Parallel Synapses with Transmission Nonlinearities Enhance Neuronal Classification Capacity.

ABSTRACT: Cortical neurons often establish multiple synaptic contacts with the same postsynaptic neuron. To avoid functional redundancy of these parallel synapses, it is crucial that each synapse exhibits distinct computational properties. Here we model the current to the soma contributed by each synapse as a sigmoidal transmission function of its presynaptic input, with learnable parameters such as amplitude, slope, and threshold. We evaluate the classification capacity of a neuron equipped with such nonlinear parallel synapses, and show that with a small number of parallel synapses per axon, it substantially exceeds that of the Perceptron. Furthermore, the number of correctly classified data points can increase superlinearly as the number of presynaptic axons grows. When training with an unrestric

SUBMITTER: Song Y 

PROVIDER: S-EPMC11244940 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature