Temporal pattern generation by associative neural network models: Theory and application to Central Pattern Generators.

Cyclic patterns of motor neuron activity are involved in the production of many rhythmic movements, such as walking, swimming, and scratching. These movements are controlled by neural circuits referred to as central pattern generators (CPGs). Some of these circuits function in the absence of both internal pacemakers and external feedback. We describe a class of associative neural network models whose dynamic behavior is similar to that of CPGs. The collective outputs of the network models consists of patterns of linear or cyclic sequences of states. Multiple patterns can be produced by the same network. Pattern generation by the models depends upon the interplay between synaptic connections that act on two different time-scales. Short-term synaptic excitation between pairs of neurons may be followed by long- term inhibition, and short-term inhibition by long-term excitation. The change in sign of the net synaptic input to the neurons as a function of time causes the network to make transitions between states.