If you are interested in learning about brain activity through a lens of computational principles, then a computational neuroscience course might be in your future. Learning about neural systems and how computers can help reveal the mind’s mysteries is becoming more exciting as the technology progresses.
Follow along as we explore the possibilities for learning computational neuroscience. We will discuss what computational neuroscience is, the differences between computational neuroscience and machine learning, and the top online and in-person courses in the industry.
What Is Computational Neuroscience?
The goal of computational neuroscience is to help us understand the information sent by neural signals by creating a replication of the nervous system. The field focuses on the study of brain functions by using mathematical models and computer algorithms.
A computational neuroscientist sees the brain as a computing machine, with neurons as information processing units. Neuroscience covers molecular and cellular studies, as well as psychophysics and human psychology. Computational neuroscience aims to describe how the brain uses electrical and chemical signals to interpret and process information.
Computational Neuroscience Themes
First, let’s look at some of the current research happening in computational neuroscience.
Single Neuron Modeling
Neurons are the building block of any nervous system and are responsible for receiving sensory input from the world around us. Most neurons have a cell body, an axon, and dendrites. Researchers use neural modeling to understand how these single cells impact our information processing and behavior.
Behavior of Neural Networks
Biological neural networks are complex compared to artificial ones. One of the aims of computational neuroscience is to recognize the computational functions of specific areas of the brain, using neural network models like the Ising model.
Development of Neural Circuits
Computational neuroscientists are always trying to answer questions on how neurons are formed, how axons migrate, and how molecular biology influences functional neural connections.
Computational Cognitive Neuroscience (CCN)
CCN is about modeling the brain’s biological activity to better understand perception, behavior, and decision-making. Computational and cognitive neuroscience often intersect in studying machine learning and neural network theories.
Neural Coding and Decoding
Neural coding is about our sensory input and our ability to encode it. Through neural coding, you will learn how to record brain activity using new technology. In contrast, neural decoding is about estimating what the brain sees, intends, or experiences based only on its neural activity.
An example of visual neural coding is what our brain sees when we hear a word like “tiger.” It is also easy for us to associate related words and actions with the word “tiger,” such as “running from a predator.”.
This process is much easier for humans than it is for machines. Experts researching neural coding are hoping to draw links between neuron activity, sensory input, and behavioral output.
Computational Neuroscience vs Machine Learning
Although there is some overlap between computational neuroscience and machine learning, they are not interchangeable. One is more focused on using computational principles and models to understand human behavior better, while the other is focused on increasing the learning abilities of a machine, and has more in common with AI technology.
Computational neuroscience experts are focused on creating realistic, dynamic neuron models. This aspect of computational neuroscience differs from machine learning. However, both machine learning and computational neuroscience use mathematical insights, learned data visualizations, and information theories.
Learning Computational Neuroscience
Computational neuroscience is an interdisciplinary field, meaning it is a mixture of different subjects. Students with strong background knowledge in mathematics, computer programming, neurology, psychology, and physics will do well in this field.
It will also be helpful for students to take classes in machine learning to get a better understanding of which area in the industry they might like to specialize in.
Very few institutions have an undergraduate computational neuroscience program. Instead, it is usually a PhD program you can attend after your graduate program.
Therefore, taking subjects like neurology, neurobiology and behavior, computer science, machine learning, engineering, and applied math in your undergraduate program is a great place to start.
Be sure to tailor your choices to your chosen career path, whether that means delving into the mathematics of chaotic neural networks, studying machine learning or decision-making behavior, working closely with clinical applications, or developing devices.
How to Learn Computational Neuroscience: Step-by-Step
Below are the steps you should take as a beginner in computational neuroscience. As it is an interdisciplinary field, there are many avenues to this career. These are our recommended steps.
- Programming languages. A great first step is to learn programming languages like Python and C++. You may want to join a coding bootcamp or attend some courses to help you apply your coding skills to your future career.
- Mathematics. Mathematics for signal processing is a basic part of computational neuroscience. Learning linear algebra, probability, statistics, and data science will help you as you enter your degree.
- Physics. The next step is to learn about the natural science behind matter in motion and behavior as it relates to time and space. You may choose to take physics courses as part of our school work, or by studying outside resources.
- Learn about brain functions. Completing some short courses on neurons before you study and taking behavioral courses, such as psychology, will provide you with more foundational knowledge.
- Take short courses. Attending a short course in computational neuroscience will give you an idea of whether you want to learn more about the field. Check out the courses below to see which ones are best for beginners.
- Neuroscience. Studying neuroscience will give you an understanding of the biological functions of the mind.
- Computer science. Computer science is another great undergraduate degree choice before heading into computational neuroscience, as you will learn all of the computational principles required for your career.
- Machine learning. If you are more interested in programming machines to learn tasks, machine learning is a great degree choice.
- Data and statistics. If you are more interested in statistics this would be a good area of study.
- Computational neuroscience. Completing a master’s and PhD in computational neuroscience is the final step. You will learn how all of the fields mentioned above come together to help us understand neuron activity, memory, and behavior.
