On the neurohydrodynamics of neural networks.

Inspired by De Broglie’s pilot wave interpretation of Quantum Mechanics and the subsequent development of Bohm’s Quantum Hydrodynamics, we propose a model for the dynamics of a neural network with reaction-diffusion processes described by a modified set of Cohen-Grossberg equations, which we call Neurohydrodynamics. In this approach, a pilot wave interpretation deterministically guides the dynamics of a neural network through the neuropotential that arises biologically from reaction-diffusion processes at the synapses of real neurons. We demonstrate that the neuropotential provides a new type of reinforcement learning useful for characterizing short-term memory and pattern formation in neural networks, and we compare our results to more traditional reinforcement learning methods through an example. Finally, we discuss extending our approach to include learning, memory, cognition and decision-making processes of the mammalian brain that are often modeled by neural networks.