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Biological systems process visual input using a distributed representation, with different areas encoding different aspects of the visual interpretation. While current engineering habits tempt us to think of this processing in terms of a pipelined sequence of filters and other feed-forward processing stages, cortical anatomy suggests quite a different architecture, using strong recurrent connectivity between visual areas. Here we design a network to interpret input from a neuromorphic sensor by means of recurrently interconnected areas, each of which encodes a different aspect of the visual interpretation, such as light intensity or optic flow. As each area of the network tries to be consistent with the information in neighboring areas, the visual interpretation converges towards global mutual consistency. Rather than applying input in a traditional feed-forward manner, the sensory input is only used to weakly influence the information flowing both ways through the middle of the network. Even with this seemingly weak use of input, this network of interacting maps is able to maintain its interpretation of the visual scene in real time, proving the viability of this interacting map approach to computation.
Cook et al. (2011) studied this question.