scholarly journals HIERARCHICAL SPATIO-TEMPORAL DYNAMICS OF A CHAOTIC NEURAL NETWORK FOR MULTISTABLE BINOCULAR RIVALRY

2009 ◽  
Vol 05 (01) ◽  
pp. 123-134 ◽  
Author(s):  
YUTA KAKIMOTO ◽  
KAZUYUKI AIHARA
Keyword(s):  
Neural Network ◽  
Binocular Rivalry ◽  
Temporal Dynamics ◽  
Visual Images ◽  
Chaotic Neural Network ◽  
Perceptual Alternation ◽  
Spatio Temporal ◽  
Characteristic Features ◽  
The Brain ◽  
Special Case

Binocular rivalry is perceptual alternation that occurs when different visual images are presented to each eye. Despite the intensive studies, the mechanism of binocular rivalry still remains unclear. In multistable binocular rivalry, which is a special case of binocular rivalry, it is known that the perceptual alternation between paired patterns is more frequent than that between unpaired patterns. This result suggests that perceptual transition in binocular rivalry is not a simple random process, and the memories stored in the brain can play an important role in the perceptual transition. In this study, we propose a hierarchical chaotic neural network model for multistable binocular rivalry and show that our model reproduces some characteristic features observed in multistable binocular rivalry.

MDTP

2021 ◽  
Vol 14 (8) ◽  
pp. 1289-1297
Author(s):  
Ziquan Fang ◽  
Lu Pan ◽  
Lu Chen ◽  
Yuntao Du ◽  
Yunjun Gao
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Real Life ◽  
Feature Modeling ◽  
Traffic Prediction ◽  
Interactive System ◽  
Trajectory Data ◽  
Spatio Temporal ◽  
Prediction Approach

Traffic prediction has drawn increasing attention for its ubiquitous real-life applications in traffic management, urban computing, public safety, and so on. Recently, the availability of massive trajectory data and the success of deep learning motivate a plethora of deep traffic prediction studies. However, the existing neural-network-based approaches tend to ignore the correlations between multiple types of moving objects located in the same spatio-temporal traffic area, which is suboptimal for traffic prediction analytics. In this paper, we propose a multi-source deep traffic prediction framework over spatio-temporal trajectory data, termed as MDTP. The framework includes two phases: spatio-temporal feature modeling and multi-source bridging. We present an enhanced graph convolutional network (GCN) model combined with long short-term memory network (LSTM) to capture the spatial dependencies and temporal dynamics of traffic in the feature modeling phase. In the multi-source bridging phase, we propose two methods, Sum and Concat, to connect the learned features from different trajectory data sources. Extensive experiments on two real-life datasets show that MDTP i) has superior efficiency, compared with classical time-series methods, machine learning methods, and state-of-the-art neural-network-based approaches; ii) offers a significant performance improvement over the single-source traffic prediction approach; and iii) performs traffic predictions in seconds even on tens of millions of trajectory data. we develop MDTP + , a user-friendly interactive system to demonstrate traffic prediction analysis.

scholarly journals STAB: a spatio-temporal cell atlas of the human brain

2020 ◽  
Vol 49 (D1) ◽  
pp. D1029-D1037
Author(s):  
Liting Song ◽  
Shaojun Pan ◽  
Zichao Zhang ◽  
Longhao Jia ◽  
Wei-Hua Chen ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Temporal Dynamics ◽  
Cell Types ◽  
Brain Regions ◽  
Molecular Characteristics ◽  
Great Effort ◽  
Brain Functions ◽  
Spatio Temporal ◽  
The Brain

Abstract The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.

scholarly journals Characterization of the Functional Dynamics in the Neonatal Brain during REM and NREM Sleep States by means of Microstate Analysis

