Characterizing contrast adaptation in a population of cat primary visual cortical neurons using Fisher information

GABA receptors mediate adaptation and sensitization processes in mouse retinal ganglion cells

Two coordinated dynamic properties (adaptation and sensitization) are observed in retinal ganglion cells (RGCs) under the contrast stimulation. During sustained high-contrast period, adaptation decreases RGCs' responses while sensitization increases RGCs' responses. In mouse retina, adaptation and sensitization respectively show OFF- and ON-pathway-dominance. However, the mechanisms which drive the differentiation between adaptation and sensitization remain unclear. In the present study, multi-electrode recordings were conducted on isolated mouse retina under full-field contrast stimulation. Dynamic property was quantified based on the trend of RGC's firing rate during high-contrast period, light sensitivity was estimated by linear-nonlinear analysis and coding ability was estimated through stimulus reconstruction algorism. γ-Aminobutyric acid (GABA) receptors were pharmacologically blocked to explore the relation between RGCs' dynamic property and the activity of GABA receptors. It was found that GABAA and GABAC receptors respectively mediated the adaptation and sensitization processes in RGCs' responses. RGCs' dynamic property changes occurred after the blockage of GABA receptors were related to the modulation of the cells' light sensitivity. Further, the blockage of GABAA (GABAC) receptor significantly decreased RGCs' overall coding ability and eliminated the functional benefits of adaptation (sensitization). Our work suggests that the dynamic property of individual RGC is related to the balance between its GABAA-receptor-mediated inputs and GABAC-receptor-mediated inputs. Blockage of GABA receptors breaks the balance of retinal circuitry for signal processing, and down-regulates the visual information coding ability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09950-2.

Attractive serial dependence overcomes repulsive neuronal adaptation

Sensory responses and behavior are strongly shaped by stimulus history. For example, perceptual reports are sometimes biased toward previously viewed stimuli (serial dependence). While behavioral studies have pointed to both perceptual and postperceptual origins of this phenomenon, neural data that could elucidate where these biases emerge is limited. We recorded functional magnetic resonance imaging (fMRI) responses while human participants (male and female) performed a delayed orientation discrimination task. While behavioral reports were attracted to the previous stimulus, response patterns in visual cortex were repelled. We reconciled these opposing neural and behavioral biases using a model where both sensory encoding and readout are shaped by stimulus history. First, neural adaptation reduces redundancy at encoding and leads to the repulsive biases that we observed in visual cortex. Second, our modeling work suggest that serial dependence is induced by readout mechanisms that account for adaptation in visual cortex. According to this account, the visual system can simultaneously improve efficiency via adaptation while still optimizing behavior based on the temporal structure of natural stimuli.

Insight from OPN1LW Gene Haplotypes into the Cause and Prevention of Myopia

Nearsightedness (myopia) is a global health problem of staggering proportions that has driven the hunt for environmental and genetic risk factors in hopes of gaining insight into the underlying mechanism and providing new avenues of intervention. Myopia is the dominant risk factor for leading causes of blindness, including myopic maculopathy and retinal detachment. The fundamental defect in myopia-an excessively elongated eyeball-causes blurry distance vision that is correctable with lenses or surgery, but the risk of blindness remains. Haplotypes of the long-wavelength and middle-wavelength cone opsin genes (OPN1LW and OPN1MW, respectively) that exhibit profound exon-3 skipping during pre-messenger RNA splicing are associated with high myopia. Cone photoreceptors expressing these haplotypes are nearly devoid of photopigment. Conversely, cones in the same retina that express non-skipping haplotypes are relatively full of photopigment. We hypothesized that abnormal contrast signals arising from adjacent cones differing in photopigment content stimulate axial elongation, and spectacles that reduce contrast may significantly slow myopia progression. We tested for an association between spherical equivalent refraction and OPN1LW haplotype in males of European ancestry as determined by long-distance PCR and Sanger sequencing and identified OPN1LW exon 3 haplotypes that increase the risk of common myopia. We also evaluated the effects of contrast-reducing spectacles lenses on myopia progression in children. The work presented here provides new insight into the cause and prevention of myopia progression.

