Edge Detection Based on Primary Visual Pathway

Visual neuroscientists seek to answer two related questions. First, what does the visual system do? Second, how does it do it? While an answer to the second question is a description based on anatomy and biophysics, an answer to the first question is a description of computations performed on images. Important steps in providing this description were made with the publication of two classical studies on primary visual cortex by Movshon et al. (1978a,b). These studies have not aged in their readability, and do not require an interpretive key to be enjoyed by the contemporary reader. As an introduction, however, this Perspectives article briefly reviews the state of the art at the time of their appearance, their main findings, and their influence on three decades of subsequent investigations. The 1960s brought important advances in the understanding of the computations performed by the retina. An influential view emerged that described retinal ganglion cells as linear filters, i.e. as processes that compute a weighted sum of the intensities in the stimulus, with weights given by the receptive field (Enroth-Cugell & Robson, 1966). This simple 'linear model' applied to the majority of ganglion cells in the cat retina (those of the X type, corresponding to P cells in primates). It was quite powerful, as it promised to predict the responses of X ganglion cells to arbitrary stimuli, on the basis of the simple knowledge of the receptive field. This purely linear view was complemented by the discovery that another kind of ganglion cells, those of Y type (which correspond to M cells in primates), perform non-linear operations, suggesting that they sum the distorted output of subunits that in turn have linear receptive fields (Enroth-Cugell & Robson, 1966; Hochstein & Shapley, 1976). This arrangement confers position invariance: for stimuli of high spatial frequency, Y cells respond equally regardless of position within the receptive field. These advances suggested new ways of looking at simple and complex cells in primary visual cortex. Hubel & Wiesel (1962) had defined simple cells as having distinct antagonistic regions in their receptive fields, and had suggested that knowing those regions, one could predict 'the responses to any shape of stimulus, stationary or moving'. They had defined complex cells as any cell that was not simple, and had reported that complex cells achieved position invariance within their receptive field: they would respond to a stimulus of the appropriate orientation regardless of position within the receptive field. These attributes of simple and complex cells resembled those of X and Y ganglion cells. Some authors proposed that the correspondence could be anatomical, i.e. that it reflected predominance of X inputs to simple cells and of Y inputs to complex cells (Stone, 1972; Movshon, 1975), a suggestion that was not later confirmed. More generally, however, the linear and non-linear models and the stimulation procedures that had been so useful to analyse X and Y cells (Enroth-Cugell & Robson, 1966; Hochstein & Shapley, 1976) constituted promising starting points for a concise and precise characterization of simple and complex cells. In addition to the breakthroughs in retinal physiology, another force was pushing towards the use of quantitative engineering techniques in primary visual cortex: such techniques were proving successful to study human perception. In particular, the University of Cambridge – where Movshon, Thompson and Tolhurst operated – was a hotbed of research into the relations between single neuron responses and perceptual phenomena. These phenomena were investigated with rigorous psychophysical measures, and described with quantitative models based on image filtering (Graham, 1989; Wandell, 1995). Much of this research rested on the concept of 'channels', which are linear filters. Simple cells in the cortex seemed to be good candidates for such a role. Did they exhibit linear summation? The time had come to apply the power of linear systems analysis and related techniques to primary visual cortex. In their first article, Movshon et al. (1978a) applied linear systems analysis to the responses of simple cells. They measured responses of simple cells to gratings and bars, and asked if such responses were consistent with the output of a linear receptive field (Fig. 1A). The models of simple and complex cells proposed by Movshon, Thompson and Tolhurst ( Movshon et al. 1978a,b) A, linear model of simple cells. The first stage is linear filtering, i. e. aweighted sum of the image intensities, with weights given by the receptive field. The second stage isrectification: only the part of the responses that is larger than a threshold is seen in the firing rate response. B, subunit model of complex cells. The first stage is linear filtering by a number of receptive fields such asthose of simple cells (here we show four of them with spatial phases offset by 90 deg). The subsequent stages involve rectification, and then summation. Much as Enroth-Cugell & Robson (1966) had done for X retinal cells, they asked if the responses to drifting sinusoidal stimuli were sinusoidal, as one would expect from a linear filter. The results supported this view, provided that the responses were rectified by the spike threshold, which shows only the part of the responses that lie above threshold (Fig. 1A). The authors then asked how the responses depended on the spatial phase of a standing grating whose contrast oscillated sinusoidally in time. This test had been applied to X and Y retinal cells by Hochstein & Shapley (1976). Here the linear model was put to a quantitative test, and the fit was good, provided again that the output of the receptive field was passed through a rectification stage that thresholded it (Fig. 1A). Movshon, Thompson and Tolhurst were even able to suggest how high the threshold should be relative to rest. They expressed this threshold in the units of firing rate responses, spikes s−1. For example, for the cell in their Fig. 4, the estimated threshold was 8 spikes s−1 (if the receptive field were to output 12 spikes s−1, the neuron would output 4 spikes s−1). Finally, the authors asked a key question: was the selectivity of simple cells predictable on the basis of the receptive field alone, as had been suggested by Hubel and Wiesel, and as would be expected of a linear filter? To test this hypothesis, they turned again to the approach that had demonstrated the linearity of X retinal cells (Enroth-Cugell & Robson, 1966). First, they measured responses to drifting gratings of various spatial frequencies (Fig. 2A). Then, they measured responses to bars flashed at various positions, thus estimating the profile of the receptive field (Fig. 2B, histograms). According to the linear hypothesis, the first data set could be used to predict the second one. This was indeed the case for the cells (Fig. 2B, curve): just as predicted by the linear model, the selectivity of simple cells for spatial frequency could be predicted on the basis of the receptive field profile. Linearity of spatial summation in simple cells the experiment in Fig. 9 of the first 1978 article by Movshon, Thompson and Tolhurst ( Movshon et al. 1978a) Responses were simulated from a model simple cell with a linear spatial receptive field that summates strongly distorted thalamic inputs (Carandini et al. 2002). A, spatial frequency tuning of the simple cell. The ordinate marks the amplitude of the sinusoidal modulation caused by drifting sinusoidal gratings, whose spatial frequency is plotted on the abscissa. B, profile of the receptive field of the simple cell. The histogram shows the firing rate elicited by flashing bars in various spatial positions across the receptive field. Negative responses indicate responses elicited when withdrawing the bar. The curve shows the prediction based on linearity, obtained by Fourier transform of the data in A. For all its success in explaining responses of simple cells, the linear model (Fig. 1A) could not possibly work for complex cells. Complex cells are insensitive to the precise position of a bar within the receptive field, and respond both to the onset and to the offset of the bar. Neither of these properties could arise from a single linear receptive field. Hubel & Wiesel (1962) had therefore described complex cells as summing the output of a number of simple cells with similar orientation preference but different receptive field profiles. Borrowing from the subunit model proposed for Y ganglion cells by Hochstein & Shapley (1976), such a description could be made quantitative by postulating a number of linear receptive fields orientated in space (subunits), whose outputs are rectified by threshold, and integrated into a single response (Fig. 1B). In their second article, Movshon et al. (1978b) went on to propose such a subunit model for complex cells (Fig. 1B), and to justify each of its components. They started by performing the same three measures that they had performed in simple cells. First, the authors studied the modulation in firing rate caused by drifting sinusoidal gratings. Complex cells responded phasically only at the lowest spatial frequencies, but as soon as the frequency approached the optimal, the responses became constant in time. This was consistent with a subunit model (Fig. 1B). Each of the subunits would respond with a sinusoid, but the rectified sinusoids would be offset in time, and therefore they would sum to an approximately constant value. Second, they asked how complex cell responses depended on the spatial phase of a standing grating whose contrast reversed in time. Just as for Y ganglion cells (Hochstein & Shapley, 1976), the cells did not care for spatial phase, giving two responses for each cycle of the stimulus (once for each sign of contrast). These results again were consistent with the subunit model (Fig. 1B): each subunit would give positive responses only once in each cycle, and only at specific spatial phases, but the sum of the positive subunit responses would rise twice in each cycle, irrespective of spatial phase. Third, they asked whether the selectivity of complex cells was predictable on the basis of the receptive field alone. The answer was a resounding no: as predicted by the subunit model (Fig. 1B), receptive fields of complex cells don't have strong distinct subregions, so it is not possible to predict their selectivity from a receptive field profile. While all these results seemed consistent with a subunit model (Fig. 1B), a key attribute of that model remained to be verified: are the receptive fields of the subunits really linear? To test this hypothesis, the authors devised an elegant experiment, in which they measured the interaction of dark and light bars in different positions of the receptive field. To understand this experiment, consider a simplified model of complex cell that includes only four subunits (Fig. 1B), and imagine placing a bright bar over the central positive region in the receptive field of the first subunit. This subunit will give a strong positive response, and will thus operate well above threshold. Conversely, the third subunit, which has the opposite receptive field, will be strongly