2021 ◽  
Author(s):  
Mohammad Khazaei ◽  
Khadijeh Raeisi ◽  
Pierpaolo Croce ◽  
Gabriella Tamburro ◽  
Anton Tokariev ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Temporal Dynamics ◽  
Functional Organization ◽  
Nrem Sleep ◽  
Quiet Sleep ◽  
Functional Dynamics ◽  
Spatio Temporal ◽  
Eeg Recordings ◽  
Microstate Analysis ◽  
The Brain

AbstractNeonates spend most of their life sleeping. During sleep, their brain experiences fast changes in its functional organization. Microstate analysis permits to capture the rapid dynamical changes occurring in the functional organization of the brain by representing the changing spatio-temporal features of the electroencephalogram (EEG) as a sequence of short-lasting scalp topographies—the microstates. In this study, we modeled the ongoing neonatal EEG into sequences of a limited number of microstates and investigated whether the extracted microstate features are altered in REM and NREM sleep (usually known as active and quiet sleep states—AS and QS—in the newborn) and depend on the EEG frequency band. 19-channel EEG recordings from 60 full-term healthy infants were analyzed using a modified version of the k-means clustering algorithm. The results show that ~ 70% of the variance in the datasets can be described using 7 dominant microstate templates. The mean duration and mean occurrence of the dominant microstates were significantly different in the two sleep states. Microstate syntax analysis demonstrated that the microstate sequences characterizing AS and QS had specific non-casual structures that differed in the two sleep states. Microstate analysis of the neonatal EEG in specific frequency bands showed a clear dependence of the explained variance on frequency. Overall, our findings demonstrate that (1) the spatio-temporal dynamics of the neonatal EEG can be described by non-casual sequences of a limited number of microstate templates; (2) the brain dynamics described by these microstate templates depends on frequency; (3) the features of the microstate sequences can well differentiate the physiological conditions characterizing AS and QS.

scholarly journals Similarity-based fusion of MEG and fMRI reveals spatio-temporal dynamics in human cortex during visual object recognition

2015 ◽  
Author(s):  
Radoslaw Cichy ◽  
Dimitrios Pantazis ◽  
Aude Oliva
Keyword(s):  
Object Recognition ◽  
Temporal Dynamics ◽  
Imaging Techniques ◽  
Visual Object ◽  
Data Sets ◽  
Visual Object Recognition ◽  
Occipital Pole ◽  
Spatio Temporal ◽  
The Brain

Every human cognitive function, such as visual object recognition, is realized in a complex spatio-temporal activity pattern in the brain. Current brain imaging techniques in isolation cannot resolve the brain's spatio-temporal dynamics because they provide either high spatial or temporal resolution but not both. To overcome this limitation, we developed a new integration approach that uses representational similarities to combine measurements from different imaging modalities - magnetoencephalography (MEG) and functional MRI (fMRI) - to yield a spatially and temporally integrated characterization of neuronal activation. Applying this approach to two independent MEG-fMRI data sets, we observed that neural activity first emerged in the occipital pole at 50-80ms, before spreading rapidly and progressively in the anterior direction along the ventral and dorsal visual streams. These results provide a novel and comprehensive, spatio-temporally resolved view of the rapid neural dynamics during the first few hundred milliseconds of object vision. They further demonstrate the feasibility of spatially unbiased representational similarity based fusion of MEG and fMRI, promising new insights into how the brain computes complex cognitive functions.

scholarly journals Spatio-temporal dynamics of cerebral capillary segments with stalling red blood cells

2017 ◽  
Vol 39 (5) ◽  
pp. 886-900 ◽  
Author(s):  
Şefik Evren Erdener ◽  
Jianbo Tang ◽  
Amir Sajjadi ◽  
Kıvılcım Kılıç ◽  
Sreekanth Kura ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Oct Angiography ◽  
Label Free ◽  
Systematic Analysis ◽  
Volumetric Imaging ◽  
Cerebral Capillary ◽  
Spatio Temporal ◽  
The Brain

