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Introduction
Published in Laurence J. Street, Introduction to Biomedical Engineering Technology, 2023
The chemicals involved in conveying nerve signals, or action potentials, between cells are called neurotransmitters and include such molecules as acetylcholine, epinephrine (adrenaline), dopamine, and nitric oxide. When an electrical nerve impulse arrives at the junction, or synapse, between the conducting nerve and the next nerve cell in the particular pathway, molecules of neurotransmitters are released into the space between the nerve cells. The neurotransmitter molecules then connect with a receptor structure on the receiving nerve cell, and this causes an electrical signal to be generated in the new cell. Signals thus travel along a chain of nerve cells to their target tissue (Figure 1.18). With the neurotransmitter steps involved, nerve signal transmission is far slower than electrical conduction speed, in the range of 200 m/s as compared to 3 × 108 m/s.
Machine Learning and Data Science in Industries
Published in Sandeep Misra, Chandana Roy, Anandarup Mukherjee, Introduction to Industrial Internet of Things and Industry 4.0, 2021
Sandeep Misra, Chandana Roy, Anandarup Mukherjee
In the biological context, synapses are structures that help to transmit the signals from one neuron to another in the brain. Similarly, in ANN, each neuron communicates with other neurons by applying signals. Further, due to the ANN-based structures, deep learning can handle a high dimension of data. In the traditional neural network, the number of hidden layers may vary from 2 to 3 [185]. However, in the case of deep learning, the network may have up to 150 (or more) hidden layers. Unlike ML, deep learning does not need algorithms to specify the steps of data processing and analysis. The performance of deep learning algorithms improves with the increase in the volume of data. Further, in ML and AI, there is a presence of potential bias in the dataset and algorithms. It is necessary to mention the features of the data in ML explicitly. Therefore, to overcome such problems, a deep learning method is best suited for computation and analysis of an enormous volume of data.
Introduction
Published in Laurence J. Street, Introduction to Biomedical Engineering Technology, 2016
The chemicals involved in conveying nerve signals, or action potentials, between cells are called neurotransmitters, and include such molecules as acetylcholine, epinephrine (adrenaline), dopamine, and nitric oxide. When an electrical nerve impulse arrives at the junction, or synapse, between the conducting nerve and the next nerve cell in the particular pathway, molecules of neurotransmitters are released into the space between the nerve cells. The neurotransmitter molecules then connect with a receptor structure on the receiving nerve cell and this causes an electrical signal to be generated in the new cell. Signals thus travel along a chain of nerve cells to their target tissue (Figure 1.18). With the neurotransmitter steps involved, nerve signal transmission is far slower than electrical conduction speed, in the range of 200 m/s as compared to 3 × 108 m/s.
Pseudo-transistors for emerging neuromorphic electronics
Published in Science and Technology of Advanced Materials, 2023
Jingwei Fu, Jie Wang, Xiang He, Jianyu Ming, Le Wang, Yiru Wang, He Shao, Chaoyue Zheng, Linghai Xie, Haifeng Ling
As the junction between pre- and postsynaptic neurons (Figure 3), synapses are the basic units of neural networks and undertake the key tasks of material interaction and information transfer between neurons [78,79]. Synapses are mainly divided into two categories: chemical synapses and electrical synapses [80]. Electrical synapses transmit information with the help of electrical signals. Chemical synapses transfer information via neurotransmitter, which are found primarily in the human body. The transmission and handling of information is a complex process: firstly, action potentials control the opening of Ca2+ channels at presynaptic neuron, releasing excitatory or inhibitory neurotransmitters in the synaptic cleft. At the postsynaptic neuron, the neurotransmitters bind to a specific protein receptor. This receptor converts the chemical signal back into an electrical signal. In this way, the neurotransmitters can initiate an electrical response that either excite or inhibit the postsynaptic neuron.
Speeding up Composite Differential Evolution for structural optimization using neural networks
Published in Journal of Information and Telecommunication, 2022
Artificial neural network (ANN) is known as one of the most powerful computational paradigms. This model was first designed in 1958 based on the understanding of the human brain's structure (Rosenblatt, 1958). In biological brains, there are billions of neurons connected to each other through synapses. The role of synapses is to transmit the electrical signal to other neurons. Similarly, ANNs are composed of nodes that simulate neurons and connections that imitate the synapses of the brain. An artificial neuron receives inputs from previous neurons, transforms them by the activation function, and sends them to the following neurons via connections. Each connection is assigned a weight to represent the magnitude of the signal. The activation function attached to artificial neurons is frequently nonlinear, allowing ANNs to capture complex data. Until now, many architectures of ANN have been introduced, for example, feed-forward neural networks (FFNN), convolution neural networks (CNN), recurrent neural networks (RNN), etc. Each architecture of ANNs is designed for a specific task. CNN is primarily used for tasks related to image processing, and RNN is suitable for time series data. In the field of structural engineering, FFNN is commonly used.
Design of a new CMOS Low-Power Analogue Neuron
Published in IETE Journal of Research, 2018
Andisheh Ghomi, Mehdi Dolatshahi
The structure of the biological neural networks include large set of parallel processors called neurons that act together to solve a problem. A neuron receives signals from other neurons through connections called synapses. The combination of these signals, in excess of a certain threshold or activation level, results in the neuron firing. Artificial neural networks are models of biological neural structures. The starting point for most artificial neural networks is a neuron model. As it is shown in Figure 1, the neuron consists of multiple inputs and a single output. Each input is multiplied by a weight. The neuron combines these weighted inputs to pass through the activation function. The artificial neuron given in Figure 1, has n inputs, denoted as x1, x2,…, xn. Weights are denoted as w1, w2, …, wn, respectively. Weights in the artificial model are corresponded to the synaptic connections in biological neurons. Processing elements are typically modelled by two equations which represent the model of an artificial neuron as follows:where a is the weighted summation of the inputs, and f (a) is the activation function of the weighted sum.