Vectorized instructive signals in cortical dendrites during a brain-computer interface task.

Autor:	Francioni V; McGovern Institute for Brain Research, MIT, Cambridge, MA, USA.; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA., Tang VD; McGovern Institute for Brain Research, MIT, Cambridge, MA, USA.; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA., Brown NJ; McGovern Institute for Brain Research, MIT, Cambridge, MA, USA.; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA., Toloza EHS; McGovern Institute for Brain Research, MIT, Cambridge, MA, USA.; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA., Harnett M; McGovern Institute for Brain Research, MIT, Cambridge, MA, USA.; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2023 Nov 05. Date of Electronic Publication: 2023 Nov 05.
DOI:	10.1101/2023.11.03.565534
Abstrakt:	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. Competing Interests: Ethics declaration The authors declare no competing interest.
Databáze:	MEDLINE
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