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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.

Recent experimental studies of hetero-synaptic interactions in various systems have shown the role of signaling in the plasticity, challenging the conventional understanding of Hebb's rule. It has also been found that activity plays a major role in plasticity, with neurotrophins acting as molecular signals translating activity into structural changes. Furthermore, role of synaptic efficacy in biasing the outcome of competition has also been revealed recently. Motivated by these experimental findings we present a model for the development of simple cell receptive field structure based on the competitive hetero-synaptic interactions for neurotrophins combined with cooperative hetero-synaptic interactions in the spatial domain. We find that with proper balance in competition and cooperation, the inputs from two populations (ON/OFF) of LGN cells segregate starting from the homogeneous state. We obtain segregated ON and OFF regions in simple cell receptive field. Our modeling study supports the experimental findings, suggesting the role of synaptic efficacy and the role of spatial signaling. We find that using this model we obtain simple cell RF, even for positively correlated activity of ON/OFF cells. We also compare different mechanism of finding the response of cortical cell and study their possible role in the sharpening of orientation selectivity. We find that degree of selectivity improvement in individual cells varies from case to case depending upon the structure of RF field and type of sharpening mechanism.

We have previously shown that during top-down attentional modulation (stimulus expectation) correlations of the beta signals across the primary visual cortex were uniform, while during bottom-up attentional processing (visual stimulation) their values were heterogeneous. These different patterns of attentional beta modulation may be caused by feed-forward lateral inhibitory interactions in the visual cortex, activated solely during stimulus processing. To test this hypothesis, we developed a large-scale computational model of the cortical network. We first identified the parameter range needed to support beta rhythm generation, and next, simulated the different activity states corresponding to experimental paradigms. The model matched our experimental data in terms of spatial organization of beta correlations during different attentional states and provided a computational confirmation of the hypothesis that the paradigm-specific beta activation spatial maps depend on the lateral inhibitory mechanism. The model also generated testable predictions that cross-correlation values depend on the distance between the activated columns and on their spatial position with respect to the location of the sensory inputs from the thalamus.

It has been suggested that an appeal to holographic and quantum properties will be ultimately required for the understanding of higher brain functions. On the other hand, successful quantum-like approaches to cognitive and behavioral processes bear witness to the usefulness of quantum prescriptions as applied to the analysis of complex non-quantum systems. Here, we show that the signal-tuned Gabor approach for modeling cortical neurons, although not based on quantum assumptions, also admits a quantum-like interpretation. Recently, the equation of motion for the signal-tuned complex cell response has been derived and proven equivalent to the Schrödinger equation for a dissipative quantum system whose solutions come under two guises: as plane-wave and Airy-packet responses. By interpreting the squared magnitude of the plane-wave solution as a probability density, in accordance with the quantum mechanics prescription, we arrive at a Poisson spiking probability — a common model of neuronal response — while spike propagation can be described by the Airy-packet solution. The signal-tuned approach is also proven consistent with holonomic brain theories, as it is based on Gabor functions which provide a holographic representation of the cell’s input, in the sense that any restricted subset of these functions still allows stimulus reconstruction.

Our previous study with functional magnetic resonance imaging (MRI) demonstrated that acupuncture stimulation of the vision-related acupoint, Bl-67, activates the visual cortex of the human brain. As a further study on the effect of Bl-67 acupuncture stimulation on the visual cortex, we examined c-Fos expression in binocularly deprived rat pups. Binocular deprivation significantly reduced the number of c-Fos-positive cells in the primary visual cortex, compared with that of normal control rat pups. Interestingly, acupuncture stimulation of Bl-67 resulted in a significant increase in the number of c-Fos-positive cells in the primary visual cortex, while acupuncture stimulation of other acupoints less important for visual function had no significant effect on c-Fos expression in the primary visual cortex. The results suggest the possibility of vision-related acupoint (Bl-67) having an influence over the activity of the primary visual cortex.

