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Compartmental Modelling Of Dendrites
Compartmental modelling of dendrites deals with multi-compartment modelling of the dendrites, to make the understanding of the electrical behavior of complex dendrites easier. Basically, compartmental modelling of dendrites is a very helpful tool to develop new biological neuron models. Dendrites are very important because they occupy the most membrane area in many of the neurons and give the neuron an ability to connect to thousands of other cells. Originally the dendrites were thought to have constant conductance and current but now it has been understood that they may have active Voltage-gated ion channels, which influences the firing properties of the neuron and also the response of neuron to synaptic inputs. Many mathematical models have been developed to understand the electric behavior of the dendrites. Dendrites tend to be very branchy and complex, so the compartmental approach to understand the electrical behavior of the dendrites makes it very useful. Lindsay, A. E., Lin ...
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Multi-compartment Model
A multi-compartment model is a type of mathematical model used for describing the way materials or energies are transmitted among the ''compartments'' of a system. Sometimes, the physical system that we try to model in equations is too complex, so it is much easier to discretize the problem and reduce the number of parameters. Each compartment is assumed to be a homogeneous entity within which the entities being modeled are equivalent. A multi-compartment model is classified as a lumped parameters model. Similar to more general mathematical models, multi-compartment models can treat variables as continuous, such as a differential equation, or as discrete, such as a Markov chain. Depending on the system being modeled, they can be treated as stochastic or deterministic. Multi-compartment models are used in many fields including pharmacokinetics, epidemiology, biomedicine, systems theory, complexity theory, engineering, physics, information science and social science. The circuits sys ...
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Dendrites Modelling
Dendrites (from Greek δένδρον ''déndron'', "tree"), also dendrons, are branched protoplasmic extensions of a nerve cell that propagate the electrochemical stimulation received from other neural cells to the cell body, or soma, of the neuron from which the dendrites project. Electrical stimulation is transmitted onto dendrites by upstream neurons (usually via their axons) via synapses which are located at various points throughout the dendritic tree. Dendrites play a critical role in integrating these synaptic inputs and in determining the extent to which action potentials are produced by the neuron. Dendritic arborization, also known as dendritic branching, is a multi-step biological process by which neurons form new dendritic trees and branches to create new synapses. The morphology of dendrites such as branch density and grouping patterns are highly correlated to the function of the neuron. Malformation of dendrites is also tightly correlated to impaired nervous syste ...
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NMDA
''N''-methyl--aspartic acid or ''N''-methyl--aspartate (NMDA) is an amino acid derivative that acts as a specific agonist at the NMDA receptor mimicking the action of glutamate, the neurotransmitter which normally acts at that receptor. Unlike glutamate, NMDA only binds to and regulates the NMDA receptor and has no effect on other glutamate receptors (such as those for AMPA and kainate). NMDA receptors are particularly important when they become overactive during, for example, withdrawal from alcohol as this causes symptoms such as agitation and, sometimes, epileptiform seizures. Biological function In 1962, J.C. Watkins reported synthesizing NMDA, an isomer of the previously know''N''-Methyl--aspartic-acid (PubChem ID 4376) NMDA is a water-soluble -alpha-amino acid — an aspartic acid derivative with an ''N''-methyl substituent and - configuration — found across Animalia from lancelets to mammals. At homeostatic levels NMDA plays an essential role as a neurotransmitter and ...
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Stimulation
Stimulation is the encouragement of development or the cause of activity generally. For example, "The press provides stimulation of political discourse." An interesting or fun activity can be described as "stimulating", regardless of its physical effects on senses. ''Stimulate'' means to act as a stimulus to; ''stimulus'' means something that rouses the recipient to activity; ''stimuli'' is the plural of ''stimulus''. A particular use of the term is physiological stimulation, which refers to sensory excitation, the action of various agents or forms of energy (Stimulus (physiology), stimuli) on receptors that generate Action potential, impulses that travel through nerves to the brain (afferents). There are sensory receptors on or near the surface of the body, such as photoreceptors in the retina of the eye, hair cells in the cochlea of the ear, touch receptors in the skin and chemical receptors in the mouth and nasal cavity. There are also sensory receptors in the muscles, joints, ...
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Artificial Neural Network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called ''edges''. Neurons and edges typically have a ''weight'' that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically ...
