Contextual modulation of V1 receptive fields depends on their spatial symmetry

The apparent receptive field characteristics of sensory neurons depend on the statistics of the stimulus ensemble—a nonlinear phenomenon often called contextual modulation. Since visual cortical receptive fields determined from simple stimuli typically do not predict responses to complex stimuli, understanding contextual modulation is crucial to understanding responses to natural scenes. To analyze contextual modulation, we examined how apparent receptive fields differ for two stimulus ensembles that are matched in first- and second-order statistics, but differ in their feature content: one ensemble is enriched in elongated contours. To identify systematic trends across the neural population, we used a multidimensional scaling method, the Procrustes transformation. We found that contextual modulation of receptive field components increases with their spatial extent. More surprisingly, we also found that odd-symmetric components change systematically, but even-symmetric components do not. This symmetry dependence suggests that contextual modulation is driven by oriented On/Off dyads, i.e., modulation of the strength of intracortically-generated signals. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Temporal pattern discrimination within the receptive field of cat retinal ganglion cells

ON-center and OFF-center receptive fields of cat retinal ganglion cells can be divided into two categories: sensitive (type N) and insensitive (type L) to three statistical temporal visual stimuli with different second order statistics but identical first order statistics (Tsukada et al. 1982). The temporal pattern sensitivity of type N response is closely related to the nonlinear stage of Y cells depending on the interaction between center and surround mechanism. The temporal pattern sensitivity of type N responses has a spatial profile within the receptive field; it is highly sensitive in the center region of the receptive field and less sensitive toward the field periphery. The temporal pattern sensitivity in the center region of the receptive field to statistical properties (irregular or regular) of a surrounding flash annulus shows modulation like a switching element: when the surrounding area is stimulated by a more regular flash stimulus with normal distribution of inter-stimulus intervals the system is sensitive (switching on) to the temporal pattern, while a change to an irregular one with an exponential distribution makes it insensitive (switching off) to the temporal pattern.     

Sparse Coding Models Can Exhibit Decreasing Sparseness while Learning Sparse Codes for Natural Images

The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive fields are optimized to accurately represent input stimuli using the least amount of neural activity. As these networks develop to represent a given class of stimulus, the receptive fields are refined so that they capture the most important stimulus features. Intuitively, this is expected to result in sparser network activity over time. Recent experiments, however, show that stimulus-evoked activity in ferret V1 becomes less sparse during development, presenting an apparent challenge to the sparse coding hypothesis. Here we demonstrate that some sparse coding models, such as those employing homeostatic mechanisms on neural firing rates, can exhibit decreasing sparseness during learning, while still achieving good agreement with mature V1 receptive field shapes and a reasonably sparse mature network state. We conclude that observed developmental trends do not rule out sparseness as a principle of neural coding per se: a mature network can perform sparse coding even if sparseness decreases somewhat during development. To make comparisons between model and physiological receptive fields, we introduce a new nonparametric method for comparing receptive field shapes using image registration techniques.     

Sensory and behavioral properties of neurons in posterior parietal cortex of the awake, trained monkey.

Neurons in posterior parietal cortex of the awake, trained monkey respond to passive visual and/or somatosensory stimuli. In general, the receptive fields of these cells are large and nonspecific. When these neurons are studied during visually guided hand movements and eye movements, most of their activity can be accounted for by passive sensory stimulation. However, for some visual cells, the response to a stimulus is enhanced when it is to be the target for a saccadic eye movement. This enhancement is selective for eye movements into the visual receptive field since it does not occur with eye movements to other parts of the visual field. Cells that discharge in association with a visual fixation task have foveal receptive fields and respond to the spots of light used as fixation targets. Cells discharging selectively in association with different directions of tracking eye movements have directionally selective responses to moving visual stimuli. Every cell in our sample discharging in association with movement could be driven by passive sensory stimuli. We conclude that the activity of neurons in posterior parietal cortex is dependent on and indicative of external stimuli but not predictive of movement.     

Receptive field properties of neurons in the electrosensory lateral line lobe of the weakly electric fish, <Emphasis Type="Italic">Gnathonemus petersii</Emphasis>

The receptive field of a sensory neuron is known as that region in sensory space where a stimulus will alter the response of the neuron. We determined the spatial dimensions and the shape of receptive fields of electrosensitive neurons in the medial zone of the electrosensory lateral line lobe of the African weakly electric fish, Gnathonemus petersii, by using single cell recordings. The medial zone receives input from sensory cells which encode the stimulus amplitude. We analysed the receptive fields of 71 neurons. The size and shape of the receptive fields were determined as a function of spike rate and first spike latency and showed differences for the two analysis methods used. Spatial diameters ranged from 2 to 36 mm (spike rate) and from 2.45 to 14.12 mm (first spike latency). Some of the receptive fields were simple consisting only of one uniform centre, whereas most receptive fields showed a complex and antagonistic centre-surround organisation. Several units had a very complex structure with multiple centres and surrounding-areas. While receptive field size did not correlate with peripheral receptor location, the complexity of the receptive fields increased from rostral to caudal along the fish's body.     

