cognitive/docs/guides/learning_paths/active_inference_neuroscience_learning_path.md

title	type	status	created	complexity	processing_priority	tags	semantic_relations
Active Inference in Neuroscience Learning Path	learning_path	stable	2024-03-15	advanced	1	active-inference neuroscience brain-dynamics cognitive-neuroscience	type links specializes active_inference_learning_path type links relates neural_dynamics_learning_path cognitive_neuroscience_learning_path brain_imaging_learning_path

Active Inference in Neuroscience Learning Path

Overview

This specialized path focuses on applying Active Inference to understand brain function, neural dynamics, and cognitive processes. It integrates neuroscientific theory with computational modeling.

Prerequisites

1. Neuroscience Foundations (4 weeks)

• Neural Systems

  • Neuroanatomy
  • Neural circuits
  • Synaptic transmission
  • Brain networks

• Brain Dynamics

  • Neural oscillations
  • Population dynamics
  • Network synchronization
  • Neuroplasticity

• Cognitive Neuroscience

  • Perception
  • Action
  • Learning
  • Memory

• Research Methods

  • Brain imaging
  • Electrophysiology
  • Data analysis
  • Experimental design

2. Technical Skills (2 weeks)

• Computational Tools
  • Python/MATLAB
  • Neural data analysis
  • Statistical methods
  • Visualization

Core Learning Path

1. Neural Implementations (4 weeks)

Week 1-2: Neural Message Passing

class NeuralMessagePassing:
    def __init__(self,
                 n_regions: int,
                 n_features: int):
        """Initialize neural message passing network."""
        self.regions = nn.ModuleList([
            BrainRegion(n_features) for _ in range(n_regions)
        ])
        self.connections = self._initialize_connections()
        
    def forward(self, 
                sensory_input: torch.Tensor) -> Dict[str, torch.Tensor]:
        """Propagate predictions and errors through network."""
        predictions = {}
        errors = {}
        
        # Bottom-up pass
        for region in self.regions:
            pred = region.generate_prediction()
            error = region.compute_error(sensory_input)
            predictions[region.name] = pred
            errors[region.name] = error
            
        return {'predictions': predictions, 'errors': errors}

Week 3-4: Neural Dynamics

class NeuralDynamics:
    def __init__(self,
                 connectivity: torch.Tensor,
                 time_constants: torch.Tensor):
        """Initialize neural dynamics model."""
        self.connectivity = connectivity
        self.tau = time_constants
        self.state = torch.zeros(connectivity.shape[0])
        
    def update(self,
              input_current: torch.Tensor,
              dt: float = 0.001) -> torch.Tensor:
        """Update neural state."""
        dxdt = (-self.state + self.connectivity @ self.state + input_current) / self.tau
        self.state += dt * dxdt
        return self.state

2. Brain Systems (6 weeks)

Week 1-2: Sensory Systems

• Visual Processing
• Auditory Processing
• Somatosensory Processing
• Multisensory Integration

Week 3-4: Motor Systems

• Motor Planning
• Action Selection
• Movement Control
• Sensorimotor Integration

Week 5-6: Cognitive Systems

• Working Memory
• Decision Making
• Learning and Plasticity
• Executive Control

3. Clinical Applications (4 weeks)

Week 1-2: Neurological Disorders

class DisorderModel:
    def __init__(self,
                 disorder_params: Dict[str, float]):
        """Initialize disorder model."""
        self.params = disorder_params
        self.baseline = self._establish_baseline()
        
    def simulate_pathology(self,
                         brain_state: torch.Tensor) -> torch.Tensor:
        """Simulate disorder effects on brain state."""
        affected_state = self.apply_disorder_effects(brain_state)
        return affected_state

Week 3-4: Therapeutic Interventions

• Treatment Design
• Intervention Modeling
• Outcome Prediction
• Personalized Medicine

4. Research Methods (4 weeks)

Week 1-2: Experimental Design

class NeuroimagingExperiment:
    def __init__(self,
                 paradigm: str,
                 conditions: List[str]):
        """Initialize neuroimaging experiment."""
        self.paradigm = paradigm
        self.conditions = conditions
        self.design_matrix = self._create_design_matrix()
        
    def run_experiment(self,
                      subject: Subject) -> Dict[str, np.ndarray]:
        """Run experimental paradigm."""
        data = {}
        for condition in self.conditions:
            response = self.present_stimulus(subject, condition)
            data[condition] = self.record_brain_activity(response)
        return data

Week 3-4: Data Analysis

• Neural Data Processing
• Statistical Analysis
• Model Comparison
• Results Interpretation

Projects

Clinical Projects

1. Disorder Modeling

   • Schizophrenia
   • Parkinson's Disease
   • Depression
   • Anxiety

2. Treatment Optimization

   • Drug Effects
   • Brain Stimulation
   • Behavioral Interventions
   • Personalized Medicine

Research Projects

1. Neural Mechanisms

   • Perception Studies
   • Action Understanding
   • Learning Experiments
   • Decision Making

2. Clinical Applications

   • Biomarker Development
   • Treatment Response
   • Disease Progression
   • Intervention Design

Assessment

Knowledge Assessment

1. Theoretical Understanding

   • Neural Mechanisms
   • Clinical Applications
   • Research Methods
   • Data Analysis

2. Practical Skills

   • Experimental Design
   • Data Collection
   • Analysis Methods
   • Result Interpretation

Final Projects

1. Research Project

   • Experimental Design
   • Data Collection
   • Analysis
   • Publication

2. Clinical Application

   • Patient Assessment
   • Treatment Design
   • Outcome Prediction
   • Validation Study

Resources

Scientific Resources

1. Research Papers

   • Foundational Papers
   • Clinical Studies
   • Methods Papers
   • Reviews

2. Books

   • Neuroscience
   • Clinical Applications
   • Research Methods
   • Data Analysis

Technical Resources

1. Software Tools

   • Analysis Packages
   • Imaging Tools
   • Statistical Software
   • Visualization Tools

2. Data Resources

   • Brain Databases
   • Clinical Data
   • Reference Datasets
   • Analysis Pipelines

Next Steps

Advanced Topics

1. computational_psychiatry_learning_path
2. brain_imaging_learning_path
3. neural_dynamics_learning_path

Research Directions

1. research_guides/computational_neuroscience
2. research_guides/clinical_neuroscience
3. research_guides/cognitive_neuroscience