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.     

High-precision coding in visual cortex

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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.     

Neural networks with constrained inputs as models for pattern formation in primate visual cortex

We present a neural network model for the formation of ocular dominance stripes on primate visual cortex and examine the generic phase behavior and dynamics of the model. The dynamical equation of ocular dominance development can be identified with a class of Langevin equations with a nonconserved order parameter. We first set up and examine an Ising model with long-range interactions in an external field, which is equivalent to the model described by the Langevin equation. We use both mean-field theory and Monte-Carlo simulations to study the equilibrium phase diagram of this equivalent Ising model. The phase diagram comprises three phases: a striped phase, a hexagonal bubble phase, and a uniform paramagnetic phase. We then examine the dynamics of the striped phase by solving the Langevin equation both numerically and by singular perturbation theory. Finally, we compare the results of the model with physiological data. The typical striped structure of the ocular dominance columns corresponds to the zero-field configurations of the model. Monocular deprivation can be simulated by allowing the system to evolve in the absence of an external field at early times and then continuing the simulation in the presence of an external field. The physical and physiological applications of our model are discussed in the conclusion.     

Extracting Wave Structure from Biological Data with Application to Responses in Turtle Visual Cortex

Waves have long been thought to be a fundamental mechanism for communicating information within a medium and are widely observed in biological systems. However, a quantitative analysis of biological waves is confounded by the variability and complexity of the response. This paper proposes a robust technique for extracting wave structure from experimental data by calculating "wave subspaces" from the KL decomposition of the data set. If a wave subspace contains a substantial portion of the data set energy during a particular time interval, one can deduce the structure of the wave and potentially isolate its information content. This paper uses the wave subspace technique to extract and compare wave structure in data from three different preparations of the turtle visual cortex. The paper demonstrates that wave subspace caricatures from the three cortical preparations have qualitative similarities. In the numerical model, where information about the underlying dynamics is available, wave subspace landmarks are related to activation and changes in behavior of other dynamic variables besides membrane potential.     

Representational drift in the mouse visual cortex

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Direct evidence for encoding of motion streaks in human visual cortex

Temporal integration in the visual system causes fast-moving objects to generate static, oriented traces (‘motion streaks’), which could be used to help judge direction of motion. While human psychophysics and single-unit studies in non-human primates are consistent with this hypothesis, direct neural evidence from the human cortex is still lacking. First, we provide psychophysical evidence that faster and slower motions are processed by distinct neural mechanisms: faster motion raised human perceptual thresholds for static orientations parallel to the direction of motion, whereas slower motion raised thresholds for orthogonal orientations. We then used functional magnetic resonance imaging to measure brain activity while human observers viewed either fast (‘streaky’) or slow random dot stimuli moving in different directions, or corresponding static-oriented stimuli. We found that local spatial patterns of brain activity in early retinotopic visual cortex reliably distinguished between static orientations. Critically, a multivariate pattern classifier trained on brain activity evoked by these static stimuli could then successfully distinguish the direction of fast (‘streaky’) but not slow motion. Thus, signals encoding static-oriented streak information are present in human early visual cortex when viewing fast motion. These experiments show that motion streaks are present in the human visual system for faster motion.     

Engaging in word recognition elicits highly specific modulations in visual cortex

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A neuronal basis of iconic memory in macaque primary visual cortex

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Perceptual stability reflected in neuronal pattern similarities in human visual cortex

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Neurometabolic coupling between neural activity,glucose, and lactate in activated visual cortex

Neural activity is closely coupled with energy metabolism but details of the association remain to be identified. One basic area involves the relationships between neural activity and the main supportive substrates of glucose and lactate. This is of fundamental significance for the interpretation of non‐invasive neural imaging. Here, we use microelectrodes with high spatial and temporal resolution to determine simultaneous co‐localized changes in glucose, lactate, and neural activity during visual activation of the cerebral cortex in the cat. Tissue glucose and lactate concentration levels are measured with electrochemical microelectrodes while neural spiking activity and local field potentials are sampled by a microelectrode. These measurements are performed simultaneously while neurons are activated by visual stimuli of different contrast levels, orientations, and sizes. We find immediate decreases in tissue glucose concentration and simultaneous increases in lactate during neural activation. Both glucose and lactate signals return to their baseline levels instantly as neurons cease firing. No sustained changes or initial dips in glucose or lactate signals are elicited by visual stimulation. However, co‐localized measurements of cerebral blood flow and neural activity demonstrate a clear delay in the cerebral blood flow signal such that it does not correlate temporally with the neural response. These results provide direct real‐time evidence regarding the coupling between co‐localized energy metabolism and neural activity during physiological stimulation. They are also relevant to a current question regarding the role of lactate in energy metabolism in the brain during neural activation.

     

Multi-regional module-based signal transmission in mouse visual cortex

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The universal fuzzy logical framework of neural cir-cuits and its application in modeling primary visual cortex

Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Al- though there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.     

Directional tunings independent of orientation in the primary visual cortex of the cat

A family of moving 'random-line' patterns was developed and used to study the directional tuning of 91 single units in cat primary visual cortex (V1). The results suggest that, in addition to the well-known orientation-dependent mechanism, there is also some kind of orientation-independent mechanism underlying the direction selectivity. The directional tuning of the neurons varies in accordance with the increase of orientation or non-orientation element in the stimulus.     

Directional tunings independent of orientation in the primary visual cortex of the cat

A family of moving ‘random-line’ patterns was developed and used to study the directional tuning of 91 single units in cat primary visual cortex (V1). The results suggest that, in addition to the well-known orientation-dependent mechanism, there is also some kind of orientationindependent mechanism underlying the direction selectivity. The directional tuning of the neurons varies in accordance with the increase of orientation or non-orientation element in the stimulus.     

Watching moving images specifically promotes development of medial area of secondary visual cortex in rat

It is generally accepted that the cortex can be divided into numerous regions depending on the type of information each processes, and that specific input is effective in improving the development of related regions. In visual cortex, many subareas are distinguished on the basis of their adequate information. However, whether the development of a subarea can be specifically improved by its particular input is still largely unknown. Here, we show the specific effects of motion information on the development of the medial area of secondary visual cortex (V2M), a subarea associated with processing the movement component of visual information. Although watching a moving or a still image had similar effects in primary visual cortex, the moving image induced multistage development of V2M in dark‐reared rats: both mRNA and protein levels of GluR2 were upregulated, the density and protein content of GluR2‐positive synapses increased, and the spine density and the frequency of spontaneous excitatory postsynaptic currents (EPSCs) of pyramidal neurons in Layer 5 were elevated. Our results suggest that rats are able to identify motion information, distribute it to V2M, and then use this input to specifically improve the development of V2M. © 2009 Wiley Periodicals, Inc. Develop Neurobiol 2009