scholarly journals Pseudocritical and Precritical States in Brain Dynamics

2021 ◽  
Author(s):  
Lei Gu ◽  
Ruqian Wu
Keyword(s):  
Neural Networks ◽  
Membrane Potential ◽  
Dynamical Systems ◽  
Critical Point ◽  
Brain Dynamics ◽  
Complex Dynamical Systems ◽  
Apparent Paradox ◽  
Scale Free ◽  
External Stimuli ◽  
The Brain

Scale-free brain dynamics under external stimuli raises an apparent paradox since the critical point of the brain dynamics locates at the limit of zero external drive. Here, we demonstrate that relaxation of the membrane potential removes the critical point but facilitates scale-free dynamics in the presence of strong external stimuli. These findings feature biological neural networks as systems that have no real critical point but bear critical-like behaviors. Attainment of such pseudocritical states relies on processing neurons into a precritical state where they are made readily activatable. We discuss supportive signatures in existing experimental observations and advise new ones for these intriguing properties. These newly revealed repertoires of neural states call for reexamination of brain's working states and open fresh avenues for the investigation of critical behaviors in complex dynamical systems.

scholarly journals Classical Soliton Theory for Studying the Dynamics and Evolution of in Network

2021 ◽  
Vol 8 (4) ◽  
pp. 01-06
Author(s):  
Sergey Belyakin
Keyword(s):  
Neural Networks ◽  
Data Processing ◽  
Dynamic Model ◽  
Mammalian Brain ◽  
Complex Task ◽  
Process Data ◽  
Brain Neurons ◽  
Soliton Theory ◽  
External Stimuli ◽  
The Brain

This paper presents the dynamic model ofthe soliton. Based on this model, it is supposed to study the state of the network. The term neural networks refersto the networks of neurons in the mammalian brain. Neurons are its main units of computation. In the brain, they are connected together in a network to process data. This can be a very complex task, and so the dynamics of neural networks in the mammalian brain in response to external stimuli can be quite complex. The inputs and outputs of each neuron change as a function of time, in the form of so-called spike chains, but the network itself also changes. We learn and improve our data processing capabilities by establishing reconnections between neurons.

System Identification of the Brain Dynamics by EEG Analysis Using Neural Networks

2003 ◽  
pp. 807-813
Author(s):  
Toshio Kawano ◽  
Masatake Akutagawa ◽  
Qinyu Zhang ◽  
Hirofumi Nagashino ◽  
Yohsuke Kinouchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
System Identification ◽  
Brain Dynamics ◽  
Eeg Analysis ◽  
The Brain

scholarly journals The Brain Dynamics Toolbox for Matlab

2017 ◽  
Author(s):  
Stewart Heitmann ◽  
Matthew J Aburn ◽  
Michael Breakspear
Keyword(s):  
Dynamical Systems ◽  
Differential Equations ◽  
Computational Neuroscience ◽  
Large Scale ◽  
Nonlinear Dynamical Systems ◽  
Brain Dynamics ◽  
Interactive Simulation ◽  
Nonlinear Dynamical ◽  
Delay Differential ◽  
The Brain

AbstractNonlinear dynamical systems are increasingly informing both theoretical and empirical branches of neuroscience. The Brain Dynamics Toolbox provides an interactive simulation platform for exploring such systems in MATLAB. It supports the major classes of differential equations that arise in computational neuroscience: Ordinary Differential Equations, Delay Differential Equations and Stochastic Differential Equations. The design of the graphical interface fosters intuitive exploration of the dynamics while still supporting scripted parameter explorations and large-scale simulations. Although the toolbox is intended for dynamical models in computational neuroscience, it can be applied to dynamical systems from any domain.

Synaptic and Neural Plasticity

2013 ◽  
pp. 64-75 ◽  
Author(s):  
Saïd Kourrich ◽  
Antonello Bonci
Keyword(s):  
Neural Networks ◽  
Information Transmission ◽  
Psychiatric Disorders ◽  
Learning And Memory ◽  
Neural Plasticity ◽  
Neuronal Transmission ◽  
Plastic Changes ◽  
External Stimuli ◽  
The Brain ◽  
Complex Organ

The brain is an extraordinarily complex organ that constantly has to process information to adapt appropriately to internal and external stimuli. This information is received, processed, and transmitted within neural networks by neurons through specialized connections called synapses. While information transmission at synapses is primarily chemical, it propagates through a neuron via electrical signals made of patterns of action potentials. The present chapter will describe the fundamental types of plastic changes that can affect neuronal transmission. Importantly, these various types of neural plasticity have been associated with both adaptive such as learning and memory or pathological conditions such as neurological and psychiatric disorders.