suppressed, and will operate much below threshold. The second and fourth subunits, in turn, will barely respond, so they will operate (as normally) slightly below threshold. Adding a second bar to the stimulus therefore will mostly reveal the receptive field properties of the first subunit. Indeed, as shown in Fig. 8 of the original article, the interaction profiles between bars resemble receptive fields. If these receptive fields of the subunits operate linearly (and if all subunits are similar in spatial frequency preferences), then it should be possible to predict the spatial frequency selectivity of the neuron based on the receptive field profile of the subunits, just as for simple cells (Fig. 2). The results of this experiment, illustrated in Fig. 9 of the original article, confirm this prediction, providing strong support for the subunit model of complex cells (Fig. 1B). The results of the two articles therefore can be summarized by two models: the linear model of simple cells (Fig. 1A), and the subunit model of complex cells (Fig. 1B). These models share two key attributes: (1) all the image processing is performed by linear filters; (2) the non-linearities operate on the time-varying signals that are output by the filters. These models have formed the basis for much that has followed in the subsequent three decades. There is obviously no space here to cover this territory, for which we refer the reader to recent reviews (e.g. Carandini et al. 2005). What might be more useful would be to discuss those aspects that, with 100% hindsight, could have been analysed differently, and would arguably have led to slightly different conclusions. First, when comparing responses to gratings to the profiles of the receptive field (Fig. 2), whether for simple cells or for the subunits of complex cells, a free scaling factor was allowed to obtain a match. Such a scaling factor should not be necessary for the simple models shown in Fig. 1, but it is necessary for actual cells, whose responsiveness depends very much on the local distribution of contrast. To account for this dependence, the models were later extended to include a divisive stage that controls responsiveness on the basis of the distribution of local contrast (for reviews see Heeger, 1992; Carandini et al. 1999). Second, the authors were perhaps wise to concentrate on one spatial dimension, and thus to avoid the contentious issue of orientation selectivity. Orientation tuning curves were not measured in this study, arguably because rotating the stimulation device involved placing one's hand within centimetres of electrocution, something that was done only reluctantly, once for each cell (J. A. Movshon, personal communication). Had the authors measured 2-dimensional receptive field profiles they could have asked whether these profiles predicted orientation selectivity. This issue remained open for decades and is not entirely closed to this date (reviewed by Ferster & Miller, 2000). Finally, perhaps the greatest limitation of these studies is that they concentrated on the spatial domain, and did not test linearity in the temporal domain. On the one hand, as was shown in the subsequent decades, the concept of spatial receptive field can be fruitfully extended to 3-dimensional space–time, to account for phenomena of direction selectivity (in fact, the nascent signs of such an extension can be seen already in the second 1978 article). On the other hand, as demonstrated later by Tolhurst et al. (1980), in primary visual cortex temporal summation is far from linear: responses are much more transient than would be expected from the frequency selectivity curves. This non-linearity is puzzling: how could a receptive field be spatially linear and temporally non-linear? It is now thought that because a cortical cell sums inputs from a variety of spatially displaced thalamic neurons, even if the individual inputs are grossly distorted by saturating and threshold non-linearities, the overall spatial summation properties of the neuron will remain approximately linear (Carandini et al. 2002; Priebe & Ferster, 2006). Indeed, the simulations shown here (Fig. 2) resulted from a model cell that summates such strongly non-linear inputs. This model cell passes the test of spatial linearity devised by the 1978 papers (Fig. 2), yet it would fail any test of temporal linearity. If it took three decades to obtain this realization, it is possibly because these 1978 papers made such a compelling case for linearity, and the field took them as evidence that every step in the visual system up to the primary visual cortex had to be linear. The reasoning, partly explicit in these papers and implicit in much of the subsequent literature, was that there could be no non-linear stages from the cones to the cortex because those stages would have prevented the cortical cells from passing the linearity tests. These are modest limitations, and they are evident only with three decades of hindsight. Overall the impact of these papers on the field was forceful and positive. As is evident from glancing at any recent review (e.g. Carandini et al. 2005) much of what was done to this date to explain responses of primary visual cortex, e.g. to explain properties such as orientation selectivity, direction selectivity and binocular integration, rested on the results of these two classical papers. Moreover, these studies succeeded in providing a foundation for the models of pattern perception based on psychophysical channels (Graham, 1989). Our only hope is that similarly powerful quantitative studies will soon appear for areas beyond the primary visual cortex.

