Strategic Objectives
• Master the information theory specifically tailored for neural spike trains.
• Implement lossy and lossless compression without sacrificing decoding accuracy.
• Optimize hardware power consumption through efficient on-chip data reduction.
• Navigate the trade-offs between signal-to-noise ratios and transmission bit-rates.
The Core Challenge
High-density neural recordings create a massive bandwidth bottleneck that traditional wireless transmission cannot handle without losing critical signal integrity.
01
The Bandwidth Bottleneck
02
Foundations of Information Theory
03
Source Coding Essentials
04
The Physics of the Signal
05
Sampling and Quantization
06
Lossless Neural Compression
07
Lossy Strategies for High Density
08
Predictive Coding Models
09
Transform Domain Compression
10
Wavelet-Based Reduction
11
Dictionary Learning
12
Compressed Sensing
13
Spike Detection and Extraction
14
Principal Component Analysis
15
Differential Pulse-Code Modulation
16
Entropy Coding Techniques
17
Hardware Implementation
18
Wireless Telemetry Constraints
19
Real-Time Processing
20
Data Integrity and Error Correction
21