Towards Brain Inspired Design for Addressing the Shortcomings of ANNs

TL;DR

This paper explores brain-inspired neural architectures, demonstrating that personalized error views in neurons can improve learning efficiency, robustness, and generalization in artificial neural networks, inspired by cerebellar mechanisms.

Contribution

It introduces a biologically inspired ANN architecture with personalized error processing, showing improved learning under data limitations and reduced shortcut strategies.

Findings

Enhanced learning with class imbalance and limited data

Reduced susceptibility to shortcut strategies

Improved generalization performance

Abstract

As our understanding of the mechanisms of brain function is enhanced, the value of insights gained from neuroscience to the development of AI algorithms deserves further consideration. Here, we draw parallels with an existing tree-based ANN architecture and a recent neuroscience study[27] arguing that the error-based organization of neurons in the cerebellum that share a preference for a personalized view of the entire error space, may account for several desirable features of behavior and learning. We then analyze the learning behavior and characteristics of the model under varying scenarios to gauge the potential benefits of a similar mechanism in ANN. Our empirical results suggest that having separate populations of neurons with personalized error views can enable efficient learning under class imbalance and limited data, and reduce the susceptibility to unintended shortcut strategies, leading to improved generalization. This work highlights the potential of translating the learning machinery of the brain into the design of a new generation of ANNs and provides further credence to the argument that biologically inspired AI may hold the key to overcoming the shortcomings of ANNs.