BioNIC: Biologically Inspired Neural Network for Image Classification Using Connectomics Principles

TL;DR

BioNIC is a biologically inspired neural network that incorporates connectomics principles and cortical features, achieving competitive emotion classification performance and demonstrating the feasibility of biologically constrained AI models.

Contribution

This work introduces BioNIC, a neural network architecture integrating detailed connectomics constraints and cortical organization for image classification tasks.

Findings

Achieved 59.77% accuracy on FER-2013.

Biological features impact model performance significantly.

Connectomics-based constraints are computationally feasible.

Abstract

We present BioNIC, a multi-layer feedforward neural network for emotion classification, inspired by detailed synaptic connectivity graphs from the MICrONs dataset. At a structural level, we incorporate architectural constraints derived from a single cortical column of the mouse Primary Visual Cortex(V1): connectivity imposed via adjacency masks, laminar organization, and graded inhibition representing inhibitory neurons. At the functional level, we implement biologically inspired learning: Hebbian synaptic plasticity with homeostatic regulation, Layer Normalization, data augmentation to model exposure to natural variability in sensory input, and synaptic noise to model neural stochasticity. We also include convolutional layers for spatial processing, mimicking retinotopic mapping. The model performance is evaluated on the Facial Emotion Recognition task FER-2013 and compared with a conventional baseline. Additionally, we investigate the impacts of each biological feature through a series of ablation experiments. While connectivity was limited to a single cortical column and biologically relevant connections, BioNIC achieved performance comparable to that of conventional models, with an accuracy of 59.77 $\pm$ 0.27% on FER-2013. Our findings demonstrate that integrating constraints derived from connectomics is a computationally plausible approach to developing biologically inspired artificial intelligence systems. This work also highlights the potential of new generation peta-scale connectomics data in advancing both neuroscience modeling and artificial intelligence.