Computing the effects of excitatory-inhibitory balance on neuronal input-output properties
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In sensory systems, stimuli are represented through the diverse firing responses and receptive fields of neurons. These features emerge from the interaction between excitatory ( E ) and inhibitory ( I ) neuron populations within the network. Changes in sensory inputs alter this balance, leading to shifts in firing patterns and the input-output properties of individual neurons and the network. While these phenomena have been studied extensively with experiments and theory, the underlying principles for combining E and I inputs are still unclear. Here, the rules for probabilistically combining E and I inputs are derived that describe how neurons in a feedforward inhibitory circuit respond to stimuli. This simple model is broadly applicable, capturing a wide range of response features that would otherwise require multiple separate models and offers insights into the cellular and network mechanisms influencing the input-output properties of neurons, gain modulation, and the emergence of diverse temporal firing patterns.
Author Summary
Sensory stimuli activate a broad network of excitatory and inhibitory neurons. The response of individual neurons is often complex and influenced by the animal’s state—such as whether it is resting, moving, or attending to a specific environmental cue. To understand how stimulus features are encoded and modulated, it is essential to examine how synaptic inputs are integrated within individual neurons and across neural networks. In this manuscript, I propose a set of rules for combining excitatory and inhibitory synaptic inputs in neural circuits. This simple, general model captures key features of sensory-driven responses.
