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Adaptive Behavior, Vol. 15, No. 4, 377-396 (2007)
DOI: 10.1177/1059712307084688

The Dynamics of Associative Learning in Evolved Model Circuits

Phattanard Phattanasri

Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, phattanardp{at}fabrinet.co.th

Hillel J. Chiel

Departments of Biology, Neurosciences, and Biomedical Engineering Case Western Reserve University Cleveland, OH 44106, hjc{at}case.edu

Randall D. Beer

Cognitive Science Program, Department of Computer Science, Department of Informatics, Indiana University, Bloomington, IN 47406, rdbeer{at}indiana.edu

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 in-depth 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.

Key Words: evolution of learning • evolutionary robotics • dynamical systems theory • synaptic plasticity • neuroscience


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