Data from three functional magnetic resonance imaging (fMRI) studies that involved in total about 100 participants and showed that the strength of several visual illusions such as the Ebbinghaus, Ponzo, and Muller-Lyer illusions depends on neuroanatomical subject measures such as visual cortex surface area and parahippocampal cortex gray matter volume were evaluated using a dynamical systems perspective to determine brain bifurcation parameters. Bifurcation parameters that involved power laws and captured relational dependencies were fitted separately to the three fMRI studies. The bifurcation parameter hypothesis that states that such parameters show unique quantities and are no longer correlated to structural systems properties was tested. The power law exponents and mean bifurcation parameter values were determined. For all three studies and three illusion types, the bifurcation parameter hypothesis was supported. Accordingly, the constructed parameters characterized the reactions of the participants under the Ebbinghaus, Ponzo, and Muller-Lyer illusions in terms of unique threshold values that no longer depended on neuroanatomical subject measures. Power law exponents in the range from 1 to 7 were found. The fMRI data describing gray matter volume of certain active regions in the parahippocampal cortex showed some interesting relationship between the mean bifurcation parameter values.

In this study, functional near-infrared spectroscopy (fNIRS) is utilized to measure the hemodynamic responses (HRs) in the visual cortex of 14 subjects (aged 22–34 years) viewing the primary red, green, and blue (RGB) colors displayed on a white screen by a beam projector. The spatiotemporal characteristics of their oxygenated and deoxygenated hemoglobins (HbO and HbR) in the visual cortex are measured using a 15-source and 15-detector optode configuration. To see whether the activation maps upon RGB-color stimuli can be distinguished or not, the $t$-values of individual channels are averaged over 14 subjects. To find the best combination of two features for classification, the HRs of activated channels are averaged over nine trials. The HbO mean, peak, slope, skewness and kurtosis values during 2–7s window for a given 10s stimulation period are analyzed. Finally, the linear discriminant analysis (LDA) for classifying three classes is applied. Individually, the best classification accuracy obtained with slope-skewness features was 74.07% (Subject 1), whereas the best overall over 14 subjects was 55.29% with peak-skewness combination. Noting that the chance level of 3-class classification is 33.33%, it can be said that RGB colors can be distinguished. The overall results reveal that fNIRS can be used for monitoring purposes of the HR patterns in the human visual cortex.

Precise mathematical modeling of the primary visual cortex (V1) is still a challenging problem. Due to the high similarity of visual system of cat and human, in this paper, we present a hybrid model to track the electrical responses of neurons that are measured by a multi-electrode array implanted in cat V1. The proposed model combines a stochastic phenomenological model with a multilayer leaky integrate-and-fire (LIF) model to predict V1 responses. Since all the existing visual cortex models do not capture the stochastic properties of synaptic changes, the proposed phenomenological model provides input currents for V1 by utilizing continuous chaotic neural equations with a quantization rule. Then a multilayer LIF model is presented to mimic the functions of lateral geniculate nucleus (LGN) and V1 neurons by their corresponding differential equations. The input current in these models is from the presynaptic neurons, which are computed using the LIF model. The LGN-V1 neuronal connections are adopted from previous studies, where the receptive fields (RFs) of LGN neurons converge onto elongated spatial structures that denote RFs of V1 neurons. The main purpose of this paper is to develop a short-term plasticity model that is more consistent with the nature of the LGN and V1 responses compared to state-of-the-art models. Previous studies have not incorporated the stochastic and quantized behaviors of neurons that in the recorded data of implemented electrodes. The experimental results show the ability of the proposed model to accurately predict spikes recorded experimentally, indicating the model outperforms the state-of-the-art method.

This paper introduces a new general framework for face identification, which is based on cortex mechanism: it describes a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating between a template matching and a maximum pooling operation, corresponding to two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier. The experiment results demonstrate the strength of the approach on face identification. In addition to its relevance for computer vision, the success of this approach suggests a plausibility proof for a class of feedforward models of object identification in cortex.

Long-term potentiation (LTP) of synaptic efficacy following repetitive (tetanic) inputs was described originally in the hippocampus, and it has been studied extensively based on the hypothesis that it represents a synaptic model for learning and memory in the brain. In the developing visual cortex, long-term depression (LTD) as well as LTP was found to be induced by tetanic stimulation of afferents, and such a synaptic modification was proposed as a basis for experience-dependent change in functional properties of cortical neurons during the critical period of postnatal development. This chapter deals with the induction of LTP and LTD in visual cortex and their possible functional significances. Among possible molecular mechanisms for the induction of LTP and LTD, those including the involvement of NMDA receptors, Ca²⁺/calmodulin dependent protein kinase II, protein kinase C, phosphatidylinositol turnover and membrane-associated cytoskeletal proteins have been reviewed, although the results obtained so far in visual cortex are only fragmentary.