Receptive Fields Of Neurons Research Articles

Resting-State Retinotopic Organization in the Absence of Retinal Input and Visual Experience.

Early visual areas have neuronal receptive fields that form a sampling mosaic of visual space, resulting in a series of retinotopic maps in which the same region of space is represented in multiple visual areas. It is not clear to what extent the development and maintenance of this retinotopic organization in humans depend on retinal waves and/or visual experience. We examined the corticocortical receptive field organization of resting-state BOLD data in normally sighted, early blind, and anophthalmic (in which both eyes fail to develop) individuals and found that resting-state correlations between V1 and V2/V3 were retinotopically organized for all subject groups. These results show that the gross retinotopic pattern of resting-state connectivity across V1-V3 requires neither retinal waves nor visual experience to develop and persist into adulthood. Significance statement: Evidence from resting-state BOLD data suggests that the connections between early visual areas develop and are maintained even in the absence of retinal waves and visual experience.

P16.05 Regional epidemiological pattern of aids-related cancers in children

<h3>Abstract</h3> The response properties of neurons to sensory stimuli have been used to identify their receptive fields and to functionally map sensory systems. In primary visual cortex, most neurons are selective to a particular orientation and spatial frequency of the visual stimulus. Using two-photon calcium imaging of neuronal populations from the primary visual cortex of mice, we have characterized the response properties of neurons to various orientations and spatial frequencies. Surprisingly, we found that the orientation selectivity of neurons actually depends on the spatial frequency of the stimulus. This dependence can be easily explained if one assumed spatially asymmetric Gabor-type receptive fields. We propose that receptive fields of neurons in layer 2/3 of visual cortex are indeed spatially asymmetric, and that this asymmetry could be used effectively by the visual system to encode natural scenes.

Formation of the priority map by the reciprocal connections between LIP and FEF

We are able to find objects in a cluttered scene quickly and effortlessly. To achieve this, the brain creates a priority map of the visual world, which incorporates both pre-attentive and cognitive inputs to represent the importance of stimuli in the scene, and which uses inhibitory tagging to track items that have been viewed. Eye movements are then guided by this map. We have previously shown that activity in the lateral intraparietal area (LIP) acts like a priority map, but the absolute responses do not correlate directly with behavior: they have to be normalized first. In this study we examined the responses of neurons in the frontal eye field (FEF), which has reciprocal connections with LIP, to test whether it contributes to the formation of the priority map. Animals searched through 5 potential targets and 5 distractors to find the target loaded with reward. After the stimuli appeared, the animals were free to move their eyes. Stimuli were spaced such that when the animal was looking at one stimulus, another was in the FEF neuron's receptive field. Most of the neurons fit into 4 main classes: neurons that responded preferentially to Ts; neurons that responded preferentially when a stimulus that had been fixated was in the response field, which could drive top-down inhibitory tagging seen in LIP; and neurons that initially showed an enhanced response to a stimulus that had been fixated, but then reversed their response preference 100-150 ms after the saccade. The late responses in this class matched our post-normalization predictions. The final class of neurons didn't differential between search objects, but preferentially responded to the goal of the next saccade. Together these data suggest that the reciprocal connection between LIP and FEF creates the priority map that guides saccadic eye movements during active, goal directed visual search. Meeting abstract presented at VSS 2015.

Contrast invariance of orientation tuning in cat primary visual cortex neurons depends on stimulus size.

