An Attractor Network-Based Model with Darwinian Dynamics

The human brain can generate new ideas, hypotheses and candidate solutions to difficult tasks with surprising ease. We argue that this process has evolutionary dynamics, with multiplication, inheritance and variability all implemented in neural matter. This inspires our model, whose main component is a population of recurrent attractor networks with palimpsest memory that can store correlated patterns. The candidate solutions are represented as output patterns of the attractor networks and they are maintained in implicit working memory until they are evaluated by selection. The best patterns are then multiplied and fed back to attractor networks as a noisy version of these patterns (inheritance with variability), thus generating a new generation of candidate hypotheses. These components implement a truly Darwinian process which is more efficient than both natural selection on genetic inheritance or learning, on their own. We argue that this type of evolutionary search with learning can be the basis of high-level cognitive processes, such as problem solving or language.