Individual Receptive Fields Research Articles

Direct Discriminative Decoder Models for Analysis of High-Dimensional Dynamical Neural Data.

With the accelerated development of neural recording technology over the past few decades, research in integrative neuroscience has become increasingly reliant on data analysis methods that are scalable to high-dimensional recordings and computationally tractable. Latent process models have shown promising results in estimating the dynamics of cognitive processes using individual models for each neuron's receptive field. However, scaling these models to work on high-dimensional neural recordings remains challenging. Not only is it impractical to build receptive field models for individual neurons of a large neural population, but most neural data analyses based on individual receptive field models discard the local history of neural activity, which has been shown to be critical in the accurate inference of the underlying cognitive processes. Here, we propose a novel, scalable latent process model that can directly estimate cognitive process dynamics without requiring precise receptive field models of individual neurons or brain nodes. We call this the direct discriminative decoder (DDD) model. The DDD model consists of (1) a discriminative process that characterizes the conditional distribution of the signal to be estimated, or state, as a function of both the current neural activity and its local history, and (2) a state transition model that characterizes the evolution of the state over a longer time period. While this modeling framework inherits advantages of existing latent process modeling methods, its computational cost is tractable. More important, the solution can incorporate any information from the history of neural activity at any timescale in computing the estimate of the state process. There are many choices in building the discriminative process, including deep neural networks or gaussian processes, which adds to the flexibility of the framework. We argue that these attributes of the proposed methodology, along with its applicability to different modalities of neural data, make it a powerful tool for high-dimensional neural data analysis. We also introduce an extension of these methods, called the discriminative-generative decoder (DGD). The DGD includes both discriminative and generative processes in characterizing observed data. As a result, we can combine physiological correlates like behavior with neural data to better estimate underlying cognitive processes. We illustrate the methods, including steps for inference and model identification, and demonstrate applications to multiple data analysis problems with high-dimensional neural recordings. The modeling results demonstrate the computational and modeling advantages of the DDD and DGD methods.

Multi-Path U-Net Architecture for Cell and Colony-Forming Unit Image Segmentation.

U-Net is the most cited and widely-used deep learning model for biomedical image segmentation. In this paper, we propose a new enhanced version of a ubiquitous U-Net architecture, which improves upon the original one in terms of generalization capabilities, while addressing several immanent shortcomings, such as constrained resolution and non-resilient receptive fields of the main pathway. Our novel multi-path architecture introduces a notion of an individual receptive field pathway, which is merged with other pathways at the bottom-most layer by concatenation and subsequent application of Layer Normalization and Spatial Dropout, which can improve generalization performance for small datasets. In general, our experiments show that the proposed multi-path architecture outperforms other state-of-the-art approaches that embark on similar ideas of pyramid structures, skip-connections, and encoder–decoder pathways. A significant improvement of the Dice similarity coefficient is attained at our proprietary colony-forming unit dataset, where a score of was achieved for the foreground class.

Estimating average single-neuron visual receptive field sizes by fMRI

The noninvasive estimation of neuronal receptive field (RF) properties in vivo allows a detailed understanding of brain organization as well as its plasticity by longitudinal following of potential changes. Visual RFs measured invasively by electrophysiology in animal models have traditionally provided a great extent of our current knowledge about the visual brain and its disorders. Voxel-based estimates of population RF (pRF) by functional magnetic resonance imaging (fMRI) in humans revolutionized the field and have been used extensively in numerous studies. However, current methods cannot estimate single-neuron RF sizes as they reflect large populations of neurons with individual RF scatter. Here, we introduce an approach to estimate RF size using spatial frequency selectivity to checkerboard patterns. This method allowed us to obtain noninvasive, average single-neuron RF estimates over a large portion of human early visual cortex. These estimates were significantly smaller compared with prior pRF methods. Furthermore, fMRI and electrophysiology experiments in nonhuman primates demonstrated an exceptionally good match, validating the approach.

Inhibition in Simple Cell Receptive Fields Is Broad and OFF-Subregion Biased.

