scholarly journals Emergence of border-ownership by large-scale consistency and long-range interactions: Neuro-computational model to reflect global configurations

2021 ◽  
Author(s):  
Naoki Kogo ◽  
Vicky Froyen
Keyword(s):  
Surface Properties ◽  
Large Scale ◽  
Computational Models ◽  
Depth Order ◽  
Global Configuration ◽  
Long Range Interactions ◽  
The Brain ◽  
Computational Processes ◽  
Advanced Version ◽  
Ground Organization

The visual system performs remarkably well to perceive depth order of surfaces without stereo disparity, indicating the importance of figure-ground organization based on pictorial cues. To understand how figure-ground organization emerges, it is essential to investigate how the global configuration of an image is reflected. In the past, many neuro-computational models developed to reproduce figure-ground organization implemented algorithms to give a bias to convex areas. However, in certain conditions, a convex area can be perceived as a hole and a non-convex area as figural. This occurs when the surface properties of the convex area are consistent with the background and, hence, are grouped together in our perception. We argue that large-scale consistency of surface properties is reflected in the border-ownership computation. We developed a model, called DISC2, that first analyzes relationships between two border-ownership signals of all possible combinations in the image. It then enhances signals if they satisfy the following conditions: 1. the two signals fit to a convex configuration, and 2. the surface properties at the locations of the two signals are consistent. The strength of the enhancement decays with distance between the signals. The model gives extremely robust responses to various images with complexities both in shape and depth order. Furthermore, we developed an advanced version of the model ("augmented model") where the global computation above interacts with local computation of curvilinearity, which further enhanced the robust nature of the model. The results suggest the involvement of similar computational processes in the brain for figure-ground organization.

Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

2021 ◽  
Vol 376 (1821) ◽  
pp. 20190765 ◽  
Author(s):  
Giovanni Pezzulo ◽  
Joshua LaPalme ◽  
Fallon Durant ◽  
Michael Levin
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
State Of The Art ◽  
Memory Representation ◽  
Theme Issue ◽  
Evolutionary Innovation ◽  
New Interpretation ◽  
The Brain

Nervous systems’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states—in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo . This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

Challenges of Complex Systems in Cognitive and Complex Systems

2010 ◽  
pp. 367-384
Author(s):  
Klaus Mainzer
Keyword(s):  
Neural Networks ◽  
Complex Systems ◽  
Large Scale ◽  
Computational Models ◽  
Self Control ◽  
Self Organization ◽  
Embodied Mind ◽  
Future Technology ◽  
Large Scale Networks ◽  
The Brain

After an introduction (1) the article analyzes complex systems and the evolution of the embodied mind (2), complex systems and the innovation of embodied robotics (3), and finally discusses challenges of handling a world with increasing complexity: Large-scale networks have the same universal properties in evolution and technology (4). Considering the evolution of the embodied mind (2), we start with an introduction of complex systems and nonlinear dynamics (2.1), apply this approach to neural self-organization (2.2), distinguish degrees of complexity of the brain (2.3), explain the emergence of cognitive states by complex systems dynamics (2.4), and discuss criteria for modeling the brain as complex nonlinear system (2.5). The innovation of embodied robotics (3) is a challenge of complex systems and future technology. We start with the distinction of symbolic and embodied AI (3.1). Embodied robotics is inspired by the evolution of life. Modern systems biology integrates the molecular, organic, human, and ecological levels of life with computational models of complex systems (3.2). Embodied robots are explained as dynamical systems (3.3). Self-organization of complex systems needs self-control of technical systems (3.4). Cellular neural networks (CNN) are an example of self-organizing complex systems offering new avenues for neurobionics (3.5). In general, technical neural networks support different kinds of learning robots (3.6). Embodied robotics aims at the development of cognitive and conscious robots (3.7).

scholarly journals Cerebral cartography and connectomics

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140173 ◽  
Author(s):  
Olaf Sporns
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Network Science ◽  
Temporal Dynamics ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Hierarchical Organization ◽  
Large Scale Networks ◽  
Definition Of ◽  
The Brain

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.

DeepSSPred: A Deep Learning Based Sulfenylation site predictor via a novel n-segmented optimize federated feature encoder

2020 ◽  
Vol 27 ◽  
Author(s):  
Zaheer Ullah Khan ◽  
Dechang Pi
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Research Work ◽  
Training Data ◽  
Training Dataset ◽  
Validation Dataset ◽  
Cytokine Signaling ◽  
Minority Class ◽  
Independent Dataset ◽  
Feature Encoding

Background: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient feature schemes, severe imbalance issues, and lack of an intelligent learning engine. Objective: In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and non-sulfenylation sites. Methods: In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via n-segmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2DConvolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication. Results: Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method. These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies. Conclusion : In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs in general.

