Dynamic properties of neural networks with adapting synapses
Abstract
Two kinds of dynamic processes take place in neural networks. One involves the change with time of the activity of each neuron. The other involves the change in strength of the connections (synapses) between neurons. When a neural network is learning or developing, both processes simultaneously take place, and their dynamics interact. This interaction is particularly important in feedback networks. A Lyapunov function is developed to help understand the combined activity and synapse dynamics for a class of such adaptive networks. The methods and viewpoint are illustrated by using them to describe the development of columnar structure of orientation-selective cells in primary visual cortex. Within this model, the columnar structure originates from symmetry breaking in feedback pathways within an area of cortex, rather than feedforward pathways between areas.