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Author: Rajesh P.N. Rao Publisher: MIT Press ISBN: 9780262264327 Category : Medical Languages : en Pages : 348
Book Description
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.
Author: Rajesh P.N. Rao Publisher: MIT Press ISBN: 9780262264327 Category : Medical Languages : en Pages : 348
Book Description
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.
Author: Kenji Doya Publisher: MIT Press ISBN: 026204238X Category : Bayesian statistical decision theory Languages : en Pages : 341
Book Description
Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control.
Author: Ahmed A. Moustafa Publisher: John Wiley & Sons ISBN: 1119159075 Category : Psychology Languages : en Pages : 588
Book Description
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.
Author: Source Wikipedia Publisher: Booksllc.Net ISBN: 9781230830841 Category : Languages : en Pages : 28
Book Description
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...
Author: David Bryant Keator Publisher: ISBN: 9781321964424 Category : Languages : en Pages : 138
Book Description
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.
Author: Charles Cadieu Publisher: ISBN: Category : Languages : en Pages : 240
Book Description
My work seeks to contribute to three broad goals: predicting the computational representations found in the brain, developing algorithms that help us infer the computations that the brain performs, and producing better statistical models of natural signals. At first glance these goals may not seem compatible; however, my work finds a common thread among them through the probabilistic modeling of phase variables. My thesis is broken down into three major chapters that reflect these three goals. Within each chapter I develop novel probabilistic models of phase variables and apply these models to the invariant representation of visual motion, to the inference of connectivity in networks of coupled neural oscillators, and to the development of statistical models of edge structure in images. First, I develop a hierarchical model of visual processing that learns from the natural world the higher-order structure of visual motion by modeling phase transformations. The model exhibits an important invariance: the model represents the way the world moves irrespective of the way it looks. This model has implications for our interpretation of biological visual processing and provides a functional roll for feedback in cortex. Second, I present a model and estimation technique that captures the dynamics of coupled oscillator systems and recovers the interactions of the oscillators from measurements. From a statistical perspective, the model is the multivariate phase distribution analogue to the multivariate Gaussian distribution and the estimation technique is then analogous to finding the inverse covariance matrix for a Gaussian distribution. From a dynamical systems perspective, the technique provides a solution to the inverse problem of the generalized Kuramoto model and infers from measurements the true connectivity between oscillators even when phase correlations or other phase measurements would lead to false conclusions. This technique can be broadly applied to a range of neurobiological phenomena including the inference of cortical dynamic functional networks from phase measurements. Third, I present a model that captures aspects of the local phase structure of edges in images. We first explore the pairwise phase statistics of local, oriented filters in response to natural images and determine that pairwise phase relationships do not explain the ̀interesting' relationships in natural images, such as long range phase alignments. Given this finding we develop a conditional latent variable model that captures the non-stationary phase structure produced by continuous edges. This model is capable of generating long range, continuous edge structure, a hallmark of natural images. The major contributions of this thesis can be divided into two types. First, this work provides demonstrative examples of how multivariate phase distributions may be modeled in a probabilistic framework. My hope is that the models I have developed will provide the basis for additional exploration of the mathematical development of probabilistic models of phase. Second, the results obtained from applying these models have important implications for understanding invariant visual representations of motion, investigating coherence mediated intracortical communication, and describing the statistical structure of edges in natural images.
Author: Edmund T. Rolls Publisher: ISBN: Category : Mathematics Languages : en Pages : 334
Book Description
The activity of neurons in the brain is noisy in that the neuronal firing times are random for a given mean rate. The Noisy Brain shows that this is fundamental to understanding many aspects of brain function, including probabilistic decision-making, perception, memory recall, short-term memory, attention, and even creativity. There are many applications too of this understanding, to for example memory and attentional disorders, aging, schizophrenia, and obsessive-compulsive disorder.
Author: Richard W. Morris Publisher: Academic Press ISBN: 0128120991 Category : Psychology Languages : en Pages : 486
Book Description
Goal-Directed Decision Making: Computations and Neural Circuits examines the role of goal-directed choice. It begins with an examination of the computations performed by associated circuits, but then moves on to in-depth examinations on how goal-directed learning interacts with other forms of choice and response selection. This is the only book that embraces the multidisciplinary nature of this area of decision-making, integrating our knowledge of goal-directed decision-making from basic, computational, clinical, and ethology research into a single resource that is invaluable for neuroscientists, psychologists and computer scientists alike. The book presents discussions on the broader field of decision-making and how it has expanded to incorporate ideas related to flexible behaviors, such as cognitive control, economic choice, and Bayesian inference, as well as the influences that motivation, context and cues have on behavior and decision-making. Details the neural circuits functionally involved in goal-directed decision-making and the computations these circuits perform Discusses changes in goal-directed decision-making spurred by development and disorders, and within real-world applications, including social contexts and addiction Synthesizes neuroscience, psychology and computer science research to offer a unique perspective on the central and emerging issues in goal-directed decision-making
Author: Sebastian Seung Publisher: HMH ISBN: 0547508174 Category : Science Languages : en Pages : 389
Book Description
“Accessible, witty . . . an important new researcher, philosopher and popularizer of brain science . . . on par with cosmology’s Brian Greene and the late Carl Sagan” (The Plain Dealer). One of the Wall Street Journal’s 10 Best Nonfiction Books of the Year and a Publishers Weekly “Top Ten in Science” Title Every person is unique, but science has struggled to pinpoint where, precisely, that uniqueness resides. Our genome may determine our eye color and even aspects of our character. But our friendships, failures, and passions also shape who we are. The question is: How? Sebastian Seung is at the forefront of a revolution in neuroscience. He believes that our identity lies not in our genes, but in the connections between our brain cells—our particular wiring. Seung and a dedicated group of researchers are leading the effort to map these connections, neuron by neuron, synapse by synapse. It’s a monumental effort, but if they succeed, they will uncover the basis of personality, identity, intelligence, memory, and perhaps disorders such as autism and schizophrenia. Connectome is a mind-bending adventure story offering a daring scientific and technological vision for understanding what makes us who we are, as individuals and as a species. “This is complicated stuff, and it is a testament to Dr. Seung’s remarkable clarity of exposition that the reader is swept along with his enthusiasm, as he moves from the basics of neuroscience out to the farthest regions of the hypothetical, sketching out a spectacularly illustrated giant map of the universe of man.” —TheNew York Times “An elegant primer on what’s known about how the brain is organized and how it grows, wires its neurons, perceives its environment, modifies or repairs itself, and stores information. Seung is a clear, lively writer who chooses vivid examples.” —TheWashington Post
Author: Concha Bielza Publisher: Cambridge University Press ISBN: 110849370X Category : Computers Languages : en Pages : 709
Book Description
Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.
Author: Rajesh P.N. Rao
Author: Rajesh P.N. Rao
Author: Kenji Doya
Author: Ahmed A. Moustafa
Author: Source Wikipedia
Author: David Bryant Keator
Author: Charles Cadieu
Author: Edmund T. Rolls
Author: Richard W. Morris
Author: Sebastian Seung
Author: Concha Bielza