Neuron as a reward-modulated combinatorial switch and a model of learning behavior
Marat M. Rvachev

TL;DR
This paper presents a model where neurons act as reward-modulated combinatorial switches, learning to respond to specific input patterns through synaptic plasticity influenced by reward signals like dopamine.
Contribution
It introduces a novel neuronal circuitry layout and plasticity principles enabling neurons to discern and learn specific input combinations via dendritic clustering and nonlinear excitation.
Findings
Neurons can learn to respond to specific input combinations based on reward signals.
Synaptic clustering on dendrites encodes permutations of input neurons.
Long-term plasticity modulates the neuron's combinatorial response.
Abstract
This paper proposes a neuronal circuitry layout and synaptic plasticity principles that allow the (pyramidal) neuron to act as a "combinatorial switch". Namely, the neuron learns to be more prone to generate spikes given those combinations of firing input neurons for which a previous spiking of the neuron had been followed by a positive global reward signal. The reward signal may be mediated by certain modulatory hormones or neurotransmitters, e.g., the dopamine. More generally, a trial-and-error learning paradigm is suggested in which a global reward signal triggers long-term enhancement or weakening of a neuron's spiking response to the preceding neuronal input firing pattern. Thus, rewards provide a feedback pathway that informs neurons whether their spiking was beneficial or detrimental for a particular input combination. The neuron's ability to discern specific combinations of…
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Taxonomy
TopicsNeural dynamics and brain function · Neuroscience and Neural Engineering · Neuroscience and Neuropharmacology Research
