What is Computational Neuroscience ?

It is not uncommon for me to get asked ‘What Computational Neuroscience is?’ as I’m pursuing a degree in the Computational Modelling track of Applied Cognitive Neuroscience and most people whom I have come across outside of this domain have hardly heard about such a field. So explaining to them what it is in layman term is challenging, as the field itself is sparsely defined. Trying to know what it is, from ‘Wikipedia’ also might not help everyone. I, myself must have taken a while to understand what the field actually is. In this blog post I will try to explain what Computational Neuroscience is, from what I have understood. Maybe I could come up with a better explanation in the future as I go deeper.

Although the name Computational Neuroscience might have emerged recently, the central motivation must have started somewhere in between late 1940s and 1980s during the days of the rise of conventional AI. In the most flamboyant language, Computational Neuroscience can be called an interdisciplinary field of Neuroscience, Cognitive Science, Computer Science and Psychology. In a more abstract language, we can define Computational Neuroscience as a field that aims at explaining the neural mechanisms behind the cognitive abilities of living beings by developing quantitative models (Neural Networks) of those mechanisms or in other words a methodological branch that exploits the resourcefulness of computational research to explain how the neural structures achieve their effect.

One of the widely held belief that has contributed to the development of this field is, understanding how human mind works will help us to design machines that can outperform today’s computer programs for visual recognition, language processing and artificial intelligence. And there is also this other half of the field trying to explain how brain works by taking inspiration from Computer Science. 

So is the brain a computer ? The answer is Yes and No. Though brain was the motivation in the early half of the computer revolution for most of its architectures, as I said in the previous paragraph, today we are coming the other way around, trying to explain functioning of our nervous system in the computational terms. But yet why can’t we accept brain as a computer? There is so much discrepancies than similarities between a brain and a computer. Unlike manufactured computers, our brains are highly plastic – they grow, develop, learn and change. And though we have managed to develop processors that have higher performance rate than our brain, even the most expensive super computer fails before the efficiency of the parallel processing of human mind (Our brains can only communicate in the range of milliseconds whereas the newest computers communicate at the speed of nano and picoseconds ).

The core organization of brain within Computational Neuroscience is loosely structured by the Marr’s levels of Analysis. Marr defined three levels to every system that makes computations. The topmost level is the (1) Computational level where in we have the abstract problem analysis breaking down it’s main components; underneath it comes the (2) algorithmic level where we define the formal procedures; and then the last level (3) hardware level or the physical implementation level of the computation.

We know what happens and how it happens millions and billions of miles away from our planet earth but not yet how our own mind works. Unlike many other branches, here a pure bottom-up approach nor a pure top-down approach is not going to be helpful, which makes it both the most challenging as well as the most exciting field to be in right now.

Getting Started in Computational Neuroscience

The demand for Computational Neuroscientists have been on the rise since the inception of BRAIN Initiative and Human Connectome Project. And today getting into a graduate program is not the only means to kickstart your journey in Computational Neuroscience.  Even if your plan is to get into a graduate program, I would strongly recommend auditing the following MOOCs:

  1. Computational Neuroscience (Coursera): The most comprehensive introductory course on Computational Neuroscience available in the internet. The course starts from the basics of neurobiology and takes you all the way upto building learning algorithms.
  2. Machine Learning by Andrew Ng (Coursera): This requires no explanation. If you have used Coursera or have googled about Machine Learning, you must have came across this name at least once. Andrew Ng is the founder of Coursera and his Machine Learning course was the first one to be offered in Coursera, which is the most taken and recommended course in Coursera.
  3. Neural Network Mathematics (Coursera): Get ready to bleed. Once you complete this course, be ready to call yourself an expert of Machine Learning.
  4. Deep Learning by Google (Udacity)
  5. Principles of fMRI 1 & fMRI 2

Other resources:

  1. [Ebook] Python in Neuroscience (Frontiers in Neuroinformatics)

Why this Blog ?

Ideas are worthless unless they can be communicated clearly and persuasively to others. And the best place to communicate them is the internet. I believe in an open culture where people from different disciplines and different nations share their ideas and contribute to each other’s works. In this fast ever changing world, the best way to accelerate your research is to keep your works open and accessible to everyone, so that they can learn, share and comment on your work!

This blog is a scientific journal of my learnings, findings, ideas and research works done in the field of Computational Neuroscience. Other than my ideas & works, I would also be sharing my reviews on stuffs that excites me!