Receptive Field Types Research Articles

Focus Affinity Perception and Super-Resolution Embedding for Multifocus Image Fusion.

Despite the fact that there is a remarkable achievement on multifocus image fusion, most of the existing methods only generate a low-resolution image if the given source images suffer from low resolution. Obviously, a naive strategy is to independently conduct image fusion and image super-resolution. However, this two-step approach would inevitably introduce and enlarge artifacts in the final result if the result from the first step meets artifacts. To address this problem, in this article, we propose a novel method to simultaneously achieve image fusion and super-resolution in one framework, avoiding step-by-step processing of fusion and super-resolution. Since a small receptive field can discriminate the focusing characteristics of pixels in detailed regions, while a large receptive field is more robust to pixels in smooth regions, a subnetwork is first proposed to compute the affinity of features under different types of receptive fields, efficiently increasing the discriminability of focused pixels. Simultaneously, in order to prevent from distortion, a gradient embedding-based super-resolution subnetwork is also proposed, in which the features from the shallow layer, the deep layer, and the gradient map are jointly taken into account, allowing us to get an upsampled image with high resolution. Compared with the existing methods, which implemented fusion and super-resolution independently, our proposed method directly achieves these two tasks in a parallel way, avoiding artifacts caused by the inferior output of image fusion or super-resolution. Experiments conducted on the real-world dataset substantiate the superiority of our proposed method compared with state of the arts.

Bipolar cell targeted optogenetic gene therapy restores parallel retinal signaling and high-level vision in the degenerated retina

Optogenetic gene therapies to restore vision are in clinical trials. Whilst current clinical approaches target the ganglion cells, the output neurons of the retina, new molecular tools enable efficient targeting of the first order retinal interneurons, the bipolar cells, with the potential to restore a higher quality of vision. Here we investigate retinal signaling and behavioral vision in blind mice treated with bipolar cell targeted optogenetic gene therapies. All tested tools, including medium-wave opsin, Opto-mGluR6, and two new melanopsin based chimeras restored visual acuity and contrast sensitivity. The best performing opsin was a melanopsin-mGluR6 chimera, which in some cases restored visual acuities and contrast sensitivities that match wild-type animals. Light responses from the ganglion cells were robust with diverse receptive-field types, inferring elaborate inner retinal signaling. Our results highlight the potential of bipolar cell targeted optogenetics to recover high-level vision in human patients with end-stage retinal degenerations.

Two Types of Auditory Spatial Receptive Fields in Different Parts of the Chicken's Midbrain.

The optic tectum (OT) is an avian midbrain structure involved in the integration of visual and auditory stimuli. Studies in the barn owl, an auditory specialist, have shown that spatial auditory information is topographically represented in the OT. Little is known about how auditory space is represented in the midbrain of birds with generalist hearing, i.e., most of avian species lacking peripheral adaptations such as facial ruffs or asymmetric ears. Thus, we conducted in vivo extracellular recordings of single neurons in the OT and in the external portion of the formatio reticularis lateralis (FRLx), a brain structure located between the inferior colliculus (IC) and the OT, in anaesthetized chickens of either sex. We found that most of the auditory spatial receptive fields (aSRFs) were spatially confined both in azimuth and elevation, divided into two main classes: round aSRFs, mainly present in the OT, and annular aSRFs, with a ring-like shape around the interaural axis, mainly present in the FRLx. Our data further indicate that interaural time difference (ITD) and interaural level difference (ILD) play a role in the formation of both aSRF classes. These results suggest that, unlike mammals and owls which have a congruent representation of visual and auditory space in the OT, generalist birds separate the computation of auditory space in two different midbrain structures. We hypothesize that the FRLx-annular aSRFs define the distance of a sound source from the axis of the lateral visual fovea, whereas the OT-round aSRFs are involved in multimodal integration of the stimulus around the lateral fovea.SIGNIFICANCE STATEMENT Previous studies implied that auditory spatial receptive fields (aSRFs) in the midbrain of generalist birds are only confined along azimuth. Interestingly, we found SRFs s in the chicken to be confined along both azimuth and elevation. Moreover, the auditory receptive fields are arranged in a concentric manner around the overlapping interaural and visual axes. These data suggest that in generalist birds, which mainly rely on vision, the auditory system mainly serves to align auditory stimuli with the visual axis, while auditory specialized birds like the barn owl compute sound sources more precisely and integrate sound positions in the multimodal space map of the optic tectum (OT).

