Fast state-space methods for inferring dendritic synaptic connectivity
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10827_2013_Article_478.pdf
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Author(s)
Pakman, Ari
Smith, Carl
Paninski, Liam
Huggins, Jonathan H.
Date Issued
September 2013
Journal
Journal of Computational Neuroscience
Publisher
Springer US
Citation
Pakman, Ari et al. “Fast State-Space Methods for Inferring Dendritic Synaptic Connectivity.” Journal of Computational Neuroscience 36.3 (2014): 415–443.
Version
Author's final manuscript
Abstract
We present fast methods for filtering voltage measurements and performing optimal inference of the location and strength of synaptic connections in large dendritic trees. Given noisy, subsampled voltage observations we develop fast l[subscript 1]-penalized regression methods for Kalman state-space models of the neuron voltage dynamics. The value of the l[subscript 1]-penalty parameter is chosen using cross-validation or, for low signal-to-noise ratio, a Mallows’ C[subscript p]-like criterion. Using low-rank approximations, we reduce the inference runtime from cubic to linear in the number of dendritic compartments. We also present an alternative, fully Bayesian approach to the inference problem using a spike-and-slab prior. We illustrate our results with simulations on toy and real neuronal geometries. We consider observation schemes that either scan the dendritic geometry uniformly or measure linear combinations of voltages across several locations with random coefficients. For the latter, we show how to choose the coefficients to offset the correlation between successive measurements imposed by the neuron dynamics. This results in a “compressed sensing” observation scheme, with an important reduction in the number of measurements required to infer the synaptic weights.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10827-013-0478-0