The process of orientation tuning is an important and well-characterized feature of neurons in primary visual cortex. The combination of ascending and descending circuits involved is not only relevant to understanding visual processing but the function of neocortex in general. The classic feed-forward model of orientation tuning predicts a broadening effect due to increasing contrast; yet, experimental results consistently report contrast invariance. We show here that contrast invariance actually depends on stimulus size such that large stimuli extending beyond the neuron's receptive field engage circuits that promote invariance, whereas optimally sized, smaller stimuli result in contrast variance that is more in line with the classical orientation tuning model. These results illustrate the importance of optimizing stimulus parameters to best reflect the sensory pathways under study and provide new clues about different circuits that may be involved in variant and invariant response properties. Selective response to stimulus orientation is a key feature of neurons in primary visual cortex, yet the underlying mechanisms generating orientation tuning are not fully understood. The combination of feed-forward and cortical mechanisms involved is not only relevant to understanding visual processing but the function of neocortex in general. The classic feed-forward model predicts that orientation tuning should broaden considerably with increasing contrast; however, experimental results consistently report contrast invariance. We show here, in primary visual cortex of anaesthetized cats under neuromuscular blockade, that contrast invariance occurs when visual stimuli are large enough to include the extraclassical surround (ECS), which is likely to involve circuits of suppression that may not be entirely feed-forward in origin. On the other hand, when stimulus size is optimized to the classical receptive field of each neuron, the population average shows a statistically significant 40% increase in tuning width at high contrast, demonstrating that contrast variance of orientation tuning can occur. Conversely, our results also suggest that the phenomenon of contrast invariance relies in part on the presence of the ECS. Moreover, these results illustrate the importance of optimizing stimulus parameters to best reflect the neural pathways under study.

Diversity priors for learning early visual features.

This paper investigates how utilizing diversity priors can discover early visual features that resemble their biological counterparts. The study is mainly motivated by the sparsity and selectivity of activations of visual neurons in area V1. Most previous work on computational modeling emphasizes selectivity or sparsity independently. However, we argue that selectivity and sparsity are just two epiphenomena of the diversity of receptive fields, which has been rarely exploited in learning. In this paper, to verify our hypothesis, restricted Boltzmann machines (RBMs) are employed to learn early visual features by modeling the statistics of natural images. Considering RBMs as neural networks, the receptive fields of neurons are formed by the inter-weights between hidden and visible nodes. Due to the conditional independence in RBMs, there is no mechanism to coordinate the activations of individual neurons or the whole population. A diversity prior is introduced in this paper for training RBMs. We find that the diversity prior indeed can assure simultaneously sparsity and selectivity of neuron activations. The learned receptive fields yield a high degree of biological similarity in comparison to physiological data. Also, corresponding visual features display a good generative capability in image reconstruction.

Development of a Self-Regulating Evolving Spiking Neural Network for classification problem

This paper presents a new spiking neural network for pattern classification problems, referred to as the Self-Regulating Evolving Spiking Neural (SRESN) classifier, that regulates the learning process of the network. It uses a two layered spiking neural network and the input layer consists of receptive field neurons, which convert a real valued input to spikes using the population coding scheme without any delays. The output layer consists of leaky integrate-and-fire neurons. Since SRESN does not use any delays, the number of network parameters for SRESN is significantly lower than that used by other spiking neural networks, used in this study. During training, the learning algorithm for SRESN, automatically evolves neurons in the output layer based on the training data stream and the current knowledge stored in the network. Depending on the knowledge in the sample and the class specific knowledge stored in the network, it can choose to either add a neuron or update the network parameters or skip learning the sample resulting in self-regulation of the learning process. In case of neuron addition, the weights for the newly added neuron are initialized using a modified rank order scheme which facilitates SRESN for use in online/sequential as well as batch learning modes. The parameter update strategy in SRESN ensures that connections with non-zero postsynaptic potential at the time of the spike are alone updated which helps prevent over training. While evaluating the performance of SRESN, first a study is conducted to assess the impact of various parameters on its performance and establish guidelines to choose suitable values for these parameters. Next, the performance of SRESN, operating in batch mode, is compared with other spiking neural classifiers, including SpikeProp and MuSpiNN, for the UCI benchmark problems of Iris flower classification and Wisconsin breast cancer. Subsequently, the performance of SRESN in online and batch learning mode is compared with an evolving spiking neural classifier for five benchmark data sets from the UCI machine learning repository. Finally, SRESN is applied to solve the practical problem of Epilepsy detection. The performance comparison clearly indicates that SRESN provides a higher generalization accuracy using fewer network parameters.

Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity.

In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational studies of sensory processing in neocortical network models equipped with synaptic plasticity.

Effects of attention and distractor contrast on the responses of middle temporal area neurons to transient motion direction changes.

