In the primate visual pathway, orientation tuning of neurons is first observed in the primary vis... more In the primate visual pathway, orientation tuning of neurons is first observed in the primary visual cortex. The LGN cells that comprise the thalamic input to V1 are not orientation tuned, but some V1 neurons are quite selective. Two main classes of theoretical models have been offered to explain orientation selectivity: feedforward models, in which inputs from spatially aligned LGN cells are summed together by one cortical neuron; and feedback models, in which an initial weak orientation bias due to convergent LGN input is sharpened and amplified by intracortical feedback. Recent data on the dynamics of orientation tuning, obtained by a crosscorrelation technique, may help to distinguish between these classes of models. To test this possibility, we simulated the measurement of orientation tuning dynamics on various receptive field models, including a simple Hubel-Wiesel type feedforward model: a linear spatio-temporal filter followed by an integrate-and-fire spike generator. The computational study reveals that simple feedforward models may account for some aspects of the experimental data, but fail to explain many salient features of orientation tuning dynamics in V1 cells. A simple feedback model of interacting cells is also considered. This model is successful in explaining the appearance of Mexican-hat orientation profiles, but other features of the data continue to be unexplained.
The visual capacity of the common barn owl (Tyto alba) was studied by quantitative analysis of th... more The visual capacity of the common barn owl (Tyto alba) was studied by quantitative analysis of the retina and optic nerve. Cell counts in the ganglion cell layer of the whole-mounted retina revealed a temporal area centralis with peak cell density of 12,500 cells/mm2 and a horizontal streak of high cell density extending from the area centralis into the nasal retina. Integration of the ganglion cell density map gave an estimated total of 1.4 million cells for the ganglion cell layer. Electron microscopy of a single, complete section of the optic nerve revealed a bimodal fiber diameter spectrum (modes at 0.3 and 0.9 microns; bin width = 0.2 microns), with diameters ranging from 0.15 microns (unmyelinated) to 6.05 microns (myelinated, sheath included). The total axon count for the optic nerve was estimated from sample counts to be about 680,000 axons (25% unmyelinated). Therefore, roughly half of the cells in the retinal ganglion cell layer do not send axons into the optic nerve. With...
SPECIAL ISSUE Rendering the Use of Visual Information from Spiking Neurons to Recognition A picture is worth thousands of trials: rendering the use of visual information from spiking neurons to recognition 141
Cognitive Science, 2004
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SPECIAL ISSUE Rendering the Use of Visual Information from Spiking Neurons to Recognition A picture is worth thousands of trials: rendering the use of visual information from spiking neurons to recognition 141
The response to contrast is one of the most important functions of the macaque primary visual cor... more The response to contrast is one of the most important functions of the macaque primary visual cortex, V1, but up to now there has not been an adequate theory for it. To fill this gap in our understanding of cortical function, we built and analyzed a new large-scale, biologically constrained model of the input layer, 4Cα, of macaque V1. We called the new model CSY2. We challenged CSY2 with a three-parameter family of visual stimuli that varied in contrast, orientation, and spatial frequency. CSY2 accurately simulated experimental data and made many new predictions. It accounted for 1) the shapes of firing-rate-versus-contrast functions, 2) orientation and spatial frequency tuning versus contrast, and 3) the approximate contrast-invariance of cortical activity maps. Post-analysis revealed that the mechanisms that were needed to produce the successful simulations of contrast response included strong recurrent excitation and inhibition that find dynamic equilibria across the cortical surface, dynamic feedback between L6 and L4, and synaptic dynamics like inhibitory synaptic depression.
Simple cells in the striate cortex respond to visual stimuli in an approximately linear manner, a... more Simple cells in the striate cortex respond to visual stimuli in an approximately linear manner, although the LGN input to the striate cortex, and the cortical network itself, are highly nonlinear. Although simple cells are vital for visual perception, there has been no satisfactory explanation of how they are produced in the cortex. To examine this question, we have developed a large-scale neuronal network model of layer 4C␣ in V1 of the macaque cortex that is based on, and constrained by, realistic cortical anatomy and physiology. This paper has two aims: (1) to show that neurons in the model respond like simple cells. To identify how the model generates this linearized response in a nonlinear network. Each neuron in the model receives nonlinear excitation from the lateral geniculate nucleus (LGN). The cells of the model receive strong (nonlinear) lateral inhibition from other neurons in the model cortex. Mathematical analysis of the dependence of membrane potential on synaptic conductances, and computer simulations, reveal that the nonlinearity of corticocortical inhibition cancels the nonlinear excitatory input from the LGN. This interaction produces linearized responses that agree with both extracellular and intracellular measurements. The model correctly accounts for experimental results about the time course of simple cell responses and also generates testable predictions about variation in linearity with position in the cortex, and the effect on the linearity of signal summation, caused by unbalancing the relative strengths of excitation and inhibition pharmacologically or with extrinsic current.
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Papers by Robert Shapley