To study and compare responses of different types of cortical neurons in the primary visual cortex in cats to grating stimuli before and during brief elevation of intraocular pressure (IOP). Single-unit electrophysiological recordings were performed in anesthetized and paralyzed cats. The IOP was elevated by injecting saline into the anterior chamber of the cat's eyes through a syringe needle. The IOP was elevated to a level at which the retinal perfusion pressure (arterial pressure minus IOP) was maintained at approximately 30 mmHg for a period of 4 minutes. The responses of simple and complex cells in the primary visual cortex to visually drifting sinusoidal gratings were measured before and during the elevation of IOP. The response amplitude of all the cortical cells in the primary visual cortex declined during a brief elevation of IOP. The decrease in the response of simple cells was always more significant than that of complex cells. The differential decrease between the two major types of cells was independent of the cell's receptive field location and cortical depth. There was a mild tendency for cells with higher preferred spatial frequencies to be more sensitive than those with lower frequencies. The preferred orientation and direction of most cortical cells remained roughly unchanged though their orientation and direction biases decreased. An increase in the animal's blood pressure, which returned the retinal perfusion pressure to a normal level, compensated for the decreased response induced by the elevation of IOP. The differential effects of a brief elevation of IOP on the response of simple and complex cells in the visual cortex are general and may originate from the retina through the lateral geniculate nucleus (LGN), where different effects of elevation of IOP are exerted on X- and Y-type retinal ganglion cells. The results may suggest differential behavior of neurons tin the parvo and magno pathways of the primate.

