The spatio-temporal receptive fields (RFs) of cells in the macaque monkey lateral geniculate nucleus (LGN) and striate cortex (V1) have been examined and two distinct sub-populations of non-directional V1 cells have been found: those with a slow largely monophasic temporal RF, and those with a fast very biphasic temporal response. These two sub-populations are in temporal quadrature, the fast biphasic cells crossing over from one response phase to the reverse just as the slow monophasic cells reach their peak response. The two sub-populations also differ in the spatial phases of their RFs. A principal components analysis of the spatio-temporal RFs of directional V1 cells shows that their RFs could be constructed by a linear combination of two components, one of which has the temporal and spatial characteristics of a fast biphasic cell, and the other the temporal and spatial characteristics of a slow monophasic cell. Magnocellular LGN cells are fast and biphasic and lead the fast-biphasic V1 subpopulation by 7 ms; parvocellular LGN cells are slow and largely monophasic and lead the slow monophasic V1 sub-population by 12 ms. We suggest that directional V1 cells get inputs in the approximate temporal and spatial quadrature required for motion detection by combining signals from the two non-directional cortical sub-populations which have been identified, and that these sub-populations have their origins in magno and parvo LGN cells, respectively.

W e (1-4) and many others (e.g., 5-8) have studied the chromatic response characteristics of cells in the lateral geniculate nucleus (LGN) and at the first processing stages within the striate cortex of the macaque monkey. We are revisiting the problem to test predictions from our recent color model (9) and from our psychophysical studies (10, 11) that certain transformations of color information should occur between the LGN and the cortex. Most previous studies of LGN and cortical cells, including our own earlier ones, used different stimuli in examining cells at the two levels, thus making direct comparisons between LGN and cortex difficult to quantify.To examine this issue more precisely, we have recorded from a considerable sample of cells in the LGN and striate cortex of macaque monkeys, by using identical stimuli in examining cells at these two successive processing levels. The stimuli also were identical in chromaticity to those used in our psychophysical studies (10, 11). These measurements should allow us to determine any changes in the chromatic information from the LGN to the cortex. Furthermore, they should also facilitate comparisons to psychophysical measurements of color appearance. MethodsMacaque monkeys (Macaca mulatta and M. fascicularis) were initially tranquilized with ketamine HCl (10-15 mg͞kg, i.m.). Anesthesia was maintained with a continuous i.v. infusion of sufentanil citrate (during surgery, 8-12 g͞kg͞h; during recording 5-8 g͞kg͞h). After surgery, paralysis was induced and maintained with pancuronium bromide (0.1-0.15 mg͞kg͞h, i.v.). Electrocardiogram, electroencephalogram, body temperature, and expired CO 2 were continuously monitored. All of the procedures were approved by the local Animal Care and Use Committee and were in accord with National Institutes of Health guidelines. Extracellular recordings were made from single neurons in the LGN and the striate cortex (V1). All recordings were from units whose receptive fields were within the central visual field. Action potentials (spikes) were recorded with a resolution of 1 msec. Visual stimuli were generated and controlled by a Sun͞TAAC (Sun Microsystems, Mountain View, CA) image processor, with on-line data analysis being performed by the Sun. The stimuli were presented on an NEC monitor (Nippon Electric, Tokyo) with a spatial resolution of 1,000 ϫ 900 pixels, a 66-Hz refresh rate, and a mean luminance of 70 cd͞m 2 .The chromatic response properties of each cell were characterized with drifting gratings that varied sinusoidally in chromaticity. These were presented for 2 sec each, in random order within a testing series. Typically, we first determined the spatial frequency tuning of a neuron and, in the case of cortical cells, the orientation tuning. The chromatic testing then proceeded with grating patches of the optimal spatial frequency and orientation. The grating patch was centered on the receptive field (RF) of the cell and was slightly larger than the classic RF. Many cortical cells and almost all LGN cells showed low-pass sp...

