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Neuron's cobweb-like cytoskeleton (its interior scaffolding)

Related:  Neuroscience

Neuron All neurons are electrically excitable, maintaining voltage gradients across their membranes by means of metabolically driven ion pumps, which combine with ion channels embedded in the membrane to generate intracellular-versus-extracellular concentration differences of ions such as sodium, potassium, chloride, and calcium. Changes in the cross-membrane voltage can alter the function of voltage-dependent ion channels. If the voltage changes by a large enough amount, an all-or-none electrochemical pulse called an action potential is generated, which travels rapidly along the cell's axon, and activates synaptic connections with other cells when it arrives. Neurons do not undergo cell division. Overview[edit] A neuron is a specialized type of cell found in the bodies of all eumetozoans. Although neurons are very diverse and there are exceptions to nearly every rule, it is convenient to begin with a schematic description of the structure and function of a "typical" neuron. Polarity[edit]

neurotransmitters and neuromodulators The soft warm living substance of the brain and nervous system stands in stark contrast to the rigid metal and plastic hardware of a modern day computer, but at the fundamental level there are clear similarities between these two apparently disparate organizational systems and, of course, one is a product of the other. Not only are the nerve cell units (neurons) self-repairing and self-wiring under the grand design built into our genes, but they can also promote, amplify, block, inhibit, or attenuate the micro-electric signals which are passed to them, and through them. In this way they give rise to signalling patterns of myriad complexity between networks of cerebral neurons, and this provides the physical substrate of mind. These key processes of signalling by one group, or family, of neurons to another is achieved largely by the secretion of tiny quantities of potent chemical substances by neuronal fibre terminals. In this way, the nerve impulses are passed on from cell to cell. 1.

Brain Atlas - Introduction The central nervous system (CNS) consists of the brain and the spinal cord, immersed in the cerebrospinal fluid (CSF). Weighing about 3 pounds (1.4 kilograms), the brain consists of three main structures: the cerebrum, the cerebellum and the brainstem. Cerebrum - divided into two hemispheres (left and right), each consists of four lobes (frontal, parietal, occipital and temporal). The outer layer of the brain is known as the cerebral cortex or the ‘grey matter’. – closely packed neuron cell bodies form the grey matter of the brain. Cerebellum – responsible for psychomotor function, the cerebellum co-ordinates sensory input from the inner ear and the muscles to provide accurate control of position and movement. Brainstem – found at the base of the brain, it forms the link between the cerebral cortex, white matter and the spinal cord. Other important areas in the brain include the basal ganglia, thalamus, hypothalamus, ventricles, limbic system, and the reticular activating system. Neurons

List all the essential neurotransmitters Acetylcholine - synthesized from Choline, Lecithin, and panthothenic acid (B5), or Diethylaminoethanol (DMAE) - Arousal and orgasm - voluntary muscular control and proper tone - enhance energy and stamina - memory - long-term planning - mental focus Dopamine - synthesized from amino acid Levodopa - Alertness - Motivation - motor control - immune function - Ego hardening, confidence, optimism - Sexual Desire - Fat gain and loss - lean muscle gain - Bone density - ability to sleep soundly - Inhibits prolactin - thinking, planning, and problem solving - Aggression - Increase psychic and creative ability - Reduction of compulsivety - Salience and paranoia - Processing of pain - Increase sociability Serotonin (5-HT) - Synthesized from amino acid L-tryptophan with co-factor Niacin (B3), through the intermediate 5-hydroxytryptophan (5-HTP) Norepinephrine - Synthesized from Dopamine with co-factor of vitamin C through the intermediate DOPAC. Vasopressin - Yan Niemczycki

UCSB scientists discover how the brain encodes memories at a cellular level (Santa Barbara, Calif.) –– Scientists at UC Santa Barbara have made a major discovery in how the brain encodes memories. The finding, published in the December 24 issue of the journal Neuron, could eventually lead to the development of new drugs to aid memory. The team of scientists is the first to uncover a central process in encoding memories that occurs at the level of the synapse, where neurons connect with each other. "When we learn new things, when we store memories, there are a number of things that have to happen," said senior author Kenneth S. Kosik, co-director and Harriman Chair in Neuroscience Research, at UCSB's Neuroscience Research Institute. "One of the most important processes is that the synapses –– which cement those memories into place –– have to be strengthened," said Kosik. This is a neuron. (Photo Credit: Sourav Banerjee) Part of strengthening a synapse involves making new proteins. When the signal comes in, the wrapping protein degrades or gets fragmented.

Introduction to Feedforward Neural Networks - Introduction Neural networks are a very popular data mining and image processing tool. Their origin stems from the attempt to model the human thought process as a an algorithm which can be efficiently run on a computer. The human brain consists of neurons that send activation signals to each other (figure on the left) thereby creating intelligent thoughts. The algorithmic version of a neural network (called an artificial neural network) also consists of neurons which send activation signals to one another (figure below). The end result is that the artificial neural network can approximate a function of multiple inputs and outputs. Because the breadth of neural networks is very large, this project focuses on feed-forward neural networks, perhaps the most common type. Algorithm Description Artificial neural networks (the ones that run on a computer as opposed to a brain) can be thought of as a model which approximates a function of multiple continuous inputs and outputs. Computing Output

The Learning Brain Gets Bigger--Then Smaller With age and enough experience, we all become connoisseurs of a sort. After years of hearing a favorite song, you might notice a subtle effect that’s lost on greener ears. Perhaps you’re a keen judge of character after a long stint working in sales. Or maybe you’re one of the supremely practiced few who tastes his money’s worth in a wine. Whatever your hard-learned skill is, your ability to hear, see, feel, or taste with more nuance than a less practiced friend is written in your brain. One classical line of work has tackled these questions by mapping out changes in brain organization following intense and prolonged sensory experience. But don’t adopt that slogan quite yet. If you were to look at the side of someone’s brain, focusing on the thin sliver of auditory cortex, it would seem fairly uniform, with only a few blood vessels to provide some bearing. And yet, some aspects of this theory invited skepticism. So what does change? Still, there’s a big question lurking here.

Feedforward Neural Networks 2.5.1 Feedforward Neural Networks Feedforward neural networks (FF networks) are the most popular and most widely used models in many practical applications. They are known by many different names, such as "multi-layer perceptrons." Figure 2.5 illustrates a one-hidden-layer FF network with inputs and output . , also called the neuron function. Figure 2.5. Mathematically the functionality of a hidden neuron is described by where the weights { } are symbolized with the arrows feeding into the neuron. The network output is formed by another weighted summation of the outputs of the neurons in the hidden layer. The neurons in the hidden layer of the network in Figure 2.5 are similar in structure to those of the perceptron, with the exception that their activation functions can be any differential function. where n is the number of inputs and nh is the number of neurons in the hidden layer. } are the parameters of the network model that are represented collectively by the parameter vector . In[1]:=

Image by Bernd Knoll at the University of Tubingen by kaspervandenberg Dec 23