Being a computational neuroscientist involves a lot. The job is as noble as it is technical. It aims at empowering humanity to understand more about the brain and create better solutions that improve our quality of life. Computational neuroscience is a broad area of study.

There are many different theories proposed as a way to explain how the brain collects and interprets information. Our senses of taste, smell, sight, and so on – are all accounted for.

An in-depth assessment of the subject shows that there are three modeling approaches. One of them is the descriptive model. Developed as a way to quantify experimental data, it involves algorithms that process neuron spike trains. There are also models meant for sensory neurons. Other normative theories clarify more brain functions.

For example, there is a computation that makes the brain better able to code sensory information by enhancing stimulus inputs. Mechanistic models are built on the two cornerstones of neuroscience. They include neuroanatomy and neurophysiology.

**Best Books on Computational Neuroscience: Our Top Picks**

If you’re looking for the best books on computational neuroscience that cover these and other topics, here are seven titles you can start with.

**1. An Introductory Course in Computational Neuroscience (Computational Neuroscience Series)**

This book is great for students with a weak foundation in mathematics and computer programming. It includes instructions that help readers create their neural models.

In it, Paul Miller explains how to dissect, comprehend, and reproduce an intricate connection of brain circuits. There are guides built on Matlab code that can create models of neural behavior. It is used to teach students how neurons function when interconnected within a framework. Details of how simulations of oscillations, post-stimulus rebounds, multistability, and chaos can ensue inside circuits as well as the work they do in the brain as highlighted.

The author gives us a basic education in mathematics, neuroscience, physics, and Matlab. He also covers conductance-based models, neuron and spike production, synaptic plasticity, Markov modeling, and Bayesian coding, among other things.

The book is a valuable tool for graduates and undergraduates alike, who want to gain a greater understanding of neuroscience and acquire better coding skills.

**Author:**Paul Miller**Publisher:**The MIT Press**Current edition:**1st edition**Pages:**408

**2. Theoretical Neuroscience (Computational and Mathematical Modeling of Neural Systems)**

Intended to furnish a quantitative foundation for communicating how nervous systems operate and what they do to function, theoretical neuroscience is a detailed field. This text presents the cardinal mathematical and computational methods involved. It explains the many different applications in learning, development, memory, vision, and sensory-motor integration.

The work has three parts. The first one deals with the sense of smell, taste, touch, sight, and sound and the neural response to each stimulus. The spotlight is on the activity within the brain circuitry and how it is captured.

The next part addresses the shaping of neurons and neural pathways on the foundation of cellular and synaptic biophysics. In the third part, there’s a deep dive into the role that plasticity plays in the learning and development process. It ends with a long list of the mathematical methods used in the work and a rundown of exercises available online.

**Author:**Peter Dayan and L. E. Abbott**Publisher:**MIT Press**Current edition:**1st edition**Pages:**480

**3. Principles of Neural Information Theory: Computational Neuroscience and Metabolic Efficiency**

Written by James V. Stone, this work is filled with illustrations that showcase the power and beauty of the human brain. Students of this complex organ weighing about 3 pounds know that its neurons are not as fast and efficient as a standard computer. This is why the focus of this book is Shannon’s mathematical theory of information. The theory is used to analyze the computational efficiency of neurons. It includes a distinct reference to coding hypotheses and visual perception.

There’s a section filled with facts and figures from different research papers. It is used to express how information theory places a defined boundary around neural processing. These same limits are explained to be determinants of the neuroanatomical microstructure of the brain and eyes.

Overall, reading this book is a good way to get acquainted with the basic principles of neural information theory. The tutorials, section for further reading, and extensive glossary are complemented by an informal style.

**Author:**James V. Stone**Publisher:**Sebtel Press**Current edition:**1st edition**Pages:**211

**4. Fundamentals of Computational Neuroscience**

This book is uncomplicated, as is expected of an introductory text for students. In it, Professor Trappenberg builds on the success of the first edition by taking the reader on a journey through the theoretical foundations of neuroscience. The essence is to address the nature of all the information processing that takes place in the human brain. Basic models of neurons fit for exploring information processing within circuits are introduced.