Phase sensitivity of complex cells in primary visual cortex

Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex.

Timing and Specificity of Feed-Forward Inhibition within the LGN

We have recorded the responses of single cells in the lateral geniculate nucleus (LGN) and striate cortex of the macaque monkey. The response characteristics of neurons at these successive visual processing levels were examined with isoluminant gratings, cone-isolating gratings, and luminance-varying gratings. The main findings were: (i) Whereas almost all parvo- and konio-cellular LGN cells are of just two opponent-cell types, either differencing the L and M cones (L(o) and M(o) cells), or the S vs. L + M cones (S(o) cells), relatively few striate cortex simple cells show chromatic responses along these two cardinal LGN axes. Rather, most are shifted away from these LGN chromatic axes as a result of combining the outputs (or the transformed outputs) of S(o) with those of L(o) and/or M(o) cells. (ii) LGN cells on average process color information linearly, exhibiting sinusoidal changes in firing rate to isoluminant stimuli that vary sinusoidally in cone contrast as a function of color angle. Some striate cortex simple cells also give linear responses, but most show an expansive response nonlinearity, resulting in narrower chromatic tuning on average at this level. (iii) There are many more +S(o) than -S(o) LGN cells, but at the striate cortex level -S(o) input to simple cells is as common as +S(o) input. (iv) Overall, the contribution of the S-opponent path is doubled at the level of the striate cortex, relative to that at the LGN.

Properties of cat retinal ganglion cells: a comparison of W-cells with X- and Y-cells.

Properties of the receptive fields of simple cells in macaque cortex were compared with properties of independent component filters generated by independent component analysis (ICA) on a large set of natural images. Histograms of spatial frequency bandwidth, orientation tuning bandwidth, aspect ratio and length of the receptive fields match well. This indicates that simple cells are well tuned to the expected statistics of natural stimuli. There is no match, however, in calculated and measured distributions for the peak of the spatial frequency response: the filters produced by ICA do not vary their spatial scale as much as simple cells do, but are fixed to scales close to the finest ones allowed by the sampling lattice. Possible ways to resolve this discrepancy are discussed.

1. Simple cells in the visual cortex have been subdivided into nondirection-selective (NDS), direction asymmetric (DA), and direction-selective (DS) cells. DA cells reverse their preferred direction with reversal of the stimulus contrast; DS2 cells respond with the same preferred direction for light and dark stimuli, whereas DS1 cells respond only to one (light or dark) contrast. Also, four velocity response groups have been distinguished: velocity broadband, low-pass, high-pass, and -tuned cells. This study describes an analytic model of feed-forward spatiotemporal interactions within a receptive field that reproduces these basic features of cortical simple cell behavior in the cat. 2. The spatial structure of the receptive fields is simulated with Gabor functions. Two neurobiologically plausible mechanisms, temporal low-pass filtering and intracortical spatial distribution of activity, are modeled. The central feature of the study is the implementation of both mechanisms in a spatially continuous way. The model is analytic, but an equivalent neural network diagram was drawn and is used to explain the features of the model. 3. First-order temporal low-pass filtering is performed both after convolving the stimulus light-intensity function with the Gabor type receptive field and also at the final output step of the model. In the circuit diagram this would correspond to low-pass filtering in lateral geniculate nucleus (LGN) and cortical cells. Filtering was adjusted to have a -3-dB drop-off frequency of 2-3 Hz, corresponding to the drop-off frequencies observed in response to temporal modulation of sine-wave gratings. 4. The mechanism that we call intracortical distribution of activity is implemented along the axis of stimulus motion. A response elicited from the part of the receptive field that is stimulated at a given time will spread out in the receptive field, influencing regions that have not been stimulated. It is equivalent to spreading of activity on the cortical surface. This mechanism extends the existing ideas of discrete interactions between subfields to a continuous scheme throughout the whole receptive field. It is based on findings that intracortical interactions exist even within single subfields. The impact of distributing the activity is assumed to decrease exponentially with the Euclidian distance between the stimulated region and the region under consideration. 5. Thresholds are implemented only at the level of the cortex. Both the activity distributing mechanism and the output of the cell being studied are thresholded.(ABSTRACT TRUNCATED AT 400 WORDS)

