Cargando…
Vectorized instructive signals in cortical dendrites during a brain-computer interface task
Backpropagation of error is the most widely used learning algorithm in artificial neural networks, forming the backbone of modern machine learning and artificial intelligence(1,2). Backpropagation provides a solution to the credit assignment problem by vectorizing an error signal tailored to individ...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635122/ https://www.ncbi.nlm.nih.gov/pubmed/37961227 http://dx.doi.org/10.1101/2023.11.03.565534 |
_version_ | 1785146291918995456 |
---|---|
author | Francioni, Valerio Tang, Vincent D Brown, Norma J. Toloza, Enrique H.S. Harnett, Mark |
author_facet | Francioni, Valerio Tang, Vincent D Brown, Norma J. Toloza, Enrique H.S. Harnett, Mark |
author_sort | Francioni, Valerio |
collection | PubMed |
description | Backpropagation of error is the most widely used learning algorithm in artificial neural networks, forming the backbone of modern machine learning and artificial intelligence(1,2). Backpropagation provides a solution to the credit assignment problem by vectorizing an error signal tailored to individual neurons. Recent theoretical models have suggested that neural circuits could implement backpropagation-like learning by semi-independently processing feedforward and feedback information streams in separate dendritic compartments(3–7). This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. We designed a neurofeedback brain-computer interface (BCI) task with an experimenter-defined reward function to evaluate the key requirements for dendrites to implement backpropagation-like learning. We trained mice to modulate the activity of two spatially intermingled populations (4 or 5 neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic versus dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals both depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. These results provide the first biological evidence of a backpropagation-like solution to the credit assignment problem in the brain. |
format | Online Article Text |
id | pubmed-10635122 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-106351222023-11-13 Vectorized instructive signals in cortical dendrites during a brain-computer interface task Francioni, Valerio Tang, Vincent D Brown, Norma J. Toloza, Enrique H.S. Harnett, Mark bioRxiv Article Backpropagation of error is the most widely used learning algorithm in artificial neural networks, forming the backbone of modern machine learning and artificial intelligence(1,2). Backpropagation provides a solution to the credit assignment problem by vectorizing an error signal tailored to individual neurons. Recent theoretical models have suggested that neural circuits could implement backpropagation-like learning by semi-independently processing feedforward and feedback information streams in separate dendritic compartments(3–7). This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. We designed a neurofeedback brain-computer interface (BCI) task with an experimenter-defined reward function to evaluate the key requirements for dendrites to implement backpropagation-like learning. We trained mice to modulate the activity of two spatially intermingled populations (4 or 5 neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic versus dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals both depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. These results provide the first biological evidence of a backpropagation-like solution to the credit assignment problem in the brain. Cold Spring Harbor Laboratory 2023-11-05 /pmc/articles/PMC10635122/ /pubmed/37961227 http://dx.doi.org/10.1101/2023.11.03.565534 Text en https://creativecommons.org/licenses/by-nd/4.0/This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Francioni, Valerio Tang, Vincent D Brown, Norma J. Toloza, Enrique H.S. Harnett, Mark Vectorized instructive signals in cortical dendrites during a brain-computer interface task |
title | Vectorized instructive signals in cortical dendrites during a brain-computer interface task |
title_full | Vectorized instructive signals in cortical dendrites during a brain-computer interface task |
title_fullStr | Vectorized instructive signals in cortical dendrites during a brain-computer interface task |
title_full_unstemmed | Vectorized instructive signals in cortical dendrites during a brain-computer interface task |
title_short | Vectorized instructive signals in cortical dendrites during a brain-computer interface task |
title_sort | vectorized instructive signals in cortical dendrites during a brain-computer interface task |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635122/ https://www.ncbi.nlm.nih.gov/pubmed/37961227 http://dx.doi.org/10.1101/2023.11.03.565534 |
work_keys_str_mv | AT francionivalerio vectorizedinstructivesignalsincorticaldendritesduringabraincomputerinterfacetask AT tangvincentd vectorizedinstructivesignalsincorticaldendritesduringabraincomputerinterfacetask AT brownnormaj vectorizedinstructivesignalsincorticaldendritesduringabraincomputerinterfacetask AT tolozaenriquehs vectorizedinstructivesignalsincorticaldendritesduringabraincomputerinterfacetask AT harnettmark vectorizedinstructivesignalsincorticaldendritesduringabraincomputerinterfacetask |