Predicting visual perceivability of scene objects through spatio-temporal modeling of retinal receptive fields

Retinal processing of a visual scene is an essential step of human visual perception. Although foveal vision is linked to the visual attention, perception is by not means limited to this region. Rather, the retinal field of view ranges from 60° nasal to 107° temporal, and from 70° superior to 80° inferior. Whether a scene object is visually perceived depends on both its visual appearance as well as its retinal location. We present a framework to evaluate the visual stimulus of a scene object with regard to different types of retinal receptive fields. Driven by gaze location provided by an eye tracker, the estimated retinal response considers the visual appearance of the object, its eccentricity in the users field of view, and the capabilities at the local retinal region. A desktop experiment shows that, in additional to foveal processing, the estimated retinal response leads to a significant increase in classification accuracy in terms of whether an object is reported as perceived by the user.

Receptive Field Properties of Koniocellular On/Off Neurons in the Lateral Geniculate Nucleus of Marmoset Monkeys.

The koniocellular (K) layers of the primate dorsal lateral geniculate nucleus house a variety of visual receptive field types, not all of which have been fully characterized. Here we made single-cell recordings targeted to the K layers of diurnal New World monkeys (marmosets). A subset of recorded cells was excited by both increments and decrements of light intensity (on/off-cells). Histological reconstruction of the location of these cells confirmed that they are segregated to K layers; we therefore refer to these cells as K-on/off cells. The K-on/off cells show high contrast sensitivity, strong bandpass spatial frequency tuning, and their response magnitude is strongly reduced by stimuli larger than the excitatory receptive field (silent suppressive surrounds). Stationary counterphase gratings evoke unmodulated spike rate increases or frequency-doubled responses in K-on/off cells; such responses are largely independent of grating spatial phase. The K-on/off cells are not orientation or direction selective. Some (but not all) properties of K-on/off cells are consistent with those of local-edge-detector/impressed-by-contrast cells reported in studies of cat retina and geniculate, and broad-thorny ganglion cells recorded in macaque monkey retina. The receptive field properties of K-on/off cells and their preferential location in the ventral K layers (K1 and K2) make them good candidates for the direct projection from geniculate to extrastriate cortical area MT/V5. If so, they could contribute to visual information processing in the dorsal ("where" or "action") visual stream.SIGNIFICANCE STATEMENT We characterize cells in an evolutionary ancient part of the visual pathway in primates. The cells are located in the lateral geniculate nucleus (the main visual afferent relay nucleus), in regions called koniocellular layers that are known to project to extrastriate visual areas as well as primary visual cortex. The cells show high contrast sensitivity and rapid, transient responses to light onset and offset. Their properties suggest they could contribute to visual processing in the dorsal ("where" or "action") visual stream.

Response Properties of Pulvinar Neurons Studied with Single-Unit Electrophysiological Recordings.

In this chapter, we discuss the types of visual receptive fields revealed by single-unit electrophysiological recordings in the pulvinar. Nearly all neurons with identifiable receptive fields responded to visual stimulation presented on the contralateral hemifield, within 25° of the fovea. The majority of the visual neurons responded to some form of moving stimulus, and some additionally exhibited direction or orientation selectivity. Most units could be driven by monocular stimulation and showed receptive fields of at least 100 square degrees in area. Finally, most of the units recorded exhibited continuous peripheral receptive fields, even though a few of them could be bilaterally activated.

Categorically distinct types of receptive fields in early visual cortex.

In the visual cortex, distinct types of neurons have been identified based on cellular morphology, response to injected current, or expression of specific markers, but neurophysiological studies have revealed visual receptive field (RF) properties that appear to be on a continuum, with only two generally recognized classes: simple and complex. Most previous studies have characterized visual responses of neurons using stereotyped stimuli such as bars, gratings, or white noise and simple system identification approaches (e.g., reverse correlation). Here we estimate visual RF models of cortical neurons using visually rich natural image stimuli and regularized regression system identification methods and characterize their spatial tuning, temporal dynamics, spatiotemporal behavior, and spiking properties. We quantitatively demonstrate the existence of three functionally distinct categories of simple cells, distinguished by their degree of orientation selectivity (isotropic or oriented) and the nature of their output nonlinearity (expansive or compressive). In addition, these three types have differing average values of several other properties. Cells with nonoriented RFs tend to have smaller RFs, shorter response durations, no direction selectivity, and high reliability. Orientation-selective neurons with an expansive output nonlinearity have Gabor-like RFs, lower spontaneous activity and responsivity, and spiking responses with higher sparseness. Oriented RFs with a compressive nonlinearity are spatially nondescript and tend to show longer response latency. Our findings indicate multiple physiologically defined types of RFs beyond the simple/complex dichotomy, suggesting that cortical neurons may have more specialized functional roles rather than lying on a multidimensional continuum.

