Best Reference Books – Computational Neuroscience

We have compiled the list of Top 10 Best Reference Books on Computational Neuroscience subject. These books are used by students of top universities, institutes and colleges. Here is the full list of top 10 best books on Computational Neuroscience along with reviews.

Kindly note that we have put a lot of effort into researching the best books on Computational Neuroscience subject and came out with a recommended list of top 10 best books. The table below contains the Name of these best books, their authors, publishers and an unbiased review of books on "Computational Neuroscience" as well as links to the Amazon website to directly purchase these books. As an Amazon Associate, we earn from qualifying purchases, but this does not impact our reviews, comparisons, and listing of these top books; the table serves as a ready reckoner list of these best books.

1. “Computational Neuroscience” by Peter Erdi and Anna Esposito

“Visual Cortex and Deep Networks” Book Review: Primates are believed to recognise objects through ventral visual streams. In recent years, scientists have tried to develop quantitative models that correspond to such biological architecture through deep convolutional neural networks without explaining the underlying physiology. This book develops the mathematical framework explaining the invariant representations in the ventral stream. It provides a thorough understanding of the ventral visual cortex which contains areas that process images in abstract ways and facilitate us to learn, recognize and categorise three-dimensional objects from arbitrary two-dimensional views. The author hypothesizes that the main computational function of the ventral stream is to compute neural representations of images. These are invariant to transformations encountered in the visual environment and are learned from unsupervised experience. After theorizing a general computational framework of invariance, the book then focuses on its application to the feedforward path of the ventral stream in the primate visual cortex.

2. “Visual Cortex and Deep Networks – Learning Invariant Representations (Computational Neuroscience Series)” by Fabio Anselmi and Tomaso A Poggio

“Dynamical Systems in Neuroscience – The Geometry of Excitability and Bursting (Computational Neuroscience)” Book Review: It is a handy book for neuroscience researchers since the dynamical system theory is often neglected in the graduate curriculum of computational neuroscience. It provides an introduction to nonlinear dynamical systems theory and its relation to electrophysiology and the computational properties of neurons. The book emphasizes that the information processing in the brain doesn’t depend only on the electrophysiological properties of neurons but also on their dynamical properties. Starting with basic concepts of one and two-dimensional Hodgkin-Huxley-type models, it further describes bursting systems. This way it provides the overview of neuroscience to Mathematicians who are interested in electrophysiology. The geometrical intuition developed beside mathematical concepts along with figures makes it equally suitable for non-mathematicians. For a better understanding, each chapter includes sample worked-out examples at the end.

3. “Dynamical Systems in Neuroscience – The Geometry of Excitability and Bursting (Computational Neuroscience)” by Eugene M Izhikevich

“Computational Neuroscience: A First Course (Springer Series in Bio-/Neuroinformatics)” Book Review: It is a fundamental book of computational neuroscience which lays down the basics of modelling the nervous system at the membrane, cellular and network level. Its three fundamental domains, namely membrane biophysics, systems theory, and artificial neural networks are covered comprehensively. The required mathematical concepts are explained in an intuitive manner so as to make it approachable for people who do not have a background in higher mathematics. This book grew out of a lecture series designed for graduate students in neuroscience with backgrounds in biology, physiology, and medicine spanning over a decade. It serves as a reference guide for all the neuroscientists who use computational methods in the day-to-day task as well as for theoretical scientists who are advancing towards the field of computational neuroscience.

4. “Computational Neuroscience: A First Course (Springer Series in Bio-/Neuroinformatics)” by Hanspeter A Mallot

“Theoretical Neuroscience – Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience Series)” Book Review: This book provides the quantitative basis of the functioning of the nervous system and lays down its general operational principle. Basic mathematical and computational methods of theoretical neuroscience are described in the book along with its application in vision, sensory-motor integration, development, learning, and memory. To make the subject material comprehensive, this volume is divided into three parts. Part I describes the relationship between sensory stimuli and neural responses. It focuses on the representation of information originating from the spiking activity of neurons. The modelling of neurons and neural circuits based on cellular and synaptic biophysics are covered in part II while part III deals with the role of plasticity in development and learning. Further, appendix at the end covers all the mathematical methods used throughout the book in detail. Exercises for better understanding of the material are available on the Book’s website.

5. “Theoretical Neuroscience – Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience Series)” by Peter Dayan

“Introducing Epigenetics: A Graphic Guide” Book Review: Epigenetics is the study of heritable phenotype changes that do not involve alterations in the DNA sequence. It explains how and why we inherit certain traits, develop diseases, age and evolve as a species. Covering one of the most exciting fields of biology today, this book introduces us to genetics, cell biology and the science behind epigenetics. It is an introductory guide to the technology which has enabled us to make unprecedented strides in medicine. Walking us through some examples like what identical twins teach us about the epigenetic effects of our environment and experiences, why certain genes are switched on and off at various age of embryonic development and how scientist have reversed the specialization of cells to clone frogs from a single gut cell, the author delves deeper into the structure of DNA and examine how the epigenetic building blocks and messengers that interpret and edit out genes makes us unique as a species.

