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Fundamentals of Physics and Chemistry of the Atmosphere
Additive and Cancellative Interacting Particle Systems (Lecture Notes in Mathematics) by David Griffeath (Repost)
PI and the AGM: A Study in Analytic Number Theory and Computational Complexity by Jonathan M. Borwein (Repost)
Advances in Complex Function Theory (Lecture Notes in Mathematics) by W. E. Kirwan (Repost)
Synthetic inorganic chemistry a course of laboratory and classroom study by Arthur Alphonzo Blanchard (Repost)
Invitation to Law and Society - An Introduction to the Study of Real Law
Matrix Mathematics - Theory, Facts, and Formulas, Second Edition
Mathematics Probability, Markov Chains, Queues, and Simulation - The Mathematical Basis of Performance Modeling
-Physics, Topology, Logic and Computation: A Rosetta Stone- by John C. Baez adn Mike Stay
-Quantum Physics for Scientists and Technologists- by Paul Sanghera (Repost)
Algebraic Aspects of Cryptography (Algorithms and Computation in Mathematics) by Neal Koblitz (Repost)
Mathematics Mathematical Foundations of Computer Science 2004 [Repost]
Mathematics Mathematical Logic for Computer Science (3rd edition)
Physics Democracy and Public Space: The Physical Sites of Democratic Performance
Spaces of Holomorphic Functions in the Unit Ball (Graduate Texts in Mathematics) by Kehe Zhu (Repost)
An Introduction to Ergodic Theory (Graduate Texts in Mathematics) by Peter Walters (Repost)
Statistical and Thermal Physics - With Computer Applications
Introduction to the Physics of the Earth's Interior (Cambridge Topics in Mineral Ph) by Jean Paul Poirier (Repost)
Mathematics Symmetry Theory in Molecular Physics with Mathematica: A new kind of tutorial book (Repost)
THE GNOMON WORKSHOP CREATING A 3D FIGURE STUDY BOOKWARE ISO-LZ0
Additive and Cancellative Interacting Particle Systems (Lecture Notes in Mathematics) by David Griffeath (Repost)
PI and the AGM: A Study in Analytic Number Theory and Computational Complexity by Jonathan M. Borwein (Repost)
Advances in Complex Function Theory (Lecture Notes in Mathematics) by W. E. Kirwan (Repost)
Synthetic inorganic chemistry a course of laboratory and classroom study by Arthur Alphonzo Blanchard (Repost)
Invitation to Law and Society - An Introduction to the Study of Real Law
Matrix Mathematics - Theory, Facts, and Formulas, Second Edition
Mathematics Probability, Markov Chains, Queues, and Simulation - The Mathematical Basis of Performance Modeling
-Physics, Topology, Logic and Computation: A Rosetta Stone- by John C. Baez adn Mike Stay
-Quantum Physics for Scientists and Technologists- by Paul Sanghera (Repost)
Algebraic Aspects of Cryptography (Algorithms and Computation in Mathematics) by Neal Koblitz (Repost)
Mathematics Mathematical Foundations of Computer Science 2004 [Repost]
Mathematics Mathematical Logic for Computer Science (3rd edition)
Physics Democracy and Public Space: The Physical Sites of Democratic Performance
Spaces of Holomorphic Functions in the Unit Ball (Graduate Texts in Mathematics) by Kehe Zhu (Repost)
An Introduction to Ergodic Theory (Graduate Texts in Mathematics) by Peter Walters (Repost)
Statistical and Thermal Physics - With Computer Applications
Introduction to the Physics of the Earth's Interior (Cambridge Topics in Mineral Ph) by Jean Paul Poirier (Repost)
Mathematics Symmetry Theory in Molecular Physics with Mathematica: A new kind of tutorial book (Repost)
THE GNOMON WORKSHOP CREATING A 3D FIGURE STUDY BOOKWARE ISO-LZ0
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Study Probabilistic Models of the Brain: Perception and Neural Function (Neural Information Processing)
Posted on 2010-03-15
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More Probabilistic Models of the Brain: Perception and Neural Function (Neural Information Processing) 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. -=Boss=- 17 Jul 2007 18:43 Thank you! ????????????? ID:581966 581966
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