The main aim of this project to explore changes in structural, biological and neuropsychological parameters in patients with early dementia or MCI after long term meditation intervention. whether such changes, if any, in the structural, biological and neuropsychological parameters after meditation in patients with dementia are sustained over long periods of follow-up and reflect the delayed progression of the illness in patients with early dementia or MCI. This project also investigates changes in empathy levels, psychological well-being, caregiver burden and quality of life of primary caregivers after long term meditation intervention. for this project, we are using neuroimaging data like resting state fMRI, DWI, and structural data.

We are using different kinds of neurocomputational tools like FSL, freesurfer, SPM, AFNI to preprocess and analyze our dataset. early dementia and MCI mostly affects our memory, thinking power and decision making. Therefore, we are mostly focusing on changes in prefrontal, medial temporal and hippocampal regions.

The project aims to create dynamic models of human brain using resting state fMRI and DTI on the TVB. This will enable us to simulate the brain using modeling parameters associated with brain dynamics after stroke and understand mechanisms underlying stroke recovery and thereby develop individualized stroke therapies for rehabilitation. As a side effort, we are also looking at integrating task-based neuronal models into TVB-like large-scale brain architectures. This will enable us to study the dynamics of the task-based fMRI activities.

In this project, we have used different kinds of images for training and testing purposes. An effort has been made for storing and recalling images with the Hopfield Neural Network Model of auto-associative memory. Images are stored by calculating a corresponding weight matrix. The proposed network consists of several cascaded single-layer Hopfield networks. The Hopfield network was trained and tested against standard images, alphabets, and digits, with noise ranging from 30-70% and got good predictions. We observed that the prediction accuracy depended on the noise level, as well as the number of images trained, due to the limited capacity of the Hopfield network.

We have used neurodynex libraries from python 3 to implement our Hopfield neural network. We have used different kinds of images for our implementation purpose like the standard skimage dataset, digits, alphabets, CIFAR10 and CIFAR100.

The aim of the this work is to verify the interference of color information with semantic processing in different durations (100,200 & 300ms). To study the response capabilities in human behaviour. and also to understand the relation of Speed of Processing Theory and Selective Attention Theory in various sub second time frames. we also investigated, can task irrelevant information be inhibited when we have more control?

we have used full factorial design for our experiment for two conditions congruent and incongruent. We have collected 30 participants data [18 male (avg age 23.67), 12 female (avg age 22.56)] from IIIT-hyderabad and the data was randomly sampled. We have used opensesame software for the designing of our experiment.

We found significant difference in reaction time for all three different durations,suggesting the performance difference in terms of processing across the conditions.Interestingly, we are getting the performance difference for 100ms, which means semantic processing is happening within 100ms which was unexpected for us.

This project was my first project in python. project was aimed to make use of python and pygame libraries to build the games. these games includes few famous games like TETROMINO (Falling shapes), and car racing.

in this project, first i learnt python then i applied that knowledge in designing these games.

Below, there are few Screen shots of games which i developed in this project.