The Best Computational Neuroscience Courses
There are many types of computational neuroscience courses and training, including in-person programs, online courses, and free online classes that will help you build up your resume. Let’s take a look at the computational neuroscience courses that will boost your interest in the field.
Best In-Person Computational Neuroscience Courses
Here are some great in-person computational neuroscience PhD programs for those who have already studied neuroscience or a related area.
Princeton University
- Quantitative and Computational Neuroscience
- Where: Princeton, NJ
- Requirement: PNI PhD
- Time: 4 years
The Quantitative Neuroscience Training Program (QNTP) is for students who are enrolled in Princeton’s neuroscience PhD program. The QNTP builds on this neuroscience foundation by giving students additional tools and training to fully function as computational neuroscientists.
UC Berkeley
- Neuroscience PhD
- Where: Berkeley, CA
- Requirements: Academic background in biology, chemistry, psychology, physics, engineering, or computer science.
- Time: 5 years
The neuroscience PhD program at UC Berkeley offers students the opportunity to specialize in molecular and cellular neuroscience, computational neuroscience, and human cognitive neuroscience.
Best Online Computational Neuroscience Courses
Below are some of the best paid online computational neuroscience courses, ranging from beginner to advanced levels.
Coursera
- Computational Neuroscience
- Where: Online, University of Washington
- Time: 26 hours
- Requirements: None
- Price: Varies
In this course provided by the University of Washington, students will gain excellent beginner knowledge of nervous systems and how they function. It covers computational principles in aspects of vision, sensory-motor control, learning, and memory.
The course also deals with spiking neurons, neural networks, and algorithms for learning. It also demonstrates how Matlab, Octave, and Python are used in computational neuroscience. The course is aimed at fourth-year undergraduates, professionals, and distance learners.
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EdX
- Computational Neuroscience: Neuronal Dynamics of Cognition
- Where: Online
- Time: 6 weeks
- Requirements: Bachelor’s in physics, math, or electrical engineering, and calculus understanding
- Price: $140 including certificate
This advanced ddX course explores the computational and mathematical models that are used in theoretical neuroscience by analyzing thousands of interacting neurons.
Best Free Online Computational Neuroscience Courses
There are many free online courses for studying computational neuroscience. They are focused on data, programming, mathematics, and computational neuroscience basics.
Neuronline
- Data Science & Data Skills for Neuroscientists
- Where: Online
- Time: About 2 hours
- Prerequisites: None
- Price: Free
In this video course, leading experts will teach basic data skills that can be applied to neuroscience. The instructors also explain how advanced data science methods can be applied to the industry.
Khan Academy
AP®︎/College Computer Science Principles
- Where: Online
- Time: Varies
- Prerequisites: None
- Price: Free
Khan Academy is a free platform that’s a great resource for learning math and programming skills. It is great for beginners in mathematics and programming. Although you won’t gain a certificate of completion, Khan Academy is a wonderful way to supplement your school learning.
University of Oxford
- How to Build a Brain from Scratch
- Where: Online Lecture Slides
- Time: 8 slides
- Prerequisites: None
- Price: Free
This advanced course covers psychology, neuroscience, machine learning, and AI research. The lectures are in the format of slides that are free to download. It also discusses parallel computational approaches in machine learning/AI and psychology and neuroscience, including reinforcement learning and deep learning.
INCF Training Space
- Computational Neuroscience: The Basics
- Where: Online
- Time: 3 hours
- Prerequisites: None
- Price: Free
This beginner-friendly video course will teach you all about neuron anatomy and signaling. It also teaches students how to model chemical computation within the brain and on a molecular level.
Best Computational Neuroscience Books
Below are a few texts to get you started on your journey toward learning computational neuroscience.
Rhythms of the Brain, Gyorgy Buzsaki
This book by Buzsaki supports the idea that spontaneous neuron activity is far from just noise, and that it is the source of all of our cognitive abilities. This book is a must-read, as the author is at the forefront of the rapidly evolving research in neuroscience.
Theoretical Neuroscience: Computational & Mathematical Modeling of Neural Systems, Peter Dayan
This text gives insight into the basic mathematical and computational methods of theoretical neuroscience. It also discusses how the methods can be obliged to vision, development, learning, sensory-motor integration, and memory.
Fundamentals of Computational Neuroscience, Thomas Trappenberg
In Trappenberg’s book, he introduces the theoretical ideas of neuroscience, focusing on information processing in the brain.
Should You Study Computational Neuroscience?
Hopefully, after reading this article, you have an understanding of how to begin studying computational neuroscience. If you want to learn about the mind’s inner workings and use mathematical and computational modeling to reveal its mysteries, then taking some of the courses listed above will be right for you.
After following these steps, you will discover how the nervous system processes information. With an education in this field, you can help others learn about mental illness and the reasons behind their behavior.
If you’re still looking for more information on how to learn coding, learning machine learning, the mathematics of machine learning, or other areas related to computational neuroscience, head over to our blog to read more.
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