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Analyzing time-to-first-spike coding schemes: A theoretical approach
Spiking neural networks (SNNs) using time-to-first-spike (TTFS) codes, in which neurons fire at most once, are appealing for rapid and low power processing. In this theoretical paper, we focus on information coding and decoding in those networks, and introduce a new unifying mathematical framework t...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9548614/ https://www.ncbi.nlm.nih.gov/pubmed/36225737 http://dx.doi.org/10.3389/fnins.2022.971937 |
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author | Bonilla, Lina Gautrais, Jacques Thorpe, Simon Masquelier, Timothée |
author_facet | Bonilla, Lina Gautrais, Jacques Thorpe, Simon Masquelier, Timothée |
author_sort | Bonilla, Lina |
collection | PubMed |
description | Spiking neural networks (SNNs) using time-to-first-spike (TTFS) codes, in which neurons fire at most once, are appealing for rapid and low power processing. In this theoretical paper, we focus on information coding and decoding in those networks, and introduce a new unifying mathematical framework that allows the comparison of various coding schemes. In an early proposal, called rank-order coding (ROC), neurons are maximally activated when inputs arrive in the order of their synaptic weights, thanks to a shunting inhibition mechanism that progressively desensitizes the neurons as spikes arrive. In another proposal, called NoM coding, only the first N spikes of M input neurons are propagated, and these “first spike patterns” can be readout by downstream neurons with homogeneous weights and no desensitization: as a result, the exact order between the first spikes does not matter. This paper also introduces a third option—“Ranked-NoM” (R-NoM), which combines features from both ROC and NoM coding schemes: only the first N input spikes are propagated, but their order is readout by downstream neurons thanks to inhomogeneous weights and linear desensitization. The unifying mathematical framework allows the three codes to be compared in terms of discriminability, which measures to what extent a neuron responds more strongly to its preferred input spike pattern than to random patterns. This discriminability turns out to be much higher for R-NoM than for the other codes, especially in the early phase of the responses. We also argue that R-NoM is much more hardware-friendly than the original ROC proposal, although NoM remains the easiest to implement in hardware because it only requires binary synapses. |
format | Online Article Text |
id | pubmed-9548614 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95486142022-10-11 Analyzing time-to-first-spike coding schemes: A theoretical approach Bonilla, Lina Gautrais, Jacques Thorpe, Simon Masquelier, Timothée Front Neurosci Neuroscience Spiking neural networks (SNNs) using time-to-first-spike (TTFS) codes, in which neurons fire at most once, are appealing for rapid and low power processing. In this theoretical paper, we focus on information coding and decoding in those networks, and introduce a new unifying mathematical framework that allows the comparison of various coding schemes. In an early proposal, called rank-order coding (ROC), neurons are maximally activated when inputs arrive in the order of their synaptic weights, thanks to a shunting inhibition mechanism that progressively desensitizes the neurons as spikes arrive. In another proposal, called NoM coding, only the first N spikes of M input neurons are propagated, and these “first spike patterns” can be readout by downstream neurons with homogeneous weights and no desensitization: as a result, the exact order between the first spikes does not matter. This paper also introduces a third option—“Ranked-NoM” (R-NoM), which combines features from both ROC and NoM coding schemes: only the first N input spikes are propagated, but their order is readout by downstream neurons thanks to inhomogeneous weights and linear desensitization. The unifying mathematical framework allows the three codes to be compared in terms of discriminability, which measures to what extent a neuron responds more strongly to its preferred input spike pattern than to random patterns. This discriminability turns out to be much higher for R-NoM than for the other codes, especially in the early phase of the responses. We also argue that R-NoM is much more hardware-friendly than the original ROC proposal, although NoM remains the easiest to implement in hardware because it only requires binary synapses. Frontiers Media S.A. 2022-09-26 /pmc/articles/PMC9548614/ /pubmed/36225737 http://dx.doi.org/10.3389/fnins.2022.971937 Text en Copyright © 2022 Bonilla, Gautrais, Thorpe and Masquelier. 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 Bonilla, Lina Gautrais, Jacques Thorpe, Simon Masquelier, Timothée Analyzing time-to-first-spike coding schemes: A theoretical approach |
title | Analyzing time-to-first-spike coding schemes: A theoretical approach |
title_full | Analyzing time-to-first-spike coding schemes: A theoretical approach |
title_fullStr | Analyzing time-to-first-spike coding schemes: A theoretical approach |
title_full_unstemmed | Analyzing time-to-first-spike coding schemes: A theoretical approach |
title_short | Analyzing time-to-first-spike coding schemes: A theoretical approach |
title_sort | analyzing time-to-first-spike coding schemes: a theoretical approach |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9548614/ https://www.ncbi.nlm.nih.gov/pubmed/36225737 http://dx.doi.org/10.3389/fnins.2022.971937 |
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