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キーワード:
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要旨:
Learning turns experience into better decisions. A key problem in learning is credit assignment—knowing how to change parameters, such as synaptic weights deep within a neural network, in order to improve behavioral performance. Artificial intelligence owes its recent bloom largely to the error-backpropagation algorithm1, which estimates the contribution of every synapse to output errors and allows rapid weight adjustment. Biological systems, however, lack an obvious mechanism to backpropagate errors. Here we show, by combining high-throughput volume electron microscopy2 and automated connectomic analysis3–5, that the synaptic architecture of songbird basal ganglia supports local credit assignment using a variant of the node perturbation algorithm proposed in a model of songbird reinforcement learning6, 7. We find that key predictions of the model hold true: first, cortical axons that encode exploratory motor variability terminate predominantly on dendritic shafts of striatal spiny neurons, while cortical axons that encode song timing terminate almost exclusively on spines. Second, synapse pairs that share a presynaptic cortical timing axon and a postsynaptic spiny dendrite are substantially more similar in size than expected, indicating Hebbian plasticity8, 9. Combined with numerical simulations, these findings provide strong evidence for a biologically plausible credit assignment mechanism6.
