Towards Brain Inspired Design for Addressing the Shortcomings of ANNs
This work addresses shortcomings in ANNs for AI researchers by proposing a biologically inspired design, though it appears incremental as it builds on existing tree-based ANN architectures and neuroscience insights.
This paper tackles the problem of improving artificial neural networks (ANNs) by drawing inspiration from the cerebellum's error-based organization, showing that separate neuron populations with personalized error views enable efficient learning under class imbalance and limited data, reduce susceptibility to shortcuts, and improve generalization.
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.