Neural network in the context of Artificial neurons

Evolutionary robotics is an embodied approach to Artificial Intelligence (AI) in which robots are automatically designed using Darwinian principles of natural selection. The design of a robot, or a subsystem of a robot such as a neural controller, is optimized against a behavioral goal (e.g. run as fast as possible). Usually, designs are evaluated in simulations as fabricating thousands or millions of designs and testing them in the real world is prohibitively expensive in terms of time, money, and safety.

An evolutionary robotics experiment starts with a population of randomly generated robot designs. The worst performing designs are discarded and replaced with mutations and/or combinations of the better designs. This evolutionary algorithm continues until a prespecified amount of time elapses or some target performance metric is surpassed.

Neurochemistry is the study of chemicals, including neurotransmitters and other molecules such as psychopharmaceuticals and neuropeptides, that control and influence the physiology of the nervous system. This particular field within neuroscience examines how neurochemicals influence the operation of neurons, synapses, and neural networks. Neurochemists analyze the biochemistry and molecular biology of organic compounds in the nervous system, and their roles in such neural processes including cortical plasticity, neurogenesis, and neural differentiation.

Neuroplasticity, also known as neural plasticity or just plasticity, is the medium of neural networks in the brain to change through growth and reorganization. Neuroplasticity refers to the brain's ability to reorganize and rewire its neural connections, enabling it to adapt and function in ways that differ from its prior state. This process can occur in response to learning new skills, experiencing environmental changes, recovering from injuries, or adapting to sensory or cognitive deficits. Such adaptability highlights the dynamic and ever-evolving nature of the brain, even into adulthood. These changes range from individual neuron pathways making new connections, to systematic adjustments like cortical remapping or neural oscillation. Other forms of neuroplasticity include homologous area adaptation, cross modal reassignment, map expansion, and compensatory masquerade. Examples of neuroplasticity include circuit and network changes that result from learning a new ability, information acquisition, environmental influences, pregnancy, caloric intake, practice/training, and psychological stress.

Neuroplasticity was once thought by neuroscientists to manifest only during childhood, but research in the later half of the 20th century showed that many aspects of the brain exhibit plasticity through adulthood. The developing brain exhibits a higher degree of plasticity than the adult brain. Activity-dependent plasticity can have significant implications for healthy development, learning, memory, and recovery from brain damage.

In neuroscience, neuronal tuning refers to the hypothesized property of brain cells by which they selectively represent a particular type of sensory, association, motor, or cognitive information. Some neuronal responses have been hypothesized to be optimally tuned to specific patterns through experience. Neuronal tuning can be strong and sharp, as observed in primary visual cortex (area V1), or weak and broad, as observed in neural ensembles. Single neurons are hypothesized to be simultaneously tuned to several modalities, such as visual, auditory, and olfactory. Neurons hypothesized to be tuned to different signals are often hypothesized to integrate information from the different sources. In computational models called neural networks, such integration is the major principle of operation. The best examples of neuronal tuning can be seen in the visual, auditory, olfactory, somatosensory, and memory systems, although due to the small number of stimuli tested the generality of neuronal tuning claims is still an open question.

In molecular biology, an interactome is the whole set of molecular interactions in a particular cell. The term specifically refers to physical interactions among molecules (such as those among proteins, also known as protein–protein interactions, PPIs; or between small molecules and proteins) but can also describe sets of indirect interactions among genes (genetic interactions).

The word "interactome" was originally coined in 1999 by a group of French scientists headed by Bernard Jacq. Mathematically, interactomes are generally displayed as graphs. While interactomes may be described as biological networks, they should not be confused with other networks such as neural networks or food webs.

A spatial network (sometimes also geometric graph) is a graph in which the vertices or edges are spatial elements associated with geometric objects, i.e., the nodes are located in a space equipped with a certain metric. The simplest mathematical realization of spatial network is a lattice or a random geometric graph (see figure in the right), where nodes are distributed uniformly at random over a two-dimensional plane; a pair of nodes are connected if the Euclidean distance is smaller than a given neighborhood radius. Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks and biological neural networks are all examples where the underlying space is relevant and where the graph's topology alone does not contain all the information. Characterizing and understanding the structure, resilience and the evolution of spatial networks is crucial for many different fields ranging from urbanism to epidemiology.

An artificial neuron is a mathematical function conceived as a model of a biological neuron in a neural network. The artificial neuron is the elementary unit of an artificial neural network.

The design of the artificial neuron was inspired by biological neural circuitry. Its inputs are analogous to excitatory postsynaptic potentials and inhibitory postsynaptic potentials at neural dendrites, or activation. Its weights are analogous to synaptic weights, and its output is analogous to a neuron's action potential which is transmitted along its axon.

A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's gain is another agent's loss.

Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proved useful for semi-supervised learning, fully supervised learning, and reinforcement learning.

"Fixed action pattern" is an ethological term describing an instinctive behavioral sequence that is highly stereotyped and species-characteristic. Fixed action patterns are said to be produced by the innate releasing mechanism, a "hard-wired" neural network, in response to a sign/key stimulus or releaser. Once released, a fixed action pattern runs to completion.

This term is often associated with Konrad Lorenz, who is the founder of the concept. Lorenz identified six characteristics of fixed action patterns. These characteristics state that fixed action patterns are stereotyped, complex, species-characteristic, released, triggered, and independent of experience.

Soft sensor or virtual sensor is a common name for software where several measurements are processed together. Commonly soft sensors are based on control theory and also receive the name of state observer. There may be dozens or even hundreds of measurements. The interaction of the signals can be used for calculating new quantities that need not be measured. Soft sensors are especially useful in data fusion, where measurements of different characteristics and dynamics are combined. It can be used for fault diagnosis as well as control applications.

Well-known software algorithms that can be seen as soft sensors include Kalman filters. More recent implementations of soft sensors use neural networks or fuzzy computing.

The floating-gate MOSFET (FGMOS), also known as a floating-gate MOS transistor or floating-gate transistor, is a type of metal–oxide–semiconductor field-effect transistor (MOSFET) where the gate is electrically isolated, creating a floating node in direct current, and a number of secondary gates or inputs are deposited above the floating gate (FG) and are electrically isolated from it. These inputs are only capacitively connected to the FG. Since the FG is surrounded by highly resistive material, the charge contained in it remains unchanged for long periods of time, typically longer than 10 years in modern devices. Usually Fowler-Nordheim tunneling or hot-carrier injection mechanisms are used to modify the amount of charge stored in the FG.

The FGMOS is commonly used as a floating-gate memory cell, the digital storage element in EPROM, EEPROM and flash memory technologies. Other uses of the FGMOS include a neuronal computational element in neural networks, analog storage element, digital potentiometers and single-transistor DACs.

Scott Elliott Fahlman (born March 21, 1948) is an American computer scientist and Professor Emeritus at Carnegie Mellon University's Language Technologies Institute and Computer Science Department. He is notable for early work on automated planning and scheduling in a blocks world, on semantic networks, on neural networks (especially the cascade correlation algorithm), on the programming languages Dylan, and Common Lisp (especially CMU Common Lisp), and he was one of the founders of Lucid Inc. During the period when it was standardized, he was recognized as "the leader of Common Lisp." From 2006 to 2015, Fahlman was engaged in developing a knowledge base named Scone, based in part on his thesis work on the NETL Semantic Network. He also is credited with coining the use of the emoticon.