Top-down and bottom-up interactions for cortical
bursting
by
Vincent D Tang
Submitted to the Department of Brain and Cognitive Sciences
in partial fulfillment of the requirements for the degree of
DOCTOR OF BRAIN AND COGNITIVE SCIENCE
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
May 2025
© 2025 Vincent D Tang. All rights reserved.
The author hereby grants to MIT a nonexclusive, worldwide, irrevocable,
royalty-free license to exercise any and all rights under copyright, including to
reproduce, preserve, distribute and publicly display copies of the thesis, or
release the thesis under an open-access license.
Authored by: Vincent D Tang
Department of Brain and Cognitive Sciences
May 2, 2025
Certified by: Mark T. Harnett
Professor of Brain and Cognitive Sciences, Thesis Supervisor
Accepted by: Mark T. Harnett
Professor of Brain and Cognitive Sciences
Graduate Officer, Department of Brain and Cognitive Sciences
2
THESIS COMMITTEE
Thesis Supervisor
Mark T. Harnett
Professor of Brain and Cognitive Sciences
Department of Brain and Cognitive Sciences
Thesis Readers
Mark Bear
Picower Professor of Neuroscience
Department of Brain and Cognitive Sciences
James J. DiCarlo
Peter de Florez Professor
Department of Brain and Cognitive Sciences
Richard Naud
Professor of Cellular and Molecular Medicine
Department of Cellular and Molecular Medicine
3
4
Top-down and bottom-up interactions for cortical bursting
by
Vincent D Tang
Submitted to the Department of Brain and Cognitive Sciences
on May 2, 2025 in partial fulfillment of the requirements for the degree of
DOCTOR OF BRAIN AND COGNITIVE SCIENCE
ABSTRACT
High-frequency burst firing occurs throughout the mammalian cortex in
vivo, yet both the underlying mechanisms and functional roles of bursts are
unclear. Burst firing in brain slices is strongly modulated by the activity of
apical dendrites, which branch extensively in layer 1 (L1) and receive long-
range inputs from higher-order cortical and thalamic areas. These properties
suggest a powerful subcellular substrate by which single pyramidal neurons
could multiplex bottom-up and top-down information via L1-independent tonic
spikes and L1-dependent bursts, respectively, and have provided a basis for
emerging theoretical models of cortical computation and learning. However,
our understanding of burst firing and subcellular processing remains critically
limited by a lack of evidence in awake animals. It is unclear whether burst
firing a) is preferentially recruited by bottom-up versus top-down inputs, and b)
requires apical dendritic engagement. To answer these questions, we performed
high-density extracellular recordings in primary visual cortex of awake mice
while presenting a battery of Gabor (bottom-up) and inverse (top-down) visual
stimuli. We report widespread high-frequency bursts in L2/3 and L5 pyramidal
neurons. Contrary to expectation, bursts exhibited extremely short response
latencies, and were most strongly recruited by Gabor stimuli. We further tested
the causal contribution(s) of apical dendrites to burst firing and top-down visual
tuning via two optogenetic manipulations: direct L5 apical tuft inhibition and
NDNF interneuron activation. Strikingly, L1 inhibition only modestly reduced
the burst fraction, and did not differentially affect Gabor vs inverse responses.
Taken together, these results challenge prevailing theories of apical dendritic
involvement in burst spike generation and feedback visual tuning, and provide
new biological constraints for future theoretical and experimental work.
Thesis supervisor: Mark T. Harnett
Title: Professor of Brain and Cognitive Sciences
5
6
Acknowledgments
It has been an honor and a privilege to pursue this thesis work over the past
six years. I am deeply thankful to my advisor, Professor Mark Harnett, for
giving me the opportunity to train and embark on this journey in his lab.
Your mentorship, rigor, and openness to chasing the unknown have profoundly
shaped my development as a scientist.
I am grateful to the members of my thesis committee, Professors Mark
Bear, Jim DiCarlo, and Richard Naud, for their time, thoughtful feedback, and
insightful discussions. It has been a true privilege to have your expertise and
perspective over the course of this work.
This work would not have been possible without my colleagues in the
Harnett lab, who have created the most supportive environment I could have
asked for. I am deeply grateful for the scientific collaborations and friendships
we have forged together.
I am thankful for all the friends and loved ones who have made the past
years so wonderful. You all have been an invaluable source of support, and I
am deeply grateful for our time together.
I owe special thanks to my family — my parents, Xianzhu and Tian, and
my brother, Andrew — for their unwavering encouragement and for nurturing
my love for science. And finally, I am deeply grateful to my partner Kelly, who
has been for being a constant companion and source of inspiration.
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8
Contents
List of Figures 11
1 Introduction 13
1.1 Anatomical organization of cortical inputs . . . . . . . . . . . 15
1.2 The role of dendritic biophysics . . . . . . . . . . . . . . . . . 20
1.3 Cortical burst spiking . . . . . . . . . . . . . . . . . . . . . . . 23
1.4 Bottom-up and top-down signal processing in mouse primary
visual cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2 Visual tuning of burst and tonic spikes 35
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.2.1 Extracellular bursts are ubiquitous and exhibit spatio-
temporal features consistent with dendritic recruitment 37
2.2.2 Feedforward recruitment of cortical bursts . . . . . . . 38
2.2.3 Inverse stimuli do not drive increased bursting . . . . . 41
2.2.4 Stimulus driven bursting is invariant to preceding stimuli 42
2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3 Apical dendritic contributions to burst spiking and top-down
visual processing in vivo 57
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.2.1 Apical dendritic activity is not required for burst firing 58
3.2.2 Apical tuft dendrites are not required for inverse tuning 61
3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4 Final Conclusions 65
4.1 Visual tuning of cortical burst and tonic spikes . . . . . . . . . 66
4.2 Causal investigation of the apical tuft . . . . . . . . . . . . . . 67
4.3 A revised framework for top-down and bottom-up integration . 68
9
A Methods 71
A.1 Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
A.2 Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
A.2.1 Acute procedures . . . . . . . . . . . . . . . . . . . . . 72
A.2.2 Chronic procedures . . . . . . . . . . . . . . . . . . . . 74
A.2.3 Silicon probe preparation . . . . . . . . . . . . . . . . . 75
A.3 Electrophysiology . . . . . . . . . . . . . . . . . . . . . . . . . 76
A.3.1 Acute recordings . . . . . . . . . . . . . . . . . . . . . 76
A.3.2 Chronic recordings . . . . . . . . . . . . . . . . . . . . 77
A.4 Optogenetics . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
A.5 Visual stimulation . . . . . . . . . . . . . . . . . . . . . . . . . 79
A.5.1 Gabor vs inverse tuning . . . . . . . . . . . . . . . . . 80
A.5.2 Size tuning . . . . . . . . . . . . . . . . . . . . . . . . 81
A.5.3 Orientation tuning . . . . . . . . . . . . . . . . . . . . 81
A.5.4 Sequential stimuli . . . . . . . . . . . . . . . . . . . . . 81
A.6 Behavioral monitoring . . . . . . . . . . . . . . . . . . . . . . 82
A.7 Histology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
A.8 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
A.8.1 Spike-sorting . . . . . . . . . . . . . . . . . . . . . . . 84
A.8.2 Layer identification . . . . . . . . . . . . . . . . . . . . 85
A.8.3 Burst spike analysis . . . . . . . . . . . . . . . . . . . . 85
A.8.4 Spike backpropagation . . . . . . . . . . . . . . . . . . 86
A.8.5 White noise analysis . . . . . . . . . . . . . . . . . . . 86
A.8.6 Receptive field mapping . . . . . . . . . . . . . . . . . 87
A.8.7 Inverse tuning . . . . . . . . . . . . . . . . . . . . . . . 87
A.8.8 Response latency . . . . . . . . . . . . . . . . . . . . . 88
A.8.9 Poisson model . . . . . . . . . . . . . . . . . . . . . . . 88
A.8.10 Optogenetic inhibition . . . . . . . . . . . . . . . . . . 89
A.8.11 Behavioral state . . . . . . . . . . . . . . . . . . . . . . 89
A.9 Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
References 91
10
List of Figures
1.1 Laminar organization of top-down and bottom-up inputs . . . 16
1.2 Axon distribution of inputs to V1 . . . . . . . . . . . . . . . . 19
1.3 V2 to V1 feedback projections . . . . . . . . . . . . . . . . . . 30
1.4 Example layer 5 pyramidal neuron morphology and biophysics 31
1.5 Functional roles of burst spiking . . . . . . . . . . . . . . . . . 32
1.6 Previous evidence for inverse tuning . . . . . . . . . . . . . . . 33
1.7 Model for the integration of top-down and bottom-up inputs via
a two-compartment neuron framework . . . . . . . . . . . . . 34
2.1 Detection and characterization of extracellular burst spikes . . 47
2.2 Spike sorting and manual curation . . . . . . . . . . . . . . . . 48
2.3 Extracellular spike bursts decrement in amplitude across time
and space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.4 Gabor stimuli drive rapid recruitment of burst spiking . . . . . 50
2.5 Unsupervised detection of early bursting neurons . . . . . . . 51
2.6 Tuning of bursts and tonic spikes to white noise stimuli . . . . 52
2.7 Poisson firing rate model . . . . . . . . . . . . . . . . . . . . . 53
2.8 Inverse stimuli do not evoke higher burst firing than Gabors. . 54
2.9 Differences in whisking do not explain lack of inverse stimuli
driven bursting . . . . . . . . . . . . . . . . . . . . . . . . . . 55
2.10 Sequential presentation of Gabor, inverse, and full-field stimuli 56
3.1 Optogenetic inhibition of the apical tuft . . . . . . . . . . . . 60
3.2 Effect of apical tuft inhibition on Gabor and inverse responses 62
11
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Chapter 1
Introduction
The mammalian neocortex is a marvel of biological evolution, orchestrating
a vast array of cognitive functions that underlie perception, behavior, and
phenomenological experience. Deciphering how the brain’s billions of neurons
and trillions of connections interact to produce these capabilities remains a
critical frontier, with far-reaching implications for both medicine and artificial
intelligence. How do we draw core functional principles from a system of
such staggering complexity? A pivotal early insight was the discovery that
the neocortex is broadly organized into distinct functional areas, each with
specialized roles ranging from sensory processing and motor control to goal-
directed decision making (Felleman & Van Essen, 1991; Miller & Cohen,
2001; Passingham et al., 2002). Furthermore, many sensory areas are broadly
organized into hierarchical networks, whereby sensory information enters the
neocortex in lower-order areas and progressively transforms into more abstract
representations as it ascends the cortical hierarchy (J. A. Harris et al., 2019;
13
Miller & Cohen, 2001; Siegle et al., 2021; Van Essen & Maunsell, 1983).
While this bottom-up framework provides a powerful explanation for basic
features of sensory encoding, cortical processing is far more intricate than a
simple feedforward flow of information. Instead, the brain is highly recurrent,
with extensive connections from higher-order cortical and thalamic areas into
their lower-order counterparts. These top-down inputs perform a critical role
in cortical function, producing changes in feedforward activity that underlie
perception, learning, and adaptive behavior (Banerjee et al., 2020; Gilbert &
Li, 2013; Makino & Komiyama, 2015; Manita et al., 2015; Noudoost et al.,
n.d.; Zhang et al., 2014). How these inputs are integrated within the cortical
microcircuit remains a significant gap in knowledge. A key organizational
principle of bottom-up and top-down inputs is their spatial segregation across
the dendritic compartments of single pyramidal neurons (Schuman et al., 2021).
These dendritic compartments, in turn, possess distinct biophysical properties
which enable powerful non-linear signal processing (M. E. Larkum, Kaiser, &
Sakmann, 1999; Major et al., 2013; Stuart & Spruston, 1998). Specifically, the
differential processing of bottom-up and top-down inputs in the peri-somatic
and distal apical dendrites (respectively) has provided a prevailing theoretical
framework, inspiring powerful algorithmic-level hypotheses for myriad cortical
computations. Despite considerable theoretical interest, however, there remains
little in vivo evidence for either the subcellular or circuit-level predictions of
these models. This is due, in large part, to the difficulty of isolating bottom-
up and top-down processes in a model system conducive to experimental
interrogation.
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This thesis investigates a prevailing framework for the integration of bottom-
up and top-down inputs via non-linear dendritic mechanisms. In Chapter 1,
I review key ex vivo evidence for this model, and identify critical assumptions
and predictions which have yet to be tested in vivo. In Chapter 2, I leverage
the recent finding of a feedback-dependent visual tuning in the mouse primary
visual cortex to test the prediction that top-down inputs preferentially drive
high-frequency burst spiking, a non-linear output mode thought to be mediated
by distal apical dendrites. In Chapter 3, I perform targeted optogenetic
perturbations to causally test the role of the apical tuft in generating burst
spikes and top-down visual tuning. Together, the results of these experiments
provide direct, in vivo evidence complicating core predictions of existing models.
In Chapter 4, I conclude by discussing the functional implications of these
findings and directions for future investigation.
1.1 Anatomical organization of cortical inputs
The neocortex exhibits a strikingly conserved laminar organization across
different cortical areas and mammalian species (Douglas & Martin, 2004; J. A.
