Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule

Matteo Saponati () and Martin Vinck ()
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Matteo Saponati: Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society
Martin Vinck: Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract Intelligent behavior depends on the brain’s ability to anticipate future events. However, the learning rules that enable neurons to predict and fire ahead of sensory inputs remain largely unknown. We propose a plasticity rule based on predictive processing, where the neuron learns a low-rank model of the synaptic input dynamics in its membrane potential. Neurons thereby amplify those synapses that maximally predict other synaptic inputs based on their temporal relations, which provide a solution to an optimization problem that can be implemented at the single-neuron level using only local information. Consequently, neurons learn sequences over long timescales and shift their spikes towards the first inputs in a sequence. We show that this mechanism can explain the development of anticipatory signalling and recall in a recurrent network. Furthermore, we demonstrate that the learning rule gives rise to several experimentally observed STDP (spike-timing-dependent plasticity) mechanisms. These findings suggest prediction as a guiding principle to orchestrate learning and synaptic plasticity in single neurons.

Date: 2023
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DOI: 10.1038/s41467-023-40651-w

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