Robert Shapley

Contrast's effect on spatial summation by macaque V1 neurons

Stimulation outside the receptive field of a primary visual cortical (V1) neuron reveals intracortical neural interactions. However, previous investigators implicitly or explicitly considered the extent of cortical spatial summation and, therefore, the size of the classical receptive field to be fixed and independent of stimulus characteristics or of surrounding context. On the contrary, we found that the extent of spatial summation in macaque V1 neurons depended on contrast, and was on average 2.3-fold greater at low contrast. This adaptive increase in spatial summation at low contrast was seen in cells throughout V1 and was independent of surround inhibition.

Cat and monkey retinal ganglion cells and their visual functional roles

Abstract Retinal ganglion cells, the integrative-output neurons of the retina, can be sorted into functional classes. In the cat, two ganglion cell classes are labelled X and Y. These are distinguished by the different retinal subnetworks that provide their input. X cells are driven by a single linear receptive field center mechanism. Y cells receive center and surround signals and additional signals from nonlinear subunits in their receptive fields. Both X and Y cells are highly sensitive to contrast. X cells project almost exclusively to the A or A1 layers of the lateral geniculate nucleus (LGN). Y cell axons terminate in the A or A1 layers and also the more ventral C layers, and also the superior colliculus. In the monkey, P cells connect the retina to the parvocellular layers of the LGN, have small receptive fields, are wavelength-selective, and are insensitive to contrast. M cells are ganglion cells that send axons to the magnocellular layers of the LGN, are not wavelength-selective, have somewhat larger receptive fields than P cells, and are very sensitive to contrast. Comparisons between cat and monkey ganglion cell classes reveal several important similarities between M cells and X cells.

The effect of contrast on the transfer properties of cat retinal ganglion cells.

1. Variation in stimulus contrast produces a marked effect on the dynamics of the cat retina. This contrast effect was investigated by measurement of the responses of X and Y ganglion cells. The stimuli were sine gratings or rectangular spots modulated by a temporal signal which was a sum of sinusoids. Fourier analysis of the neural response to such a stimulus allowed us to calculate first order and second order frequency kernels. 2. The first order frequency kernel of both X and Y ganglion cells became more sharply tuned at higher contrasts. The peak amplitude also shifted to higher temporal frequency at higher contrasts. Responses to low frequencies of modulation (less than 1 Hz) grew less than proportionally with contrast. However, response amplitudes at higher modulation frequencies (greater than 4 Hz) scaled approximately proportionally with contrast. Also, there was a marked phase advance in these latter components as contrast increased. 3. The contrast effect was significantly larger for Y cells than for X cells. 4. The first order frequency kernel was measured with single sine waves as well as with the sum of sinusoids as a modulation signal. The transfer function measured in this way was much less affected by increases in contrast. This implied that stimulus energy at one temporal frequency could affect the response amplitude and phase shift at another temporal frequency. 5. Direct proof was found that modulation at one frequency modifies the response at other frequencies. This was demonstrated by perturbation experiments in which the modulation stimulus was the sum of one strong perturbing sinusoid and seven weak test sinusoids. 6. The shape of the graph of the amplitude of the first order frequency kernel vs. temporal frequency did not depend on the amplitudes of the first order components, but rather on local retinal contrast. This was shown in an experiment with a sine grating placed at different positions in the visual field. The shape of the first order kernel did not vary with spatial phase, while the magnitudes of the first order responses varied greatly with spatial phase. 7. Models for the contrast gain control mechanism are considered in the Discussion.

Orientation selectivity in macaque V1: diversity and laminar dependence.

