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.     

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.     

A reaction-diffusion model to capture disparity selectivity in primary visual cortex

Decades of experimental studies are available on disparity selective cells in visual cortex of macaque and cat. Recently, local disparity map for iso-orientation sites for near-vertical edge preference is reported in area 18 of cat visual cortex. No experiment is yet reported on complete disparity map in V1. Disparity map for layer IV in V1 can provide insight into how disparity selective complex cell receptive field is organized from simple cell subunits. Though substantial amounts of experimental data on disparity selective cells is available, no model on receptive field development of such cells or disparity map development exists in literature. We model disparity selectivity in layer IV of cat V1 using a reaction-diffusion two-eye paradigm. In this model, the wiring between LGN and cortical layer IV is determined by resource an LGN cell has for supporting connections to cortical cells and competition for target space in layer IV. While competing for target space, the same type of LGN cells, irrespective of whether it belongs to left-eye-specific or right-eye-specific LGN layer, cooperate with each other while trying to push off the other type. Our model captures realistic 2D disparity selective simple cell receptive fields, their response properties and disparity map along with orientation and ocular dominance maps. There is lack of correlation between ocular dominance and disparity selectivity at the cell population level. At the map level, disparity selectivity topography is not random but weakly clustered for similar preferred disparities. This is similar to the experimental result reported for macaque. The details of weakly clustered disparity selectivity map in V1 indicate two types of complex cell receptive field organization.     

Development of spatiotemporal receptive fields of simple cells: I. Model formulation

A model for the development of spatiotemporal receptive fields of simple cells in the visual cortex is proposed. The model is based on the 1990 hypothesis of Saul and Humphrey that the convergence of four types of input onto a cortical cell, viz. non-lagged ON and OFF inputs and lagged ON and OFF inputs, underlies the spatial and temporal structure of the receptive fields. It therefore explains both orientation and direction selectivity of simple cells. The response properties of the four types of input are described by the product of linear spatial and temporal response functions. Extending the 1994 model of one of the authors (K.D. Miller), we describe the development of spatiotemporal receptive fields as a Hebbian learning process taking into account not only spatial but also temporal correlations between the different inputs. We derive the correlation functions that drive the development both for the period before and after eye-opening and demonstrate how the joint development of orientation and direction selectivity can be understood in the framework of correlation-based learning. Our investigation is split into two parts that are presented in two papers. In the first, the model for the response properties and for the development of direction-selective receptive fields is presented. In the second paper we present simulation results that are compared with experimental data, and also provide a first analysis of our model. Received: 18 June 1997 / Accepted: 16 September 1997     

Receptive fields of disparity-tuned simple cells in macaque V1

Binocular simple cells in primary visual cortex (V1) are the first cells along the mammalian visual pathway to receive input from both eyes. Two models of how binocular simple cells could extract disparity information have been put forward. The phase-shift model proposes that the receptive fields in the two eyes have different subunit organizations, while the position-shift model proposes that they have different overall locations. In five fixating macaque monkeys, we recorded from 30 disparity-tuned simple cells that showed selectivity to the disparity in a random dot stereogram. High-resolution maps of the left and right eye receptive fields indicated that both phase and position shifts were common. Single cells usually showed a combination of the two, and the optimum disparity was best correlated with the sum of receptive field phase and position shift.     

A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field Structure

Visual attention has many effects on neural responses, producing complex changes in firing rates, as well as modifying the structure and size of receptive fields, both in topological and feature space. Several existing models of attention suggest that these effects arise from selective modulation of neural inputs. However, anatomical and physiological observations suggest that attentional modulation targets higher levels of the visual system (such as V4 or MT) rather than input areas (such as V1). Here we propose a simple mechanism that explains how a top-down attentional modulation, falling on higher visual areas, can produce the observed effects of attention on neural responses. Our model requires only the existence of modulatory feedback connections between areas, and short-range lateral inhibition within each area. Feedback connections redistribute the top-down modulation to lower areas, which in turn alters the inputs of other higher-area cells, including those that did not receive the initial modulation. This produces firing rate modulations and receptive field shifts. Simultaneously, short-range lateral inhibition between neighboring cells produce competitive effects that are automatically scaled to receptive field size in any given area. Our model reproduces the observed attentional effects on response rates (response gain, input gain, biased competition automatically scaled to receptive field size) and receptive field structure (shifts and resizing of receptive fields both spatially and in complex feature space), without modifying model parameters. Our model also makes the novel prediction that attentional effects on response curves should shift from response gain to contrast gain as the spatial focus of attention drifts away from the studied cell.     

