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Nicholas V. Swindale
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Journal Articles
Publisher: Journals Gateway
Neural Computation (2008) 20 (1): 176–204.
Published: 01 January 2008
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A mechanism is proposed by which feedback pathways model spatial patterns of feedforward activity in cortical maps. The mechanism can be viewed equivalently as readout of a content-addressable memory or as decoding of a population code. The model is based on the evidence that cortical receptive fields can often be described as a separable product of functions along several dimensions, each represented in a spatially ordered map. Given this, it is shown that for an N -dimensional map, accurate modeling and decoding of x N feedforward activity patterns can be done with Nx fibers, N of which must be active at any one time. The proposed mechanism explains several known properties of the cortex and pyramidal neurons: (1) the integration of signals by dendrites with a narrow tangential distribution, that is, apical dendrites; (2) the presence of fast-conducting feedback projections with broad tangential distributions; (3) the multiplicative effects of attention on receptive field profiles; and (4) the existence of multiplicative interactions between subthreshold feedforward inputs to basal dendrites and inputs to apical dendrites.
Journal Articles
Publisher: Journals Gateway
Neural Computation (2002) 14 (9): 2053–2056.
Published: 01 September 2002
Journal Articles
Publisher: Journals Gateway
Neural Computation (1999) 11 (7): 1519–1526.
Published: 01 October 1999
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It has recently been shown that orientation and retinotopic position, both of which are mapped in primary visual cortex, can show correlated jumps (Das & Gilbert, 1997). This is not consistent with maps generated by Kohonen's algorithm (Kohonen, 1982), where changes in mapped variables tend to be anticorrelated. We show that it is possible to obtain correlated jumps by introducing a Hebbian component (Hebb, 1949) into Kohonen's algorithm. This corresponds to a volume learning mechanism where synaptic facilitation depends not only on the spread of a signal from a maximally active neuron but also requires postsynaptic activity at a synapse. The maps generated by this algorithm show discontinuities across which both orientation and retinotopic position change rapidly, but these regions, which include the orientation singularities, are also aligned with the edges of ocular dominance columns, and this is not a realistic feature of cortical maps. We conclude that cortical maps are better modeled by standard, non-Hebbian volume learning, perhaps coupled with some other mechanism (e.g., that of Ernst, Pawelzik, Tsodyks, & Sejnowski, 1999) to produce receptive field shifts.