Neural tuning and representational geometry

A central goal of neuroscience is to understand the representations formed by brain activity patterns and their connection to behaviour. The classic approach is to investigate how individual neurons encode stimuli and how their tuning determines the fidelity of the neural representation. Tuning analyses often use the Fisher information to characterize the sensitivity of neural responses to small changes of the stimulus. In recent decades, measurements of large populations of neurons have motivated a complementary approach, which focuses on the information available to linear decoders. The decodable information is captured by the geometry of the representational patterns in the multivariate response space. Here we review neural tuning and representational geometry with the goal of clarifying the relationship between them. The tuning induces the geometry, but different sets of tuned neurons can induce the same geometry. The geometry determines the Fisher information, the mutual information and the behavioural performance of an ideal observer in a range of psychophysical tasks. We argue that future studies can benefit from considering both tuning and geometry to understand neural codes and reveal the connections between stimuli, brain activity and behaviour.

Sensory Adaptation in the Whisker-Mediated Tactile System: Physiology, Theory, and Function

In the natural environment, organisms are constantly exposed to a continuous stream of sensory input. The dynamics of sensory input changes with organism's behaviour and environmental context. The contextual variations may induce >100-fold change in the parameters of the stimulation that an animal experiences. Thus, it is vital for the organism to adapt to the new diet of stimulation. The response properties of neurons, in turn, dynamically adjust to the prevailing properties of sensory stimulation, a process known as "neuronal adaptation." Neuronal adaptation is a ubiquitous phenomenon across all sensory modalities and occurs at different stages of processing from periphery to cortex. In spite of the wealth of research on contextual modulation and neuronal adaptation in visual and auditory systems, the neuronal and computational basis of sensory adaptation in somatosensory system is less understood. Here, we summarise the recent finding and views about the neuronal adaptation in the rodent whisker-mediated tactile system and further summarise the functional effect of neuronal adaptation on the response dynamics and encoding efficiency of neurons at single cell and population levels along the whisker-mediated touch system in rodents. Based on direct and indirect pieces of evidence presented here, we suggest sensory adaptation provides context-dependent functional mechanisms for noise reduction in sensory processing, salience processing and deviant stimulus detection, shift between integration and coincidence detection, band-pass frequency filtering, adjusting neuronal receptive fields, enhancing neural coding and improving discriminability around adapting stimuli, energy conservation, and disambiguating encoding of principal features of tactile stimuli.

Functional-pathway-dominant contrast adaptation and sensitization in mouse retinal ganglion cells

Retinal ganglion cells (RGCs) reduce their light sensitivity during persistent high-contrast stimulation to prevent saturation to strong inputs and improve coding efficiency. This process is known as contrast adaptation. However, contrast adaptation also reduces RGCs' light response to weak inputs. On the other hand, some RGCs undergo contrast sensitization, and these RGCs respond to weak inputs following high contrast stimulation. In the present study, multi-electrode recordings were conducted on isolated mouse retinas under full-field visual stimulation with different contrast levels. Adaptation and sensitization were mainly observed in OFF and ON pathways, respectively. The results of linear-nonlinear analysis and stimulus reconstruction revealed that both the light sensitivity and encoded information were changed in opposite directions in adaptation and sensitization processes. Our work suggests that contrast adaptation and sensitization are two opposite dynamic processes. In mouse retina, OFF RGCs utilize adaptation to increase the discrimination of strong OFF inputs. On the other hand, ON RGCs use sensitization to increase the sensitivity to weak ON inputs. This functional differentiation might be meaningful for the mouse's survival as it lives in environments in which strong OFF stimuli often indicate potential predators while weak ON stimuli are usually related to movement and might be important for predation.

Comparison of contrast-dependent phase sensitivity in primary visual cortex of mouse, cat and macaque

Neurones in the primary visual cortex (V1) are classified into simple and complex types. Simple cells are phase-sensitive, that is, they modulate their responses according to the position and brightness polarity of edges in their receptive fields. Complex cells are phase invariant, that is, they respond to edges in their receptive fields regardless of location or brightness polarity. Simple and complex cells are quantified by the degree of sensitivity to the spatial phases of drifting sinusoidal gratings. Some V1 complex cells become more phase-sensitive at low contrasts. Here we use a standardized analysis method for data derived from grating stimuli developed for macaques to reanalyse data previously collected from cats, and also collect and analyse the responses of 73 mouse V1 neurons. The analysis provides the first consistent comparative study of contrast-dependent phase sensitivity in V1 of mouse, cat and macaque monkey.

