🧬Objectives
🧠 Introduce tools that are essential for starting work in computational neuroscience.
🧠 It includes pre-requisite knowledge such as
👩💻 Programming with Python
👩💻 Linear Algebra
👩💻 Calculus
👩💻 Statistics
👩💻 Basic Neuroscience
| 🧠Topic | 🧠Tutorial | 🧠Video | 🧠Slides |
|---|---|---|---|
| Python (W1D1&2) |  |
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| Basics of Neurosciences 1 (W1D3) | |
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| Basics of Neurosciences 2 (W1D4) | TBA | ||
| Neural Data (W1D5) | |
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| Linear Algebra Tutorial 1 (W2D1) |  |
TBA | |
| Linear Algebra Tutorial 2 (W2D2) | |
TBA | |
| Linear Algebra Tutorial 3 (W2D3) | |
|
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| Calculus Tutorial 1 (W2D4) | |
TBA | |
| Calculus Tutorial 2 (W2D5) | |
TBA | |
| Calculus Tutorial 3 (W2D5) | |
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| Statistics Tutorial 1 (W3D1) | |
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| Statistics Tutorial 2 (W3D2) | |
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- Mon, July 31: Python Day 1
- Tue, August 1: Python Day 2
- Wed, August 2: Basics of Neurosciences Day 1
- Thu, August 3: Basics of Neurosciences Day 2
- Fri, August 4: Neural Data
- Mon, August 7: Linear Algebra Day 1 (Vectors)
- Tue, August 8: Linear Algebra Day 2 (Matrices)
- Wed, August 9: Linear Algebra Day 3 (Eigen Values and Eigen Vectors)
- Thu, August 10: Calculus Day 1 (Differentiation and Integration)
- Fri, August 11: Calculus Day 2 (Differential Equations)
- Mon, August 14: Statistics Day 1 (Probability)
- Tue, August 15: Statistics Day 2 (Inference)
- Wed, August 16: Guest Speakers
- Thu, August 17: Project Day 1
- Fri, August 18: Project Day 2
| Topic🧠 | Code🧠 |
|---|---|
| Our Project | |
The objective of this project is to analyze neural activity data obtained from brain regions involved in visual perception tasks. The project aims to decode the neural responses to different visual stimuli and evaluate the accuracy of decoding across various regions of interest (ROIs) in the brain.
The project starts with downloading and installing necessary libraries and datasets required for analysis. This includes downloading data files (data.h5 and features.h5), ImageNet labels (imagenet_classes.txt), and installing required Python libraries (bdpy, seaborn_image, etc)
The neural activity data is stored in an HDF5 file (data.h5). The data includes voxel data from different brain regions, along with metadata such as data type, run number, labels, and ROI indicators.
The project includes functions for extracting data from specific ROIs and filtering based on image types (similar, different, or imagery). Logistic Regression models are trained using the extracted data to decode neural responses and predict image labels. The accuracy of the decoding models is evaluated using test data, and accuracy scores are calculated for each ROI.
Visualizations are generated to illustrate the decoding accuracy across different ROIs. Plots such as bar graphs are used to compare the accuracy of decoding across various brain regions.
Data Extraction Functions: Functions to extract neural activity data from specific ROIs and filter based on image types. Model Training and Evaluation: Training logistic regression models to decode neural responses and evaluate accuracy. Visualization: Generating visualizations to depict decoding accuracy across different brain regions.
The project aims to provide insights into how neural activity in different brain regions correlates with visual perception tasks. By analyzing decoding accuracy across ROIs, the project aims to identify regions that are most informative for specific visual stimuli.