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Role of Chaos in Trial-and-error Problem Solving by an Artificial Neural Network

One role of chaotic neural activity is illustrated by means of computer simulations of an imaginary agent's goal-oriented behavior. The agent has a simplified neural network with seven neurons and three legs. The neuronal network consists of one photosensory neuron and three pairs of inter- and motor neurons. The three legs whose movements are governed by the three motor neurons allow the agent to walk in six concentric radial directions on a plane. It is intended that the neural network causes the agent to walk in a direction of greater brightness, to reach finally the most brightly lit place on the plane. The presence of only one sensory neuron has an important meaning. That is, no immediate information on directions of greater brightness is sensed by the agent. In other words, random walking in the manner of trial- and error problem solving must be involved in the agent's walking. Chaotic firing of the motor neurons is intended to play a crucial role in generating the random walking. Brief random walking and rapid straight walking in a direction of greater brightness were observed to occur alternately in the computer simulation. Controlled chaos in naturally occurring neural networks may play a similar role.