Harris et al., 2019; Mountcastle, 1997; Rakic, 2009)(Fig. 1.1). This layered
structure has long been hypothesized to support a functional organization of
inputs, with specific layers and dendritic compartments within each cortical
area preferentially receiving inputs from different cortical and thalamic areas.
Early retrograde tracing studies found that feedforward axons originating
from primary sensory thalamic areas (e.g. lateral and medial geniculate nuclei)
15
Figure 1.1: Laminar organization of top-down and bottom-up inputs. Bottom
up (feedforward) inputs from lower order thalamic and cortical areas are
preferentially targeted towards L4, and are distributed locally to L2/3 and L5.
In contrast, top-down (feedback) inputs from higher order areas largely avoid
L4. Instead, they are highly enriched in L1, where they impinge on the apical
dendrites of L2/3 and L5 pyramidal neurons, and in deeper layers.
are predominantly received in layer 4 of primary sensory cortex (Douglas &
Martin, 2004; Gilbert & Wiesel, 1979; E. G. Jones & Powell, 1970; Rockland
& Pandya, 1979). Subsequent axon bouton reconstruction and functional
studies demonstrated that bottom-up projections from lateral geniculate nucleus
synapse heavily onto the peri-somatic dendrites of L4 pyramidal neurons (Ferster
& Lindström, 1983; Freund et al., 1985; Lien & Scanziani, 2018b), although they
also target the apical oblique dendrites of L5 pyramidal neurons (Constantinople
& Bruno, 2013). These inputs are then processed recurrently within the local
microcircuits, where they are further distributed to the peri-somatic dendrites
of pyramidal neurons in layers 2/3 (L2/3) and 5 (L5) within the same area
(Rockland & Pandya, 1979).
16
In contrast, feedback inputs from many higher-order thalamic and cortical
regions are noticeably attenuated in L4, and are instead preferentially targeted
towards layer 1 (L1) and deeper layers (Fig. 1.1). In the primary visual cortex
(V1), for instance, inputs from the lateral geniculate nucleus (LGN) - the
primary source of visual information from the retina - terminate predominantly
in L4 (Douglas & Martin, 2004; Gilbert & Wiesel, 1979; E. G. Jones & Powell,
1970; Rockland & Pandya, 1979), while inputs from higher order visual areas
such as the lateromedial visual cortex (LM) and posteriomedial visual cortex
(PM) are targeted towards L1 and L5/6 (Fig. 1.2). The observation of similar
connectivity motifs in higher-order cortical areas (Felleman & Van Essen, 1991;
Rockland & Pandya, 1979; Van Essen & Maunsell, 1983) has led to the proposal
that this laminar segregation of feedforward and feedback inputs represents a
canonical organizational principle of the neocortex (Douglas & Martin, 2004;
Felleman & Van Essen, 1991).
This structured organization is thought to be essential for the integration
of externally driven signals (bottom-up) with internally generated predictions,
prior knowledge, and contextual information (top-down). Previous studies
have shown that descending cortical signals play a crucial role in modulating
perception and behavior, contributing to processes such as contextual mod-
ulation, predictive coding, attention, expectation, multisensory integration,
and learning (Doron et al., 2020; Gilbert & Li, 2013; Jordan & Keller, 2020;
Nurminen et al., 2018; Pak et al., 2020; N. L. Xu et al., 2012; Zanto et al., 2011;
Zipser et al., 1996). A critical implication of these findings is that long range
inputs carrying top-down information which must be adaptively integrated with
17
bottom-up sensory signals, instead of simply overwriting them. However, it
remains unclear to what extent the vast array of long range higher order signals
share the same canonical laminar organization that was first derived from the
hierarchical areas within individual sensory modalities (Felleman & Van Essen,
1991). In V1, for instance, feedback axons from higher order cortical and
thalamic areas such as the anterior cingulate cortex (ACC), orbitofrontal cortex
(ORB), and the lateral posterior nucleus of the pulvinar (LP) differ noticeably
in their laminar organization, with differences in their innervation of deeper
layers (ACC preferentially targets L6, ORB targets L5, and LP innervates both
L5 and L6), and the relative density of their L1 innervation (Ährlund-Richter
et al., 2024; Liu et al., 2024). Even for feedback axons from V2 to V1, previous
studies have reported differing laminar distributions, complicating efforts to
validate a unified functional motif (Shen et al., 2022; Young et al., 2021).
An important caveat to these studies is that axon density does not directly
equate to functional connectivity (Oh et al., 2014). Here, direct functional
assays such as subcellular channelrhodopsin assisted circuit mapping (sCRACM)
have begun to provide critical evidence by distinguishing the cell types and
subcompartments which are functionally targeted by specific inputs (Lafourcade
et al., 2022; Petreanu et al., 2009; Young et al., 2021). sCRACM mapping of
V2 inputs to V1 have found that V2 inputs strongly target the peri-somatic
compartments of looped V1 pyramidal neurons which send inputs back to V2
(Young et al., 2021). This evidence is consistent with the substantial deeper
layer innervation of V2 axons (J. A. Harris et al., 2019; Marques, Nguyen, et al.,
2018; Young et al., 2021), suggesting that they could represent a substantial
18
Figure 1.2: Axon distribution of inputs to V1. Inputs from LGN are highly
localized to L4, while inputs from higher order thalamus (LP) and cortex
(ACC) are strongly biased towards L1. Inputs from lateromedial (LM) visual
cortex are present in L1, but also exhibit substantial innervation in deeper
layers (L5 and L6). LGN, LP, and ACC panels adapted from Schuman et al.,
2021, images from the Allen Connectivity Map.
parallel pathway for V2 to V1 feedback (Fig. 1.3). However, these deep
feedback projections remain markedly understudied compared to feedback
projections to L1 (Doron et al., 2020; Fişek et al., 2023; Huang et al., 2024;
Keller et al., 2020; Marques, Summers, et al., 2018), and their functional role
remains unclear. Instead, top-down processing remains strongly associated
with L1 inputs (M. E. Larkum et al., 2009; Manita et al., 2015; Rao et al.,
2021; Schuman et al., 2021; Siegel et al., 2000; S. Xu et al., 2012).
The predominant hypothesis that L1 serves as a privileged integration hub
for top-down inputs is due, in part, to its conserved targeting by many long
19
range inputs and its unique anatomical features (Schuman et al., 2021). In
addition to being heavily innervated by feedback from higher order cortical
and thalamic areas, L1 receives little excitatory input from within the local
microcircuit (Chu et al., 2003; Jiang et al., 2015; Petreanu et al., 2009).
Furthermore, L1 is unique among cortical layers in that it does not contain
any pyramidal neurons. Instead, the dense excitatory inputs from higher-order
areas impinge on the apical dendrites of L2/3 and L5 pyramidal neurons, and
a unique population of L1-residing interneurons (Cohen-Kashi Malina et al.,
2021). Notably, the apical dendrites of the primary thalamo-recipient neurons,
L4, are significantly attenuated compared to those of neurons in other layers 1.1.
Given this connectivity, L2/3 and L5 pyramidal neurons are uniquely positioned
to integrate both bottom-up sensory inputs and top-down modulatory signals.
These neurons, with their extensive apical dendrites in L1 and peri-somatic
dendrites in deeper layers, may function as powerful computational units that
leverage this dendritic compartmentalization to differentially process bottom-up
and top-down inputs.
1.2 The role of dendritic biophysics
Dendrites receive the vast majority of synaptic inputs in a pyramidal cell.
Instead of simply conveying these inputs to the soma unchanged, they leverage
a complex array of biophysical properties to transform inputs prior to action
potential (AP) generation.
Dendrites exhibit passive cable properties that produce significant distance-
20
dependent signal attenuation (Stuart & Spruston, 1998). This electrical com-
partmentalization is particularly apparent in large layer 5 pyramidal neurons,
whose dendrites span all cortical layers (Mason & Larkman, 1990). Second,
the apical dendrites of both layer 2/3 and 5 pyramidal neurons arborize ex-
tensively in L1 and possess an array of voltage-dependent ion channels that
allow them to actively produce Na+, N-Methyl-D-Aspartate (NMDA), and
Ca2+ spikes that mediate supra-linear signal transformation (Palmer et al.,
2014; Stuart et al., 1997). The combination of these properties allows dendrites
to non-linearly integrate information, and “gate” subthreshold signals. Third,
somatic action potentials can backpropagate into dendritic branches (Fig. 1.4)
(M. E. Larkum, Kaiser, & Sakmann, 1999; Stuart et al., 1997). The extensive
branching and electrotonic properties of apical dendrites have motivated models
of single neurons as multi-layer neural networks (David et al., 2019). This
differs significantly from most current research in machine learning and artificial
intelligence, which overwhelmingly models individual neurons as simple rate
or integrate-and-fire units (Higgins et al., 2020; Li & DiCarlo, 2008; Yamins
et al., 2014).
In layer 5 pyramidal neurons, the expression of high-voltage-activated Ca2+
channels in the apical trunk provides an additional mechanism for supra-
linear signal processing. When subthreshold events in the apical dendrite
are temporally coincident with somatic spiking, the additional activation of a
dendritic Ca2+ spike can generate powerful regenerative currents, transforming
single action potentials into high-frequency bursting events (M. E. Larkum,
Zhu, & Sakmann, 1999). These regenerative currents can also be engaged by
21
activation of the apical tuft alone, allowing strong distal inputs to overcome
their electrotonic isolation and elicit high-frequency burst firing at the soma
(Fig. 1.4) (M. Larkum, 2013; M. E. Larkum, Zhu, & Sakmann, 1999; Major
et al., 2013).
The distinct electrotonic and active properties of the apical tuft versus
the peri-somatic dendrites, combined with their differential sampling of feed-
back inputs, suggests a powerful two-compartment framework for single cell
computation 1.7 (M. Larkum, 2013; Shai et al., 2015; Siegel et al., 2000; Yi
et al., 2017). A growing body of theoretical work has leveraged these prop-
erties to propose computational models at both the single cell and circuit
levels, providing algorithmic-level hypotheses for how cortical circuits could
perform both complex signal processing (Hawkins & Ahmad, 2016; Naud et al.,
2023) and implement powerful learning rules (Greedy et al., 2022; Guerguiev
et al., 2017; Körding & König, 2001; Richards & Lillicrap, 2019; Sacramento
et al., 2018). Despite its functional implications, a significant limitation of the
two-compartment framework is that its supporting evidence remains almost
entirely ex vivo. Ex vivo measurements of neural activity differ substantially
from intact circuits in awake animals, with critical differences including the
lack of ongoing synaptic activity, spatio-temporally patterned synaptic input,
and neuromodulator release, all of which can dramatically affect integrative
properties at both the single cell and circuit levels (Destexhe & Paré, 1999;
Destexhe et al., 2003; Hasselmo, 1995; London & Häusser, 2005; Marder &
Thirumalai, 2002; McCormick, 1992; Okun & Lampl, 2008; Opitz et al., 2017).
In fact, the constant barrage of synaptic inputs in vivo has been theorized to
22
drive cells into a high conductance state, under which the non-linear properties
of different dendritic compartments are largely normalized (Destexhe et al.,
2003).
Recent studies of in vivo dendritic activity have only highlighted this gap
in our knowledge. Given the high electrotonic separation between the apical
tuft and soma, it was widely theorized that apical tuft activity would be
decoupled from the soma, and exhibit a substantial amount of independent
activity (driven by active spikes). However, recent in vivo studies leveraging two-
photon calcium imaging to record simultaneous activity in apical dendrites and
their parent somata have found that GCaMP signals are highly synchronized
across the two compartments, with little to no independent encoding in the
apical tuft (Beaulieu-Laroche et al., 2019; Francioni et al., 2019; Kerlin et
al., 2019; Schoenfeld et al., 2021, but see Voigts and Harnett, 2020). Here,
electrophysiological recordings of burst firing offers promising complement to
these studies, since it can be assayed across large numbers of neurons in vivo,
and has a strong biophysical link to apical dendritic engagement. Moreover, the
top-down recruitment of burst firing is a direct assumption of a growing body
of powerful theoretical models that leverage a two compartment framework.
1.3 Cortical burst spiking
Burst spiking powerfully alters neural output, increasing the efficacy and
precision of synaptic transmission (Lisman, 1997) and providing a mechanism
for multiplexing top-down and bottom-up information in the output of a
23
single pyramidal neuron 1.5 (Kepecs & Lisman, 2003; Naud & Sprekeler, 2018;
Naud et al., 2023). In addition to their putative roles in moment-to-moment
information processing, bursts are also strongly implicated in learning, where
their high-frequency firing and the associated intracellular influx of Ca2+
offer mechanisms to for long-term synaptic modifications (Cichon & Gan,
2015; Froemke et al., 2006; Kampa et al., 2006). In the hippocampus, bursts
have been shown to mediate a powerful form of one-shot plasticity known
as behavioral timescale synaptic plasticity (Bittner et al., 2017). Emerging
theoretical work has consolidated these ideas to suggest that apical dendrite
mediated bursting mediates credit assignment within biological neural networks
(Greedy et al., 2022; Payeur et al., 2020; Richards & Lillicrap, 2019).
