Complex Cell Receptive Fields Research Articles

Neural mechanisms for processing binocular information II. Complex cells.

Complex cells in the striate cortex exhibit extensive spatiotemporal nonlinearities, presumably due to a convergence of various subunits. Because these subunits essentially determine many aspects of a complex cell receptive field (RF), such as tuning for orientation, spatial frequency, and binocular disparity, examination of the RF properties of subunits is important for understanding functional roles of complex cells. Although monocular aspects of these subunits have been studied, little is known about their binocular properties. Using a sophisticated RF mapping technique that employs binary m-sequences, we have examined binocular interactions exhibited by complex cells in the cat's striate cortex and the binocular RF properties of their underlying functional subunits. We find that binocular interaction RFs of complex cells exhibit subregions that are elongated along the frontoparallel axis at different binocular disparities. Therefore responses of complex cells are largely independent of monocular stimulus position or phase as long as the binocular disparity of the stimulus is kept constant. The binocular interaction RF is well described by a sum of binocular interaction RFs of underlying functional subunits, which exhibit simple cell-like RFs and a preference for different monocular phases but the same binocular disparity. For more than half of the complex cells examined, subunits of each cell are consistent with the characteristics specified by an energy model, with respect to the number of subunits as well as relationships between the subunit properties. Subunits exhibit RF binocular disparities that are largely consistent with a phase mechanism for encoding binocular disparity. These results indicate that binocular interactions of complex cells are derived from simple cell-like subunits, which exhibit multiplicative binocular interactions. Therefore binocular interactions of complex cells are also multiplicative. This suggests that complex cells compute something analogous to an interocular cross-correlation of images for a local region of visual space. The result of this computation can be used for solving the stereo correspondence problem.

The relation between V1 neuronal responses and eye movement-like stimulus presentations

Primates normally make 2–3 saccadic eye movements/s to explore the environment. To investigate how these eye movements might influence visual responses, we compared the dynamics of stimuli arriving on V1 complex cell receptive fields by switching stimuli in sequence while a monkey fixated to the responses occurring when the stimulus appears due to saccadic eye movements. During the image sequences, information was greater when each image remained on the receptive fields longer, up to 200 ms; information was greatest when there was a gap of 50 ms between images. Responses were more variable when the image appeared due to a saccadic eye movement. The amount of stimulus-related information was lower in the early phase of the post-saccadic time, but increased during the post-saccadic fixation, so that after 400 ms there was almost as much stimulus-related information available as during the image switching. Eye position showed much larger variability after saccades, with the variability decreasing over 350–400ms to reach the level seen during long fixations. The dynamics of information accumulation in V1 complex cells appear to be well matched to the manner in which the environment is normally viewed.

A neural network model for the development of simple and complex cell receptive fields within cortical maps of orientation and ocular dominance

Prenatal development of the primary visual cortex leads to simple cells with spatially distinct and oriented ON and OFF subregions. These simple cells are organized into spatial maps of orientation and ocular dominance that exhibit singularities, fractures, and linear zones. On a finer spatial scale, simple cells occur that are sensitive to similar orientations but opposite contrast polarities, and exhibit both even-symmetric and odd-symmetric receptive fields. Pooling of outputs from oppositely polarized simple cells leads to complex cells that respond to both contrast polarities. A neural network model is described which simulates how simple and complex cells self-organize starting from unsegregated and unoriented geniculocortical inputs during prenatal development. Neighboring simple cells that are sensitive to opposite contrast polarities develop from a combination of spatially short-range inhibition and high-gain recurrent habituative excitation between cells that obey membrane equations. Habituation, or depression, of synapses controls reset of cell activations both through enhanced ON responses and OFF antagonistic rebounds. Orientation and ocular dominance maps form when high-gain medium-range recurrent excitation and long-range inhibition interact with the short-range mechanisms. The resulting structure clarifies how simple and complex cells contribute to perceptual processes such as texture segregation and perceptual grouping.

