一种新的视觉皮层简单细胞工作机制的模型

提出了一种基于独立元分析（ICA）的视觉皮层简单细胞工作机制的模型。用Gabor函数逼近对自然图像进行ICA而获得的基函数,揭示了ICA基函数与视觉皮层简单细胞感受野反应间存在内在的关系。并对水平条纹的图像进行ICA,模拟在特殊视觉环境下生长的幼年动物的视觉皮层发育过程,证实了1970年Blakemore和Cooper在幼猫上的实验结果。从而说明ICA可以模拟动物的视觉皮层简单细胞工作过程。     

初级视觉皮层“简单”与“复杂”神经元动力学的计算建模

本文回顾了我们在哺乳动物视觉皮层的建模工作.利用初级视觉皮层的大规模神经元网络模型,我们解释了初级视觉皮层里“简单”与“复杂”神经元现象的网络机制.所谓的“简单”细胞对视觉刺激的反应近似线性,而“复杂”细胞对视觉刺激是非线性的.我们的模型成功地再现了简单和复杂细胞分布的实验数据.     

Correction of Distortion in Flattened Representations of the Cortical Surface Allows Prediction of V1-V3 Functional Organization from Anatomy

Several domains of neuroscience offer map-like models that link location on the cortical surface to properties of sensory representation. Within cortical visual areas V1, V2, and V3, algebraic transformations can relate position in the visual field to the retinotopic representation on the flattened cortical sheet. A limit to the practical application of this structure-function model is that the cortex, while topologically a two-dimensional surface, is curved. Flattening of the curved surface to a plane unavoidably introduces local geometric distortions that are not accounted for in idealized models. Here, we show that this limitation is overcome by correcting the geometric distortion induced by cortical flattening. We use a mass-spring-damper simulation to create a registration between functional MRI retinotopic mapping data of visual areas V1, V2, and V3 and an algebraic model of retinotopy. This registration is then applied to the flattened cortical surface anatomy to create an anatomical template that is linked to the algebraic retinotopic model. This registered cortical template can be used to accurately predict the location and retinotopic organization of these early visual areas from cortical anatomy alone. Moreover, we show that prediction accuracy remains when extrapolating beyond the range of data used to inform the model, indicating that the registration reflects the retinotopic organization of visual cortex. We provide code for the mass-spring-damper technique, which has general utility for the registration of cortical structure and function beyond the visual cortex.     

Spike suppression in a local cortical circuit induced by transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) noninvasively interferes with human cortical function, and is widely used as an effective technique for probing causal links between neural activity and cognitive function. However, the physiological mechanisms underlying TMS-induced effects on neural activity remain unclear. We examined the mechanism by which TMS disrupts neural activity in a local circuit in early visual cortex using a computational model consisting of conductance-based spiking neurons with excitatory and inhibitory synaptic connections. We found that single-pulse TMS suppressed spiking activity in a local circuit model, disrupting the population response. Spike suppression was observed when TMS was applied to the local circuit within a limited time window after the local circuit received sensory afferent input, as observed in experiments investigating suppression of visual perception with TMS targeting early visual cortex. Quantitative analyses revealed that the magnitude of suppression was significantly larger for synaptically-connected neurons than for isolated individual neurons, suggesting that intracortical inhibitory synaptic coupling also plays an important role in TMS-induced suppression. A conventional local circuit model of early visual cortex explained only the early period of visual suppression observed in experiments. However, models either involving strong recurrent excitatory synaptic connections or sustained excitatory input were able to reproduce the late period of visual suppression. These results suggest that TMS targeting early visual cortex disrupts functionally distinct neural signals, possibly corresponding to feedforward and recurrent information processing, by imposing inhibitory effects through intracortical inhibitory synaptic connections.     

A Network Model of Motion Processing in Area MT of Primates

A simple and biologically plausible model is proposed to simulatethe visual motion processing taking place in the middle temporal (MT) areaof the visual cortex in the primate brain. The model is ahierarchical neural network composed of multiple competitive learninglayers. The input layer of the network simulates the neurons in the primaryvisual cortex (V1), which are sensitive to the orientation and motionvelocity of the visual stimuli, and the middle and output layers of thenetwork simulate the component MT and pattern MT neurons, which areselectively responsive to local and global motions, respectively. Thenetwork model was tested with various simulated motion patterns (random dotsof different direction correlations, transparent motion, grating and plaidpatterns, and so on). The response properties of the model closely resemblemany of the known features of the MT neurons found neurophysiologically.These results show that the sophisticated response behaviors of the MTneurons can emerge naturally from some very simple models, such as acompetitive learning network.     

l-Dopa methyl ester attenuates amblyopia-induced neuronal injury in visual cortex of amblyopic cat

