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Artificial neural networks (ANNs), inspired by the processing systems present in simple nervous systems, are now widely used for the extraction of patterns or meaning from complicated or imprecise data sets (Arbib, 2003; Enquist & Ghirlanda, 2005). Although modern ANNs have progressed considerably from the early, basic feedforward models to systems of significant sophistication, some with varying levels of feedback, modulation, adaptation, learning, etc. (Minsky & Papert, 1969; Gurney, 1997; Vogels et al., 2005), they rarely contain the full processing capabilities or adaptive power of real assemblies of nerve cells. Part of the problem in modelling such capabilities is that the detailed mechanisms underlying the operation of biological neural networks are not themselves fully identified or well understood, for there is a dearth of good biological model systems that possess a wide range of processing mechanisms but whose physiological processes and cellular interconnections can be fully investigated and characterised. One of the best biological model systems available is the vertebrate visual system, but even here the full range of cellular connections and interactions have not yet been characterised and hence cannot be developed into equivalent models (van Hemmen et al., 2001; Wassle, 2004).
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