Modeling Neural Circuits Made Simple With Python

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Modeling Neural Circuits Made Simple with Python

Author : Robert Rosenbaum
Publisher : MIT Press
ISBN 13 : 0262548089
Total Pages : 169 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Modeling Neural Circuits Made Simple with Python by : Robert Rosenbaum

Download or read book Modeling Neural Circuits Made Simple with Python written by Robert Rosenbaum and published by MIT Press. This book was released on 2024-03-19 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: An accessible undergraduate textbook in computational neuroscience that provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Understanding the brain is a major frontier of modern science. Given the complexity of neural circuits, advancing that understanding requires mathematical and computational approaches. This accessible undergraduate textbook in computational neuroscience provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Starting with the biophysics of single neurons, Robert Rosenbaum incrementally builds to explanations of neural coding, learning, and the relationship between biological and artificial neural networks. Examples with real neural data demonstrate how computational models can be used to understand phenomena observed in neural recordings. Based on years of classroom experience, the material has been carefully streamlined to provide all the content needed to build a foundation for modeling neural circuits in a one-semester course. Proven in the classroom Example-rich, student-friendly approach Includes Python code and a mathematical appendix reviewing the requisite background in calculus, linear algebra, and probability Ideal for engineering, science, and mathematics majors and for self-study

Modeling Neural Circuits Made Simple with Python

Author : Robert Rosenbaum (Researcher in computational neuroscience)
Publisher :
ISBN 13 : 9780262378741
Total Pages : 0 pages
Book Rating : 4.3/5 (787 download)

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Book Synopsis Modeling Neural Circuits Made Simple with Python by : Robert Rosenbaum (Researcher in computational neuroscience)

Download or read book Modeling Neural Circuits Made Simple with Python written by Robert Rosenbaum (Researcher in computational neuroscience) and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Rosenbaum explains at an advanced undergrad level, the core mathematical and computational models of neurons and networks of neurons, focusing primarily on neurons in the cerebral cortex"--

Modeling and Simulation in Python

Author : Allen B. Downey
Publisher : No Starch Press
ISBN 13 : 1718502176
Total Pages : 277 pages
Book Rating : 4.7/5 (185 download)

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Book Synopsis Modeling and Simulation in Python by : Allen B. Downey

Download or read book Modeling and Simulation in Python written by Allen B. Downey and published by No Starch Press. This book was released on 2023-05-30 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling and Simulation in Python teaches readers how to analyze real-world scenarios using the Python programming language, requiring no more than a background in high school math. Modeling and Simulation in Python is a thorough but easy-to-follow introduction to physical modeling—that is, the art of describing and simulating real-world systems. Readers are guided through modeling things like world population growth, infectious disease, bungee jumping, baseball flight trajectories, celestial mechanics, and more while simultaneously developing a strong understanding of fundamental programming concepts like loops, vectors, and functions. Clear and concise, with a focus on learning by doing, the author spares the reader abstract, theoretical complexities and gets right to hands-on examples that show how to produce useful models and simulations.

Python in Neuroscience

Author : Eilif Muller
Publisher : Frontiers Media SA
ISBN 13 : 2889196089
Total Pages : 275 pages
Book Rating : 4.8/5 (891 download)

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Book Synopsis Python in Neuroscience by : Eilif Muller

Download or read book Python in Neuroscience written by Eilif Muller and published by Frontiers Media SA. This book was released on 2015-07-23 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Python is rapidly becoming the de facto standard language for systems integration. Python has a large user and developer-base external to theneuroscience community, and a vast module library that facilitates rapid and maintainable development of complex and intricate systems. In this Research Topic, we highlight recent efforts to develop Python modules for the domain of neuroscience software and neuroinformatics: - simulators and simulator interfaces - data collection and analysis - sharing, re-use, storage and databasing of models and data - stimulus generation - parameter search and optimization - visualization - VLSI hardware interfacing. Moreover, we seek to provide a representative overview of existing mature Python modules for neuroscience and neuroinformatics, to demonstrate a critical mass and show that Python is an appropriate choice of interpreter interface for future neuroscience software development.