Modeling development in retinal afferents: retinotopy, segregation, and ephrinA/EphA mutants.

During neural development, neurons extend axons to target areas of the brain. Through processes of growth, branching and retraction these axons establish stereotypic patterns of connectivity. In the visual system, these patterns include retinotopic organization and the segregation of individual axons onto different subsets of target neurons based on the eye of origin (ocular dominance) or receptive field type (ON or OFF). Characteristic disruptions to these patterns occur when neural activity or guidance molecule expression is perturbed. In this paper we present a model that explains how these developmental patterns might emerge as a result of the coordinated growth and retraction of individual axons and synapses responding to position-specific markers, trophic factors and spontaneous neural activity. This model derives from one presented earlier (Godfrey et al., 2009) but which is here extended to account for a wider range of phenomena than previously described. These include ocular dominance and ON-OFF segregation and the results of altered ephrinA and EphA guidance molecule expression. The model takes into account molecular guidance factors, realistic patterns of spontaneous retinal wave activity, trophic molecules, homeostatic mechanisms, axon branching and retraction rules and intra-axonal signaling mechanisms that contribute to the survival of nearby synapses on an axon. We show that, collectively, these mechanisms can account for a wider range of phenomena than previous models of retino-tectal development.

Vector model for mapping of visual space to subjective 4-D sphere

Here we present a mathematical model of binocular vision that maps a visible physical world to a subjective perception of it. The subjective space is a set of 4-D vectors whose components are outputs of four monocular neurons from each of the two eyes. Monocular neurons have one of the four types of concentric receptive fields with Gabor-like weighting coefficients. Next this vector representation of binocular vision is implemented as a pool of neurons where each of them is selective to the object's particular location in a 3-D visual space. Formally each point of the visual space is being projected onto a 4-D sphere. Proposed model allows determination of subjective distances in depth and direction, provides computational means for determination of Panum's area and explains diplopia and allelotropia.

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.

Classification of frequency response areas in the inferior colliculus reveals continua not discrete classes.

A differential response to sound frequency is a fundamental property of auditory neurons. Frequency analysis in the cochlea gives rise to V-shaped tuning functions in auditory nerve fibres, but by the level of the inferior colliculus (IC), the midbrain nucleus of the auditory pathway, neuronal receptive fields display diverse shapes that reflect the interplay of excitation and inhibition. The origin and nature of these frequency receptive field types is still open to question. One proposed hypothesis is that the frequency response class of any given neuron in the IC is predominantly inherited from one of three major afferent pathways projecting to the IC, giving rise to three distinct receptive field classes. Here, we applied subjective classification, principal component analysis, cluster analysis, and other objective statistical measures, to a large population (2826) of frequency response areas from single neurons recorded in the IC of the anaesthetised guinea pig. Subjectively, we recognised seven frequency response classes (V-shaped, non-monotonic Vs, narrow, closed, tilt down, tilt up and double-peaked), that were represented at all frequencies. We could identify similar classes using our objective classification tools. Importantly, however, many neurons exhibited properties intermediate between these classes, and none of the objective methods used here showed evidence of discrete response classes. Thus receptive field shapes in the IC form continua rather than discrete classes, a finding consistent with the integration of afferent inputs in the generation of frequency response areas. The frequency disposition of inhibition in the response areas of some neurons suggests that across-frequency inputs originating at or below the level of the IC are involved in their generation.

Receptive Field Properties of Color Opponent Neurons in the Cat Lateral Geniculate Nucleus

Most nonprimate mammals possess dichromatic ("red-green color blind") color vision based on short-wavelength-sensitive (S) and medium/long-wavelength-sensitive (ML) cone photoreceptor classes. However, the neural pathways carrying signals underlying the primitive "blue-yellow" axis of color vision in nonprimate mammals are largely unexplored. Here, we have characterized a population of color opponent (blue-ON) cells in recordings from the dorsal lateral geniculate nucleus of anesthetized cats. We found five points of similarity to previous descriptions of primate blue-ON cells. First, cat blue-ON cells receive ON-type excitation from S-cones, and OFF-type excitation from ML-cones. We found no blue-OFF cells. Second, the S- and ML-cone-driven receptive field regions of cat blue-ON cells are closely matched in size, consistent with specialization for detecting color contrast. Third, the receptive field center diameter of cat blue-ON cells is approximately three times larger than the center diameter of non-color opponent receptive fields at any eccentricity. Fourth, S- and ML-cones contribute weak surround inhibition to cat blue-ON cells. These data show that blue-ON receptive fields in cats are functionally very similar to blue-ON type receptive fields previously described in macaque and marmoset monkeys. Finally, cat blue-ON cells are found in the same layers as W-cells, which are thought to be homologous to the primate koniocellular system. Based on these data, we suggest that cat blue-ON cells are part of a "blue-yellow" color opponent system that is the evolutionary homolog of the blue-ON division of the koniocellular pathway in primates.

