Neural correlates of contrast detection at threshold

Human psychophysical studies have demonstrated that, for stimuli near the threshold of visibility, detection of motion in one direction is unaffected by the superimposition of motion in the opposite direction. To investigate the neural basis for this perceptual phenomenon, we recorded from directionally selective neurons in macaque visual area MT (middle temporal visual area). Contrast thresholds obtained for single gratings moving in a neuron's preferred direction were compared with those obtained for motion presented simultaneously in the neuron's preferred and antipreferred directions. A simple model based on probability summation between neurons tuned to opposite directions could sufficiently account for contrast thresholds revealed psychophysically, suggesting that area MT is likely to provide the neural basis for contrast detection of stimuli modulated in time.     

A multilayer neural network model for perception of rotational motion

A multilayer neural nerwork model for the perception of rotational motion has been developed usingReichardt's motion detector array of correlation type, Kohonen's self-organized feature map and Schuster-Wagner's oscillating neural network. It is shown that the unsupervised learning could make the neurons on the second layer of the network tend to be self-organized in a form resembling columnar organization of selective directions in area MT of the primate's visual cortex. The output layer can interpret rotation information and give the directions and velocities of rotational motion. The computer simulation results are in agreement with some psychophysical observations of rotation-al perception. It is demonstrated that the temporal correlation between the oscillating neurons would be powerful for solving the "binding problem" of shear components of rotational motion.     

采用Reichardt运动检测器和Boltzmann Machine神经网络的运动计算

建立了一个探讨灵长类视皮层从Ｖ１区到ＭＴ区的运动信息加工原理的计算模型,这个过程的突出特征是视觉运动信息经过了从局部检测进步到整体感知。模型的第一层由用于抽提运动模式的局部速度以及结构性质的Ｒｅｉｃｈａｒｄｔ运动检测器组成,进一步的加工是通过Ｂｏｌｔｚｍａｎｎ Ｍａｃｈｉｎｅ神经网络来实现的。这种网络的学习算法具有局部更新的显著性质,在学习阶段,网络不断地修改联结权重以形成对于记录在网络的显单元上     

Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus

Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain.     

Relationship between Size Summation Properties,Contrast Sensitivity and Response Latency in the Dorsomedial and Middle Temporal Areas of the Primate Extrastriate Cortex

Analysis of the physiological properties of single neurons in visual cortex has demonstrated that both the extent of their receptive fields and the latency of their responses depend on stimulus contrast. Here, we explore the question of whether there are also systematic relationships between these response properties across different cells in a neuronal population. Single unit recordings were obtained from the middle temporal (MT) and dorsomedial (DM) extrastriate areas of anaesthetized marmoset monkeys. For each cell, spatial integration properties (length and width summation, as well as the presence of end- and side-inhibition within 15° of the receptive field centre) were determined using gratings of optimal direction of motion and spatial and temporal frequencies, at 60% contrast. Following this, contrast sensitivity was assessed using gratings of near-optimal length and width. In both areas, we found a relationship between spatial integration and contrast sensitivity properties: cells that summated over smaller areas of the visual field, and cells that displayed response inhibition at larger stimulus sizes, tended to show higher contrast sensitivity. In a sample of MT neurons, we found that cells showing longer latency responses also tended to summate over larger expanses of visual space in comparison with neurons that had shorter latencies. In addition, longer-latency neurons also tended to show less obvious surround inhibition. Interestingly, all of these effects were stronger and more consistent with respect to the selectivity for stimulus width and strength of side-inhibition than for length selectivity and end-inhibition. The results are partially consistent with a hierarchical model whereby more extensive receptive fields require convergence of information from larger pools of “feedforward” afferent neurons to reach near-optimal responses. They also suggest that a common gain normalization mechanism within MT and DM is involved, the spatial extent of which is more evident along the cell’s preferred axis of motion.     

Sensitive period for a multimodal response in human visual motion area MT/MST

The middle temporal complex (MT/MST) is a brain region specialized for the perception of motion in the visual modality. However, this specialization is modified by visual experience: after long-standing blindness, MT/MST responds to sound. Recent evidence also suggests that the auditory response of MT/MST is selective for motion. The developmental time course of this plasticity is not known. To test for a sensitive period in MT/MST development, we used fMRI to compare MT/MST function in congenitally blind, late-blind, and sighted adults. MT/MST responded to sound in congenitally blind adults, but not in late-blind or sighted adults, and not in an individual who lost his vision between ages of 2 and 3 years. All blind adults had reduced functional connectivity between MT/MST and other visual regions. Functional connectivity was increased between MT/MST and lateral prefrontal areas in congenitally blind relative to sighted and late-blind adults. These data suggest that early blindness affects the function of feedback projections from prefrontal cortex to MT/MST. We conclude that there is a sensitive period for visual specialization in MT/MST. During typical development, early visual experience either maintains or creates a vision-dominated response. Once established, this response profile is not altered by long-standing blindness.     

