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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
Neural Coding
What is a Neural Code
Definition and significance
Information representation
Neural computation
Types of neural codes
Rate codes
Temporal codes
Population codes
Sparse codes
Coding principles
Efficiency
Robustness
Flexibility
Rate Coding
Firing Rate as Information Carrier
Spike count codes
Time window selection
Rate estimation methods
Tuning Curves
Sensory tuning
Orientation tuning
Direction tuning
Frequency tuning
Motor tuning
Movement direction
Force coding
Tuning curve shapes
Gaussian tuning
Cosine tuning
Sigmoidal tuning
Limitations of rate coding
Temporal resolution
Information loss
Metabolic costs
Temporal Coding
Spike Timing
Precise timing
Timing reliability
Temporal precision limits
Synchrony
Spike synchronization
Correlation strength
Synchrony detection
Phase-of-Firing Codes
Oscillatory phase
Phase precession
Phase coding capacity
Temporal precision
Millisecond precision
Jitter analysis
Information content
Burst Coding
Burst detection
Burst information
Tonic vs burst modes
Population Coding
Encoding by Ensembles
Distributed representation
Population responses
Ensemble dynamics
Population Vectors
Vector summation
Preferred directions
Decoding accuracy
Sparse Coding
Sparsity measures
Lifetime sparsity
Population sparsity
Dense Coding
Distributed activity
Redundant coding
Noise tolerance
Redundancy and decorrelation
Information redundancy
Noise correlations
Efficient coding
Decoding Neural Activity
Spike-Triggered Average
Reverse correlation
Receptive field mapping
Linear filters
Bayesian Decoding
Posterior probability
Prior knowledge
Likelihood functions
Linear Classifiers
Support vector machines
Linear discriminant analysis
Perceptron algorithm
Maximum likelihood decoding
ML estimation
Decoding accuracy
Confidence intervals
Neural decoding applications
Brain-machine interfaces
Prosthetic control
Cognitive state decoding
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7. Modeling Neural Networks