Intracortical inhibition contributes to direction selectivity in primary visual cortex, but how it acts has been unclear. We investigated this problem in simple cells of cat area 17 by taking advantage of the link between spatiotemporal (S-T) receptive-field structure and direction selectivity. Most cells in layer 4 have S-T-oriented receptive fields in which gradients of response timing across the field confer a preferred direction of motion. Linear summation of responses across the receptive field, followed by a static nonlinear amplification, has been shown previously to account for directional tuning in layer 4. We tested the hypotheses that inhibition acts by altering S-T structure or the static nonlinearity or both. Drifting and counterphasing sine wave gratings were used to measure direction selectivity and S-T structure, respectively, in 17 layer 4 simple cells before and during iontophoresis of bicuculline methiodide (BMI), a GABAA antagonist. S-T orientation was quantified from fits to response temporal phase versus stimulus spatial phase data. Bicuculline reduced direction selectivity and S-T orientation in nearly all cells, and reductions in the two measures were well correlated (r = 0.81) and reversible. Using conventional linear predictions based on response phase and amplitude, we found that BMI-induced changes in S-T structure also accounted well for absolute changes in the amplitude and phase of responses to gratings drifting in the preferred and nonpreferred direction. For each cell we also calculated an exponent used to estimate the static nonlinearity. Bicuculline reduced the exponent in most cells, but the changes were not correlated with reductions in direction selectivity. We conclude that GABAA-mediated inhibition influences directional tuning in layer 4 primarily by sculpting S-T receptive-field structure. The source of the inhibition is likely to be other simple cells with certain spatiotemporal relationships to their target. Despite reductions in the two measures, most receptive fields maintained some directional tuning and S-T orientation during BMI. This suggests that their excitatory inputs, arising from the lateral geniculate nucleus and within area 17, are sufficient to create some S-T orientation and that inhibition accentuates it. Finally, BMI also reduced direction selectivity in 8 of 10 simple cells tested in layer 6, but the reductions were not accompanied by systematic changes in S-T structure. This reflects the fact that S-T orientation, as revealed by our first-order measures of the receptive field, is weak there normally. Inhibition likely affects layer 6 cells via more complex, nonlinear interactions.

Comparative study on the offset responses of simple cells and complex cells in the primary visual cortex of the cat

In this research we concentrate on adaptive texture feature extractors which are automatically extracted from an image collection. We use independent component analysis (ICA) to extract independent component filters (ICF). ICF have previously been shown to include edge filters, having properties similar to the receptive fields of simple cells of the human visual cortex. In this thesis we evaluate the utility of ICF-based collection-specific features in the context of content-based image retrieval (CBIR), with the view to demonstrate the viability of using such automatic collection-specific features. We find that global features extracted using a small number of ICF outperform those extracted by a bank of Gabor filters, even when very large number of Gabors are used. We also present comparisons against a variety of state-of-the-art features and show that ICF-based features perform better than these, without the need for any hand-tuning. ICA extracts large number of filters. In order to find a useful smaller subset we evaluate a previously-published variance-based filter selection method. We identify the shortcomings of this method. Our proposed improvements to filter-based feature selection and extraction, response scaling and locally normalised convolution, was a result of trying to address some of these shortcomings. Even with these improvements the variance-based method has an intrinsic flaw of being susceptible to selecting redundant filters. We propose a new filter selection method based on normalised cross-correlation and clustering. We show that our method selects a more useful subset compared to the previously published variance-based method. We also propose a salient-point based local feature which uses ICF and show that our proposed feature performs better than SIFT features and also the global ICF features. We further illustrate the utility of ICFSIFT by developing a grid-based adaptive local feature and we demonstrate that ICFSIFT extracts different and useful information compared to the ICF-based global and grid-based local features. After applying the techniques proposed in this thesis to four standard texture collections, we show that, while hand-tuned features can work well for their target collection, the automatic adaptive features perform better for the general case.