Moth olfactory receptor neurons adjust their encoding efficiency to temporal statistics of pheromone fluctuations

The efficient coding hypothesis predicts that sensory neurons adjust their coding resources to optimally represent the stimulus statistics of their environment. To test this prediction in the moth olfactory system, we have developed a stimulation protocol that mimics the natural temporal structure within a turbulent pheromone plume. We report that responses of antennal olfactory receptor neurons to pheromone encounters follow the temporal fluctuations in such a way that the most frequent stimulus timescales are encoded with maximum accuracy. We also observe that the average coding precision of the neurons adjusted to the stimulus-timescale statistics at a given distance from the pheromone source is higher than if the same encoding model is applied at a shorter, non-matching, distance. Finally, the coding accuracy profile and the stimulus-timescale distribution are related in the manner predicted by the information theory for the many-to-one convergence scenario of the moth peripheral sensory system.

Efficiency of rate and latency coding with respect to metabolic cost and time

Recent studies on the theoretical performance of latency and rate code in single neurons have revealed that the ultimate accuracy is affected in a nontrivial way by aspects such as the level of spontaneous activity of presynaptic neurons, amount of neuronal noise or the duration of the time window used to determine the firing rate. This study explores how the optimal decoding performance and the corresponding conditions change when the energy expenditure of a neuron in order to spike and maintain the resting membrane potential is accounted for. It is shown that a nonzero amount of spontaneous activity remains essential for both the latency and the rate coding. Moreover, the optimal level of spontaneous activity does not change so much with respect to the intensity of the applied stimulus. Furthermore, the efficiency of the temporal and the rate code converge to an identical finite value if the neuronal activity is observed for an unlimited period of time.

Accuracy of rate coding: When shorter time window and higher spontaneous activity help

It is widely accepted that neuronal firing rates contain a significant amount of information about the stimulus intensity. Nevertheless, theoretical studies on the coding accuracy inferred from the exact spike counting distributions are rare. We present an analysis based on the number of observed spikes assuming the stochastic perfect integrate-and-fire model with a change point, representing the stimulus onset, for which we calculate the corresponding Fisher information to investigate the accuracy of rate coding. We analyze the effect of changing the duration of the time window and the influence of several parameters of the model, in particular the level of the presynaptic spontaneous activity and the level of random fluctuation of the membrane potential, which can be interpreted as noise of the system. The results show that the Fisher information is nonmonotonic with respect to the length of the observation period. This counterintuitive result is caused by the discrete nature of the count of spikes. We observe also that the signal can be enhanced by noise, since the Fisher information is nonmonotonic with respect to the level of spontaneous activity and, in some cases, also with respect to the level of fluctuation of the membrane potential.

Presynaptic Spontaneous Activity Enhances the Accuracy of Latency Coding

The time to the first spike after stimulus onset typically varies with the stimulation intensity. Experimental evidence suggests that neural systems use such response latency to encode information about the stimulus. We investigate the decoding accuracy of the latency code in relation to the level of noise in the form of presynaptic spontaneous activity. Paradoxically, the optimal performance is achieved at a nonzero level of noise and suprathreshold stimulus intensities. We argue that this phenomenon results from the influence of the spontaneous activity on the stabilization of the membrane potential in the absence of stimulation. The reported decoding accuracy improvement represents a novel manifestation of the noise-aided signal enhancement.

Effect of spontaneous activity on stimulus detection in a simple neuronal model

It is studied what level of a continuous-valued signal is optimally estimable on the basis of first-spike latency neuronal data. When a spontaneous neuronal activity is present, the first spike after the stimulus onset may be caused either by the stimulus itself, or it may be a result of the prevailing spontaneous activity. Under certain regularity conditions, Fisher information is the inverse of the variance of the best estimator. It can be considered as a function of the signal intensity and then indicates accuracy of the estimation for each signal level. The Fisher information is normalized with respect to the time needed to obtain an observation. The accuracy of signal level estimation is investigated in basic discharge patterns modelled by a Poisson and a renewal process and the impact of the complex interaction between spontaneous activity and a delay of the response is shown.