Pattern-dependent response modulations in motion-sensitive visual interneurons--a model study

Even if a stimulus pattern moves at a constant velocity across the receptive field of motion-sensitive neurons, such as lobula plate tangential cells (LPTCs) of flies, the response amplitude modulates over time. The amplitude of these response modulations is related to local pattern properties of the moving retinal image. On the one hand, pattern-dependent response modulations have previously been interpreted as 'pattern-noise', because they deteriorate the neuron's ability to provide unambiguous velocity information. On the other hand, these modulations might also provide the system with valuable information about the textural properties of the environment. We analyzed the influence of the size and shape of receptive fields by simulations of four versions of LPTC models consisting of arrays of elementary motion detectors of the correlation type (EMDs). These models have previously been suggested to account for many aspects of LPTC response properties. Pattern-dependent response modulations decrease with an increasing number of EMDs included in the receptive field of the LPTC models, since spatial changes within the visual field are smoothed out by the summation of spatially displaced EMD responses. This effect depends on the shape of the receptive field, being the more pronounced--for a given total size--the more elongated the receptive field is along the direction of motion. Large elongated receptive fields improve the quality of velocity signals. However, if motion signals need to be localized the velocity coding is only poor but the signal provides--potentially useful--local pattern information. These modelling results suggest that motion vision by correlation type movement detectors is subject to uncertainty: you cannot obtain both an unambiguous and a localized velocity signal from the output of a single cell. Hence, the size and shape of receptive fields of motion sensitive neurons should be matched to their potential computational task.     

Simple model for encoding natural images by retinal ganglion cells with nonlinear spatial integration

A central goal in sensory neuroscience is to understand the neuronal signal processing involved in the encoding of natural stimuli. A critical step towards this goal is the development of successful computational encoding models. For ganglion cells in the vertebrate retina, the development of satisfactory models for responses to natural visual scenes is an ongoing challenge. Standard models typically apply linear integration of visual stimuli over space, yet many ganglion cells are known to show nonlinear spatial integration, in particular when stimulated with contrast-reversing gratings. We here study the influence of spatial nonlinearities in the encoding of natural images by ganglion cells, using multielectrode-array recordings from isolated salamander and mouse retinas. We assess how responses to natural images depend on first- and second-order statistics of spatial patterns inside the receptive field. This leads us to a simple extension of current standard ganglion cell models. We show that taking not only the weighted average of light intensity inside the receptive field into account but also its variance over space can partly account for nonlinear integration and substantially improve response predictions of responses to novel images. For salamander ganglion cells, we find that response predictions for cell classes with large receptive fields profit most from including spatial contrast information. Finally, we demonstrate how this model framework can be used to assess the spatial scale of nonlinear integration. Our results underscore that nonlinear spatial stimulus integration translates to stimulation with natural images. Furthermore, the introduced model framework provides a simple, yet powerful extension of standard models and may serve as a benchmark for the development of more detailed models of the nonlinear structure of receptive fields.     

Cooperative nonlinearities in auditory cortical neurons

Cortical receptive fields represent the signal preferences of sensory neurons. Receptive fields are thought to provide a representation of sensory experience from which the cerebral cortex may make interpretations. While it is essential to determine a neuron's receptive field, it remains unclear which features of the acoustic environment are specifically represented by neurons in the primary auditory cortex (AI). We characterized cat AI spectrotemporal receptive fields (STRFs) by finding both the spike-triggered average (STA) and stimulus dimensions that maximized the mutual information between response and stimulus. We derived a nonlinearity relating spiking to stimulus projection onto two maximally informative dimensions (MIDs). The STA was highly correlated with the first MID. Generally, the nonlinearity for the first MID was asymmetric and often monotonic in shape, while the second MID nonlinearity was symmetric and nonmonotonic. The joint nonlinearity for both MIDs revealed that most first and second MIDs were synergistic and thus should be considered conjointly. The difference between the nonlinearities suggests different possible roles for the MIDs in auditory processing.     

