Local, calcium- and reward-based synaptic learning rule that enhances dendritic nonlinearities can solve the nonlinear feature binding problem

This study explores the computational potential of single striatal projection neurons (SPN), emphasizing dendritic nonlinearities and their crucial role in solving complex integration problems. Utilizing a biophysically detailed multicompartmental model of an SPN, we introduce a calcium-based, local synaptic learning rule that leverages dendritic plateau potentials. According to what is known about excitatory corticostriatal synapses, the learning rule is governed by local calcium dynamics from NMDA and L-type calcium channels and dopaminergic reward signals. In addition, we incorporated metaplasticity in order to devise a self-adjusting learning rule which ensures stability for individual synaptic weights. We demonstrate that this rule allows single neurons to solve the nonlinear feature binding problem (NFBP), a task traditionally attributed to neuronal networks. We also detail an inhibitory plasticity mechanism, critical for dendritic compartmentalization, further enhancing computational efficiency in dendrites. Thisin silicostudy underscores the computational capacity of individual neurons, extending our understanding of neuronal processing and the brain’s ability to perform complex computations.