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.

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.

Neural plasticity: The substratum of music-based interventions in neurorehabilitation

2021 ◽  
pp. 1-12
Author(s):  
Diya Chatterjee ◽  
Shantala Hegde ◽  
Michael Thaut
Keyword(s):  
Neural Networks ◽  
Music Therapy ◽  
Neural Plasticity ◽  
Neurological Disorders ◽  
Structural Changes ◽  
Brain Plasticity ◽  
Neurological Rehabilitation ◽  
Predictive Role ◽  
The Brain ◽  
And Training

BACKGROUND: The plastic nature of the human brain lends itself to experience and training-based structural changes leading to functional recovery. Music, with its multimodal activation of the brain, serves as a useful model for neurorehabilitation through neuroplastic changes in dysfunctional or impaired networks. Neurologic Music Therapy (NMT) contributes to the field of neurorehabilitation using this rationale. OBJECTIVE: The purpose of this article is to present a discourse on the concept of neuroplasticity and music-based neuroplasticity through the techniques of NMT in the domain of neurological rehabilitation. METHODS: The article draws on observations and findings made by researchers in the areas of neuroplasticity, music-based neuroplastic changes, NMT in neurological disorders and the implication of further research in this field. RESULTS: A commentary on previous research reveal that interventions based on the NMT paradigm have been successfully used to train neural networks using music-based tasks and paradigms which have been explained to have cross-modal effects on sensorimotor, language and cognitive and affective functions. CONCLUSIONS: Multimodal gains using music-based interventions highlight the brain plasticity inducing function of music. Individual differences do play a predictive role in neurological gains associated with such interventions. This area deserves further exploration and application-based studies.

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 Το νευροεπιστημονικό υπόβαθρο της ψυχοθεραπείας: μηχανισμοί και εγκεφαλικές δομές που επηρεάζονται από την ψυχοθεραπευτική διαδικασία

2020 ◽  
Vol 22 (2) ◽  
pp. 1
Author(s):  
Θεοδώρα Σεληνιωτάκη ◽  
Ιωάννης Νέστορος
Keyword(s):  
Mental Health ◽  
Psychiatric Disorders ◽  
Learning And Memory ◽  
Large Volume ◽  
Recent Literature ◽  
General Tendency ◽  
Brain Functions ◽  
Ancient Times ◽  
The Brain

Recently we witness a general tendency to synthesize psychotherapeutic models, as well as, a tendency to explore the effects of psychotherapy on the brain. This article summarizes a large volume of literature on the neuroscientific substrate of psychotherapy starting with scientific findings located in Ancient times till recent literature. The published literature that deals with the effects of psychotherapy on the brain includes studies, usually neuroimaging ones, which examine the neurological aspects of the most popular models of psychotherapy and pharmacotherapy. All researchers draw the conclusion that psychotherapy affects the brain functions, such as neuroplasticity,learning and memory, neurogenesis, mood and emotions, thus leading to an improvement of mental health. The discussion leads to the constitution of a new discipline, the Neuropsychotherapy, whichis promising for the liberation from the grip of psychiatric disorders.

scholarly journals Modeling Neurodegeneration in silico With Deep Learning

2021 ◽  
Vol 15 ◽  
Author(s):  
Anup Tuladhar ◽  
Jasmine A. Moore ◽  
Zahinoor Ismail ◽  
Nils D. Forkert
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Recognition ◽  
Language Processing ◽  
Neural Plasticity ◽  
In Silico ◽  
Cortical Atrophy ◽  
Visual Object ◽  
Deep Convolutional Neural Networks ◽  
The Brain

Deep neural networks, inspired by information processing in the brain, can achieve human-like performance for various tasks. However, research efforts to use these networks as models of the brain have primarily focused on modeling healthy brain function so far. In this work, we propose a paradigm for modeling neural diseases in silico with deep learning and demonstrate its use in modeling posterior cortical atrophy (PCA), an atypical form of Alzheimer’s disease affecting the visual cortex. We simulated PCA in deep convolutional neural networks (DCNNs) trained for visual object recognition by randomly injuring connections between artificial neurons. Results showed that injured networks progressively lost their object recognition capability. Simulated PCA impacted learned representations hierarchically, as networks lost object-level representations before category-level representations. Incorporating this paradigm in computational neuroscience will be essential for developing in silico models of the brain and neurological diseases. The paradigm can be expanded to incorporate elements of neural plasticity and to other cognitive domains such as motor control, auditory cognition, language processing, and decision making.

scholarly journals Effect of steady-state response versus excitatory/inhibitory balance on spiking synchronization in neural networks with log-normal synaptic weight distribution

2021 ◽  
Author(s):  
Sou Nobukawa ◽  
Nobuhiko Wagatsuma ◽  
Takashi Ikeda ◽  
Chiaki Hasegawa ◽  
Mitsuru Kikuchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Steady State ◽  
Psychiatric Disorders ◽  
Neural Activity ◽  
Amplitude Distribution ◽  
Inhibitory Neurons ◽  
Steady State Response ◽  
State Response ◽  
Log Normal ◽  
External Stimuli

