|
Attractor Network
An attractor network is a type of recurrent dynamical network, that evolves toward a stable pattern over time. Nodes in the attractor network converge toward a pattern that may either be fixed-point (a single state), cyclic (with regularly recurring states), chaotic (locally but not globally unstable) or random (stochastic). Attractor networks have largely been used in computational neuroscience to model neuronal processes such as associative memory and motor behavior, as well as in biologically inspired methods of machine learning. An attractor network contains a set of ''n'' nodes, which can be represented as vectors in a ''d''-dimensional space where ''n''>''d''. Over time, the network state tends toward one of a set of predefined states on a ''d''-manifold; these are the attractors. Overview In attractor networks, an ''attractor'' (or ''attracting set'') is a closed subset of states ''A'' toward which the system of nodes evolves. A stationary attractor is a state or sets of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dynamical System
In mathematics, a dynamical system is a system in which a Function (mathematics), function describes the time dependence of a Point (geometry), point in an ambient space, such as in a parametric curve. Examples include the mathematical models that describe the swinging of a clock pendulum, fluid dynamics, the flow of water in a pipe, the Brownian motion, random motion of particles in the air, and population dynamics, the number of fish each springtime in a lake. The most general definition unifies several concepts in mathematics such as ordinary differential equations and ergodic theory by allowing different choices of the space and how time is measured. Time can be measured by integers, by real number, real or complex numbers or can be a more general algebraic object, losing the memory of its physical origin, and the space may be a manifold or simply a Set (mathematics), set, without the need of a Differentiability, smooth space-time structure defined on it. At any given time, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Entorhinal Cortex
The entorhinal cortex (EC) is an area of the brain's allocortex, located in the medial temporal lobe, whose functions include being a widespread network hub for memory, navigation, and the perception of time.Integrating time from experience in the lateral entorhinal cortex Albert Tsao, Jørgen Sugar, Li Lu, Cheng Wang, James J. Knierim, May-Britt Moser & Edvard I. Moser Naturevolume 561, pages57–62 (2018) The EC is the main interface between the hippocampus and neocortex. The EC-hippocampus system plays an important role in declarative (autobiographical/episodic/semantic) memories and in particular spatial memories including memory formation, memory consolidation, and memory optimization in sleep. The EC is also responsible for the pre-processing (familiarity) of the input signals in the reflex nictitating membrane response of classical trace conditioning; the association of impulses from the eye and the ear occurs in the entorhinal cortex. Anatomy The entorhina ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Memory Reconsolidation
Memory consolidation is a category of processes that stabilize a memory trace after its initial acquisition. A memory trace is a change in the nervous system caused by memorizing something. Consolidation is distinguished into two specific processes. The first, synaptic consolidation, which is thought to correspond to late-phase long-term potentiation, occurs on a small scale in the synaptic connections and neural circuits within the first few hours after learning. The second process is systems consolidation, occurring on a much larger scale in the brain, rendering hippocampus-dependent memories independent of the hippocampus over a period of weeks to years. Recently, a third process has become the focus of research, reconsolidation, in which previously consolidated memories can be made labile again through reactivation of the memory trace. History Memory consolidation was first referred to in the writings of the renowned Roman teacher of rhetoric Quintillian. He noted the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Priming (psychology)
Priming is a concept in psychology and psycholinguistics to describe how exposure to one stimulus may influence a response to a subsequent stimulus, without conscious guidance or intention. The priming effect is the positive or negative effect of a rapidly presented stimulus (priming stimulus) on the processing of a second stimulus (target stimulus) that appears shortly after. Generally speaking, the generation of priming effect depends on the existence of some positive or negative relationship between priming and target stimuli. For example, the word ''nurse'' might be recognized more quickly following the word ''doctor'' than following the word ''bread''. Priming can be perceptual, associative, repetitive, positive, negative, affective, semantic, or conceptual. Priming effects involve word recognition, semantic processing, attention, unconscious processing, and many other issues, and are related to differences in various writing systems. How quickly this effect occurs is conte ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mixture Model
In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs. Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population. However, while problems associated with "mixture distributions" relate to deriving the properties of the overall population from those of the sub-populations, "mixture models" are used to make statistical inferences about the properties of the sub-populations given only observations on the pooled population, without sub-population identity information. Mixture models are used for clustering, under the name model-based clustering, and also for density estimation. Mixture models should not be confused with models for compositional data, i.e., data whose components are constra ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Expectation–maximization Algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the ''E'' step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step. It can be used, for example, to estimate a mixture of gaussians, or to solve the multiple linear regression problem. History The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. They pointed out that the method had been "proposed man ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Block Matrix
