  An ongoing challenge in neural information processing is: how do neurons
adjust their connectivity to improve task performance over time (i.e.,
actualize learning)? It is widely believed that there is a consistent,
synaptic-level learning mechanism in specific brain regions that actualizes
learning. However, the exact nature of this mechanism remains unclear. Here we
propose an algorithm based on reinforcement learning (RL) to generate and apply
a simple synaptic-level learning policy for multi-layer perceptron (MLP)
models. In this algorithm, the action space for each MLP synapse consists of a
small increase, decrease, or null action on the synapse weight, and the state
for each synapse consists of the last two actions and reward signals. A binary
reward signal indicates improvement or deterioration in task performance. The
static policy produces superior training relative to the adaptive policy and is
agnostic to activation function, network shape, and task. Trained MLPs yield
character recognition performance comparable to identically shaped networks
trained with gradient descent. 0 hidden unit character recognition tests
yielded an average validation accuracy of 88.28%, 1.86$\pm$0.47% higher than
the same MLP trained with gradient descent. 32 hidden unit character
recognition tests yielded an average validation accuracy of 88.45%,
1.11$\pm$0.79% lower than the same MLP trained with gradient descent. The
robustness and lack of reliance on gradient computations opens the door for new
techniques for training difficult-to-differentiate artificial neural networks
such as spiking neural networks (SNNs) and recurrent neural networks (RNNs).
Further, the method's simplicity provides a unique opportunity for further
development of local rule-driven multi-agent connectionist models for machine
intelligence analogous to cellular automata.
