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Microfluidics in Neuroscience
Published in Tuhin S. Santra, Microfluidics and Bio-MEMS, 2020
Pallavi Gupta, Nandhini Balasubramaniam, Kiran Kaladharan, Fan-Gang Tseng, Moeto Nagai, Hwan-You Chang, Tuhin S. Santra
Dendrites act as conduits of information influx and integration, from synapse to nucleus and vice versa. Interconnected compartments with μFDs also control dendrite probing in subregions. To mimic the synaptic release, neurotransmitters were administered inside ultrathin fluid streams of chemoattractants, placed or relocated across neurons in μFDs [99]. The advancements in controlling the microenvironments of dendrites have helped resolve the fundamental synapse formation and regulation mechanism [101, 102]. This approach has proven useful in resolving mechanisms of BDNF induction of transcription-mediated synapse strengthening. Compartmentalization and fluid isolation of dendrites from the somatic body of the rat embryonic cortical neurons in μFDs has helped discover a novel, dendrite-localized neurotrophin signaling pathway (Fig. 4.18b). Treatment by BDNF and other pharmacological agents can be restricted to the dendritic somatic part inside the above-described device. The study revealed that the dendrite-to-nucleus induction of c-Fos expression induced by BDNF is dependent on Ca2+, Trk-activity independent in dendrites, and also mediated via mitogen-activated protein kinase (MAPKK or MEK1/2). However, dendritic signal processing is different from MEK5-mediated axonal retrograde signaling, and it is dependent on intradendritic mRNA translation for the expression of the immediate early gene. All these factors separate dendritic pathways from the retrograde neurotrophin signaling existing in axons [97].
Comb Models for Transport along Spiny Dendrites
Published in Christos H. Skiadas, Charilaos Skiadas, Handbook of Applications of Chaos Theory, 2017
Méndez Vicenç, Iomin Alexander
Comb-like models have been applied to mimic ramified structures as spiny dendrites of neuron cells [27,35] or percolation clusters with dangling bonds [48]. We are interested in the first example, where a comb structure with one-sided teeth of infinite length can be used to describe the movement and binding dynamics of particles inside the spines of dendrites. These spines are small protrusions from many types of neurons located on the surface of a neuronal dendrite. They receive most of the excitatory inputs and their physiological role is still unclear, although most spines are thought to be key elements in neuronal information processing and plasticity [49]. Spines are composed of a head (~1μm) and a thin neck (~0.1 μm) attached to the surface of dendrites (see Figure 31.2).
Methods of visual perception and memory modelling
Published in Limiao Deng, Cognitive and Neural Modelling for Visual Information Representation and Memorization, 2022
Neurons, also known as nerve cells, are the basic units of the structure and function of the nervous system. Neurons are composed of cell bodies, dendrites, and axons, as shown in Fig. 2.5. Dendrites are multi-rooted and multi-branched processes from the cell body, which are mainly used to receive incoming information. A neuron has multiple dendrites, but only one axon. Neurons transmit information through axon terminals located at the end of axons and dendrites of other neurons19, which are called synapses. The neural basis of learning and memory is the high plasticity of the central nervous system, and synaptic plasticity is the basis of the central nervous system20.
Performance analysis and process parameters optimisation on specific cutting energy in the abrasive waterjet cutting
Published in International Journal of Ambient Energy, 2022
Ketan D. Panchal, Abdul Hafiz Shaikh
ANN is widely used in machine learning to develop predictive model to approximate the response of various manufacturing processes based on input parameters. They are powerful and effective tools for various complex manufacturing processes like AWJM and EDM which involves many process parameters. They are also used for system identification, optimisation as well as to reorganise the pattern. ANN comprises of different input layers, which in turn multiplied by synoptic weights, and afterward assessed by a numerical planning which registers the actuation of the neuron. Dendrites sends inputs to neurons and depending upon these inputs, dendrites produce output which in turn gets transferred to upcoming neuron via axon. This principle basic fundamentals for distinct algorithms. Neural networks are enough capable to learn from the experimental data and to accomplish nonlinear plotting.
Neurophysiological and molecular approaches to understanding the mechanisms of learning and memory
Published in Journal of the Royal Society of New Zealand, 2021
Shruthi Sateesh, Wickliffe C. Abraham
The plasticity of functional synaptic properties appears to go hand-in-hand with plasticity of the synaptic structure (Muller et al. 2000; Williams et al. 2003). Dendritic spines, which are the small postsynaptic protrusions where excitatory synaptic contacts are made, are dynamic structures that can be formed, modified in their shape or eliminated under the influence of activity. Matsuzaki et al. (2004) showed that synaptic potentiation is closely related to the enlargement of spine heads, as revealed by two-photon photolysis of caged glutamate and concurrent real-time imaging of spine morphology and electrophysiological recording of synaptic responses at single spines of hippocampal CA1 pyramidal neurons. This key finding has been complemented by static analyses of synaptic structure at fixed times post-HFS using electron microscopy. Bourne and Harris (2011) undertook three-dimensional reconstructions of CA1 dendrites and observed an increase in synapse size and number for both excitatory and inhibitory synapses following induction of LTP. In addition, using a combination of electron microscopy and calcium precipitation, there was an increase in the number of spines making contact with the same presynaptic terminal, indicating that LTP induces the multiplication of existing axonal-dendritic contacts (Toni et al. 1999). Interestingly, changes in number and morphology of neuronal structures have also been captured in behaving animals using high-resolution imaging suggesting that structural plasticity is an ongoing process in the mammalian adult brain (Holtmaat and Svoboda 2009).
Prediction of abrasive wears behavior of dental composites using an artificial neural network
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Abhijeet Shivaji Suryawanshi, Niranjana Behera
Other neurons send signals to the dendrites that are responsible for receiving them. A dendrite works as a receptor for a specific neuron and transmits information to the cell body, where it is processed. It now either activates or does not activate depending on the volume of information. This activity is determined by the neuron's threshold value. It will not fire if the incoming signal value is less than that threshold; otherwise, it will activate. The terminals, which are attached to the dendrites of other neurons, constitute the third component. Terminals are in charge of transmitting the output of a certain neuron to other pertinent connections.