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Biology
Neurobiology/Neuroscience
Computational Neuroscience
1. Introduction to Computational Neuroscience
2. Foundations in Neuroscience
3. Mathematical and Physical Foundations
4. Modeling Single Neurons
5. Synaptic Plasticity and Learning
6. Neural Coding
7. Modeling Neural Networks
8. Models of Learning and Memory
9. Models of Sensory and Motor Systems
10. Models of Higher Cognitive Functions
11. Tools and Techniques
Models of Higher Cognitive Functions
Decision Making
Signal Detection Theory
Sensitivity and bias
d-prime measures
Criterion setting
ROC curves
Neural implementations
Drift-Diffusion Model
Evidence accumulation
Drift rate
Decision boundaries
Reaction time distributions
Neural correlates
Parietal cortex
Frontal cortex
Multi-alternative decisions
Race models
Leaky competing accumulator
Urgency signals
Value-based decisions
Economic choice
Neuroeconomics
Utility functions
Attention
Biased Competition Model
Competitive interactions
Top-down biasing
Bottom-up salience
Saliency Maps
Feature integration
Winner-take-all dynamics
Spatial attention
Neural mechanisms
Attentional modulation
Gamma synchronization
Frontoparietal networks
Attention and consciousness
Global workspace theory
Integrated information theory
Access consciousness
Working Memory
Persistent Activity Models
Prefrontal cortex
Delay period activity
Attractor dynamics
Network mechanisms
Recurrent excitation
Balanced inhibition
Capacity and limitations
Interference models
Resource models
Discrete slot models
Maintenance mechanisms
Synaptic facilitation
Calcium dynamics
Network oscillations
Executive Control
Cognitive control
Conflict monitoring
Error detection
Performance adjustment
Inhibitory control
Response inhibition
Interference resolution
Stop-signal tasks
Task switching
Set shifting
Switch costs
Cognitive flexibility
The Bayesian Brain Hypothesis
Perception as Inference
Probabilistic inference
Likelihood and priors
Posterior computation
Predictive Coding
Prediction errors
Hierarchical processing
Free energy principle
Hierarchical Bayesian models
Multi-level inference
Empirical Bayes
Neural implementations
Uncertainty representation
Probabilistic population codes
Sampling-based codes
Distributional codes
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11. Tools and Techniques