The ability of primates to detect transient changes in a visual scene can be influenced by the allocation of attention, as well as by the presence of distractors. We investigated the neural substrates of these effects by recording the responses of neurons in the middle temporal area (MT) of two monkeys while they detected a transient motion direction change in a moving target. We found that positioning a distractor near the target impaired the change-detection performance of the animals. This impairment monotonically decreased as the distractor's contrast decreased. A neural correlate of this effect was a decrease in the ability of MT neurons to signal the direction change (detection sensitivity or DS) when a distractor was near the target, both located inside the neuron's receptive field. Moreover, decreasing distractor contrast increased neuronal DS. On the other hand, directing attention away from the target decreased neuronal DS. At the level of individual neurons, we found a negative correlation between the degree of response normalization and the DS. Finally, the intensity of a neuron's response to the change was predictive of the animal's reaction time, suggesting that the activity of our recorded neurons was linked to the animal's detection performance. Our results suggest that the ability of an MT neuron to signal a transient direction change is regulated by the degree of inhibitory drive into the cell. The presence of distractors, their contrast and the allocation of attention influence such inhibitory drive, therefore modulating the ability of the neurons to signal transient changes in stimulus features and consequently behavioral performance.

Fast and robust estimation of spectro-temporal receptive fields using stochastic approximations

BackgroundThe receptive field (RF) represents the signal preferences of sensory neurons and is the primary analysis method for understanding sensory coding. While it is essential to estimate a neuron's RF, finding numerical solutions to increasingly complex RF models can become computationally intensive, in particular for high-dimensional stimuli or when many neurons are involved. New methodHere we propose an optimization scheme based on stochastic approximations that facilitate this task. The basic idea is to derive solutions on a random subset rather than computing the full solution on the available data set. To test this, we applied different optimization schemes based on stochastic gradient descent (SGD) to both the generalized linear model (GLM) and a recently developed classification-based RF estimation approach. Results and comparison with existing methodUsing simulated and recorded responses, we demonstrate that RF parameter optimization based on state-of-the-art SGD algorithms produces robust estimates of the spectro-temporal receptive field (STRF). Results on recordings from the auditory midbrain demonstrate that stochastic approximations preserve both predictive power and tuning properties of STRFs. A correlation of 0.93 with the STRF derived from the full solution may be obtained in less than 10% of the full solution's estimation time. We also present an on-line algorithm that allows simultaneous monitoring of STRF properties of more than 30 neurons on a single computer. ConclusionsThe proposed approach may not only prove helpful for large-scale recordings but also provides a more comprehensive characterization of neural tuning in experiments than standard tuning curves.

Continuous Updating of Visuospatial Memory in Superior Colliculus during Slow Eye Movements

Primates can remember and spatially update the visual direction of previously viewed objects during various types of self-motion. It is known that the brain "remaps" visual memory traces relative to gaze just before and after, but not during, discrete gaze shifts called saccades. However, it is not known how visual memory is updated during slow, continuous motion of the eyes. Here, we recorded the midbrain superior colliculus (SC) of two rhesus monkeys that were trained to spatially update the location of a saccade target across an intervening smooth pursuit (SP) eye movement. Saccade target location was varied across trials so that it passed through the neuron's receptive field at different points of the SP trajectory. Nearly all (99% of) visual responsive neurons, but no motor neurons, showed a transient memory response that continuously updated the saccade goal during SP. These responses were gaze centered (i.e., shifting across the SC's retinotopic map in opposition to gaze). Furthermore, this response was strongly enhanced by attention and/or saccade target selection. This is the first demonstration of continuous updating of visual memory responses during eye motion. We expect that this would generalize to other visuomotor structures when gaze shifts in a continuous, unpredictable fashion.

Examination of rhythmicity of extracellularly recorded neurons in the entorhinal cortex.

