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Achieving stable dynamics in neural circuits

Leo Kozachkov, View ORCID ProfileMikael Lundqvist, Jean-Jacques Slotine, View ORCID ProfileEarl K. Miller
doi: https://doi.org/10.1101/2020.01.17.910174
Leo Kozachkov
The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USADepartment of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USANonlinear Systems Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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Mikael Lundqvist
The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USADepartment of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
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  • ORCID record for Mikael Lundqvist
Jean-Jacques Slotine
The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USANonlinear Systems Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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Earl K. Miller
The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USADepartment of Brain & Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
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  • ORCID record for Earl K. Miller
  • For correspondence: ekmiller@mit.edu
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1 Abstract

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.

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  • ↵* co-first authors

  • ↵¥ co-senior PIs

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 17, 2020.
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Achieving stable dynamics in neural circuits
Leo Kozachkov, Mikael Lundqvist, Jean-Jacques Slotine, Earl K. Miller
bioRxiv 2020.01.17.910174; doi: https://doi.org/10.1101/2020.01.17.910174
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Achieving stable dynamics in neural circuits
Leo Kozachkov, Mikael Lundqvist, Jean-Jacques Slotine, Earl K. Miller
bioRxiv 2020.01.17.910174; doi: https://doi.org/10.1101/2020.01.17.910174

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