Systems analysis of socio-political processes using neuron network models. II

1997 ◽  
Vol 33 (1) ◽  
pp. 58-65
Author(s):  
M. Z. Zgurovskii ◽  
A. V. Dobronogov
Keyword(s):  
Systems Analysis ◽  
Network Models ◽  
Neuron Network ◽  
Political Processes

scholarly journals NetPyNE, a tool for data-driven multiscale modeling of brain circuits

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Salvador Dura-Bernal ◽  
Benjamin A Suter ◽  
Padraig Gleeson ◽  
Matteo Cantarelli ◽  
Adrian Quintana ◽  
...  
Keyword(s):  
Multiple Scales ◽  
Network Models ◽  
Brain Regions ◽  
Data Driven ◽  
Model Parameters ◽  
Biophysical Modeling ◽  
Neuron Network ◽  
Graphical Interfaces ◽  
Information Theoretic Measures ◽  
High Level

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.

scholarly journals On Stationary Distributions of Stochastic Neural Networks

2014 ◽  
Vol 51 (3) ◽  
pp. 837-857
Author(s):  
K. Borovkov ◽  
G. Decrouez ◽  
M. Gilson
Keyword(s):  
Stationary Distribution ◽  
Hebbian Learning ◽  
Memory Span ◽  
Network Models ◽  
The State ◽  
Stochastic Neural Networks ◽  
Learning Mechanisms ◽  
Neuron Network ◽  
Bounded Memory ◽  
Network Process

The paper deals with nonlinear Poisson neuron network models with bounded memory dynamics, which can include both Hebbian learning mechanisms and refractory periods. The state of the network is described by the times elapsed since its neurons fired within the post-synaptic transfer kernel memory span, and the current strengths of synaptic connections, the state spaces of our models being hierarchies of finite-dimensional components. We prove the ergodicity of the stochastic processes describing the behaviour of the networks, establish the existence of continuously differentiable stationary distribution densities (with respect to the Lebesgue measures of corresponding dimensionality) on the components of the state space, and find upper bounds for them. For the density components, we derive a system of differential equations that can be solved in a few simplest cases only. Approaches to approximate computation of the stationary density are discussed. One approach is to reduce the dimensionality of the problem by modifying the network so that each neuron cannot fire if the number of spikes it emitted within the post-synaptic transfer kernel memory span reaches a given threshold. We show that the stationary distribution of this ‘truncated’ network converges to that of the unrestricted network as the threshold increases, and that the convergence is at a superexponential rate. A complementary approach uses discrete Markov chain approximations to the network process.

scholarly journals Predictive regulatory and metabolic network models for systems analysis of Clostridioides difficile

2020 ◽  
Author(s):  
Mario L. Arrieta-Ortiz ◽  
Selva Rupa Christinal Immanuel ◽  
Serdar Turkarslan ◽  
Wei Ju Wu ◽  
Brintha P. Girinathan ◽  
...  
Keyword(s):  
Systems Analysis ◽  
Network Models ◽  
Transcriptional Regulators ◽  
Metabolic Model ◽  
Research Community ◽  
Collaborative Systems ◽  
Human Gut ◽  
Clostridioides Difficile ◽  
New Genes

