Download or read book Probabilistic Models of the Brain written by Rajesh P.N. Rao and published by MIT Press. This book was released on 2002-03-29 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Bayesian Brain written by Kenji Doya and published by MIT Press. This book was released on 2007 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control.

Download or read book Computational Models of Brain and Behavior written by Ahmed A. Moustafa and published by John Wiley & Sons. This book was released on 2017-09-11 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Probabilistic Models written by Source Wikipedia and published by Booksllc.Net. This book was released on 2013-09 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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...

Download or read book Probabilistic Models for Brain Image Collection Classication and Functional Connectivity written by David Bryant Keator and published by . This book was released on 2015 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Goal Directed Decision Making written by Richard W. Morris and published by Academic Press. This book was released on 2018-08-23 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Probabilistic Models of Phase Variables for Visual Representation and Neural Dynamics written by Charles Cadieu and published by . This book was released on 2009 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Connectome written by Sebastian Seung and published by HMH. This book was released on 2012-02-07 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: “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

Download or read book The Noisy Brain written by Edmund T. Rolls and published by . This book was released on 2010-01-28 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Driven Computational Neuroscience written by Concha Bielza and published by Cambridge University Press. This book was released on 2020-11-26 with total page 709 pages. Available in PDF, EPUB and Kindle. Book excerpt: Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.

Download or read book Active Inference written by Thomas Parr and published by MIT Press. This book was released on 2022-03-29 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines. Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston over years of groundbreaking research, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy. Active inference puts the action into perception. This book offers the first comprehensive treatment of active inference, covering theory, applications, and cognitive domains. Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. The book emphasizes the implications of the free energy principle for understanding how the brain works. It first introduces active inference both conceptually and formally, contextualizing it within current theories of cognition. It then provides specific examples of computational models that use active inference to explain such cognitive phenomena as perception, attention, memory, and planning.

Download or read book Information Processing in Medical Imaging written by Chris Taylor and published by Springer Science & Business Media. This book was released on 2003-07-11 with total page 714 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refeered proceedings of the 18th Interational Conference on Information Processing in Medical Imaging, IPMI 2003, held in UK, in July 2003. The 57 revised full papers presented were carefully reviewed and selected from submissions. The papers are organized in topical sections shape modeling, shape analysis, segmentation, color, performance characterization, registration and modeling similarity, registration and modeling deformation, cardiac motion, fMRI analysis, and diffusion imaging and tractography.

Download or read book The Perceptron written by Frank Rosenblatt and published by . This book was released on 1958 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Decisions Uncertainty and the Brain written by Paul W. Glimcher and published by MIT Press. This book was released on 2004-09-17 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this provocative book, Paul Glimcher argues that economic theory may provide an alternative to the classical Cartesian model of the brain and behavior. Glimcher argues that Cartesian dualism operates from the false premise that the reflex is able to describe behavior in the real world that animals inhabit. A mathematically rich cognitive theory, he claims, could solve the most difficult problems that any environment could present, eliminating the need for dualism by eliminating the need for a reflex theory. Such a mathematically rigorous description of the neural processes that connect sensation and action, he explains, will have its roots in microeconomic theory. Economic theory allows physiologists to define both the optimal course of action that an animal might select and a mathematical route by which that optimal solution can be derived. Glimcher outlines what an economics-based cognitive model might look like and how one would begin to test it empirically. Along the way, he presents a fascinating history of neuroscience. He also discusses related questions about determinism, free will, and the stochastic nature of complex behavior.

Download or read book Bayesian Rationality written by Mike Oaksford and published by Oxford University Press. This book was released on 2007-02-22 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: For almost 2,500 years, the Western concept of what is to be human has been dominated by the idea that the mind is the seat of reason - humans are, almost by definition, the rational animal. In this text a more radical suggestion for explaining these puzzling aspects of human reasoning is put forward.

Download or read book Surfing Uncertainty written by Andy Clark and published by Oxford University Press, USA. This book was released on 2016 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exciting new theories in neuroscience, psychology, and artificial intelligence are revealing minds like ours as predictive minds, forever trying to guess the incoming streams of sensory stimulation before they arrive. In this up-to-the-minute treatment, philosopher and cognitive scientist Andy Clark explores new ways of thinking about perception, action, and the embodied mind.

Download or read book Probabilistic Methods for Learning Variations of High dimensional Neuroimaging Data written by Ke Zeng and published by . This book was released on 2018 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building quantitative models to summarize the structural variability of the human brain is an essential task in brain image analysis. Such quantitative models can be used to measure the normative variation of healthy brains, to capture their change over time, and to find imaging patterns of a diseased group. These model can be further applied to individual brain scans for tissue segmentation, lesion delineation, abnormality detection and image registration. A common approach to derive a representation of a population is through the use of atlases (i.e., characteristic brains) that are either manually determined or automatically inferred. However, atlases are first-order statistical measures that do not convey information about the amount and direction of variability within a population and are therefore inadequate for many applications. Most previous works on statistical modeling of imaging data have resorted to voxel-based constructions in which image values at different voxels are assumed to be statistically independent. Although voxel-based methods can identify structural variations that are well localized, they are myopic to correlations between different regions and cannot capture any global patterns of the underlying data. Contrarily, classical multivariate statistical methods can be useful for finding the most dominant trends of variability. However, they are incapable of providing a statistically consistent estimate of the full covariance structure or the joint probabilistic density function of high-dimensional image data with a limited amount of samples. In this thesis, we introduce a multivariate framework for learning probability distributions over high-dimensional image data to capture the inter-subject structural variability of the brain. Specifically, we adopt the divide and conquer strategy by breaking the challenging task of learning high-dimensional image data into a collection of smaller, more tractable problems. In Chapter 2, we present a generative model built upon the aforementioned strategy to capture normative variations of image appearance. The model is incorporated within a novel framework for locating imaging abnormalities. In particular, a 3-Dimensional image volume is modeled as an ensemble of overlapping local regions. A sparse probabilistic model is used to approximate the marginal distribution of local intensity patterns, while pairwise potentials are incorporated to account for correlations across local regions. To tackle the difficulties associated with registering an image of a healthy brain to a scan of a diseased brain, we develop an iterative procedure that interleaves abnormality detection with registration. The method was evaluated using simulated data and tested using images with real lesions. Experimental results demonstrate that the framework can achieve accurate registration and abnormality detection simultaneously.In Chapter 3, we introduce a generative probabilistic model of high-dimensional spatial transformations. To make use of linear statistical methods while preserving diffeomorphisms, we adopt the Log-Euclidean framework and parametrize diffeomorphisms as exponentials of stationary velocity fields. Following the divide and conquer principle, we treat a velocity field as a collection of local velocities that reside in much lower-dimensional sub-spaces. Differing from the model for image appearances, principal component analysis is used to estimate the covariance structure for each local velocity and canonical correlation analysis is used to learn the dependencies between pairs of local velocities. The learned model is used as the foundation of a statistically constrained diffeomorphic registration algorithm. The method was tested using both simulated and real data. The results indicate that the proposed model is able to capture the normative variations of deformations with sub-millimeter accuracy and that the learned statistical constraints lead to substantially more robust registration results in the presence of abnormalities. Lastly, in Chapter 4, we shift our attention to the segmentation of specific pathological structures in a supervised setting. In particular, we demonstrate how a generative model similar to the one described in Chapter 2 can be combined with discriminative learning techniques to form a hybrid segmentation framework. The hybrid method was validated using 132 scans of patients with high-grade gliomas. Quantitative evaluation of the segmentation shows that the hybrid approach outperforms both the baseline generative method and the baseline discriminative model.