TA
Neuroinformatics & Methods in Neuroscience
| Recording | Description | Slides and Code NBs |
|---|---|---|
| Session 0 | Intro to the couse | Slides_Session_0 |
| Session 1 | Intro to EEG, sources of EEG, EEG acquisition, data format & preprocessing | Slides_Session_1 |
| Session 2 | Visualizing EEG and ERPs; Fundamentals of time-frequency representation; filtering, sampling & rereferencing | Slides_Session_2 |
| Session 5 | Intro to signals and types of signals, Euler’s representation | Slides_Session_5,TypesOfSignals |
| Session 6 | Fundamental operations on signals, Periodic signals & Fourier series, Fourier series to fourier transform | Slides_Session_6, Fourier_Series |
Brain, Behavior and Beyond: 2 Day Seminar (April 2023)
| File | Description |
|---|---|
| Session 1 | Feedback Control in the Brain |
| Session 2 | Reading the Minds: Decoding the Brain |
| Video | Recording of the session |
Demystifying the Brain: (Feb - Mar 2023),NPTEL
| File | Description |
|---|---|
| Session 1 | Introduction to Neuroscience and Neuroanatomy |
| Session 2 | Biophysics of neuron signalling and Multilayer perceptron |
| Session 3 | Neuroscience of Vision, Self-Organizing Maps and Sub-cortical structures |
| Session 4 | Memory, Hopfield Networks, Emotion and Reward processing |
| Lectures | Recording of the sessions |
AVD 614 Pattern Recognition and Machine Learning: (Aug - Dec, 2020), IIST
| File | Description |
|---|---|
| Programming Assignment 1 | Fundamentals of Linear Algebra and Probability |
| Programming Assignemnt 2 | Bayesian Decision Theory and K-NN |
| Programming Assignment 3 | Density Estimation: MLE, MAP and Kernel Density Estimation |
| Programming Tutorial 1 | jupyter notebook file for tutorial 1 |
| Programming Tutorial 2 | jupyter notebook file for tutorial 2 |