Visual Cortex Of Brain Research Articles

Large-scale parameters framework with large convolutional kernel for encoding visual fMRI activity information.

Visual encoding models often use deep neural networks to describe the brain's visual cortex response to external stimuli. Inspired by biological findings, researchers found that large receptive fields built with large convolutional kernels improve convolutional encoding model performance. Inspired by scaling laws in recent years, this article investigates the performance of large convolutional kernel encoding models on larger parameter scales. This paper proposes a large-scale parameters framework with a sizeable convolutional kernel for encoding visual functional magnetic resonance imaging activity information. The proposed framework consists of three parts: First, the stimulus image feature extraction module is constructed using a large-kernel convolutional network while increasing channel numbers to expand the parameter size of the framework. Second, enlarging the input data during the training stage through the multi-subject fusion module to accommodate the increase in parameters. Third, the voxel mapping module maps from stimulus image features to functional magnetic resonance imaging signals. Compared to sizeable convolutional kernel visual encoding networks with base parameter scale, our visual encoding framework improves by approximately 7% on the Natural Scenes Dataset, the dedicated dataset for the Algonauts 2023 Challenge. We further analyze that our encoding framework made a trade-off between encoding performance and trainability. This paper confirms that expanding parameters in visual coding can bring performance improvements.

3D Visual Discomfort Assessment With a Weakly Supervised Graph Convolution Neural Network Based on Inaccurately Labeled EEG.

Visual discomfort significantly limits the broader application of stereoscopic display technology. Hence, the accurate assessment of stereoscopic visual discomfort is a crucial topic in this field. Electroencephalography (EEG) data, which can reflect changes in brain activity, have received increasing attention in objective assessment research. However, inaccurately labeled data, resulting from the presence of individual differences, restrict the effectiveness of the widely used supervised learning methods in visual discomfort assessment tasks. Simultaneously, visual discomfort assessment methods should pay greater attention to the information provided by the visual cortical areas of the brain. To tackle these challenges, we need to consider two key aspects: maximizing the utilization of inaccurately labeled data for enhanced learning and integrating information from the brain's visual cortex for feature representation purposes. Therefore, we propose the weakly supervised graph convolution neural network for visual discomfort (WSGCN-VD). In the classification part, a center correction loss serves as a weakly supervised loss, employing a progressive selection strategy to identify accurately labeled data while constraining the involvement of inaccurately labeled data that are influenced by individual differences during the model learning process. In the feature extraction part, a feature graph module pays particular attention to the construction of spatial connections among the channels in the visual regions of the brain and combines them with high-dimensional temporal features to obtain visually dependent spatio-temporal representations. Through extensive experiments conducted in various scenarios, we demonstrate the effectiveness of our proposed model. Further analysis reveals that the proposed model mitigates the impact of inaccurately labeled data on the accuracy of assessment.

Relationship of SSVEP response between flash frequency conditions.

This study delves into the application of Brain-Computer Interfaces (BCIs), focusing on exploiting Steady-State Visual Evoked Potentials (SSVEPs) as communication tools for individuals facing mobility impairments. SSVEP-BCI systems can swiftly transmit substantial volumes of information, rendering them suitable for diverse applications. However, the efficacy of SSVEP responses can be influenced by variables such as the frequency and color of visual stimuli. Through experiments involving participants equipped with electrodes on the brain's visual cortex, visual stimuli were administered at 4, 17, 25, and 40Hz, using white, red, yellow, green, and blue light sources. The results reveal that white and green stimuli evoke higher SSVEP responses at lower frequencies, with color's impact diminishing at higher frequencies. At low light intensity (1W), white and green stimuli elicit significantly higher SSVEP responses, while at high intensity (3W), responses across colors tend to equalize. Notably, due to seizure risks, red and blue lights should be used cautiously, with white and green lights preferred for SSVEP-BCI systems. This underscores the critical consideration of color and frequency in the design of effective and safe SSVEP-BCI systems, necessitating further research to optimize designs for clinical applications.