Together, these models introduce multiple algorithmic-level descriptions
of how cortical circuits could utilize high-frequency bursts to perform both
computation and learning. However, key assumptions and predictions of these
models have yet to be validated in intact cortical neural circuits. Much of the
existing literature on burst spiking, and its potential differences from tonic
spikes, derives instead from studies in weakly electric fish (Clarke & Maler, 2017;
Gabbiani et al., 1996; Metzen et al., 2016; Metzner et al., 1998; Oswald et al.,
2004), thalamus (Alitto et al., 2005; Contreras et al., 1992; Guido et al., 1992;
Llinás & Steriade, 2006; Reinagel et al., 1999; Sherman, 2001), or hippocampus
(Lowet et al., 2023). Although these studies provide powerful proof-of-principle
that burst spikes could serve distinct functional roles within neural circuits,
they were conducted in circuits and neurons that differ significantly from those
within mammalian neocortex. As such, it remains an open question whether the
24
theorized computational roles of burst spikes represent generalized functional
principles. Within cortical circuits, burst spikes have been reported in early
studies in the cat and primate visual cortex, where they were found to be more
discriminative of stimulus orientation than tonic spikes during microsaccadic
eye movements (Cattaneo et al., 1981; Livingstone et al., 1996; Martinez-Conde
et al., 2002). Although these studies suggested that cortical burst spikes
may play a role in sensory processing, these bursts shared tuning features
with tonic spikes, and thus were hypothesized to reflect stimuli salience and
optimality rather than encode unique visual features (Martinez-Conde et al.,
2002). In the rat primary somatosensory cortex, previous studies have shown
that high-frequency burst spikes encode active, but not passive touch (de Kock
et al., 2021; De Kock & Sakmann, 2008; De Kock et al., 2007). Interestingly,
several studies have shown linked cortical burst firing with the detectability
of stimuli during either active whisking or electrical stimulation in the barrel
cortex (Doron et al., 2014; Takahashi et al., 2016).
As such, few studies have attempted to explicitly disambiguate whether
cortical bursting events and tonic spikes differentially encode top-down and
bottom-up information (Anderson et al., 2011, 2013). It remains unclear
whether bursts are primarily recruited to process bottom-up inputs (i.e. serving
a thresholding role in sensory detection or enhancing information transmission),
or serve to preferentially encode a privileged class of higher-order signals. In
order to distinguish between these two hypotheses, it is critical to directly
compare the degree to which burst spikes are recruited by bottom-up and
top-down inputs. Furthermore, it remains an open question to what extent
25
burst spikes remain biophysically distinct from tonic spikes in vivo. Although
the recruitment of burst spikes by the apical tuft is central to many two-
compartment neuron frameworks (M. Larkum, 2013; Shai et al., 2015), a strong
causal link has yet to be established in awake animals. Interestingly, although
burst spikes are attributed to a subpopulation of L5 neurons with large apical
dendritic processes in slice and in anesthetized animals, they have been observed
in a broader population of neurons (e.g. L2/3 and putative L5 IT) in awake,
in vivo recordings, suggesting that additional biophysical mechanisms may be
at play in awake animals (De Kock & Sakmann, 2008; De Kock et al., 2007;
Sakmann, 2017). Indeed, other models have generated high-frequency bursts
without direct inputs to the distal apical tuft (Bast et al., 2023; Izhikevich,
2003; Rhodes & Gray, 1994), and suggested that bursting may instead serve as
an input slope detector (Kepecs et al., 2002). Interestingly, it has also been
suggested that bottom-up inputs from sensory thalamus can directly recruit
active dendritic processing, bypassing the need for top-down signals altogether
(Bast et al., 2023). Directly testing these hypotheses in intact neural circuits is
therefore critical for understanding the role of burst spikes cortical computation,
and will provide new biological constraints to guide theoretical work.
1.4 Bottom-up and top-down signal processing
in mouse primary visual cortex
The lack of in vivo evidence for either the subcellular or circuit-level predictions
of these models is due, in part, to the difficulty of isolating bottom-up and
26
top-down processes in a model system amenable to experimental interrogation.
Here, recent advances in our understanding of the mouse primary visual cortex
(V1) present a compelling opportunity to overcome these hurdles.
The response properties of neurons in primary visual cortex (V1) have been
extensively studied over the past decades (Angelucci et al., 2017; DeAngelis
et al., 1993; DeAngelis et al., 1995; Hubel & Wiesel, 1962; Marques, Nguyen,
et al., 2018; Niell & Stryker, 2008), and V1’s connectivity to other regions is
the most highly explored of all cortical areas (Angelucci et al., 2002; Zhang
et al., 2014).
As the first cortical visual processing region, V1 receives strong bottom-up
visual input from the lateral geniculate nucleus. These inputs are processed
on single neurons and within the cortical column, providing a mechanistic
basis for classical receptive field localization, and properties such as ON/OFF
tuning and orientation selectivity (Angelucci & Bressloff, 2006; Angelucci et al.,
2002, 2017; Federer et al., 2021; Hubel & Wiesel, 1959; Hubel & Wiesel, 1962;
Iacaruso et al., 2017; Lien & Scanziani, 2018a; Nurminen et al., 2018; Siu et al.,
2021). This feedforward tuning is thought to be well approximated as a Gabor
filter (J. P. Jones & Palmer, 1987).
However, mounting evidence also highlights the role of top-down inputs
even in early stages of visual processing, demonstrating that V1 neurons are
far more than bottom-up feature detectors. Previous studies have shown that
V1 encodes visual flow, reward, and spatial information (Diamanti et al., 2019;
Jordan & Keller, 2020; Saleem et al., 2018; Zhang et al., 2014; Zong et al.,
2021), and possesses higher-order visual tuning properties such as figure-ground
27
modulation and extra-classical receptive fields (Keller et al., 2020; Lamme
et al., 1998; Schnabel et al., 2018; Walker et al., 2019; Zipser et al., 1996).
These tuning properties are thought to be mediated by feedback inputs from
higher-order visual areas, as well as anterior cingulate cortex and retro-splenial
cortex (de Vries et al., 2020; Makino & Komiyama, 2015; Marques, Nguyen,
et al., 2018; Marques, Summers, et al., 2018; Nurminen et al., 2018).
Of particular interest is the recent discovery that strong extra-classical
receptive field tuning in mouse V1 is driven by feedback from V2 (Keller et al.,
2020). The tuning to these inverse stimuli exhibits a long temporal latency
consistent with a top-down origin, and is abolished by inactivation of V2 areas
(Fig. 1.6). Notably, inverse tuning is present in L2/3 and L5 pyramidal neurons
but not L4, suggesting that L1 inputs may play a critical role. Previous work
investigated this possibility by imaging feedback axons from lateral medial
visual (LM) cortex into L1 of V1, finding that L1 feedback inputs are not
inverse tuned themselves, but contain the requisite information to generate
inverse tuning (Fig. 1.6). Therefore, a supra-linear integration of L1 inputs
in the apical dendrites of L2/3 and L5 pyramidal neurons would provide a
possible explanation for feedback driven inverse tuning. The feasibility of
such a mechanism was directly investigated in a recent study suggesting that
inverse stimuli preferentially active dendritic events in the apical dendrites
of L5 pyramidal neurons in V1 (Fişek et al., 2023). Together, these studies
provide substantial evidence that V1 could differentially process top-down and
bottom-up visual inputs via the two compartment neuron framework 1.7. This
hypothesis leads to two experimentally testable predictions: 1. Burst and tonic
28
spikes should preferentially encode inverse and Gabor stimuli, respectively, and
2. apical tuft inhibition should selectively abolish burst spike generation and
the inverse response.
29
Figure 1.3: V2 to V1 feedback projections. A-B. Axon tracing of inputs from
lateral visual cortices (LM) into V1 shows substantial innervation of L5 and L6
in addition to L1. Despite targeting the same area, the laminar specificity varies
across studies. C. Subcellular assisted circuit mapping (sCRACM) shows the
subcellular targeting of V2 to V1 inputs for a subset of L2/3 and L5 neurons.
D. Same data as in C, aligned to the cell body of each neuron instead of laminar
depth. L2/3 and L5 neurons receive strong peri-somatic drive from V2 to V1
inputs, with only L5 IT neurons exhibiting significant apical dendritic input
from V2. A. adapted from Shen et al., 2022, B,C,D. adapted from Young et al.,
2021.
30
Figure 1.4: Example layer 5 pyramidal neuron morphology and biophysics. A.
Top-down inputs to single neurons are received in the apical tuft dendrites (L1),
while bottom-up inputs are preferentially targeted to the soma. B-D describe
simultaneous triple patch experiments performed in vitro. B. Subthreshold
stimulation near the apical trunk attenuates heavily along the dendritic trunk.
C. Single action potentials evoked in the soma back-propagate to dendritic
compartments. D. Combination of subthreshold apical trunk stimulation and
a single action potential evokes a supra-linear bursting event. E. Strong apical
dendritic stimulation can independently generate somatic bursting. Adapted
from M. E. Larkum, Zhu, and Sakmann, 1999
31
Figure 1.5: Functional roles of burst spiking. A. The selective recruitment of
burst spikes by distinct input patterns allows single neurons to multiplex dif-
ferent information streams. Pathway 1 (putative peri-somatic input) generates
tonic spikes when activated alone, but burst spikes when combined with inputs
from pathway 2 (putative apical dendritic input) B. Short-term potentiation
and depression at the post-synaptic neuron could allow burst spikes to be
differentially decoded. C. Differential pairing of bursts and single spikes at
the pre and postsynaptic level may lead to either long term potentiation or
depression. Adapted from Friedenberger et al., 2023.
32
Figure 1.6: Previous evidence for inverse tuning. A-B. Classical and inverse
stimuli share a spatial receptive field. C-D. V2 inactivation selectively abolishes
inverse (bottom panel), but not classical (top panel) tuning. E. Inverse responses
exhibit a longer latency than classical, consistent with a feedback recruitment.
F. Imaging of LM axons in layer 1 of V1 (left panel) showed that LM inputs
are not inverse tuned themselves (middle), but the sum of LM activity contains
the requisite information to generate inverse tuning (right). G-H. Imaging of
L5 apical dendrites suggests that inverse stimuli recruit more active dendritic
events than other stimuli. Panels A-F adapted from Keller et al., 2020, panels
G-H adapted from Fişek et al., 2023.
33
Figure 1.7: Model for the integration of top-down and bottom-up inputs via a
two-compartment neuron framework. Bottom-up inputs are localized to the
perisomatic compartments, while top-down inputs are targeted to the apical
dendrites in L1. The differential recruitment of voltage-gated supra-linearities
by these two streams is hypothesized to produce a differential recruitment
of burst and tonic spikes. In the primary visual cortex, Gabor and inverse
stimuli are thought to drive bottom-up and top-down inputs, respectively, thus
resulting in a differential recruitment of tonic and burst spikes.
34
Chapter 2
Visual tuning of burst and tonic
spikes
2.1 Introduction
High-frequency burst spiking offers a powerful mechanism by which single
pyramidal neurons can multiplex multiple information streams, and is thought
to be preferentially recruited by top-down signal processing in apical dendrites.
Although a growing number of theoretical models have posited that cortical
circuits could leverage burst spikes to perform powerful computation functions,
there remains little characterization of their response properties in vivo. It is
unknown whether cortical burst and tonic spikes encode different information,
and specifically, whether they are preferentially tuned to top-down and bottom-
up inputs.
In the mouse primary visual cortex, masked drifting gratings (inverse stimuli)
35
can powerfully drive individual V1 neurons, even in the absence of stimuli in
their feedforward receptive fields (Keller et al., 2020). Unlike classic bottom-up
stimuli (Gabor patches), the tuning to inverse stimuli requires top-down inputs
from V2, providing an experimental model for testing the coding of burst and
tonic spikes.
2.2 Results
We performed extracellular electrophysiology using a battery of inverse and
Gabor visual stimuli to test whether burst and single spikes code for distinct
visual information in the primary visual cortex of awake, head-fixed mice
(Fig. 2.1A). For the Gabor presentation experiments, mice were chronically
implanted with Neuropixels 2.0 silicon electrodes, allowing us to record large
populations of neurons across all cortical layers with minimal acute damage to
layer 1 (Fig. 2.1A). While this extracellular recording approach enabled us to
stably record hundreds of neurons for the time periods necessary for our visual
stimuli battery, the non-stationarity of waveform shape and amplitude across
the course of a burst (Allen et al., 2018; Connors et al., 1982; Fee et al., 1996;
Henze et al., 2000; McCormick et al., 1985) poses a considerable challenge for
the spike sorting algorithms used to process these data (K. D. Harris et al.,
2000; Lewicki, 1998). In particular, the decrementing waveform amplitudes of
consecutive spikes within a burst commonly lead to under-detection of burst
spikes, and the over-splitting of single bursty neurons into multiple units (Fig.
