Connectivity and Candidate Structures

Introduction

We have earlier considered the foundations of recruitment learning, an approach grounded in the understanding of neural organisation and psychological function which emerged during the second half of the twentieth century. While the revolutionary findings in respect of adult neurogenesis explored in section 1.5 have deep implications for recruitment learning, we focus initially on Arbib’s earlier contention that “... a certain critical degree of structural complexity is required of a network before it can become self-modifying ... in a way that we could consider intelligent”. This chapter begins a computational exploration of these ideas, adopting the strategy of converging constraints as pioneered by Feldman and Valiant to help delineate the tasks which might be realised through the recruitment of general purpose circuits.