Physiological computation of binocular disparity

We previously proposed a physiologically realistic model for stereo vision based on the quantitative binocular receptive field profiles mapped by Freeman and coworkers. Here we present several new results about the model that shed light on the physiological processes involved in disparity computation. First, we show that our model can be extended to a much more general class of receptive field profiles than the commonly used Gabor functions. Second, we demonstrate that there is, however, an advantage of using the Gabor filters: similar to our perception, the stereo algorithm with the Gabor filters has a small bias towards zero disparity. Third, we prove that the complex cells as described by Freeman et al. compute disparity by effectively summing up two related cross products between the band-pass filtered left and right retinal image patches. This operation is related to cross-correlation but it overcomes some major problems with the standard correlator. Fourth, we demonstrate that as few as two complex cells at each spatial location are sufficient for a reasonable estimation of binocular disparity. Fifth, we find that our model can be significantly improved by considering the fact that complex cell receptive fields are, on average, larger than those of simple cells. This fact is incorporated into the model by averaging over several quadrature pairs of simple cells with nearby and overlapping receptive fields to construct a model complex cell. The disparity tuning curve of the resulting complex cell is much more reliable than that constructed from a single quadrature pair of simple cells used previously, and the computed disparity maps for random dot stereograms with the new algorithm are very similar to human perception, with sharp transitions at disparity boundaries. Finally, we show that under most circumstances our algorithm works equally well with either of the two well-known receptive field models in the literature.

Computational mechanisms underlying the nonlinear spatial structure of V1 complex cell receptive fields

Computational mechanisms underlying the nonlinear spatial structure of V1 complex cell receptive fields

2803 Computational mechanisms underlying the nonlinear spatial structure of V1 complex cell receptive fields

2803 Computational mechanisms underlying the nonlinear spatial structure of V1 complex cell receptive fields

Position-specific adaptation in complex cell receptive fields of the cat striate cortex

1. Responses of complex cells in cat striate cortex were studied with flashed light slit stimuli. The responses to slits flashed in different positions in the receptive field were assessed quantitatively before and after periods of prolonged stimulation of one small region of the receptive field. This type of prolonged stimulation resulted in reduced responsivity over a limited zone within the complex cell receptive field. 2. The adaptation-induced responsivity decrement was generally observed in both the ON and OFF response profiles but could also be restricted to one or the other. In general, the magnitude of the response decrements was greatest in the ON response profiles. The adaptation-induced response decrement did not necessarily spread throughout the receptive field but was restricted to a small region surrounding the adapted receptive field position (RFP). Adaptation spread equally widely across the ON and OFF response profiles despite the smaller adaptation effects in the OFF profile. 3. The adaptation effects from repeated stimulation at a single RFP did not spread symmetrically across the receptive field, and a given cell's preferred direction of motion indicated the direction of the asymmetric spread of the adaptation. RFPs that would be stimulated by a light slit originating at the point of adaptation and moving in the preferred direction (preferred side) showed greater adaptation-induced response decrements than did RFPs that would be stimulated by a light slit moving in the opposite direction from the point of adaptation (nonpreferred side). There was significant enhancement of responses at some RFPs on the non-preferred side of the point of adaptation. This asymmetric spread of adaptation could be caused by adaptation of inhibitory connections that contribute to complex cell direction selectivity. 4. The asymmetry of adaptation was significantly different for the ON and OFF response profiles. The asymmetric spread of adaptation for the ON response profile was similar to that observed previously in simple cells with greater decrements in the preferred direction side of the point of adaptation. However, the OFF response profiles showed less directional asymmetry in the spread of adaptation and showed greater decrements at RFPs in the nonpreferred direction side of the point of adaptation. 5. The similarity between the spread of adaptation in simple and complex cells suggests that the adaptation in these cells is occurring through a common mechanism. The directional asymmetry of the spread of adaptation is likely due to a local postsynaptic mechanism of adaptation rather than presynaptic transmitter depletion.

The two-dimensional spatial structure of nonlinear subunits in the receptive fields of complex cells

We have estimated the second-order response properties of complex cells in two spatial dimensions by cross-correlating their spike trains with a binary approximation of a Gaussian white noise stimulus ensemble. Wiener-like kernels were computed and generally consisted of two or three parallel, elongated subrogions alternating between augmented and suppressed response. These submits were scattered across the receptive fields of complex cells and their axes of elongation agreed with the optimal orientation determined with drifting gratings.