2.2A). In order to mitigate the under-detection of burst spikes during spike
36
sorting, we utilized state-of-the-art automated algorithms (Kilosort 2.5 and
4) utilizing template-based spike detection and template subtraction, which
improve the detection of lower amplitude waveforms and reduce the impact
of overlapping spikes, respectively (Pachitariu et al., 2016, 2024). After the
initial automated spike detection, we corrected for the over-splitting of bursty
units by performing extensive manual curation (Rossant & Harris, 2013). In
order to maximize the recovery of over-split bursts, we examined all units
on nearby channels, and used cross-correlogram and raw trace inspection in
addition to waveform similarity metrics (Fig. 2.2B). After merging, waveform
quality control, and removing putative fast spiking interneurons, we identified
1158 neurons across 7 mice.
2.2.1 Extracellular bursts are ubiquitous and exhibit spatio-
temporal features consistent with dendritic recruit-
ment
We defined bursts as events where at least three consecutive spikes occurred
with a <10 ms inter-spike interval (Fig. 2.1B). Although the exact criteria
of a cortical burst has been debated (Krahe & Gabbiani, 2004; Naud et al.,
2023), we chose a stringent burst criteria, consistent with previous literature,
in order to maximize distinction between burst and tonic events (Bast et al.,
2023). Although our extracellular recordings likely under-represent the true
number of bursts, we were able to recover high-frequency bursts in both L2/3
and L5, including those with strongly decrementing waveform amplitudes (Fig.
37
2.1C). Burst spikes were particularly prevalent in L2/3 and deep L5 (Fig.
2.1D), consistent with previous in vivo measurements and the hypothesized
differentiation of L5a vs L5b (Senzai et al., 2019). In a subset of recordings
made perpendicular to the cortical surface using acute Neuropixel 1.0 probes,
we found that consecutive spikes within burst events decreased in their spatial
backpropagation along the laminar probe (Fig. 2.3), consistent with previous
extracellular reports (Bereshpolova et al., 2007), and with the engagement of
unique biophysical mechanisms during high frequency bursts (Connors et al.,
1982; Fee et al., 1996; McCormick et al., 1985).
2.2.2 Feedforward recruitment of cortical bursts
To investigate the coding properties of these cortical bursts, we first charac-
terized their tuning and onset latencies in response to classical feedforward
stimuli. We mapped each neurons classical receptive field (RF) (Fig. 2.4A).
We identified 669 neurons (n=7 mice) that had a classical spatial receptive
field, and performed the subsequent analyses on these neurons. We found that
the receptive field tuning for tonic and burst spikes was largely identical for
neurons with a significant bursting response to visual stimuli (n=461), and
then analyzed the response of burst vs tonic spikes at each neuron’s preferred
RF location. Contrary to expectation, bursts exhibited short response latencies,
and were recruited in the initial feedforward response to visual stimuli (Fig.
2.4B,C). To further characterize the relative contribution of burst spikes to the
visual response, we calculated the burst fraction as the number of burst spikes
in a given time bin divided by the total number of spikes (Fig. 2.4D. We found
38
that the majority of Gabor tuned neurons (65.7%, 303 of 461 neurons) had a
short latency peak in the burst fraction, defined as a significant burst response
within 150 ms after stimuli presentation. When we analyzed the relative timing
of the burst fraction and firing rate for these early bursting neurons, we found
that the half-peak latency of burst fraction was significantly different, and
preceded that of the firing rate at the population level (p=0.0004, Fig. 2.4F,G).
This indicated that across the population level, burst spikes were recruited in
the initial onset of the visual response, instead of exhibiting a longer latency
characteristic of feedback recruitment. To verify our latency threshold for early
bursting (150 ms), we performed a k-means clustering on the firing rate and
burst fraction latencies, which similarly revealed that the majority of visually
responsive neurons resided within an early bursting cluster (Fig. 2.5A, 300 of
461 neurons are assigned to cluster 1). For these neurons, their burst fraction
again peaked earlier than their firing rate (Fig. 2.5B,C). In order to test
whether the Gabor evoked bursting could explained by the increase in firing
rate, we used a Poisson firing rate model to simulate artificial spikes which
matched the firing response of each neuron (Fig. 2.7A). We then calculated
a model estimated burst rate for each neuron, which correlated closely with
the firing rate and provided an estimate of the burst rate predicted solely by
firing rate statistics (Fig. 2.7B). The Poisson estimated burst rate differed
substantially from the actual burst rate, under-predicting the initial burst rate
ramp and over-predicting the subsequent response (Fig. 2.7B,D). The same
was true when we analyzed the burst fraction instead of the burst rate (Fig.
2.7C,E), indicating that the Gabor evoked bursting is poorly explained by
39
firing rate alone. Although both the burst rate and burst fraction were well
approximated at the population level, the large mean absolute error of the
Poisson model indicates that the Poisson model fits extremely poorly on the
individual neuron level (Fig. 2.7D,E).
Although the burst fraction predominantly preceded the spiking response to
the Gabor stimuli, the absolute response latency was likely over-estimated due
to the fixed temporal phase at which each Gabor stimulus was presented. In
order to perform a more precise estimate of the response latency of burst spikes,
we additionally assessed burst vs tonic spike tuning latency by presenting white
noise stimuli in a subset of experiments performed using acute recordings with
Neuropixel 1.0 probes (Fig. 2.6A). To do so, we first extracted the receptive
field of each neuron by calculating tonic and burst spike triggered averages
(tSTAs and bSTAs, respectively). Of the 561 neurons we analyzed in these
experiments (n=8 mice), the majority (391 neurons) exhibited a significant
tuning to white noise stimuli. In individual neurons with both a significant
tSTA and bSTA (n=125 neurons), we assessed the white noise response latency
by finding the first temporal lag at which a significant tuning was present in
the tSTA and bSTA. In contrast to the Gabor driven response latencies, the
white noise response latencies were extremely rapid (mean response latency,
68 ms), consistent with previous estimates of the initial LGN driven response
latency in V1 (Niell & Stryker, 2008). Interestingly, the tuning latency of burst
and tonic spikes (bSTA and tSTA) were not significantly different for bSTA
tuned neurons (Fig. 2.6B), again indicating that the initial visual response of
V1 neurons is comprised of a high proportion of burst spikes. We additionally
40
compared the white noise response latencies of neurons with a significant bSTA
to those without (n=266 only tSTA tuned neurons), and found that bSTA
tuned neurons had a significantly faster response latency (p<0.0001, Fig. 2.6C).
These results were further inconsistent with the idea that the early bursting
responses we observed were due to intra-cortical recurrence or feedback.
2.2.3 Inverse stimuli do not drive increased bursting
Having characterized the burst response to classical stimuli, we next tested
whether feedback driven visual responses would preferentially recruit burst
spiking. We mapped the receptive fields using both Gabor and inverse stimuli,
and presented full-field drifting gratings to calculate inverse tuning as in
previous literature (Fig. 2.8A) (Keller et al., 2020). Similar to previous reports,
we find that neurons in L2/3 and L5 exhibit an inverse receptive field (n=178
neurons, 7 mice), where a masked full field stimuli centered on each neurons
classical receptive field produces a spiking response larger than that to a full
field grating. These extra-classical responses showed a delayed onset relative
to feedforward stimuli, consistent with their recruitment by a feedback process
(Fig. 2.8B,C,E). We next compared the burst fraction driven by Gabor and
inverse stimuli. Contrary to our expectations, responses to inverse stimuli did
not exhibit a higher burst fraction than classical stimuli, even when restricting
analysis to the late visual evoked component of the response(Fig. 2.8D,F,G).
Instead, the initial feedforward response to classical stimuli recruited a higher
fraction of burst spikes than all other conditions (Fig. 2.8). These results
suggest that, despite the mechanistic capability of inverse stimuli to drive
41
active dendritic events in the apical tuft, dendritically evoked bursts do not
contribute to the generation of inverse tuning.
We next investigated whether the differences in stimuli evoked burst fractions
could be explained by differences in behavioral state. Previous studies have
shown that orofacial movements can explain a large fraction of neural activity
of mouse V1 (Stringer et al., 2019), and correlate with metrics of general
arousal (Nestvogel & McCormick, 2022; Salkoff et al., 2020). To control for
these effects, we used FaceMap to extract orofacial movements from concurrent
video recordings of the mouse during visual stimuli experiments, and restricted
our analyses to only trials in which the mouse was still (see Methods). However,
the differences between Gabor and inverse evoked burst fraction remained the
same, indicating that differences in behavioral state do not drive the observed
effects (Fig. 2.9A,B).
2.2.4 Stimulus driven bursting is invariant to preceding
stimuli
The relative absence of top-down driven burst spiking, and the prevalence of
burst spikes in the onset response to bottom-up stimuli instead were both
inconsistent with our initial two-compartment model. We reasoned that the
lack of matched bottom-up inputs during the inverse stimuli may have left
the top-down inputs unable to independently drive burst spiking. In this case,
the model would predict that the coincidence of bottom-up and top-down
inputs would produce increased bursting, consistent with theories that the
extra-classical response represents a form of visual prediction. In order to test
42
this, we presented a new stimuli battery in which retinotopically matched Gabor
and inverse stimuli were presented sequentially (Fig. 2.10A,B). As a control, we
also presented a Gabor preceded by a full-field grating. Here, we reasoned that
the full-field gratings would recruit increased surround suppression compared
to the inverse response, and as such would produce less apical dendritic drive.
However, if the inverse stimuli were recruiting top-down inputs to the apical
dendrites to serve as a predictive signal, then the presentation of a Gabor
stimuli immediately following its retinotopically matched inverse stimuli (or
visa versa) should generate a coincidence of top-down and bottom-up signals
which evoke a larger burst fraction response. For these analyses, we compared
the firing rate and burst fraction elicited by Gabor and inverse stimuli with
different preceding stimuli without subtracting the baseline due to the previous
stimuli, since we reasoned that the absolute amplitude of the response should
differ. We found that the firing rate elicited by the Gabor stimuli did not
change depending on whether they were preceded by inverse or full-field stimuli
(n=130 neurons, 4 mice) Fig. 2.10C,D). Contrary to our initial hypothesis,
the Gabor-evoked burst fraction did not increase when the Gabor stimuli was
preceded by inverse, and instead exhibited a modest decrease compared to when
it was preceded by a full-field stimulus (p=0.035, Fig. 2.10E,F). Furthermore,
we did not find an amplification of the burst fraction when the inverse stimuli
was preceded by its RF matched Gabor compared to when it was preceded by
gray screen (Fig. 2.10G,H). These results were inconsistent with the idea that
inverse stimuli preferentially recruit top-down signals to the apical dendrites to
serve as a predictive coincidence detector.
43
2.3 Discussion
Our results provide evidence that burst spiking in mouse V1 is not uniquely,
nor preferentially, driven by top-down stimuli, but is instead robustly recruited
by classical bottom-up stimuli (Gabors). Notably, Gabor-evoked increases in
the burst fraction were largely recruited in the initial feedforward component
of the visual response. These rapid onset times are more consistent with
feedforward drive from the LGN or local recurrence than with delayed feedback
from higher cortical regions. These findings are inconsistent with previous
theories in which the burst fraction selectively encode top-down information,
and the prediction that cortical burst spiking is dependent on feedback inputs
to the apical tuft. Importantly, our findings are not easily explained by models
that predict burst generation based solely on firing rate. Burst fractions were
highest at stimulus onset, preceding peak firing rates, and neither the latency
nor magnitude of these visually evoked bursts could be predicted by a Poisson
model. Furthermore, the burst fraction and firing rate profiles across laminar
depth differ substantially, suggesting that burst spike generation is decoupled
from firing rate in a cell-type dependent manner. Taken together, this suggests
that the burst spikes we measured reflect an intrinsic biophysical or circuit-level
computation beyond simple rate coding.
Interestingly, our results are consistent with a previous study suggesting
that sensory thalamic inputs can directly recruit active calcium channels along
the apical trunk to generate early sensory evoked bursting (Bast et al., 2023).
Another possibility is that the spatio-temporal pattern of bottom-up inputs
44
within in vivo neural circuits generates enough somatic depolarization to
recruit active apical dendritic voltage channels through the backpropagation of
somatic action potentials, again allowing the rapid recruitment of burst spiking
without longer-latency feedback signals. Such a regime would allow bottom-up
driven bursts to act as a nonlinear thresholding mechanism, encoding the rapid
temporal derivative, or slope, of incoming excitation.
Despite previous evidence that inverse stimuli drive feedback inputs to L1 of
V1, and that these L1 inputs can engage active dendritic events, we did not find
that inverse stimuli drove a higher burst fraction than our bottom-up control
stimuli. These results provide direct in vivo evidence contradicting previous
predictions of top-down encoding via burst spikes. The lack of inverse driven
increase in burst fraction (relative to Gabor) could have several explanations.
One, despite the strong ex vivo results tying apical dendritic inputs to burst
spiking, the concurrent recruitment of balanced circuit level inhibition could
prevent apical tuft inputs from generating somatic burst spikes. Second, it is
possible that inverse stimuli are not causally dependent on feedback to the
apical tuft. Previous studies have shown that a substantial amount of V2
feedback arrives peri-somatically, both for L2/3 and L5 neurons. Whether
these deeper-layer inputs carry the requisite information to generate inverse
tuning and are causally involved remains an open question.
