A neural model of motion perception simulates psychophysical data concerning first-order and second-order motion stimuli, including the reversal of perceived motion direction with distance from the stimulus (\Gamma display), and data about directional judgments as a function of relative spatial phase or spatial and temporal frequency. Many other second-order motion percepts that have been ascribed to a second non-Fourier processing stream can also be explained in the model by interactions between ON and OFF cells within a single, neurobiologically interpreted magnocellular processing stream. Yet other percepts may be traced to interactions between form and motion processing streams, rather than to processing within multiple motion processing streams. The model hereby explains why monkeys with lesions of of the parvocellular layers, but not the magnocellular layers, of the lateral geniculate nucleus (LGN) are capable of detecting the correct direction of second-order motion, why most ce...

Visually guided navigation through a cluttered natural scene is a challenging problem that animals and humans accomplish with ease. The ViSTARS neural model proposes how primates use motion information to segment objects and determine heading for purposes of goal approach and obstacle avoidance in response to video inputs from real and virtual environments. The model produces trajectories similar to those of human navigators. It does so by predicting how computationally complementary processes in cortical areas MT- /MSTv and MT + /MSTd compute object motion for tracking and self-motion for navigation, respectively. The model retina responds to transients in the input stream. Model V1 generates a local speed and direction estimate. This local motion estimate is ambiguous due to the neural aperture problem. Model MT + interacts with MSTd via an attentive feedback loop to compute accurate heading estimates in MSTd that quantitatively simulate properties of human heading estimation data. Model MT- interacts with MSTv via an attentive feedback loop to compute accurate estimates of speed, direction and position of moving objects. This object information is combined with heading information to produce steering decisions wherein goals behave like attractors and obstacles behave like repellers. These steering decisions lead to navigational trajectories that closely match human performance.

Intuitively, dynamic visual stimuli, such as moving objects or flashing lights, attract attention. Visual search tasks have revealed that dynamic targets among static distractors can indeed efficiently guide attention. The present study shows that the reverse case, a static target among dynamic distractors, allows for relatively efficient selection in certain but not all cases. A static target was relatively efficiently found among distractors that featured apparent motion, corroborating earlier findings. The important new finding was that static targets were equally easily found among distractors that blinked on and off continuously, even when each individual item blinked at a random rate. However, search for a static target was less efficient when distractors abruptly varied in luminance but did not completely disappear. The authors suggest that the division into the parvocellular pathway dealing with static visual information, on the one hand, and the magnocellular pathway common to motion and new object onset detection, on the other hand, allows for efficient filtering of dynamic and static information.

Abstract--Data concerning the possible role of a corollary discharge mechanism in the regulation of visual-oculomotor interactions are reviewed. Several modes of action for such a mechanism on the processing of visual information are discussed. Mere suppression of visual input during saccades is considered mostly as a peripheral mechanism. It is proposed that corollary discharge could either produce an active cancellation of the effects of eye movements on vision, or contribute to the evaluation that a given visual change is provoked by a saccade. Cancellation could occur at subcortical levels of visual processing although evaluation could occur at the cortical level.

This report describes research done within the Artificial Intelligence Laboratory and the Cen,er for Biological Irfforma'iion Frocessing (Whitaker College) at the Massachusetts Institute of Techno!ogy. The Center's support is provided in part by the SIon Foundation and in part by the Whitaker College

An overview over the different approaches to mathematical modelling in neuroscience in general, with special emphasis on the early visual system, is presented. Questions such as Why do we make mathematical models at all?", What makes a mathematical model good?", What types of mathematical models exist?", and What is the right level of detail in a model?" are addressed. Results from a project on constructing mechanistic models of the spatial receptive-field organization of cells in the dorsal lateral geniculate nucleus (dLGN) are also presented. In contrast to the traditional descriptive modelling based on the difference-of-Gaussians model, our model takes the known physiological couplings between retina and dLGN and within dLGN into account. The advantage of this modelling approach is that in addition to providing mathematical descriptions of the receptive fields of dLGN neurons, it also make explicit the contributions from the geniculate circuit. Moreover, the model parameters have direct physiological relevance and can be manipulated and measured experimentally. The model is applied to experimental data on neural responses to spots of varying sizes for X dLGN cells and for their retinal input (S-potentials). The model is able to account for these results. Moreover, model predictions regarding receptive-field center sizes of interneurons, distances between neighboring retinal ganglion cells providing input to interneurons, and the amount of center-surround antagonism for interneurons compared to relay cells, are all compatible with data available in the literature. 1.

