Achieving stable dynamics in neural circuits
Name
pcbi.1007659.pdf
Description
Published version
Size
1.06 MB
Format
Adobe PDF
Checksum (MD5)
c1fcac20b4e2d8f82330fdcbc7a41c04
Author(s)
Kozachkov, Leo
Lundqvist, Mikael
Slotine, Jean-Jacques
Miller, Earl K
Date Issued
2020
Journal
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Version
Final published version
Abstract
© 2020 Kozachkov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The brain consists of many interconnected networks with time-varying, partially autonomous activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable, reproducible state (or sequence of states) for its computations to make sense. We approached this problem from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included inhibitory Hebbian plasticity, excitatory anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. Our findings shed light on how stable computations might be achieved despite biological complexity. Crucially, our analysis is not limited to analyzing the stability of fixed geometric objects in state space (e.g points, lines, planes), but rather the stability of state trajectories which may be complex and time-varying.
MIT Department
Picower Institute for Learning and Memory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Nonlinear Systems Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/JOURNAL.PCBI.1007659