An Introductory Course in Computational Neuroscience

Author : Paul Miller
Publisher : MIT Press
ISBN 13 : 0262038250
Total Pages : 405 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis An Introductory Course in Computational Neuroscience by : Paul Miller

Download or read book An Introductory Course in Computational Neuroscience written by Paul Miller and published by MIT Press. This book was released on 2018-10-02 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook for students with limited background in mathematics and computer coding, emphasizing computer tutorials that guide readers in producing models of neural behavior. This introductory text teaches students to understand, simulate, and analyze the complex behaviors of individual neurons and brain circuits. It is built around computer tutorials that guide students in producing models of neural behavior, with the associated Matlab code freely available online. From these models students learn how individual neurons function and how, when connected, neurons cooperate in a circuit. The book demonstrates through simulated models how oscillations, multistability, post-stimulus rebounds, and chaos can arise within either single neurons or circuits, and it explores their roles in the brain. The book first presents essential background in neuroscience, physics, mathematics, and Matlab, with explanations illustrated by many example problems. Subsequent chapters cover the neuron and spike production; single spike trains and the underlying cognitive processes; conductance-based models; the simulation of synaptic connections; firing-rate models of large-scale circuit operation; dynamical systems and their components; synaptic plasticity; and techniques for analysis of neuron population datasets, including principal components analysis, hidden Markov modeling, and Bayesian decoding. Accessible to undergraduates in life sciences with limited background in mathematics and computer coding, the book can be used in a “flipped” or “inverted” teaching approach, with class time devoted to hands-on work on the computer tutorials. It can also be a resource for graduate students in the life sciences who wish to gain computing skills and a deeper knowledge of neural function and neural circuits.

Neuronal Dynamics

Author : Wulfram Gerstner
Publisher : Cambridge University Press
ISBN 13 : 1107060834
Total Pages : 591 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Neuronal Dynamics by : Wulfram Gerstner

Download or read book Neuronal Dynamics written by Wulfram Gerstner and published by Cambridge University Press. This book was released on 2014-07-24 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.

The NEURON Book

Author : Nicholas T. Carnevale
Publisher : Cambridge University Press
ISBN 13 : 1139447831
Total Pages : 399 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis The NEURON Book by : Nicholas T. Carnevale

Download or read book The NEURON Book written by Nicholas T. Carnevale and published by Cambridge University Press. This book was released on 2006-01-12 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authoritative reference on NEURON, the simulation environment for modeling biological neurons and neural networks that enjoys wide use in the experimental and computational neuroscience communities. This book shows how to use NEURON to construct and apply empirically based models. Written primarily for neuroscience investigators, teachers, and students, it assumes no previous knowledge of computer programming or numerical methods. Readers with a background in the physical sciences or mathematics, who have some knowledge about brain cells and circuits and are interested in computational modeling, will also find it helpful. The NEURON Book covers material that ranges from the inner workings of this program, to practical considerations involved in specifying the anatomical and biophysical properties that are to be represented in models. It uses a problem-solving approach, with many working examples that readers can try for themselves.

Pulsed Neural Networks

Author : Wolfgang Maass
Publisher : MIT Press
ISBN 13 : 9780262632218
Total Pages : 414 pages
Book Rating : 4.6/5 (322 download)

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Book Synopsis Pulsed Neural Networks by : Wolfgang Maass

Download or read book Pulsed Neural Networks written by Wolfgang Maass and published by MIT Press. This book was released on 2001-01-26 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most practical applications of artificial neural networks are based on a computational model involving the propagation of continuous variables from one processing unit to the next. In recent years, data from neurobiological experiments have made it increasingly clear that biological neural networks, which communicate through pulses, use the timing of the pulses to transmit information and perform computation. This realization has stimulated significant research on pulsed neural networks, including theoretical analyses and model development, neurobiological modeling, and hardware implementation. This book presents the complete spectrum of current research in pulsed neural networks and includes the most important work from many of the key scientists in the field. Terrence J. Sejnowski's foreword, "Neural Pulse Coding," presents an overview of the topic. The first half of the book consists of longer tutorial articles spanning neurobiology, theory, algorithms, and hardware. The second half contains a larger number of shorter research chapters that present more advanced concepts. The contributors use consistent notation and terminology throughout the book. Contributors Peter S. Burge, Stephen R. Deiss, Rodney J. Douglas, John G. Elias, Wulfram Gerstner, Alister Hamilton, David Horn, Axel Jahnke, Richard Kempter, Wolfgang Maass, Alessandro Mortara, Alan F. Murray, David P. M. Northmore, Irit Opher, Kostas A. Papathanasiou, Michael Recce, Barry J. P. Rising, Ulrich Roth, Tim Schönauer, Terrence J. Sejnowski, John Shawe-Taylor, Max R. van Daalen, J. Leo van Hemmen, Philippe Venier, Hermann Wagner, Adrian M. Whatley, Anthony M. Zador

What Makes Us Smart

Author : Samuel Gershman
Publisher : Princeton University Press
ISBN 13 : 0691225990
Total Pages : 218 pages
Book Rating : 4.6/5 (912 download)