Stereo boundary fusion by cortical complex cells: A system of maps, filters, and feedback networks for multiplexing distributed data

A neural network model of multiple-scale binocular fusion and rivalry in visual cortex is described and simulated on the computer. The model consists of three parts: a distributed spatial representation of binocular input patterns among simple cells that are organized into ocular dominance columns; an adaptive filter from simple cells to complex cells; and a nonlinear on-center off-surround shunting feedback network that joins together the complex cells. This data structure generates complex cell receptive fields which multiplex input position, orientation, spatial frequency, positional disparity, and orientational disparity, and which are insensitive to direction-of-contrast in the image. Multiple copies of this circuit are replicated in the model using receptive fields of different sizes. Within each such circuit, the simple cell and complex cell receptive field sizes covary. Together these circuits define a self-similar multiple-scale network. The self-similarity property across spatial scales enables the network to exhibit a size-disparity correlation, whereby simultaneous binocular fusion and rivalry can occur among the spatial scales corresponding to a given retinal region. It is shown that a laminar organization of the model interactions among the complex cells gives rise to conceptually simple growth rules for intercellular connections. The output patterns of the model complex cells are designed to feed into the model hypercomplex cells at the first competitive stage of a Boundary Contour System network, where they trigger a process of multiple-scale emergent binocular boundary segmentation. The modeling results are compared with psychophysical data about binocular fusion and rivalry, as well as with the cepstrum stereo model of Yeshurun and Schwartz. The results indicate that analogous self-similar multiple-scale neural networks may be used to carry out data fusion of many other types of spatially organized data structures.

Diversity of complex cell responses to even- and odd-symmetric luminance profiles in the visual cortex of the cat.

We have tested the hypothesis that complex cell receptive fields are made up of subfields which, for a given cell, have either exclusively even or exclusively odd symmetry. To do this we have measured the response of complex cells in the visual cortex of the cat to members of pairs of spatially limited even-symmetric stimuli (single light and dark bars) and pairs of odd-symmetric stimuli ("light-dark" and "dark-light" double bars) successively drifting across their receptive fields. The strength of a cell's response was estimated by measuring the sum of all spikes produced by a stimulus. Some complex cells respond about equally to single light and dark bars; others respond appreciably more to either the light or dark bar. The central tendency of average response histograms was estimated by measuring the mean with respect to position across the width of the receptive field. Many complex cells show distinct spatial offsets between the mean for narrow single light and that for dark bars as well as between means to double bars of opposite phase. Combined offset plots were constructed with the spatial offsets between means for single light bars and single dark bars along the x axis and the offsets between means to double bars of opposite phase along the y axis. There is significant scatter in the combined offset points; some falling at the origin, some at significant distances from the origin along the axes, and others well within each of the four quadrants. These diverse localizations in the offset plots rule out the simple models of complex cell spatial substructure described above and, therefore, imply considerable heterogeneity within the population of complex cells.

Association amacrine cells could mediate directional selectivity in pigeon retina.

Ganglion cell receptive fields in the vertebrate retina are classified as ‘simple’ if they display mutually antagonistic, concentric on and off responses to stimulation by light1, or ‘complex’ if they respond maximally to a stimulus which moves in a certain direction or has a particular orientation2. One complex unit frequently reported is directionally selective3–6. It is excited by stimuli moving through its receptive field in only one direction, the preferred direction, and is inhibited by motion in the reverse or null direction. In species such as cats and monkeys which have a predominantly simple centre-surround organization of ganglion cell receptive fields, there is a low ratio of conventional (amacrine cell) to ribbon (bipolar cell) synapses in the inner plexiform layer, while in species such as pigeons and frogs which have a large percentage of complex ganglion cell receptive fields, there is a high ratio of conventional to ribbon synapses. It is therefore thought that amacrine cells, which are laterally interconnecting neurones in the inner plexiform layer, form the complex ganglion cell receptive field7,8. Although quantitative electron microscopy of the inner plexiform layer has indicated that amacrine cells may form the complex receptive field properties of ganglion cells, it offers no clues to the particular morphology of amacrine cells involved. I have therefore now studied amacrine cells in pigeon retina using Golgi impregnation in both whole-mounted and radially sectioned material to see whether there is a correlation between the morphology of amacrine cells and the receptive fields of ganglion cells. In 30 Golgi-impregnated retinas, I have found many different morphological types of amacrine cell. One of these is so clearly different from all other amacrine cells that it requires the separation of pigeon amacrine cells into two distinct classes, those which lack axons (class I cells) and those which have axons (class II cells).