Receptive field organization of electrosensory neurons in the paddlefish (Polyodon spathula)

Paddlefish use their electrosense to locate small water fleas (daphnia), their primary prey, in three-dimensional space. High sensitivity and a representation of object location are essential for this task. High sensitivity can be achieved by convergence of information from a large number of receptors and object location is usually represented in the nervous system by topographic maps. However the first electrosensory center in the brain, the dorsal octavolateral nucleus in the hindbrain, is neither topographically organized nor does it show a higher sensitivity than primary afferent fibers. Here, we investigated the response properties of electrosensory neurons in the dorsal octavolateral nucleus (DON), the lateral mesencephalic nucleus (LMN) and the tectum mesencephali (TM). LMN units are characterized by large receptive fields, which suggest a high degree of convergence. TM units have small receptive fields and are topographically arranged, at least in the rostro-caudal axis, the only dimension we could test. Well-defined receptive fields, however, could only be detected in the TM with a moving DC stimulus. The receptive fields of TM units, as determined by slowly scanning the rostrum and head with a 5 Hz stimulus, were very large and frequently two or more receptive fields were present. The receptive fields for LMN units were located in the anterior half of the rostrum whereas TM units had receptive fields predominantly on the head and at the base of the rostrum. A detailed analysis of the prey catching behavior revealed that it consists of two phases that coincide with the location of the receptive fields in LMN and TM, respectively. This suggests that LMN units are responsible for the initial orienting response that occurs when the prey is alongside the anterior first half of the rostrum. TM units, in contrast, had receptive fields at locations where the prey is located when the fish opens its mouth and attempts the final strike.     

Are V1 Simple Cells Optimized for Visual Occlusions? A Comparative Study

Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex.     

Visual analysis in unspecialized receptive fields as an orthogonal series expansion

It was postulated on the basis of results obtained by investigating retinal receptive fields of cats that experimental data reflecting the pattern of sensitivity of the receptive field coincide with certain orthogonal polynomials. These special functions have certain properties which can be regarded as spatial characteristics of the receptive fields whose differential sensitivity they describe. Within the framework of the suggested hypothesis visual analysis in the retina from the mathematical point of view can be regarded as a process of expansion of the curve of spatial distribution of brightness within the receptive fields in an orthogonal series. Physiologically speaking the retinal receptive fields can be interpreted as filters distinguishing an elementary orthogonal harmonic from the signal. This method of analysis results in maximal economy of the coding of visual information in the retina. The concepts put forward provide a fresh explanation of a number of physiological facts and they provide precise quantitative evaluations of the response of the receptive field to an arbitrary stimulus.     

15th Annual spring brain conference,march 10–13, 2004,summary report

The present study was designed (1) to characterize the subliminal responses of dorsal horn neurons to stimulation of the sural nerve, and (2) to correlate the type of response to this stimulus with the responses to natural mechanical stimulation of the skin. To accomplish this, intracellular and extracellular recordings were carried out in L⁶ and L⁷ dorsal horn neurons in the cat. The excitatory responses of each cell to electrical stimulation of the sural nerve and to mechanical stimulation of the skin were noted.

Of 35 dorsal horn cells recorded intracellularly, 11 responded with impulses to sural nerve stimulation, 9 responded with excitatory postsynaptic potentials (EPSPs) but not impulses, and 15 had no excitatory responses to this stimulus. The type of response to sural nerve stimulation was strongly correlated with receptive field modality. Most cells receiving an input from high-threshold cutaneous mechanoreceptors responded with impulses or gave no excitatory response to sural nerve stimulation, whereas most cells that had only low-threshold mechanoreceptor input responded with EPSPs only or gave no response. In cells with only low-threshold (LT) mechanoreceptive input, response to sural nerve stimulation was highly correlated with receptive field locus. Those LT cells with no excitatory responses to sural nerve stimulation had receptive fields confined to the foot and/or toes, whereas those that gave EPSPs had more proximal receptive fields. The possible significance of these data with reference to changes observed after lesions, such as increased response to sural nerve stimulation, increased receptive field size, and somatotopic reorganization, is discussed.     

Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus

Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain.     

Evaluating Population Receptive Field Estimation Frameworks in Terms of Robustness and Reproducibility

Within vision research retinotopic mapping and the more general receptive field estimation approach constitute not only an active field of research in itself but also underlie a plethora of interesting applications. This necessitates not only good estimation of population receptive fields (pRFs) but also that these receptive fields are consistent across time rather than dynamically changing. It is therefore of interest to maximize the accuracy with which population receptive fields can be estimated in a functional magnetic resonance imaging (fMRI) setting. This, in turn, requires an adequate estimation framework providing the data for population receptive field mapping. More specifically, adequate decisions with regard to stimulus choice and mode of presentation need to be made. Additionally, it needs to be evaluated whether the stimulation protocol should entail mean luminance periods and whether it is advantageous to average the blood oxygenation level dependent (BOLD) signal across stimulus cycles or not. By systematically studying the effects of these decisions on pRF estimates in an empirical as well as simulation setting we come to the conclusion that a bar stimulus presented at random positions and interspersed with mean luminance periods is generally most favorable. Finally, using this optimal estimation framework we furthermore tested the assumption of temporal consistency of population receptive fields. We show that the estimation of pRFs from two temporally separated sessions leads to highly similar pRF parameters.     

