Brain Mapping with Python





Brain Mapping Using Python:

Over the past few years, machine learning and artificial intelligence has been making headlines and advancing quickly by creating products that can make optimistic decisions. Now this machine learning technology can be implemented in making a machine which can perform complex actions just like in brain which can make human life easier. Now the real challenge is

can we create a neural network model which can perform complex actions like human brain?

How Python can be used to accomplish this task and how far we can achieve this feat? This talk will be focusing on the methods approached by brain to generate conclusions or make decisions which can be replicated in computers and generate results.

Contents of the talk

  1. About me - Basic introduction of myself.
  2. What is Brain Mapping?
  3. Functionalities of Human Brain.
  4. Neural Networks Using Python.
  5. Types of Data Summarisation techniques in Python.
  6. How Computers can make decisions.
  7. What can we expect from Brain Mapping in future.


  • basic syntax knowledge of python
  • basic machine learning terminology
  • neural network models functionality

Content URLs:

wikipedia article on the brain computer interface

Text Summarizer neural network model code is in the following link

Speaker Info:

Rohith Pudari


Rohith is a B Tech student who is passionate about integrating the most complex organ known to human which is brain with computers. He is winner of the Hyderabad best coder championship conducted by JNTUH. He is one of the few persons in India who is selected for the google Udacity scholarship. He is always interested in decreasing the interaction gap between computers and humans and started his research in creating an interface which will allow humans to interact with computers in a more natural way. He created a neural network model which generates a summary of a given essay which won the title "Best innovative idea" at IIT Kanpur.

Speaker Links:

you can see the projects and previous work of Rohith in the following link to his github profile. and linkedIn profile

Rohith contributed to the following open source projects:

  1. Atom- open source code Editor
  2. OpenWISP- software platform that implements a complete Wi-Fi service
  3. Sugar Labs- desktop environment and learning platform
  4. Sustainable Computing Research Group (SCoRe)

Section: Data science
Type: Talks
Target Audience: Intermediate
Last Updated:

The proposal is bound to ring bells if it lives up to the expectation. Can you help me with the following queries ?

  • The title "brain mapping with python" needs a little more explaination.
  • You have not mentioned how you are going to perform the above mentioned feats.
  • Also I think your idea of text summarisation is cool but how does it relate to BCI ? Gensim has a pretty neat implementation of their own summarization techniques what novelty does your summarizer brings on the table?
Mohit Rathore (~markroxor)
  • The title Brain Mapping with python emphasises on the methods approached by brain to generate conclusions or make decisions which can be replicated in computers and generate results.
  • I am going to show the results of few research activities on BCI and also my deductions in it. As it is a topic of ongoing research it is not possible to come to a conclusion whether we can or cannot achieve the complexity of brain but I will be talking on the ways which we can at least make it work forgetting the efficiency.
  • The data which we give to computers will obviously be large and to increase computing capability and making it efficient we need to remove the unnecessary data that's where summarisation gets into picture. There are still developments going on to generate summary for different types of data and we have achieved it for text itself. My summarisation model functionality mostly focuses on diverse forms of data, for now text summarisation is achieved and I am continuing my research to implement it for video and audio data also.

Login to add a new comment.