Organization of cat visual cortex as investigated by cross-correlation technique.

Organization of cat visual cortex as investigated by cross-correlation technique.K Toyama, M Kimura, and K TanakaK Toyama, M Kimura, and K TanakaPublished Online:01 Aug 1981https://doi.org/10.1152/jn.1981.46.2.202MoreSectionsPDF (2 MB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInEmailWeChat Previous Back to Top Next Download PDF FiguresReferencesRelatedInformationCited ByRelationship between the Dynamics of Orientation Tuning and Spatiotemporal Receptive Field Structures of Cat LGN NeuronsNeuroscience, Vol. 377Five key factors determining pairwise correlations in visual cortexDavid P. A. Schulz, Maneesh Sahani, and Matteo Carandini1 August 2015 | Journal of Neurophysiology, Vol. 114, No. 2Spatial range and laminar structures of neuronal correlations in the cat primary visual cortexHiroki Tanaka, Hiroshi Tamura, and Izumi Ohzawa1 August 2014 | Journal of Neurophysiology, Vol. 112, No. 3Computational principles of microcircuits for visual object processing in the macaque temporal cortexTrends in Neurosciences, Vol. 37, No. 3Circuit mechanisms revealed by spike-timing correlations in macaque area MTXin Huang, and Stephen G. Lisberger1 February 2013 | Journal of Neurophysiology, Vol. 109, No. 3Synaptic Interactions Between Forelimb-Related Motor Cortex Neurons in Behaving PrimatesW. S. Smith and E. E. Fetz1 August 2009 | Journal of Neurophysiology, Vol. 102, No. 2Internal Spatial Organization of Receptive Fields of Complex Cells in the Early Visual CortexKota S. Sasaki, and Izumi Ohzawa1 September 2007 | Journal of Neurophysiology, Vol. 98, No. 3Complex Receptive Fields in Primary Visual Cortex29 June 2016 | The Neuroscientist, Vol. 9, No. 5Spatiotemporal Patterns of Excitation and Inhibition Evoked by the Horizontal Network in Layer 2/3 of Ferret Visual CortexThomas R. Tucker, and Lawrence C. Katz1 January 2003 | Journal of Neurophysiology, Vol. 89, No. 1Local Correlation-Based Circuitry Can Account for Responses to Multi-Grating Stimuli in a Model of Cat V1T. Z. Lauritzen, A. E. Krukowski, and K. D. Miller1 October 2001 | Journal of Neurophysiology, Vol. 86, No. 4Chapter 8 Synchronization and assembly formation in the visual cortexDynamic Properties of Recurrent Inhibition in Primary Visual Cortex: Contrast and Orientation Dependence of Contextual EffectsValentin Dragoi, and Mriganka Sur1 February 2000 | Journal of Neurophysiology, Vol. 83, No. 2The Binding ProblemNeuron, Vol. 24, No. 1The Psychophysical Evidence for a Binding Problem in Human VisionNeuron, Vol. 24, No. 1The Role of Neural Mechanisms of Attention in Solving the Binding ProblemNeuron, Vol. 24, No. 1The Temporal Correlation Hypothesis of Visual Feature IntegrationNeuron, Vol. 24, No. 1Neuronal Synchrony: A Versatile Code for the Definition of Relations?Neuron, Vol. 24, No. 1Synchrony UnboundNeuron, Vol. 24, No. 1Specialized Representations in Visual CortexNeuron, Vol. 24, No. 1Are Cortical Models Really Bound by the “Binding Problem”?Neuron, Vol. 24, No. 1The What and Why of BindingNeuron, Vol. 24, No. 1Solutions to the Binding ProblemNeuron, Vol. 24, No. 1Synchronization of Local Neural Networks in the Somatosensory Cortex: A Comparison of Stationary and Moving StimuliStephane Roy, and Kevin D. Alloway1 March 1999 | Journal of Neurophysiology, Vol. 81, No. 3Synchronous Membrane Potential Fluctuations in Neurons of the Cat Visual CortexNeuron, Vol. 22, No. 2Review : Inhibitory Processes in the Visual Cortex29 June 2016 | The Neuroscientist, Vol. 4, No. 1Encoding of Binocular Disparity by Complex Cells in the Cat's Visual CortexIzumi Ohzawa, Gregory C. Deangelis, and Ralph D. Freeman1 June 1997 | Journal of Neurophysiology, Vol. 77, No. 6Two models of the recognition and detection of texture-defined letters comparedBiological Cybernetics, Vol. 72, No. 5The nature and origin of orientation specificity in neurons of the visual pathwaysProgress in Neurobiology, Vol. 43, No. 4-5Sub-millisecond coincidence detection in active dendritic treesNeuroscience, Vol. 58, No. 1Theoretical mechanics of biological neural networksNeural Networks, Vol. 7, No. 5Cortical column design: a link between the maps of preferred orientation and orientation tuning strength?Biological Cybernetics, Vol. 70, No. 1Inhibition contributes to orientation selectivity in visual cortex of cat27 October 1988 | Nature, Vol. 335, No. 6193Neuronal synchrony in monkey striate cortex: interocular signal flow and dependency on spike ratesExperimental Brain Research, Vol. 72, No. 1Locally correlated activity in the goldfish tectum in the absence of optic innervationDevelopmental Brain Research, Vol. 41, No. 1-2Cortical auditory neuron interactions during presentation of 3-tone sequences: effective connectivityBrain Research, Vol. 450, No. 1-2Intracortical connectivity revealed by spike-triggered averaging in slice preparations of cat visual cortexBrain Research, Vol. 442, No. 2Evaluation of neuronal connectivity: Sensitivity of cross-correlationBrain Research, Vol. 340, No. 2Simultaneous recordings from pairs of cat somatosensory cortical neurons with overlapping peripheral receptive fieldsBrain Research, Vol. 341, No. 1Synaptic connections of morphologically identified and physiologically characterized large basket cells in the striate cortex of catNeuroscience, Vol. 10, No. 2The statistical reliability of signals in single neurons in cat and monkey visual cortexVision Research, Vol. 23, No. 8A computational model of texture segmentation More from this issue > Volume 46Issue 2August 1981Pages 202-14 https://doi.org/10.1152/jn.1981.46.2.202PubMed6267212History Published online 1 August 1981 Published in print 1 August 1981 Metrics