Composed complex-cue histograms: An investigation of the information content in receptive field based image descriptors for object recognition

Recent work has shown that effective methods for recognizing objects and spatio-temporal events can be constructed based on histograms of receptive field like image operations.This paper presents the results of an extensive study of the performance of different types of receptive field like image descriptors for histogram-based object recognition, based on different combinations of image cues in terms of Gaussian derivatives or differential invariants applied to either intensity information, color-opponent channels or both. A rich set of composed complex-cue image descriptors is introduced and evaluated with respect to the problems of (i) recognizing previously seen object instances from previously unseen views, and (ii) classifying previously unseen objects into visual categories.It is shown that there exist novel histogram descriptors with significantly better recognition performance compared to previously used histogram features within the same class. Specifically, the experiments show that it is possible to obtain more discriminative features by combining lower-dimensional scale-space features into composed complex-cue histograms. Furthermore, different types of image descriptors have different relative advantages with respect to the problems of object instance recognition vs. object category classification. These conclusions are obtained from extensive evaluations on two mutually independent data sets.For the task of recognizing specific object instances, combined histograms of spatial and spatio-chromatic derivatives are highly discriminative, and several image descriptors in terms rotationally invariant (intensity and spatio-chromatic) differential invariants up to order two lead to very high recognition rates. For category classification, primary information is contained in both first-and second-order derivatives, where second-order partial derivatives constitute the most discriminative cue.Dimensionality reduction by principal component analysis and variance normalization prior to training and recognition can in many cases lead to a significant increase in recognition or classification performance. Surprisingly high recognition rates can even be obtained with binary histograms that reveal the polarity of local scale-space features, and which can be expected to be particularly robust to illumination variations.An overall conclusion from this study is that compared to previously used lower-dimensional histograms, the use of composed complex-cue histograms of higher dimensionality reveals the co-variation of multiple cues and enables much better recognition performance, both with regard to the problems of recognizing previously seen objects from novel views and for classifying previously unseen objects into visual categories.

Organized Representation of Spectrotemporal Features in Songbird Auditory Forebrain

Much of our understanding of the functional organization of auditory cortex is based on relatively simple stimuli, and the cortical representation of complex sounds remains poorly understood. With their rich vocal communication and learning behaviors, songbirds can offer insights into the neural processing of complex acoustic signals analogous to human speech. In the primary forebrain auditory area field L (primary auditory cortex analog) of zebra finches, previous studies identified a limited set of spectrotemporal receptive field (STRF) types, but whether these were spatially organized remained unclear. Here, we investigated the spatial organization of field L in multiple anatomical planes, using multielectrode array recordings and a stimulus that captures aspects of spectrotemporal modulations of song. Our data demonstrate two separate axes along which spectrotemporal aspects of sound are mapped: width of spectral tuning changes systematically from narrow to broad mediolaterally, whereas width of temporal tuning changes markedly, from narrow to broad, from the input to the output layers. These spatial patterns, which are evident in STRFs computed from both multiple and single units, result in a mapping of basic STRF types to subregions of field L. This highly organized representation of spectrotemporal features of sound contrasts with current views of mammalian auditory cortex, where no strong spatial organization of STRF shapes has been seen thus far. Our data identify a coherent initial cortical representation of sound features and suggest that forebrain filtering for basic perceptual qualities of sound occurs in a spatially organized and segregated manner in the songbird auditory system.