Inhibition in thalamorecipient layer 4 simple cells of primary visual cortex is believed to play important roles in establishing visual response properties and integrating visual inputs across their receptive fields (RFs). Simple cell RFs are characterized by nonoverlapping, spatially restricted subregions in which visual stimuli can either increase or decrease the firing rate of the cell, depending on contrast. Inhibition is believed to be triggered exclusively from visual stimulation of individual RF subregions. However, this view is at odds with the known anatomy of layer 4 interneurons in visual cortex and differs from recent findings in mouse visual cortex. Here we show with in vivo intracellular recordings in cats that while excitation is restricted to RF subregions, inhibition spans the width of simple cell RFs. Consequently, excitatory stimuli within a subregion concomitantly drive excitation and inhibition. Furthermore, we found that the distribution of inhibition across the RF is stronger toward OFF subregions. This inhibitory OFF-subregion bias has a functional consequence on spatial integration of inputs across the RF. A model based on the known anatomy of layer 4 demonstrates that the known proportion and connectivity of inhibitory neurons in layer 4 of primary visual cortex is sufficient to explain broad inhibition with an OFF-subregion bias while generating a variety of phase relations, including antiphase, between excitation and inhibition in response to drifting gratings.SIGNIFICANCE STATEMENT The wiring of excitatory and inhibitory neurons in cortical circuits is key to determining the response properties in sensory cortex. In the visual cortex, the first cells that receive visual input are simple cells in layer 4. The underlying circuitry responsible for the response properties of simple cells is not yet known. In this study, we challenge a long-held view concerning the pattern of inhibitory input and provide results that agree with current known anatomy. We show here that inhibition is evoked broadly across the receptive fields of simple cells, and we identify a surprising bias in inhibition within the receptive field. Our findings represent a step toward a unified view of inhibition across different species and sensory systems.

Spatial precision of population activity in primate area MT.

The middle temporal (MT) area is a cortical area integral to the "where" pathway of primate visual processing, signaling the movement and position of objects in the visual world. The receptive field of a single MT neuron is sensitive to the direction of object motion but is too large to signal precise spatial position. Here, we asked if the activity of MT neurons could be combined to support the high spatial precision required in the where pathway. With the use of multielectrode arrays, we recorded simultaneously neural activity at 24-65 sites in area MT of anesthetized marmoset monkeys. We found that although individual receptive fields span more than 5° of the visual field, the combined population response can support fine spatial discriminations (<0.2°). This is because receptive fields at neighboring sites overlapped substantially, and changes in spatial position are therefore projected onto neural activity in a large ensemble of neurons. This fine spatial discrimination is supported primarily by neurons with receptive fields flanking the target locations. Population performance is degraded (by 13-22%) when correlations in neural activity are ignored, further reflecting the contribution of population neural interactions. Our results show that population signals can provide high spatial precision despite large receptive fields, allowing area MT to represent both the motion and the position of objects in the visual world.

Long-term modification of cortical synapses improves sensory perception

Synapses and receptive fields of the cerebral cortex are plastic. However, changes to specific inputs must be coordinated within neural networks to ensure that excitability and feature selectivity are appropriately configured for perception of the sensory environment. Long-lasting enhancements and decrements to rat primary auditory cortical excitatory synaptic strength were induced by pairing acoustic stimuli with activation of the nucleus basalis neuromodulatory system. Here we report that these synaptic modifications were approximately balanced across individual receptive fields, conserving mean excitation while reducing overall response variability. Decreased response variability should increase detection and recognition of near-threshold or previously imperceptible stimuli, as we found in behaving animals. Thus, modification of cortical inputs leads to wide-scale synaptic changes, which are related to improved sensory perception and enhanced behavioral performance.

Feedforward Origins of Response Variability Underlying Contrast Invariant Orientation Tuning in Cat Visual Cortex

Contrast invariant orientation tuning in simple cells of the visual cortex depends critically on contrast dependent trial-to-trial variability in their membrane potential responses. This observation raises the question of whether this variability originates from within the cortical circuit or the feedforward inputs from the lateral geniculate nucleus (LGN). To distinguish between these two sources of variability, we first measured membrane potential responses while inactivating the surrounding cortex, and found that response variability was nearly unaffected. We then studied variability in the LGN, including contrast dependence, and the trial-to-trial correlation in responses between nearby neurons. Variability decreased significantly with contrast, whereas correlation changed little. When these experimentally measured parameters of variability were applied to a feedforward model of simple cells that included realistic mechanisms of synaptic integration, contrast-dependent, orientation independent variability emerged in the membrane potential responses. Analogous mechanisms might contribute to the stimulus dependence and propagation of variability throughout the neocortex.

