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A synaptic learning rule for exploiting nonlinear dendritic computation
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DOI：10.1016/j.neuron.2021.09.044.png)
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Abstract
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Information processing in the brain depends on the integration of synaptic input distributed throughout neuronal dendrites. Dendritic integration is a hierarchical process, proposed to be equivalent to integration by a multilayer network, potentially endowing single neurons with substantial computational power. However, whether neurons can learn to harness dendritic properties to realize this potential is unknown. Here, we develop a learning rule from dendritic cable theory and use it to investigate the processing capacity of a detailed pyramidal neuron model. We show that computations using spatial or temporal features of synaptic input patterns can be learned, and even synergistically combined, to solve a canonical nonlinear feature-binding problem. The voltage dependence of the learning rule drives coactive synapses to engage dendritic nonlinearities, whereas spike-timing dependence shapes the time course of subthreshold potentials. Dendritic input-output relationships can therefore be flexibly tuned through synaptic plasticity, allowing optimal implementation of nonlinear functions by single neurons.
Keywords:
PYRAMIDAL NEURON
ACTION-POTENTIALS
BASAL DENDRITES
NMDA SPIKES
INFORMATION-STORAGE
THIN DENDRITES
MODEL
INTEGRATION
PLASTICITY
PROPAGATION
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