In the present study, we aimed to assess the potential anti-amblyopic effects of l-dopa methyl ester (LDME) on visual cortex area 17 in an amblyopic feline model induced by monocular vision deprivation. After LDME administration, pathophysiologic and ultrastructural observations were utilized to examine the morphological changes of nerve cells in visual cortex area 17. Dopamine (DA) and its metabolite contents in visual cortex area 17 were investigated through HPLC analysis. Apoptotic cells in visual cortex area 17 were evaluated by TUNEL assay. Additionally, the c-fos expression both at gene and protein levels was assessed using RT-PCR and immunohistochemistry analyses, respectively. The contents of DA and its metabolites were elevated in visual cortex area 17. Neuronal rejuvenation which occurred in visual cortex area 17 was observed through anatomical and physiological assessments. Similarly, TUNEL results showed that neuronal apoptosis was inhibited in the visual cortex of amblyopic cats by both l-dopa and LDME therapies. Meanwhile, the c-fos expression was notably up-regulated at both the mRNA and protein levels by the treatments. These findings suggested that LDME treatment could effectively increase DA and its metabolite contents, and restrain the apoptotic process, as well as elevate the c-fos expression in nerve cells of visual cortex area 17. Taken together, LDME might ameliorate the functional cytoarchitecture in visual cortex area 17 through mechanisms that elevate DA content and increase endogenous c-fos expression, as well as inhibit neuronal lesion in visual cortex tissue.     

reflection of stimulus orientation in reactions of visual cortex neurons

A mathematical model of neuronal target structure with spatial anisotropy of lateral inhibition is discussed. The positions of the neuronal target to oriented sensory stimuli are investigated by computer simulation. It is suggested that visual stimuli orientation is coded in the late phase of dynamic responses of cortical neurons. This idea is in agreement with the data obtained in experiments on guinea pig visual cortex neurons.     

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.     

M-cells: A logic circuit model to account for some features of CNS inhibition

A truth table definition for neural inhibition is used to develop a model for stimulus specificity in a sensory system. An example from the mammalian visual system, that of orientation selectivity in visual cortex, is worked out in detail. Using the assumption that logical processing of signals may take place in a nerve cell's dendritic tree, a digital circuit is transformed into a neural model. An important feature of the model is an inhibitory neuron, termed an M-cell, which by its actions confers response specificity on neighboring principal cells. The M-cell is shown to have several properties in common with basket cells seen in cerebellar, hippocampal and cerebral cortex.     

Energy and phase orientation mechanisms: a computational model

A computational model is proposed for spatial orientation processing beyond the initial stage of linear filtering in visual cortex. The model accounts for orientation pop-out, edge location and orientation, and bar location and orientation. It naturally extends to higher order orientation symmetries. The model is consistent with much of the current understanding of early processing in mammalian visual cortex. It builds on the notions of orientation and spatial frequency specific simple cells, any subsequent non-linearity, and orientation 'pooling'. The processing treats simple cell energy, real, and imaginary responses in a unified way to generate 'feature maps'. The 'pooling' operation in each case is a discrete Fourier transform of the simple cell responses over orientation. The suggested processing has implications for psychophysics (e.g. providing an explanation of why orientation discrimination thresholds are more than an order of magnitude less than simple cell orientation bandwidths), provides some understanding of the variety of 'complex-cell' properties found in visual cortex, and provides a plausible starting point for subsequent processing.     

Computational model for perception of objects and motions

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist ‘What’ and ‘Where’ pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives ‘where’, for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The computational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.     

Computational model for perception of objects and motions

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist 'What' and 'Where' pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives 'where', for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The compu- tational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.     

Metabolic Changes in the Visual Cortex of Binocular Blindness Macaque Monkeys: A Proton Magnetic Resonance Spectroscopy Study

Purpose

To evaluate proton magnetic resonance spectroscopy (¹H-MRS) in a study of cross-modal plasticity in the visual cortex of binocular blindness macaque monkeys.

Materials and Methods

Four healthy neonatal macaque monkeys were randomly divided into 2 groups, with 2 in each group. Optic nerve transection was performed in both monkeys in the experimental group (group B) to obtain binocular blindness. Two healthy macaque monkeys served as a control group (group A). After sixteen months post-procedure, ¹H-MRS was performed in the visual cortex of all monkeys. We compared the peak areas of NAA, Cr, Cho, Glx and Ins and the ratios of NAA/Cr, Cho/Cr, Glx/Cr and Ins/Cr of each monkey in group B with group A.

Results

The peak area of NAA and the NAA/Cr ratio in the visual cortex of monkey 4 in group B were found to be dramatically decreased, the peak area of NAA slightly decreased and the NAA/Cr ratio clearly decreased in visual cortex of monkey 3 in group B than those in group A. The peak area of Ins and the Ins/Cr ratio in the visual cortex of monkey 4 in group B slightly increased. The peak area of Cho and the Cho/Cr ratio in the visual cortex of all monkeys in group B dramatically increased compared with group A. The peak area of Glx in the visual cortex of all monkeys in group B slightly increased compared with group A.

Conclusions

¹H-MRS could detect biochemical and metabolic changes in the visual cortex and therefore this technique can be used to provide valuable information for investigating the mechanisms of cross-modal plasticity of binocular blindness in a macaque monkey model.     

