Propagating waves in visual cortex: a large-scale model of turtle visual cortex

This article describes a large-scale model of turtle visual cortex that simulates the propagating waves of activity seen in real turtle cortex. The cortex model contains 744 multicompartment models of pyramidal cells, stellate cells, and horizontal cells. Input is provided by an array of 201 geniculate neurons modeled as single compartments with spike-generating mechanisms and axons modeled as delay lines. Diffuse retinal flashes or presentation of spots of light to the retina are simulated by activating groups of geniculate neurons. The model is limited in that it does not have a retina to provide realistic input to the geniculate, and the cortex and does not incorporate all of the biophysical details of real cortical neurons. However, the model does reproduce the fundamental features of planar propagating waves. Activation of geniculate neurons produces a wave of activity that originates at the rostrolateral pole of the cortex at the point where a high density of geniculate afferents enter the cortex. Waves propagate across the cortex with velocities of 4 m/ms to 70 m/ms and occasionally reflect from the caudolateral border of the cortex.     

Orientation scanning effect monitored in neurons of the cat visual cortex

Incidence of the phenomenon of dynamic scanning of a portion of the orientation range during the development of neuronal response in cells of the primary visual cortex was monitored in immobilized cats using a technique involving time bins, having smoothed latencies and estimating only the highly significant portions of their response. It was found that this effect persisted in 13 out of 17 test neurons, actually remaining invariable in seven units, and modified in a further six cells owing to a shift in the starting point of the scanning process along the directional range, either extending the latter or producing a change in the direction of scanning. Directional tuning stabilized in 4 cells only following smoothing of latent periods. Findings indicate that dynamic changes in directional tuning are associated with a restructuring of the time course of response in most neurons, indicative of spatio-temporal directional coding.Institute of Higher Nervous Activity and Neurophysiology, Academy of Sciences of the USSR, Moscow. Translating for Neirofiziologiya, Vol. 19, No. 2, pp. 179–185, March–April, 1987.     

Orientation preference patterns in mammalian visual cortex: a wire length minimization approach

In the visual cortex of many mammals, orientation preference changes smoothly along the cortical surface, with the exception of singularities such as pinwheels and fractures. The reason for the existence of these singularities has remained elusive, suggesting that they are developmental artifacts. We show that singularities reduce the length of intracortical neuronal connections for some connection rules. Therefore, pinwheels and fractures could be evolutionary adaptations keeping cortical volume to a minimum. Wire length minimization approach suggests that interspecies differences in orientation preference maps reflect differences in intracortical neuronal circuits, thus leading to experimentally testable predictions. We discuss application of our model to direction preference maps.     

Mathematical model of the simple cells in the visual cortex

A mathematical model for the spatial computations performed by simple cells in the mammalian visual cortex is derived. The construction uses as organizing principles the experimentally observed simple cell linearity and rotational symmetry breaking, together with the constraint that simple cell inputs must effectively be ganglion cell outputs. This leads to a closed form expression for the simple cellkernel in terms of Jacobi-functions. Using a-function identity, it is also shown how Gabor sampling arises as an approximation to this exact kernel for most cells. In addition, the model provides a natural mechanism for introducing the type of nonlinearity observed in some simple cells. The cell's responses to a variety of visual stimuli are calculated using the exact kernel and compared to single cell recordings. In all cases, the model's predictions are in agreement with available experimental data.Work supported by the National Science Foundation, grant PHYS86-20266Work supported by the Department of Energy, contract DE-AC02-76ERO2220     

Emergent dynamics in a model of visual cortex

This paper proposes that the network dynamics of the mammalian visual cortex are highly structured and strongly shaped by temporally localized barrages of excitatory and inhibitory firing we call ‘multiple-firing events’ (MFEs). Our proposal is based on careful study of a network of spiking neurons built to reflect the coarse physiology of a small patch of layer 2/3 of V1. When appropriately benchmarked this network is capable of reproducing the qualitative features of a range of phenomena observed in the real visual cortex, including spontaneous background patterns, orientation-specific responses, surround suppression and gamma-band oscillations. Detailed investigation into the relevant regimes reveals causal relationships among dynamical events driven by a strong competition between the excitatory and inhibitory populations. It suggests that along with firing rates, MFE characteristics can be a powerful signature of a regime. Testable predictions based on model observations and dynamical analysis are proposed.     