Coding accuracy on the psychophysical scale

Sensory neurons are often reported to adjust their coding accuracy to the stimulus statistics. The observed match is not always perfect and the maximal accuracy does not align with the most frequent stimuli. As an alternative to a physiological explanation we show that the match critically depends on the chosen stimulus measurement scale. More generally, we argue that if we measure the stimulus intensity on the scale which is proportional to the perception intensity, an improved adjustment in the coding accuracy is revealed. The unique feature of stimulus units based on the psychophysical scale is that the coding accuracy can be meaningfully compared for different stimuli intensities, unlike in the standard case of a metric scale.

Contrast and response gain control depend on cortical map architecture

Visual cortical neurons are sensitive to visual stimulus contrast and most cells adapt their sensitivity to the prevailing visual environment. Specifically, they match the steepest region of their contrast response function to the prevailing contrast (contrast gain control), and reduce spike rates to limit saturation (response gain control). Most neurons are also tuned for stimulus orientation, and neurons with similar orientation preference are clustered together into iso-orientation zones arranged around pinwheels, i.e. points where all orientations are represented. Here we investigated the relationship between the contrast adaptation properties of neurons and their location relative to pinwheels in the orientation preference map. We measured orientation preference maps in cat cortex using optical intrinsic signal imaging. We then characterized the contrast adaptation properties of single neurons located close to pinwheels, in iso-orientation zones, and at regions in between. We found little evidence of differential contrast sensitivity of neurons adapted to zero contrast. However, after adaptation to their preferred orientation at high contrast, changes in both contrast and response gain were greater for neurons near pinwheels compared with other map regions. Therefore, in the adapted state, which is probably typical during natural viewing, there is a spatial map of contrast sensitivity that is associated with the orientation preference map. This differential adaptation revealed a new dimension of cortical functional organization, linking the contrast adaptation of cells with the orientation preference of their nearest neighbours.

The influence of population size, noise strength and behavioral task on best-encoded stimulus for neurons with unimodal or monotonic tuning curves

Tuning curves and receptive fields are widely used to describe the selectivity of sensory neurons, but the relationship between firing rates and information is not always intuitive. Neither high firing rates nor high tuning curve gradients necessarily mean that stimuli at that part of the tuning curve are well represented by a neuron. Recent research has shown that trial-to-trial variability (noise) and population size can strongly affect which stimuli are most precisely represented by a neuron in the context of a population code (the best-encoded stimulus), and that different measures of information can give conflicting indications. Specifically, the Fisher information is greatest where the tuning curve gradient is greatest, such as on the flanks of peaked tuning curves, but the stimulus-specific information (SSI) is greatest at the tuning curve peak for small populations with high trial-to-trial variability. Previous research in this area has focussed upon unimodal (peaked) tuning curves, and in this article we extend these analyses to monotonic tuning curves. In addition, we examine how stimulus spacing in forced choice tasks affects the best-encoded stimulus. Our results show that, regardless of the tuning curve, Fisher information correctly predicts the best-encoded stimulus for large populations and where the stimuli are closely spaced in forced choice tasks. In smaller populations with high variability, or in forced choice tasks with widely-spaced choices, the best-encoded stimulus falls at the peak of unimodal tuning curves, but is more variable for monotonic tuning curves. Task, population size and variability all need to be considered when assessing which stimuli a neuron represents, but the best-encoded stimulus can be estimated on a case-by case basis using commonly available computing facilities.

Population decoding in rat barrel cortex: optimizing the linear readout of correlated population responses

Sensory information is encoded in the response of neuronal populations. How might this information be decoded by downstream neurons? Here we analyzed the responses of simultaneously recorded barrel cortex neurons to sinusoidal vibrations of varying amplitudes preceded by three adapting stimuli of 0, 6 and 12 µm in amplitude. Using the framework of signal detection theory, we quantified the performance of a linear decoder which sums the responses of neurons after applying an optimum set of weights. Optimum weights were found by the analytical solution that maximized the average signal-to-noise ratio based on Fisher linear discriminant analysis. This provided a biologically plausible decoder that took into account the neuronal variability, covariability, and signal correlations. The optimal decoder achieved consistent improvement in discrimination performance over simple pooling. Decorrelating neuronal responses by trial shuffling revealed that, unlike pooling, the performance of the optimal decoder was minimally affected by noise correlation. In the non-adapted state, noise correlation enhanced the performance of the optimal decoder for some populations. Under adaptation, however, noise correlation always degraded the performance of the optimal decoder. Nonetheless, sensory adaptation improved the performance of the optimal decoder mainly by increasing signal correlation more than noise correlation. Adaptation induced little systematic change in the relative direction of signal and noise. Thus, a decoder which was optimized under the non-adapted state generalized well across states of adaptation.

In the natural environment, animals are constantly exposed to sensory stimulation. A key question in systems neuroscience is how attributes of a sensory stimulus can be “read out” from the activity of a population of brain cells. We chose to investigate this question in the whisker-mediated touch system of rats because of its well-established anatomy and exquisite functionality. The whisker system is one of the major channels through which rodents acquire sensory information about their surrounding environment. The response properties of brain cells dynamically adjust to the prevailing diet of sensory stimulation, a process termed sensory adaptation. Here, we applied a biologically plausible scheme whereby different brain cells contribute to sensory readout with different weights. We established the set of weights that provide the optimal readout under different states of adaptation. The results yield an upper bound for the efficiency of coding sensory information. We found that the ability to decode sensory information improves with adaptation. However, a readout mechanism that does not adjust to the state of adaptation can still perform remarkably well.

Texture-dependent motion signals in primate middle temporal area

Neurons in the middle temporal (MT) area of primate cortex provide an important stage in the analysis of visual motion. For simple stimuli such as bars and plaids some neurons in area MT – pattern cells – seem to signal motion independent of contour orientation, but many neurons – component cells – do not. Why area MT supports both types of receptive field is unclear. To address this we made extracellular recordings from single units in area MT of anaesthetised marmoset monkeys and examined responses to two-dimensional images with a large range of orientations and spatial frequencies. Component and pattern cell response remained distinct during presentation of these complex spatial textures. Direction tuning curves were sharpest in component cells when a texture contained a narrow range of orientations, but were similar across all neurons for textures containing all orientations. Response magnitude of pattern cells, but not component cells, increased with the spatial bandwidth of the texture. In addition, response variability in all neurons was reduced when the stimulus was rich in spatial texture. Fisher information analysis showed that component cells provide more informative responses than pattern cells when a texture contains a narrow range of orientations, but pattern cells had more informative responses for broadband textures. Component cells and pattern cells may therefore coexist because they provide complementary and parallel motion signals.

Adaptation improves neural coding efficiency despite increasing correlations in variability

Exposure of cortical cells to sustained sensory stimuli results in changes in the neuronal response function. This phenomenon, known as adaptation, is a common feature across sensory modalities. Here, we quantified the functional effect of adaptation on the ensemble activity of cortical neurons in the rat whisker-barrel system. A multishank array of electrodes was used to allow simultaneous sampling of neuronal activity. We characterized the response of neurons to sinusoidal whisker vibrations of varying amplitude in three states of adaptation. The adaptors produced a systematic rightward shift in the neuronal response function. Consistently, mutual information revealed that peak discrimination performance was not aligned to the adaptor but to test amplitudes 3-9 μm higher. Stimulus presentation reduced single neuron trial-to-trial response variability (captured by Fano factor) and correlations in the population response variability (noise correlation). We found that these two types of variability were inversely proportional to the average firing rate regardless of the adaptation state. Adaptation transferred the neuronal operating regime to lower rates with higher Fano factor and noise correlations. Noise correlations were positive and in the direction of signal, and thus detrimental to coding efficiency. Interestingly, across all population sizes, the net effect of adaptation was to increase the total information despite increasing the noise correlation between neurons.