We additionally tested whether the sequential presentation of bottom-up
and top-down stimuli would cause an amplification of the burst fraction, as
coincidence detection or visual predictive coding models might predict. Again
contrary to these models, our data showed that the sequential presentation
45
of top-down and bottom-up stimuli (or visa versa) did not produce further
increases to the burst fraction. Instead, we found that the response to Gabor
stimuli was consistently recruited regardless of its preceding stimuli. These
data suggest that inverse stimuli do not form a type of visual prediction which
amplifies future presentations of classical stimuli (or visa versa).
Taken together, these findings suggest a need to revise current theories
of burst coding in the cortex. Rather than acting as a dedicated channel for
top-down information, burst spiking may play a broader role in encoding salient
or strongly driving features of the sensory environment - functions typically
associated with bottom-up pathways. This reframing has implications for both
computational models of cortical processing and our understanding of how
dendritic integration contributes to perception and behavior.
46
Figure 2.1: Detection and characterization of extracellular burst spikes. A.
Extracellular electrophysiological recordings were performed in the primary
visual cortex of awake, head-fixed mice using both acute Neuropixels 1.0 probes
and chronically implanted Neuropixels 2.0 probes. B. Burst criteria and example
burst and single spikes (denoted by blue and red arrows, respectively) from an
example L5 pyramidal neuron. C. Example bursts recorded from neurons in
L2/3 and L5. D. Firing rate and burst fraction as a function of distance from
the L5 MUA peak. Putative L5a neurons (centered on the MUA peak) exhibit
the highest firing rate, whereas burst fraction peaks are centered on L2/3 and
just below the MUA peak (putative L5b).
47
Figure 2.2: Spike sorting and manual curation. A. Raw Neuropixel data was
time-shifted (t-shift) to correct for asynchronous temporal sampling across
channels and artifact-corrected using a common average reference (CAR).
Extracellular spike sorting was performed to identify putative single units
using Kilosort 2.5 and 4. After automated spike sorting, bursty units can be
erroneously split into separate units due to characteristic differences in burst
amplitude and shape. B. Following spike sorting, extensive manual curation
was performed to re-merge over-split bursty units. Left panel: For each unit,
cross-correlograms (CCGs) were examined for all units on adjacent channels,
with a particular focus on identifying heavily asymmetric CCGs with minimal
refractory period violations. Right panel: Putative mis-splits identified via
CCG profiles were further inspected for waveform similarities, and raw trace
inspection was used to confirm that mis-splits units spiked consecutively during
high-frequency bouts of action potentials.
48
Figure 2.3: Extracellular spike bursts decrement in amplitude across time and
space. A. Averaged waveforms at the peak channel (putative somatic location)
for an example L5 neuron. Waveforms are grouped by tonic vs burst spikes
and the position of each spike within a burst. Somatic waveform amplitude
decreases across the duration of a burst. B. Spike-triggered LFP (sLFP) reveals
action potential backpropagation across the cortical depth. Scale denotes
voltage clipped between ±8 µV. C. Spike-triggered CSD (sCSD) profiles of
the field potentials shown in B. Markers indicate significant sources, showing
decreasing action potential backpropagation over the course of a burst. Scale
denotes boot-strapped z-score clipped between ±4.
49
Figure 2.4: Gabor stimuli drive rapid recruitment of burst spiking. A. Ex-
perimental design. Extracellular Neuropixel recording in V1 while presenting
Gabor stimuli. B. Raster plot showing burst and tonic spike firing in response
to a Gabor stimuli presented in the receptive field of an example neuron. Inset
shows long trains of burst spikes occurring at the initial onset of the visual
response. C. Peri-stimulus time histogram of the same neuron as in B. D.
PSTH showing the response of all spikes, and the separation of burst and tonic
spikes across the population (n=669 neurons, 7 mice). Dashed lines indicate
the mean peak latency of each spike type across the population. E. Firing rate
and burst fraction of an example neuron. F. Firing rate and burst fraction
across the population (n=461 neurons, 7 mice). G. Half-peak latencies of burst
spikes compared to all spikes for all neurons which exhibit an early bursting
response (significant burst fraction peak within 150 ms of the Gabor onset,
n=303/461 neurons). The burst spike response occurs significantly earlier
across the population (p<0.001).
50
Figure 2.5: Unsupervised detection of early bursting neurons. A. K-means
clustering of burst vs all-spike response latency for all neurons with a significant
bursting response. Inset: selection of clusters based on within-cluster sum of
squares. B. Same analysis of burst and all spike response latency as in Fig.
2.4G, but for early bursting neurons identified using k-means clustering instead
of a latency threshold (cluster 1, n=300/461 neurons). Burst spikes respond
at a significantly shorter latency than all spikes (p<0.0001). C. Burst - all
spike latency for all neurons. The majority of neurons are in cluster 1, which
exhibits a faster burst latency (significantly left shifted relative to 0).
51
Figure 2.6: Tuning of bursts and tonic spikes to white noise stimuli. A. Full-
field gaussian white noise stimuli are presented for 32 ms with no inter-stim
interval. B. Example tonic and burst spike triggered averages (tSTA and
bSTA, respectively) for a single neuron. C. In single neurons with a significant
bSTA (n=125, 7 mice), the tuning latency of burst and tonic spikes was not
significantly different. D. Neurons with a significantly tuned bSTA exhibit a
faster tuning onset than those without a significant burst spike tuning (n=266
neurons, 7 mice). The rapid tuning latency of burst spikes, even to spatio-
temporally random noise stimuli, is inconsistent with their dependence on
cortical feedback.
52
Figure 2.7: Poisson firing rate model. A. Example Poisson model fitting to a
single neuron. Poisson spike trains are generated to match the firing rate of
the Gabor response, and then the same burst spike criteria is applied to the
Poisson generated spike trains. Top: Raster plot of tonic spikes (orange) and
burst spikes (purple) for an example L5 neuron. Middle: Raster plot of Poisson
simulated spikes and bursts for the sample example neuron. Bottom: Same
data as in the top and middle panels, but zoomed in on the initial onset window
(50-200ms). Poisson generated bursts lack the trial-to-trial structure and early
onset latency of the actual neuron. B. Actual vs Poisson predicted burst rate
for the example neuron in A. Top: Actual firing rate and burst rate plotted
against the burst rate predicted by the Poisson model. Bottom: Model error
(actual - predicted burst rate) across time. The Poisson model fails to capture
the early onset of the actual burst rate, and then over-predicts over the course
of the response. C. Same as B, but plotted using the burst fraction instead of
the burst rate. The burst fraction is left shifted compared to the burst rate
due to periods during which few spikes are fired before the response onset,
but shows the same general trend as the burst rate. D. Actual and Poisson
predicted burst rates across the population (n=461 neurons). Although the
Poisson model approximates burst rate at population level, the mean absolute
error (model error, red) indicates an extremely poor fit for individual neurons.
Model error peaks early, and remains high throughout the response window. E.
Same as in D, but for the population burst fraction.
53
Figure 2.8: Inverse stimuli do not evoke higher burst firing than Gabors.
A. Visual stimuli battery of Gabor, inverse, and full field drifting gratings.
Matching Gabor and inverse receptive fields for an example neuron. B. Example
tuning of a single pyramidal neuron with receptive fields shown in A. Top
and middle: Raster plot and PSTH of Gabor, inverse, and full-field responses.
The response to inverse stimuli is delayed in onset compared to Gabor, and
significantly greater than the full-field response. Bottom: Burst fraction
driven by different grating stimuli. C. Population level firing rate (n=178
neurons, 7 mice). D. Population level burst fraction. E. Firing rate for different
visual stimuli separate by short and long latency bins (short and long latency
responses are defined as 0-150 ms and 150-500 ms, respectively). F. Burst
fraction depicted in the same format as E. The short latency response to Gabor
stimuli evokes a larger burst fraction than all other conditions (p<0.0001).
Inverse stimuli do not drive a higher burst fraction than Gabor in any condition.
F. Same data as in F, but with the pairwise difference between Gabor and
inverse stimuli driven burst fractions for each neuron. Gabor stimuli evoke a
higher burst fraction in the short (p<0.0001) but not long latency conditions
(p=0.083).
54
Figure 2.9: Differences in whisking do not explain lack of inverse stimuli driven
bursting. A. Population firing rate for inverse and Gabor stimuli (n=87, n=89,
respectively) during trials in which animals were not whisking. B. Burst fraction
for inverse and Gabor stimuli during the same stationary trials as in A. Gabor
stimuli drive equal or more bursting than inverse stimuli at all latencies.
55
Figure 2.10: Sequential presentation of Gabor, inverse, and full-field stimuli.
A. Visual stimuli battery. Inverse stimuli are presented directly following
retinotopically matched Gabor stimuli, and visa versa. As a control condition,
Gabor stimuli are presented directly following full-field stimuli. B. Population
level firing rate and burst fraction across the three stimuli conditions. Dotted
line indicates when the second visual stimuli in the sequence was presented. C.
Population firing rate across the three conditions, synchronized to the onset of
the Gabor stimuli (n=130 neurons, 4 mice). D. Mean normalized firing rate
between 75-275 ms after Gabor presentation. Mean firing rate does not change
depending on whether the Gabor was preceded by an inverse of full-field stimuli.
E. Population burst fraction locked to the onset of Gabor stimuli. F. Burst
fraction quantified for the same time period as in D. The burst fraction is not
higher when Gabor stimuli are preceded by inverse compared to any condition.
Instead, Gabors preceded by full-field stimuli recruit a slightly larger burst
fraction than those preceded by inverse stimuli (p=0.035) G. Population burst
fraction aligned to the onset of inverse stimuli. H. Burst fraction quantification
of the data shown in G. Inverse stimuli do not recruit different burst fractions
when preceded by Gabor vs full-field stimuli.
56
Chapter 3
Apical dendritic contributions to
burst spiking and top-down visual
processing in vivo
3.1 Introduction
Although decades of ex vivo physiology have shown that apical dendrites can
powerfully mediate burst spiking, the causal contribution of apical dendrites
to burst spike generation in awake animals remains unknown. In vivo neural
circuits differ substantially in the nature of their synaptic activity, which could
dramatically alter the input-output functions of single neurons. For instance,
top-down inputs to the apical tuft could be accompanied by circuit-mediated
changes in excitatory-inhibitory balance, resulting in a lower burst rate despite
changes in tuning (Anderson et al., 2013).
57
Independent from their role in driving burst spiking, it is also unclear the
extent to what excitatory inputs to the apical tuft are responsible for feedback
driven visual tuning. Compared to top-down inputs from LP or ACC, feedback
projections from V2 are more heavily innervated in the deeper layers of V1, and
functional mapping has shown that LM inputs strongly target the peri-somatic
dendrites of both L2/3 and L5 neurons (Fig. 1.3) (Shen et al., 2022; Young
et al., 2021). Although previous studies have largely focused on the functional
role of L1 inputs (Fişek et al., 2023; Keller et al., 2020; Marques, Nguyen, et al.,
2018), it remains possible that these deep layer and/or peri-somatic projections
are heavily involved in visual feedback tuning.
3.2 Results
We therefore combined extracellular electrophysiology with targeted optogenetic
perturbations to directly investigate the causal role of apical dendrites in both
burst spike generation and top-down visual tuning.
3.2.1 Apical dendritic activity is not required for burst
firing
We first performed graded inhibition of the apical tuft vs soma via a direct
optogenetic inhibition of L5 pyramidal neurons. To do so, we used a Cre-
dependent viral approach to express a halorhodopsin variant (eNphR3.0) in
a semi-sparse population of L5 (Fig. 3.1A) and performed acute Neuropixels
recordings (n=40 neurons, 4 mice). Similar to previous work, we delivered
58
titrated optogenetic inhibition using a fiber optic positioned at the surface of
the brain (Ranganathan et al., 2018). Due to the exponential drop-off of light
intensity as a function of distance and diffusion through cortical tissue, we rea-
soned that this optogenetic inhibition would preferentially inactivate the apical
tuft, particularly at lower LED powers. We validated that electrophysiological
recordings were co-localized with the opsin expression and selectively targeted
to L5 neurons (n=4 mice, Fig. 3.1A,B). Strikingly, the burst fraction during
optogenetic inhibition remained the same as control even at the maximal laser
power, which corresponded to a 40% reduction in firing rate (Fig. 3.1C,D).
As a complementary approach, we next performed a selective inactivation
of apical tuft activity via activation of layer 1 neuron-derived neurotrophic
factor positive (NDNF) interneurons (Fig. 3.1E). NDNF interneurons represent
a distinct subclass of interneurons which are localized to L1 and form direct
inhibitory synapses with the apical tuft dendrites of L2/3 and L5 pyramidal
neurons. NDNF driven inhibition is thought to be mediated by a combination
of GABAA and GABAB release, allowing for long-timescale dendritic inhibi-
tion(Cohen-Kashi Malina et al., 2021). NDNF interneurons additionally target
parvalbumin-positive (PV) interneurons in deeper cortical layers. Thus, NDNF
interneurons provide long lasting apical dendritic inhibition paired with somatic
disinhibition, and are thought to enable a circuit level switch from top-down
to bottom-up signal processing. In order to test the effects of NDNF mediated
inhibition on cortical bursting, we expressed the excitatory opsin ChrMine
in a genetically targeted population of NDNF interneurons, and performed
chronic electrophysiology using Neuropixels 2.0 probes. We found that transient
59
Figure 3.1: Optogenetic inhibition of the apical tuft. A. Experimental design
for L5 inhibition experiments. ENpHR3 is expressed in a subset of layer
5 pyramidal neurons targeted using rbp4-Cre mice (n=40 neurons, 4 mice).
