Strategic Objectives
• Master the core Bayesian logic required to filter neural noise.
• Understand how prior knowledge stabilizes erratic brain-machine interfaces.
• Learn the mathematical frameworks for estimating intended movement.
• Explore the frontier of uncertainty management in neurotechnology.
The Core Challenge
Neural signals are notoriously noisy and unpredictable, making precise thought-to-action decoding a mathematical minefield.
01
The Probabilistic Mind
02
Foundations of Neural Coding
03
Prior Knowledge and Expectations
04
Likelihood and Neural Evidence
05
The Mathematics of Uncertainty
06
The Posterior Distribution
07
Population Coding
08
Maximum A Posteriori Estimation
09
Recursive Estimation with Kalman Filters
10
Hidden Markov Models in Thought
11
The Cost of Error
12
Information Theory and Neural Capacity
13
Variational Inference
14
The Predictive Brain
15
Point Process Models
16
Non-Parametric Bayesian Methods
17
Signal Processing and Noise
18
Stochastic Processes in the Brain
19
Computational Complexity
20
Machine Learning Integration
21