Direction selectivity of simple cells in the primary visual cortex: comparison of two alternative mathematical models. I: response to drifting gratings

Two models of a single hypercolumn in the primary visual cortex are presented, and used for the analysis of direction selectivity in simple cells. The two models differ as to the arrangement of inhibitory connections: in the first ("antiphase model") inhibition is in phase opposition with excitation, but with a similar orientation tuning; in the second ("in-phase model"), inhibition is in phase with excitation, but with broader orientation tuning. Simulation results, performed by using drifting gratings with different orientations, and different spatial and temporal frequencies, show that both models are able to explain the origin of direction preference of simple cells.

Comparing Acceleration and Speed Tuning in Macaque MT: Physiology and Modeling

Studies of individual neurons in area MT have traditionally investigated their sensitivity to constant speeds. We investigated acceleration sensitivity in MT neurons by comparing their responses to constant steps and linear ramps in stimulus speed. Speed ramps constituted constant accelerations and decelerations between 0 and 240°/s. Our results suggest that MT neurons do not have explicit acceleration sensitivity, although speed changes affected their responses in three main ways. First, accelerations typically evoked higher responses than the corresponding deceleration rate at all rates tested. We show that this can be explained by adaptation mechanisms rather than differential processing of positive and negative speed gradients. Second, we inferred a cell's preferred speed from the responses to speed ramps by finding the stimulus speed at the latency-adjusted time when response amplitude peaked. In most cells, the preferred speeds inferred from deceleration were higher than those for accelerations of the same rate or from steps in stimulus speed. Third, neuron responses to speed ramps were not well predicted by the transient or sustained responses to steps in stimulus speed. Based on these findings, we developed a model incorporating adaptation and a neuron's speed tuning that predicted the higher inferred speeds and lower spike rates for deceleration responses compared with acceleration responses. This model did not predict acceleration-specific responses, in accordance with the lack of acceleration sensitivity in the neurons. The outputs of this single-cell model were passed to a population-vector–based model used to estimate stimulus speed and acceleration. We show that such a model can accurately estimate relative speed and acceleration using information from the population of neurons in area MT.

Thalamocortical NMDA conductances and intracortical inhibition can explain cortical temporal tuning

Cells in cerebral cortex fail to respond to fast-moving stimuli that evoke strong responses in the thalamic nuclei innervating the cortex. The reason for this behavior has remained a mystery. We study an experimentally motivated model of the thalamic input-recipient layer of cat primary visual cortex that accounts for many aspects of cortical orientation tuning. In this circuit, inhibition dominates over excitation, but temporal modulations of excitation and inhibition occur out of phase with one another, allowing excitation to transiently drive cells. We show that this circuit provides a natural explanation of cortical low-pass temporal frequency tuning, provided N-methyl-D-aspartate (NMDA) receptors are present in thalamocortical synapses in proportions measured experimentally. This suggests a new and unanticipated role for NMDA conductances in shaping the temporal response properties of cortical cells, and suggests that common cortical circuit mechanisms underlie both spatial and temporal response tuning.

Analysis of direction selectivity arising from recurrent cortical interactions

The relative contributions of feedforward and recurrent connectivity to the direction-selective responses of cells in layer IVB of primary visual cortex are currently the subject of debate in the neuroscience community. Recently, biophysically detailed simulations have shown that realistic direction-selective responses can be achieved via recurrent cortical interactions between cells with nondirection-selective feedforward input (Suarez et al., 1995; Maex & Orban, 1996). Unfortunately these models, while desirable for detailed comparison with biology, are complex and thus difficult to analyze mathematically. In this article, a relatively simple cortical dynamical model is used to analyze the emergence of direction-selective responses via recurrent interactions. A comparison between a model based on our analysis and physiological data is presented. The approach also allows analysis of the recurrently propagated signal, revealing the predictive nature of the implementation.