In mathematics, a block matrix or a partitioned matrix is a matrix that is interpreted as having been broken into sections called blocks or submatrices. Intuitively, a matrix interpreted as a block matrix can be visualized as the original matrix with a collection of horizontal and vertical lines, which break it up, or partition it, into a collection of smaller matrices. For example, the 3x4 matrix presented below is divided by horizontal and vertical lines into four blocks: the top-left 2x3 block, the top-right 2x1 block, the bottom-left 1x3 block, and the bottom-right 1x1 block. : \left \begin a_ & a_ & a_ & b_ \\ a_ & a_ & a_ & b_ \\ \hline c_ & c_ & c_ & d \end \right Any matrix may be interpreted as a block matrix in one or more ways, with each interpretation defined by how its rows and columns are partitioned. This notion can be made more precise for an n by m matrix M by partitioning n into a collection \text, and then partitioning m into a collection \text. The original m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bidirectional Associative Memory
Bidirectional associative memory (BAM) is a type of recurrent neural network. BAM was introduced by Bart Kosko in 1988. There are two types of associative memory, auto-associative and hetero-associative. BAM is hetero-associative, meaning given a pattern it can return another pattern which is potentially of a different size. It is similar to the Hopfield network in that they are both forms of associative memory. However, Hopfield nets return patterns of the same size. It is said to be bi-directional as it can respond to inputs from either the input or the output layer. Topology A BAM contains two layers of neurons, which we shall denote X and Y. Layers X and Y are fully connected to each other. Once the weights have been established, input into layer X presents the pattern in layer Y, and vice versa. The layers can be connected in both directions (bidirectional) with the result the weight matrix sent from the X layer to the Y layer is W and the weight matrix for signals sent ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Associative Memory (psychology)
In psychology, associative memory is defined as the ability to Learning, learn and Memory, remember the relationship between unrelated items. This would include, for example, remembering the name of someone or the aroma of a particular perfume. This type of memory deals specifically with the relationship between these different objects or concepts. A normal associative memory task involves testing participants on their Recall (memory), recall of pairs of unrelated items, such as face-name pairs.Matzen, Laura E., Michael C. Trumbo, Ryan C. Leach, and Eric D. Leshikar. "Effects of Non-invasive Brain Stimulation on Associative Memory". ''Brain Research'' 1624 (2015): 286-296. Associative memory is a declarative memory structure and Episodic memory, episodically based. Conditioning Two important processes for learning association (psychology), associations, and thus forming associative memories, are operant conditioning and classical conditioning. Operant conditioning refers to a typ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
K-nearest Neighbors Algorithm
In statistics, the ''k''-nearest neighbors algorithm (''k''-NN) is a Non-parametric statistics, non-parametric supervised learning method. It was first developed by Evelyn Fix and Joseph Lawson Hodges Jr., Joseph Hodges in 1951, and later expanded by Thomas M. Cover, Thomas Cover. Most often, it is used for statistical classification, classification, as a ''k''-NN classifier, the output of which is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its ''k'' nearest neighbors (''k'' is a positive integer, typically small). If ''k'' = 1, then the object is simply assigned to the class of that single nearest neighbor. The ''k''-NN algorithm can also be generalized for regression analysis, regression. In ''-NN regression'', also known as ''nearest neighbor smoothing'', the output is the property value for the object. This value is the average of the values of ''k'' nearest neighbo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Episodic Memory
Episodic memory is the memory of everyday events (such as times, location geography, associated emotions, and other contextual information) that can be explicitly stated or conjured. It is the collection of past personal experiences that occurred at particular times and places; for example, the party on one's 7th birthday. Along with semantic memory, it comprises the category of explicit memory, one of the two major divisions of long-term memory (the other being implicit memory). The term "episodic memory" was coined by Endel Tulving in 1972, referring to the distinction between knowing and remembering: ''knowing'' is factual recollection (semantic) whereas ''remembering'' is a feeling that is located in the past (episodic). One of the main components of episodic memory is the process of recollection, which elicits the retrieval of contextual information pertaining to a specific event or experience that has occurred. Tulving seminally defined three key properties of episodi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Grid Cell
A grid cell is a type of neuron within the entorhinal cortex that fires at regular intervals as an animal navigates an open area, allowing it to understand its position in space by storing and integrating information about location, distance, and direction. Grid cells have been found in many animals, including Laboratory rat, rats, laboratory mouse, mice, bats, Monkey#In experiments, monkeys, and humans. Grid cells were discovered in 2005 by Edvard Moser, May-Britt Moser, and their students Torkel Hafting, Marianne Fyhn, and Sturla Molden at the Kavli Institute for Systems Neuroscience and Centre for the Biology of Memory, Centre for the Biology of Memory (CBM) in Norway. They were awarded the 2014 Nobel Prize in Physiology or Medicine together with John O'Keefe (neuroscientist), John O'Keefe for their discoveries of cells that constitute a positioning system in the brain. The arrangement of spatial firing fields, all at equal distances from their neighbors, led to a hypothesis t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
