Biologically Inspired Semantic Lateral Connectivity for Convolutional Neural Networks

TL;DR

This paper introduces a biologically inspired lateral connectivity pattern in CNNs that enhances classification accuracy and mimics visual cortex organization without increasing trainable parameters.

Contribution

It proposes a Mexican hat lateral connectivity profile for CNNs, improving accuracy and interpretability without adding trainable parameters, and analytically models filter activation distributions.

Findings

Improved classification accuracy across lightweight CNNs.

The Mexican hat profile sharpens filter tuning curves.

Filter organization resembles visual cortex topography.

Abstract

Lateral connections play an important role for sensory processing in visual cortex by supporting discriminable neuronal responses even to highly similar features. In the present work, we show that establishing a biologically inspired Mexican hat lateral connectivity profile along the filter domain can significantly improve the classification accuracy of a variety of lightweight convolutional neural networks without the addition of trainable network parameters. Moreover, we demonstrate that it is possible to analytically determine the stationary distribution of modulated filter activations and thereby avoid using recurrence for modeling temporal dynamics. We furthermore reveal that the Mexican hat connectivity profile has the effect of ordering filters in a sequence resembling the topographic organization of feature selectivity in early visual cortex. In an ordered filter sequence, this profile then sharpens the filters' tuning curves.