Granular Neural Networks and Their Development Through Context-Based Clustering and Adjustable Dimensionality of Receptive Fields

In this study, we present a new architecture of a granular neural network and provide a comprehensive design methodology as well as elaborate on an algorithmic setup supporting its development. The proposed neural network relates to a broad category of radial basis function neural networks (RBFNNs) in the sense that its topology involves a collection of receptive fields. In contrast to the standard architectures encountered in RBFNNs, here we form individual receptive fields in subspaces of the original input space rather than in the entire input space. These subspaces could be different for different receptive fields. The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-means clustering while the information granules in the multidimensional input space are formed by using the so-called context-based fuzzy C-means, which takes into account the structure being already formed in the output space. The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization [genetic algorithms (GAs), to be more specific] to determine the optimal input subspaces. A series of numeric studies exploiting synthetic data and data coming from the Machine Learning Repository, University of California at Irvine, provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

Cortical influences on rapid brainstem plasticity

Cortical contributions to brainstem plasticity in the somatosensory system are poorly understood. Tactile receptive fields (RFs) of brainstem dorsal column nuclei (DCN) neurons rapidly enlarge when peripheral inputs are disrupted by local anesthetic blocks with lidocaine (LID). Cortical inputs appear to influence this plasticity because enlargements have been shown to be greater when cortical inputs are disrupted. Like disruptions of peripheral inputs, disruptions of DCN inhibition by DCN administration of the GABAA receptor antagonist bicuculline methiodide (BMI) also cause rapid enlargements of DCN RFs when cortical inputs are intact. These findings leave questions about interactions between cortical inputs, DCN inhibition, and DCN RF plasticity. To study potential interactions, the present experiments evaluated RF sizes of DCN tactilely responsive neurons in anesthetized rats following DCN microinjection of BMI when cortical inputs were acutely disrupted or intact. These tests were also supplemented by subsequent LID tests to directly compare post-BMI and post-LID effects on the same RF. BMI caused DCN RF enlargements when cortical inputs were disrupted or intact; however, enlargements after cortical input disruption were greater than when cortical inputs were intact. Following RF enlargement and retraction after BMI, LID often caused a second enlargement of the same RF, across skin that partially matched skin involved in the enlargement after BMI. This occurred when cortical inputs were disrupted or intact. We hypothesize that cortical inputs are not required for BMI and LID to initiate partially matching enlargements in individual DCN tactile RFs, however, cortical inputs constrain magnitudes of these enlargements.

Influence of Lateral Masking on Contrast Detection Thresholds of Bars Differing in Width

Contrast detection thresholds for bars 1, 2, or 3 arcmin wide and 1 deg long were estimated in the presence of high-contrast flank bars of the same size. The distance between the flank and the test bars varied from 1 to 30 arcmin. Regardless of the bar width, the thresholds increased when the test and flank bars were in a summation zone 6–8 arcmin wide. With increasing distance between the test and flank bars, the thresholds decreased and reached a plateau. The results testified to an influence of surrounding patterns on the responses of individual receptive fields of neurons processing the test stimuli rather than to lateral interactions between the fields. An attempt was made to apply this explanation to the results of earlier studies concerning the lateral interactions of spatial frequency channels in tasks consisting of detection of objects masked in the same manner.

Separable features of visual cortical plasticity revealed by N-methyl-D-aspartate receptor 2A signaling.

