In this article, we evolve and analyze continuous-time recurrent neural networks capable of associating the smells of different foods with edibility or inedibility in different environments. First, we present an indepth analysis of this task, highlighting the evolutionary challenges it poses and how these challenges informed our experimental design. Next, we describe the evolution of nonplastic neural circuits that can solve this food edibility learning problem. We then show that the dynamics of the best evolved nonplastic circuits instantiate finite state machines that capture the combinatorial structure of this task. Finally, we demonstrate that successful circuits with Hebbian synaptic plasticity can also be evolved, but that such circuits do not utilize their synaptic plasticity in a traditional way.
Phattanasri, P., Chiel, H.J. and Beer, R.D. (2007). The dynamics of associative learning in evolved model circuits. Adaptive Behavior 15(4):377-396.