Title: Learning Hippo: A Hippocampal Neural Architecture for Deep Learning
Primary area: applications to neuroscience & cognitive science
Keywords: Deep Learning, Neural Architecture, Hippocampus, CA3 Region, Compartmental Models, Shunting Inhibition, Dendritic Integration, Robustness to Noise

Abstract: Current neural networks exhibit known limitations due to their fragility against corrupted data and the requirement for large amounts of data for training. These limitations are in deep contrast to the efficiency and robustness of many biological systems such as the human brain. In this work, we present \textit{Hippo-1}, a biological neuron model along with a specific architecture inspired by the CA3 region of the hippocampus. Our biological model is based on a justified compartmental simplification of the neuron which allows us to preserve essential non-linear dendritic integration and on a specific interaction between excitatory and inhibitory neuron populations through a shunting inhibition mechanism. 
To validate the approach, we evaluated the performance of three simple \textit{Hippo-1} models (named High, Medium, and Low) on standard image classification tasks (i.e., MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, Tiny ImageNet), comparing them with Multi-Layer Perceptron (MLP) networks of comparable parametric complexity. The experimental results demonstrate that \textit{Hippo-1} not only achieves competitive or superior performance compared to the standard MLPs but also shows a superior robustness to noise (Gaussian and salt-and-pepper), with a more gradual performance degradation as input corruption increases. Gains in adopting Hippo-1 models increase with the complexity of the task, being approximately 5\% on Cifar-10, around 10\% on Cifar-100 and as high as 50\% on Tiny-ImageNet. In fact, our work shows that integrating targeted neuroscientific principles, rather than a complete morphological simulation, represents an effective strategy for building more resilient models, combining biological plausibility with computational efficiency.

TL;DR: We introduce Hippo-1, a neural architecture inspired by the hippocampal CA3 region, which uses compartmental simplification and shunting inhibition to achieve superior performance and robustness to noise compared to standard MLPs.