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.

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 Cellular compartment analysis of temporal activity by fluorescent in situ hybridization (catFISH) in the transcardially perfused rat brain

2019 ◽  
Author(s):  
Ali Gheidi ◽  
Vivek Kumar ◽  
Christopher J Fitzpatrick ◽  
Rachel L Atkinson ◽  
Jonathan D Morrow
Keyword(s):  
Mammalian Brain ◽  
Compartment Analysis ◽  
Spatiotemporal Resolution ◽  
Adult Rats ◽  
Cellular Compartment ◽  
Study Gene Expression ◽  
Resolution Mapping ◽  
External Stimuli ◽  
The Brain

AbstractCellular compartment analysis of temporal activity by fluorescent in situ hybridization (catFISH) allows high spatiotemporal resolution mapping of immediate early genes in the brain in response to internal/external stimuli. One caveat of this technique and indeed other methods of in situ hybridization is the necessity of flash-freezing the brain prior to staining. Often however, the mammalian brain is transcardially perfused to use the brain tissue for immunohistochemistry, the most widely-used technique to study gene expression. The present study illustrates how the original catFISH protocol can be modified for use in adult rats that have been transcardially perfused with 4% paraformaldehyde. c-Fos activity induced by either an auditory tone or status epilepticus was visualized using the catFISH procedure. Analysis of the rat prefrontal cortex, hippocampus and amygdala shows that a clear distinction can be made between the compartmental distribution of c-Fos mRNA in the nuclei and cytoplasmic regions. Furthermore, the qualitative proportion of c-Fos compartmentalization is similar to previous reports of c-Fos expression pattern in rodents navigating novel environments. c-Fos catFISH on perfused rodent brains is an valuable addition to the traditional histological methods using fluorescently labeled riboprobes, and opens several avenues for future investigations.

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.

The three attentional networks and the two hemispheric mechanisms

1995 ◽  
Vol 18 (2) ◽  
pp. 343-344 ◽  
Author(s):  
Uri Fidelman
Keyword(s):  
Neural Networks ◽  
Data Processing ◽  
Methodological Problem ◽  
Neural System ◽  
Attentional Networks ◽  
Hemispheric Mechanisms ◽  
The Brain ◽  
Processing Mechanism

AbstractA methodological problem may distort the implications derived from the metabolism scans of the brain, but Posner & Raichle may have found neural networks which underlie the analytical and synthetical hemispheric data processing mechanism. This methodological problem is that a large regional consumption of energy, detected by the PET technique, is not necessarily related to more data processing. It may be related to the inefficiency of the neural system at this region.

scholarly journals An architectonic type principle in the development of laminar patterns of cortico-cortical connections

2021 ◽  
Author(s):  
Sarah F. Beul ◽  
Alexandros Goulas ◽  
Claus C. Hilgetag
Keyword(s):  
Structural Connectivity ◽  
Macaque Monkey ◽  
Brain Regions ◽  
Adult Brain ◽  
Mammalian Brain ◽  
Cortical Connections ◽  
Cortical Projections ◽  
Structural Connections ◽  
High Degree ◽  
The Brain

AbstractStructural connections between cortical areas form an intricate network with a high degree of specificity. Many aspects of this complex network organization in the adult mammalian cortex are captured by an architectonic type principle, which relates structural connections to the architectonic differentiation of brain regions. In particular, the laminar patterns of projection origins are a prominent feature of structural connections that varies in a graded manner with the relative architectonic differentiation of connected areas in the adult brain. Here we show that the architectonic type principle is already apparent for the laminar origins of cortico-cortical projections in the immature cortex of the macaque monkey. We find that prenatal and neonatal laminar patterns correlate with cortical architectonic differentiation, and that the relation of laminar patterns to architectonic differences between connected areas is not substantially altered by the complete loss of visual input. Moreover, we find that the degree of change in laminar patterns that projections undergo during development varies in proportion to the relative architectonic differentiation of the connected areas. Hence, it appears that initial biases in laminar projection patterns become progressively strengthened by later developmental processes. These findings suggest that early neurogenetic processes during the formation of the brain are sufficient to establish the characteristic laminar projection patterns. This conclusion is in line with previously suggested mechanistic explanations underlying the emergence of the architectonic type principle and provides further constraints for exploring the fundamental factors that shape structural connectivity in the mammalian brain.