综述: 初级视皮层的亮度信息加工

亮度(luminance)是最基本的视觉信息.与其他视觉特征相比,由于视神经元对亮度刺激的反应较弱,并且许多神经元对均匀亮度无反应,对亮度信息编码的神经机制知之甚少.初级视皮层部分神经元对亮度的反应要慢于对比度反应,被认为是由边界对比度诱导的亮度知觉(brightness)的神经基础.我们的研究表明,初级视皮层许多神经元的亮度反应要快于对比度反应,并且这些神经元偏好低的空间频率、高的时间频率和高的运动速度,提示皮层下具有低空间频率和高运动速度通路的信息输入对产生初级视皮层神经元的亮度反应有贡献.已经知道初级视皮层神经元对空间频率反应的时间过程是从低空间频率到高空间频率,我们发现的早期亮度反应是对极低空间频率的反应,与这一时间过程是一致的,是这一从粗到细的视觉信息加工过程的第一步,揭示了处理最早的粗的视觉信息的神经基础.另外,初级视皮层含有偏好亮度下降和高运动速度的神经元,这群神经元的活动有助于在光照差的环境中检测高速运动的低亮度物体.     

Perceptual Learning of Motion Direction Discrimination with Suppressed and Unsuppressed MT in Humans: An fMRI Study

The middle temporal area of the extrastriate visual cortex (area MT) is integral to motion perception and is thought to play a key role in the perceptual learning of motion tasks. We have previously found, however, that perceptual learning of a motion discrimination task is possible even when the training stimulus contains locally balanced, motion opponent signals that putatively suppress the response of MT. Assuming at least partial suppression of MT, possible explanations for this learning are that 1) training made MT more responsive by reducing motion opponency, 2) MT remained suppressed and alternative visual areas such as V1 enabled learning and/or 3) suppression of MT increased with training, possibly to reduce noise. Here we used fMRI to test these possibilities. We first confirmed that the motion opponent stimulus did indeed suppress the BOLD response within hMT+ compared to an almost identical stimulus without locally balanced motion signals. We then trained participants on motion opponent or non-opponent stimuli. Training with the motion opponent stimulus reduced the BOLD response within hMT+ and greater reductions in BOLD response were correlated with greater amounts of learning. The opposite relationship between BOLD and behaviour was found at V1 for the group trained on the motion-opponent stimulus and at both V1 and hMT+ for the group trained on the non-opponent motion stimulus. As the average response of many cells within MT to motion opponent stimuli is the same as their response to non-directional flickering noise, the reduced activation of hMT+ after training may reflect noise reduction.     

Mathematical model for self-organization of direction columns in the primate middle temporal area

We attempted to reproduce modular structures for direction selectivity characteristic of the primate middle temporal area (MT) based on our thermodynamic model for the activity-dependent self-organization of neural networks. We assumed that excitatory afferent input to MT neurons arises from V1 and/or V2 neurons which are selective to both orientation of a visual stimulus and direction of its motion, and that such input is modifiable and becomes selectively connected through the process of self-organization. By contrast, local circuit connections within MT are unmodifiable and remain nonselectively connected (isotropic). The present simulations reproduced characteristic patterns of organization in the cortex of MT in that: (1) preferred directions of the afferent input gradually shifted, except for singularity lines where direction abruptly changed by 180°; (2) model MT neurons located between the singularity lines responded to unidirectionally moving stimuli, closely reflecting preferred direction of the afferent input; (3) neurons responding to stimuli moving in two opposite directions were located along the singularity lines; and (4) neurons responding to stimuli moving in any direction were clustered at the ends of the singularity lines. When the strength of the lateral inhibition was decreased, direction selectivity of MT neurons was reduced. Therefore, the lateral inhibition, even if isotropic, strengthens the direction selectivity of MT neurons. Expression of singularities changed depending on a parameter that represents the relative dominance of the direction selectivity to the orientation selectivity of the afferent input. When the direction selectivity was predominant, singularity points were formed, while when the orientation selectivity prevailed, the MT was covered by two-dimensional singularity networks. Line singularities similar to those experimentally observed were reproduced when these two types of selectivity were in balance. Received: 15 October 1992/Accepted in revised form: 27 June 1993     

End-stopping and the aperture problem: two-dimensional motion signals in macaque V1

Our perception of fine visual detail relies on small receptive fields at early stages of visual processing. However, small receptive fields tend to confound the orientation and velocity of moving edges, leading to ambiguous or inaccurate motion measurements (the aperture problem). Thus, it is often assumed that neurons in primary visual cortex (V1) carry only ambiguous motion information. Here we show that a subpopulation of V1 neurons is capable of signaling motion direction in a manner that is independent of contour orientation. Specifically, end-stopped V1 neurons obtain accurate motion measurements by responding only to the endpoints of long contours, a strategy which renders them largely immune to the aperture problem. Furthermore, the time course of end-stopping is similar to the time course of motion integration by MT neurons. These results suggest that cortical neurons might represent object motion by responding selectively to two-dimensional discontinuities in the visual scene.     

