Neural Networks for Control of Artificial Life Form

Abstract
An artificial life form under the control of a spiking neural network has been created in a chessboard environment which consists of 60*60 grids using Matlab GUI. The spiking neural network consists of 8 neurons simulated using Izhikevich model which combines the property of both biological plausibility and computational efficiency. The neurons within the network are fully connected with each other. The ‘intelligence’ of the artificial life form is stored as value of weights in the synaptic connections of neurons. STDP is the learning rule implemented to the network in this project. STDP adjusts the synaptic weights according to the precise timing of pre and postsynaptic spikes.

The artificial life form itself has been designed to complete certain tasks such as avoiding obstacles and catching food in the chessboard. The behavior of the artificial life form under the control of STDP in various situations will be investigated. Experiments will be carried out at the same time trying to improve the behavior of the artificial life form so that the artificial life form can evolve and show some adaption abilities according to the external environments.