A number of studies have examined the theta-rhythmic modulation of neuronal firing in the hippocampal circuit. For extracellular recordings, this is often done by examining spectral properties of the spike-time autocorrelogram, most significantly, for validating the presence or absence of theta modulation across species. These techniques can show significant rhythmicity for high firing rate, highly rhythmic neurons; however, they are substantially biased by several factors including the peak firing rate of the neuron, the amount of time spent in the neuron's receptive field, and other temporal properties of the rhythmicity such as cycle-skipping. These limitations make it difficult to examine rhythmic modulation in neurons with low firing rates or when an animal has short dwell times within the firing field and difficult to compare rhythmicity under disparate experimental conditions when these factors frequently differ. Here, we describe in detail the challenges that researchers face when using these techniques and apply our findings to recent recordings from bat entorhinal grid cells, suggesting that they may have lacked enough data to examine theta rhythmicity robustly. We describe a more sensitive and statistically rigorous method using maximum likelihood estimation (MLE) of a parametric model of the lags within the autocorrelation window, which helps to alleviate some of the problems of traditional methods and was also unable to detect rhythmicity in bat grid cells. Using large batteries of simulated data, we explored the boundaries for which the MLE technique and the theta index can detect rhythmicity. The MLE technique is less sensitive to many features of the autocorrelogram and provides a framework for statistical testing to detect rhythmicity as well as changes in rhythmicity in individual sessions providing a substantial improvement over previous methods.

Möbius-strip-like columnar functional connections are revealed in somato-sensory receptive field centroids.

Receptive fields of neurons in the forelimb region of areas 3b and 1 of primary somatosensory cortex, in cats and monkeys, were mapped using extracellular recordings obtained sequentially from nearly radial penetrations. Locations of the field centroids indicated the presence of a functional system in which cortical homotypic representations of the limb surfaces are entwined in three-dimensional Möbius-strip-like patterns of synaptic connections. Boundaries of somatosensory receptive field in nested groups irregularly overlie the centroid order, and are interpreted as arising from the superposition of learned connections upon the embryonic order. Since the theory of embryonic synaptic self-organization used to model these results was devised and earlier used to explain findings in primary visual cortex, the present findings suggest the theory may be of general application throughout cortex and may reveal a modular functional synaptic system, which, only in some parts of the cortex, and in some species, is manifest as anatomical ordering into columns.

Saccadic remapping of object-selective information

Saccadic remapping, a presaccadic increase in neural activity when a saccade is about to bring an object into a neuron's receptive field, may be crucial for our perception of a stable world. Studies of perception and saccadic remapping, like ours, focus on the presaccadic acquisition of information from the saccade target, with no direct reference to underlying physiology. While information is known to be acquired prior to a saccade, it is unclear whether object-selective or feature-specific information is remapped. To test this, we performed a series of psychophysical experiments in which we presented a peripheral, nonfoveated face as a presaccadic target. The target face disappeared at saccade onset. After making a saccade to the location of the peripheral target face (which was no longer visible), subjects misperceived the expression of a subsequent, foveally presented neutral face as being repelled away from the peripheral presaccadic face target. This effect was similar to a sequential shape contrast or negative aftereffect but required a saccade, because covert attention was not sufficient to generate the illusion. Additional experiments further revealed that inverting the faces disrupted the illusion, suggesting that presaccadic remapping is object-selective and not based on low-level features. Our results demonstrate that saccadic remapping can be an object-selective process, spatially tuned to the target of the saccade and distinct from covert attention in the absence of a saccade.

Large-scale spiking circuit simulation of spatio-temporal dynamics in superior colliculus

We present computational simulations of the intrinsic circuitry of the superior colliculus using large-scale spiking neural circuit models. We reproduce recent results from slice experiments that showed different spatio-temporal patterns of interaction within the visual layers versus the eye-movement related layers of the superior colliculus. Specifically, the receptive fields of neurons in the visual layers implement a “center-surround” pattern of spatial competition, and furthermore additional input within the central region sums super-linearly. In contrast, the receptive fields of neurons in the motor-related regions implement spatially symmetric fields of overlaid excitation and inhibition, and additional inputs sum linearly. Our simulations investigate the circuit mechanisms and dynamics that differentiate the computational roles of these distinct but related regions. We constructed full-scale simulations of mouse brain slices using spiking neuron models. Connection parameters were then fit within physiological constraints to reproduce experimental data. Figure ​Figure1A1A shows results from a best fit of the superficial (visual) layers. Stimulation was applied at different horizontal distances from a central neuron in a tangential slice. The figure shows integrated post-synaptic potential (PSP) during stimulation at the indicated distance (purple). We also reproduced data from a “two-point” paradigm in which stimulation was simultaneously applied to the indicated electrode and the central electrode, simulating a “large” stimulus (red). The net response of the neuron sums super-linearly in comparison to the linear summation of the results from the two points independently (naive summation in black). Insets show corresponding slice data. Interestingly, anatomically distinct inhibitory and excitatory populations were not necessary to reproduce spatial asymmetry (“center-surround”) in the visual superficial layers. Rather, asymmetries in the temporal properties (synaptic dynamics) of connections were better able to account for observed data under all conditions. A further population of small disinhibitory neurons was necessary to maintain linear summation in the periphery while summing super-linearly in the center. Figure 1 This contrasts with the motor-related intermediate layers (Figure ​(Figure1B),1B), whose response to stimulation was best explained by both spatial and temporal symmetry between inhibitory and excitatory neural connections. Overall, the regions seem to have taken advantage of both spatial and temporal dynamics in their connections to specialize their computational function: The visual layers seem geared towards spatial competition and strengthening of salient stimuli, whereas the motor-related regions seem geared towards broad and long-term integration of input.