Response histogram shapes and tuning curves: The predicted responses of several cortical cell types to drifting gratings stimuli

The responses to visual stimuli of simple cortical cells show linear spatial summation within and between their receptive field subunits. Complex cortical cells do not show this linearity. We analyzed the simulated responses to drifting sinusoidal grating stimuli of simple and of several types of complex cells. The complex cells, whose responses are seen to be half-wave rectified before pooling, have receptive fields consisting of two or more DOG (difference-of-Gaussians) shaped subunits. In both cases of stimulation by contrast-reversal gratings or drifting gratings, the cells' response as a function of spatial frequency is affected by the subunit distances 2 and the stimulation frequency . Furthermore, an increased number of subunits (a larger receptive field) yields a narrower peak tuning curve with decreased modulation depth for many of the spatial frequencies. The average and the peak response tuning curves are compared for the different receptive field types.     

Rabbit visual cortical neurons with simple and complex receptive fields

Receptive fields of neurons of the rabbit visual cortex selective for stimulus orientation were investigated. These receptive fields were less well differentiated than those of the analogous neurons of the cat visual cortex (large in size and circular in shape). Two mechanisms of selectivity for stimulus orientation were observed: inhibition between on and off zones of the receptive field (sample type) and oriented lateral inhibition within the same zone of the receptive field (complex type). Lateral inhibition within the same zone of the receptive field also took place in unselective neurons; "complex" selective neurons differed from them in the orientation of this inhibition. A combination of both mechanisms was possible in the receptive field of the same neuron. It is suggested that both simple and complex receptive fields are derivatives of unselective receptive fields and that "complex" neurons are not the basis for a higher level of analysis of visual information than in "simple" neurons.A. N. Severtsov Institute of Evolutionary Morphology and Ecology of Animals, Academy of Sciences of the USSR, Moscow. Translated from Neirofiziologiya, Vol. 10, No. 1, pp. 13–21, January–February, 1978.     

Visual receptive field organization

Increasingly systematic approaches to quantifying receptive fields in primary visual cortex, combined with inspired ideas about functional circuitry, non-linearities, and visual stimuli, are bringing new interest to classical problems. This includes the distinction and hierarchy between simple and complex cells, the mechanisms underlying the receptive field surround, and debates about optimal stimuli for mapping receptive fields. An important new problem arises from recent observations of stimulus-dependent spatial and temporal summation in primary visual cortex. It appears that the receptive field can no longer be considered unique, and we might have to relinquish this cherished notion as the embodiment of neuronal function in primary visual cortex.     

Is sparse and distributed the coding goal of simple cells?

The question of why the receptive fields of simple cells in the primary visual cortex are Gabor-like is a crucial one in vision research. Many research efforts (Olshausen and Field 1996, 1997; van Hateren and Ruderman 1998; van Hateren and van der Schaaf 1998) that yield a set of localized, oriented, and bandpass Gabor-like receptive fields believe that sparse and distributed is the coding goal of simple cells. This paper investigates a more general coding strategy that measures equally any departure from normality in the simple cells responses. That is, we investigate the possibility that highly kurtotic response histograms may result if simple cells explicitly seek, not maximally kurtotic, but rather maximally non-Gaussian response histograms to natural images. It is found that, under this coding strategy, the simulations produce a majority of localized, oriented, bandpass (Gabor-like) receptive fields. Some receptive fields, however, are spatially distributed and show little oriented structure. Nearly all receptive fields, regardless of whether they are Gabor-like or non-Gabor-like, yield highly kurtotic response histograms to natural images. Thus, in seeking maximally non-Gaussian response histograms, receptive fields spontaneously yield highly kurtotic histograms. The presence in our ensemble of nonlocalized, nonoriented receptive fields may be due to the artificial requirement that receptive fields be orthonormal. We conclude that the high kurtoses observed in the response histograms of simple-cell receptive fields to natural images may reflect a property of natural images themselves rather than an explicit coding goal used to structure simple-cell receptive fields.Acknowledgement This work was supported by the US Office of Naval Research under agreement number N68936-00-2-0002.     

Receptive field properties of near neighbor orientation selective neurons in the visual cortex: a modeling study

The primary visual cortex is organized into clusters of cells having similar receptive fields (RFs). A purely feedforward model has been shown to produce realistic simple cell receptive fields. The modeled cells capture a wide range of receptive field properties of orientation selective cortical cells. We have analyzed the responses of 78 nearby cell pairs to study which RF properties are clustered. Orientation preference shows strongest clustering. Orientation tuning width (hwhh) and tuning height (spikes/sec) at the preferred orientation are not as tightly clustered. Spatial frequency is also not as tightly clustered and RF phase has the least clustering. Clustering property of orientation preference, orientation tuning height and width depend on the location of cells in the orientation map. No such location dependence is observed for spatial frequency and RF phase. Our results agree well with experimental data.     

