Achieving stable dynamics in neural circuits

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 thi...

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Bibliographic Details
Published in:PLoS computational biology 2020-08, Vol.16 (8), p.e1007659-e1007659
Main Authors: Kozachkov, Leo, Lundqvist, Mikael, Slotine, Jean-Jacques, Miller, Earl K, Graham, Lyle J, Haith, Adrian M
Format: Article
Language:English
Subjects:
Analysis
Authorship
Biology and Life Sciences
brain
Complexity
computation
Computational biology
Computer and Information Sciences
Contraction
Control theory
Coordinate transformations
Dynamic stability
Hebbian plasticity
Laboratories
Mechanical properties
Medicine and Health Sciences
Memory
Metastability
networks
Neural circuitry
neural circuits
Neural networks
Norms
Physical Sciences
Plasticity
Psychology
psykologi
Recurrent neural networks
Stability analysis
state trajectories
Synaptic plasticity
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1007659