Methods for dynamic study of the biological neural network at the cellular level using computer processing

2021 ◽  
Vol 3 ◽  
Author(s):  
A.V. Medievsky ◽  
◽  
A.G. Zotin ◽  
K.V. Simonov ◽  
A.S. Kruglyakov
Keyword(s):  
Neural Network ◽  
Cellular Level ◽  
Computational Method ◽  
Color Coding ◽  
Low Contrast ◽  
Biological Neural Network ◽  
Fundamental Knowledge ◽  
The Brain ◽  
Contrast Image ◽  
Specific Neuron

The study of the principles of formation and development of the structure of the brain is necessary to replenish fundamental knowledge both in the field of neurophysiology and in medicine. A detailed description of all the features of the brain will allow you to choose the most effective therapy method, or check the effectiveness of the drugs being developed. The basis for creating a model of a biological neural network is a map of nerve cells and their connections. To obtain it, it is necessary to carry out microscopy of the cell culture. This will produce a low-contrast image. The study of these images is a difficult task therefore a computational method for processing images based on the Shearlet transform algorithm with contrast using color coding has been developed, designed to improve the process of creating a neural network model. To assess the functional characteristics of each cell a modified version of the MEA method is proposed. The new version will have movable microelectrodes capable of homing to the desired coordinates in accordance with the data from the analyzed microscopic images and interacting with a specific neuron. The contact of a microelectrode with a single cell allows one to study its individual adhesions with minimal noise from the excitation of neighboring cells.

scholarly journals A model biological neural network: the cephalopod vestibular system

2007 ◽  
Vol 362 (1479) ◽  
pp. 473-481 ◽  
Author(s):  
Roddy Williamson ◽  
Abdul Chrachri
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Vestibular System ◽  
Sensory Information ◽  
Model Systems ◽  
Sense Organs ◽  
Biological Neural Network ◽  
Physiological Investigation ◽  
The Brain ◽  
Number Of Cells

Artificial neural networks (ANNs) have become increasingly sophisticated and are widely used for the extraction of patterns or meaning from complicated or imprecise datasets. At the same time, our knowledge of the biological systems that inspired these ANNs has also progressed and a range of model systems are emerging where there is detailed information not only on the architecture and components of the system but also on their ontogeny, plasticity and the adaptive characteristics of their interconnections. We describe here a biological neural network contained in the cephalopod statocysts; the statocysts are analogous to the vertebrae vestibular system and provide the animal with sensory information on its orientation and movements in space. The statocyst network comprises only a small number of cells, made up of just three classes of neurons but, in combination with the large efferent innervation from the brain, forms an ‘active’ sense organs that uses feedback and feed-forward mechanisms to alter and dynamically modulate the activity within cells and how the various components are interconnected. The neurons are fully accessible to physiological investigation and the system provides an excellent model for describing the mechanisms underlying the operation of a sophisticated neural network.

scholarly journals Computational Model for Biological Neural Network for Localisation of Sound in the Vertical Plane

2020 ◽  
Author(s):  
Anandita De ◽  
Daniel Cox
Keyword(s):  
Neural Network ◽  
Brain Stem ◽  
Neural Circuit ◽  
Broad Band ◽  
Vertical Plane ◽  
Rate Model ◽  
Biological Neural Network ◽  
Band Noise ◽  
Pure Tones ◽  
The Brain

AbstractWe build a computational rate model for a biological neural network found in mammals that is thought to be important in the localisation of the sound in the vertical plane. We find the response of neurons in the brain stem that participate in the localisation neural circuit to pure tones, broad band noise and notched noise and compare them to experimentally obtained response of these neurons. Our model is able to reproduce the sensitivity of these neurons in the brain stem to spectral properties of sounds that are important in localisation. This is the first rate based population model that elucidates all the response properties of the neurons in the vertical localisation pathway to our knowledge.

scholarly journals Connections

2020 ◽  
Vol 2 (3(September-December)) ◽  
pp. e642020
Author(s):  
Ricardo Santos De Oliveira
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Nervous System ◽  
Human Brain ◽  
Nerve Cells ◽  
Seer Database ◽  
Biological Neural Network ◽  
Artificial Neural ◽  
The Brain

