The function and nature of inhibition of neurons in the visual cortex have been the focus of both experimental and theoretical investigations. There are two ways in which inhibition can suppress synaptic excitation. In hyperpolarizing inhibition, negative and positive currents sum linearly to produce a net change in membrane potential. In contrast, shunting inhibition acts nonlinearly by causing an increase in membrane conductance; this divides the amplitude of the excitatory response. Visually evoked changes in membrane conductance have been reported to be nonsignificant or weak, supporting the hyperpolarization mode of inhibition. Here we present a new approach to studying inhibition that is based on in vivo whole-cell voltage clamping. This technique allows the continuous measurement of conductance dynamics during visual activation. We show, in neurons of cat primary visual cortex, that the response to optimally orientated flashed bars can increase the somatic input conductance to more than three times that of the resting state. The short latency of the visually evoked peak of conductance, and its apparent reversal potential suggest a dominant contribution from gamma-aminobutyric acid ((GABA)A) receptor-mediated synapses. We propose that nonlinear shunting inhibition may act during the initial stage of visual cortical processing, setting the balance between opponent 'On' and 'Off' responses in different locations of the visual receptive field.

This intracellular study investigates synaptic mechanisms of orientation and direction selectivity in cat area 17. Visually evoked inhibition was analyzed in 88 cells by detecting spike suppression, hyperpolarization, and reduction of trial-to-trial variability of membrane potential. In 25 of these cells, inhibition visibility was enhanced by depolarization and spike inactivation and by direct measurement of synaptic conductances. We conclude that excitatory and inhibitory inputs share the tuning preference of spiking output in 60% of cases, whereas inhibition is tuned to a different orientation in 40% of cases. For this latter type of cells, conductance measurements showed that excitation shared either the preference of the spiking output or that of the inhibition. This diversity of input combinations may reflect inhomogeneities in functional intracortical connectivity regulated by correlation-based activity-dependent processes.

We review here the development of Hodgkin-Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are "fast spiking", "regular spiking", "intrinsically bursting" and "low-threshold spike" cells. For each class, we fit "minimal" HH type models to experimental data. The models contain the minimal set of voltage-dependent currents to account for the data. To obtain models as generic as possible, we used data from different preparations in vivo and in vitro, such as rat somatosensory cortex and thalamus, guinea-pig visual and frontal cortex, ferret visual cortex, cat visual cortex and cat association cortex. For two cell classes, we used automatic fitting procedures applied to several cells, which revealed substantial cell-to-cell variability within each class. The selection of such cellular models constitutes a necessary step towards building network simulations of the thalamocortical system with realistic cellular dynamical properties.

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