Cargando…

Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma

When an action potential arrives at a synapse there is a large probability that no neurotransmitter is released. Surprisingly, simple computational models suggest that these synaptic failures enable information processing at lower metabolic costs. However, these models only consider information tran...

Descripción completa

Detalles Bibliográficos
Autores principales: Schug, Simon, Benzing, Frederik, Steger, Angelika
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8716105/
https://www.ncbi.nlm.nih.gov/pubmed/34661525
http://dx.doi.org/10.7554/eLife.69884
_version_ 1784624252500049920
author Schug, Simon
Benzing, Frederik
Steger, Angelika
author_facet Schug, Simon
Benzing, Frederik
Steger, Angelika
author_sort Schug, Simon
collection PubMed
description When an action potential arrives at a synapse there is a large probability that no neurotransmitter is released. Surprisingly, simple computational models suggest that these synaptic failures enable information processing at lower metabolic costs. However, these models only consider information transmission at single synapses ignoring the remainder of the neural network as well as its overall computational goal. Here, we investigate how synaptic failures affect the energy efficiency of models of entire neural networks that solve a goal-driven task. We find that presynaptic stochasticity and plasticity improve energy efficiency and show that the network allocates most energy to a sparse subset of important synapses. We demonstrate that stabilising these synapses helps to alleviate the stability-plasticity dilemma, thus connecting a presynaptic notion of importance to a computational role in lifelong learning. Overall, our findings present a set of hypotheses for how presynaptic plasticity and stochasticity contribute to sparsity, energy efficiency and improved trade-offs in the stability-plasticity dilemma.
format Online
Article
Text
id pubmed-8716105
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher eLife Sciences Publications, Ltd
record_format MEDLINE/PubMed
spelling pubmed-87161052022-01-05 Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma Schug, Simon Benzing, Frederik Steger, Angelika eLife Neuroscience When an action potential arrives at a synapse there is a large probability that no neurotransmitter is released. Surprisingly, simple computational models suggest that these synaptic failures enable information processing at lower metabolic costs. However, these models only consider information transmission at single synapses ignoring the remainder of the neural network as well as its overall computational goal. Here, we investigate how synaptic failures affect the energy efficiency of models of entire neural networks that solve a goal-driven task. We find that presynaptic stochasticity and plasticity improve energy efficiency and show that the network allocates most energy to a sparse subset of important synapses. We demonstrate that stabilising these synapses helps to alleviate the stability-plasticity dilemma, thus connecting a presynaptic notion of importance to a computational role in lifelong learning. Overall, our findings present a set of hypotheses for how presynaptic plasticity and stochasticity contribute to sparsity, energy efficiency and improved trade-offs in the stability-plasticity dilemma. eLife Sciences Publications, Ltd 2021-10-18 /pmc/articles/PMC8716105/ /pubmed/34661525 http://dx.doi.org/10.7554/eLife.69884 Text en © 2021, Schug et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Schug, Simon
Benzing, Frederik
Steger, Angelika
Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma
title Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma
title_full Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma
title_fullStr Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma
title_full_unstemmed Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma
title_short Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma
title_sort presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8716105/
https://www.ncbi.nlm.nih.gov/pubmed/34661525
http://dx.doi.org/10.7554/eLife.69884
work_keys_str_mv AT schugsimon presynapticstochasticityimprovesenergyefficiencyandhelpsalleviatethestabilityplasticitydilemma
AT benzingfrederik presynapticstochasticityimprovesenergyefficiencyandhelpsalleviatethestabilityplasticitydilemma
AT stegerangelika presynapticstochasticityimprovesenergyefficiencyandhelpsalleviatethestabilityplasticitydilemma