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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
Modeling Neural Networks
Network Architectures
Feedforward Networks
Layered structure
Input layers
Hidden layers
Output layers
Input-output mapping
Function approximation
Pattern recognition
Deep networks
Hierarchical processing
Feature extraction
Recurrent Networks
Feedback connections
Local recurrence
Global recurrence
Memory and dynamics
Working memory
Temporal processing
Reservoir computing
Echo state networks
Liquid state machines
Small-World Networks
Clustering coefficient
Path length
Small-world index
Biological relevance
Scale-Free Networks
Degree distribution
Power-law scaling
Hub nodes
Preferential attachment
Network robustness
Modular Networks
Community structure
Modularity measures
Hierarchical organization
Firing-Rate Models
Input-Output Transfer Functions
Linear transfer functions
Nonlinear transfer functions
Sigmoid functions
Threshold functions
Rectified linear functions
Linear Networks
Matrix formulation
Eigenvalue analysis
Stability conditions
Nonlinear Networks
Fixed point analysis
Attractor dynamics
Chaos and complexity
Stability analysis
Lyapunov stability
Linear stability analysis
Bifurcation theory
Wilson-Cowan Model
Excitatory-inhibitory dynamics
Population equations
Oscillatory behavior
Spiking Network Models
Integrate-and-Fire Networks
Network connectivity
Synaptic interactions
Collective dynamics
Synaptic connectivity
Random connectivity
Structured connectivity
Plastic connectivity
Balanced Excitation and Inhibition
Balance conditions
Asynchronous irregular states
Critical dynamics
Network simulations
Event-driven simulation
Time-driven simulation
Parallel computing
Liquid State Machines
Reservoir dynamics
Readout mechanisms
Computational capacity
Network Dynamics
Oscillations and Rhythms
Gamma oscillations
Fast gamma
Slow gamma
Gamma generation mechanisms
Theta oscillations
Hippocampal theta
Theta-gamma coupling
Beta oscillations
Motor beta
Cognitive beta
Alpha oscillations
Sensory alpha
Attention and alpha
Delta oscillations
Sleep delta
Cortical slow waves
Synchrony
Phase synchronization
Lag synchronization
Generalized synchronization
Phase Locking
Phase-locking value
Cross-frequency coupling
Phase-amplitude coupling
Attractor Dynamics
Point Attractors
Memory storage
Pattern completion
Hopfield networks
Line Attractors
Head direction systems
Eye position control
Continuous attractors
Ring Attractors
Orientation tuning
Spatial navigation
Bump attractors
Strange Attractors
Chaotic attractors
Fractal dimensions
Chaotic Dynamics
Sensitivity to initial conditions
Lyapunov exponents
Butterfly effect
Implications for computation
Edge of chaos
Computational benefits
Information processing
Criticality
Self-organized criticality
Neuronal avalanches
Scale-free dynamics
Critical brain hypothesis
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8. Models of Learning and Memory