The retina as a window to the brain in Alzheimer's disease? Insights from an animal model

AbstractPurpose: The retina and the brain share the same embryonic origin, and the retina is considered an extension of the brain. In recent years, the concept ‘the retina as a window to the brain’ gained increasing interest to the study of brain degenerative diseases, such as Alzheimer's disease (AD). A better understanding of the natural history of AD and its temporal trajectories, also including alterations in the retina, requires longitudinal studies. Our main aim was to understand when changes (molecular, cellular, structural, physiological) start appearing in the retina and brain, how detected changes progress along time and if they can be correlated. We performed a longitudinal study in a triple transgenic mouse model of AD (3 × Tg‐AD), at 4, 8, 12 and 16 months of age.Methods: Behavioural assessment included the open field and novel object recognition (assessment of recognition memory) tests. The levels of amyloid β (Aβ) and hyperphosphorylated tau, two hallmarks of AD, were assessed in the hippocampus, a brain region associated with memory (impairment). By magnetic resonance imaging (MRI) we assessed hippocampal and visual cortex structural integrity (voxel‐based morphometry). Retinal structure and physiology were evaluated in vivo using optical coherence tomography (OCT) and electroretinography (ERG).Results: 3 × Tg‐AD animals presented altered locomotor activity, and recognition memory impairment at all time points. Significant reduction of hippocampal and visual cortex volumes started early, at month 4, and was maintained until 16 months. The overall retinal thickness of 3 × Tg‐AD mice, as well as the thickness of several retinal layers, decreased at all time points, except for the outer nuclear layer, where an increased thickness was observed. Regarding retinal physiology, the amplitude of scotopic and photopic responses of 3 × Tg‐AD increased at all time points. Similarly, higher amplitude and lower phase values were observed in the photopic flicker response. No differences were found in the activity of retinal ganglion cells. Moreover, a positive correlation between retinal thickness and visual cortex volume was found.Conclusions: This animal model (3xTg‐AD) mimics pathological and neurobehavioral features of AD, presenting early onset recognition memory impairment and hippocampal reduced grey matter volume. Moreover, 3xTg‐AD mice show similar neural changes in the retina and brain visual cortex, namely retinal and brain thinning. These observations support the possibility of using the retina as an additional tool for the early diagnosis and therapeutic monitoring of AD.Support: Santa Casa Mantero Belard Award 2015 (MB‐1049‐2015), FCT (SFRH/BD/52045/2012, SFRH/BPD/93672/2013, PEst UID/NEU/04539/2013, UID/NEU/04539/2019, UIDB/04539/2020 and UIDP/04539/2020, and MEDPERSYST SAICTPAC/0010/2015), COMPETE‐FEDER (POCI‐01‐0145‐FEDER‐007440 and POCI‐01‐0145‐FEDER‐016428) and Centro 2020 Regional Operational Programme (CENTRO‐01‐0145‐FEDER‐000008: BrainHealth 2020 and CENTRO‐01‐0145‐FEDER‐000016:BIGDATIMAGE).

MSP Patches Based Optimized EEG Source Localization and Validation in Visual Cortex of Human Brain

This paper is focused on optimizing EEG source localization of visual neural activities generated in the posterior lobe of the human brain. The visual and neural systems of the human brain process the captured images of faces or scenes into optical and chemical neurons in order to produce electrical potentials over the scalp surface, where EEG electrodes measure these signals to sense the underneath visual brain activity. However, it is categorically hard to localize the true neural sources in the human brain's visual cortex due to overlapping and interaction of other active areas of the lobes of the brain. Thus, a novel algorithm of MSP inversion-based Bayesian framework with varying patches for providing the optimal free energy and minimum location in the visual cortex is proposed to address this issue. This algorithm is integrated with a synthetic EEG dataset generation scheme to validate active neural sources. This proposed algorithm provides satisfactory results in terms of optimal free energy with minimum localization error and validates true active sources in the brain. The SPM12 Toolbox is applied in processing the visual EEG dataset in this research. The application of this proposed algorithm is beneficial in terms of localizing the optimum visual sources and identifying the visual disorders or diseases in the human brain.

Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network.