Receptive field organization of complex cells in the cat's striate cortex.

1. All complex cells in the cat's striate cortex exhibit gross non-linearities of spatial summation when tested with sinusoidal grating stimuli. Their responses to moving gratings of all but the lowest spatial frequencies are usually dominated by a component that is not modulated by the passage of the bars of the grating across the receptive field. They give responses to temporally modulated stationary gratings that consist mostly of even harmonics of the stimulus frequency and that vary little in amplitude or wave form as the spatial phase of the grating is varied. 2. We compared complex cells' receptive fields with their sensitivity to sinusoidal gratings of different spatial frequencies. Qualitatively, the receptive fields are usually two to five times wider than the bars of the gratings that stimulate them most effectively. Quantitatively, the receptive field profiles of complex cells are invariably broader than those predicted by Fourier synthesis of their spatial frequency tuning curves, and in particular lack predicted spatially antagonistic regions. 3. We further examined the receptive field organization of these cells, using pairs of stationary lines flashed synchronously on their receptive fields. If both lines are of the same polarity (bright or dark), complex cells respond to the paired stimulus much less well than they do to either of its component bars, unless the bars are separated by less than about one quarter of the width of the receptive field. If the lines are of opposite polarity, one bright and one dark, the opposite situation obtains: closely spaced bars elicit small responses, while paired bars of larger separation are much more effective. In either case, the results are independent in general character of the absolute positions of the stimuli within the receptive field; rather, they depend in a manner characteristic of each cell on the relative positions of the two bars. 4. The two-line interaction profile that plots the change in a complex cell's response to one bar as a function of the position of a second added bar corresponds closely to the receptive field profile predicted from Fourier synthesis of the cell's spatial frequency tuning curve. These profiles may thus reveal the spatial characteristics of subunits within complex cell-receptive fields. We examined the nature of the interaction between these subunits by performing several two-line interaction experiments in which the onset of the second bar was delayed some time after the onset of the first. The results suggest that neighbouring subunits interact in a facilitatory fashion: for an interval after the presentation of one bar, responses to neighbouring bars are enhanced. 5. The subunits of a complex receptive field may, by their spatial properties, determine the spatial selectivities of complex cells, while the nature of the interaction among the subunits may determine these cells' sensitivity and selectivity for moving visual stimuli...