How individual receptive field properties are formed in the maturing sensory neocortex remains largely unknown. The shortening of N-methyl-d-aspartate (NMDA) receptor currents by 2A subunit (NR2A) insertion has been proposed to delimit the critical period for experience-dependent refinement of circuits in visual cortex. In mice engineered to maintain prolonged NMDA responses by targeted deletion of NR2A, the sensitivity to monocular deprivation was surprisingly weakened but restricted to the typical critical period and delayed normally by dark rearing from birth. Orientation preference instead failed to mature, occluding further effects of dark rearing. Interestingly, a full ocular dominance plasticity (but not orientation bias) was selectively restored by enhanced inhibition, reflecting an imbalanced excitation in the absence of NR2A. Many of the downstream pathways involved in NMDA signaling are coupled to the receptor through a variety of protein-protein interactions and adaptor molecules. To further investigate a mechanistic dissociation of receptive field properties in the developing visual system, mice carrying a targeted disruption of the NR2A-associated 95-kDa postsynaptic density (PSD95) scaffolding protein were analyzed. Although the development and plasticity of ocular dominance was unaffected, orientation preference again failed to mature in these mice. Taken together, our results demonstrate that the cellular basis generating individual sensory response properties is separable in the developing neocortex.

Enhanced sensitivity for peripherally‐presented collinearly‐aligned stimulus elements: contour detection or spatial summation?

Background: It has been shown that the detection threshold for a foveally presented Gabor stimulus (Gaussian‐windowed sinusoidal grating) is reduced in the presence of two suprathreshold flanking Gabors and that such facilitation is orientation and configuration dependent, with maximal threshold reductions occurring with collinear flanking elements.Methods: We assessed the effect of varying relative orientation on detection thresholds for chains of three Gabors, with 0.96d̀ centre‐to‐centre spacing, presented at 4.8d̀ in the periphery. The central Gabor element in each chain was either collinear with its flanking elements or oriented orthogonally to them.Results: Thresholds for the configuration as a whole were found to be lower using the collinear configuration compared with the orthogonal configuration, consistent with the proposal that orientation‐specific long‐range intracortical interactions are involved. However, this configuration‐dependent threshold difference disappeared when the spatial frequency of the Gabors was doubled from 3.33 cpd to 6.66 cpd.Conclusions: Configurational dependencies may disappear at higher spatial frequencies because, despite increased spatial summation at detection threshold contrast levels, the intrinsically smaller receptive field sizes of neurons tuned to high spatial frequencies render them incapable of encapsulating more than one of the spatially separated Gabors within their individual receptive fields.

High-resolution two-dimensional spatial mapping of cat striate cortex using a 100-microelectrode array

Much of our understanding of the visuotopic organization of striate cortex results from single-electrode penetrations and serial recording of receptive field properties. However, the quality of these maps is limited by imprecision in quantifying electrode position, combining data from multiple laminae, and eye drift during the measurement of the receptive field properties. We have addressed these concerns by using an array of 100 closely spaced microelectrodes to investigate the two-dimensional visuotopic organization of layer IV in cat striate cortex. This array allowed simultaneous measurement of the receptive field properties of multiple single units on a regularly spaced grid. We found the relationship between cortical and visual space to be locally non-conformal: the receptive field locations associated with a closely spaced line of electrodes appeared randomly scattered in visual space. To quantify the scatter, we fitted a linear transformation of electrode sites onto the associated receptive field locations. We found that the distribution of the difference between the predicted receptive field location and the measured location had standard deviations of 0.59° and 0.45° in the horizontal and the vertical axes, respectively. Although individual receptive field positions differed from the predicted locations in a non-conformal sense, the trend across multiple receptive fields followed the maps described elsewhere. We found, on average, that the 13 mm 2 of cortex sampled by the array mapped onto a 5.8-degrees 2 region of visual space. From the scaling of this map and a combination of the statistics of the receptive field size (2.7±1.5 degrees 2) and scatter, we have explored the impact of electrode spacing on the completeness and redundancy in coverage of visual space sampled by an array. The simulation indicated an array with 1.2-mm spacing would completely sample the region of visual space addressed by the array. These results have implications for neuroprosthetic applications. Assuming phosphene organization resembles the visuotopic organization, remapping of visual space may be necessary to accommodate the scatter in phosphene locations.

The Gaussian derivative model for spatial-temporal vision: I. Cortical model.