BACKGROUNDA convolutional neural network (CNN) is a deep learning algorithm based on the principle of human brain visual cortex processing and image recognition.AIMTo automatically identify the invasion depth and origin of esophageal lesions based on a CNN.METHODSA total of 1670 white-light images were used to train and validate the CNN system. The method proposed in this paper included the following two parts: (1) Location module, an object detection network, locating the classified main image feature regions of the image for subsequent classification tasks; and (2) Classification module, a traditional classification CNN, classifying the images cut out by the object detection network.RESULTSThe CNN system proposed in this study achieved an overall accuracy of 82.49%, sensitivity of 80.23%, and specificity of 90.56%. In this study, after follow-up pathology, 726 patients were compared for endoscopic pathology. The misdiagnosis rate of endoscopic diagnosis in the lesion invasion range was approximately 9.5%; 41 patients showed no lesion invasion to the muscularis propria, but 36 of them pathologically showed invasion to the superficial muscularis propria. The patients with invasion of the tunica adventitia were all treated by surgery with an accuracy rate of 100%. For the examination of submucosal lesions, the accuracy of endoscopic ultrasonography (EUS) was approximately 99.3%. Results of this study showed that EUS had a high accuracy rate for the origin of submucosal lesions, whereas the misdiagnosis rate was slightly high in the evaluation of the invasion scope of lesions. Misdiagnosis could be due to different operating and diagnostic levels of endoscopists, unclear ultrasound probes, and unclear lesions.CONCLUSIONThis study is the first to recognize esophageal EUS images through deep learning, which can automatically identify the invasion depth and lesion origin of submucosal tumors and classify such tumors, thereby achieving good accuracy. In future studies, this method can provide guidance and help to clinical endoscopists.

Nanoscale Memristor‐Based Spike Timing‐Dependent Plasticity Learning in a Radix‐X Quantized Retinal Neural Network

The human retina sends visual signals to the brain's visual cortex from photoreceptors (rod and cone cells) through various synaptic pathways and performs crucial early vision processing before signals are passed to higher brain regions. Herein, an artificial retina system implemented based on the leaky integrate‐and‐fire spiking neuron model is presented. The architecture of the proposed retina system consists of a multilayer convolutional neural network (CNN), and the system uses spike timing‐dependent plasticity (STDP) as a feedforward learning rule. In addition, the system integrates a feedback plasticity learning rule to expedite learning convergence. The system weights are implemented using nanoscale memristor arrays, taking on a constrained (radix‐X) range of conductance states. The proposed system produces an output image of 25 × 25 pixels, corresponding to the output retina ganglion cells that act as the interface between the retina and the visual cortex, using an input image of 100 × 100 pixels.

Multifunctional Optoelectronic Synapse Based on Ferroelectric Van der Waals Heterostructure for Emulating the Entire Human Visual System

AbstractThe development of optoelectronic synapses can provide an important breakthrough toward creating a sophisticated and adaptable artificial visual system analogous to that of humans. However, it remains a great challenge to implement the various functions of the biological visual neuromorphic system at the single device level. Intriguingly, 2D van der Waals (vdW) heterostructure may offer a platform to address the issue. Here, a novel multifunctional synaptic device based on ferroelectric α‐In2Se3/GaSe vdW heterojunction is proposed to emulate the entire biological visual system. Essential synaptic behaviors are observed in response to light and electrical stimuli; additionally, the retina‐like selectivity for light wavelengths and the achievement of Pavlov's dog experiment demonstrate the device's capacity for processing complex electrical and optical inputs. Beyond the optoelectronic synaptic behaviors, the device incorporates memory and logic functions analogous to those in the brain's visual cortex. The results of artificial neural network simulations show that the vdW heterojunction‐based device is completely capable of performing logic operations and recognizing images with a high degree of accuracy. The study indicates that versatile devices with a rationally designed construction have great potential for efficiently processing complex visual information and may simplify the design of artificial visual systems.

GaborNet Visual Encoding: A Lightweight Region-Based Visual Encoding Model With Good Expressiveness and Biological Interpretability.

Computational visual encoding models play a key role in understanding the stimulus–response characteristics of neuronal populations in the brain visual cortex. However, building such models typically faces challenges in the effective construction of non-linear feature spaces to fit the neuronal responses. In this work, we propose the GaborNet visual encoding (GaborNet-VE) model, a novel end-to-end encoding model for the visual ventral stream. This model comprises a Gabor convolutional layer, two regular convolutional layers, and a fully connected layer. The key design principle for the GaborNet-VE model is to replace regular convolutional kernels in the first convolutional layer with Gabor kernels with learnable parameters. One GaborNet-VE model efficiently and simultaneously encodes all voxels in one region of interest of functional magnetic resonance imaging data. The experimental results show that the proposed model achieves state-of-the-art prediction performance for the primary visual cortex. Moreover, the visualizations demonstrate the regularity of the region of interest fitting to the visual features and the estimated receptive fields. These results suggest that the lightweight region-based GaborNet-VE model based on combining handcrafted and deep learning features exhibits good expressiveness and biological interpretability.

