Reverse engineering of the visual system using networks of spiking neurons
Recent research has shown that the speed of image processing achieved by the human visual system is incompatible with conventional neural network approaches that use standard coding schemes based on firing rate. An alternative is to use networks of asynchronously firing spiking neurons and use the order of firing across a population of neurons as a code. In this paper we summarize results that demonstrate a number of advantages of such coding schemes: (1) they allow very efficient transmission of information, (2) they are intrinsically invariant to variations in stimulus intensity and contrast, (3) they can be used in very large scale processing architectures to solve difficult problems including categorization of objects in natural scenes, and (4) they are particularly suited for implementation in low-cost multi-processor hardware.