How do we see the motion of objects as well as their shapes? The Gaussian Derivative (GD) spatial model is extended to time to help answer this question. The GD spatio-temporal model requires only two numbers to describe the complete three-dimensional space-time shapes of individual receptive fields in primate visual cortex. These two numbers are the derivative numbers along the respective spatial and temporal principal axes of a given receptive field. Nine transformation parameters allow for a standard geometric association of these intrinsic axes with the extrinsic environment. The GD spatio-temporal model describes in one framework the following properties of primate simple cell fields: motion properties, number of lobes in space-time, spatial orientation. location, and size. A discrete difference-of-offset-Gaussians (DOOG) model provides a plausible physiological mechanism to form GD-like model fields in both space and time. The GD model hypothesizes that receptive fields at the first stage of processing in the visual cortex approximate 'derivative analyzers' that estimate local spatial and temporal derivatives of the intensity profile in the visual environment. The receptive fields as modeled provide operators that can allow later stages of processing in either a biological or machine vision system to estimate the motion as well as the shapes of objects in the environment.

A possible basic cortical microcircuit called "cascaded inhibition." Results from cortical network models and recording experiments from striate simple cells.

The robust behavior, the degree of response linearity, and the aspect of contrast gain control in visual cortical simple cells are (amongst others) the result of the interplay between excitatory and inhibitory afferent and intracortical connections. The goal of this study was to suggest a simple intracortical connection pattern, which could also play a role in other cortical substructures, in order to generically obtain these desired effects within large physiological parameter ranges. To this end we explored the degree of linearity of spatial summation in visual simple cells experimentally and in different models based on half-wave rectifying cells ("push-pull models"). Visual cortical push-pull connection schemes originated from antagonistic motor-control models. Thus, this model class is widely applicable but normally requires a rather specific design. On the other hand we showed that a more generic version of a push-pull model, the so-called cascaded inhibitory intracortical connection scheme, which we implemented in a biologically realistic simulation, naturally explains much of the experimental data. We investigated the influence of the afferent and intracortical connection structure on the measured linearity of spatial summation in simple cells. The analysis made use of the relative modulation measure, which is easy to apply but is limited to moving sinusoidal grating stimuli. We introduced two basic push-pull models, where the order of threshold nonlinearity and linear summation is reversed. Very little difference is observed with the relative modulation measure for these models. Alterative models, like half-wave squaring models, were also briefly discussed. Of all model parameters, the ratio of excitation to inhibition in the simple cell exerts the most crucial influence on the relative modulation. Linearity deteriorates as soon as excitatory and inhibitory inputs are imbalanced and the relative modulation drops. This prediction was tested experimentally by extracellular recordings from cat area 17 simple cells and we found that about 62% showed a significant deviation from linear behavior. The problem that individual basic push-pull models are hard to distinguish experimentally led us to suggest a different solution. In order to generically account for the observed behavior (e.g., imbalance of excitation versus inhibition), we suggested a rather generic version of a push-pull model where it no longer mattered about (the hard-to-distinguish) fine differences in connectivity. Thus, we introduced a new class of biophysically realistic models ("cascaded inhibition"). This model class requires very little connection specificity and is therefore highly robust against parameter variations. Up to 25 cells are connected to each target cell. Thereby a highly interconnected network is generated, which also leads to disinhibition at some parts of an individual receptive field. We showed that the performance of these models simulates the degree of linearity and its variability in recal simple cells with comparatively high accuracy. This behavior can be explained by the self-regulating properties of a cascaded inhibitory connection scheme by which the balance between excitation and inhibition at a given cell is improved by the joint network effects. The virtues and the generic design of this connection pattern, therefore, allow to speculate that it is used also in other parts of the cortex.

Spatial invariance of visual receptive fields in parietal cortex neurons.

Spatial information is conveyed to the primary visual cortex in retinal coordinates. Movement trajectory programming, however, requires a transformation from this sensory frame of reference into a frame appropriate for the selected part of the body, such as the eye, head or arms. To achieve this transformation, visual information must be combined with information from other sources: for instance, the location of an object of interest can be defined with respect to the observer's head if the position of the eyes in the orbit is known and is added to the object's retinal coordinates. Here we show that in a subdivision of the monkey parietal lobe, the ventral intraparietal area (VIP), the activity of visual neurons is modulated by eye-position signals, as in many other areas of the cortical visual system. We find that individual receptive fields of a population of VIP neurons are organized along a continuum, from eye to head coordinates. In the latter case, neurons encode the azimuth and/or elevation of a visual stimulus, independently of the direction in which the eyes are looking, thus representing spatial locations explicitly in at least a head-centred frame of reference.