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Non-linearity
In mathematics and science, a nonlinear system is a system in which the change of the output is not proportional to the change of the input. Nonlinear problems are of interest to engineers, biologists, physicists, mathematicians, and many other scientists because most systems are inherently nonlinear in nature. Nonlinear dynamical systems, describing changes in variables over time, may appear chaotic, unpredictable, or counterintuitive, contrasting with much simpler linear systems. Typically, the behavior of a nonlinear system is described in mathematics by a nonlinear system of equations, which is a set of simultaneous equations in which the unknowns (or the unknown functions in the case of differential equations) appear as variables of a polynomial of degree higher than one or in the argument of a function which is not a polynomial of degree one. In other words, in a nonlinear system of equations, the equation(s) to be solved cannot be written as a linear combination of the un ...
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Threshold Potential
In electrophysiology, the threshold potential is the critical level to which a membrane potential must be depolarized to initiate an action potential. In neuroscience, threshold potentials are necessary to regulate and propagate signaling in both the central nervous system (CNS) and the peripheral nervous system (PNS). Most often, the threshold potential is a membrane potential value between –50 and –55 mV, but can vary based upon several factors. A neuron's resting membrane potential (–70 mV) can be altered to either increase or decrease likelihood of reaching threshold via sodium and potassium ions. An influx of sodium into the cell through open, voltage-gated sodium channels can depolarize the membrane past threshold and thus excite it while an efflux of potassium or influx of chloride can hyperpolarize the cell and thus inhibit threshold from being reached. Discovery Initial experiments revolved around the concept that any electrical change that is brought about in neu ...
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Sigmoid Function
A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve. A common example of a sigmoid function is the logistic function shown in the first figure and defined by the formula: :S(x) = \frac = \frac=1-S(-x). Other standard sigmoid functions are given in the Examples section. In some fields, most notably in the context of artificial neural networks, the term "sigmoid function" is used as an alias for the logistic function. Special cases of the sigmoid function include the Gompertz curve (used in modeling systems that saturate at large values of x) and the ogee curve (used in the spillway of some dams). Sigmoid functions have domain of all real numbers, with return (response) value commonly monotonically increasing but could be decreasing. Sigmoid functions most often show a return value (y axis) in the range 0 to 1. Another commonly used range is from −1 to 1. A wide variety of sigmoid functions including the logistic and hype ...
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Oblique Dendrite
An oblique dendrite is a dendrite that branches from an apical dendrite that emerges from the apex of a pyramidal cell. Oblique dendrites typically branch one to two times before terminating. Dendrites are extensions of the cell body of a neuron. Growth and development The growth and development of oblique dendrites in rats has been linked to the type of environment, or condition, they are placed in. This is also known as environmental enrichment. The three types of conditions used in studies are an enriched conditioned, standard condition, and impoverished condition. The enriched condition contains mazes, an exercise wheel, other rats, and toys. The standard condition generally has a wheel for voluntary exercise and other rats. The impoverished condition only contains fellow rats. Animals placed in an enriched environment had heavier, thicker cortexes and an increase in the number of dendritic branches (including oblique dendrites) in the hippocampus than the standard or impove ...
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Biophysical
Biophysics is an interdisciplinary science that applies approaches and methods traditionally used in physics to study biological phenomena. Biophysics covers all scales of biological organization, from molecular to organismic and populations. Biophysical research shares significant overlap with biochemistry, molecular biology, physical chemistry, physiology, nanotechnology, bioengineering, computational biology, biomechanics, developmental biology and systems biology. The term ''biophysics'' was originally introduced by Karl Pearson in 1892.Roland Glaser. Biophysics: An Introduction'. Springer; 23 April 2012. . The term ''biophysics'' is also regularly used in academia to indicate the study of the physical quantities (e.g. electric current, temperature, stress, entropy) in biological systems. Other biological sciences also perform research on the biophysical properties of living organisms including molecular biology, cell biology, chemical biology, and biochemistry. Overview Mol ...
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Molecular
A molecule is a group of two or more atoms held together by attractive forces known as chemical bonds; depending on context, the term may or may not include ions which satisfy this criterion. In quantum physics, organic chemistry, and biochemistry, the distinction from ions is dropped and ''molecule'' is often used when referring to polyatomic ions. A molecule may be homonuclear, that is, it consists of atoms of one chemical element, e.g. two atoms in the oxygen molecule (O2); or it may be heteronuclear, a chemical compound composed of more than one element, e.g. water (molecule), water (two hydrogen atoms and one oxygen atom; H2O). In the kinetic theory of gases, the term ''molecule'' is often used for any gaseous particle regardless of its composition. This relaxes the requirement that a molecule contains two or more atoms, since the noble gases are individual atoms. Atoms and complexes connected by non-covalent interactions, such as hydrogen bonds or ionic bonds, are typic ...
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