Receptive Field Inference with Localized Priors

The linear receptive field describes a mapping from sensory stimuli to a one-dimensional variable governing a neuron's spike response. However, traditional receptive field estimators such as the spike-triggered average converge slowly and often require large amounts of data. Bayesian methods seek to overcome this problem by biasing estimates towards solutions that are more likely a priori, typically those with small, smooth, or sparse coefficients. Here we introduce a novel Bayesian receptive field estimator designed to incorporate locality, a powerful form of prior information about receptive field structure. The key to our approach is a hierarchical receptive field model that flexibly adapts to localized structure in both spacetime and spatiotemporal frequency, using an inference method known as empirical Bayes. We refer to our method as automatic locality determination (ALD), and show that it can accurately recover various types of smooth, sparse, and localized receptive fields. We apply ALD to neural data from retinal ganglion cells and V1 simple cells, and find it achieves error rates several times lower than standard estimators. Thus, estimates of comparable accuracy can be achieved with substantially less data. Finally, we introduce a computationally efficient Markov Chain Monte Carlo (MCMC) algorithm for fully Bayesian inference under the ALD prior, yielding accurate Bayesian confidence intervals for small or noisy datasets.

An information integration model of the primary visual cortex under grating stimulations

During the course of information processing, a visual system extracts characteristic information of the visual image and integrates the spatial and temporal visual information simultaneously. In this study, we investigate the integration effect of neurons in the primary visual cortex (V1 area) under the grating stimulation. First, an information integration model was established based on the receptive field properties of the extracted features of the visual images features, the interaction between neurons and the nonlinear integration of those neurons. Then the neuropsychological experiments were designed both to provide parameters for the model and to verify its effect. The experimental results with factual visual image were largely consistent with the model’s forecast output. This demonstrates that our model can truly reflect the integration effect of the primary visual system when being subjected to grating stimulations with different orientations. Our results indicate the primary visual system integrates the visual information in the following manner: it first extracts visual information through different types of receptive field, and then its neurons interact with each other in a non-linear manner, finally the neurons fire spikes recorded as responses to the visual stimulus.

Complex cell receptive fields: evidence for a hierarchical mechanism

Simple cells in the primary visual cortex have segregated ON and OFF subregions in their receptive fields, while complex cells have overlapping ON and OFF subregions. These two cell types form the extremes at each end of a continuum of receptive field types. Hubel and Wiesel in 1962 suggested a hierarchical scheme of processing whereby spatially offset simple cells drive complex cells. Simple and complex cells are often classified by their responses to moving sine wave gratings: simple cells have oscillatory responses while complex cells produce unmodulated responses. Here, using moving gratings as stimuli, we show that a significant number of cells that display low levels of response modulation at high contrasts demonstrate high levels of response modulation at low contrasts. Most often a drifting low contrast grating generates a large phasic response at the fundamental frequency of the grating (F(1)) and a smaller but significant phasic response that is approximately 180 deg out-of-phase with the F(1) component. We present several models capable of capturing the effects of stimulus contrast on complex cell responses. The model that best reproduces our experimental results is a variation of the classical hierarchical model. In our model several spatially offset simple cells provide input to a complex cell, with each simple cell exhibiting a different contrast response function. At low contrasts only one of these simple cells is sufficiently excited to reveal its receptive field properties. As contrast is increased additional spatially offset simple cells with higher contrast thresholds add their responses to the overall spiking activity.

