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

ABSTRACT: Vectorization of teaching signals is a key element of virtually 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 compartments^1-5. This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. We leveraged a neurofeedback brain-computer interface (BCI) task with an experimenter-defined rew

SUBMITTER: Francioni V 

PROVIDER: S-EPMC10635122 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature