A neural network model for texture discrimination

A model of texture discrimination in visual cortex was built using a feedforward network with lateral interactions among relatively realistic spiking neural elements. The elements have various membrane currents, equilibrium potentials and time constants, with action potentials and synapses. The model is derived from the modified programs of MacGregor (1987). Gabor-like filters are applied to overlapping regions in the original image; the neural network with lateral excitatory and inhibitory interactions then compares and adjusts the Gabor amplitudes in order to produce the actual texture discrimination. Finally, a combination layer selects and groups various representations in the output of the network to form the final transformed image material. We show that both texture segmentation and detection of texture boundaries can be represented in the firing activity of such a network for a wide variety of synthetic to natural images. Performance details depend most strongly on the global balance of strengths of the excitatory and inhibitory lateral interconnections. The spatial distribution of lateral connective strengths has relatively little effect. Detailed temporal firing activities of single elements in the lateral connected network were examined under various stimulus conditions. Results show (as in area 17 of cortex) that a single element's response to image features local to its receptive field can be altered by changes in the global context.     

A stable biologically motivated learning mechanism for visual feature extraction to handle facial categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task.     

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.     

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.     

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。     

Relating Information,Encoding and Adaptation: Decoding the Population Firing Rate in Visual Areas 17/18 in Response to a Stimulus Transition

Neurons in the primary visual cortex typically reach their highest firing rate after an abrupt image transition. Since the mutual information between the firing rate and the currently presented image is largest during this early firing period it is tempting to conclude this early firing encodes the current image. This view is, however, made more complicated by the fact that the response to the current image is dependent on the preceding image. Therefore we hypothesize that neurons encode a combination of current and previous images, and that the strength of the current image relative to the previous image changes over time. The temporal encoding is interesting, first, because neurons are, at different time points, sensitive to different features such as luminance, edges and textures; second, because the temporal evolution provides temporal constraints for deciphering the instantaneous population activity. To study the temporal evolution of the encoding we presented a sequence of 250 ms stimulus patterns during multiunit recordings in areas 17 and 18 of the anaesthetized ferret. Using a novel method we decoded the pattern given the instantaneous population-firing rate. Following a stimulus transition from stimulus A to B the decoded stimulus during the first 90ms was more correlated with the difference between A and B (B-A) than with B alone. After 90ms the decoded stimulus was more correlated with stimulus B than with B-A. Finally we related our results to information measures of previous (B) and current stimulus (A). Despite that the initial transient conveys the majority of the stimulus-related information; we show that it actually encodes a difference image which can be independent of the stimulus. Only later on, spikes gradually encode the stimulus more exclusively.     

Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex.

Simple cells in the primary visual cortex process incoming visual information with receptive fields localized in space and time, bandpass in spatial and temporal frequency, tuned in orientation, and commonly selective for the direction of movement. It is shown that performing independent component analysis (ICA) on video sequences of natural scenes produces results with qualitatively similar spatio-temporal properties. Whereas the independent components of video resemble moving edges or bars, the independent component filters, i.e. the analogues of receptive fields, resemble moving sinusoids windowed by steady Gaussian envelopes. Contrary to earlier ICA results on static images, which gave only filters at the finest possible spatial scale, the spatio-temporal analysis yields filters at a range of spatial and temporal scales. Filters centred at low spatial frequencies are generally tuned to faster movement than those at high spatial frequencies.     

Corticothalamic interactions in the transfer of visual information

Thalamic function does not stand apart, as a discrete processing step, from the cortical circuitry. The thalamus receives extensive feedback from the cortex and this influences the firing pattern, synchronization and sensory response mode of relay cells. A crucial question concerns the extent to which the feedback simply controls the state and transmission mode of relay cells and the extent to which the feedback participates in the specific processing of sensory information. Using examples from experiments examining the influence of feedback from the visual cortex to the lateral geniculate nucleus (LGN), we argue that thalamic mechanisms are selectively focused by visually driven feedback to optimize the thalamic contribution to segmentation and global integration. This involves effects on both the temporal and spatial parameters characterizing the responses of LGN cells and includes, for example, motion-driven feedback effects from MT (middle temporal visual area) relayed via layer 6 of V1 (primary visual cortex).     

Sparse Coding Models Can Exhibit Decreasing Sparseness while Learning Sparse Codes for Natural Images

The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive fields are optimized to accurately represent input stimuli using the least amount of neural activity. As these networks develop to represent a given class of stimulus, the receptive fields are refined so that they capture the most important stimulus features. Intuitively, this is expected to result in sparser network activity over time. Recent experiments, however, show that stimulus-evoked activity in ferret V1 becomes less sparse during development, presenting an apparent challenge to the sparse coding hypothesis. Here we demonstrate that some sparse coding models, such as those employing homeostatic mechanisms on neural firing rates, can exhibit decreasing sparseness during learning, while still achieving good agreement with mature V1 receptive field shapes and a reasonably sparse mature network state. We conclude that observed developmental trends do not rule out sparseness as a principle of neural coding per se: a mature network can perform sparse coding even if sparseness decreases somewhat during development. To make comparisons between model and physiological receptive fields, we introduce a new nonparametric method for comparing receptive field shapes using image registration techniques.     

Are V1 Simple Cells Optimized for Visual Occlusions? A Comparative Study

Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex.     

Slow Feature Analysis on Retinal Waves Leads to V1 Complex Cells

The developing visual system of many mammalian species is partially structured and organized even before the onset of vision. Spontaneous neural activity, which spreads in waves across the retina, has been suggested to play a major role in these prenatal structuring processes. Recently, it has been shown that when employing an efficient coding strategy, such as sparse coding, these retinal activity patterns lead to basis functions that resemble optimal stimuli of simple cells in primary visual cortex (V1). Here we present the results of applying a coding strategy that optimizes for temporal slowness, namely Slow Feature Analysis (SFA), to a biologically plausible model of retinal waves. Previously, SFA has been successfully applied to model parts of the visual system, most notably in reproducing a rich set of complex-cell features by training SFA with quasi-natural image sequences. In the present work, we obtain SFA units that share a number of properties with cortical complex-cells by training on simulated retinal waves. The emergence of two distinct properties of the SFA units (phase invariance and orientation tuning) is thoroughly investigated via control experiments and mathematical analysis of the input-output functions found by SFA. The results support the idea that retinal waves share relevant temporal and spatial properties with natural visual input. Hence, retinal waves seem suitable training stimuli to learn invariances and thereby shape the developing early visual system such that it is best prepared for coding input from the natural world.     

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.     

Different dynamics of performance and brain activation in the time course of perceptual learning

Perceptual learning is regarded as a manifestation of experience-dependent plasticity in the sensory systems, yet the underlying neural mechanisms remain unclear. We measured the dynamics of performance on a visual task and brain activation in the human primary visual cortex (V1) across the time course of perceptual learning. Within the first few weeks of training, brain activation in a V1 subregion corresponding to the trained visual field quadrant and task performance both increased. However, while performance levels then saturated and were maintained at a constant level, brain activation in the corresponding areas decreased to the level observed before training. These findings indicate that there are distinct temporal phases in the time course of perceptual learning, related to differential dynamics of BOLD activity in visual cortex.     

Deep Supervised,but Not Unsupervised,Models May Explain IT Cortical Representation

Inferior temporal (IT) cortex in human and nonhuman primates serves visual object recognition. Computational object-vision models, although continually improving, do not yet reach human performance. It is unclear to what extent the internal representations of computational models can explain the IT representation. Here we investigate a wide range of computational model representations (37 in total), testing their categorization performance and their ability to account for the IT representational geometry. The models include well-known neuroscientific object-recognition models (e.g. HMAX, VisNet) along with several models from computer vision (e.g. SIFT, GIST, self-similarity features, and a deep convolutional neural network). We compared the representational dissimilarity matrices (RDMs) of the model representations with the RDMs obtained from human IT (measured with fMRI) and monkey IT (measured with cell recording) for the same set of stimuli (not used in training the models). Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category. In addition, better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities. Representational geometries were significantly correlated between IT and many of the models. However, the categorical clustering observed in IT was largely unexplained by the unsupervised models. The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data. Overall, our results suggest that explaining IT requires computational features trained through supervised learning to emphasize the behaviorally important categorical divisions prominently reflected in IT.     

Spectral Characteristics of Phase Sensitivity and Discharge Rate of Neurons in the Ascending Tectofugal Visual System

Drifting gratings can modulate the activity of visual neurons at the temporal frequency of the stimulus. In order to characterize the temporal frequency modulation in the cat’s ascending tectofugal visual system, we recorded the activity of single neurons in the superior colliculus, the suprageniculate nucleus, and the anterior ectosylvian cortex during visual stimulation with drifting sine-wave gratings. In response to such stimuli, neurons in each structure showed an increase in firing rate and/or oscillatory modulated firing at the temporal frequency of the stimulus (phase sensitivity). To obtain a more complete characterization of the neural responses in spatiotemporal frequency domain, we analyzed the mean firing rate and the strength of the oscillatory modulations measured by the standardized Fourier component of the response at the temporal frequency of the stimulus. We show that the spatiotemporal stimulus parameters that elicit maximal oscillations often differ from those that elicit a maximal discharge rate. Furthermore, the temporal modulation and discharge-rate spectral receptive fields often do not overlap, suggesting that the detection range for visual stimuli provided jointly by modulated and unmodulated response components is larger than the range provided by a one response component.     

A neuron-inspired computational architecture for spatiotemporal visual processing

In this article, we present a neurologically motivated computational architecture for visual information processing. The computational architecture’s focus lies in multiple strategies: hierarchical processing, parallel and concurrent processing, and modularity. The architecture is modular and expandable in both hardware and software, so that it can also cope with multisensory integrations – making it an ideal tool for validating and applying computational neuroscience models in real time under real-world conditions. We apply our architecture in real time to validate a long-standing biologically inspired visual object recognition model, HMAX. In this context, the overall aim is to supply a humanoid robot with the ability to perceive and understand its environment with a focus on the active aspect of real-time spatiotemporal visual processing. We show that our approach is capable of simulating information processing in the visual cortex in real time and that our entropy-adaptive modification of HMAX has a higher efficiency and classification performance than the standard model (up to \(\sim \!+6\,\% \) ).     

Early vision is early in time

Roelfsema, Tolboom, and Khayat have found that neurons in primary visual cortex, V1, increase their spike firing rates to signal image segmentation and attention. V1 responses were in a temporal sequence: first to image motion, next to segmentation, last to attentional signals. The involvement of V1 with segmentation and attention suggests modifying the hierarchical view of visual perception.     

Temporal codes and sparse representations: A key to understanding rapid processing in the visual system

Where neural information processing is concerned, there is no debate about the fact that spikes are the basic currency for transmitting information between neurons. How the brain actually uses them to encode information remains more controversial. It is commonly assumed that neuronal firing rate is the key variable, but the speed with which images can be analysed by the visual system poses a major challenge for rate-based approaches. We will thus expose here the possibility that the brain makes use of the spatio-temporal structure of spike patterns to encode information. We then consider how such rapid selective neural responses can be generated rapidly through spike-timing-dependent plasticity (STDP) and how these selectivities can be used for visual representation and recognition. Finally, we show how temporal codes and sparse representations may very well arise one from another and explain some of the remarkable features of processing in the visual system.     

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.