Optical coherence tomography (OCT) allows label-free imaging of red blood cell (RBC) flux within capillaries with high spatio-temporal resolution. In this study, we utilized time-series OCT-angiography to demonstrate interruptions in capillary RBC flux in mouse brain in vivo. We noticed ∼7.5% of ∼200 capillaries had at least one stall in awake mice with chronic windows during a 9-min recording. At any instant, ∼0.45% of capillaries were stalled. Average stall duration was ∼15 s but could last over 1 min. Stalls were more frequent and longer lasting in acute window preparations. Further, isoflurane anesthesia in chronic preparations caused an increase in the number of stalls. In repeated imaging, the same segments had a tendency to stall again over a period of one month. In awake animals, functional stimulation decreased the observance of stalling events. Stalling segments were located distally, away from the first couple of arteriolar-side capillary branches and their average RBC and plasma velocities were lower than nonstalling capillaries within the same region. This first systematic analysis of capillary RBC stalls in the brain, enabled by rapid and continuous volumetric imaging of capillaries with OCT-angiography, will lead to future investigations of the potential role of stalling events in cerebral pathologies.

scholarly journals Low-Dimensional Spatio-Temporal Dynamics Underlie Cortex-Wide Neural Activity

2020 ◽  
Author(s):  
Camden J. MacDowell ◽  
Timothy J. Buschman
Keyword(s):  
Sensory Processing ◽  
Neural Activity ◽  
Calcium Imaging ◽  
Temporal Dynamics ◽  
Dorsal Cortex ◽  
Dynamic Flow ◽  
Efficient Control ◽  
Spatio Temporal ◽  
Low Dimensional ◽  
The Brain

AbstractCognition arises from the dynamic flow of neural activity through the brain. To capture these dynamics, we used mesoscale calcium imaging to record neural activity across the dorsal cortex of awake mice. We found that the large majority of variance in cortex-wide activity (∼75%) could be explained by a limited set of ∼14 ‘motifs’ of neural activity. Each motif captured a unique spatio-temporal pattern of neural activity across the cortex. These motifs generalized across animals and were seen in multiple behavioral environments. Motif expression differed across behavioral states and specific motifs were engaged by sensory processing, suggesting the motifs reflect core cortical computations. Together, our results show that cortex-wide neural activity is highly dynamic, but that these dynamics are restricted to a low-dimensional set of motifs, potentially to allow for efficient control of behavior.

scholarly journals ST-PCNN: Spatio-Temporal Physics-Coupled Neural Networks for Dynamics Forecasting

2021 ◽  
Author(s):  
Yu Huang ◽  
James Li ◽  
Min Shi ◽  
Hanqi Zhuang ◽  
Yufei Tang ◽  
...  
Keyword(s):  
Neural Network ◽  
Temporal Dynamics ◽  
Temporal Correlation ◽  
Current Data ◽  
Ocean Current ◽  
Coupled Neural Networks ◽  
Proposed Model ◽  
Essential Components ◽  
Spatio Temporal ◽  
Learn Physics

Abstract Ocean current, fluid mechanics, and many other physical systems with spatio-temporal dynamics are essential components of the universe. One key characteristic of such systems is that they can be represented by certain physics laws, such as ordinary/partial differential equations (ODEs/PDEs), irrespective of time or location. Physics-informed machine learning has recently emerged to learn physics from data for accurate prediction, but they often lack a mechanism to leverage localized spatial and temporal correlation or rely on hard-coded physics parameters. In this paper, we advocate a physics-coupled neural network model to learn parameters governing the physics of the system, and further couple the learned physics to assist the learning of recurring dynamics. Here a spatio-temporal physics-coupled neural network (ST-PCNN) model is proposed to achieve three goals: (1) learning the underlying physics parameters, (2) transition of local information between spatio-temporal regions, and (3) forecasting future values for the dynamical system. The physics-coupled learning ensures that the proposed model can be tremendously improved by using learned physics parameters, and can achieve useful long-range forecasting (e.g., more than two weeks). Experiments using simulated wave propagation and field-collected ocean current data validate that ST-PCNN outperforms typical deep learning models and existing physics-informed models.

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