A minimal model for synaptic integration in simple neurons

TL;DR

This paper introduces a minimal mathematical model for synaptic integration in neurons, focusing on how excitatory inputs are summed and how this affects neuronal response to noise-driven processes, with minimal parameters and dendritic complexity.

Contribution

The paper presents a simple, parameter-efficient model for synaptic summation that isolates the core integration mechanisms in neurons, useful for analyzing noise effects.

Findings

Model effectively characterizes neuronal responses to stochastic resonance.

Highlights the role of input summation in neuronal computation.

Useful for studying neurons with minimal dendritic processing.

Abstract

Synaptic integration is a prominent aspect of neuronal information processing. The detailed mechanisms that modulate synaptic inputs determine the computational properties of any given neuron. We study a simple model for the summation of excitatory inputs from synapses and illustrate its use by characterizing some functional properties of postsynaptic neurons. In this regard, we study the response of postsynaptic neurons as defined by the model to two well known noise driven processes: stochastic and coherence resonance. The model requires a small number of parameters and is especially useful to isolate the role of integration mechanisms that rely on summation of inputs with little dendritic processing.