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Book Synopsis What Makes Us Smart by : Samuel Gershman

Download or read book What Makes Us Smart written by Samuel Gershman and published by Princeton University Press. This book was released on 2021-10-19 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: How a computational framework can account for the successes and failures of human cognition At the heart of human intelligence rests a fundamental puzzle: How are we incredibly smart and stupid at the same time? No existing machine can match the power and flexibility of human perception, language, and reasoning. Yet, we routinely commit errors that reveal the failures of our thought processes. What Makes Us Smart makes sense of this paradox by arguing that our cognitive errors are not haphazard. Rather, they are the inevitable consequences of a brain optimized for efficient inference and decision making within the constraints of time, energy, and memory—in other words, data and resource limitations. Framing human intelligence in terms of these constraints, Samuel Gershman shows how a deeper computational logic underpins the “stupid” errors of human cognition. Embarking on a journey across psychology, neuroscience, computer science, linguistics, and economics, Gershman presents unifying principles that govern human intelligence. First, inductive bias: any system that makes inferences based on limited data must constrain its hypotheses in some way before observing data. Second, approximation bias: any system that makes inferences and decisions with limited resources must make approximations. Applying these principles to a range of computational errors made by humans, Gershman demonstrates that intelligent systems designed to meet these constraints yield characteristically human errors. Examining how humans make intelligent and maladaptive decisions, What Makes Us Smart delves into the successes and failures of cognition.

The Synaptic Organization of the Brain

Author : Gordon M. Shepherd
Publisher : Oxford University Press
ISBN 13 : 019515956X
Total Pages : 736 pages
Book Rating : 4.1/5 (951 download)

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Book Synopsis The Synaptic Organization of the Brain by : Gordon M. Shepherd

Download or read book The Synaptic Organization of the Brain written by Gordon M. Shepherd and published by Oxford University Press. This book was released on 2004 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a thorough revision of the standard text on local circuits in the different regions of the brain. In this fifth edition, the results of the mouse and human genome projects are incorporated for the first time. Also for the first time, the reader is oriented to supporting neuroscience databases. Among the new advances covered are 2-photon confocal laser microscopy of dendrites and dendritic spines, biochemical analyses, and dual patch and multielectrode recordings, applied together with an increasing range of behavioral and gene-targeting methods.

Brain Dynamics

Author : Hermann Haken
Publisher : Springer Science & Business Media
ISBN 13 : 3540752382
Total Pages : 333 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Brain Dynamics by : Hermann Haken

Download or read book Brain Dynamics written by Hermann Haken and published by Springer Science & Business Media. This book was released on 2007-12-22 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an excellent introduction for graduate students and nonspecialists to the field of mathematical and computational neurosciences. The book approaches the subject via pulsed-coupled neural networks, which have at their core the lighthouse and integrate-and-fire models. These allow for highly flexible modeling of realistic synaptic activity, synchronization and spatio-temporal pattern formation. The more advanced pulse-averaged equations are discussed.

Algorithms in Structural Molecular Biology

Author : Bruce R. Donald
Publisher : MIT Press
ISBN 13 : 0262548798
Total Pages : 497 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Algorithms in Structural Molecular Biology by : Bruce R. Donald

Download or read book Algorithms in Structural Molecular Biology written by Bruce R. Donald and published by MIT Press. This book was released on 2023-08-15 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of algorithms important to computational structural biology that addresses such topics as NMR and design and analysis of proteins.Using the tools of information technology to understand the molecular machinery of the cell offers both challenges and opportunities to computational scientists. Over the past decade, novel algorithms have been developed both for analyzing biological data and for synthetic biology problems such as protein engineering. This book explains the algorithmic foundations and computational approaches underlying areas of structural biology including NMR (nuclear magnetic resonance); X-ray crystallography; and the design and analysis of proteins, peptides, and small molecules. Each chapter offers a concise overview of important concepts, focusing on a key topic in the field. Four chapters offer a short course in algorithmic and computational issues related to NMR structural biology, giving the reader a useful toolkit with which to approach the fascinating yet thorny computational problems in this area. A recurrent theme is understanding the interplay between biophysical experiments and computational algorithms. The text emphasizes the mathematical foundations of structural biology while maintaining a balance between algorithms and a nuanced understanding of experimental data. Three emerging areas, particularly fertile ground for research students, are highlighted: NMR methodology, design of proteins and other molecules, and the modeling of protein flexibility. The next generation of computational structural biologists will need training in geometric algorithms, provably good approximation algorithms, scientific computation, and an array of techniques for handling noise and uncertainty in combinatorial geometry and computational biophysics. This book is an essential guide for young scientists on their way to research success in this exciting field.