ABSTRACTThough Clostridioides difficile is among the most studied anaerobes, we know little about the systems level interplay of metabolism and regulation that underlies its ability to negotiate complex immune and commensal interactions while colonizing the human gut. We have compiled publicly available resources, generated through decades of work by the research community, into two models and a portal to support comprehensive systems analysis of C. difficile. First, by compiling a compendium of 148 transcriptomes from 11 studies we have generated an Environment and Gene Regulatory Influence Network (EGRIN) model that organizes 90% of all genes in the C. difficile genome into 297 high quality modules based on evidence for their conditional co-regulation by at least 120 transcription factors. EGRIN predictions, validated with independently-generated datasets, have recapitulated previously characterized C. difficile regulons of key transcriptional regulators, refined and extended membership of genes within regulons, and implicated new genes for sporulation, carbohydrate transport and metabolism. Findings further predict pathogen behaviors in in vivo colonization, and interactions with beneficial and detrimental commensals. Second, by advancing a constraints-based metabolic model, we have discovered that 15 amino acids, diverse carbohydrates, and 24 genes across glyoxylate, Wood-Ljungdahl, nucleotide, amino acid, and carbohydrate metabolism are essential to support growth of C. difficile within an intestinal environment. Models and supporting resources are accessible through an interactive web portal (http://networks.systemsbiology.net/cdiff-portal/) to support collaborative systems analyses of C. difficile.

scholarly journals An attempt to fit all parameters of a dynamical recurrent neural network from sensory neural spiking data

2018 ◽  
Author(s):  
Ozgur Doruk ◽  
Kechen Zhang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Model Fitting ◽  
Network Models ◽  
Neuron Network ◽  
Stimulus Component ◽  
Component Size ◽  
Stimulus Response ◽  
Sensory Neural ◽  
Comparison Of The Results

A simulation based study on model fitting for sensory neurons from stimulus/response data is presented. The employed model is a continuous time recurrent neural network (CTRNN) which is a member of models with known universal approximation features. This feature of the recurrent dynamical neuron network models allow us to describe excitatory-inhibitory characteristics of an actual sensory neural network with any desired number of neurons. This work will be a continuation of a previous study where the parameters associated with the sigmoidal gain functions are not taken into account. In this work, we will construct a similar framework but all parameters associated with the model are estimated. The stimulus data is generated by a Phased Cosine Fourier series having fixed amplitude and frequency but a randomly shot phase. Various values of amplitude, stimulus component size and sample size are applied in order to examine the effect of stimulus to the identification process. Results are presented in tabular and graphical forms at the end of this text. In addition a comparison of the results with previous researches including will be presented.

scholarly journals On the applicability of STDP-based learning mechanisms to spiking neuron network models

AIP Advances ◽  
2016 ◽  
Vol 6 (11) ◽  
pp. 111305 ◽  
Author(s):  
A. Sboev ◽  
D. Vlasov ◽  
A. Serenko ◽  
R. Rybka ◽  
I. Moloshnikov
Keyword(s):  
Network Models ◽  
Spiking Neuron ◽  
Learning Mechanisms ◽  
Neuron Network ◽  
Spiking Neuron Network

Investigation of socio-political processes by methods of systems analysis

1997 ◽  
Vol 33 (1) ◽  
pp. 50-50
Author(s):  
M. Z. Zgurovskii ◽  
T. N. Pomerantseva ◽  
A. V. Dobronogov ◽  
A. Yu. Artemov
Keyword(s):  
Systems Analysis ◽  
Political Processes

scholarly journals Short-term wind speed prediction based on MLP and NARX network models

2020 ◽  
Vol 18 (1) ◽  
pp. 150
Author(s):  
Yousra Amellas ◽  
Outman El Bakkali ◽  
Abdelouahed Djebli ◽  
Adil Echchelh
Keyword(s):  
Wind Speed ◽  
Back Propagation ◽  
Network Models ◽  
Activation Function ◽  
Back Propagation Algorithm ◽  
Neuron Network ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Single Output ◽  
Speed Prediction

The article aims to predict the wind speed by two artificial neural network’s models. The first model is a multilayer perceptron (MLP) treated by back-propagation algorithm and the second one is a recurrent neuron network type, processed by the NARX model. The two models having the same Network’s structure, which they are composed by 4 Inputs layers (Wind Speed, Pressure Temperature and Humidity), an intermediate layer defined by 20 neurons and an activation function, as well as a single output layer characterized by wind speed and a linear function. NARX shows the best results with a regression coefficient R = 0.984 et RMSE = 0.314.

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