The Emergence of Contrast-Invariant Orientation Tuning in Simple Cells of Cat Visual Cortex

Abstract

Simple cells in primary visual cortex exhibit contrast-invariant orientation tuning, in seeming contradiction to feed-forward models that rely on lateral geniculate nucleus (LGN) input alone. Contrast invariance has therefore been thought to depend on the presence of intracortical lateral inhibition. In vivo intracellular recordings instead suggest that contrast invariance can be explained by three properties of the excitatory pathway. (1) Depolarizations evoked by orthogonal stimuli are determined by the amount of excitation a cell receives from the LGN, relative to the excitation it receives from other cortical cells. (2) Depolarizations evoked by preferred stimuli saturate at lower contrasts than the spike output of LGN relay cells. (3) Visual stimuli evoke contrast-dependent changes in trial-to-trial variability, which lead to contrast-dependent changes in the relationship between membrane potential and spike rate. Thus, high-contrast, orthogonally oriented stimuli that evoke significant depolarizations evoke few spikes. Together these mechanisms, without lateral inhibition, can account for contrast-invariant stimulus selectivity.

Untuned Suppression Makes a Major Contribution to the Enhancement of Orientation Selectivity in Macaque V1

“…For example, for the representative cell in Figure 5A, such tuning curves are shown in Figure 7A. One might expect that, if the membrane potential of a cell were far below spike firing threshold in the drifting grating experiment, the measured orientation selectivity would be greater than that of the predicted tuning curve because of the iceberg effect (Priebe and Ferster, 2006;Finn et al, 2007). Good agreement between predicted and measured orientation selectivity would suggest the opposite, that the membrane potential of the cell was not far below spike-firing threshold during the drifting grating experiments.…”

“…Many experimental studies (Nelson and Frost, 1978;Sillito et al, 1980;Bonds, 1989;Volgushev et al, 1993;Sato et al, 1996;Ringach et al, 2002a,b;Shapley et al, 2003) concluded that suppressive or inhibitory cortical mechanisms were involved. However, more recent studies Freeman et al, 2002;Priebe and Ferster, 2006;Finn et al, 2007;Koelling et al, 2008) suggested that the suppressive effects studied earlier in experiments that used plaids and drifting gratings as stimuli might be due to nonlinearities in the retina and/or LGN. These reexaminations of the neuronal mechanism of orientation selectivity motivated us to make a quantitative assessment of the contribution of untuned suppression.…”

“…The spiking threshold acting as an iceberg effect (Rose and Blakemore, 1974) is unlikely to account for orientation selectivity (Sompolinsky and Troyer et al, 1998), but more recent work (Priebe and Ferster, 2006;Finn et al, 2007) proposed a more sophisticated model regarding spike-firing threshold. In this model, the spike-firing threshold makes a visual cortical neuron transform its membrane potential into cycle-averaged firing rates like a power law transducer (Hansel and van Vreeswijk, 2002;Miller and Troyer, 2002).…”

Anticipation of natural stimuli modulates EEG dynamics: physiology and simulation

“…Using this equation for our simulation yielded qualitatively similar results. The noise that comes with such balanced input has recently been shown to account for contrast normalization in the primary visual cortex of cats (Finn et al 2007). A second mechanism that could implement gain modulation is synchrony of inhibitory cells (Tiesinga et al 2004).…”

The role of fluctuations in coarse-grained descriptions of neuronal networks

“…Experimentally, it was shown that cortical neurons' degree of orientation selectivity is independent of the stimulus contrast, and that this prompts the network to operate in a high-gain regime, as close to the transition to bistability as possible, but that no bistable behavior of simple or complex cells has been observed in the input layer of the primary visual cortex [3,5,46,47,107,110,111,119]. The constraints laid out by this fluctuation-controlled criticality scenario allows us to tune the large-scale model network so that it operates in the regime just below the critical transition point to bistability [100,101,130], whose existence and properties are well understood through our kinetic-theoretic coarse-graining approach.…”