Abstract

Donald Hebb proposed a hypothesis that specialised groups of neurons, called cell-assemblies (CAs), form the basis for neural encoding of symbols in human mind. It is not clear, however, how CAs can be re-used and combined to form new representations as in classical symbolic systems. We demonstrate that Hebbian learning of synaptic weights alone is not adequate for the task, and that additional meta-control process should be involved. We describe a proposed earlier architecture implementing such a process, and then evaluate it by modelling the probability matching phenomenon in a classical twochoice task. The model and its results are discussed in view of mathematical theory of learning, existing cognitive architectures as well as some hypotheses about neural functioning in the brain.

Citations