Over the past 15 years, we have developed software image processing systems that attempt to reproduce the sorts of spike-based processing strategies used in biological vision. The basic idea is that sophisticated visual processing can be achieved with a single wave of spikes by using the relative timing of spikes in different neurons as an efficient code. While software simulations are certainly an option, it is now becoming clear that it may well be possible to reproduce the same sorts of ideas in specific hardware. Firstly, several groups have now developed spiking retina chips in which the pixel elements send the equivalent of spikes in response to particular events such as increases or a decreases in local luminance. Importantly, such chips are fully asynchronous, allowing image processing to break free of the standard frame based approach. We have recently shown how simple neural network architectures can use the output of such dynamic spiking retinas to perform sophisticated tasks by using a biologically inspired learning rule based on Spike-Time Dependent Plasticity (STOP). Such systems can learn to detect meaningful patterns that repeat in a purely unsupervised way. For example, after just a few minutes of training, a network composed of a first layer of 60 neurons and a second layer of 10 neurons was able to form neurons that could effectively count the number of cars going by on the different lanes of a freeway. For the moment, this work has just used simulations. However, there is a real possibility that the same processing strategies could be implemented in memristor-based hardware devices. If so, it will become possible to build intelligent image processing systems capable of learning to recognize significant events without the need for conventional computational hardware.
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