• Abstract Vectorization of teaching signals is a key element of almost all modern machine learning algorithms, including backpropagation, target propagation and reinforcement learning • Vectorization allows a scalable and computationally efficient solution to the credit assignment problem by tailoring instructive signals to individual neurons • Recent theoretical models have suggested that neural circuits could implement single-phase vectorized learning at the cellular level by processing feedforward and feedback information streams in separate dendritic compartments1,2,3,4,5 • This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain • Here we used a neurofeedback brain-computer interface task with an experimenter-defined reward function to test for vectorized instructive signals in dendrites • We trained mice to modulate the activity of two spatially intermingled populations (four or five 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

Article Summaries:

  • Abstract Vectorization of teaching signals is a key element of almost all modern machine learning algorithms, including backpropagation, target propagation and reinforcement learning. Vectorization allows a scalable and computationally efficient solution to the credit assignment problem by tailoring instructive signals to individual neurons. Recent theoretical models have suggested that neural circuits could implement single-phase vectorized learning at the cellular level by processing feedforward and feedback information streams in separate dendritic compartments1,2,3,4,5. This presents a com

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