Interscale interactions in cortical neural networks

2000 ◽  
Vol 23 (3) ◽  
pp. 408-409
Author(s):  
Hans Liljenström
Keyword(s):  
Neural Networks ◽  
Mental State ◽  
Large Scale ◽  
Brain Dynamics ◽  
Spontaneous Firing ◽  
Cortical Dynamics ◽  
Target Article ◽  
Electromagnetic Effects ◽  
The Brain

This commentary focuses on how the large-scale cortical dynamics described in Nunez's target article are related to various phenomena at different scales, both spatial and temporal, in particular, how the brain dynamics measured with EEG could relate to (i) experience and mental state, (ii) neuromodulatory effects, and (iii) spontaneous firing and autogenerated electromagnetic effects.

scholarly journals Testing Jump-Diffusion in Epileptic Brain Dynamics: Impact of Daily Rhythms

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 309
Author(s):  
Jutta G. Kurth ◽  
Thorsten Rings ◽  
Klaus Lehnertz
Keyword(s):  
Dynamical Systems ◽  
Evolution Equations ◽  
Time Series Data ◽  
Diffusion Processes ◽  
Jump Diffusion ◽  
Series Data ◽  
Brain Dynamics ◽  
Complex Dynamical Systems ◽  
Jump Diffusion Processes ◽  
External Forcings

Stochastic approaches to complex dynamical systems have recently provided broader insights into spatial-temporal aspects of epileptic brain dynamics. Stochastic qualifiers based on higher-order Kramers-Moyal coefficients derived directly from time series data indicate improved differentiability between physiological and pathophysiological brain dynamics. It remains unclear, however, to what extent stochastic qualifiers of brain dynamics are affected by other endogenous and/or exogenous influencing factors. Addressing this issue, we investigate multi-day, multi-channel electroencephalographic recordings from a subject with epilepsy. We apply a recently proposed criterion to differentiate between Langevin-type and jump-diffusion processes and observe the type of process most qualified to describe brain dynamics to change with time. Stochastic qualifiers of brain dynamics are strongly affected by endogenous and exogenous rhythms acting on various time scales—ranging from hours to days. Such influences would need to be taken into account when constructing evolution equations for the epileptic brain or other complex dynamical systems subject to external forcings.

scholarly journals Phase Transitions: In the Brain, Socio-­dramatic Play and Meaningful Early Learning

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Doris Pronin Fromberg
Keyword(s):  
Dynamical Systems ◽  
Young Children ◽  
Interpersonal Relationships ◽  
Early Learning ◽  
Dramatic Play ◽  
Complex Dynamical Systems ◽  
Linear Complex ◽  
Brain Functions ◽  
Non Linear ◽  
The Brain

There are similar, non-linear complex dynamical systems that underlie the epigenetic development of young children. This paper discusses the confluence of research on brain functions; a body or research that informs the characteristics of young children’s play and imagination; and the ways in which young children acquire fresh perceptions and cognitions. Focus on the spaces among components of physical and interpersonal relationships can illuminate the processes of these non-linear, complex, dynamical systems. Particular implications are relevant for educational practices.

Dynamic Ridge Polynomial Higher Order Neural Network

2010 ◽  
pp. 255-268 ◽  
Author(s):  
Rozaida Ghazali ◽  
Abir Hussain ◽  
Nazri Mohd Nawi
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
Time Series ◽  
Dynamical Systems ◽  
Dynamic Structure ◽  
Higher Order ◽  
System Control ◽  
Internal States ◽  
The Brain

This chapter proposes a novel Dynamic Ridge Polynomial Higher Order Neural Network (DRPHONN). The architecture of the new DRPHONN incorporates recurrent links into the structure of the ordinary Ridge Polynomial Higher Order Neural Network (RPHONN) (Shin & Ghosh, 1995). RPHONN is a type of feedforward Higher Order Neural Network (HONN) (Giles & Maxwell, 1987) which implements a static mapping of the input vectors. In order to model dynamical functions of the brain, it is essential to utilize a system that is capable of storing internal states and can implement complex dynamic system. Neural networks with recurrent connections are dynamical systems with temporal state representations. The dynamic structure approach has been successfully used for solving varieties of problems, such as time series forecasting (Zhang & Chan, 2000; Steil, 2006), approximating a dynamical system (Kimura & Nakano, 2000), forecasting a stream flow (Chang et al, 2004), and system control (Reyes et al, 2000). Motivated by the ability of recurrent dynamic systems in real world applications, the proposed DRPHONN architecture is presented in this chapter.

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