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    Failure tolerance of spike phase synchronization in coupled neural networks

    , Article Chaos (Woodbury, N.Y.) ; Volume 21, Issue 3 , 2011 , Pages 033126- ; 10897682 (ISSN) Jalili, M ; Sharif University of Technology
    Abstract
    Neuronal synchronization plays an important role in the various functionality of nervous system such as binding, cognition, information processing, and computation. In this paper, we investigated how random and intentional failures in the nodes of a network influence its phase synchronization properties. We considered both artificially constructed networks using models such as preferential attachment, Watts-Strogatz, and Erdo{combining double acute accent} s-Rényi as well as a number of real neuronal networks. The failure strategy was either random or intentional based on properties of the nodes such as degree, clustering coefficient, betweenness centrality, and vulnerability. Hindmarsh-Rose... 

    Neural fields with fast learning dynamic kernel

    , Article Biological Cybernetics ; Volume 106, Issue 1 , January , 2012 , Pages 15-26 ; 03401200 (ISSN) Abbassian, A. H ; Fotouhi, M ; Heidari, M ; Sharif University of Technology
    Abstract
    We introduce a modified-firing-rate model based on Hebbian-type changing synaptic connections. The existence and stability of solutions such as rest state, bumps, and traveling waves are shown for this type of model. Three types of kernels, namely exponential, Mexican hat, and periodic synaptic connections, are considered. In the former two cases, the existence of a rest state solution is proved and the conditions for their stability are found. Bump solutions are shown for two kinds of synaptic kernels, and their stability is investigated by constructing a corresponding Evans function that holds for a specific range of values of the kernel coefficient strength (KCS). Applying a similar... 

    EEG-based functional brain networks: does the network size matter?

    , Article PloS one ; Volume 7, Issue 4 , 2012 ; 19326203 (ISSN) Joudaki, A ; Salehi, N ; Jalili, M ; Knyazeva, M. G ; Sharif University of Technology
    PLOS  2012
    Abstract
    Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of... 

    Failure tolerance of motif structure in biological networks

    , Article PLoS ONE ; Volume 6, Issue 5 , May , 2011 ; 19326203 (ISSN) Mirzasoleiman, B ; Jalili, M ; Sharif University of Technology
    2011
    Abstract
    Complex networks serve as generic models for many biological systems that have been shown to share a number of common structural properties such as power-law degree distribution and small-worldness. Real-world networks are composed of building blocks called motifs that are indeed specific subgraphs of (usually) small number of nodes. Network motifs are important in the functionality of complex networks, and the role of some motifs such as feed-forward loop in many biological networks has been heavily studied. On the other hand, many biological networks have shown some degrees of robustness in terms of their efficiency and connectedness against failures in their components. In this paper we... 

    Individual differences in nucleus accumbens dopamine receptors predict development of addiction-like behavior: A computational approach

    , Article Neural Computation ; Volume 22, Issue 9 , 2010 , Pages 2334-2368 ; 08997667 (ISSN) Piray, P ; Keramati, M. M ; Dezfouli, A ; Lucas, C ; Mokri, A ; Sharif University of Technology
    2010
    Abstract
    Clinical and experimental observations show individual differences in the development of addiction. Increasing evidence supports the hypothesis that dopamine receptor availability in the nucleus accumbens (NAc) predisposes drug reinforcement. Here, modeling striatal-midbrain dopaminergic circuit, we propose a reinforcement learning model for addiction based on the actor-critic model of striatum. Modeling dopamine receptors in the NAc as modulators of learning rate for appetitive-but not aversive-stimuli in the critic-but not the actor-we define vulnerability to addiction as a relatively lower learning rate for the appetitive stimuli, compared to aversive stimuli, in the critic. We... 

    Synchronizing hindmarsh-rose neurons over newman-watts networks

    , Article Chaos ; Volume 19, Issue 3 , 2009 ; 10541500 (ISSN) Jalili, M ; Sharif University of Technology
    American Institute of Physics Inc  2009
    Abstract
    In this paper, the synchronization behavior of the Hindmarsh-Rose neuron model over Newman-Watts networks is investigated. The uniform synchronizing coupling strength is determined through both numerically solving the network's differential equations and the master-stability-function method. As the average degree is increased, the gap between the global synchronizing coupling strength, i.e., the one obtained through the numerical analysis, and the strength necessary for the local stability of the synchronization manifold, i.e., the one obtained through the master-stability-function approach, increases. We also find that this gap is independent of network size, at least in a class of networks... 

    Adjustable primitive pattern generator: A novel cerebellar model for reaching movements

    , Article Neuroscience Letters ; Volume 406, Issue 3 , 2006 , Pages 232-234 ; 03043940 (ISSN) Vahdat, S ; Maghsoudi, A ; Haji Hasani, M ; Towhidkhah, F ; Gharibzadeh, S ; Jahed, M ; Sharif University of Technology
    2006
    Abstract
    Cerebellum has been assumed as an array of adjustable pattern generators (APGs). In recent years, electrophysiological researches have suggested the existence of modular structures in spinal cord called motor primitives. In our proposed model, each "adjustable primitive pattern generator" (APPG) module in the cerebellum is consisted of a large number of parallel APGs, the output of each module being the weighted sum of the outputs of these APGs. Each spinal field is tuned by a coefficient, representing a descending supraspinal command, which is modulated by ith APPG correspondingly. According to this model, motor control can be interpreted in terms of the modification of these coefficients.... 