1. We have examined the spatial and temporal tuning properties of 238 cortical neurones, recorded using conventional techniques from acutely prepared anaesthetized cats. We determined spatial and temporal frequency tuning curves using sinusoidal grating stimuli presented to each neurone's receptive field by a digital computer on a cathode ray tube. 2. We measured tuning curves either by determining response amplitude as a function of spatial or temporal frequency, or by measuring contrast sensitivity (the inverse of the contrast of the grating that just elicited a detectable response). The two measures give very similar tuning curves in all cases. 3. We recorded from 184 neurones in area 17; of these 156 had receptive fields within 5 ° of the area centralis. The range of preferred spatial frequency for these neurones was 0-3-3 c/deg, and their spatial frequency tuning band widths varied from 0 7 to 3-2 octaves at half-amplitude. The most common band width was roughly 1-3 octaves. Simple and complex cells in area 17 did not differ in their distributions of preferred spatial frequency, although complex cells were, on average, slightly less

1. The activity of single units was recorded from the striate cortex (area 17) of anaesthetized, paralysed cats. Reponses to stimuli moving at different velocities were examined. 2. Peak evoked firing frequency, rather than total evoked spikes, is used throughout as a measure of response. The former measure gives curves of response vs. velocity that correlate well with curves of contrast sensitivity vs. velocity, whereas the latter does not. 3. Cortical receptive fields were classified according to the criteria of Hubel & Wiesel. Simple cells were found to prefer lower velocities (mean 2-2 deg sec-') than complex cells (mean 18-8 deg sec-'). The response of simple cells to stimuli moving faster than 20 deg sec ' is generally poor; complex cells usually discharge briskly to these speeds. 4. Cells classified as hypercomplex by the end-inhibition criterion were further characterized as type I or type II, according to the suggestion of Dreher (1972). Type I units are indistinguishable from simple cells in their velocity tuning, and type II units equally clearly resemble complex cells. These results are therefore consistent with Dreher's subdivision. 5. The selectivity of cells for velocity is variable but can be quite marked. The average selectivities of simple and complex cells are not significantly different. There is an inverse correlation between preferred velocity and the sharpness of velocity selectivity for simple cells; no trend is apparent for other cell types. 6. No clear correlation is observed between the velocity preferences of units and their degree of direction selectivity, or receptive field arrangement. Simple cells with 'sustained ' temporal responses to flashed stimuli tend to prefer slower rates of movement than 'transient ' ones, and to be less selective for velocity.

A knowledge of the dynamics (temporal properties) of neuronal populations is essential for an understanding of their function, and is also crucial when one attempts to develop computational or mathematical models of the neurons. Here we review the temporal properties of the receptive fields (RFs) of the two best-studied types of ganglion cells in the primate retina, those that project to the parvocellular (P) and magnocellular (M) layers of the dorsal lateral geniculate nucleus. The center and surround mechanisms of the P RFs are approximately linear, and their impulse responses are very similar, although the surround lags the center by a few milliseconds. The center and surround are chromatically opponent. With the appropriate stimulus one can find significant nonlinearities in their responses, and also in the interaction between the center and surround. The phase lag between the responses of the center and surround depends on the temporal frequency, so that at high temporal frequency the antagonism between them is reduced or abolished. The temporal responses of M cells are nonlinear, and with increasing contrast they show the effects of a contrast gain control. The different dynamical properties of the two populations suggest that M cells participate in motion analysis, while P cells are used for the analysis of form, texture, and perhaps color.