Joint Motion Information Extraction and Human Behavior Recognition in Video Based on Deep Learning

Video surveillance has played a huge role in the safety and security tasks of various places for many years, and its application range is also expanding. The core problem that needs to be addressed in intelligent video surveillance is the identification and analysis of human behavior in video. In this paper, the structure and joint motion feature extraction of the two-channel deep convolutional neural network model are studied and designed. In the design of the network structure, the ventral and dorsal channels used in the processing of visual signals by the brain visual cortex were simulated. The spatial channel network and the time channel network are used to process static information and dynamic information respectively, and the two types of features are separately extracted. The superiority of the dual-channel structure is verified by comparing the recognition effects of the two-channel model with the single-channel model. Finally, experiments were performed on the KTH behavioral dataset. The results show that the human behavior recognition algorithm based on deep learning can achieve high recognition accuracy based on the good extraction of joint motion information.

Frequency Detection of Single Channel Steady State Visual Evoked Potential using Canonical Correlation Analysis

Wave generated into visual cortex of brain, when subject focused his/her attention on visual stimulus flickers at certain frequency. The main challenge with SSVEP Based Brain computer interface (BCI) System is to detect the stimulus frequency from recorded brain signal. Canonical Correlation analysis (CCA) is one of the most popular methods to recognize the frequency of Steady state visual evoked potential (SSVEP). This paper focuses on the study of CCA algorithm to recognize the SSVEP signal frequency. For experiment purpose, a single channel data with flickering frequency in the range of (6Hz-10Hz) is used. The performance of the BCI System is measured in terms of detection accuracy and Information transmission rate (ITR). The maximum accuracy is obtained as 83.90% and ITR is 15.35 at stimulus frequency of 8.2Hz

Retinal thinning of inner sub-layers is associated with cortical atrophy in a mouse model of Alzheimer\u2019s disease: a longitudinal multimodal in vivo study

BackgroundIt has been claimed that the retina can be used as a window to study brain disorders. However, concerning Alzheimer’s disease (AD), it still remains controversial whether changes occurring in the brain and retina are associated. We aim to understand when changes start appearing in the retina and brain, how changes progress, and if they are correlated.MethodsWe carried out a unique longitudinal study, at 4, 8, 12, and 16 months of age, in a triple transgenic mouse model of AD (3×Tg-AD), which mimics pathological and neurobehavioral features of AD, as we have already shown. Retinal structure and physiology were evaluated in vivo using optical coherence tomography and electroretinography. Brain visual cortex structure was evaluated in vivo using magnetic resonance imaging.ResultsThe retinal thickness of 3×Tg-AD decreased, at all time points, except for the outer nuclear layer, where the opposite alteration was observed. Amplitudes in scotopic and photopic responses were increased throughout the study. Similarly, higher amplitude and lower phase values were observed in the photopic flicker response. No differences were found in the activity of retinal ganglion cells. Visual cortex gray matter volume was significantly reduced.ConclusionsOur results show that this animal model shows similar neural changes in the retina and brain visual cortex, i.e., retinal and brain thinning. Moreover, since similar changes occur in the retina and brain visual cortex, these observations support the possibility of using the eye as an additional tool (noninvasive) for early AD diagnosis and therapeutic monitoring.

Nonlinear statistical properties of fMRI signals in human visual cortex during resting-state

The purpose of the current work is to study nonlinear dynamics in neuronal activity within human brain visual cortex based on blood-oxygen-level dependent (BOLD) contrast imaging. In particular, based on functional magnetic resonance imaging (fMRI) signals, measures of fractality, complexity, and state disorder are estimated from central and peripheral eccentricity bands across three visual areas. Statistical results from analysis of 48750 resting-state fMRI signals show evidence that nonlinear dynamics of neuronal activity in resting-state in central and peripheral eccentricity bands of human visual cortex are persistent. However, they exhibit heterogeneous variability across eccentricity bands and visual areas. Also, information content in first visual area is more ordered than in the second one, whilst information content in the third visual area is the least ordered. These interesting nonlinear statistical properties are a further step toward understanding neuronal activity and nonlinear dynamics in human brain visual cortex.

3D computer vision based on machine learning with deep neural networks: A review

AbstractRecent advances in the field of computer vision can be attributed to the emergence of deep learning techniques, in particular convolutional neural networks. Neural networks, partially inspired by the brain's visual cortex, enable a computer to “learn” the most important features of the images it is shown in relation to a specific, specified task. Given sufficient data and time, (deep) convolutional neural networks offer more easily designed, more generalizable, and significantly more accurate end‐to‐end systems than is possible with previously employed computer vision techniques. This review paper seeks to provide an overview of deep learning in the field of computer vision with an emphasis on recent progress in tasks involving 3D visual data. Through a backdrop of the mammalian visual processing system, we hope to also provide inspiration for future advances in automated visual processing.