    Spike phase synchronization in delayed-coupled neural networks: Uniform vs. non-uniform transmission delay

    , Article Chaos ; Volume 23, Issue 1 , 2013 ; 10541500 (ISSN) Jalili, M ; Sharif University of Technology
    2013
    Abstract
    In this paper, we investigated phase synchronization in delayed dynamical networks. Non-identical spiking Hindmarsh-Rose neurons were considered as individual dynamical systems and coupled through a number of network structures such as scale-free, Erdos-Rényi, and modular. The individual neurons were coupled through excitatory chemical synapses with uniform or distributed time delays. The profile of spike phase synchrony was different when the delay was uniform across the edges as compared to the case when it was distributed, i.e., different delays for the edges. When an identical transmission delay was considered, a quasi-periodic pattern was observed in the spike phase synchrony. There... 

    Synchronizability of EEG-based functional networks in early alzheimer's disease

    , Article IEEE Transactions on Neural Systems and Rehabilitation Engineering ; Volume 20, Issue 5 , 2012 , Pages 636-641 ; 15344320 (ISSN) Tahaei, M. S ; Jalili, M ; Knyazeva, M. G ; Sharif University of Technology
    IEEE  2012
    Abstract
    Recently graph theory and complex networks have been widely used as a mean to model functionality of the brain. Among different neuroimaging techniques available for constructing the brain functional networks, electroencephalography (EEG) with its high temporal resolution is a useful instrument of the analysis of functional interdependencies between different brain regions. Alzheimer's disease (AD) is a neurodegenerative disease, which leads to substantial cognitive decline, and eventually, dementia in aged people. To achieve a deeper insight into the behavior of functional cerebral networks in AD, here we study their synchronizability in 17 newly diagnosed AD patients compared to 17 healthy... 

    Constructing brain functional networks from EEG: Partial and unpartial correlations

    , Article Journal of Integrative Neuroscience ; Volume 10, Issue 2 , 2011 , Pages 213-232 ; 02196352 (ISSN) Jalili, M ; Knyazeva, M. G ; Sharif University of Technology
    Abstract
    We consider electroencephalograms (EEGs) of healthy individuals and compare the properties of the brain functional networks found through two methods: unpartialized and partialized cross-correlations. The networks obtained by partial correlations are fundamentally different from those constructed through unpartial correlations in terms of graph metrics. In particular, they have completely different connection efficiency, clustering coefficient, assortativity, degree variability, and synchronization properties. Unpartial correlations are simple to compute and they can be easily applied to large-scale systems, yet they cannot prevent the prediction of non-direct edges. In contrast, partial... 

    Deep sparse graph functional connectivity analysis in AD patients using fMRI data

    , Article Computer Methods and Programs in Biomedicine ; Volume 201 , 2021 ; 01692607 (ISSN) Ahmadi, H ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    Elsevier Ireland Ltd  2021
    Abstract
    Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to... 

    Deep sparse graph functional connectivity analysis in AD patients using fMRI data

    , Article Computer Methods and Programs in Biomedicine ; Volume 201 , 2021 ; 01692607 (ISSN) Ahmadi, H ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    Elsevier Ireland Ltd  2021
    Abstract
    Functional magnetic resonance imaging (fMRI) is a non-invasive method that helps to analyze brain function based on BOLD signal fluctuations. Functional Connectivity (FC) catches the transient relationship between various brain regions usually measured by correlation analysis. The elements of the correlation matrix are between -1 to 1. Some of them are very small values usually related to weak and spurious correlations due to noises and artifacts. They can not be concluded as real strong correlations between brain regions and their existence could make a misconception and leads to fake results. It is crucial to make a conclusion based on reliable and informative correlations. In order to... 

    Identifying brain functional connectivity alterations during different stages of Alzheimer’s disease

    , Article International Journal of Neuroscience ; Volume 132, Issue 10 , 2022 , Pages 1005-1013 ; 00207454 (ISSN) Ahmadi, H ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    Taylor and Francis Ltd  2022
    Abstract
    Purpose: Alzheimer's disease (AD) starts years before its signs and symptoms including the dementia become apparent. Diagnosis of the AD in the early stages is important to reduce the speed of brain decline. Aim of the study: Identifying the alterations in the functional connectivity of the brain during the disease stages is among the main important issues in this regard. Therefore, in this study, the changes in the functional connectivity during the AD stages were analyzed. Materials and methods: By employing the functional magnetic resonance imaging (fMRI) data and graph theory, weighted undirected graphs of the whole-brain and default mode network (DMN) network were investigated... 

    Digital implementation of a biological astrocyte model and its application

    , Article IEEE Transactions on Neural Networks and Learning Systems ; Volume 26, Issue 1 , 2014 , Pages 127-139 ; 2162237X (ISSN) Soleimani, H ; Bavandpour, M ; Ahmadi, A ; Abbott, D ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2014
    Abstract
    This paper presents a modified astrocyte model that allows a convenient digital implementation. This model is aimed at reproducing relevant biological astrocyte behaviors, which provide appropriate feedback control in regulating neuronal activities in the central nervous system. Accordingly, we investigate the feasibility of a digital implementation for a single astrocyte and a biological neuronal network model constructed by connecting two limit-cycle Hopf oscillators to an implementation of the proposed astrocyte model using oscillator-astrocyte interactions with weak coupling. Hardware synthesis, physical implementation on field-programmable gate array, and theoretical analysis confirm...