Fisher and Shannon Information in Finite Neural Populations

The precision of the neural code is commonly investigated using two families of statistical measures: Shannon mutual information and derived quantities when investigating very small populations of neurons and Fisher information when studying large populations. These statistical tools are no longer the preserve of theorists and are being applied by experimental research groups in the analysis of empirical data. Although the relationship between information-theoretic and Fisher-based measures in the limit of infinite populations is relatively well understood, how these measures compare in finite-size populations has not yet been systematically explored. We aim to close this gap. We are particularly interested in understanding which stimuli are best encoded by a given neuron within a population and how this depends on the chosen measure. We use a novel Monte Carlo approach to compute a stimulus-specific decomposition of the mutual information (the SSI) for populations of up to 256 neurons and show that Fisher information can be used to accurately estimate both mutual information and SSI for populations of the order of 100 neurons, even in the presence of biologically realistic variability, noise correlations, and experimentally relevant integration times. According to both measures, the stimuli that are best encoded are those falling at the flanks of the neuron's tuning curve. In populations of fewer than around 50 neurons, however, Fisher information can be misleading.

Gain control in the response of human visual cortex to plaids

A recent intrinsic signal optical imaging study in tree shrew showed, surprisingly, that the population response of V1 to plaid patterns comprising grating components of equal contrast is predicted by the average of the responses to the individual components (MacEvoy SP, Tucker TR, Fitzpatrick D. Nat Neurosci 12: 637-645, 2009). This prompted us to compare responses to plaids and gratings in human visual cortex as a function of contrast and orientation. We found that the functional MRI (fMRI) blood oxygenation level-dependent (BOLD) responses of areas V1-V3 to a plaid comprising superposed grating components of equal contrast are significantly higher than the responses to a single component. Furthermore, the orientation response profile of a plaid is poorly predicted from a linear combination of the responses to its components. Together, these results indicate that the model of MacEvoy et al. (2009) cannot, without modification, account for the fMRI BOLD response to plaids in human visual cortex.

Is the homunculus "aware" of sensory adaptation?

Neural activity and perception are both affected by sensory history. The work presented here explores the relationship between the physiological effects of adaptation and their perceptual consequences. Perception is modeled as arising from an encoder-decoder cascade, in which the encoder is defined by the probabilistic response of a population of neurons, and the decoder transforms this population activity into a perceptual estimate. Adaptation is assumed to produce changes in the encoder, and we examine the conditions under which the decoder behavior is consistent with observed perceptual effects in terms of both bias and discriminability. We show that for all decoders, discriminability is bounded from below by the inverse Fisher information. Estimation bias, on the other hand, can arise for a variety of different reasons and can range from zero to substantial. We specifically examine biases that arise when the decoder is fixed, "unaware" of the changes in the encoding population (as opposed to "aware" of the adaptation and changing accordingly). We simulate the effects of adaptation on two well-studied sensory attributes, motion direction and contrast, assuming a gain change description of encoder adaptation. Although we cannot uniquely constrain the source of decoder bias, we find for both motion and contrast that an "unaware" decoder that maximizes the likelihood of the percept given by the preadaptation encoder leads to predictions that are consistent with behavioral data. This model implies that adaptation-induced biases arise as a result of temporary suboptimality of the decoder.

Ideal observer analysis of signal quality in retinal circuits

The function of the retina is crucial, for it must encode visual signals so the brain can detect objects in the visual world. However, the biological mechanisms of the retina add noise to the visual signal and therefore reduce its quality and capacity to inform about the world. Because an organism's survival depends on its ability to unambiguously detect visual stimuli in the presence of noise, its retinal circuits must have evolved to maximize signal quality, suggesting that each retinal circuit has a specific functional role. Here we explain how an ideal observer can measure signal quality to determine the functional roles of retinal circuits. In a visual discrimination task the ideal observer can measure from a neural response the increment threshold, the number of distinguishable response levels, and the neural code, which are fundamental measures of signal quality relevant to behavior. It can compare the signal quality in stimulus and response to determine the optimal stimulus, and can measure the specific loss of signal quality by a neuron's receptive field for non-optimal stimuli. Taking into account noise correlations, the ideal observer can track the signal-to-noise ratio available from one stage to the next, allowing one to determine each stage's role in preserving signal quality. A comparison between the ideal performance of the photon flux absorbed from the stimulus and actual performance of a retinal ganglion cell shows that in daylight a ganglion cell and its presynaptic circuit loses a factor of approximately 10-fold in contrast sensitivity, suggesting specific signal-processing roles for synaptic connections and other neural circuit elements. The ideal observer is a powerful tool for characterizing signal processing in single neurons and arrays along a neural pathway.