Example histology shows co-localization of the recording electrode and viral
expression. B. Optogenetic inhibition is restricted to layer 5 pyramidal neurons.
C. Firing rate inhibition caused by optogenetic perturbation. D. Burst fraction
does not differ between opto and control conditions at the maximal LED
intensity. E. Experimental design for NDNF-interneuron mediated dendritic
inhibition. ChrMine is expressed in L1 NDNF neurons via NDNF-Cre mice. F.
Opto/control for both firing rate and burst fraction. Burst fraction and firing
rate modulation are not significantly different . G. Burst fraction during control
and opto conditions. H. Distribution of burst fraction differences between opto
and control conditions.
activation of NDNF inteneurons significantly decreased the burst fraction (134
neurons, 3 mice, Fig. 3.1G,H). However, the magnitude of this inhibition was
fairly modest (20% decrease in burst fraction), and not significantly different
from the decrease in firing rate (Fig. 3.1F). Contrary to the prevailing model
of burst spike generation, these results showed that apical tuft engagement
60
was not necessary for the generation of the majority of the burst spikes we
observed in vivo.
3.2.2 Apical tuft dendrites are not required for inverse
tuning
Despite the relatively small contribution of apical tuft activity to burst spike
generation, we reasoned that it was still possible that tuft inputs were selectively
involved in inverse stimuli processing. As such, we performed simultaneous
visual stimuli and apical tuft inhibition via NDNF interneuron activation, and
analyzed the effects of L1 inhibition on both the overall firing rate response
and burst fraction driven by classical and inverse stimuli (n=130 Gabor tuned
neurons, 48 inverse tuned neurons, Fig. 3.2A-D). We found no differences
between the degree of firing rate or burst fraction inhibition between Gabor and
inverse stimuli, strongly suggesting that inverse stimuli are not preferentially
processed by apical tuft inputs (Fig. 3.2B,D). Interestingly, inverse-tuned
neurons were significantly inhibited in their firing rate, but showed no burst
fraction inhibition (Fig. 3.2D). Finally, we tested whether the size tuning
curves to Gabor and inverse stimuli were differentially affected by L1 inhibition.
Previous work has shown that direct inhibition of higher order visual areas
preferentially abolishes the size tuning to inverse, but not Gabor stimuli (Keller
et al., 2020). However, we found that selective apical tuft inhibition did not
affect the size tuning for either Gabor or inverse stimuli, and instead produced
a uniform decrease in response amplitude (Fig. 3.2E).
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Figure 3.2: Effect of apical tuft inhibition on Gabor and inverse responses.
A. Population firing rate during control and optogenetic inhibition during
Gabor (left panel) and inverse (right panel) stimuli presentation. Responses
are normalized to the maximum firing rate during Gabor presentation for each
neuron. B. Opto-control firing rate for both Gabor and inverse conditions. L1
inactivation does not differentially Gabor and inverse firing rates across time.
C. Normalized burst fraction across the population in response to Gabor and
inverse stimuli (left and right panels, respectively). D. Burst fraction difference
(opto-control) for Gabor and inverse. E. Top: Tuning to increasing sizes of
Gabor stimuli centered at the RF location for opto and control conditions.
Bottom: Size tuning for RF centered inverse stimuli. Similar to Gabors, the
size tuning to inverse stimuli is not abolished by L1 inactivation.
3.3 Discussion
In these experiments, we performed the first causal test of whether the apical
tuft is required for in vivo burst spiking or feedback-driven visual tuning.
Contrary to prevailing models derived from ex vivo and computational studies,
our results reveal that apical tuft engagement is not a necessary condition for
the generation of burst spikes in awake animals. Both direct optogenetic and
62
interneuron-mediated inhibition of the apical dendrites failed to abolish the
majority of burst firing, despite robust suppression of overall neuronal activity.
This dissociation between firing rate and burst fraction suggests that burst
generation can occur through mechanisms that do not rely on apical tuft input,
and perhaps more surprisingly, may be predominantly governed by peri-somatic
processes in the intact brain.
These findings challenge a widely held assumption that burst firing is
a hallmark of apical dendrite-mediated top-down integration. While some
component of burst activity may still be supported by dendritic spikes in
the tuft, our data indicate that this is not the dominant mode in awake, non-
anesthetized animals. One interpretation is that the tightly regulated excitatory-
inhibitory (EI) balance in vivo, particularly in L1, actively suppresses the
conditions required for dendritic spike initiation. This aligns with prior reports
suggesting that apical dendrites may serve more modulatory or subthreshold
integrative functions, rather than acting as discrete burst generators under
passive behavioral states.
Furthermore, our experiments show that top-down visual tuning, such as
size tuning to inverse stimuli, is not selectively abolished by inhibition of the
apical tuft. Despite anatomical projections from higher-order areas like LP or
V2 into L1, and prior work implicating these projections in feedback modulation,
we found no evidence that tuft input is necessary for stimulus-specific tuning
effects. Even in superficial layers, top-down inputs from V2 show substantial
peri-somatic targeting to L2/3 pyramidal neurons in V1, suggesting that L1
inputs need not be integrated by the apical tuft. Another possibility is that
63
feedback-driven tuning may be mediated predominantly through alternative
pathways—specifically, projections to deeper layers of V1, which are often
overlooked in favor of L1 inputs. This hypothesis is supported by anatomical
studies showing that V2 and other feedback sources robustly innervate layers
beyond L1, including L5 and L6.
Importantly, our findings do not entirely rule out the functional significance
of apical tuft activity. It is possible that under specific behavioral states, such
as multi-sensory processing or active learning, the apical tuft could play a more
prominent role in modulating output or biasing sensory processing. Future
experiments could explore these possibilities using more complex behavioral
paradigms paired with temporally precise dendritic inhibition.
64
Chapter 4
Final Conclusions
The ability to integrate sensory information from the outside world with inter-
nal predictions, contextual knowledge, and goals is a fundamental computation
performed by the neocortex. Decades of anatomical and physiological research
have suggested a framework for how this computation could be implemented
at the single cell and circuit level, through the combination of spatially segre-
gated inputs and dendritic biophysics. This framework, in turn, has informed
theoretical models proposing algorithmic-level explanations for how the brain
could integrate different streams of information to perform both computation
and learning. At present, these models have exposed key predictions for how
bottom-up and top-down information are processed at the single cell level which
have not yet been validated in vivo. Here, we bridge this gap by specifically
investigating the role of burst and tonic spikes in encoding top-down and
bottom-up information, and the causal contributions of apical tuft dendrites
to burst spiking and top-down visual processing.
65
4.1 Visual tuning of cortical burst and tonic
spikes
In Chapter 2, we found that burst spiking is not preferentially recruited by
top-down visual stimuli. Instead, the highest burst fraction that we observed
was in the short latency response to a classical receptive-field stimuli thought to
be mediated primarily by bottom-up inputs. When we sequentially presented
top-down and bottom-up stimuli, we did not observe supra-linear amplification
of the responses that would be predicted by theories of dendritic coincidence
detection. In these experiments, we found that the Gabor driven firing and
burst fraction were not affected by the preceding stimuli. Taken together, results
strongly contradict the idea that the burst fraction preferentially encodes higher-
order inputs to the apical tuft. Instead, the strong feedforward recruitment
of high-frequency firing suggests that burst generation in vivo is regulated
by strong peri-somatic drive. Interestingly, many of the extracellular burst
spikes that we recorded showed decrements in both spike amplitude and signal
backpropagation, which are both consistent with the presence of the regenerative
currents characteristic of apical-dendrite mediated bursting ex vivo. These
findings pose a considerable biological constraint for models which rely on burst
spiking to encode top-down driven activity for either computational or learning
related functions. The lack of inverse stimuli driven burst spiking posed a
further question - did we fail to observe these events because (1) the apical
tuft is not strongly coupled to burst spiking, or (2) the inverse stimuli did not
strongly drive tuft activation?
66
4.2 Causal investigation of the apical tuft
To answer these questions, we directly tested the causal involvement of apical
dendrites in Chapter 3. Contrary to the ex vivo evidence linking apical dendritic
engagement to burst firing, we found that inhibition of apical dendrites—either
via direct optogenetic silencing of L5 apical tufts or NDNF interneurons L1
inhibition—had a minimal effect on the burst fraction. This suggests that apical
dendrites are not essential for generating the majority of the burst spikes we
observed in vivo, consistent with our finding that they are strongly recruited by
bottom-up stimuli. This may be explained by more cell-intrinsic mechanisms
of burst firing (Izhikevich, 2003; Kepecs et al., 2002). Our perturbation
experiments also showed that the burst fraction is highly robust to decreases in
the overall firing rate. In particular, it was striking that the burst fraction did
not change at any laser titration during our halorhodpsin-mediated inhibition
of L5 neurons - even when the firing rate was inhibited by 40%.
Although inverse did not drive a higher burst fraction than Gabor stimuli,
we reasoned that it was still possible that their tuning was preferentially
mediated by the apical tuft. This could be the case if the inverse driven
inputs simultaneously recruited feedback inhibition. However, we found that
optogenetic activation of NDNF-interneurons did not preferentially affect either
the firing rate or burst fraction for inverse versus Gabor stimuli. Unlike the
direct inhibition of V2 reported in previous work (Keller et al., 2020), the
targeted inhibition of layer 1 in V1 did not cause a selective abolishment of
inverse tuning to stimuli size. Instead, the uniform decreases across both Gabor
67
and inverse stimuli in response to apical dendritic inhibition suggests that
inverse tuning is not driven by feedback projections into L1. Thus, our findings
challenge the conventional view that top-down feedback relies predominantly
on L1 and apical tuft signaling, and instead suggest a much more prominent
role for deeper layer feedback projections.
4.3 A revised framework for top-down and bottom-
up integration
Taken together, these results provide new insights into the mechanisms of
burst spike generation and top-down visual processing in vivo. They suggest
that, contrary to prevailing models, apical dendrites are not critical for high-
frequency burst firing, and that feedback modulation of visual processing is
not exclusive to the canonical L1 pathway.
At the cellular level, the strong bottom-up recruitment of burst firing
supports previous theories that bursts serve to enhance the transmission of
sensory information (lisman_bursts_1997; Kepecs et al., 2002). Instead
of relying on apical dendritic inputs, these bursts may be strongly driven by
mechanisms such as spatio-temporally precise inputs or more proximal-driven
activation of the apical trunk (Bast et al., 2023). These results suggest that
the burst fraction does not uniquely encode a multiplexed representation of
top-down and bottom-up inputs. However, it remains possible that bursts could
be strongly recruited by top-down inputs or learning signals under different
experimental conditions. One caveat to these experiments is that it is unclear
68
whether the extracellular recording technique utilized in these experiments can
capture action potentials when neurons enter the depolarization block associated
with large intracellular plateau potentials. As a result, these experiments may be
unable to sample the intracellular plateau potential driven bursts most strongly
associated with plasticity. In the hippocampus, for instance, high-frequency
bursts are ubiquitous and contain different information than tonic spikes (Lowet
et al., 2023), and yet are clearly distinct from the plateau potentials which
cause one-shot remapping of place cell tuning (bittner_conjunctive_2015;
Bittner et al., 2017). Do tonic spikes, bursts, and plateaus represent three
distinct output modes that single neurons can differentially leverage? Future
work, including ground-truth validation of extracellular techniques, will be
critical for a more nuanced understanding of these phenomena.
At the circuit level, these findings point towards a prominent role for deep
layer feedback inputs from V2 to V1 in generating extra-classical visual tuning.
Despite the fact that these projections represent the majority of V2 to V1
feedback, they remain critically under-characterized. It remains unknown
whether their functional tuning or progenitor neurons overlap with those of
the superficial V2 to V1 projections. Compared to higher order visual cortices,
feedback projections from many motor and prefrontal cortices and high-order
thalamus to V1 are more precisely restricted to L1. This raises the possibility
that different long range inputs may exploit functionally different laminar
targeting despite having a qualitatively similar organization (i.e. targeting to L1
and deeper layers). The significance of this differentiation between hierarchical
feedback within the same sensory modality and cross-modal projections offers
69
a promising direction for further investigation. That the apical dendrites in
L1 do not process passive visual information may actually represent a critical
feature for more complex functions of top-down processing, although it remains
an open question what their role is in vivo. Here, laminar specific inactivation
approaches such as the direct apical and NDNF-activation strategies utilized
in this work present powerful tools for future causal studies of apical dendritic
computation.