Modeling student cohesiveness by waving the sociometric test with the picture apperception value test

One of the main problems why students and also other categories of stakeholders within various organizations are not productive and satisfied with their jobs and organizations is because there is a lack of group cohesiveness. Since the latter influence directly the performance and job satisfaction, then there are strong reasons to find effective and efficient ways to stimulate social and task cohesion. The aim of this study is to model student cohesiveness by using simultaneously the Sociometric Test (ST) for group formation and Picture Apperception Value Test (PAVT) for consonance or social cohesion. The study is based on the method of literature review, theory development, and simulation modeling of students’ behavior. Findings of the study shows the way how can be integrated a sociometric test with a psychometric test for a common purpose (i.e. student cohesiveness referring to their values system). During simulation modeling a relevant point to be emphasized is the role of images during PAVT that serve as a substitute mechanism of questions to be answered. They activate emotions through the brain visual cortex and in them are anchored values. Therefore, through the mention procedure it is possible to understand social cohesion of different small groups of students. Regarding originality, value, and practical applications, this framework is novel and is supposed to increase students’ productivity/performance in terms of perception, learning, and task execution. Furthermore, it is supposed also to increase student satisfaction during project works.

Neuroscience: The cortical connection.

Neurons in the brain's visual cortex receive inputs from thousands of other neurons. But it now emerges that each is strongly connected to only a few others: those most similar to itself. See Letter p.399 The degree to which a neuron influences the activity of others is dependent on the strength of the synaptic connections it makes with its partners, and it is known that this connection strength can vary over two orders of magnitude. Using a combination of two-photon calcium imaging and simultaneous intracellular recordings from pairs of neurons, Thomas Mrsic-Flogel and colleagues show that layer 2/3 neurons in mouse primary visual cortex (V1) follow a simple rule: strong connections are sparse and occur between neurons with correlated responses to visual stimuli, whereas only weak connections link neurons with uncorrelated responses. This bias in functional connection strength may be a means by which neuronal selectivity for visual features is computed in areas downstream of V1.

Synchronous SSVEP Data Acquisition System

AbstractSteady State Visually Evoked Potentials have been known for several decades and they appear in the primary visual cortex of brain as a result of light stimulation of the sense of sight. In this article a simple method for electroencephalographic data acquisition is presented. The system is based on the DSM-51 unit connected to goggles with blinking diodes and Mindset-1000 EEG amplifier with 16 channels. We present self-developed hardware and method of effective synchronization for the light stimulation and brain activity recording.

Universality in the evolution of orientation columns in the visual cortex.

The brain's visual cortex processes information concerning form, pattern, and motion within functional maps that reflect the layout of neuronal circuits. We analyzed functional maps of orientation preference in the ferret, tree shrew, and galago--three species separated since the basal radiation of placental mammals more than 65 million years ago--and found a common organizing principle. A symmetry-based class of models for the self-organization of cortical networks predicts all essential features of the layout of these neuronal circuits, but only if suppressive long-range interactions dominate development. We show mathematically that orientation-selective long-range connectivity can mediate the required interactions. Our results suggest that self-organization has canalized the evolution of the neuronal circuitry underlying orientation preference maps into a single common design.

“Induced” Eyes Suggest a Path to Retinal Repair

“Induced” Eyes Suggest a Path to Retinal Repair

Oscillatory network with self-organized dynamical connections for synchronization-based image segmentation

An oscillatory network of columnar architecture located in 3D spatial lattice was recently designed by the authors as oscillatory model of the brain visual cortex. Single network oscillator is a relaxational neural oscillator with internal dynamics tunable by visual image characteristics — local brightness and elementary bar orientation. It is able to demonstrate either activity state (stable undamped oscillations) or “silence” (quickly damped oscillations). Self-organized nonlocal dynamical connections of oscillators depend on oscillator activity levels and orientations of cortical receptive fields. Network performance consists in transfer into a state of clusterized synchronization. At current stage grey-level image segmentation tasks are carried out by 2D oscillatory network, obtained as a limit version of the source model. Due to supplemented network coupling strength control the 2D reduced network provides synchronization-based image segmentation. New results on segmentation of brightness and texture images presented in the paper demonstrate accurate network performance and informative visualization of segmentation results, inherent in the model.