scholarly journals Cognitive neural prosthetics – the way from experiment to clinical application

2021 ◽  
pp. 64-72
Author(s):  
S. V. Kravchenko ◽  
A. Kh. Kade ◽  
A. I. Trofimenko ◽  
S. P. Vcherashnyuk ◽  
V. V. Malyshko
Keyword(s):  
Neural Networks ◽  
Prefrontal Cortex ◽  
Clinical Practice ◽  
Clinical Application ◽  
Brain Structures ◽  
Operating Conditions ◽  
Artificial Organs ◽  
Structural Elements ◽  
Clinical Issues ◽  
Biological Neural Networks

Accepted: September 3, 2021. Objective of this review is to highlight some aspects of the development and use of cognitive neuroprostheses, such as the technological background for their developing and key modern projects in this field. The literature sources were analyzed and the place of neuroprostheses among other artificial organs and tissues, which are under development or already used in clinical practice, was defined. The main principles of their implementation, structural elements and operating conditions were described. Also, this review presents some examples of diseases which can be corrected by cognitive neuroprostheses. The mechanisms of compensation for the functions of the damaged brain structures when using neuroprostheses are described on the basis of the principles of their interaction with biological neural networks. Descriptions of advanced developments that are currently relevant are given. Moreover, information is provided on the protocols and results of tests on animals and humans of the artificial hippocampus, as well as the results of testing a prosthesis that allows restoring the functions of the prefrontal cortex in animals. The examples considered in the review allow us to conclude that cognitive neuroprostheses are not just a hypothetic concept. They are implemented as specialized experimental solutions for practical clinical issues. Currently, the greatest success has been achieved in restoring the hippocampus functions.

scholarly journals Big data, machine learning and artificial intelligence: a neurologist’s guide

2020 ◽  
pp. practneurol-2020-002688
Author(s):  
Stephen D Auger ◽  
Benjamin M Jacobs ◽  
Ruth Dobson ◽  
Charles R Marshall ◽  
Alastair J Noyce
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Big Data ◽  
Clinical Practice ◽  
Clinical Data ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Basic Principles ◽  
Biological Neural Networks

Modern clinical practice requires the integration and interpretation of ever-expanding volumes of clinical data. There is, therefore, an imperative to develop efficient ways to process and understand these large amounts of data. Neurologists work to understand the function of biological neural networks, but artificial neural networks and other forms of machine learning algorithm are likely to be increasingly encountered in clinical practice. As their use increases, clinicians will need to understand the basic principles and common types of algorithm. We aim to provide a coherent introduction to this jargon-heavy subject and equip neurologists with the tools to understand, critically appraise and apply insights from this burgeoning field.

Phytochemistry, Pharmacological Activities, Toxicity and Clinical Application of Momordica cochinchinensis

2019 ◽  
Vol 25 (6) ◽  
pp. 715-728 ◽  
Author(s):  
Hai-Yue Lan ◽  
Bin Zhao ◽  
Yu-Li Shen ◽  
Xiao-Qin Li ◽  
Su-Juan Wang ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Southeast Asia ◽  
Clinical Application ◽  
Chemical Constituents ◽  
Folk Medicine ◽  
Pharmacological Activities ◽  
Pharmacological Studies ◽  
Momordica Cochinchinensis

Momordica cochinchinensis (Lour.) Spreng (M. cochinchinensis) is a deciduous vine that grows in Southeast Asia. It is known as gac in Vietnam and as Red Melon in English. Gac is reputed to be extremely benificial for health and has been widely used as food and folk medicine in Southeast Asia. In China, the seed of M. cochinchinensis (Chinese name: Mu biezi) is used as traditional Chinese medicine (TCM) for the treatment of various diseases. More than 60 chemical constituents have been isolated from M. cochinchinensis. Modern pharmacological studies and clinical practice demonstrate that some chemical constituents of M. cochinchinensis possess wide pharmacological activities, such as anti-tumor, anti-oxidation, anti-inflammatory, etc. This paper reviews the phytochemistry, pharmacological activities, toxicity, and clinical application of M. cochinchinensis, aiming to bring new insights into further research and application of this ancient herb.

scholarly journals Glia Not Neurons: Uncovering Brain Dysmaturation in a Rat Model of Alzheimer’s Disease

Biomedicines ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 823
Author(s):  
Ekaterina A. Rudnitskaya ◽  
Tatiana A. Kozlova ◽  
Alena O. Burnyasheva ◽  
Natalia A. Stefanova ◽  
Nataliya G. Kolosova
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prefrontal Cortex ◽  
Brain Structures ◽  
Time Frame ◽  
Oxys Rats ◽  
Unknown Etiology ◽  
Suitable Model ◽  
Model Of Alzheimer’S Disease ◽  
The Brain

Sporadic Alzheimer’s disease (AD) is a severe disorder of unknown etiology with no definite time frame of onset. Recent studies suggest that middle age is a critical period for the relevant pathological processes of AD. Nonetheless, sufficient data have accumulated supporting the hypothesis of “neurodevelopmental origin of neurodegenerative disorders”: prerequisites for neurodegeneration may occur during early brain development. Therefore, we investigated the development of the most AD-affected brain structures (hippocampus and prefrontal cortex) using an immunohistochemical approach in senescence-accelerated OXYS rats, which are considered a suitable model of the most common—sporadic—type of AD. We noticed an additional peak of neurogenesis, which coincides in time with the peak of apoptosis in the hippocampus of OXYS rats on postnatal day three. Besides, we showed signs of delayed migration of neurons to the prefrontal cortex as well as disturbances in astrocytic and microglial support of the hippocampus and prefrontal cortex during the first postnatal week. Altogether, our results point to dysmaturation during early development of the brain—especially insufficient glial support—as a possible “first hit” leading to neurodegenerative processes and AD pathology manifestation later in life.