Learning retina implants with epiretinal contacts.

Retina implants are currently being developed by several interdisciplinary research consortia worldwide for blind humans with various retinal degenerative diseases. It is the aim of our retina implant project to develop a novel type of visual prosthesis to regain a moderate amount of vision such as perception of location and shape of large objects in the first stage and to approach reading quality in a subsequent stage. In our planned retina implant, a retina encoder (RE) outside the eye has to replace the information processing of the retina. A retina stimulator (RS), implanted adjacently to the retinal ganglion cell layer, has to contact a sufficient number of retinal ganglion cells/fibers for electrical elicitation of spikes. A wireless signal and energy transmission system has to provide the communication between the RE and RS. This paper outlines the retina implant project of our consortium of 14 expert groups and describes first results of the learning RE. The RE approximates the typical receptive field (RF) properties of primate retinal ganglion cells by means of individually tunable spatiotemporal RF filters. The RE as a cluster of RF filters maps visual patterns onto spike trains for a number of contacted ganglion cells. A concept is presented to train the individual RF filters in an unsupervised learning process, which employs neural networks in a dialog with the individual human subject. The desired aim of this dialog is an optimization of the visual perception by matching the various RF filter properties with those 'expected' by the central visual system for each contacted ganglion cell.

Analyzing phase transitions in high-dimensional self-organizing maps

The self-organizing map (SOM), a widely used algorithm for the unsupervised learning of neural maps, can be formulated in a low-dimensional ‘feature map’ variant which requires prespecified parameters (‘features’) for the description of receptive fields, or in a more general high-dimensional variant which allows self-organization of the structure of individual receptive fields as well as their arrangement in a map. We present here a new analytical method for deriving conditions for the emergence of structure in SOMs which is particularly suited for the as yet inaccessible high-dimensional SOM variant. Our approach is based on an evaluation of a map distortion function. It involves only an ansatz for the way stimuli are distributed among map neurons; the receptive fields of the map need not be known explicitly. Using this method we first calculate regions of stability for four possible states of SOMs projecting from a rectangular input space to a ring of neurons. We then analyze the transition from nonoriented to oriented receptive fields in a SOM-based model for the development of orientation maps. In both cases, the analytical results are well corroborated by the results of computer simulations.

Representation and integration of multiple sensory inputs in primate superior colliculus.

1. The properties of visual-, auditory-, and somatosensory-responsive neurons, as well as of neurons responsive to multiple sensory cues (i.e., multisensory), were examined in the superior colliculus of the rhesus monkey. Although superficial layer neurons responded exclusively to visual stimuli and visual inputs predominated in deeper layers, there was also a rich nonvisual and multisensory representation in the superior colliculus. More than a quarter (27.8%) of the deep layer population responded to stimuli from more than a single sensory modality. In contrast, 37% responded only to visual cues, 17.6% to auditory cues, and 17.6% to somatosensory cues. Unimodal- and multisensory-responsive neurons were clustered by modality. Each of these modalities was represented in map-like fashion, and the different representations were in alignment with one another. 2. Most deep layer visually responsive neurons were binocular and exhibited poor selectivity for such stimulus characteristics as orientation, velocity, and direction of movement. Similarly, most auditory-responsive neurons had contralateral receptive fields and were binaural, but had little frequency selectivity and preferred complex, broad-band sounds. Somatosensory-responsive neurons were overwhelmingly contralateral, high velocity, and rapidly adapting. Only rarely did somatosensory-responsive neurons require distortion of subcutaneous tissue for activation. 3. The spatial congruence among the different receptive fields of multisensory neurons was a critical feature underlying their ability to synthesize cross-modal information. 4. Combinations of stimuli could have very different consequences in the same neuron, depending on their temporal and spatial relationships. Generally, multisensory interactions were evident when pairs of stimuli were separated from one another by < 500 ms, and the products of these interactions far exceeded the sum of their unimodal components. Whether the combination of stimuli produced response enhancement, response depression, or no interaction depended on the location of the stimuli relative to one another and to their respective receptive fields. Maximal response enhancements were observed when stimuli originated from similar locations in space (as when derived from the same event) because they fell within the excitatory receptive fields of the same multisensory neurons. If, however, the stimuli were spatially disparate such that one fell beyond the excitatory borders of its receptive field, either no interaction was produced or this stimulus depressed the effectiveness of the other. Furthermore, maximal response interactions were seen with the pairing of weakly effective unimodal stimuli. As the individual unimodal stimuli became increasingly effective, the levels of response enhancement to stimulus combinations declined, a principle referred to as inverse effectiveness. Many of the integrative principles seen here in the primate superior colliculus are strikingly similar to those observed in the cat. These observations indicate that a set of common principles of multisensory integration is adaptable in widely divergent species living in very different ecological situations. 5. Surprisingly, a few multisensory neurons had individual receptive fields that were not in register with one another. This has not been noted in multisensory neurons of other species, and these "anomalous" receptive fields could present a daunting problem: stimuli originating from the same general location in space cannot simultaneously fall within their respective receptive fields, a stimulus pairing that may result in response depression. Conversely, stimuli that originate from separate events and disparate locations (and fall within their receptive fields) may result in response enhancement. However, the spatial principle of multisensory integration did not apply in these cases. (ABSTRACT TRUNCATED)

