Role of the posterolateral nucleus of the thalamus in mechanism of information transmission to the visual cortex

Reliability and Representational Bandwidth in the Auditory Cortex

Visual areas I and II of cerebral cortex of rabbit.

Organization of auditory, somatic sensory, and visual projection to association fields of cerebral cortex in the cat.

A paired-stimuli paradigm combined with fMRI was utilized to study the effect of gradient acoustic noise on fMRI response in the human primary visual cortex (V1) in terms of the auditory-visual cross-modal neural interaction. The gradient noise generated during the fMRI acquisition was used as the primary stimulus, and a single flashing light was used as the secondary stimulus. An interstimulus interval (ISI) separated the two. Six tasks were designed with different ISIs ranging from 50 to 700 ms. Both BOLD signal intensity and the number of activated pixels in V1 were analyzed and examined, and they showed a significant reduction when the gradient noise preceded the flashing light by approximately 300 ms. These results indicate that the gradient acoustic noise generated during fMRI acquisitions does interfere with neural behavior and the BOLD signal in the human visual cortex. This interference is modulated by the delay between the gradient noise and visual stimulation, and it can be studied quantitatively when the stimulation paradigm is designed appropriately. This study provides evidence of the auditory-visual interaction during fMRI studies, and the results should have an impact on fMRI applications.

Autonomic versus cortical arousal in schizophrenics and non-psychotics

Variability in cortical neuronal responses to sensory stimuli and in perceptual decision making performance is substantial. Moment-to-moment fluctuations in waking state or arousal can account for much of this variability. Yet, this variability is rarely characterized across the full spectrum of waking states, leaving the characteristics of the optimal state for sensory processing unresolved. Using pupillometry in concert with extracellular multiunit and intracellular whole-cell recordings, we found that the magnitude and reliability of visually evoked responses in primary visual cortex (V1) of awake, passively behaving male mice increase as a function of arousal and are largest during sustained locomotion periods. During these high-arousal, sustained locomotion periods, cortical neuronal membrane potential was at its most depolarized and least variable. Contrastingly, behavioral performance of mice on two distinct visual detection tasks was generally best at a range of intermediate arousal levels, but worst during high arousal with locomotion. These results suggest that large, reliable responses to visual stimuli in V1 occur at a distinct arousal level from that associated with optimal visual detection performance. Our results clarify the relation between neuronal responsiveness and the continuum of waking states, and suggest new complexities in the relation between primary sensory cortical activity and behavior.SIGNIFICANCE STATEMENT Cortical sensory processing strongly depends on arousal. In the mouse visual system, locomotion (associated with high arousal) has previously been shown to enhance the sensory responses of neurons in primary visual cortex (V1). Yet, arousal fluctuates on a moment-to-moment basis, even during quiescent periods. The characteristics of V1 sensory processing across the continuum of arousal are unclear. Furthermore, the arousal level corresponding to optimal visual detection performance is unknown. We show that the magnitude and reliability of sensory-evoked V1 responses are monotonic increasing functions of arousal, and largest during locomotion. Visual detection behavior, however, is suboptimal during high arousal with locomotion, and usually best during intermediate arousal. Our study provides a more complete picture of the dependence of V1 sensory processing on arousal.

Induction of long-lasting potentiation in the secondary somatosensory cortex by thalamic stimulation requires cortico-cortical pathways from the primary somatosensory cortex.

Perception, working memory, and long-term memory each evoke neural responses in visual cortex. While previous neuroimaging research on the role of visual cortex in memory has largely emphasized similarities between perception and memory, we hypothesized that responses in visual cortex would differ depending on the origins of the inputs. Using fMRI, we quantified spatial tuning in visual cortex while participants (both sexes) viewed, maintained in working memory, or retrieved from long-term memory a peripheral target. In each condition, BOLD responses were spatially tuned and aligned with the target’s polar angle in all measured visual field maps including V1. As expected given the increasing sizes of receptive fields, polar angle tuning during perception increased in width up the visual hierarchy from V1 to V2, V3, hV4, and beyond. In stark contrast, the tuned responses were broad across the visual hierarchy during long-term memory (replicating a prior result) and during working memory. This pattern is consistent with the idea that mnemonic responses in V1 stem from top-down sources, even when the stimulus was recently viewed and is held in working memory. Moreover, in long-term memory, trial-to-trial biases in these tuned responses (clockwise or counterclockwise of target), predicted matched biases in memory, suggesting that the reinstated cortical responses influence memory guided behavior. We conclude that feedback widens spatial tuning in visual cortex during memory, where earlier visual maps inherit broader tuning from later maps thereby impacting the precision of memory.

