Search

This paper explores the idea that auto-teaching neural networks with evolved selfsupervision signals can lead to improved performance in dynamic environments where there is insufficient training data available within an individual’s lifetime. Results are presented from a series of artificial life experiments which investigate whether, when, and how this approach can lead to performance enhancements, in a simple problem domain that captures season dependent foraging behaviour.

After a review of the purpose and biological background of Head Direction Cells, and existing models of them, we introduce an improved neural model that integrates visual flow information with the vestibular inputs found in earlier models. Simulation results using real video inputs are presented to explore and validate the new model, the evolutionary advantages for the proposed arrangement are noted, its utility for use in robot controllers is discussed, and suggestions are made for further work in this area.

Attempts in the past to use evolutionary simulations to model the emergence of modular neural architectures has led to conflicting results. Here, we present some preliminary work on the use of computational embryogeny to model the evolution of neural architecture at a less coarse level of description. We believe that such an approach will lead to much more reliable and biologically realistic simulations of brain evolution.

Catastrophic forgetting is a well-known failing of many neural network systems whereby training on new patterns causes them to forget previously learned patterns. Humans have evolved mechanisms to minimize this problem, and in this paper we present our preliminary attempts to use simulated evolution to generate neural networks that suffer significantly less from catastrophic forgetting than traditionally formulated networks.