| Popis: |
We demonstrate a spiking neural network for navigation motivated by the chemotaxis circuit of Caenorhabditis elegans. Our network uses information regarding temporal gradients in intensity of local variables such as chemical concentration, temperature, radiation, etc., to make navigational decisions for contour tracking and obstacle avoidance. The gradient information is determined by mimicking the underlying mechanisms of the ASE neurons of C. elegans. Simulations show that our software-worm is able to identify the set-point with 92% efficiency, 68.5% higher than an optimal memoryless Levy foraging strategy and 33% higher than an equivalent non-spiking neural network configuration. The software-worm is able to track the set-point with an average deviation of 1% from the set-point, and this performance degrades merely by 1.8% in the presence of intense salt and pepper noise in the local tracking variable. We also develop a VLSI implementation for the main gradient detector neurons, which could be integrated with standard comparator circuitry to develop robust circuits for navigation and contour tracking. We demonstrate noise-resilience of our network to environmental, architectural and circuit noise. |
