  Biological nervous systems consist of networks of diverse, sophisticated
information processors in the form of neurons of different classes. In most
artificial neural networks (ANNs), neural computation is abstracted to an
activation function that is usually shared between all neurons within a layer
or even the whole network; training of ANNs focuses on synaptic optimization.
In this paper, we propose the optimization of neuro-centric parameters to
attain a set of diverse neurons that can perform complex computations.
Demonstrating the promise of the approach, we show that evolving neural
parameters alone allows agents to solve various reinforcement learning tasks
without optimizing any synaptic weights. While not aiming to be an accurate
biological model, parameterizing neurons to a larger degree than the current
common practice, allows us to ask questions about the computational abilities
afforded by neural diversity in random neural networks. The presented results
open up interesting future research directions, such as combining evolved
neural diversity with activity-dependent plasticity.