We studied the steady-state orientation selectivity of single neurons in macaque primary visual cortex (V1). To analyze the data, two measures of orientation tuning selectivity, circular variance and orientation bandwidth, were computed from the tuning curves. Circular variance is a global measure of the shape of the tuning curve, whereas orientation bandwidth is a local measure of the sharpness of the tuning curve around its peak. Circular variance in V1 was distributed broadly, indicating a great diversity of orientation selectivity. This diversity was also reflected in the individual cortical layers. However, there was a tendency for neurons with high circular variance, meaning low selectivity for orientation, to be concentrated in layers 4C, 3B, and 5. The relative variation of orientation bandwidth across the cortical layers was less than for circular variance, but it showed a similar laminar dependence. Neurons with large orientation bandwidth were found predominantly in layers 4C and 3B. There was a weak correlation between orientation selectivity and the level of spontaneous activity of the neurons. We also assigned a response modulation ratio for each cell, which is a measure of the linearity of spatial summation. Cells with low modulation ratios tended to have higher circular variance and bandwidth than those with high modulation ratios. These findings suggest a revision to the classical view that nonoriented receptive fields are principally found in layer 4C and the cytochrome oxidase-rich blobs in layer 2/3. Instead, a broad distribution of tuning selectivity is found in all cortical layers, and neurons that are weakly tuned for orientation are ubiquitous in V1 cortex.

Linear and nonlinear spatial subunits in Y cat retinal ganglion cells.

1. The mechanism which makes Y cells different from X cells was investigated. 2. Spatial frequency contrast sensitivity functions for the fundamental and second harmonic responses of Y cells to alternating phase gratings were determined. 3. The fundamental spatial frequency response was predicted by the Fourier transform of the sensitivity profile of the Y cell. The high spatial frequency cut‐off of a Y cells fundamental response was in this way related to the centre of the cells receptive field. 4. The second harmonic response of a Y cell did not cut off at such a low spatial frequency as the fundamental response. This result indicated that the source of the second harmonic was a spatial subunit of the receptive field smaller in spatial extent than the centre. 5. Contrast sensitivity vs. spatial phase for a Y cell was measured under three conditions: a full grating, a grating seen through a centrally located window, a grating partially obscured by a visual shutter. The 2nd/1st harmonic sensitivity ratio went down with the window and up with the shutter. These results implied that the centre of Y cells was linear and also that the nonlinear subunits extended into the receptive field surround. 6. Spatial localization of the nonlinear subunits was determined by means of a spatial dipole stimulus. The nonlinear subunits overlapped the centre and surround of the receptive field and extended beyond both. 7. The nature of the Y cell nonlinearity was found to be rectification, as determined from measurements of the second harmonic response as a function of contrast. 8. Spatial models for the Y cell receptive field are proposed.

The spatial transformation of color in the primary visual cortex of the macaque monkey

Perceptually, color is used to discriminate objects by hue and to identify color boundaries. The primate retina and the lateral geniculate nucleus (LGN) have cell populations sensitive to color modulation, but the role of the primary visual cortex (V1) in color signal processing is uncertain. We re-evaluated color processing in V1 by studying single-neuron responses to luminance and to equiluminant color patterns equated for cone contrast. Many neurons respond robustly to both equiluminant color and luminance modulation (color-luminance cells). Also, there are neurons that prefer luminance (luminance cells), and a few neurons that prefer color (color cells). Surprisingly, most color-luminance cells are spatial-frequency tuned, with approximately equal selectivity for chromatic and achromatic patterns. Therefore, V1 retains the color sensitivity provided by the LGN, and adds spatial selectivity for color boundaries.Perceptually, color is used to discriminate objects by hue and to identify color boundaries. The primate retina and the lateral geniculate nucleus (LGN) have cell populations sensitive to color modulation, but the role of the primary visual cortex (V1) in color signal processing is uncertain. We re-evaluated color processing in V1 by studying single-neuron responses to luminance and to equiluminant color patterns equated for cone contrast. Many neurons respond robustly to both equiluminant color and luminance modulation (color-luminance cells). Also, there are neurons that prefer luminance (luminance cells), and a few neurons that prefer color (color cells). Surprisingly, most color-luminance cells are spatial-frequency tuned, with approximately equal selectivity for chromatic and achromatic patterns. Therefore, V1 retains the color sensitivity provided by the LGN, and adds spatial selectivity for color boundaries.

