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Sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware

With the ongoing growth in the field of neuro-inspired computing, newly arriving computational architectures demand extensive validation and testing against existing benchmarks to establish their competence and value. In our work, we break down the validation step into two parts—(1) establishing a m...

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Autores principales: Dey, Srijanie, Dimitrov, Alexander G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484528/
https://www.ncbi.nlm.nih.gov/pubmed/37694108
http://dx.doi.org/10.3389/fnins.2023.1198282
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author Dey, Srijanie
Dimitrov, Alexander G.
author_facet Dey, Srijanie
Dimitrov, Alexander G.
author_sort Dey, Srijanie
collection PubMed
description With the ongoing growth in the field of neuro-inspired computing, newly arriving computational architectures demand extensive validation and testing against existing benchmarks to establish their competence and value. In our work, we break down the validation step into two parts—(1) establishing a methodological and numerical groundwork to establish a comparison between neuromorphic and conventional platforms and, (2) performing a sensitivity analysis on the obtained model regime to assess its robustness. We study the neuronal dynamics based on the Leaky Integrate and Fire (LIF) model, which is built upon data from the mouse visual cortex spanning a set of anatomical and physiological constraints. Intel Corp.'s first neuromorphic chip “Loihi” serves as our neuromorphic platform and results on it are validated against the classical simulations. After setting up a model that allows a seamless mapping between the Loihi and the classical simulations, we find that Loihi replicates classical simulations very efficiently with high precision. This model is then subjected to the second phase of validation, through sensitivity analysis, by assessing the impact on the cost function as values of the significant model parameters are varied. The work is done in two steps—(1) assessing the impact while changing one parameter at a time, (2) assessing the impact while changing two parameters at a time. We observe that the model is quite robust for majority of the parameters with slight change in the cost function. We also identify a subset of the model parameters changes which make the model more sensitive and thus, need to be defined more precisely.
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spelling pubmed-104845282023-09-08 Sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware Dey, Srijanie Dimitrov, Alexander G. Front Neurosci Neuroscience With the ongoing growth in the field of neuro-inspired computing, newly arriving computational architectures demand extensive validation and testing against existing benchmarks to establish their competence and value. In our work, we break down the validation step into two parts—(1) establishing a methodological and numerical groundwork to establish a comparison between neuromorphic and conventional platforms and, (2) performing a sensitivity analysis on the obtained model regime to assess its robustness. We study the neuronal dynamics based on the Leaky Integrate and Fire (LIF) model, which is built upon data from the mouse visual cortex spanning a set of anatomical and physiological constraints. Intel Corp.'s first neuromorphic chip “Loihi” serves as our neuromorphic platform and results on it are validated against the classical simulations. After setting up a model that allows a seamless mapping between the Loihi and the classical simulations, we find that Loihi replicates classical simulations very efficiently with high precision. This model is then subjected to the second phase of validation, through sensitivity analysis, by assessing the impact on the cost function as values of the significant model parameters are varied. The work is done in two steps—(1) assessing the impact while changing one parameter at a time, (2) assessing the impact while changing two parameters at a time. We observe that the model is quite robust for majority of the parameters with slight change in the cost function. We also identify a subset of the model parameters changes which make the model more sensitive and thus, need to be defined more precisely. Frontiers Media S.A. 2023-08-24 /pmc/articles/PMC10484528/ /pubmed/37694108 http://dx.doi.org/10.3389/fnins.2023.1198282 Text en Copyright © 2023 Dey and Dimitrov. 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
Dey, Srijanie
Dimitrov, Alexander G.
Sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware
title Sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware
title_full Sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware
title_fullStr Sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware
title_full_unstemmed Sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware
title_short Sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware
title_sort sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484528/
https://www.ncbi.nlm.nih.gov/pubmed/37694108
http://dx.doi.org/10.3389/fnins.2023.1198282
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