The human brain contains around 86 billion nerve cells and about as many glial cells [1]. In addition, there are about 100 trillion connections between the nerve cells alone. While mapping all the connections of a human brain remains out of reach, scientists have started to address the problem on a smaller scale. The term artificial neural networks (ANNs or simply neural networks (NNs), encompassing a family of nonlinear computational methods that, at least in the early stage of their development, were inspired by the functioning of the human brain. Indeed, the first ANNs were nothing more than integrated circuits devised to reproduce and understand the transmission of nerve stimuli and signals in the human central nervous system [2]. The correct way of doing it is to the first study human behavior. The human brain has a biological neural network that has billions of interconnections. As the brain learns, these connections are either formed, changed or removed, similar to how an artificial neural network adjusts its weights to account for a new training example. This complexity is the reason why it is said that practice makes one perfect since a greater number of learning instances allow the biological neural network to become better at whatever it is doing. Depending upon the stimulus, only a certain subset of neurons are activated in the nervous system. Recently, Moreau et al., [3] published an interesting paper studying how artificial intelligence can help doctors and patients with meningiomas make better treatment decisions, according to a new study. They demonstrated that their models were capable of predicting meaningful individual-specific clinical outcome variables and show good generalizability across the Surveillance, Epidemiology, and End Results (SEER) database to predict meningioma malignancy and survival after specific treatments. Statistical learning models were trained and validated on 62,844 patients from the SEER database and a model scoring for the malignancy model was performed using a series of metrics. A free smartphone and web application were also provided for readers to access and test the predictive models (www.meningioma.app). The use of artificial intelligence techniques is gradually bringing efficient theoretical solutions to a large number of real-world clinical problems related to the brain (4). Specifically, recently, thanks to the accumulation of relevant data and the development of increasingly effective algorithms, it has been possible to significantly increase the understanding of complex brain mechanisms. The researchers' efforts are creating increasingly sophisticated and interpretable algorithms, which could favor a more intensive use of “intelligent” technologies in practical clinical contexts. Brain and machine working together will improve the power of these methods to make individual-patient predictions could lead to improved diagnosis, patient counseling, and outcomes.

scholarly journals Spinal vertebrae localization and analysis on disproportionality in curvature using radiography—a comprehensive review

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Joddat Fatima ◽  
Muhammad Usman Akram ◽  
Amina Jameel ◽  
Adeel Muzaffar Syed
Keyword(s):  
Spinal Cord ◽  
Template Matching ◽  
Human Anatomy ◽  
Comprehensive Overview ◽  
Low Contrast ◽  
Active Shape Models ◽  
Harris Corner ◽  
Illumination Changes ◽  
The Central Nervous System ◽  
The Brain

AbstractIn human anatomy, the central nervous system (CNS) acts as a significant processing hub. CNS is clinically divided into two major parts: the brain and the spinal cord. The spinal cord assists the overall communication network of the human anatomy through the brain. The mobility of body and the structure of the whole skeleton is also balanced with the help of the spinal bone, along with reflex control. According to the Global Burden of Disease 2010, worldwide, back pain issues are the leading cause of disability. The clinical specialists in the field estimate almost 80% of the population with experience of back issues. The segmentation of the vertebrae is considered a difficult procedure through imaging. The problem has been catered by different researchers using diverse hand-crafted features like Harris corner, template matching, active shape models, and Hough transform. Existing methods do not handle the illumination changes and shape-based variations. The low-contrast and unclear view of the vertebrae also makes it difficult to get good results. In recent times, convolutional nnural Network (CNN) has taken the research to the next level, producing high-accuracy results. Different architectures of CNN such as UNet, FCN, and ResNet have been used for segmentation and deformity analysis. The aim of this review article is to give a comprehensive overview of how different authors in different times have addressed these issues and proposed different mythologies for the localization and analysis of curvature deformity of the vertebrae in the spinal cord.

scholarly journals DNN-m6A: A Cross-Species Method for Identifying RNA N6-Methyladenosine Sites Based on Deep Neural Network with Multi-Information Fusion

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 354
Author(s):  
Lu Zhang ◽  
Xinyi Qin ◽  
Min Liu ◽  
Ziwei Xu ◽  
Guangzhong Liu
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Area Under The Curve ◽  
Nucleotide Composition ◽  
Computational Method ◽  
Feature Subset ◽  
Accurate Identification ◽  
Genome Wide ◽  
Dinucleotide Composition ◽  
Optimal Feature Subset

As a prevalent existing post-transcriptional modification of RNA, N6-methyladenosine (m6A) plays a crucial role in various biological processes. To better radically reveal its regulatory mechanism and provide new insights for drug design, the accurate identification of m6A sites in genome-wide is vital. As the traditional experimental methods are time-consuming and cost-prohibitive, it is necessary to design a more efficient computational method to detect the m6A sites. In this study, we propose a novel cross-species computational method DNN-m6A based on the deep neural network (DNN) to identify m6A sites in multiple tissues of human, mouse and rat. Firstly, binary encoding (BE), tri-nucleotide composition (TNC), enhanced nucleic acid composition (ENAC), K-spaced nucleotide pair frequencies (KSNPFs), nucleotide chemical property (NCP), pseudo dinucleotide composition (PseDNC), position-specific nucleotide propensity (PSNP) and position-specific dinucleotide propensity (PSDP) are employed to extract RNA sequence features which are subsequently fused to construct the initial feature vector set. Secondly, we use elastic net to eliminate redundant features while building the optimal feature subset. Finally, the hyper-parameters of DNN are tuned with Bayesian hyper-parameter optimization based on the selected feature subset. The five-fold cross-validation test on training datasets show that the proposed DNN-m6A method outperformed the state-of-the-art method for predicting m6A sites, with an accuracy (ACC) of 73.58%–83.38% and an area under the curve (AUC) of 81.39%–91.04%. Furthermore, the independent datasets achieved an ACC of 72.95%–83.04% and an AUC of 80.79%–91.09%, which shows an excellent generalization ability of our proposed method.

Sign in / Sign up

Export Citation Format

Share Document