How does a single retinal ganglion cell (RGC) affect the firing of simple cells in the visual cortex? Although much is known of the functional connections between the retina and the lateral geniculate nucleus (LGN) and between LGN and visual cortex, it is hard to infer the effect of disynaptic connections from retina to visual cortex. Most importantly, there is considerable divergence from retina to LGN, so cortical neurons might be influenced by ganglion cells through multiple feedforward pathways. We recorded simultaneously from ganglion cells in the retina and cortical simple cells in the striate cortex with overlapping receptive fields and evaluated disynaptic connections with cross-correlation analysis. In all disynaptically connected pairs, the retinal receptive field center and overlapping cortical subregion always shared the same sign (either both ON or both OFF). Connected pairs were similar in other respects, such as relative position and timing of their receptive fields, and thus obeyed the same rules of connectivity found previously for retinothalamic and thalamocortical connections. We found that a single RGC directly contributed on average to approximately 3% of the activity of its cortical target. The relative timing of pairs of spikes from the retinal cell affected their efficacy in driving the cortical cell. When two retinal spikes were closely spaced (<10 msec), the second spike was several times more likely to drive the cortical target. The relative magnitude of this disynaptic paired spike enhancement was considerably larger than has been found previously for retinogeniculate and geniculocortical connections. The amplified paired spike enhancement from retina to cortex ensures that signal transmission from retina to cortex is particularly effective when the retina fires a series of closely spaced action potentials.

In unequal alternating monocular exposure, each eye receives normal patterned input, but on alternate days and for unequal periods. This imbalance in stimulation produces a behavioral deficit for the less-experienced eye and alters the ability of that eye to activate cortical cells. To determine whether unequal alternating exposure also affects the sizes of cells in the lateral geniculate nucleus (LGN), we measured the cross-sectional areas of geniculate neurons in seven normally reared cats, 14 cats reared with equal alternating exposure, and 17 cats reared with unequal alternating exposure. We found that, in the LGNs of cats reared with unequal alternating monocular exposure, cells in layers that received their input from the less-experienced eye were smaller than those in layers that received their input from the more-experienced eye. This effect was restricted to the binocular segments of the nucleus, and the difference in cell size was a function of the imbalanced exposure, rather than the length of exposure per se. In control groups given balanced alternating exposure, cell size was not correlated with the length of daily exposure. In cats reared with unequal exposure, the change in cell size was greater in the nucleus ipsilateral to the less-experienced eye. Further, the size of the effect was correlated with the size of the imbalance imposed during rearing: Cats reared with a moderate imbalance (8 hours/day vs. 4 hours/day) showed less change in cell size than cats reared with a large imbalance (8 hours/day vs. 1 hour/day). These results are consistent with those of behavorial and physiological studies and strongly suggest (1) that unequal alternating monocular exposure affects the sizes of cells in the LGN by altering the normal competitive balance between the retinogeniculocortical pathways from the left and right eyes, and (2) that the contralateral pathway has some inherent advantage in this competition. We also found a slight shrinkage of cells in the LGNs of cats reared with equal alternating monocular exposure. Since this effect was restricted to the binocular segments of the nucleus, and was not related to the length of exposure given, it was probably caused by the imbalanced binocular competition that occurred during each day's monocular exposure.

According to a modern view, simple cells of the cat striate cortex are considered to operate as a part of Fourier analysis system thus leading to the idea that the operational mechanism of the visual cortex is concerned with the analysis of spatial frequencies. Nevertheless if simple cells are really concerned only with the analysis of spatial frequencies there should exist a strict relationship between their spatial frequency selectivity and the spatial organization of their receptive fields. This is because it is the spatial organization of the spatial frequency detector i.e. the cell's receptive field that determines the cell's spatial frequency selectivity. Since the quantitative analysis of the spatial organization of simple cell receptive fields has shown that the receptive fields of these cells are made up of two to four antagonistic subregions it follows that striate simple cells are concerned with the analysis of only a selected range of spatial frequencies. Therefore striate simple cells must be considered analysers of visual signals both in spatial frequencies as well as in spatial positions.