White noise analysis of temporal properties in simple receptive fields of cat cortex

Abstract

We studied the linear and nonlinear temporal response properties of simple cells in cat visual cortex by presenting at single positions in the receptive field an optimally oriented bar stimulus whose luminance was modulated in a random, binary fashion. By crosscorrelating a cell's response with the input it was possible to obtain the zeroth-, first-, and second-order Wiener kernels at each RF location. Simple cells showed pronounced nonlinear temporal properties as revealed by the presence of prominent second-order kernels. A more conventional type of response histogram was also calculated by time-locking a histogram on the occurrence of the desired stimulus in the random sequence. A comparison of the time course of this time-locked response with that of the kernel prediction indicated that nonlinear temporal effects of order higher than two are unimportant. The temporal properties of simple cells were well represented by a cascade model composed of a linear filter followed by a static nonlinearity. These modelling results suggested that for simple cells, the nonlinearity occurs late and probably is a soft threshold associated with the spike generating mechanism of the cortical cell itself. This result is surprising in view of the known threshold nonlinearities in preceding lateral geniculate and retinal neurons. It suggests that geniculocortical connectivity cancels the earlier nonlinearities to create a highly linear representation inside cortical simple cells.

Natural Stimulus Statistics Alter the Receptive Field Structure of V1 Neurons

“…This inhibition is much weaker or even absent in STRFs estimated using grating sequences. The changes in temporal inhibition depend on differences in temporal stimulus statistics and are consistent with previous observations of nonlinear temporal summation (Tolhurst et al, 1980;Mancini et al, 1990;Reid et al, 1992). Grating sequences are temporally white (up to 72 Hz), whereas natural vision movies are biased toward saccade frequencies (3-4 Hz).…”

“…However, this approach has proved less successful when applied to nonlinear neurons [e.g., complex cells (DeAngelis et al, 1995)] or at higher stages of visual processing (Mazer et al, 2000). Specialized algorithms have been developed to estimate secondorder kernels (Mancini et al, 1990;Touryan et al, 2002), but these methods require spatially and temporally restricted stimuli to achieve adequate signal-to-noise levels.…”

The Role of Delayed Suppression in Slow and Fast Contrast Adaptation in V1 Simple Cells

“…Slow adaptation likely relies on both intrinsic and cortically based synaptic mechanisms (Maffei et al, 1973;Ohzawa et al, 1985;Carandini and Ferster, 1997;Sanchez-vives et al, 2000;Nowak et al, 2005). In the case of fast contrast adaptation, the relative roles of the spike threshold, subcortical sources, and intracortical synaptic inhibition are still debated (Geisler and Albrecht, 1992;Heeger, 1992;Borg-Graham et al, 1998;Hirsch et al, 2003;Lauritzen and Miller, 2003;Finn et al, 2007;Katzner et al, 2011). Part of the issue may be that "slow" adaptation in V1 actually spans a diversity of timescales, from Ͻ1 s (Bonds, 1991;Müller et al, 1999) to seconds (Ohzawa et al, 1982(Ohzawa et al, , 1985 to minutes (Sharpee et al, 2006); moreover, the fine dynamics of the fast (Ͻ100 ms; Albrecht et al, 2002) contrast gain control are still unknown.…”

Predicting neuronal responses during natural vision

“…These include higher order Volterra series (Mancini et al 1990;Rust et al 2005;Touryan et al 2005) and non-parametric methods, such as artificial neural networks (Lehky et al 1992;Lau et al 2002;Prenger et al 2004). Many of these models can be formulated in terms of linearization.…”