Normalization of neuronal responses in cortical area MT across signal strengths and motion directions

Multiple visual stimuli are common in natural scenes, yet it remains unclear how multiple stimuli interact to influence neuronal responses. We investigated this question by manipulating relative signal strengths of two stimuli moving simultaneously within the receptive fields (RFs) of neurons in the extrastriate middle temporal (MT) cortex. Visual stimuli were overlapping random-dot patterns moving in two directions separated by 90°. We first varied the motion coherence of each random-dot pattern and characterized, across the direction tuning curve, the relationship between neuronal responses elicited by bidirectional stimuli and by the constituent motion components. The tuning curve for bidirectional stimuli showed response normalization and can be accounted for by a weighted sum of the responses to the motion components. Allowing nonlinear, multiplicative interaction between the two component responses significantly improved the data fit for some neurons, and the interaction mainly had a suppressive effect on the neuronal response. The weighting of the component responses was not fixed but dependent on relative signal strengths. When two stimulus components moved at different coherence levels, the response weight for the higher-coherence component was significantly greater than that for the lower-coherence component. We also varied relative luminance levels of two coherently moving stimuli and found that MT response weight for the higher-luminance component was also greater. These results suggest that competition between multiple stimuli within a neuron's RF depends on relative signal strengths of the stimuli and that multiplicative nonlinearity may play an important role in shaping the response tuning for multiple stimuli.

Global selection of saccadic target features by neurons in area v4.

Psychophysical and neurophysiological studies indicate that during the preparation of saccades, visual processing at the target location is facilitated automatically by the deployment of attention. It has been assumed that the neural mechanisms involved in presaccadic shifts of attention are purely spatial in nature. Saccade preparation modulates the visual responses of neurons within extrastriate area V4, where the responses to targets are enhanced and responses to nontargets are suppressed. We tested whether this effect also engages a nonspatial form of modulation. We measured the responses of area V4 neurons to oriented gratings in two monkeys (Macaca mulatta) making delayed saccades to targets distant from the neuronal receptive field (RF). We varied the orientation of both the RF stimulus and the saccadic target. We found that, in addition to the spatial modulation, saccade preparation involves a feature-dependent modulation of V4 neuronal responses. Specifically, we found that the suppression of area V4 responses to nontarget stimuli during the preparation of saccades depends on the features of the saccadic target. Presaccadic suppression was absent when the features of the saccadic target matched the features preferred by individual V4 neurons. This feature-dependent modulation occurred in the absence of any feature-attention task. We show that our observations are consistent with a computational framework in which feature-based effects automatically emerge from saccade-related feedback signals that are spatial in nature.

Discriminative Learning of Receptive Fields from Responses to Non-Gaussian Stimulus Ensembles

Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design.

