Interactive control of computational power in a model of the basal ganglia-thalamocortical circuit by a supervised attractor-based learning procedure

Abstract

The attractor-based complexity of a Boolean neural network refers to its ability to discriminate among the possible input streams, by means of alternations between meaningful and spurious attractor dynamics. The higher the complexity, the greater the computational power of the network. The fine tuning of the interactivity – the network’s feedback output combined with the current input stream – can maintain a high degree of complexity within stable domains of the parameters’ space. In addition, the attractor-based complexity of the network is related to the degree of discrimination of specific input streams. We present a novel supervised attractor-based learning procedure aimed at achieving a maximal discriminability degree of a selected input stream. With a predefined target value of discriminability degree and in the absence of changes in the internal connectivity matrix of the network, the learning procedure updates solely the weights of the feedback projections. In a Boolean model of the basal ganglia-thalamocortical circuit, we show how the learning trajectories starting from different configurations can converge to final configurations associated with same high discriminability degree. We discuss the possibility that the limbic system may play the role of the interactive feedback to the network studied here.