The ability to distinguish colour from intensity variations is a difficult computational problem for the visual system because each of the three cone photoreceptor types absorb all wavelengths of light, although their peak sensitivities are at relatively short (S cones), medium (M cones), or long (L cones) wavelengths. The first stage in colour processing is the comparison of the outputs of different cone types by spectrally opponent neurons in the retina and upstream in the lateral geniculate nucleus. Some neurons receive opponent inputs from L and M cones, whereas others receive input from S cones opposed by combined signals from L and M cones. Here we report how the outputs of the L/M- and S-opponent geniculate cell types are combined in time at the next stage of colour processing, in the macaque primary visual cortex (V1). Some V1 neurons respond to a single chromatic region, with either a short (68-95 ms) or a longer (96-135 ms) latency, whereas others respond to two chromatic regions with a difference in latency of 20-30 ms. Across all types, short latency responses are mostly evoked by L/M-opponent inputs whereas longer latency responses are evoked mostly by S-opponent inputs. Furthermore, neurons with late S-cone inputs exhibit dynamic changes in the sharpness of their chromatic tuning over time. We propose that the sparse, S-opponent signal in the lateral geniculate nucleus is amplified in area V1, possibly through recurrent excitatory networks. This results in a delayed, sluggish cortical S-cone signal which is then integrated with L/M-opponent signals to rotate the lateral geniculate nucleus chromatic axes.

# $We present a computational-observer model of the human spatial contrast-sensitivity function based on the Image Systems Engineering Toolbox for Biology (ISETBio) simulation framework. We demonstrate that ISETBioderived contrast-sensitivity functions agree well with ones derived using traditional ideal-observer approaches, when the mosaic, optics, and inference engine are matched. Further simulations extend earlier work by considering more realistic cone mosaics, more recent measurements of human physiological optics, and the effect of varying the inference engine used to link visual representations to psychophysical performance. Relative to earlier calculations, our simulations show that the spatial structure of realistic cone mosaics reduces the upper bounds on performance at low spatial frequencies, whereas realistic optics derived from modern wave-front measurements lead to increased upper bounds at high spatial frequencies. Finally, we demonstrate that the type of inference engine used has a substantial effect on the absolute level of predicted performance. Indeed, the performance gap between an ideal observer with exact knowledge of the relevant signals and human observers is greatly reduced when the inference engine has to learn aspects of the visual task. ISETBio-derived estimates of stimulus representations at various stages along the visual pathway provide a powerful tool for computing the limits of human performance. Cottaris et al. Commercial relationships: none.Corresponding author: Nicolas P. Cottaris. Figure 13. Stimulus-matched spatial-pooling template for the SVM-Template-Linear inference engine. (A) The spatial contrast modulation for the 16-c/8 stimulus. (B) The cone mosaic used. (C) The spatial-pooling kernel, or template, for this stimulus and this mosaic, with which cone responses are weighted before pooled. Each disk corresponds to a cone; the color of the disk indicates the weight value-red for positive, blue for negative-and the color saturation indicates the weight strength.

We considered the problem of determining how the retinal network may interact with electrical epiretinal stimulation in shaping the spike trains of ON and OFF ganglion cells, and thus the synaptic input to first-stage cortical neurons. To do so, we developed a biophysical model of the retinal network with nine stacked neuronal mosaics. Here, we describe the model's behavior under (i) electrical stimulation of a retina with complete cone photoreceptor loss, but an otherwise intact circuitry and (ii) electrical stimulation of a fully-functional retina. Our results show that electrical stimulation alone results in indiscriminate excitation of ON and OFF ganglion cells and a patchy input to the cortex with islands of excitation among regions of no net excitation. Activation of the retinal network biases the excitation of ON relative to OFF ganglion cells, and in addition, gradually interpolates and focuses the initial, patchy synaptic input to the cortex. As stimulation level increases, the cortical input spreads beyond the area occupied by the electrode contact. Further, at very strong stimulation levels, ganglion cell responses begin to saturate, resulting in a significant distortion in the spatial profile of the cortical input. These findings occur in both the normal and the degenerated retina simulations, but the normal retina exhibits a tighter spatiotemporal response. The complex spatiotemporal dynamics of the prosthetic input to the cortex that are revealed by our model should be addressed by prosthetic image encoders and by studies that simulate prosthetic vision.