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Appendix A
Methods
A.1 Animals
All experiments were compliant with guidance and regulation from the NIH and
the Massachusetts Institute of Technology Committee on Animal Care. We used
C57Bl6 mice for acute Neuropixel 1.0 and chronic Neuropixel 2.0 experiments,
Rbp4-Cre heterozygous mice for all acute Neuropixel 1.0 experiments with L5
optogenetic inactivations, and NDNF-Cre homo/heterozygous for all chronic
Neuropixel 2.0 experiments with NDNF interneuron mediated suppression of
L1. Male and female mice were used for all experiments, and were 8-12 weeks
of age at the beginning of experiments. All mice were maintained on a 12-hour
light/dark cycle in a temperature and humidity-controlled room with ad libitum
food and water access.
71
A.2 Surgery
Mice were anesthetized using 4% isoflurane in 1 l/min oxygen and subsequently
maintained using 1-2% isoflurane over the course of the surgery. Following
anesthetization, mice were placed on a closed-loop heating pad to maintain
physiological body temperature, and eye ointment (Bepanthen, Bayer) was
applied to prevent the eyes from drying. We delivered Dexamethasone (4 mg/kg)
and Buprenorphine (extended release, 0.5 mg/kg) subcutaneously to provide
post-surgical analgesia. The scalp was first shaved using hair clippers, and then
remaining hairs were removed using a depilatory cream (Nair). The position of
the head was leveled along the anterior-posterior (AP) and mediolateral (ML)
axes. The exposed skin and surrounding area were cleaned using alternating
passes of iodine solution and ethanol, and the skin over the skull was excised.
We then marked the target location over left monocular V1 (3.6 mm AP, 2.7
mm ML from bregma), which was the same for all experiments. Additional
surgical procedures were performed for acute and chronic electrophysiology
experiments as follows.
A.2.1 Acute procedures
For acute recordings without optogenetic perturbations, a custom metal head-
plate was fixed to the skull using clear dental cement (C&B Metabond, Parkell),
and mice were transferred to post-operative care. For L5 inhibition experiments
in Rbp4-Cre mice, a small burr hole (2-300 µm) was made at the target site, and
a glass pipette containing pAAV-hSyn-eNpHR 3.0-EYFP (Addgene, catalog #
72
26972-AAV5) at a dilution of 1x1012 vg/ml was lowered to 575 µm below the
cortical surface. We waited at least 5 minutes for the pipette to settle , and
then pressure injected 100 nL of virus at a rate of 20 nL/min. The virus was
allowed to diffuse for at least 5 minutes following completion of the injection
before the pipette was removed. The burr hole was covered using a clear dental
cement (Ivoclar Vivadent Tetric EvoFlow), and a custom metal headplate was
then fixed to the skull using opaque cement (C&B Metabond, Parkell). Mice
were subcutaneously injected with up to 25 mL/kg of Ringer’s solution at
the end of the procedure. Recordings were performed 4-6 weeks post-surgery.
Prior to the recording experiment, we habituated mice to human handling and
head fixation on a treadmill for at least 3 consecutive days, and progressively
increased head fixation duration up to 45 minutes.
On the day of recording, we performed a short second surgery to expose
the cortex. For these surgeries, mice were induced at 2% isoflurane, and
maintained at 1-2% isoflurane over the course of the surgery. Body temperature
was maintained using a heating pad as described above, and a protective eye
ointment was applied immediately following anesthetization (Bepanthen, Bayer).
We removed the clear cement over the target recording site, and performed
either a burr hole or small craniotomy to expose the cortical surface. The
dura was left intact. In order to maintain a saline well during recording,
and to limit off-target light leakage, a small circular well was made with
dental cement surrounding the exposed cortex. This well was filled with a
mixture of low melting point agarose (Durand et al., 2023), followed by a
layer of silicon elastiomer (Kwik-Cast, WPI). We applied thin stripes of dental
73
cement over the well to provide additional mechanical protection. Mice were
subcutaneously injected with Ringer’s solution to maintain hydration, then
removed from anesthesia and allowed to recover in a heated cage for 3-4 hours
before recordings.
A.2.2 Chronic procedures
For chronic Neuropixel implants, we drilled a 2 mm craniotomy centered over
the same V1 target location described above (3.6 mm AP, 2.7 mm ML from
bregma). The dura was left intact, and surgical foam (Surgifoam) was used to
maintain cortical hydration. We drilled a small burr hole over the cerebellum of
the opposite hemisphere, and implanted a sharpened silver wire for grounding.
A custom-printed plastic base plate was affixed to the skull using dental cement
(Ivoclar Vivadent Tetric EvoFlow). We then centered a custom-fabricated
microdrive (see A.x.x Silicon probe preparation) with a Neuropixel 2.0 probe
over the V1 target site. The four shanks of the probe were angled at a 45°angle
relative to the midline. Small adjustments in the angle and placement of
the four shanks (+-100 µm AP, ML) were made to avoid blood vessels. To
accommodate for these adjustments to the four-shank placement, we positioned
the probe before performing viral injections for optogenetic experiments to
ensure maximal co-localization between the injection and recording sites. For
these experiments, we took reference images of each shank at the cortical
surface. We then removed the probe, and used a glass pipette to inject 100 nL
of pAAV-nEF-Con/Foff 2.0-ChRmine-oScarlet (Addgene, catalog # 137161-
AAV8) at each of the four electrode shank positions. Injections were performed
74
at a depth of 175 µm below the cortical surface, and injected at a rate of 20
nL/s. To prevent backflow, we waited at least 5 minutes before and after each
injection. We then repositioned the probe microdrive to the same location,
and performed the same implant procedure for NDNF-Cre and BL6 mice.
Probes were inserted to a depth of 3.5-4 mm at a speed of 10 µm/s using
a programmable micromanipulator (Scientifica). Following probe insertion,
we attached the microdrive to the skull using dental cement, and filled the
craniotomy using Dura-gel (Cambridge NeuroTech). The implanted ground
wire was soldered to the previously prepared grounding/reference wire on the
Neuropixel probe. We installed an electrically shielded cage to the base plate,
and used a self-adhesive bandage to enclose the implant. The design of the
cage and base plate system was adapted from Vöröslakos et al., 2021. We
delivered multiple injections of Ringers solution (2-3 deliveries) subcutaneously
over the course of the surgery to maintain hydration, up to a final dose of 25
mL/kg. Mice were allowed to recover for at least three days post-operation,
and were habituated to human handling and head fixation for at least 3 days
prior to the beginning of experiments.
A.2.3 Silicon probe preparation
To minimize damage to cortical tissue during probe insertion, we sharpened all
Neuropixel probes (1.0 and 2.0) using a custom-built sharpening device, follow-
ing a publicly available protocol (https://github.com/billkarsh/SpikeGLX/blob/gh-
pages/Support/NPix_sharpening.docx). We soldered a silver ground wire to
the ground and reference pins for all probes. Probes were coated in either DiI or
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DiO lipophilic dyes (Invitrogen, ThermoFisher) immediately prior to insertion
or implantation to allow for histological reconstruction of the recording site.
Acute recordings were performed using Neuropixel 1.0 probes. Following each
acute recording, probes were soaked in overnight in 1% Terg-a-zyme (Sigma-
Aldrich) and then rinsed with DiI water and sterilized in ethanol. We used
Neuropixels 2.0 probes for all chronic recording experiments, and attached
them to recoverable, custom-fabricated metal microdrives (design adapted from
Vöröslakos et al., 2021). To clean probes recovered from chronic implants, we
soaked the electrode shanks in alternating baths of 1% Terg-a-zyme, Dowsil
DS-2025 (Dow-Corning), and ethanol.
A.3 Electrophysiology
A.3.1 Acute recordings
Acute electrophysiological recordings were performed at least 3-4 hours after
the burr hole surgery (section A.2.1), and after mice exhibited full motor
recovery and self-groomed to remove the protective eye cream. We first head
fixed the mice and removed the protective layers of dental cement and Kwik-
cast. We then filled the well over the recording site was with saline, and
placed a chlorinated silver wire into the bath and connected to the ground
and reference sites of the Neuropixel probe. A micromanipulator (Luigs &
Neumann) was used to position a Neuropixel 1.0 probe at the burr hole site.
We adjusted the angle of the probe in order to approximate a normal insertion
relative to the cortical surface. For optogenetic experiments, we used a second
76
micromanipulator to position an optical fiber directly above the cortical surface
before probe insertion. The probe was then advanced at 1.5 µm/s until the
cortical depth was spanned ( 1500 µm), and then allowed to settle for 15 minutes
before the beginning of recording. Data acquisition was performed using a
National Instruments system (PXIe-1071) and bandpass filtered into AP and
LFP bands (0.3-10 kHz and 0.5-500 Hz, respectively) through SpikeGLX. AP
and LFP band data were collected at 30 kHz and 2.5 kHz, respectively. At
the end of each session, we slowly withdrew the electrode at the same speed
as insertion (1.5 µm/s) and covered the recording sites with Kwik-cast before
preparing the animal for histology.
A.3.2 Chronic recordings
On each day of chronic recording, mice were head fixed and the implant
covering was removed. For optogenetic experiments in NDNF-Cre mice, we
used a micromanipulator to position an optical fiber over the craniotomy site,
and wrapped the entire implant with opaque tape in order to minimize light
leakage. The recording headstage was then attached, and a custom channel
map was loaded for each animal. We sampled uniformly across all four shanks,
resulting in 96 channels spanning 720 µm of contiguous cortical depth per shank.
Due to constraints in the flexibility of channel selection, we biased channel
selection to maximize coverage of cortical layers 1-5. Spiking and LFP data
were acquired in the same data stream at 30 kHz using a National Instruments
system (PXIe-1083) and bandpass filtered (0.5 Hz - 10 kHz) through SpikeGLX.
In a subset of mice, we performed freely behaving recordings in the home cage
77
to capture periods of putative sleep. For these recordings, we temporarily fixed
the headstage to the implant body and then replaced the implant covering. We
then removed the mice from head fixation and transferred them into their home
cages. At the end of each recording session, we removed the headstage and
replaced the implant covering. We recorded from individual mice for up to two
months after probe implantation, and recovered the probes before histology.
A.4 Optogenetics
We controlled light delivery for optogenetic perturbations using a Cyclops
LED driver (OpenEphys, Catalog # OEPS-6602) triggered by an external
Arduino signal. The Cyclops driver was connected to a fiber-coupled LED,
which terminated in a 400 µm optical fiber positioned above the cortical surface.
We used a 595 nm LED (8.7 mW, 1000 mA, ThorLabs catalog # M595F2) to
provide excitation for ChRmine experiments, and a 455 LED (17 mW, 1000
mA, Thorlabs catalog # M455F3) for eNpHR3.0 experiments. Titrations in
light intensity were programmed through the Cyclops library (Newman et al.,
2015). Light delivery was initiated at the onset of visual stimuli, and was
performed for a fixed duration (400 or 500 ms) with a square wave profile. For
direct L5 pyramidal neuron inhibition experiments (eNpHR3.0), we activated
the LED for the entire duration of the visual stimuli (500 ms), and for NDNF
interneuron activation experiments (ChRmine) we reduced the optogenetic
perturbation to 400 ms to account for the long timescale of NDNF mediated
inhibition (Cohen-Kashi Malina et al., 2021).
78
A.5 Visual stimulation
We generated visual stimuli using the open-source PsychoPy toolbox based
in Python (Peirce et al., 2019). All stimuli were presented on an LED-backlit
LCD monitor with a refresh rate of 60 Hz, at a distance of 15 cm to the right
eye (approximately 135° x 75° coverage of visual space). We used screen refresh
cycles to define presentation time in order to maximize timing accuracy, and
sent TTL pulses at the onset of each visual stimuli for post hoc synchronization
to the electrophysiological data. All visual stimuli were block randomized.
Our visual stimuli battery contained white noise stimuli and three different
types of drifting gratings: Gabor stimuli, inverse stimuli, and full field drifting
gratings. For the white noise stimuli, we generated a 25 x 14 grid of squares
spanning the full screen ( 5.4 °per square), and randomly sampled the color
(black to white) of each square from a uniform distribution. White noise stimuli
were presented for 2 frames ( 33.3 ms), with no inter-trial interval between
stimuli. Gabor stimuli consisted of a circular drifting grating patch presented
on a full-field gray screen, and inverse stimuli consisted of a full-field drifting
grating masked by a circular gray patch. The location of Gabor and inverse
stimuli in visual space were defined as the center of their respective circular
patches. To mitigate confounds caused by visual edge effects, we used a raised
cosine filter to blur the edges of the patches for both classical and extra-classical
stimuli. All drifting gratings were sinusoidal (0.04 cycles per degree, 2 Hz
temporal frequency) and were presented at 100% contrast. (Keller et al., 2020).
Unless otherwise specified, grating stimuli were presented for 30 frames (500
79
ms) and separated by an inter-trial interval of 450-500 ms, during which the
screen was held an equiluminant gray. We presented all drifting gratings at the
same initial phase (0°, black grating centered at onset) to ensure that all latency
analyses were performed across phase-matched conditions (see A.9.x Response
latency). Full field gratings were centered at the center of the screen unless
otherwise specified. For optogenetic perturbation experiments, we multiplied
the number of trials by the number of laser power titrations, and adjusted
the spatial sampling of Gabor and inverse stimuli as necessary. Optogenetic
perturbation trials were block randomized (i.e. different light titrations and
non-opto control trials were randomly interleaved).