From Neuron to Cognition via Computational Neuroscience

Author : Michael A. Arbib
Publisher : MIT Press
ISBN 13 : 0262335271
Total Pages : 808 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis From Neuron to Cognition via Computational Neuroscience by : Michael A. Arbib

Download or read book From Neuron to Cognition via Computational Neuroscience written by Michael A. Arbib and published by MIT Press. This book was released on 2016-11-04 with total page 808 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive, integrated, and accessible textbook presenting core neuroscientific topics from a computational perspective, tracing a path from cells and circuits to behavior and cognition. This textbook presents a wide range of subjects in neuroscience from a computational perspective. It offers a comprehensive, integrated introduction to core topics, using computational tools to trace a path from neurons and circuits to behavior and cognition. Moreover, the chapters show how computational neuroscience—methods for modeling the causal interactions underlying neural systems—complements empirical research in advancing the understanding of brain and behavior. The chapters—all by leaders in the field, and carefully integrated by the editors—cover such subjects as action and motor control; neuroplasticity, neuromodulation, and reinforcement learning; vision; and language—the core of human cognition. The book can be used for advanced undergraduate or graduate level courses. It presents all necessary background in neuroscience beyond basic facts about neurons and synapses and general ideas about the structure and function of the human brain. Students should be familiar with differential equations and probability theory, and be able to pick up the basics of programming in MATLAB and/or Python. Slides, exercises, and other ancillary materials are freely available online, and many of the models described in the chapters are documented in the brain operation database, BODB (which is also described in a book chapter). Contributors Michael A. Arbib, Joseph Ayers, James Bednar, Andrej Bicanski, James J. Bonaiuto, Nicolas Brunel, Jean-Marie Cabelguen, Carmen Canavier, Angelo Cangelosi, Richard P. Cooper, Carlos R. Cortes, Nathaniel Daw, Paul Dean, Peter Ford Dominey, Pierre Enel, Jean-Marc Fellous, Stefano Fusi, Wulfram Gerstner, Frank Grasso, Jacqueline A. Griego, Ziad M. Hafed, Michael E. Hasselmo, Auke Ijspeert, Stephanie Jones, Daniel Kersten, Jeremie Knuesel, Owen Lewis, William W. Lytton, Tomaso Poggio, John Porrill, Tony J. Prescott, John Rinzel, Edmund Rolls, Jonathan Rubin, Nicolas Schweighofer, Mohamed A. Sherif, Malle A. Tagamets, Paul F. M. J. Verschure, Nathan Vierling-Claasen, Xiao-Jing Wang, Christopher Williams, Ransom Winder, Alan L. Yuille

The Book of GENESIS

Author : James M. Bower
Publisher : Springer Verlag
ISBN 13 :
Total Pages : 440 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis The Book of GENESIS by : James M. Bower

Download or read book The Book of GENESIS written by James M. Bower and published by Springer Verlag. This book was released on 1995 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: This title introduces and guides the reader through Genesis, a simulation and modeling software tool that is delivered on-line via the Internet from a California Institute of Technology file server. It contains a contribution of models and simulations, plus step-by-step tutorials. 50 illustrations. Approx.

An Introduction to Lifted Probabilistic Inference

Author : Guy Van den Broeck
Publisher : MIT Press
ISBN 13 : 0262542595
Total Pages : 455 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis An Introduction to Lifted Probabilistic Inference by : Guy Van den Broeck

Download or read book An Introduction to Lifted Probabilistic Inference written by Guy Van den Broeck and published by MIT Press. This book was released on 2021-08-17 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field. After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.

Neural Engineering

Author : Chris Eliasmith
Publisher : MIT Press
ISBN 13 : 9780262550604
Total Pages : 384 pages
Book Rating : 4.5/5 (56 download)

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Book Synopsis Neural Engineering by : Chris Eliasmith

Download or read book Neural Engineering written by Chris Eliasmith and published by MIT Press. This book was released on 2003 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: A synthesis of current approaches to adapting engineering tools to the study of neurobiological systems.

Dive Into Deep Learning

Author : Joanne Quinn
Publisher : Corwin Press
ISBN 13 : 1544385404
Total Pages : 297 pages
Book Rating : 4.5/5 (443 download)

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Book Synopsis Dive Into Deep Learning by : Joanne Quinn

Download or read book Dive Into Deep Learning written by Joanne Quinn and published by Corwin Press. This book was released on 2019-07-15 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.