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A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks

Recurrent circuitry components are distributed widely within the brain, including both excitatory and inhibitory synaptic connections. Recurrent neuronal networks have potential stability problems, perhaps a predisposition to epilepsy. More generally, instability risks making internal representation...

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Autores principales: Rongala, Udaya B., Enander, Jonas M. D., Kohler, Matthias, Loeb, Gerald E., Jörntell, Henrik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173185/
https://www.ncbi.nlm.nih.gov/pubmed/34093156
http://dx.doi.org/10.3389/fncom.2021.656401
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author Rongala, Udaya B.
Enander, Jonas M. D.
Kohler, Matthias
Loeb, Gerald E.
Jörntell, Henrik
author_facet Rongala, Udaya B.
Enander, Jonas M. D.
Kohler, Matthias
Loeb, Gerald E.
Jörntell, Henrik
author_sort Rongala, Udaya B.
collection PubMed
description Recurrent circuitry components are distributed widely within the brain, including both excitatory and inhibitory synaptic connections. Recurrent neuronal networks have potential stability problems, perhaps a predisposition to epilepsy. More generally, instability risks making internal representations of information unreliable. To assess the inherent stability properties of such recurrent networks, we tested a linear summation, non-spiking neuron model with and without a “dynamic leak”, corresponding to the low-pass filtering of synaptic input current by the RC circuit of the biological membrane. We first show that the output of this neuron model, in either of its two forms, follows its input at a higher fidelity than a wide range of spiking neuron models across a range of input frequencies. Then we constructed fully connected recurrent networks with equal numbers of excitatory and inhibitory neurons and randomly distributed weights across all synapses. When the networks were driven by pseudorandom sensory inputs with varying frequency, the recurrent network activity tended to induce high frequency self-amplifying components, sometimes evident as distinct transients, which were not present in the input data. The addition of a dynamic leak based on known membrane properties consistently removed such spurious high frequency noise across all networks. Furthermore, we found that the neuron model with dynamic leak imparts a network stability that seamlessly scales with the size of the network, conduction delays, the input density of the sensory signal and a wide range of synaptic weight distributions. Our findings suggest that neuronal dynamic leak serves the beneficial function of protecting recurrent neuronal circuitry from the self-induction of spurious high frequency signals, thereby permitting the brain to utilize this architectural circuitry component regardless of network size or recurrency.
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spelling pubmed-81731852021-06-04 A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks Rongala, Udaya B. Enander, Jonas M. D. Kohler, Matthias Loeb, Gerald E. Jörntell, Henrik Front Comput Neurosci Neuroscience Recurrent circuitry components are distributed widely within the brain, including both excitatory and inhibitory synaptic connections. Recurrent neuronal networks have potential stability problems, perhaps a predisposition to epilepsy. More generally, instability risks making internal representations of information unreliable. To assess the inherent stability properties of such recurrent networks, we tested a linear summation, non-spiking neuron model with and without a “dynamic leak”, corresponding to the low-pass filtering of synaptic input current by the RC circuit of the biological membrane. We first show that the output of this neuron model, in either of its two forms, follows its input at a higher fidelity than a wide range of spiking neuron models across a range of input frequencies. Then we constructed fully connected recurrent networks with equal numbers of excitatory and inhibitory neurons and randomly distributed weights across all synapses. When the networks were driven by pseudorandom sensory inputs with varying frequency, the recurrent network activity tended to induce high frequency self-amplifying components, sometimes evident as distinct transients, which were not present in the input data. The addition of a dynamic leak based on known membrane properties consistently removed such spurious high frequency noise across all networks. Furthermore, we found that the neuron model with dynamic leak imparts a network stability that seamlessly scales with the size of the network, conduction delays, the input density of the sensory signal and a wide range of synaptic weight distributions. Our findings suggest that neuronal dynamic leak serves the beneficial function of protecting recurrent neuronal circuitry from the self-induction of spurious high frequency signals, thereby permitting the brain to utilize this architectural circuitry component regardless of network size or recurrency. Frontiers Media S.A. 2021-05-20 /pmc/articles/PMC8173185/ /pubmed/34093156 http://dx.doi.org/10.3389/fncom.2021.656401 Text en Copyright © 2021 Rongala, Enander, Kohler, Loeb and Jörntell. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Rongala, Udaya B.
Enander, Jonas M. D.
Kohler, Matthias
Loeb, Gerald E.
Jörntell, Henrik
A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks
title A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks
title_full A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks
title_fullStr A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks
title_full_unstemmed A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks
title_short A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks
title_sort non-spiking neuron model with dynamic leak to avoid instability in recurrent networks
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8173185/
https://www.ncbi.nlm.nih.gov/pubmed/34093156
http://dx.doi.org/10.3389/fncom.2021.656401
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