Self-regulating neurons. A model for synaptic plasticity in recurrent neural networks.

In our approach of evolutionary robotics we generate recurrent neural network of arbitrary structure to control autonomous robots in different environments performing various tasks. In dynamically changing and partially unknown environments it is hard if not impossible to define error functions for an on-line learning rule. Therefore, in this context, a learning method must be local and unsupervised. Most of today's local and unsupervised learning rules are variations of the Hebbian Learning Rule, including its limitations, or reinforcement techniques.

In the proposed model each neuron is a self-regulating unit, motivated by Ashby's Homeostat, stabilising its activity towards a target value. In the sensorimotor loop, when such a recurrent neural network with self-regulating neurons is controlling a robot, this regulation process is constantly disturbed by external stimuli. In order to compensate for these disturbances, each neuron has two additional internal properties, motivated by receptors and transmitters of biological neurons. The overall behaviour is the result of the interplay of the self-regulating neurons.

In my talk I will present the followed approach of evolutionary robotics, the software framework which I have designed and written in large parts, as well as the neuron model.