  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.