Local and global motion preferences in descending neurons of the fly

For a moving animal, optic flow is an important source of information about its ego-motion. In flies, the processing of optic flow is performed by motion sensitive tangential cells in the lobula plate. Amongst them, cells of the vertical system (VS cells) have receptive fields with similarities to optic flows generated during rotations around different body axes. Their output signals are further processed by pre-motor descending neurons. Here, we investigate the local motion preferences of two descending neurons called descending neurons of the ocellar and vertical system (DNOVS1 and DNOVS2). Using an LED arena subtending 240° × 95° of visual space, we mapped the receptive fields of DNOVS1 and DNOVS2 as well as those of their presynaptic elements, i.e. VS cells 1–10 and V2. The receptive field of DNOVS1 can be predicted in detail from the receptive fields of those VS cells that are most strongly coupled to the cell. The receptive field of DNOVS2 is a combination of V2 and VS cells receptive fields. Predicting the global motion preferences from the receptive field revealed a linear spatial integration in DNOVS1 and a superlinear spatial integration in DNOVS2. In addition, the superlinear integration of V2 output is necessary for DNOVS2 to differentiate between a roll rotation and a lift translation of the fly.     

Laminar distribution of neurons with different types of receptive fields in the rabbit visual cortex

Investigation of receptive fields of 232 primary visual cortical neurons in rabbits by the use of shaped visual stimuli showed that 21.1% are unselective for stimulus orientation, and 34.1% have simple, 16.4% complex, and 18.5% hypercomplex receptive fields, and 9.9% have other types. Neurons with different types of receptive fields also differed in spontaneous activity, selectivity for rate of stimulus movement, and acuteness of orientational selectivity. Neurons not selective to orientation were found more frequently in layer IV than in other layers, and very rarely in layer VI. Cells with simple receptive fields were numerous in all layers but predominated in layer VI. Neurons with complex receptive fields were rare in layer IV and more numerous in layers V and VI. Neurons with hypercomplex receptive fields were found frequently in layers II + III and IV, rarely in layers V and VI. Spontaneous unit activity in layer II + III was lowest on average, and highest in layer V. Acuteness or orientational selectivity of neurons with simple and complex receptive fields in layers II + III and V significantly exceeded the analogous parameter in layers IV and VI.A. N. Severtsov Institute of Evolutionary Morphology and Ecology of Animals, Academy of Sciences of the USSR, Moscow. Translated from Neirofiziologiya, Vol. 17, No. 1, pp. 19–27, January–February, 1985.     

Invariance and selectivity in the ventral visual pathway.

Pattern recognition systems that are invariant to shape, pose, lighting and texture are never sufficiently selective; they suffer a high rate of "false alarms". How are biological vision systems both invariant and selective? Specifically, how are proper arrangements of sub-patterns distinguished from the chance arrangements that defeat selectivity in artificial systems? The answer may lie in the nonlinear dynamics that characterize complex and other invariant cell types: these cells are temporarily more receptive to some inputs than to others (functional connectivity). One consequence is that pairs of such cells with overlapping receptive fields will possess a related property that might be termed functional common input. Functional common input would induce high correlation exactly when there is a match in the sub-patterns appearing in the overlapping receptive fields. These correlations, possibly expressed as a partial and highly local synchrony, would preserve the selectivity otherwise lost to invariance.     

Spike-based synaptic plasticity and the emergence of direction selective simple cells: simulation results

Direction selectivity (DS) of simple cells in the primary visual cortex was recently suggested to arise from short-term synaptic depression in thalamocortical afferents (Chance F, Nelson S, Abbott L (1998), J. Neuroscience 18(12): 4785–4799). In the model, two groups of afferents with spatially displaced receptive fields project through either depressing and non-depressing synapses onto the V1 cell. The degree of synaptic depression determines the temporal phase advance of the response to drifting gratings. We show that the spatial displacement and the appropriate degree of synaptic depression required for DS can develop within an unbiased input scenario by means of temporally asymmetric spike-timing dependent plasticity (STDP) which modifies both the synaptic strength and the degree of synaptic depression. Moving stimuli of random velocities and directions break any initial receptive field symmetry and produce DS. Frequency tuning curves and subthreshold membrane potentials akin to those measured for non-directional simple cells are thereby changed into those measured for directional cells. If STDP is such that down-regulation dominates up-regulation the overall synaptic strength adapts in a self-organizing way such that eventually the postsynaptic response for the non-preferred direction becomes subthreshold. To prevent unlearning of the acquired DS by randomly changing stimulus directions an additional learning threshold is necessary. To further protect the development of the simple cell properties against noise in the stimulus, asynchronous and irregular synaptic inputs are required.     

A neural network model on self-organizing emergence of simple-cell receptive field with orientation selectivity in visual cortex

In order to probe into the self-organizing emergence of simple cell orientation selectivity, we tried to construct a neural network model that consists of LGN neurons and simple cells in visual cortex and obeys the Hebbian learning rule. We investigated the neural coding and representation of simple cells to a natural image by means of this model. The results show that the structures of their receptive fields are determined by the preferred orientation selectivity of simple cells. However, they are also decided by the emergence of self-organization in the unsupervision learning process. This kind of orientation selectivity results from dynamic self-organization based on the interactions between LGN and cortex.