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.

Which neural processes underlie our conscious experience? One theoretical view argues that the neural correlates of consciousness (NCC) reside in local activity in sensory cortices. Accordingly, local category-specific gamma band responses in visual cortex correlate with conscious perception. However, as most studies manipulated conscious perception by altering the amount of sensory evidence, it is possible that they reflect prerequisites or consequences of consciousness rather than the actual NCC. Here we directly address this issue by developing a new experimental paradigm in which conscious perception is modulated either by sensory evidence or by previous exposure of the images while recording intracranial EEG from the higher-order visual cortex of human epilepsy patients. A clear prediction is that neural processes directly reflecting conscious perception should be present regardless of how it comes about. In contrast, we observed that although subjective reports were modulated both by sensory evidence and by previous exposure, gamma band responses solely reflected sensory evidence. This result contradicts the proposal that local gamma band responses in the higher-order visual cortex reflect conscious perception.

Listening to Npas4: a transcription factor is the prescription for restoring youthful plasticity in the mature brain

Inactivation of prefrontal cortex abolishes cortical acetylcholine release evoked by sensory or sensory pathway stimulation in the rat

The use of phosphenes evoked by transcorneal electrical stimulation (TcES) has been proposed as a means of residual visual function evaluation and candidate selection before implantation of retinal prostheses. Compared to the subjective measures, measurement of neuronal activity in visual cortex can objectively and quantitatively explore their response properties to electrical stimulation. The purpose of this study was to investigate systematically the properties of cortical responses evoked by TcES. The visual cortical responses were recorded using a multiwavelength optical imaging of intrinsic signals (OIS) combining with electrophysiological recording by a multichannel electrode array. The effects of different parameters of TcES on cortical responses, including the changes of hemoglobin oxygenation and cerebral blood volume, were examined. We found consistent OIS activation regions in visual cortex after TcES, which also showed strong evoked field potentials according to electrophysiological results. The OIS response regions were located mainly in cortical areas representing peripheral visual field. The extent of activation areas and strength of intrinsic signals were increased with higher current intensities and longer pulse widths, and the largest responses were acquired in the frequency range 10 to 20 Hz. Use of TcES through the ERG-jet corneal electrode may preferentially activate peripheral retina. Revealing the hemodynamic changes in visual cortex occurred after electrical stimulation can contribute to comprehension of neurophysiological underpinnings underlying prosthetic vision. This study provided an objective foundation for optimizing parameters of TcES and would bring considerable benefits in the application of TcES for assessment and screening in patients.

Neural selectivity to orientation is one of the simplest and most thoroughly-studied cortical sensory features. Here, we show that a large body of research that purported to measure orientation tuning may have in fact been inadvertently measuring sensitivity to second-order changes in luminance, a phenomenon we term 'vignetting'. Using a computational model of neural responses in primary visual cortex (V1), we demonstrate the impact of vignetting on simulated V1 responses. We then used the model to generate a set of predictions, which we confirmed with functional MRI experiments in human observers. Our results demonstrate that stimulus vignetting can wholly determine the orientation selectivity of responses in visual cortex measured at a macroscopic scale, and suggest a reinterpretation of a well-established literature on orientation processing in visual cortex.

PurposeElectronic retinal prostheses restore vision in people with outer retinal degeneration by electrically stimulating the inner retina. We characterized visual cortex electrophysiologic response elicited by electrical stimulation of retina in normally sighted and retinal degenerate rats.MethodsNine normally sighted Long Evans and 11 S334ter line 3 retinal degenerate (rd) rats were used to map cortical responses elicited by epiretinal electrical stimulation in four quadrants of the retina. Six normal and six rd rats were used to compare the dendritic spine density of neurons in the visual cortex.ResultsThe rd rats required higher stimulus amplitudes to elicit responses in the visual cortex. The cortical electrically evoked responses (EERs) for both healthy and rd rats show a dose-response characteristic with respect to the stimulus amplitude. The EER maps in healthy rats show retinotopic organization. For rd rats, cortical retinotopy is not well preserved. The neurons in the visual cortex of rd rats show a 10% higher dendritic spine density than in the healthy rats.ConclusionsCortical activity maps, produced when epiretinal stimulation is applied to quadrants of the retina, exhibit retinotopy in normal but not rd rats. This is likely due to a combination of degeneration of the retina and increased stimulus thresholds in rd, which broadens the activated area of the retina.Translational RelevanceLoss of retinotopy is evident in rd rats. If a similar loss of retinotopy is present in humans, retinal prostheses design must include flexibility to account for patient specific variability.