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Three-factor Learning
In neuroscience and machine learning, three-factor learning is the combinaison of Hebbian plasticity with a third modulatory factor to stabilise and enhance synaptic learning. This third factor can represent various signals such as reward, punishment, error, surprise, or novelty, often implemented through neuromodulators. Description Three-factor learning introduces the concept of eligibility traces, which flag synapses for potential modification pending the arrival of the third factor, and helps temporal credit assignement by bridging the gap between rapid neuronal firing and slower behavioral timescales, from which learning can be done. Biological basis for Three-factor learning rules have been supported by experimental evidence. This approach addresses the instability of classical Hebbian learning by minimizing autocorrelation and maximizing cross-correlation In signal processing, cross-correlation is a measure of similarity of two series as a function of the displaceme ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hebbian Theory
Hebbian theory is a neuropsychological theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell. It is an attempt to explain synaptic plasticity, the adaptation of neurons during the learning process. Hebbian theory was introduced by Donald Hebb in his 1949 book '' The Organization of Behavior.'' The theory is also called Hebb's rule, Hebb's postulate, and cell assembly theory. Hebb states it as follows: Let us assume that the persistence or repetition of a reverberatory activity (or "trace") tends to induce lasting cellular changes that add to its stability. ... When an axon of cell ''A'' is near enough to excite a cell ''B'' and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that ''A''’s efficiency, as one of the cells firing ''B'', is increased. The theory is often summarized as "Neurons that fire togethe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neuromodulation
Neuromodulation is the physiological process by which a given neuron uses one or more chemicals to regulate diverse populations of neurons. Neuromodulators typically bind to metabotropic, G-protein coupled receptors (GPCRs) to initiate a second messenger signaling cascade that induces a broad, long-lasting signal. This modulation can last for hundreds of milliseconds to several minutes. Some of the effects of neuromodulators include altering intrinsic firing activity, increasing or decreasing voltage-dependent currents, altering synaptic efficacy, increasing bursting activity and reconfiguring synaptic connectivity. Major neuromodulators in the central nervous system include: dopamine, serotonin, acetylcholine, histamine, norepinephrine, nitric oxide, and several neuropeptides. Cannabinoids can also be powerful CNS neuromodulators. Neuromodulators can be packaged into vesicles and released by neurons, secreted as hormones and delivered through the circulatory system ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Eligibility Trace
Eligibility may refer to: * The right to run for office (in elections), sometimes called ''passive suffrage'' or ''voting eligibility'' * Desirability as a marriage partner, as in the term ''eligible bachelor'' * Validity for participation, as in eligibility to enter a Competition * Eligibility for the NBA draft * Eligible receiver, gridiron football rules for catching a pass * NCAA eligibility The National Collegiate Athletic Association (NCAA) is a nonprofit organization that regulates student athletics among about 1,100 schools in the United States, and 1 in Canada. It also organizes the athletic programs of colleges and helps ..., requirements to play college sports in the National Collegiate Athletic Association * FIFA eligibility rules, requirement to play for a national team in association football {{disambig ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Credit Assignment Problem
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) with the goal of maximizing the cumulative reward (the feedback of which might be incomplete or delayed). The search for this balance is known as the exploration–exploitation dilemma. The environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dynami ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Autocorrelation
Autocorrelation, sometimes known as serial correlation in the discrete time case, measures the correlation of a signal with a delayed copy of itself. Essentially, it quantifies the similarity between observations of a random variable at different points in time. The analysis of autocorrelation is a mathematical tool for identifying repeating patterns or hidden periodicities within a signal obscured by noise. Autocorrelation is widely used in signal processing, time domain and time series analysis to understand the behavior of data over time. Different fields of study define autocorrelation differently, and not all of these definitions are equivalent. In some fields, the term is used interchangeably with autocovariance. Various time series models incorporate autocorrelation, such as unit root processes, trend-stationary processes, autoregressive processes, and moving average processes. Autocorrelation of stochastic processes In statistics, the autocorrelation of a real ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cross-correlation
In signal processing, cross-correlation is a measure of similarity of two series as a function of the displacement of one relative to the other. This is also known as a ''sliding dot product'' or ''sliding inner-product''. It is commonly used for searching a long signal for a shorter, known feature. It has applications in pattern recognition, single particle analysis, electron tomography, averaging, cryptanalysis, and neurophysiology. The cross-correlation is similar in nature to the convolution of two functions. In an autocorrelation, which is the cross-correlation of a signal with itself, there will always be a peak at a lag of zero, and its size will be the signal energy. In probability and statistics, the term ''cross-correlations'' refers to the correlations between the entries of two random vectors \mathbf and \mathbf, while the ''correlations'' of a random vector \mathbf are the correlations between the entries of \mathbf itself, those forming the correlation mat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