Conductance-based refractory density model of primary visual cortex

A layered continual population model of primary visual cortex has been constructed, which reproduces a set of experimental data, including postsynaptic responses of single neurons on extracellular electric stimulation and spatially distributed activity patterns in response to visual stimulation. In the model, synaptically interacting excitatory and inhibitory neuronal populations are described by a conductance-based refractory density approach. Populations of two-compartment excitatory and inhibitory neurons in cortical layers 2/3 and 4 are distributed in the 2-d cortical space and connected by AMPA, NMDA and GABA type synapses. The external connections are pinwheel-like, according to the orientation of a stimulus. Intracortical connections are isotropic local and patchy between neurons with similar orientations. The model proposes better temporal resolution and more detailed elaboration than conventional mean-field models. In comparison to large network simulations, it excludes a posteriori statistical data manipulation and provides better computational efficiency and minimal parametrization.     

A mathematical model of the primary visual cortex and hypercolumn

A mathematical model of the primary visual cortex is presented. Basically, the model comprises two features. Firstly, in analogy with the principle of the computerized tomography (CT), it assumes that simple cells in each hypercolumn are not merely detecting line segments in images as features, but rather that they are as a whole representing the local image with a certain representation. Secondly, it assumes that each hypercolumn is performing spatial frequency analyses of local images using that representation, and that the resultant spectra are represented by complex cells. The model is analyzed using numerical simulations and its advantages are discussed from the viewpoint of visual information processing. It is shown that 1) the proposed processing is tolerant to shifts in position of input images, and that 2) spatial frequency filtering operations can be easily performed in the model.     

Cerebral cortex: the singular precision of visual cortex maps

A remarkable new technique, two-photon confocal fluorescence microscopy, has revealed an extraordinarily precise organization in the visual cortex. The methodology seems set to become the tool of choice for studying cortical maps.     

Synchronized oscillations in the visual cortex — a synergetic model

We present an oscillator network model for the synchronization of oscillatory neuronal activity underlying visual processing. The single neuron is modeled by means of a limit cycle oscillator with an eigenfrequency corresponding to visual stimulation. The eigenfrequency may be time dependent. The mutual coupling strengths are unsymmetrical and activity dependent, and they scatter within the network. Synchronized clusters (groups) of neurons emerge in the network due to the visual stimulation. The different clusters correspond to different visual stimuli. There is no limitation of the number of stimuli. Distinct clusters do not perturb each other, although the coupling strength between all model neurons is of the same order of magnitude. Our analysis is not restricted to weak coupling strength. The scatter of the couplings causes shifts of the cluster frequencies. The model's behavior is compared with the experimental findings. The coupling mechanism is extended in order to model the influence of bicucullin upon the neural network. We additionally investigate repulsive couplings, which lead to constant phase differences between clusters of the same frequency. Finally, we consider the problem of selective attention from the viewpoint of our model.     

Attention: control in the visual cortex

A recent study in which the human visual cortex was directly stimulated to create visual percepts has shown that visual spatial attention can act directly on neural activity in sensory cortex without involving attentional modulation of subcortical visual inputs.     

A computational model of the vertical anatomical organization of primary visual cortex

A method for modeling anatomical connectivity for a vertically organized slab of cortical tissue in mammalian primary visual cortex has been developed. The modeled slab covers 500 × 500 m of cortical surface and extends vertically throughout the full depth of the cortex. The model slab was divided into 6 laminae and neuronal somata were distributed in three dimensions through the slab in accordance with experimentally derived cell densities. Axonal and dendritic arborizations were modeled as line segments. A total of 17 morphological types of neurons were included. Connectivity was established based on proximity between axonal and dendritic arbors. There is good general agreement between the vertical distribution of connections generated by the model and the vertical distribution of synapses observed for cat area 17. In all layers, fewer connections were generated in the model than synapses in cat area 17. This is due, at least in part, to the exclusion of long range intracortical projections and sources of afferent input other than the dorsal lateral geniculate nucleus from the model. The connection scheme described here will be used in conjunction with a physiology model to model vertical signal flow, and will be expanded further to model receptive fields of cortical neurons.Supported in part by a grant from Cray Research Inc.     