Electron Microscopic Analysis of the Inner Synaptic Layer in the River Lamprey(Lampetra fluviatilis)

Different types of synaptic contacts between bipolar, amacrine and ganglion cells were scored on random electron micrographs and on montages comprising the entire thickness of the inner synaptic layer. Currently accepted criteria were used when classifying the different cell processes. The percental distribution of dyads was estimated to 56 % amacrine-amacrine dyads, 34 % amacrine-ganglion dyads and 10 % ganglion-ganglion dyads. The ratio of amacrine conventional synapses to bipolar ribbon synapses was 6.8 : 1. The density per unit area of conventional synapses (0.035/μm2) and ribbon synapses (0.005/ μm2) was found markedly low as compared with other vertebrate species except the carp. The inner synaptic layer of the river lamprey is suggested to be of the intermediate type in which both simple and complex ganglion cell receptive fields may be expected.

Periodic excitability changes across the receptive fields of complex cells in the striate and parastriate cortex of the cat.

1. Complex cells in cortical areas 17 and 18 of the cat have been studied in response to narrow slits and edges moving across the receptive field in the preferred direction and also to stationary slits of different widths. 2. Average response histograms, recorded as a narrow slit was moved across the receptive field, displayed a periodic series of peaks above a base line level. The response histogram for most area 17 and 18 cells contained five principal peaks; sometimes one or two weaker peaks were present at receptive field borders. The histogram for one cell located at the area 17-18 border showed thirteen distinct peaks. Periodic response patterns were also generated as an extended edge was moved across the receptive field. Plots of cell responses versus slit width for stationary slits of different widths also indicated periodic response pattern. 3. The accuracy of determining the preferred slit orientation was the single most important requirement for demonstrating the periodic response pattern. Significant changes in the appearance of the periodic pattern occurred even upon 5 degrees rotations away from the preferred orientation. 4. Average response histograms were also studied over a wide range of moving slit velocities. The number of peaks across corresponding spacings within the recewptive field remained constant over a range of velocities. Response amplitudes, however, were velocity dependent. Thus the response peaks remain associated with fixed positions within visual space independent of stimulus velocity, even though temporal as well as spatial factors may be involved in response selectivity and the periodic modulation. The most striking periodic response histograms were generated at the velocities which produced the greatest cell firing rates. Area 17 complex cells responded well to velocities of less than 0-5 degrees to 6-0 degrees/sec, but cells in area 18 generally required higher velocities, sometimes as high as 20 degrees--30 degrees/sec, for a good response. 5. Spatial frequencies for the periodic component of the receptive field for area 17 cells in the central visual area covered a range of three octaves up to 5 cycles/degree, and area 18 cells included another octave on the low frequency side. The spatial frequency of a cell was found to be roughly inversely proportional to the receptive field width. Only a small sample of area 18 cells was studied, but these cells tended to represent low spatial frequencies and to respond selectively to high velocity stimuli...

The inner plexiform layer of the vertebrate retina: A quantitative and comparative electron microscopic analysis

AbstractThe inner plexiform layer of human, monkey, cat, rat, rabbit, ground squirrel, frog and pigeon retinas was studied by electron microscopy. All showed the same qualitative synaptic arrangements: bipolar cells made dyad ribbon synapses onto amacrine and ganglion cells; amacrine cells made conventional synaptic contacts onto bipolar, ganglion cells; amacrine cells montage of electron micrographs through the full thickness of the inner plexiform layer were made for each species and were scored for synaptic contacts. Both absolute and relative quantitative differences were found between species. The ratio of amacrine cell (conventional) synapses to bipolar cell (ribbon) synapses, the absolute number of amacrine cell synapses and the number of inter‐amacrine cell synapses were all found to be higher in those animals which are known to have relatively complex retinal ganglion cell receptive field properties. It is suggested that the amacrine cell is involved in mediating complex visual transformations in certain vertebrate retinas.