Spatial gradients and inhibitory summation in the rat whisker barrel system

1. Extracellular single-unit recordings and controlled whisker stimuli were used to compare response properties of cells in the barreloids of the ventral posterior medial nucleus of the thalamus and the barrels in the rat primary somatosensory cortex. Whiskers were deflected alone or in combinations involving up to four immediately adjacent whiskers to assess their relative inhibitory and excitatory contributions to individual receptive fields. Quantitative data were obtained from 51 thalamocortical units (TCUs), 79 "regular-spiking" barrel neurons (RSUs), and 5 "fast-spiking" barrel neurons (FSUs) in 28 normal female adult rats. 2. A random-noise generator was used to produce small, continuously varying whisker movements that were applied to one to four adjacent whiskers while the principal (columnar) whisker was displaced with the use of a ramp-and-hold deflection. RSUs displayed adjacent whisker-evoked inhibition that increased as the number of adjacent whiskers stimulated was incremented. Asymptotic levels of inhibition were reached with the application of the noise stimulus to two or three adjacent whiskers depending on which particular combinations were deflected. By contrast, TCUs and FSUs showed weak, or no, surround inhibition. 3. As the number of adjacent whiskers stimulated increased, the background (prestimulus) activity in TCUs and FSUs increased, whereas displayed background activity in RSUs was relatively unaffected. The increase in background activity observed in the FSUs is hypothesized to mediate adjacent whisker-evoked inhibition in the RSUs. 4. A spatial gradient of adjacent whisker inhibition was observed in RSUs. The caudally adjacent whisker evoked more inhibition than the rostrally adjacent whisker, and the ventral more than the dorsal. A cortical origin for the gradient is suggested by the finding that TCUs did not show a spatial inhibitory gradient. 5. As the noise stimulus was applied to an increasing number of adjacent whiskers, RSUs became more sharply tuned for deflection angles. Neither TCUs nor FSUs showed increases in angular tuning. 6. Inhibition worked disproportionately in RSUs to inhibit those responses that were initially the least robust. For example, inhibition was most effective at reducing responses to nonpreferred versus preferred whisker deflection angles. 7. To assess the principal whisker's effect on adjacent whisker excitatory responses, the noise stimulus was applied to the principal whisker. In RSUs, principal whisker-evoked inhibition was more potent than adjacent whisker-evoked inhibition. FSUs were excited to a greater extent by the application of the noise stimulus to the principal whisker than to adjacent whiskers. TCUs did not display principal whisker-evoked inhibition. 8. Inhibition within the barrel serves as a contrast enhancement mechanism to differentiate small versus large magnitude responses. Less vigorous responses, such as those associated with perturbations of noncolumnar whiskers and inputs from nonoptimal deflection angles, are more strongly suppressed. During active touch, when many whiskers simultaneously palpate an object, these inhibitory interactions could effectively increase the "principal whiskerness" of the cortical column.