Implantable neural interfaces

2018 ◽  
Author(s):  
Stefano Vassanelli
Keyword(s):  
Brain Function ◽  
Large Scale ◽  
Ionic Channels ◽  
Patch Clamp Technique ◽  
Advantages And Disadvantages ◽  
Optogenetic Stimulation ◽  
Physical Interfaces ◽  
The Brain

Establishing direct communication with the brain through physical interfaces is a fundamental strategy to investigate brain function. Starting with the patch-clamp technique in the seventies, neuroscience has moved from detailed characterization of ionic channels to the analysis of single neurons and, more recently, microcircuits in brain neuronal networks. Development of new biohybrid probes with electrodes for recording and stimulating neurons in the living animal is a natural consequence of this trend. The recent introduction of optogenetic stimulation and advanced high-resolution large-scale electrical recording approaches demonstrates this need. Brain implants for real-time neurophysiology are also opening new avenues for neuroprosthetics to restore brain function after injury or in neurological disorders. This chapter provides an overview on existing and emergent neurophysiology technologies with particular focus on those intended to interface neuronal microcircuits in vivo. Chemical, electrical, and optogenetic-based interfaces are presented, with an analysis of advantages and disadvantages of the different technical approaches.

What Has Direct Cortical and Subcortical Electrostimulation Taught Us about Neurolinguistics?

2019 ◽  
pp. 185-211
Author(s):  
Hugues Duffau
Keyword(s):  
White Matter ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Great Accuracy ◽  
Subcortical White Matter ◽  
Cerebral Lesions ◽  
Physiological Basis ◽  
Postoperative Mri ◽  
The Brain

Investigating the neural and physiological basis of language is one of the most important challenges in neurosciences. Direct electrical stimulation (DES), usually performed in awake patients during surgery for cerebral lesions, is a reliable tool for detecting both cortical and subcortical (white matter and deep grey nuclei) regions crucial for cognitive functions, especially language. DES transiently interacts locally with a small cortical or axonal site, but also nonlocally, as the focal perturbation will disrupt the entire subnetwork sustaining a given function. Thus, in contrast to functional neuroimaging, DES represents a unique opportunity to identify with great accuracy and reproducibility, in vivo in humans, the structures that are actually indispensable to the function, by inducing a transient virtual lesion based on the inhibition of a subcircuit lasting a few seconds. Currently, this is the sole technique that is able to directly investigate the functional role of white matter tracts in humans. Thus, combining transient disturbances elicited by DES with the anatomical data provided by pre- and postoperative MRI enables to achieve reliable anatomo-functional correlations, supporting a network organization of the brain, and leading to the reappraisal of models of language representation. Finally, combining serial peri-operative functional neuroimaging and online intraoperative DES allows the study of mechanisms underlying neuroplasticity. This chapter critically reviews the basic principles of DES, its advantages and limitations, and what DES can reveal about the neural foundations of language, that is, the large-scale distribution of language areas in the brain, their connectivity, and their ability to reorganize.

scholarly journals Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

2021 ◽  
Author(s):  
Pooja Prabhu ◽  
A. K. Karunakar ◽  
Sanjib Sinha ◽  
N. Mariyappa ◽  
G. K. Bhargava ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Mri ◽  
Similarity Measures ◽  
Principal Component ◽  
Brain Anatomy ◽  
Morphological Operations ◽  
Mr Images ◽  
Tilt Correction ◽  
Brain Mr Images ◽  
The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

scholarly journals Engineered glycomaterial implants orchestrate large-scale functional repair of brain tissue chronically after severe traumatic brain injury

2021 ◽  
Vol 7 (10) ◽  
pp. eabe0207
Author(s):  
Charles-Francois V. Latchoumane ◽  
Martha I. Betancur ◽  
Gregory A. Simchick ◽  
Min Kyoung Sun ◽  
Rameen Forghani ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Severe Traumatic Brain Injury ◽  
Large Scale ◽  
Growth Factor Signaling ◽  
Neuronal Circuit ◽  
Reach To Grasp ◽  
Function Recovery ◽  
Cellular Repair ◽  
The Brain

Severe traumatic brain injury (sTBI) survivors experience permanent functional disabilities due to significant volume loss and the brain’s poor capacity to regenerate. Chondroitin sulfate glycosaminoglycans (CS-GAGs) are key regulators of growth factor signaling and neural stem cell homeostasis in the brain. However, the efficacy of engineered CS (eCS) matrices in mediating structural and functional recovery chronically after sTBI has not been investigated. We report that neurotrophic factor functionalized acellular eCS matrices implanted into the rat M1 region acutely after sTBI significantly enhanced cellular repair and gross motor function recovery when compared to controls 20 weeks after sTBI. Animals subjected to M2 region injuries followed by eCS matrix implantations demonstrated the significant recovery of “reach-to-grasp” function. This was attributed to enhanced volumetric vascularization, activity-regulated cytoskeleton (Arc) protein expression, and perilesional sensorimotor connectivity. These findings indicate that eCS matrices implanted acutely after sTBI can support complex cellular, vascular, and neuronal circuit repair chronically after sTBI.

Sign in / Sign up

Export Citation Format

Share Document