In the first recording session for each chronic mouse, we presented 20000
white noise stimuli and/or Gabor stimuli tiling the entire screen. The Gabor
stimuli (20°visual size) covered an even grid pattern with 15°of separation
between the center of each stimuli. We presented 2 different directions per
location, and repeated each unique Gabor stimuli for 10-15 trials. We estimated
the receptive field of each neuron offline using either the white noise or Gabor
(see A.9.3 Receptive field mapping). In subsequent experiments, we used the
averaged population RF to reposition the monitor as necessary, and to fine-tune
the positioning of Gabor and inverse stimuli.
A.5.1 Gabor vs inverse tuning
For baseline comparisons between Gabor and inverse, we presented Gabor,
inverse, and full field stimuli. The Gabor and inverse stimuli were tiled along
a square grid of visual locations (15°spacing) centered at the population RF
80
estimated for each mouse. The patch size was identical (20°) for Gabor and
inverse stimuli. Each stimuli was presented at 2 different directions (0 and 90°)
per location, and each unique stimuli was presented for 15-20 trials.
A.5.2 Size tuning
To evaluate size tuning, we presented patches of 6 different sizes (5, 15, 25,
35, 45, and 65°). We presented each unique stimuli at 2 different directions,
and adjusted the extent of spatial tiling for the Gabor and inverse stimuli to
maintain at least 15 trials per stimulus.
A.5.3 Orientation tuning
For orientation tuning experiments, we presented Gabor, inverse, and full field
stimuli at 8 different directions (equally spaced from 0 to 360°, 20 trials per
stimulus). For these experiments, we presented 20°Gabor stimuli, and increased
the size of the gray mask in the inverse stimuli to 35°to minimize classical
RF-related contamination of the inverse response.
A.5.4 Sequential stimuli
We created three types of sequential stimuli (Gabor -> inverse, inverse ->
Gabor, full field -> Gabor) by presenting pairs of drifting grating stimuli with
no inter-trial interval. Sequentially delivered Gabor and inverse stimuli (and
visa-versa) were presented at the same location and had the same patch size
(20°), such that the drifting grating and gray portions of the screen were exactly
81
inverted at each transition. For these sequential stimuli, we also matched the
"center" location of each full field grating to their paired Gabor stimuli. All
pairs of stimuli were presented with at the same direction (0 or 90°), initial
phase (0°), and spatial frequency (2 Hz). Each individual stimuli was presented
for 500 ms, resulting in an exact phase match (occurring after 1 full cycle of
the preceding sinusoidal grating) at the sequential transitions. Each pair of
sequential stimuli was followed by an inter-trial gray screen (450-500 ms). We
presented 20 trials for each unique configuration.
A.6 Behavioral monitoring
Mice were awake and free to run on a treadmill during all head-fixed experiments.
We recorded locomotion using an optical encoder (E6, US Digital, 2500 cycles
per revolution), and facial movements using an infrared camera (Flir Blackfly,
Teledyne) trained on the left side of the mouse’s face. Behavioral data was
acquired and saved using Bonsai (Lopes et al., 2015), and synchronized to the
neural recording post-hoc via shared TTLs. During homecage recordings, the
monitor was turned off, and an overhead infrared camera was used to track
motion.
A.7 Histology
For chronic Neuropixel experiments, the probe microdrives were recovered
before histology. We anesthetized mice with 5% isoflurane, and maintained
anesthesia at 1-2%. The implant covering and protective cage were removed,
82
and the silver ground wire connecting the probe to the skull was carefully
disconnected using wire clippers. The probe microdrive was affixed to a
stereotaxic arm, and then withdrawn from the brain at 2.5 µm/s. Following
probe removal, the Dura-gel covering the craniotomy was reinforced using
Kwik-Cast, and mice were prepared for histology.
Mice were deeply anesthetized with 5% isoflurane and transcardially per-
fused with phosphate-buffered saline (PBS) followed by 4% paraformaldehyde
(PFA) in PBS. After overnight fixation in 4% PFA, we washed the brains in
PBS and took 100 µm thick coronal sections using a floating section vibratome
(Leica VT1000s). Slices were mounted and coverslipped in a mounting medium
containing DAPI (Vectashield H-1500-10, Vector Laboratories). Confocal im-
ages were acquired using a Leica TCS SP8 microscope with a 10x objective
(NA 0.40).
Image pre-processing and curation was performed using Fiji (Schindelin
et al., 2012), and all slices containing probe tracks (identified by fluorescent
DiI or DiO tracks) were registered to the Allen Common Coordinate Frame-
work (CCFv3) using SHARP-Track (Shamash et al., 2018). SHARP-Track
reconstructions were used to validate the targeting of probes to V1. We used
eYFP (co-expressed with eNpHR3.0) and oScarlet (co-expressed with ChRmine)
fluorescence to verify that opsin expression was co-localized with the electrode
tracks for the apical tuft perturbation experiments.
83
A.8 Data analysis
A.8.1 Spike-sorting
Raw electrophysiological data was time-shifted and common-average referenced
using SpikeInterface (Buccino et al., 2020). We performed automated spike sort-
ing using either Kilosort 2.5 or 4 (Pachitariu et al., 2016, 2024). Putative single
units were manually curated using Phy 2.0 (Rossant & Harris, 2013) as follows.
First, all clusters that were identified by Kilosort as ‘mua’ or ‘noise’, or had a fir-
ing rate of < 0.1 Hz, were automatically rejected. Then, we split or merged clus-
ters based on the auto and cross correlograms, following the procedures detailed
in the Manual Clustering Practical User’s Guide (by S. Lenzi and N. Steinmetz,
publicly available https://phy.readthedocs.io/en/latest/sorting_user_guide/).
Special care was taken to look for erroneously split clusters stemming from
single bursting cells. These over splits are likely due to changes in waveform
shape and amplitude across consecutive action potentials within a burst, which
has been characterized in both intra and extracellular recordings (Allen et al.,
2018; Connors et al., 1982; Fee et al., 1996; Henze et al., 2000; McCormick et al.,
1985). We separated regular spiking (RS) and fast spiking (FS) units based on
the peak-to-trough time of each unit’s average waveform. Putative excitatory
neurons (RS) were defined as those with a peak-to-trough greater than 0.4 ms,
and putative inhibitory neurons (FS) as those with a peak-to-trough less than
or equal to 0.4 ms (Barthó et al., 2004; Niell & Stryker, 2008; Sirota et al.,
2008). RS and FS units were separated for all analyses.
84
A.8.2 Layer identification
We used the depth profile of previously reported physiological landmarks to
estimate laminar depth (Senzai et al., 2019). We first bandpass filtered the
local field potential (LFP) between 0.5-500 Hz and averaged it over visual
stimuli presentations. Neuropixel 2.0 data was temporally downsampled to 2.5
kHz to match Neuropixel 1.0. For L4 estimation, we spatially smoothed the
LFP (Gaussian kernel, σ = 30 µm) and took its second spatial derivative to
estimate the current source density (CSD) (Mitzdorf, 1985). L4 was identified
by the prominent short latency current sink (Niell & Stryker, 2008). To identify
L5, we band-pass filtered the raw AP band signal between 0.5 - 5000 Hz, and
used the Hilbert transform to calculate the analytic amplitude at each channel
(MUA power). The channel with the highest MUA power was designated the
center of L5 (Oude Lohuis et al., 2024; Senzai et al., 2019). For layer specific
analysis, we conservatively assigned units within +-100 µm of the MUA peak
to L5, and units >275 µm above MUA peak to L2/3.
A.8.3 Burst spike analysis
We defined high-frequency bursts as events with three or more consecutive
spikes with ISIs 10 ms. We classified burst spikes (b) as all events occurring
within a burst, and tonic spikes (t) as all events outside a burst. The burst
fraction (BF ) was defined as the number of burst spikes divided by the total
number of spikes (BF = b/(b+t)). For estimating the burst fraction across time,
we first calculated smoothed peri-stimulus time histograms (PSTHs) separately
85
for burst and tonic spikes (1 ms binning, Gaussian smoothing with σ = 5-10 ms).
The resulting smoothed burst and tonic firing rates were then used to estimate
an instantaneous burst fraction similar to above (BF = FRb/(FRb + FRt)).
A.8.4 Spike backpropagation
For a subset of recordings, we calculated the spatial spread of action potentials
along the vertical axis of the recording electrode. For individual neurons, we
extracted AP band (0.5 - 10 kHz) data across all channels triggered on spikes
detected at the soma. We averaged these signals separately for burst and tonic
spikes, and took the second spatial derivative to remove volume conducted
signals. Spike triggered activity at each channel was z-scored against the
window directly preceding the spike.
A.8.5 White noise analysis
For each neuron, we computed calculated the spike-triggered average (STA)
of the white noise stimuli using burst, tonic, and all spikes. STAs were
calculated by reverse correlating each spike with the preceding white noise
stimuli (Chichilnisky, n.d.; Schwartz et al., 2006). To identify tuned neurons,
we used a shuffled STA (generated by randomly circularly shuffling the timing
of the stimuli with respect to the spikes) to compute a z-scored significance.
We considered neurons visually tuned if they exhibited a significant response
(z-score > 4) at any point within 200 ms. For each neuron, we defined the
response latency as the time at which z-scored tuning crossed threshold, and
the classical receptive field as the maximally tuned position in visual space.
86
A.8.6 Receptive field mapping
Classical receptive fields were mapped using white noise (see above) and Gabor
stimuli. For each neuron, we computed the evoked response (average firing rate
from 0-500 ms) to Gabor stimuli at each spatial location. We next generated a
null distribution by computing the evoked response after shuffling the location
across all Gabor presentations. We considered neurons receptive field tuned if
they exhibited a significant evoked response at any spatial location (p < 0.05
compared to the shuffle distribution) and their evoked response was significantly
greater than the pre-stimulus baseline (average firing rate 100 ms before stimuli
presentation). We defined the classical receptive field as the position at which
each RF tuned neuron responded maximally to Gabor stimuli. Only Gabor
stimuli 25°in size were used for receptive field mapping.
A.8.7 Inverse tuning
For all neurons with a classical receptive field, we calculated the evoked response
to inverse stimuli centered at the classical RF location. Neuron were considered
inverse tuned if their cRF centered inverse response (of any size) was significantly
greater than baseline (100 ms preceding) and the evoked response to full-field
stimuli. We did not approximate the response to full-field stimuli using Gabor
or inverse stimuli of different sizes.
87
A.8.8 Response latency
To estimate response latency, we calculated the smoothed firing rate and burst
fraction for grating stimuli presented in each neuron’s cRF as described above
(see A.x.x Burst fraction). We defined the response latency as the half-peak
time of the first peak for both firing rate and burst fraction. For burst latency
comparisons, we excluded units which did not exhibit a significant burst fraction
peak. For analyses in Fig. ??, we considered neurons to be early bursting if
their burst fraction response latency was < 150 ms.
We performed an unsupervised categorization of response latency using
k-means clustering of each neuron’s firing rate and burst fraction latencies. The
optimal number of clusters was determined by calculating the within-cluster
sum of squares (inertia) across different cluster sizes (Fig. 2.6.
A.8.9 Poisson model
We used a Poisson spiking model to examine whether the Gabor stimuli evoked
bursting could be explained by firing rate (Fig. 2.7). To do so, we simulated the
spiking response for individual neurons using an inhomogeneous Poisson model
matched to each neuron’s smoothed firing rate responses (Marmarelis & Berger,
2005). We then calculated the smoothed burst fraction of the simulated spike
train using the same criteria as for the neural data (see Burst spike analysis).
The model error for each neuron was defined as the absolute difference between
the actual and Poisson burst fractions.
88
A.8.10 Optogenetic inhibition
To identify optogenetically inhibited neurons, we compared the baseline cor-
rected firing rates (trial averaged) across opto and non-opto trials. For sessions
in which multiple LED powers were delivered, we used the maximum power
to identify inhibited neurons. Only neurons which were significantly inhibited
were included in subsequent analyses.
A.8.11 Behavioral state
We used FaceMap (Syeda et al., 2024) to extract whisking and eye movement at
a sampling frequency of 200 Hz. These behavioral variables, were trial synchro-
nized to visual stimuli presentation. We calculated the trial averaged changes
in behavior for each variable. For each distribution of trial averaged behavioral
data, we noticed that the median value corresponded to a stationary state (i.e.
no whisking or eye movement). To separate stationary from movement trials,
we fit a half-Gaussian model to data below the median. We then classified
stationary trials as those within 2 standard deviations (based on the Gaussian
model) of the median value.
A.9 Statistics
All statistics were performed using the scipy package in Python. For normally
distributed data, we used Student’s t-tests for paired and independent sample
comparisons. Otherwise, Wilcoxon signed-rank tests were used for independent
89
sample comparisons, and Mann-Whitney U tests were used for paired tests.
Barplot error bars indicate the 95% confidence interval. All other error bars
represent the standard error of the mean (SEM).
90
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