Compressive Sensing of Neural Signals

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Compression: Prefix-Free Codes

Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.

Auto-encoder and GANs

Covers auto-encoders for data compression and GANs for data generation.

Sampling: Signal Reconstruction and Aliasing

Covers the importance of sampling, signal reconstruction, and aliasing in digital representation.

SVD: Singular Value Decomposition

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

Neural Signals and Signal Processing

Delves into neural signals, GLM, ANOVA, brain mapping, connectivity, and multivariate methods.

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

Neural Signals and Signal Processing: Modeling and Simulation

Covers the fundamentals of neural signals and signal processing, focusing on modeling and simulation of neural systems.

Data Compression and Shannon's Theorem: Lossy Compression

Explores data compression, including lossless methods and the necessity of lossy compression for real numbers and signals.

Neural Signals and Signal Processing

Explores nuclear magnetic resonance, MRI principles, pulse sequences, image reconstruction, safety considerations, and volume normalization in brain imaging.

Neural Signals and Signal Processing

Explores neuronal signals, brain organization, measurement techniques, and MRI principles.