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Temporal Difference
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods, and perform updates based on current estimates, like dynamic programming methods. While Monte Carlo methods only adjust their estimates once the final outcome is known, TD methods adjust predictions to match later, more accurate, predictions about the future before the final outcome is known. (A revised version is available oRichard Sutton's publication page) This is a form of bootstrapping, as illustrated with the following example: :"Suppose you wish to predict the weather for Saturday, and you have some model that predicts Saturday's weather, given the weather of each day in the week. In the standard case, you would wait until Saturday and then adjust all your models. However, when it is, for example, Friday, you should have a pretty go ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Model-free (reinforcement Learning)
In reinforcement learning (RL), a model-free algorithm (as opposed to a model-based one) is an algorithm which does not use the ''transition probability distribution'' (and the ''reward function'') associated with the Markov decision process (MDP), which, in RL, represents the problem to be solved. The transition probability distribution (or transition model) and the reward function are often collectively called the "model" of the environment (or MDP), hence the name "model-free". A model-free RL algorithm can be thought of as an "explicit" trial-and-error algorithm. An example of a model-free algorithm is Q-learning ''Q''-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions an .... Key 'Model-Free' reinforcement learning algorithms {, class="wikitable sortable" style="font-size: 96%;" !Algo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neuroscience
Neuroscience is the scientific study of the nervous system (the brain, spinal cord, and peripheral nervous system), its functions and disorders. It is a multidisciplinary science that combines physiology, anatomy, molecular biology, developmental biology, cytology, psychology, physics, computer science, chemistry, medicine, statistics, and Mathematical Modeling, mathematical modeling to understand the fundamental and emergent properties of neurons, glia and neural circuits. The understanding of the biological basis of learning, memory, behavior, perception, and consciousness has been described by Eric Kandel as the "epic challenge" of the Biology, biological sciences. The scope of neuroscience has broadened over time to include different approaches used to study the nervous system at different scales. The techniques used by neuroscientists have expanded enormously, from molecular biology, molecular and cell biology, cellular studies of individual neurons to neuroimaging, imaging ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Reinforcement Learning
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of 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). The environment is typically stated in the form of a Markov decision process (MDP), because many reinforcement learning algorithms for this context use dynamic programming techniques. The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematica ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computational Neuroscience
Computational neuroscience (also known as theoretical neuroscience or mathematical neuroscience) is a branch of neuroscience which employs mathematical models, computer simulations, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system. Computational neuroscience employs computational simulations to validate and solve mathematical models, and so can be seen as a sub-field of theoretical neuroscience; however, the two fields are often synonymous. The term mathematical neuroscience is also used sometimes, to stress the quantitative nature of the field. Computational neuroscience focuses on the description of biologically plausible neurons (and neural systems) and their physiology and dynamics, and it is therefore not directly concerned with biologically unrealistic models used in connectionism, control theory, cybernetics, quantitative psychology, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
PVLV
The primary value learned value (PVLV) model is a possible explanation for the reward-predictive firing properties of dopamine (DA) neurons. It simulates behavioral and neural data on Pavlovian conditioning and the midbrain dopaminergic neurons that fire in proportion to unexpected rewards. It is an alternative to the temporal-differences (TD) algorithm. It is used as part of Leabra Leabra stands for local, error-driven and associative, biologically realistic algorithm. It is a model of learning which is a balance between Hebbian and error-driven learning with other network-derived characteristics. This model is used to mathe .... References Computational neuroscience Machine learning algorithms {{neuroscience-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Q-learning
''Q''-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations. For any finite Markov decision process (FMDP), ''Q''-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state. ''Q''-learning can identify an optimal action-selection policy for any given FMDP, given infinite exploration time and a partly-random policy. "Q" refers to the function that the algorithm computes – the expected rewards for an action taken in a given state. Reinforcement learning Reinforcement learning involves an agent, a set of ''states'' , and a set of ''actions'' per state. By performing an action a \in A, the agent transitions from state to state. Executing an action i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Schizophrenia
Schizophrenia is a mental disorder characterized by continuous or relapsing episodes of psychosis. Major symptoms include hallucinations (typically hearing voices), delusions, and disorganized thinking. Other symptoms include social withdrawal, decreased emotional expression, and apathy. Symptoms typically develop gradually, begin during young adulthood, and in many cases never become resolved. There is no objective diagnostic test; diagnosis is based on observed behavior, a history that includes the person's reported experiences, and reports of others familiar with the person. To be diagnosed with schizophrenia, symptoms and functional impairment need to be present for six months (DSM-5) or one month (ICD-11). Many people with schizophrenia have other mental disorders, especially substance use disorders, depressive disorders, anxiety disorders, and obsessive–compulsive disorder. About 0.3% to 0.7% of people are diagnosed with schizophrenia during their lifetime. In 2 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dopamine
Dopamine (DA, a contraction of 3,4-dihydroxyphenethylamine) is a neuromodulatory molecule that plays several important roles in cells. It is an organic compound, organic chemical of the catecholamine and phenethylamine families. Dopamine constitutes about 80% of the catecholamine content in the brain. It is an amine synthesized by removing a carboxyl group from a molecule of its precursor (chemistry), precursor chemical, L-DOPA, which is biosynthesis, synthesized in the brain and kidneys. Dopamine is also synthesized in plants and most animals. In the brain, dopamine functions as a neurotransmitter—a chemical released by neurons (nerve cells) to send signals to other nerve cells. Neurotransmitters are synthesized in specific regions of the brain, but affect many regions systemically. The brain includes several distinct dopaminergic pathway, dopamine pathways, one of which plays a major role in the motivational component of reward system, reward-motivated behavior. The anticipa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Reward System
The reward system (the mesocorticolimbic circuit) is a group of neural structures responsible for incentive salience (i.e., "wanting"; desire or craving for a reward and motivation), associative learning (primarily positive reinforcement and classical conditioning), and positively-valenced emotions, particularly ones involving pleasure as a core component (e.g., joy, euphoria and ecstasy). Reward is the attractive and motivational property of a stimulus that induces appetitive behavior, also known as approach behavior, and consummatory behavior. A rewarding stimulus has been described as "any stimulus, object, event, activity, or situation that has the potential to make us approach and consume it is by definition a reward". In operant conditioning, rewarding stimuli function as positive reinforcers; however, the converse statement also holds true: positive reinforcers are rewarding. The reward system motivates animals to approach stimuli or engage in behaviour that increases ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Substantia Nigra
The substantia nigra (SN) is a basal ganglia structure located in the midbrain that plays an important role in reward and movement. ''Substantia nigra'' is Latin for "black substance", reflecting the fact that parts of the substantia nigra appear darker than neighboring areas due to high levels of neuromelanin in dopaminergic neurons. Parkinson's disease is characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta. Although the substantia nigra appears as a continuous band in brain sections, anatomical studies have found that it actually consists of two parts with very different connections and functions: the pars compacta (SNpc) and the pars reticulata (SNpr). The pars compacta serves mainly as a projection to the basal ganglia circuit, supplying the striatum with dopamine. The pars reticulata conveys signals from the basal ganglia to numerous other brain structures. Structure The substantia nigra, along with four other nuclei, is part ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
