Learning in neural models with complex dynamics

Interest in the ANN field has recently focused on dynamical neural networks for performing temporal operations, as more realistic models of biological information processing, and to extend ANN learning techniques. While this represents a step towards realism, it is important to note that individual neurons are complex dynamical systems, interacting through nonlinear, nonmonotonic connections. The result is that the ANN concept of learning, even when applied to a single synaptic connection, is a nontrivial subject. Based on recent results from living and simulated neurons, a first pass is made at clarifying this problem. We summarize how synaptic changes in a 2-neuron, single synapse neural network can change system behavior and how this constrains the type of modification scheme that one might want to use for realistic neuron-like processors