Cargando…
A Model of Pattern Separation by Single Neurons
For efficient processing, spatiotemporal spike patterns representing similar input must be able to transform into a less similar output. A new computational model with physiologically plausible parameters shows how the neuronal process referred to as “pattern separation” can be very well achieved by...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103200/ https://www.ncbi.nlm.nih.gov/pubmed/35573263 http://dx.doi.org/10.3389/fncom.2022.858353 |
_version_ | 1784707504862658560 |
---|---|
author | Löffler, Hubert Gupta, Daya Shankar |
author_facet | Löffler, Hubert Gupta, Daya Shankar |
author_sort | Löffler, Hubert |
collection | PubMed |
description | For efficient processing, spatiotemporal spike patterns representing similar input must be able to transform into a less similar output. A new computational model with physiologically plausible parameters shows how the neuronal process referred to as “pattern separation” can be very well achieved by single neurons if the temporal qualities of the output patterns are considered. Spike patterns generated by a varying number of neurons firing with fixed different frequencies within a gamma range are used as input. The temporal and spatial summation of dendritic input combined with theta-oscillating excitability in the output neuron by subthreshold membrane potential oscillations (SMOs) lead to high temporal separation by different delays of output spikes of similar input patterns. A Winner Takes All (WTA) mechanism with backward inhibition suffices to transform the spatial overlap of input patterns to much less temporal overlap of the output patterns. The conversion of spatial patterns input into an output with differently delayed spikes enables high separation effects. Incomplete random connectivity spreads the times up to the first spike across a spatially expanded ensemble of output neurons. With the expansion, random connectivity becomes the spatial distribution mechanism of temporal features. Additionally, a “synfire chain” circuit is proposed to reconvert temporal differences into spatial ones. |
format | Online Article Text |
id | pubmed-9103200 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91032002022-05-14 A Model of Pattern Separation by Single Neurons Löffler, Hubert Gupta, Daya Shankar Front Comput Neurosci Neuroscience For efficient processing, spatiotemporal spike patterns representing similar input must be able to transform into a less similar output. A new computational model with physiologically plausible parameters shows how the neuronal process referred to as “pattern separation” can be very well achieved by single neurons if the temporal qualities of the output patterns are considered. Spike patterns generated by a varying number of neurons firing with fixed different frequencies within a gamma range are used as input. The temporal and spatial summation of dendritic input combined with theta-oscillating excitability in the output neuron by subthreshold membrane potential oscillations (SMOs) lead to high temporal separation by different delays of output spikes of similar input patterns. A Winner Takes All (WTA) mechanism with backward inhibition suffices to transform the spatial overlap of input patterns to much less temporal overlap of the output patterns. The conversion of spatial patterns input into an output with differently delayed spikes enables high separation effects. Incomplete random connectivity spreads the times up to the first spike across a spatially expanded ensemble of output neurons. With the expansion, random connectivity becomes the spatial distribution mechanism of temporal features. Additionally, a “synfire chain” circuit is proposed to reconvert temporal differences into spatial ones. Frontiers Media S.A. 2022-04-29 /pmc/articles/PMC9103200/ /pubmed/35573263 http://dx.doi.org/10.3389/fncom.2022.858353 Text en Copyright © 2022 Löffler and Gupta. 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 Löffler, Hubert Gupta, Daya Shankar A Model of Pattern Separation by Single Neurons |
title | A Model of Pattern Separation by Single Neurons |
title_full | A Model of Pattern Separation by Single Neurons |
title_fullStr | A Model of Pattern Separation by Single Neurons |
title_full_unstemmed | A Model of Pattern Separation by Single Neurons |
title_short | A Model of Pattern Separation by Single Neurons |
title_sort | model of pattern separation by single neurons |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103200/ https://www.ncbi.nlm.nih.gov/pubmed/35573263 http://dx.doi.org/10.3389/fncom.2022.858353 |
work_keys_str_mv | AT lofflerhubert amodelofpatternseparationbysingleneurons AT guptadayashankar amodelofpatternseparationbysingleneurons AT lofflerhubert modelofpatternseparationbysingleneurons AT guptadayashankar modelofpatternseparationbysingleneurons |