Direction and contrast tuning of macaque MSTd neurons during saccades

Saccades are rapid eye movements that change the direction of gaze, although the full-field image motion associated with these movements is rarely perceived. The attenuation of visual perception during saccades is referred to as saccadic suppression. The mechanisms that produce saccadic suppression are not well understood. We recorded from neurons in the dorsal medial superior temporal area (MSTd) of alert macaque monkeys and compared the neural responses produced by the retinal slip associated with saccades (active motion) to responses evoked by identical motion presented during fixation (passive motion). We provide evidence for a neural correlate of saccadic suppression and expand on two contentious results from previous studies. First, we confirm the finding that some neurons in MSTd reverse their preferred direction during saccades. We quantify this effect by calculating changes in direction tuning index for a large cell population. Second, it has been noted that neural activity associated with saccades can arrive in the parietal cortex <or=30 ms earlier than activity produced by similar visual stimulation during fixation. This led to the question of whether the saccade-related responses were visual in origin or were motor signals arising from saccade-planning areas of the brain. By comparing the responses to saccades made over textured backgrounds of different contrasts, we provide strong evidence that saccade-related responses were visual in origin. Refinements of the possible models of saccadic suppression are discussed.

Dynamic contrast change produces rapid gain control in visual cortex

During normal vision, objects moving in the environment, our own body movements and our eye movements ensure that the receptive fields of visual neurons are being presented with continually changing contrasts. Thus, the visual input during normal behaviour differs from the type of stimuli traditionally used to study contrast coding, which are presented in a step-like manner with abrupt changes in contrast followed by prolonged exposure to a constant stimulus. The abrupt changes in contrast typically elicit brief periods of intense firing with low variability called onset transients. Onset transients provide the visual system with a powerful and reliable cue that the visual input has changed. In this paper we investigate visual processing in the primary visual cortex of cats in response to stimuli that change contrast dynamically. We show that 1-4 s presentations of dynamic increases and decreases in contrast can generate stronger contrast gain control than several minutes exposure to a stimulus of constant contrast. Thus, transient mechanisms of contrast coding are not only less variable than sustained responses but are also more rapid and flexible. Finally, we propose a quantitative model of contrast coding which accounts for changes in spike rate over time in response to dynamically changing image contrast.

Influence of adapting speed on speed and contrast coding in the primary visual cortex of the cat

Adaptation is a ubiquitous property of the visual system. Adaptation often improves the ability to discriminate between stimuli and increases the operating range of the system, but is also associated with a reduced ability to veridically code stimulus attributes. Adaptation to luminance levels, contrast, orientation, direction and spatial frequency has been studied extensively, but knowledge about adaptation to image speed is less well understood. Here we examined how the speed tuning of neurons in cat primary visual cortex was altered after adaptation to speeds that were slow, optimal, or fast relative to each neuron's speed response function. We found that the preferred speed (defined as the speed eliciting the peak firing rate) of the neurons following adaptation was dependent on the speed at which they were adapted. At the population level cells showed decreases in preferred speed following adaptation to speeds at or above the non-adapted speed, but the preferred speed did not change following adaptation to speeds lower than the non-adapted peak. Almost all cells showed response gain control (reductions in absolute firing capacity) following speed adaptation. We also investigated the speed dependence of contrast adaptation and found that most cells showed contrast gain control (rightward shifts of their contrast response functions) and response gain control following adaptation at any speed. We conclude that contrast adaptation may produce the response gain control associated with speed adaptation, but shifts in preferred speed require an additional level of processing beyond contrast adaptation. A simple model is presented that is able to capture most of the findings.