6. “Introducing Epigenetics: A Graphic Guide” by Oliver Pugh and Cath Ennis

“The Computing Dendrite (Springer Series in Computational Neuroscience)” Book Review: Neuronal dendritic trees enable cells to perform powerful computations and allow for high neural interconnectivity. With the recent advances in experimental techniques, we have gained a new perspective into these complex structures with unprecedented details. This Springer series book summarizes some of the cutting edge experimental, computational and mathematical investigations conducted to understand the functions of dendrites in different neural systems. This allows us to gain a broad perspective on the diversity of mechanisms that dendrite employs to shape neural computations. We first look into the morphological properties of dendrites whose characterization ranges from the study of fractal principles (to describe dendrite topologies) to the consequences of optimization principles for dendrite shape. The second part of the book focuses on the contribution of dendrites to the neural computations. It explains the effects of dendritic morphology and the distribution of synapses and membrane properties over the dendritic tree on the output to a neuronal stimulus.

7. “The Computing Dendrite (Springer Series in Computational Neuroscience)” by Hermann Cuntz and Michiel W H Remme

“Biological Learning and Control – How the Brain Builds Representations, Predicts Events, and Makes Decisions (Computational Neuroscience Series)” Book Review: Our brain acquires essential surviving skills based on internal models that enable it to predict events and actions based on past observations. These models describe what should happen and then combine this prediction with inputs from the sensory system to form a belief. In this book, the authors lay down a novel theoretical framework for understanding the brain’s perception, its reaction to sensory stimuli and motor control. Despite biomechanical similarities among young and old, healthy and unhealthy, humans and other animals, there are certain variations in the brain’s motor commands. They are economic decisions made weighing effort and reward. The authors also argue that the brain prefers to receive a reward sooner than later and thus the value of reward depreciates with the passage of time. With this discounting change in reward, the shape of our movements also change. The concepts presented in the book describe the probable rationale for the regularity in our movement, our learning pattern and the ability of our brain to predict events.

8. “Biological Learning and Control – How the Brain Builds Representations, Predicts Events, and Makes Decisions (Computational Neuroscience Series)” by Reza Shadmehr and Sandro Mussa–ivaldi

“Natural Image Statistics: A Probabilistic Approach to Early Computational Vision. (Computational Imaging and Vision)” Book Review: Natural images are the photographs of the typical environment we live in. In this book, the natural images are explored through different statistical models whose parameters are estimated from image samples. Its main aim is to computationally model the biological visual system. This research also finds its application in Computer Science and Engineering where it helps in the development of better image processing and computer vision methods. Besides being an introductory textbook, it is also a research monograph on modelling the statistical structure of natural images, providing a unified view of different models and approaches. This theoretical framework considers that the properties of our visual system are essentially reflections of the statistical structure of natural images. This happens because of evolutionary adaptation processes.

9. “Natural Image Statistics: A Probabilistic Approach to Early Computational Vision. (Computational Imaging and Vision)” by Aapo Hyvärinen and Jarmo Hurri

“Fundamentals of Computational Neuroscience” Book Review: Computational neuroscience is a scientific discipline that concerns the theoretical study of the brain. It uncovers the principles and mechanisms guiding the development, organization and information processing in the nervous system. This book serves as an introductory text for this fascinating and complex topic for anyone interested in brain sciences. The readers are introduced to the theoretical foundation of neuroscience, focusing on the nature of information processing that happens inside the brain. ‘Fundamentals of Computational Neuroscience’, now in its second edition, introduces us to some of the simplified computational models of neurons. They are suitable for exploring how information is processed in large brain-like networks. We are also introduced to several fundamental network architectures related to this mechanism along with models that perform higher-order cognitive functions. Simple MATLAB programs have also been included to better understand the computational models explained in the book.

10. “Fundamentals of Computational Neuroscience” by Thomas Trappenberg

“Computational Neuroscience: Cortical Dynamics” Book Review: Cortical dynamics concerns the processing, transmission, and imprinting of information in the brain. It also constitutes important functions of the cortical area of the brain such as cortical rhythm, cortical neural plasticity, their structural basis and functional significance. This book serves as a reference text on models of cortical dynamics from a Neuroscience and Physics perspective. It contains the papers presented on cordial dynamics at the 8th edition of International Summer School held in Sicily, Italy. Compiling some of the most recent experimental and theoretical findings, this book aims at providing a high-level coverage of this field. It is divided into two sections. The first second lays down the fundamentals of cortical dynamics such as dynamics of storage and recall in memory networks, hierarchical organization of a central nervous system, etc. The second section explains some of the advanced mathematical models of cortical dynamics including mean-field methods, chaotic neuron dynamics, etc.

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