Relationship between Size Summation Properties,Contrast Sensitivity and Response Latency in the Dorsomedial and Middle Temporal Areas of the Primate Extrastriate Cortex

Analysis of the physiological properties of single neurons in visual cortex has demonstrated that both the extent of their receptive fields and the latency of their responses depend on stimulus contrast. Here, we explore the question of whether there are also systematic relationships between these response properties across different cells in a neuronal population. Single unit recordings were obtained from the middle temporal (MT) and dorsomedial (DM) extrastriate areas of anaesthetized marmoset monkeys. For each cell, spatial integration properties (length and width summation, as well as the presence of end- and side-inhibition within 15° of the receptive field centre) were determined using gratings of optimal direction of motion and spatial and temporal frequencies, at 60% contrast. Following this, contrast sensitivity was assessed using gratings of near-optimal length and width. In both areas, we found a relationship between spatial integration and contrast sensitivity properties: cells that summated over smaller areas of the visual field, and cells that displayed response inhibition at larger stimulus sizes, tended to show higher contrast sensitivity. In a sample of MT neurons, we found that cells showing longer latency responses also tended to summate over larger expanses of visual space in comparison with neurons that had shorter latencies. In addition, longer-latency neurons also tended to show less obvious surround inhibition. Interestingly, all of these effects were stronger and more consistent with respect to the selectivity for stimulus width and strength of side-inhibition than for length selectivity and end-inhibition. The results are partially consistent with a hierarchical model whereby more extensive receptive fields require convergence of information from larger pools of “feedforward” afferent neurons to reach near-optimal responses. They also suggest that a common gain normalization mechanism within MT and DM is involved, the spatial extent of which is more evident along the cell’s preferred axis of motion.     

Independent component filters of natural images compared with simple cells in primary visual cortex.

Properties of the receptive fields of simple cells in macaque cortex were compared with properties of independent component filters generated by independent component analysis (ICA) on a large set of natural images. Histograms of spatial frequency bandwidth, orientation tuning bandwidth, aspect ratio and length of the receptive fields match well. This indicates that simple cells are well tuned to the expected statistics of natural stimuli. There is no match, however, in calculated and measured distributions for the peak of the spatial frequency response: the filters produced by ICA do not vary their spatial scale as much as simple cells do, but are fixed to scales close to the finest ones allowed by the sampling lattice. Possible ways to resolve this discrepancy are discussed.     

Lack of homogeneity of receptive fields of visual neurons in the cortical area 18 of the cat

The receptive fields of complex neurons within area 18 of the cerebral cortex of the cat were determined by a computer-assisted method using a moving light bar substantially shorter than the long diameter of the receptive field as a visual stimulus. The visual cells repeatedly generated nerve impulses when the stimulus crossed well-defined active points within their receptive fields. Outside of these active points, the cells remained silent. It is suggested that the receptive fields are formed by a discontinuous accumulation of such active points. When the electrical activities of two neighbouring visual neurons are recorded simultaneously, their active points do not coincide. In addition, some active points were located outside the most prominent excitatory part of the receptive field of the studied cells. Individual visual cells typically differ in the number and distribution of active points. Since these cells best respond to a stimulus moving in a certain direction, it is suggested that they may act as direction of movement and/or velocity detectors. Alternate firing of a number of neighboring cells connected to a distributed pattern of peripheral receptors may form a system which is able to code for velocity and direction of the moving stimulus.     

A functional model of the wiring of the simple cells of visual cortex

A computer model of the simple cells in the mammalian visual cortex was constructed. The model cells received inputs from a great number of isopolar centre/surround cells assumed to be located in the lateral geniculate nucleus (LGN). The distribution of input to the model simple cells was either inhibitory/excitatory or inhibitory/excitatory/inhibitory. Such arrangements produced receptive fields containing four or five consecutively antagonistic subfields. Responses produced by the model cells to different types of stimuli (periodical as well as nonperiodical) were obtained and compared to responses of living cells reported from various laboratories under comparable stimulus conditions. In all the situations tested, the responses of the model cells corresponded qualitatively very well to those of living cells. It was seen that the same wiring mechanism was able to account for orientation selectivity, spatial frequency filtering, various phase relationships between stimulus and response, subfield orientational selectivity, and slight end-inhibition. Furthermore, the receptive fields of the model simple cells closely resemble Gabor functions.