cognitive/docs/guides/learning_paths/predictive_processing.md

title	type	status	created	tags	semantic_relations
Predictive Processing Learning Path	learning_path	stable	2024-02-12	learning-path predictive-processing guide	type links implements ../documentation_standards type links relates ../../knowledge_base/cognitive/predictive_processing active_inference_learning_path

Predictive Processing Learning Path

Overview

This learning path guides you through understanding and implementing predictive processing principles in cognitive modeling.

Prerequisites

Mathematics

1. knowledge_base/mathematics/probability_theory

   • Conditional probability
   • Bayesian inference
   • Probabilistic graphical models

2. knowledge_base/mathematics/information_theory

   • Entropy
   • Mutual information
   • KL divergence

3. knowledge_base/mathematics/optimization

   • Gradient descent
   • Error minimization
   • Loss functions

Programming

1. Python Fundamentals

   • NumPy/SciPy
   • PyTorch/TensorFlow
   • Scientific computing

2. Software Engineering

   • Object-oriented programming
   • Testing frameworks
   • Version control

Learning Path

1. Theoretical Foundations

Week 1: Core Concepts

1. knowledge_base/cognitive/predictive_coding

   • Neural basis
   • Hierarchical processing
   • Error minimization

2. knowledge_base/cognitive/hierarchical_inference

   • Layer organization
   • Information flow
   • Prediction errors

Week 2: Advanced Theory

1. knowledge_base/cognitive/precision_weighting

   • Uncertainty estimation
   • Attention mechanisms
   • Dynamic control

2. knowledge_base/cognitive/temporal_prediction

   • Time series prediction
   • Sequence learning
   • Dynamic models

2. Implementation Basics

Week 3: Basic Implementation

1. docs/guides/implementation/predictive_network

   • Network architecture
   • Layer implementation
   • Error computation

2. docs/guides/implementation/error_propagation

   • Forward predictions
   • Backward errors
   • Update mechanisms

Week 4: Core Mechanisms

1. docs/guides/implementation/precision_mechanisms

   • Precision estimation
   • Attention control
   • Uncertainty handling

2. docs/guides/implementation/temporal_models

   • Sequence prediction
   • Time series handling
   • Dynamic updating

3. Advanced Applications

Week 5: Complex Systems

1. docs/guides/implementation/hierarchical_systems

   • Multi-layer networks
   • Deep architectures
   • Information integration

2. docs/guides/implementation/multimodal_processing

   • Sensory integration
   • Cross-modal prediction
   • Feature binding

Week 6: Real-world Applications

1. docs/guides/implementation/perception_systems

   • Visual processing
   • Auditory analysis
   • Sensory integration

2. docs/guides/implementation/cognitive_tasks

   • Decision making
   • Learning tasks
   • Memory systems

4. Research Topics

Week 7: Current Research

1. docs/guides/research/current_developments

   • Latest findings
   • Research directions
   • Open questions

2. docs/guides/research/advanced_architectures

   • Novel approaches
   • Hybrid systems
   • Performance optimization

Week 8: Applications

1. docs/guides/implementation/clinical_applications

   • Psychiatric models
   • Neurological disorders
   • Therapeutic applications

2. docs/guides/implementation/technological_applications

   • Robotics
   • Computer vision
   • Signal processing

Projects

Beginner Projects

1. docs/examples/simple_prediction

   • Basic prediction
   • Error computation
   • Learning rules

2. docs/examples/visual_prediction

   • Image processing
   • Feature extraction
   • Pattern completion

Intermediate Projects

1. docs/examples/temporal_sequence

   • Sequence learning
   • Time series prediction
   • Dynamic patterns

2. docs/examples/multimodal_integration

   • Sensory fusion
   • Cross-modal prediction
   • Feature binding

Advanced Projects

1. docs/examples/cognitive_architecture

   • Full system implementation
   • Multiple cognitive functions
   • Real-world applications

2. docs/examples/clinical_model

   • Disorder modeling
   • Intervention testing
   • Treatment simulation

Resources

Reading Materials

1. Core Papers

   • Foundational papers
   • Implementation papers
   • Review articles

2. Books

   • Theoretical texts
   • Implementation guides
   • Application studies

Tools and Libraries

1. Software Tools

   • Neural networks
   • Predictive models
   • Analysis tools

2. Development Resources

   • Code repositories
   • Documentation
   • Community resources

Assessment

Knowledge Checks

1. Theoretical Understanding

   • Concept tests
   • Mathematical problems
   • Design challenges

2. Implementation Skills

   • Coding exercises
   • System design
   • Performance analysis

Final Projects

1. Research Implementation

   • Novel contribution
   • Experimental validation
   • Documentation

2. Practical Application

   • Real-world problem
   • Solution design
   • Performance evaluation

Next Steps

Advanced Topics

1. docs/guides/learning_paths/advanced_predictive_processing

   • Complex architectures
   • Novel applications
   • Research directions

2. docs/guides/learning_paths/research_directions

   • Current challenges
   • Open questions
   • Future developments

Related Paths

1. docs/guides/learning_paths/active_inference
2. docs/guides/learning_paths/deep_learning
3. docs/guides/learning_paths/cognitive_science

Related Documentation

• docs/guides/machine_learning
• docs/guides/research
• docs/guides/implementation/predictive_processing_implementation