5-Month Online
Research Training
A mentorship-driven program introducing modern computational neuroscience, from brain modeling to biologically inspired deep networks and research publication.
Program Vision
A long-form, mentorship-driven training program designed to introduce students to modern computational neuroscience.
Brain Modeling
Model important brain structures including the basal ganglia, hippocampus, and sensory-motor systems.
Deep Networks
Learn biologically inspired deep networks, oscillatory neural models, and computational approaches.
Research Track
Transition to supervised research with the possibility of co-authoring a conference abstract.
Phase 1 — Coursework
Video lectures + graded assignments + 1 hr/week live mentor call
Goal: Ensure all students acquire core mathematical and computational foundations
Foundations of Computational Neuroscience
- Hodgkin-Huxley (HH) Models
- Leaky Integrate and Fire (LIF) Neuron
- McCulloch-Pitts Neurons
Python, NumPy, PyTorch
Assignment: Build and Simulate Single Neurons
Deep Neural Networks and the Brain
- MLPs and Perceptrons
- Convolutional Neural Networks
- DNNs as Brain Metaphors
PyTorch, Brain Organization
Assignment: Projects using Deep Neural Networks
Sensory-Motor Function in DNNs
- Visual System Modeling
- Auditory Pathway Simulation
- Motor System Dynamics
TensorFlow, Vision Labs
Assignment: DNN applications in vision neuroscience
Modelling the Dynamic Neuron
- Phase plane analysis
- Modeling Limit Cycles
- Limit Cycle-based neuron models (e.g., Fitzhugh-Nagumo, Morris-Lecar)
- Izhikevich neuron models – 1D and 2D
Xppaut
Assignment:Modeling dynamic single neuron models and small networks
Modelling the Dynamic Neuron
- Spiking-neuron networks
- Oscillatory Neural Networks
- Deep oscillatory neural networks
Python, NumPy, PyTorch
Assignment: Build Deep oscillatory-networks for classification, Spiking neuron networks to model epilepsy
Phase 2 — Research Project
Choose a specialized research direction under weekly supervision
Mentors supervise weekly and help with modeling choices, testing plans, implementation, and writing
Modeling Basal Ganglia & DBS
Investigate how deep brain stimulation can be used therapeutically for Parkinson's disease.
Deep Oscillatory Neural Networks
Develop and apply deep oscillatory networks to classify complex signal and video data.
Modeling Stroke with Constrained DNNs
Build biologically constrained models to understand mechanisms and interventions for stroke.
Modeling the Spatial Cells in the Hippocampus
Investigate computational models of spatial cells including place cells, grid cells, and other spatially-tuned neurons in the hippocampal formation.
AI Applications in Electroencephalography (EEG) Signal Processing
Apply advanced AI techniques to process and analyze EEG signals for applications in brain-computer interfaces, clinical diagnosis, and cognitive neuroscience.
Admission Requirements
Fee Structure
Phase 1: Coursework
Weeks 1-10
₹25,000
INR
Phase 2: Research
Weeks 11-20
₹35,000
INR
Expected Outcomes
Conference-Ready Abstract
Co-authored with mentor upon satisfactory progress
GitHub Repository
Reproducible code for your research project
4 Graded Assignments
Comprehensive coursework across all modules
Project Presentation
5-7 minute presentation of your research
Mentorship Structure
Weekly Mentor Call (1hr)
Clarify concepts, debug models, review progress
Continuous Chat Support
Email and messaging support throughout the program
Assignment Review
Detailed feedback on all submitted work
Career Guidance
Advice on research paths and academic opportunities
Lead Instructor
Prof. V. Srinivasa Chakravarthy
IIT Madras
Professor with joint appointments in the Department of Biotechnology and the Department of Medical Science and Technology at IIT Madras. Currently heads the Computational Neuroscience Lab (CNS Lab), the Parkinson's Therapeutics Lab, and Neural Engineering Lab.