Mathematical modeling and parameter estimation of axonal cargo transport

This paper is about how cortical recurrent interactions in primary visual cortex (V1) together with feedback from extrastriate cortex can account for spectral peaks in the V1 local field potential (LFP). Recent studies showed that visual stimulation enhances the γ-band (25–90 Hz) of the LFP power spectrum in macaque V1. The height and location of the γ-band peak in the LFP spectrum were correlated with visual stimulus size. Extensive spatial summation, possibly mediated by feedback connections from extrastriate cortex and long-range horizontal connections in V1, must play a crucial role in the size dependence of the LFP. To analyze stimulus-effects on the LFP of V1 cortex, we propose a network model for the visual cortex that includes two populations of V1 neurons, excitatory and inhibitory, and also includes feedback to V1 from extrastriate cortex. The neural network model for V1 was a resonant system. The model’s resonance frequency (ResF) was in the γ-band and varied up or down in frequency depending on cortical feedback. The model’s ResF shifted downward with stimulus size, as in the real cortex, because increased size recruited more activity in extrastriate cortex and V1 thereby causing stronger feedback. The model needed to have strong local recurrent inhibition within V1 to obtain ResFs that agree with cortical data. Network resonance as a consequence of recurrent excitation and inhibition appears to be a likely explanation for γ-band peaks in the LFP power spectrum of the primary visual cortex.     

Topographical maps of orientation specificity

A spatially congruent framework for orientation encoding in the primate striate visual cortex is proposed and discussed. This framework, which is based on the foot-of-normal representation of straight lines, not only provides a reasonable explanation for the centric organization of the orientation specificity in the primate striate visual cortex but also accounts for a series of experimentally verified intriguing phenomena such as the lack of orientation specificity around the centres of the orientation modules (i.e. the singularities), the increased neural activity at these same places, and the relatively uniform distribution of the singularities along the ocular dominance columns. The proposed framework can also explain and predict the possible existence of centric modules in other cortical regions containing topographical maps of two-dimensional sensory spaces (e.g. pre-striate and somatic sensory cortex). A simple one-layer neural model of the basic centric module in the framework is presented, and simulation results are discussed.     

Visual Cortex: How Are Faces and Objects Represented?

The way in which information about complex objects and faces is represented in visual cortex is controversial. One model posits that information is processed in modules, highly specialized for different categories of objects; an opposing model appeals to a distributed representation across a large network of visual areas. A recent paper uses a novel imaging technique to address this controversy.     

Online learning and stimulus-driven responses of neurons in visual cortex

In understanding how visual scene is processed in visual cortex, it has been an intriguing problem for theoretical and experimental neuroscientists to examine the relationship between visual stimuli and the induced responses of visual cortex. In particular, it is less explored whether and how the collective responses of visual neurons are patterned to reflect the geometrical regularities. In this paper, through a computation model and statistical analysis, we show that the orientation preference maps induced from correlated visual stimuli exhibit geometrical regularities similar as observed in natural images.     

Emergence of visual saliency from natural scenes via context-mediated probability distributions coding

Visual saliency is the perceptual quality that makes some items in visual scenes stand out from their immediate contexts. Visual saliency plays important roles in natural vision in that saliency can direct eye movements, deploy attention, and facilitate tasks like object detection and scene understanding. A central unsolved issue is: What features should be encoded in the early visual cortex for detecting salient features in natural scenes? To explore this important issue, we propose a hypothesis that visual saliency is based on efficient encoding of the probability distributions (PDs) of visual variables in specific contexts in natural scenes, referred to as context-mediated PDs in natural scenes. In this concept, computational units in the model of the early visual system do not act as feature detectors but rather as estimators of the context-mediated PDs of a full range of visual variables in natural scenes, which directly give rise to a measure of visual saliency of any input stimulus. To test this hypothesis, we developed a model of the context-mediated PDs in natural scenes using a modified algorithm for independent component analysis (ICA) and derived a measure of visual saliency based on these PDs estimated from a set of natural scenes. We demonstrated that visual saliency based on the context-mediated PDs in natural scenes effectively predicts human gaze in free-viewing of both static and dynamic natural scenes. This study suggests that the computation based on the context-mediated PDs of visual variables in natural scenes may underlie the neural mechanism in the early visual cortex for detecting salient features in natural scenes.     

Ageing and monoamine turnover in the lateral geniculate nucleus and visual cortex of the rat

The effects of ageing on the turnover of dopamine, noradrenaline and serotonin in the lateral geniculate nucleus and the visual cortex were evaluated, using high performance liquid chromatography (HPLC) with electrochemical detection. Compared to adult animals, aged rats showed more changes in the visual cortex than in the lateral geniculate nucleus, with dopamine turnover decreased in both structures and noradrenaline turnover unaltered. Changes in serotonin turnover were witnessed only in the visual cortex. A decrease in the monoamine oxidase-A to -B ratio was also observed with increased age for both the lateral geniculate nucleus and visual cortex.