Many core network architectures are highlighted as well as their relevance in processing information. A few examples of higher-order cognitive models serve to show the depth of insight such findings can bring.

The text is written in a simple way. Each chapter begins by leading with some experimental evidence and conceptual issues connected with brain function. Some basic Matlab programs are also explained, with a section for downloadable programs.

Although computational neuroscience is not a new field of endeavor, it has recently become a proper scientific discipline. This book will help you get a good start in the field.

**Author:**Thomas P. Trappenberg**Publisher:**Oxford University Press**Current edition:**2nd edition**Pages:**416

**5. Computational Neuroscience: A First Course (Springer Series in Bio/Neuroinformatics**

Authored by a Professor of Cognitive Neuroscience, this book is a guide for any aspiring neuroscientist who wants to use computational methods. It gives the reader a framework to understand computational modeling from the membrane to the network level.

The result of a series of lectures held for over a decade and presented to graduate neuroscience students from biological and medical backgrounds, this book is a thorough work. It explores the core areas of computational neuroscience. They include artificial neural networks, membrane biophysics, and systems theory.

Readers who don’t have a firm grasp of mathematics are not left out in this text. The abstract concepts are easy to understand. It also delves into mathematical neuromodeling, differential calculus, and partial differential equations. There is a focus on applying them to the dynamics of the neural code.

All who desire a firm foundation in computational neuroscience and modeling do well to grab a copy of this book. It’s interesting to note that the writer is an alumnus of the Massachusetts Institute of Technology, the Ruhr-Univerisity Bochum, the Max-Planck Institute for Biological Cybernetics, among others.

**Author:**Hanspeter A. Mallot**Publisher:**Springer**Current edition:**2013 edition**Pages:**146

**6. Principles of Computational Modeling in Neuroscience**

It is said that principles are the foundation upon which everything rests. That is true both in the humanities and the sciences. This work is the brainchild of four professionals in the field of neuroscience.

The book details how adopting computational modeling can give you a broad comprehension of the entire nervous system. Many different elements are interacting with one another within this system. Understanding it entails the construction and deconstruction of many computational models. Luckily, there’s a step-by-step guide in this text on modeling neural circuitry to get acquainted with the length and breadth of the nervous system – from ion channels to networks.

It begins with neurons as basic models and branches out into the impact of intracellular signaling, neural morphology, ion channels, and synapses. There are even chapters that focus on abstraction principles, simplifying models, and how those models can be utilized within networks.

The last chapter deals with modeling the growth of the nervous system. Readers with a basic foundation in mathematics and neuroscience will find this textbook very helpful.

**Author:**David Sterratt, Bruce Graham, Andrew Gilles, David Willshaw**Publisher:**Cambridge University Press**Current edition:**1st edition**Pages:**404

**7. Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting**

Mathematical neurophysiology enthusiasts will find that this book is an excellent addition to their library. The author was a Senior Fellow in Theoretical Neurobiology at the Neurosciences Institute, San Diego.

This work sheds light on the mathematics behind the dynamics of neurons. It gives a qualitative as well as a quantitative insight into the world of neurons. In it, neural models are grouped according to type and the most significant things about them are identified.

The author demonstrates how to use Matlab to recreate the models he describes. Plenty of examples and explanations make this an easy read. It goes beyond the basics in providing an outline of how to apply non-linear dynamics to neuroscience. Care is taken to explain all mathematical concepts in detail. This makes it easy to assimilate.

Overall, this book studies the relationship between bifurcations, electrophysiology, and computational properties of neurons. Concepts of non-linear dynamics such as stability, equilibrium, limit cycle attractor, and bifurcations are also explored. Biomathematicians and neuroscientists, in general, will find that it is a great reference tool.

**Author:**Eugene M. Izhikevich**Publisher:**The MIT Press**Current edition:**3rd edition**Pages:**464

**Conclusion**

All these books are worth a read, especially for those who want a deeper understanding of computational neuroscience and its many fields. Each of them represents a painstaking effort at enriching your knowledge of the subject, and that is why they made the list.