Attention to surfaces modulates motion processing in extrastriate area MT

In the visual system, early atomized representations are grouped into higher-level entities through processes of perceptual organization. Here we present neurophysiological evidence that a representation of a simple object, a surface defined by color and motion, can be the unit of attentional selection at an early stage of visual processing. Monkeys were cued by the color of a fixation spot to attend to one of two transparent random-dot surfaces, one red and one green, which occupied the same region of space. Motion of the attended surface drove neurons in the middle temporal (MT) visual area more strongly than physically identical motion of the non-attended surface, even though both occurred within the spotlight of attention. Surface-based effects of attention persisted even without differential surface coloring, but attentional modulation was stronger with color. These results show that attention can select surface representations to modulate visual processing as early as cortical area MT.     

A neural network model on self-organizing emergence of simple-cell receptive field with orientation selectivity in visual cortex

In order to probe into the self-organizing emergence of simple cell orientation selectivity, we tried to construct a neural network model that consists of LGN neurons and simple cells in visual cortex and obeys the Hebbian learning rule. We investigated the neural coding and representation of simple cells to a natural image by means of this model. The results show that the structures of their receptive fields are determined by the preferred orientation selectivity of simple cells. However, they are also decided by the emergence of self-organization in the unsupervision learning process. This kind of orientation selectivity results from dynamic self-organization based on the interactions between LGN and cortex.     

Mapping the organization of axis of motion selective features in human area MT using high-field fMRI

Functional magnetic resonance imaging (fMRI) at high magnetic fields has made it possible to investigate the columnar organization of the human brain in vivo with high degrees of accuracy and sensitivity. Until now, these results have been limited to the organization principles of early visual cortex (V1). While the middle temporal area (MT) has been the first identified extra-striate visual area shown to exhibit a columnar organization in monkeys, evidence of MT's columnar response properties and topographic layout in humans has remained elusive. Research using various approaches suggests similar response properties as in monkeys but failed to provide direct evidence for direction or axis of motion selectivity in human area MT. By combining state of the art pulse sequence design, high spatial resolution in all three dimensions (0.8 mm isotropic), optimized coil design, ultrahigh field magnets (7 Tesla) and novel high resolution cortical grid sampling analysis tools, we provide the first direct evidence for large-scale axis of motion selective feature organization in human area MT closely matching predictions from topographic columnar-level simulations.     

A neural network model on self-organizing emergence of simple-cell receptive field with orientation selectivity in visual cortex

In order to probe into the self-organizing emergence of simple cell orientation selectivity, we tried to construct a neural network model that consists of LGN neurons and simple cells in visual cortex and obeys the Hebbian learning rule. We investigated the neural coding and representation of simple cells to a natural image by means of this model. The results show that the structures of their receptive fields are determined by the preferred orientation selectivity of simple cells. However, they are also decided by the emergence of self-organization in the unsupervision learning process. This kind of orientation selectivity results from dynamic self-organization based on the interactions between LGN and cortex.     

Visual stability and the motion aftereffect: a psychophysical study revealing spatial updating

Eye movements create an ever-changing image of the world on the retina. In particular, frequent saccades call for a compensatory mechanism to transform the changing visual information into a stable percept. To this end, the brain presumably uses internal copies of motor commands. Electrophysiological recordings of visual neurons in the primate lateral intraparietal cortex, the frontal eye fields, and the superior colliculus suggest that the receptive fields (RFs) of special neurons shift towards their post-saccadic positions before the onset of a saccade. However, the perceptual consequences of these shifts remain controversial. We wanted to test in humans whether a remapping of motion adaptation occurs in visual perception.The motion aftereffect (MAE) occurs after viewing of a moving stimulus as an apparent movement to the opposite direction. We designed a saccade paradigm suitable for revealing pre-saccadic remapping of the MAE. Indeed, a transfer of motion adaptation from pre-saccadic to post-saccadic position could be observed when subjects prepared saccades. In the remapping condition, the strength of the MAE was comparable to the effect measured in a control condition (33±7% vs. 27±4%). Contrary, after a saccade or without saccade planning, the MAE was weak or absent when adaptation and test stimulus were located at different retinal locations, i.e. the effect was clearly retinotopic. Regarding visual cognition, our study reveals for the first time predictive remapping of the MAE but no spatiotopic transfer across saccades. Since the cortical sites involved in motion adaptation in primates are most likely the primary visual cortex and the middle temporal area (MT/V5) corresponding to human MT, our results suggest that pre-saccadic remapping extends to these areas, which have been associated with strict retinotopy and therefore with classical RF organization. The pre-saccadic transfer of visual features demonstrated here may be a crucial determinant for a stable percept despite saccades.     

Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity

One of the reasons the visual cortex has attracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large-scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spiking with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behavior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical characteristics of LGN spike trains are important for V1’s function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes.     

Activation of Multimodal Areas in the Human Cerebral Cortex in Response to Biological Motion Sounds

Functional magnetic resonance imaging (fMRI) was used to investigate activation of the multimodal areas in the cerebral cortex–supramarginal and angular gyri, precuneus, and middle temporal visual cortex (MT/V5)–in response to motion of biologically significant sounds (human footsteps). The subjects listened to approaching or receding footstep sounds during 45 s, and such stimulation was supposed to evoke auditory adaptation to biological motion. Listening conditions alternated with stimulation-free control. To reveal activity in the regions of interest, the periods before and during stimulation were compared. Most stable and voluminous activation was detected in the supramarginal and angular gyri, being registered for all footstep sound types–approaching, receding and steps in place. Listening to human approaching steps activated the precuneus area, with the volume of activation clusters varying considerably between subjects. In the MT/V5 area, activation was revealed in 5 of 21 subjects. The involvement of the tested multimodal cortical areas in analyzing biological motion is discussed.     

Multivoxel Pattern Analysis Reveals Auditory Motion Information in MT+ of Both Congenitally Blind and Sighted Individuals

Cross-modal plasticity refers to the recruitment of cortical regions involved in the processing of one modality (e.g. vision) for processing other modalities (e.g. audition). The principles determining how and where cross-modal plasticity occurs remain poorly understood. Here, we investigate these principles by testing responses to auditory motion in visual motion area MT+ of congenitally blind and sighted individuals. Replicating previous reports, we find that MT+ as a whole shows a strong and selective responses to auditory motion in congenitally blind but not sighted individuals, suggesting that the emergence of this univariate response depends on experience. Importantly, however, multivoxel pattern analyses showed that MT+ contained information about different auditory motion conditions in both blind and sighted individuals. These results were specific to MT+ and not found in early visual cortex. Basic sensitivity to auditory motion in MT+ is thus experience-independent, which may be a basis for the region''s strong cross-modal recruitment in congenital blindness.     

A Normalization Model of Attentional Modulation of Single Unit Responses

Although many studies have shown that attention to a stimulus can enhance the responses of individual cortical sensory neurons, little is known about how attention accomplishes this change in response. Here, we propose that attention-based changes in neuronal responses depend on the same response normalization mechanism that adjusts sensory responses whenever multiple stimuli are present. We have implemented a model of attention that assumes that attention works only through this normalization mechanism, and show that it can replicate key effects of attention. The model successfully explains how attention changes the gain of responses to individual stimuli and also why modulation by attention is more robust and not a simple gain change when multiple stimuli are present inside a neuron''s receptive field. Additionally, the model accounts well for physiological data that measure separately attentional modulation and sensory normalization of the responses of individual neurons in area MT in visual cortex. The proposal that attention works through a normalization mechanism sheds new light a broad range of observations on how attention alters the representation of sensory information in cerebral cortex.     

A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Analysis and an Application to Orientation Tuning

We explore a computationally efficient method of simulating realistic networks of neurons introduced by Knight, Manin, and Sirovich (1996) in which integrate-and-fire neurons are grouped into large populations of similar neurons. For each population, we form a probability density that represents the distribution of neurons over all possible states. The populations are coupled via stochastic synapses in which the conductance of a neuron is modulated according to the firing rates of its presynaptic populations. The evolution equation for each of these probability densities is a partial differential-integral equation, which we solve numerically. Results obtained for several example networks are tested against conventional computations for groups of individual neurons.We apply this approach to modeling orientation tuning in the visual cortex. Our population density model is based on the recurrent feedback model of a hypercolumn in cat visual cortex of Somers et al. (1995). We simulate the response to oriented flashed bars. As in the Somers model, a weak orientation bias provided by feed-forward lateral geniculate input is transformed by intracortical circuitry into sharper orientation tuning that is independent of stimulus contrast.The population density approach appears to be a viable method for simulating large neural networks. Its computational efficiency overcomes some of the restrictions imposed by computation time in individual neuron simulations, allowing one to build more complex networks and to explore parameter space more easily. The method produces smooth rate functions with one pass of the stimulus and does not require signal averaging. At the same time, this model captures the dynamics of single-neuron activity that are missed in simple firing-rate models.