New perspectives on the mechanisms for orientation selectivity

Since the discovery of orientation selectivity by Hubel and Wiesel, the mechanisms responsible for this remarkable operation in the visual cortex have been controversial. Experimental studies over the past year have highlighted the contribution of feedforward thalamo-cortical afferents, as proposed originally by Hubel and Wiesel, but they have also indicated that this contribution alone is insufficient to account for the sharp orientation tuning observed in the visual cortex. Recent advances in understanding the functional architecture of local cortical circuitry have led to new proposals for the involvement of intracortical recurrent excitation and inhibition in orientation selectivity. Establishing how these two mechanisms work together remains an important experimental and theoretical challenge.

Chapter 7 New views of primate retinal function

This review will focus on some of the recent advances in our knowledge of the monkey retina, particularly those dealing with the physiological properties of retinal ganglion cells and thalamic neurons in macaque monkey

Temporal-frequency selectivity in monkey visual cortex

We investigated the dynamics of neurons in the striate cortex (V1) and the lateral geniculate nucleus (LGN) to study the transformation in temporal-frequency tuning between the LGN and V1. Furthermore, we compared the temporal-frequency tuning of simple with that of complex cells and direction-selective cells with nondirection-selective cells, in order to determine whether there are significant differences in temporal-frequency tuning among distinct functional classes of cells within V1. In addition, we compared the cells in the primary input layers of V1 (4a, 4c alpha, and 4c beta) with cells in the layers that are predominantly second and higher order (2, 3, 4b, 5, and 6). We measured temporal-frequency responses to drifting sinusoidal gratings. For LGN neurons and simple cells, we used the amplitude and phase of the fundamental response. For complex cells, the elevation of impulse rate (F0) to a drifting grating was the response measure. There is significant low-pass filtering between the LGN and the input layers of V1 accompanied by a small, 3-ms increase in visual delay. There is further low-pass filtering between V1 input layers and the second- and higher-order neurons in V1. This results in an average decrease in high cutoff temporal-frequency between the LGN and V1 output layers of about 20 Hz and an increase in average visual latency of about 12-14 ms. One of the most salient results is the increased diversity of the dynamic properties seen in V1 when compared to the cells of the lateral geniculate, possibly reflecting specialization of function among cells in V1. Simple and complex cells had distributions of temporal-frequency tuning properties that were similar to each other. Direction-selective and nondirection-selective cells had similar preferred and high cutoff temporal frequencies, but direction-selective cells were almost exclusively band-pass while nondirection-selective cells distributed equally between band-pass and low-pass categories. Integration time, a measure of visual delay, was about 10 ms longer for V1 than LGN. In V1 there was a relatively broad distribution of integration times from 40-80 ms for simple cells and 60-100 ms for complex cells while in the LGN the distribution was narrower.

The use of m-sequences in the analysis of visual neurons: Linear receptive field properties

We have used Sutters (1987) spatiotemporal m-sequence method to map the receptive fields of neurons in the visual system of the cat. The stimulus consisted of a grid of 16 x 16 square regions, each of which was modulated in time by a pseudorandom binary signal, known as an m-sequence. Several strategies for displaying the m-sequence stimulus are presented. The results of the method are illustrated with two examples. For both geniculate neurons and cortical simple cells, the measurement of first-order response properties with the m-sequence method provided a detailed characterization of classical receptive-field structures. First, we measured a spatiotemporal map of both the center and surround of a Y-cell in the lateral geniculate nucleus (LGN). The time courses of the center responses was biphasic: OFF at short latencies, ON at longer latencies. The surround was also biphasic--ON then OFF--but somewhat slower. Second, we mapped the response properties of an area 17 directional simple cell. The response dynamics of the ON and OFF subregions varied considerably; the time to peak ranged over more than a factor of two. This spatiotemporal inseparability is related to the cells directional selectivity (Reid et al., 1987, 1991; McLean & Palmer, 1989; McLean et al., 1994). The detail with which the time course of response can be measured at many different positions is one of the strengths of the m-sequence method.