AbstractSynchronization of neural activity, especially at the gamma band, contributes to perceptual functions. In several psychiatric disorders, deficits of perceptual functions are reflected in synchronization abnormalities. Plausible cause of this impairment is an alteration in the balance between excitation and inhibition (E/I balance); a disruption in the E/I balance leads to abnormal neural interactions reminiscent of pathological states. Moreover, the local lateral excitatory-excitatory synaptic connections in the cortex exhibit excitatory postsynaptic potentials (EPSPs) that follow a log-normal amplitude distribution. This long-tailed distribution is considered an important factor for the emergence of spatiotemporal neural activity. In this context, we hypothesized that manipulating the EPSP distribution under abnormal E/I balance conditions would provide insights into psychiatric disorders characterized by deficits in perceptual functions, potentially revealing the mechanisms underlying pathological neural behaviors. In this study, we evaluated the synchronization of neural activity with external periodic stimuli in spiking neural networks in cases of both E/I balance and imbalance with or without a long-tailed EPSP amplitude distribution. The results showed that external stimuli of a high frequency lead to a decrease in the degree of synchronization with an increasing ratio of excitatory to inhibitory neurons in the presence, but not in the absence, of high-amplitude EPSPs. This monotonic reduction can be interpreted as an autonomous, strong-EPSP-dependent spiking activity selectively interfering with the responses to external stimuli. This observation is consistent with pathological findings. Thus, our modeling approach has potential to improve the understanding of the steady-state response in both healthy and pathological states.

Functions of adiponectin signaling in regulating neural plasticity and its application as the therapeutic target to neurological and psychiatric diseases

2019 ◽  
Vol 30 (5) ◽  
pp. 485-495 ◽  
Author(s):  
Li-na Sun ◽  
Xiao-li Liu
Keyword(s):  
Synaptic Plasticity ◽  
Psychiatric Disorders ◽  
Neural Plasticity ◽  
Treatment Method ◽  
Psychiatric Diseases ◽  
Treatment Target ◽  
Different Types ◽  
Current Article ◽  
New Treatment ◽  
The Brain

Abstract Convergent lines of evidence indicate the critical roles of adiponectin in regulating neural functions on different levels. Because of the importance in maintaining neural plasticity including adult neurogenesis and synaptic plasticity, adiponectin has the potential to serve as the treatment targets in therapies of neurological and psychiatric disorders. Hence, systematic review is needed to summarize how adiponectin works in the brain, and how the adiponectin pathway is employed as the treatment method needs to be determined. Moreover, the benefits of adiponectin as the regulator for neural plasticity such as synaptic plasticity and neurogenesis have been supported by many literatures. In the current article, we reviewed the functions of adiponectin in different types of neural plasticity. We also demonstrated the potential value of adiponectin as the treatment target for different types of neurodegenerative and psychiatric disorders. Taken together, this review offers a new insight about adiponectin as the ideal target to develop the new treatment methods against neurodegeneration or psychiatric diseases.

scholarly journals Astrocytic adenosine: from synapses to psychiatric disorders

2014 ◽  
Vol 369 (1654) ◽  
pp. 20130594 ◽  
Author(s):  
Dustin J. Hines ◽  
Philip G. Haydon
Keyword(s):  
Psychiatric Disorders ◽  
Neuronal Cells ◽  
Atp Release ◽  
General Term ◽  
The Body ◽  
Signalling Molecules ◽  
Major Class ◽  
Tnf Α ◽  
The Brain ◽  
Complex Organ

Although it is considered to be the most complex organ in the body, the brain can be broadly classified into two major types of cells, neuronal cells and glial cells. Glia is a general term that encompasses multiple types of non-neuronal cells that function to maintain homeostasis, form myelin, and provide support and protection for neurons. Astrocytes, a major class of glial cell, have historically been viewed as passive support cells, but recently it has been discovered that astrocytes participate in signalling activities both with the vasculature and with neurons at the synapse. These cells have been shown to release d -serine, TNF-α, glutamate, atrial natriuretic peptide (ANP) and ATP among other signalling molecules. ATP and its metabolites are well established as important signalling molecules, and astrocytes represent a major source of ATP release in the nervous system. Novel molecular and genetic tools have recently shown that astrocytic release of ATP and other signalling molecules has a major impact on synaptic transmission. Via actions at the synapse, astrocytes have now been shown to regulate complex network signalling in the whole organism with impacts on respiration and the sleep–wake cycle. In addition, new roles for astrocytes are being uncovered in psychiatric disorders, and astrocyte signalling mechanisms represents an attractive target for novel therapeutic agents.

A Hybrid Neural Network of Addressable and Content-Addressable Memory

2003 ◽  
Vol 13 (03) ◽  
pp. 205-213 ◽  
Author(s):  
Jinwen Ma
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Theoretical Analysis ◽  
Learning And Memory ◽  
Competitive Learning ◽  
Memory Structure ◽  
Hybrid Neural Network ◽  
Content Addressable Memory ◽  
Learning Rules ◽  
The Brain

We investigate the memory structure and retrieval of the brain and propose a hybrid neural network of addressable and content-addressable memory which is a special database model and can memorize and retrieve any piece of information (a binary pattern) both addressably and content-addressably. The architecture of this hybrid neural network is hierarchical and takes the form of a tree of slabs which consist of binary neurons with the same array. Simplex memory neural networks are considered as the slabs of basic memory units, being distributed on the terminal vertexes of the tree. It is shown by theoretical analysis that the hybrid neural network is able to be constructed with Hebbian and competitive learning rules, and some other important characteristics of its learning and memory behavior are also consistent with those of the brain. Moreover, we demonstrate the hybrid neural network on a set of ten binary numeral patters.

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