# Neuronal heterogeneity of normalization strength in a circuit model

## TL;DR

This paper explores how differences in inhibition among neurons lead to varied normalization strengths, improving visual information processing in the brain.

## Contribution

The study provides a mechanistic explanation linking inhibitory currents to normalization heterogeneity and its computational benefits.

## Key findings

- Neuronal normalization strength heterogeneity correlates with the level of inhibitory current received.
- Stronger normalization leads to more efficient information encoding in visual stimuli processing.
- Greater normalization heterogeneity increases network information capacity and encoding efficiency.

## Abstract

Neurons in higher-order visual areas integrate information through a canonical computation called normalization. The strength of normalization is highly heterogeneous across neurons, and this heterogeneity correlates with attention-mediated modulations in neural responses. However, the circuit mechanism underlying the heterogeneous normalization strength is unclear. In this work, we study normalization in a spiking neuron network model of visual cortex. Our model reveals that the heterogeneity of normalization strength is highly correlated with the inhibitory current each neuron receives. The correlation between inhibition and other synaptic inputs explains the experimentally observed dependence of spike count correlations on normalization strength. Further, we find that neurons with stronger normalization encode information more efficiently, and that networks with more heterogeneity in normalization encode visual stimuli with higher information and capacity. Together, our model provides a mechanistic explanation of heterogeneous normalization strengths in the visual cortex and sheds light on the computational benefits of neuronal heterogeneity.

Diverse inhibitory currents generate heterogeneous normalization that enhances visual stimulus encoding.