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Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks
Overview: We model energy constraints in a network of spiking neurons, while exploring general questions of resource limitation on network function abstractly. Background: Metabolic states like dietary ketosis or hypoglycemia have a large impact on brain function and disease outcomes. Glia provide m...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5326782/ https://www.ncbi.nlm.nih.gov/pubmed/28289370 http://dx.doi.org/10.3389/fnins.2017.00080 |
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author | Burroni, Javier Taylor, P. Corey, Cassian Vachnadze, Tengiz Siegelmann, Hava T. |
author_facet | Burroni, Javier Taylor, P. Corey, Cassian Vachnadze, Tengiz Siegelmann, Hava T. |
author_sort | Burroni, Javier |
collection | PubMed |
description | Overview: We model energy constraints in a network of spiking neurons, while exploring general questions of resource limitation on network function abstractly. Background: Metabolic states like dietary ketosis or hypoglycemia have a large impact on brain function and disease outcomes. Glia provide metabolic support for neurons, among other functions. Yet, in computational models of glia-neuron cooperation, there have been no previous attempts to explore the effects of direct realistic energy costs on network activity in spiking neurons. Currently, biologically realistic spiking neural networks assume that membrane potential is the main driving factor for neural spiking, and do not take into consideration energetic costs. Methods: We define local energy pools to constrain a neuron model, termed Spiking Neuron Energy Pool (SNEP), which explicitly incorporates energy limitations. Each neuron requires energy to spike, and resources in the pool regenerate over time. Our simulation displays an easy-to-use GUI, which can be run locally in a web browser, and is freely available. Results: Energy dependence drastically changes behavior of these neural networks, causing emergent oscillations similar to those in networks of biological neurons. We analyze the system via Lotka-Volterra equations, producing several observations: (1) energy can drive self-sustained oscillations, (2) the energetic cost of spiking modulates the degree and type of oscillations, (3) harmonics emerge with frequencies determined by energy parameters, and (4) varying energetic costs have non-linear effects on energy consumption and firing rates. Conclusions: Models of neuron function which attempt biological realism may benefit from including energy constraints. Further, we assert that observed oscillatory effects of energy limitations exist in networks of many kinds, and that these findings generalize to abstract graphs and technological applications. |
format | Online Article Text |
id | pubmed-5326782 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-53267822017-03-13 Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks Burroni, Javier Taylor, P. Corey, Cassian Vachnadze, Tengiz Siegelmann, Hava T. Front Neurosci Neuroscience Overview: We model energy constraints in a network of spiking neurons, while exploring general questions of resource limitation on network function abstractly. Background: Metabolic states like dietary ketosis or hypoglycemia have a large impact on brain function and disease outcomes. Glia provide metabolic support for neurons, among other functions. Yet, in computational models of glia-neuron cooperation, there have been no previous attempts to explore the effects of direct realistic energy costs on network activity in spiking neurons. Currently, biologically realistic spiking neural networks assume that membrane potential is the main driving factor for neural spiking, and do not take into consideration energetic costs. Methods: We define local energy pools to constrain a neuron model, termed Spiking Neuron Energy Pool (SNEP), which explicitly incorporates energy limitations. Each neuron requires energy to spike, and resources in the pool regenerate over time. Our simulation displays an easy-to-use GUI, which can be run locally in a web browser, and is freely available. Results: Energy dependence drastically changes behavior of these neural networks, causing emergent oscillations similar to those in networks of biological neurons. We analyze the system via Lotka-Volterra equations, producing several observations: (1) energy can drive self-sustained oscillations, (2) the energetic cost of spiking modulates the degree and type of oscillations, (3) harmonics emerge with frequencies determined by energy parameters, and (4) varying energetic costs have non-linear effects on energy consumption and firing rates. Conclusions: Models of neuron function which attempt biological realism may benefit from including energy constraints. Further, we assert that observed oscillatory effects of energy limitations exist in networks of many kinds, and that these findings generalize to abstract graphs and technological applications. Frontiers Media S.A. 2017-02-27 /pmc/articles/PMC5326782/ /pubmed/28289370 http://dx.doi.org/10.3389/fnins.2017.00080 Text en Copyright © 2017 Burroni, Taylor, Corey, Vachnadze and Siegelmann. http://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) or licensor 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 Burroni, Javier Taylor, P. Corey, Cassian Vachnadze, Tengiz Siegelmann, Hava T. Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks |
title | Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks |
title_full | Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks |
title_fullStr | Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks |
title_full_unstemmed | Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks |
title_short | Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks |
title_sort | energetic constraints produce self-sustained oscillatory dynamics in neuronal networks |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5326782/ https://www.ncbi.nlm.nih.gov/pubmed/28289370 http://dx.doi.org/10.3389/fnins.2017.00080 |
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