The computational resources available to the brain can be summarized in terms of a number of numerical parameters. These include the number of neurons, the average number of neurons each neuron synapses with, and the basic response time of a neuron to its inputs. A fourth parameter is the strength of influence that a neuron has through its synapses on a typical neuron with which it synapses. Given the limited resources that the brain has, as summarized by these four parameters as currently understood, it turns out to be highly challenging to explain how the brain can perform even the most basic information processing tasks. In particular, the sparsity of the interconnection pattern in conjunction with generally weak synapses appears to impose very considerable constraints. The aim of the proposed research is to explore, using abstract computational models, how information processing is possible at all under these constraints. In particular the challenge is to explain how multiple tasks, such as memorization, association and inductive learning, can be supported together in such a system.
