Demyelinating diseaseA demyelinating disease refers to any disease affecting the nervous system where the myelin sheath surrounding neurons is damaged. This damage disrupts the transmission of signals through the affected nerves, resulting in a decrease in their conduction ability. Consequently, this reduction in conduction can lead to deficiencies in sensation, movement, cognition, or other functions depending on the nerves affected. Various factors can contribute to the development of demyelinating diseases, including genetic predisposition, infectious agents, autoimmune reactions, and other unknown factors.
Artificial neural networkArtificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting 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.
Recurrent neural networkA recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.
Lesional demyelinations of the central nervous systemMultiple sclerosis and other demyelinating diseases of the central nervous system (CNS) produce lesions (demyelinated areas in the CNS) and glial scars or scleroses. They present different shapes and histological findings according to the underlying condition that produces them. Demyelinating diseases are traditionally classified in two kinds: demyelinating myelinoclastic diseases and demyelinating leukodystrophic diseases. In the first group a normal and healthy myelin is destroyed by a toxic, chemical or autoimmune substance.
Pathophysiology of multiple sclerosisMultiple sclerosis is an inflammatory demyelinating disease of the CNS in which activated immune cells invade the central nervous system and cause inflammation, neurodegeneration, and tissue damage. The underlying cause is currently unknown. Current research in neuropathology, neuroimmunology, neurobiology, and neuroimaging, together with clinical neurology, provide support for the notion that MS is not a single disease but rather a spectrum.
Chronic inflammatory demyelinating polyneuropathyChronic inflammatory demyelinating polyneuropathy (CIDP) is an acquired autoimmune disease of the peripheral nervous system characterized by progressive weakness and impaired sensory function in the legs and arms. The disorder is sometimes called chronic relapsing polyneuropathy (CRP) or chronic inflammatory demyelinating polyradiculoneuropathy (because it involves the nerve roots). CIDP is closely related to Guillain–Barré syndrome and it is considered the chronic counterpart of that acute disease.
AxonAn axon (from Greek ἄξων áxōn, axis), or nerve fiber (or nerve fibre: see spelling differences), is a long, slender projection of a nerve cell, or neuron, in vertebrates, that typically conducts electrical impulses known as action potentials away from the nerve cell body. The function of the axon is to transmit information to different neurons, muscles, and glands.
Fish locomotionFish locomotion is the various types of animal locomotion used by fish, principally by swimming. This is achieved in different groups of fish by a variety of mechanisms of propulsion, most often by wave-like lateral flexions of the fish's body and tail in the water, and in various specialised fish by motions of the fins.
Language modelA language model is a probabilistic model of a natural language that can generate probabilities of a series of words, based on text corpora in one or multiple languages it was trained on. Large language models, as their most advanced form, are a combination of feedforward neural networks and transformers. They have superseded recurrent neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model.
Normal distributionIn statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is The parameter is the mean or expectation of the distribution (and also its median and mode), while the parameter is its standard deviation. The variance of the distribution is . A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate.
Convolutional neural networkConvolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.
Cauchy distributionThe Cauchy distribution, named after Augustin Cauchy, is a continuous probability distribution. It is also known, especially among physicists, as the Lorentz distribution (after Hendrik Lorentz), Cauchy–Lorentz distribution, Lorentz(ian) function, or Breit–Wigner distribution. The Cauchy distribution is the distribution of the x-intercept of a ray issuing from with a uniformly distributed angle. It is also the distribution of the ratio of two independent normally distributed random variables with mean zero.
Axon guidanceAxon guidance (also called axon pathfinding) is a subfield of neural development concerning the process by which neurons send out axons to reach their correct targets. Axons often follow very precise paths in the nervous system, and how they manage to find their way so accurately is an area of ongoing research. Axon growth takes place from a region called the growth cone and reaching the axon target is accomplished with relatively few guidance molecules. Growth cone receptors respond to the guidance cues.
Terrestrial locomotionTerrestrial locomotion has evolved as animals adapted from aquatic to terrestrial environments. Locomotion on land raises different problems than that in water, with reduced friction being replaced by the increased effects of gravity. As viewed from evolutionary taxonomy, there are three basic forms of animal locomotion in the terrestrial environment: legged – moving by using appendages limbless locomotion – moving without legs, primarily using the body itself as a propulsive structure.
Central pattern generatorCentral pattern generators (CPGs) are self-organizing biological neural circuits that produce rhythmic outputs in the absence of rhythmic input. They are the source of the tightly-coupled patterns of neural activity that drive rhythmic and stereotyped motor behaviors like walking, swimming, breathing, or chewing. The ability to function without input from higher brain areas still requires modulatory inputs, and their outputs are not fixed. Flexibility in response to sensory input is a fundamental quality of CPG-driven behavior.
Animal locomotionAnimal locomotion, in ethology, is any of a variety of methods that animals use to move from one place to another. Some modes of locomotion are (initially) self-propelled, e.g., running, swimming, jumping, flying, hopping, soaring and gliding. There are also many animal species that depend on their environment for transportation, a type of mobility called passive locomotion, e.g., sailing (some jellyfish), kiting (spiders), rolling (some beetles and spiders) or riding other animals (phoresis).
Neural networkA neural network can refer to a neural circuit of biological neurons (sometimes also called a biological neural network), a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed.
Spinal cordThe spinal cord is a long, thin, tubular structure made up of nervous tissue, which extends from the medulla oblongata in the brainstem to the lumbar region of the vertebral column (backbone). The backbone encloses the central canal of the spinal cord, which contains cerebrospinal fluid. The brain and spinal cord together make up the central nervous system (CNS). In humans, the spinal cord begins at the occipital bone, passing through the foramen magnum and then enters the spinal canal at the beginning of the cervical vertebrae.
Physical neural networkA physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse or a higher-order (dendritic) neuron model. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.
Stable distributionIn probability theory, a distribution is said to be stable if a linear combination of two independent random variables with this distribution has the same distribution, up to location and scale parameters. A random variable is said to be stable if its distribution is stable. The stable distribution family is also sometimes referred to as the Lévy alpha-stable distribution, after Paul Lévy, the first mathematician to have studied it. Of the four parameters defining the family, most attention has been focused on the stability parameter, (see panel).
