Collision avoidance : a biologically inspired neural network for the detection of approaching objects

Abstract

The frequently studied lobula giant movement detector (LGMD) system of the locust responds most strongly to approaching objects. This thesis describes simulations which were designed with the ultimate aim of constructing a comprehensive model of the neural circuitry showing the effects of individual neurons on the overall responses of the system. The Rind and Bramwell neural network model of the LGMD was studied using new stimuli which revealed that the responses of the model are dependent on the shape of the stimulus. A modification of the model removes this dependence and allows the model to respond to more complex stimuli. Two models of a locust photoreceptor were developed with the aim of producing a detailed model of a light-adapting photoreceptor which could be used to study the responses of the LGMD to natural scenes. The first model, an electrical model of the cell membrane which describes the principal ionic conductances, was found to be overly complex for use in large scale simulations. However, the model was used to calculate from the photoreceptor's impulse response the average conductance change produced by individual photons. The second photoreceptor model, which is suitable for large scale simulations, uses two leaky integrators to mimic the effects of light adaptation on the photoreceptor's response. An electrical model of the lamina region of the optic lobe allowed the proposal that inhibition in the lamina is produced by electrical presynaptic inhibition to be studied, along with the possible effects of this inhibition on the visual input to the LGMD. The responses of the model correspond well with those measured from the LMCs of locusts and other insects, and their implications for the LGMD system are discussed