MSTd neurons during ocular following and smooth pursuit perturbation.

MSTd neurons in the behaving monkey were investigated during step-ramp smooth pursuit eye movements (SPEM), short perturbations of the small visual target during ongoing pursuit, and large-field visual stimulation inducing ocular following responses (OFR). Neurons responded with short latencies to visual motion during OFR. In contrast the non-retinal responses during SPEM and perturbations followed the eye movements by 100-150 ms and were in the opposite direction to the OFR response. Often neurons were not modulated by the perturbation. Although, both the OFR and the perturbation response are involuntary eye movements due to visual motion, it seems very unlikely that these MSTd neurons with non-retinal responses are involved in their direct control. Based on these responses, we suggest that our MSTd neurons may code for gaze direction in space based on visual estimates of self-motion and extraretinal estimates of eye-in-head motion.

Learning the selectivity of V2 and V4 neurons using non-linear multi-layer wavelet networks.

We investigate a non-linear network with two processing stages optimized to reduce the statistical dependencies in natural images. This network serves as a model for the neural information processing in the higher visual areas of primates (visual cortices V2-V4). The resulting population is analyzed with regard to non-linear selectivity and invariance properties. We find units that are very selective with respect to the space spanned by all possible input signals and units that are invariant with respect to certain stimulus classes. In comparison to the measured distribution of selectivity in V2 neurons, the selectivity histogram of the network units shows an even more pronounced tendency towards higher selectivities. A special property of the system is the emergence of non-linear interactions between coefficients from different scales and orientations, which are necessary for the exploitation of higher-order statistical redundancies of natural images. We extend the concept to multi-layer systems and present some simulation results.

Natural scene statistics and nonlinear neural interactions between frequency-selective mechanisms.

Linear filtering is a basic concept in neural models of early sensory information processing. In particular the visual system has been described to perform a wavelet-like multi-channel decomposition by a set of independent spatial-frequency selective filter mechanisms. Here we suggest that this principle of linear filtering deserves a critical re-evaluation. We propose that an optimal adaptation to natural scene statistics would require AND-like nonlinear interactions between the frequency-selective filter channels. We describe how this hypothesis can be tested by predicted violations of the principle of linearity that should be observable if cortical neurons would actually implement the proposed nonlinearities. We further explain why these effects might have been easily overlooked in earlier tests of the linearity of neurons in primary visual cortex.