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.     

States of high conductance in a large-scale model of the visual cortex

This paper reports on the consequences of large, activity dependent, synaptic conductances for neurons in a large-scale neuronal network model of the input layer 4C of the Macaque primary visual cortex (Area V1). This high conductance state accounts for experimental observations about orientation selectivity, dynamics, and response magnitude (D. McLaughlin et al. (2000) Proc. Natl. Acad. Sci. USA 97: 8087–8092), and the linear dependence of Simple cells on visual stimuli (J. Wielaard et al. (2001) J. Neuroscience 21: 5203–5211). The source of large conductances in the model can be traced to inhibitory corticocortical synapses, and the model's predictions of large conductance changes are consistent with recent intracellular measurements (L. Borg-Graham et al. (1998) Nature 393: 369–373; J. Hirsch et al. (1998) J. Neuroscience 15: 9517–9528; J.S. Anderson et al. (2000) J. Neurophysiol. 84: 909–926). During visual stimulation, these conductances are large enough that their associated time-scales become the shortest in the model cortex, even below that of synaptic interactions. One consequence of this activity driven separation of time-scales is that a neuron responds very quickly to temporal changes in its synaptic drive, with its intracellular membrane potential tracking closely an effective reversal potential composed of the instantaneous synaptic inputs. From the effective potential and large synaptic conductance, the spiking activity of a cell can be expressed in an interesting and simplified manner, with the result suggesting how accurate and smoothly graded responses are achieved in the model network. Further, since neurons in this high-conductance state respond quickly, they are also good candidates as coincidence detectors and burst transmitters.     

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.     

Towards a unifying model of neural net activity in the visual cortex

A neural net model describing the non-linear interactions between axonal spikes is presented. It reconciles aspects of pattern recognition (as action of an associative memory) with those of spike synchronization and phase locking. The stability of the synchronized state is studied in detail.     

A computational model of vertical signal propagation in the primary visual cortex

A computational model of the flow of activity in a vertically organized slab of cat primary visual cortex (area 17) has been developed. The membrane potential of each cell in the model, as a function of time, is given by the solution of a system of first order, coupled, non-linear differential equations. When firing threshold is exceeded, an action potential waveform is pasted in. The behavior of the model following a brief simulated stimulus to afferents from the dorsal lateral geniculate nucleus (dLGN) is explored. Excitatory and inhibitory post-synaptic potential (E and IPSP) latencies, as a function of cortical depth, were generated by the model. These data were compared with the experimental literature. In general, good agreement was found for EPSPs. Many disynaptic inhibitory inputs were found to be masked by the firing of action potentials in the model. To our knowledge this phenomenon has not been reported in the experimental literature. The model demonstrates that whether a cell exhibits disynaptic or polysynaptic PSP latencies is not a fixed consequence of anatomical connectivity, but rather, can be influenced by connection strengths, and may be influenced by the ongoing pattern of activity in the cortex.Supported by a grant from Cray Research Inc.     

A model for the economical encoding of the visual image in cerebral cortex

We propose a model for the first stage of the cortical transformation of the visual image based on the principle that the cortex encodes the information with the minimum number of channels mathematically needed. We restrict our model to be consistent with the data on size adaptation, the known relationships of acuity and the inverse of magnification factor with eccentricity, and the electrophysiological findings on the physiological uniformity of the striate cortex. Assuming that each hypercolumn analyzes a limited spatial domain, we apply the sampling theorem to show that only 16 channels, composed of 4 sizes, are needed for one dimension. The extension to 2 dimensions leads to a possible scheme for the number, spacing, and orientational disposition of the elements, together with predictions about the number of inputs from the eyes and the total number of hypercolumns. Since all these predictions are consistent with physical and neural estimates, we conclude that the cortex may analyze the image along the lines we suggest.Supported by NIH grants EY 03412 and EY 02621