Probabilistic Models of the Brain - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Probabilistic Models of the Brain write by Rajesh P.N. Rao. This book was released on 2002-03-29. Probabilistic Models of the Brain available in PDF, EPUB and Kindle. A survey of probabilistic approaches to modeling and understanding brain function. Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed behavior. A fundamental question that is seldom addressed by these studies, however, is why the brain uses the types of representations it does and what evolutionary advantage, if any, these representations confer. It is difficult to address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and Bayesian inference to derive models of brain function. This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples focus on vision, many of the models and techniques are applicable to other modalities as well. The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception, probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals.
Bayesian Brain
Bayesian Brain - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Brain write by Kenji Doya. This book was released on 2007. Bayesian Brain available in PDF, EPUB and Kindle. Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control.
Computational Models of Brain and Behavior
Computational Models of Brain and Behavior - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Computational Models of Brain and Behavior write by Ahmed A. Moustafa. This book was released on 2017-09-11. Computational Models of Brain and Behavior available in PDF, EPUB and Kindle. A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.
Probabilistic Models
Probabilistic Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Probabilistic Models write by Source Wikipedia. This book was released on 2013-09. Probabilistic Models available in PDF, EPUB and Kindle. Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 26. Chapters: Bayesian brain, Binary Independence Model, Constellation model, Continuum structure function, Divergence-from-randomness model, Factored language model, First-order reliability method, Generative model, Latent Dirichlet allocation, Maier's theorem, Mixture model, N-gram, Probabilistic automaton, Probabilistic relational model, Probabilistic relational programming language, Probabilistic relevance model, Probabilistic voting model, Stochastic context-free grammar, Stochastic grammar, Voter model. Excerpt: 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. Some ways of implementing mixture models involve steps that attribute postulated sub-population-identities to individual observations (or weights towards such sub-populations), in which case these can be regarded as types of unsupervised learning or clustering procedures. However not all inference procedures involve such steps. Mixture models should not be confused with models for compositional data, i.e., data whose components are constrained to sum to a constant value (1, 100%, etc.). A typical finite-dimensional mixture model is a hierarchical model consisting...
Probabilistic Models for Brain Image Collection, Classication, and Functional Connectivity
Probabilistic Models for Brain Image Collection, Classication, and Functional Connectivity - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Probabilistic Models for Brain Image Collection, Classication, and Functional Connectivity write by David Bryant Keator. This book was released on 2015. Probabilistic Models for Brain Image Collection, Classication, and Functional Connectivity available in PDF, EPUB and Kindle. The use of functional neuroimaging to evaluate brain disorders has become pervasive in the scientific community. The technique provides researchers with a means to evaluate dynamic in-vivo brain function. Over the last thirty years of using neuroimaging techniques to evaluate brain disorders, there is evidence suggesting some illnesses are characterized by differences in regional brain function whereas others by differences in regional connectivity. Disorders with gross anatomical and functional changes such as Alzheimer's disease and traumatic brain injury are often visually discernible in brain scans and differences quantifiable using typical mass univariate analysis techniques. Conversely, disorders with subtle functional changes (e.g. depression) or subtle changes in how the brain communicates (e.g. schizophrenia) are less amiable to existing analysis techniques. Detecting these subtle differences in molecular imaging data, often plagued by noisy measurements from the imaging system, further impedes our ability to gain valuable insights into brain disorders. In this dissertation we use a variety of tools from machine learning and probabilistic modeling to develop new models for decreasing noise in data captured from our imaging systems, improve feature extraction for detecting differences in regional brain function, and evaluate group-based functional connectivity models and their performance in settings with small sample sizes. Each of these models are presented separately with experiments designed to show improvements over existing methodologies and measures of accuracy in both disease classification and recovering gold-standard functional relationships in the brain.
