---
title: Active Inference in Neuroscience Learning Path
type: learning_path
status: stable
created: 2024-03-15
complexity: advanced
processing_priority: 1
tags:
  - active-inference
  - neuroscience
  - brain-dynamics
  - cognitive-neuroscience
semantic_relations:
  - type: specializes
    links: [[active_inference_learning_path]]
  - type: relates
    links:
      - [[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
```python
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
```python
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
```python
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
```python
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|Computational Psychiatry]]
2. [[brain_imaging_learning_path|Brain Imaging]]
3. [[neural_dynamics_learning_path|Neural Dynamics]]

### Research Directions
1. [[research_guides/computational_neuroscience|Computational Neuroscience]]
2. [[research_guides/clinical_neuroscience|Clinical Neuroscience]]
3. [[research_guides/cognitive_neuroscience|Cognitive Neuroscience]]