It is shown how the well-known algorithm of B. Horn and B.C. Schunk (1981) for computing optical flow, based on minimizing a quadratic functional using a relaxation scheme, maps onto two different kinds of massive par...
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It is shown how the well-known algorithm of B. Horn and B.C. Schunk (1981) for computing optical flow, based on minimizing a quadratic functional using a relaxation scheme, maps onto two different kinds of massive parallel hardware: either resistive networks which are attractive for their technological potential, or neuronal networks related to the ones occurring in the motion pathway in the primate's visual system. If the x and y components of the motion field are coded explicitly as voltages within electrical circuits, simple resistive networks solve for the optical flow in the presence of motion discontinuities. These networks are being implemented into analog, subthreshold CMOS VLSI (complementary metal oxide semiconductor very large-scale integration) circuits. If velocity is represented within a population of direction selective cells, the resulting neuronal network maps onto the primate's striate and extrastriate visual cortex (middle temporal area). The performance of the network mimicks a large number of psychological illusions as well as electrophysical findings.< >
Based on anatomical and physiological data, we have developed a computer simulation of piri-form (olfactory) cortex which is capable of reproducing spatial and temporal patterns of actual cortical activity under a var...
Based on anatomical and physiological data, we have developed a computer simulation of piri-form (olfactory) cortex which is capable of reproducing spatial and temporal patterns of actual cortical activity under a variety of conditions. Using a simple Hebb-type learning rule in conjunction with the cortical dynamics which emerge from the anatomical and physiological organization of the model, the simulations are capable of establishing cortical representations for different input patterns. The basis of these representations lies in the interaction of sparsely distributed, highly divergent/convergent interconnections between modeled neurons. We have shown that different representations can be stored with minimal interference. and that following learning these representations are resistant to input degradation, allowing reconstruction of a representation following only a partial presentation of an original training stimulus. Further, we have demonstrated that the degree of overlap of cortical representations for different stimuli can also be modulated. For instance similar input patterns can be induced to generate distinct cortical representations (discrimination). while dissimilar inputs can be induced to generate overlapping representations (accommodation). Both features are presumably important in classifying olfactory stimuli.
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