Exploring Three Possibilities in Network Design: Spontaneous Node Activity, Node Plasticity and Temporal Coding

The relationship between two massively parallel techniques, namely relaxation labelling and synaptically-based neural learning, is sketched. Within this framework, neuron-centered learning can be viewed as second-order relaxation. This type of learning has been explored through simulation of a lateral-inhibition toroidal network of 10 × 10 plastic pacemaker neurons. Two frequencies successively presented to the network have been encoded in separate groups of neurons, without learning of the second frequency disrupting memory of the first one. Some implications of these results for both Computational Neuroscience and Computer Network Design are pointed out.