Topic: excitation/inhibition

Artificial neural networks (ANNs) for simulated motor control and robotics often adopt generic architectures like fully connected multi-layer perceptrons (MLPs) and randomly connected recurrent neural networks (RNNs). While general, these tabula rasa architectures rely on large amounts of experience to learn, and their internal dynamics are difficult to interpret. In nature, animals are born with highly structured connectivity in their nervous systems shaped by evolution; this innate circuitry acts synergistically with learning mechanisms to provide inductive biases that enable most animals to