Recent Advances in Lower Bound Shakedown Analysis

2009 ◽  
Author(s):  
Dieter Weichert ◽  
Abdelkader Hachemi
Keyword(s):  
Finite Element ◽  
Lower Bound ◽  
Process Engineering ◽  
Operating Conditions ◽  
Structural Elements ◽  
Shakedown Analysis ◽  
Material Models ◽  
Practical Engineering ◽  
Safe Operating ◽  
New Algorithms

The special interest in lower bound shakedown analysis is that it provides, at least in principle, safe operating conditions for sensitive structures or structural elements under fluctuating thermo-mechanical loading as to be found in power- and process engineering. In this paper achievements obtained over the last years to introduce more sophisticated material models into the framework of shakedown analysis are developed. Also new algorithms will be presented that allow using the addressed numerical methods as post-processor for commercial finite element codes. Examples from practical engineering will illustrate the potential of the methodology.

scholarly journals DeepFogSim: A Toolbox for Execution and Performance Evaluation of the Inference Phase of Conditional Deep Neural Networks with Early Exits Atop Distributed Fog Platforms

2021 ◽  
Vol 11 (1) ◽  
pp. 377
Author(s):  
Michele Scarpiniti ◽  
Enzo Baccarelli ◽  
Alireza Momenzadeh ◽  
Sima Sarv Ahrabi
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Future Internet ◽  
Operating Conditions ◽  
Graphic User Interface ◽  
Energy Aware ◽  
Internet Applications ◽  
The Real ◽  
Delay Performance ◽  
Hard Constraints

The recent introduction of the so-called Conditional Neural Networks (CDNNs) with multiple early exits, executed atop virtualized multi-tier Fog platforms, makes feasible the real-time and energy-efficient execution of analytics required by future Internet applications. However, until now, toolkits for the evaluation of energy-vs.-delay performance of the inference phase of CDNNs executed on such platforms, have not been available. Motivated by these considerations, in this contribution, we present DeepFogSim. It is a MATLAB-supported software toolbox aiming at testing the performance of virtualized technological platforms for the real-time distributed execution of the inference phase of CDNNs with early exits under IoT realms. The main peculiar features of the proposed DeepFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the Fog-hosted computing-networking resources under hard constraints on the tolerated inference delays; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall Fog execution platform; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operating conditions and/or failure events; and (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering. Some numerical results give evidence for about the actual capabilities of the proposed DeepFogSim toolbox.

Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network

2015 ◽  
Author(s):  
M. A. Rafe Biswas ◽  
Melvin D. Robinson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Operating Conditions ◽  
Direct Methanol ◽  
Artificial Neural ◽  
Methanol Fuel Cell

A direct methanol fuel cell can convert chemical energy in the form of a liquid fuel into electrical energy to power devices, while simultaneously operating at low temperatures and producing virtually no greenhouse gases. Since the direct methanol fuel cell performance characteristics are inherently nonlinear and complex, it can be postulated that artificial neural networks represent a marked improvement in performance prediction capabilities. Artificial neural networks have long been used as a tool in predictive modeling. In this work, an artificial neural network is employed to predict the performance of a direct methanol fuel cell under various operating conditions. This work on the experimental analysis of a uniquely designed fuel cell and the computational modeling of a unique algorithm has not been found in prior literature outside of the authors and their affiliations. The fuel cell input variables for the performance analysis consist not only of the methanol concentration, fuel cell temperature, and current density, but also the number of cells and anode flow rate. The addition of the two typically unconventional variables allows for a more distinctive model when compared to prior neural network models. The key performance indicator of our neural network model is the cell voltage, which is an average voltage across the stack and ranges from 0 to 0:8V. Experimental studies were carried out using DMFC stacks custom-fabricated, with a membrane electrode assembly consisting of an additional unique liquid barrier layer to minimize water loss through the cathode side to the atmosphere. To determine the best fit of the model to the experimental cell voltage data, the model is trained using two different second order training algorithms: OWO-Newton and Levenberg-Marquardt (LM). The OWO-Newton algorithm has a topology that is slightly different from the topology of the LM algorithm by the employment of bypass weights. It can be concluded that the application of artificial neural networks can rapidly construct a predictive model of the cell voltage for a wide range of operating conditions with an accuracy of 10−3 to 10−4. The results were comparable with existing literature. The added dimensionality of the number of cells provided insight into scalability where the coefficient of the determination of the results for the two multi-cell stacks using LM algorithm were up to 0:9998. The model was also evaluated with empirical data of a single-cell stack.

Pathology, Phenomenology and the Dopamine Hypothesis of Schizophrenia

1987 ◽  
Vol 151 (3) ◽  
pp. 288-301 ◽  
Author(s):  
P. J. McKenna
Keyword(s):  
Prefrontal Cortex ◽  
Basal Ganglia ◽  
Functional Role ◽  
Ventral Striatum ◽  
Brain Structures ◽  
Schizophrenic Symptom ◽  
Dopamine Hypothesis ◽  
Dopamine Innervation

The dopamine hypothesis of schizophrenia implies that positive schizophrenic symptoms should be understandable by reference to brain structures receiving a dopamine innervation, or in terms of the functional role of dopamine itself. The basal ganglia, ventral striatum, septo-hippocampal system, and prefrontal cortex, sites of mesotelencephalic dopamine innervation, are examined and it is argued that their dysfunction could form the basis of particular schizophrenic symptom classes. The postulated involvement of dopamine in reinforcement processes might further assist such interpretations. This type of analysis can be extended to other categories of schizophrenic psychopathology.

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