Evolutionary and Computational Advantages of Neuromodulated Plasticity

Abstract

The integration of modulatory neurons into evolutionary artificial neural networks is proposed here. A model of modulatory neurons was devised to describe a plasticity mechanism at the low level of synapses and neurons. No initial assumptions were made on the network structures or on the system level dynamics. The work of this thesis studied the outset of high level system dynamics that emerged employing the low level mechanism of neuromodulated plasticity. Fully-fledged control networks were designed by simulated evolution: an evolutionary algorithm could evolve networks with arbitrary size and topology using standard and modulatory neurons as building blocks. A set of dynamic, reward-based environments was implemented with the purpose of eliciting the outset of learning and memory in networks. The evolutionary time and the performance of solutions were compared for networks that could or could not use modulatory neurons. The experimental results demonstrated that modulatory neurons provide an evolutionary advantage that increases with the complexity of the control problem. Networks with modulatory neurons were also observed to evolve alternative neural control structures with respect to networks without neuromodulation. Different network topologies were observed to lead to a computational advantage such as faster input-output signal processing. The evolutionary and computational advantages induced by modulatory neurons strongly suggest the important role of neuromodulated plasticity for the evolution of networks that require temporal neural dynamics, adaptivity and memory functions