  Neural networks are based on a simplified model of the brain. In this
project, we wanted to relax the simplifying assumptions of a traditional neural
network by making a model that more closely emulates the low level interactions
of neurons. Like in an RNN, our model has a state that persists between time
steps, so that the energies of neurons persist. However, unlike an RNN, our
state consists of a 2 dimensional matrix, rather than a 1 dimensional vector,
thereby introducing a concept of distance to other neurons within the state. In
our model, neurons can only fire to adjacent neurons, as in the brain. Like in
the brain, we only allow neurons to fire in a time step if they contain enough
energy, or excitement. We also enforce a notion of conservation of energy, so
that a neuron cannot excite its neighbors more than the excitement it already
contained at that time step. Taken together, these two features allow signals
in the form of activations to flow around in our network over time, making our
neural mesh more closely model signals traveling through the brain the brain.
Although our main goal is to design an architecture to more closely emulate the
brain in the hope of having a correct internal representation of information by
the time we know how to properly train a general intelligence, we did benchmark
our neural mash on a specific task. We found that by increasing the runtime of
the mesh, we were able to increase its accuracy without increasing the number
of parameters.