A model for learning color selective receptive cells from natural scenes

Event Abstract Back to Event A model for learning color selective receptive cells from natural scenes Jan Wiltschut1*, Martina Truschzinski2, Thorsten Hansen3 and Fred H. Hamker2 1 Westfälische Wilhelms-Universität, Department of Psychology, Germany 2 University of Technology, Department of Artificial Intelligence, Germany 3 Justus-Liebig-University, Department of General and Experimental Psychology, Germany Different from the standard view of color perception that proposes largely different pathways for color and shape perception, recently it has been discovered that in the primary visual cortex, color and shape are not processed apart from one another. Electrophysiological studies suggest that cells do not only respond to stimuli of a certain orientation or shape but at the same time they can be color selective [1]. Different receptive field types have been reported: color-responsive single-opponent cells, color-responsive double-opponent cells (circular and orientated) and non-color-responsive cells [1]. We here show that such receptive fields can emerge from Hebbian learning when presenting colored natural scenes to a model of V1 that has previously been proven to learn “edge-detecting” receptive fields (RFs) out of gray-scale images similar to those of primary visual cortex of macaque monkey [2]. As a first step we simulate color receptive (L-, M- and S-) cones of the lateral geniculate nucleus (LGN) [4]. Second, the LGN output is fed into our algorithm of learning. The Figure shows the receptive fields of miscellaneous categorized cells and their responses to changes in orientation and spatial frequency of the underlying (fitted) Gabor-filter. The learnt RFs are either only responsive to stimuli of a certain color, a specific achromatic edge or to stimuli with different colors and orientations. In correspondence to the literature [1,3], the cells are categorized by means of their orientation- and frequency-selectivity as well as their response to different colors. Figure: Presentation of examples for a non-color-responsive, a single-opponent and a double-opponent cell. For each cell the following is shown: the RF (left) and the tuning characteristic to orientation (middle) and frequency (right). The green line in the tuning characteristics denotes the response to the original stimuli, the red line the response to the corresponding chromatic stimuli and black to the corresponding luminance stimuli. Figure 1 References [1] EN Johnson, MJ Hawken und R Shapley: The Orientation Selectivity of Color-Responsive Neurons in Macaque V1, J Neurosci, 28: 8096-8106, 2008. [2]J Wiltschut and FH Hamker: Efficient coding correlates with spatial frequency tuning in a model of V1 receptive field organization, Vis Neurosci, 26: 21-34, 2009 [3]EN Johnson, MJ Hawken und R Shapley: Cone Inputs in Macaque Primary Visual Cortex, J Neurophysiol, 92: 2501-2514, 2004. [4] R Clay Reid and RM Shapley: Space and Time Maps of Cone Photoreceptor Signals in Macaque Lateral Geniculate Nucleus, J Neurosci, 22: 6158-6175, 2002 Learning*; Information Processing; Modelling; Neural Dynamics; Neural Encoding; Sensory Processing Keywords: Information Processing, Learning*, modelling, neural dynamics, Neural Encoding, sensory processing Conference: Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010. Presentation Type: Presentation Topic: Bernstein Conference on Computational Neuroscience Citation: Wiltschut J, Truschzinski M, Hansen T and Hamker FH (2010). A model for learning color selective receptive cells from natural scenes. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00122 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 31 Aug 2010; Published Online: 23 Sep 2010. * Correspondence: Dr. Jan Wiltschut, Westfälische Wilhelms-Universität, Department of Psychology, Münster, Germany, wiltschj@uni-muenster.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Jan Wiltschut Martina Truschzinski Thorsten Hansen Fred H Hamker Google Jan Wiltschut Martina Truschzinski Thorsten Hansen Fred H Hamker Google Scholar Jan Wiltschut Martina Truschzinski Thorsten Hansen Fred H Hamker PubMed Jan Wiltschut Martina Truschzinski Thorsten Hansen Fred H Hamker Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

The firing statistics of Poisson neuron models driven by slow stimuli

The coding properties of cells with different types of receptive fields have been studied for decades. ON-type neurons fire in response to positive fluctuations of the time-dependent stimulus, whereas OFF cells are driven by negative stimulus segments. Biphasic cells, in turn, are selective to up/down or down/up stimulus upstrokes. In this article, we explore the way in which different receptive fields affect the firing statistics of Poisson neuron models, when driven with slow stimuli. We find analytical expressions for the time-dependent peri-stimulus time histogram and the inter-spike interval distribution in terms of the incoming signal. Our results enable us to understand the interplay between the intrinsic and extrinsic factors that regulate the statistics of spike trains. The former depend on biophysical neural properties, whereas the latter hinge on the temporal characteristics of the input signal.

Dynamics of orientation tuning in cat V1 neurons depend on location within layers and orientation maps

Analysis of the timecourse of the orientation tuning of responses in primary visual cortex (V1) can provide insight into the circuitry underlying tuning. Several studies have examined the temporal evolution of orientation selectivity in V1 neurons, but there is no consensus regarding the stability of orientation tuning properties over the timecourse of the response. We have used reverse-correlation analysis of the responses to dynamic grating stimuli to re-examine this issue in cat V1 neurons. We find that the preferred orientation and tuning curve shape are stable in the majority of neurons; however, more than forty percent of cells show a significant change in either preferred orientation or tuning width between early and late portions of the response. To examine the influence of the local cortical circuit connectivity, we analyzed the timecourse of responses as a function of receptive field type, laminar position, and orientation map position. Simple cells are more selective, and reach peak selectivity earlier, than complex cells. There are pronounced laminar differences in the timing of responses: middle layer cells respond faster, deep layer cells have prolonged response decay, and superficial cells are intermediate in timing. The average timing of neurons near and far from pinwheel centers is similar, but there is more variability in the timecourse of responses near pinwheel centers. This result was reproduced in an established network model of V1 operating in a regime of balanced excitatory and inhibitory recurrent connections, confirming previous results. Thus, response dynamics of cortical neurons reflect circuitry based on both vertical and horizontal location within cortical networks.