Visual Stability During Saccades is Achieved through Transient Changes in Perceptual Space and Time

To interact rapidly and effectively with our environment, our brain needs access to a dynamic neural representation – or map – of the spatial layout of the external world. One of the major challenges to constructing such a map is the frequent rapid movements of the eyes, which displace the images on our retinae, up to three times per second. Much neurophysiological research shows that just before saccades, the receptive fields of neurons in many visual areas are subject to large shifts, in the direction of the saccade (Duhamel, Colby, & Goldberg, 1992). While the neural mechanisms of this dynamic process are now well understood (Sommer & Wurtz, 2006), it is less clear how a shift in the direction of the saccade benefits perception.We suggest that the purpose of the shifts in receptive fields is to prepare for the effects of saccades before they commence, and that the real work of stabilizing occurs when they relax back into their standard position during the actual saccade (Burr & Morrone, 2011, 2012; Cicchini, Binda, Burr, & Morrone, 2012). It is this relaxation that compensates for the saccadic displacement. The relaxation process is best described as a shift in both space and in time, and this can be well described by spatiotemporal receptive field that is slanted in space-time (see Figure 9 from Cicchini, Binda, Burr, & Morrone, 2012).We have accumulated a wealth of psychophysical evidence supporting the notion of the spatiotemporal shift. Perhaps the strongest evidence is the temporal lag that occurs during saccades, a clear signature of a spatiotemporal transformation. During saccades, visual flashes are perceived to be delayed by 100ms compared with normal viewing (Binda, Cicchini, Burr, & Morrone, 2009), and it is this delay that creates the spatiotemporal tuning (as do motion perception mechanisms, on a different timescale; Burr, Ross, & Morrone, 1986). There is also strong temporal compression, so stimuli 100ms long are perceived as 50ms (Morrone, Ross, & Burr, 2005); we have developed a simple model that explains how the temporal compression results from a spatiotemporal shift (Cicchini & Morrone, 2012). Recently, neurophysiological studies find similar delays and compression in neurones of cortical area FEF (Joiner, Cavanaugh, & Wurtz, 2013). Finally, we have explicitly measured the spatiotemporal interaction field of stimuli flashed around the time of the saccade, and report a field like that shown in Figure 9 of Cicchini, Binda, Burr, and Morrone (2012).All up these studies show clear evidence for a spatiotemporal mechanism that creates transient spatiotopy at the time of saccades, allowing perceptual systems to momentarily bridge the transition between one fixation and another. This process cannot be understood as only a spatial transform, as it is inherently spatiotemporal, resulting in strong and measureable delays and compression of time.

Eye position information is used to compensate the consequences of ocular torsion on V1 receptive fields

It is commonly held that the receptive fields (RFs) of neurons in primary visual cortex (V1) are fixed relative to the retina. Hence, V1 should be unable to distinguish between retinal image shifts due to object motion and image shifts resulting from ego motion. Here we show that, in contrast to this belief, a particular class of neurons in V1 of non-human primates have RFs that are actually head centred, despite intervening eye movements. They use eye position information to shift their RFs location and to change their orientation tuning on the retina so as to fully compensate for the retinal consequences of a particular type of reflexive eye movements, ocular counter-roll, an eye rotation around the line of sight partially counterpoising head tilt. In other words, V1 uses eye position information to resolve the ambiguity if retinal image tilt is the result of the tilting of an object or of the ocular counter-roll.

Topographic generalization of tactile perceptual learning.

Perceptual learning can improve our sensory abilities. Understanding its underlying mechanisms, in particular, when perceptual learning generalizes, has become a focus of research and controversy. Specifically, there is little consensus regarding the extent to which tactile perceptual learning generalizes across fingers. We measured tactile orientation discrimination abilities on 4 fingers (index and middle fingers of both hands), using psychophysical measures, before and after 4 training sessions on 1 finger. Given the somatotopic organization of the hand representation in the somatosensory cortex, the topography of the cortical areas underlying tactile perceptual learning can be inferred from the pattern of generalization across fingers; only fingers sharing cortical representation with the trained finger ought to improve with it. Following training, performance improved not only for the trained finger but also for its adjacent and homologous fingers. Although these fingers were not exposed to training, they nevertheless demonstrated similar levels of learning as the trained finger. Conversely, the performance of the finger that was neither adjacent nor homologous to the trained finger was unaffected by training, despite the fact that our procedure was designed to enhance generalization, as described in recent visual perceptual learning research. This pattern of improved performance is compatible with previous reports of neuronal receptive fields (RFs) in the primary somatosensory cortex (SI) spanning adjacent and homologous digits. We conclude that perceptual learning rooted in low-level cortex can still generalize, and suggest potential applications for the neurorehabilitation of syndromes associated with maladaptive plasticity in SI.