scholarly journals Modeling temperature- and Cav3 subtype-dependent alterations in T-type calcium channel mediated burst firing

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Fernando R. Fernandez ◽  
Mircea C. Iftinca ◽  
Gerald W. Zamponi ◽  
Ray W. Turner
Keyword(s):  
Calcium Channel ◽  
Neuronal Excitability ◽  
Neuronal Firing ◽  
Mammalian Brain ◽  
Peripheral Tissues ◽  
Window Current ◽  
Modeling Data ◽  
The Brain ◽  
Firing Neuron ◽  
Cav3 Channel

AbstractT-type calcium channels are important regulators of neuronal excitability. The mammalian brain expresses three T-type channel isoforms (Cav3.1, Cav3.2 and Cav3.3) with distinct biophysical properties that are critically regulated by temperature. Here, we test the effects of how temperature affects spike output in a reduced firing neuron model expressing specific Cav3 channel isoforms. The modeling data revealed only a minimal effect on baseline spontaneous firing near rest, but a dramatic increase in rebound burst discharge frequency for Cav3.1 compared to Cav3.2 or Cav3.3 due to differences in window current or activation/recovery time constants. The reduced response by Cav3.2 could optimize its activity where it is expressed in peripheral tissues more subject to temperature variations than Cav3.1 or Cav3.3 channels expressed prominently in the brain. These tests thus reveal that aspects of neuronal firing behavior are critically dependent on both temperature and T-type calcium channel subtype.

scholarly journals Fast Motion Model of Road Vehicles with Artificial Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 928
Author(s):  
Ferenc Hegedüs ◽  
Péter Gáspár ◽  
Tamás Bécsi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dynamic Model ◽  
Piecewise Linear ◽  
Complex Problem ◽  
Computational Effort ◽  
Road Vehicles ◽  
Vehicle Motion ◽  
Artificial Neural ◽  
Original Dynamic

Nonlinear optimization-based motion planning algorithms have been successfully used for dynamically feasible trajectory planning of road vehicles. However, the main drawback of these methods is their significant computational effort and thus high runtime, which makes real-time application a complex problem. Addressing this field, this paper proposes an algorithm for fast simulation of road vehicle motion based on artificial neural networks that can be used in optimization-based trajectory planners. The neural networks are trained with supervised learning techniques to predict the future state of the vehicle based on its current state and driving inputs. Learning data is provided for a wide variety of randomly generated driving scenarios by simulation of a dynamic vehicle model. The realistic random driving maneuvers are created on the basis of piecewise linear travel velocity and road curvature profiles that are used for the planning of public roads. The trained neural networks are then used in a feedback loop with several variables being calculated by additional numerical integration to provide all the outputs of the original dynamic model. The presented model can be capable of short-term vehicle motion simulation with sufficient precision while having a considerably faster runtime than the original dynamic model.

XVI. Note on the minute structure of the grey matter of the convolutions of the brain of man, the sheep, cat, and dog

1863 ◽  
Vol 12 ◽  
pp. 671-673
Keyword(s):  
Grey Matter ◽  
Nerve Cells ◽  
Mammalian Brain ◽  
New Process ◽  
The Brain ◽  
Thick Fibre

By a new process of investigation, I have succeeded in demonstrating the connexion between the nerve-cells and fibres in the grey matter of the convolutions and in other parts of the mammalian brain, and have followed individual fibres for a much greater distance than can be effected in sections prepared by other processes of investigation which I have tried. In many instances one thick fibre is continuous with one or other extremity of the “cell,” while from its opposite portion from three to six or eight thinner fibres diverge in a direction onwards and outwards. This arrangement is particularly distinct in the grey matter of the sheep’s brain.

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