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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 Single Neurons
The Neuron as an Electrical Circuit
Equivalent circuit model
Membrane resistance
Membrane capacitance
Intracellular resistance
Cable Theory
The Cable Equation
Derivation and assumptions
Steady-state solutions
Transient solutions
Solutions for simple geometries
Infinite cable
Semi-infinite cable
Finite cable
Length constant
Time constant
Passive Dendritic Trees
Signal attenuation
Electrotonic length
Voltage attenuation
Dendritic filtering
Frequency response
Signal integration
Input resistance
Dendritic democracy
Simplified Neuron Models
Leaky Integrate-and-Fire Model
Model equations
Membrane equation
Threshold condition
Threshold and reset mechanism
Spike generation
Reset potential
Input-output relationships
f-I curves
Gain modulation
Quadratic Integrate-and-Fire Model
Nonlinear dynamics
Type I excitability
Bifurcation analysis
Exponential Integrate-and-Fire Model
Exponential spike initiation
Spike slope factor
Threshold dynamics
Adaptation currents
Spike-frequency adaptation
Burst firing
Izhikevich Model
Two-variable model
Rich firing patterns
Computational efficiency
Biophysically Detailed Models
The Hodgkin-Huxley Model
Model equations
Membrane current equation
Ionic current equations
Modeling Ion Channels
Sodium channels
Activation and inactivation
Voltage dependence
Potassium channels
Delayed rectifier
Voltage dependence
Leak channels
Linear conductance
Gating Variables
Activation variable m
Inactivation variable h
Potassium variable n
Voltage dependence
Time constants
Steady-state values
Voltage Clamp Experiments
Ionic current isolation
Conductance measurements
Space Clamp Experiments
Uniform voltage control
Multi-Compartment Models
Compartmental Modeling
Dividing neurons into segments
Compartment coupling
Simulating complex morphologies
Dendritic trees
Axonal arbors
NEURON simulator
Model specification
Simulation protocols
Ion Channel Kinetics
Markov models
State transitions
Rate constants
Single-channel recordings
Channel noise
Analysis of Firing Patterns
Firing Rate
Instantaneous firing rate
Time-averaged firing rate
Population firing rate
Interspike Interval Analysis
ISI distributions
Coefficient of variation
Serial correlations
Phase-Plane Analysis
Nullclines
V-nullcline
Recovery variable nullcline
Fixed points
Stability analysis
Eigenvalue analysis
Limit cycles
Oscillatory behavior
Hopf bifurcations
Spike-Frequency Adaptation
Calcium-activated potassium channels
Sodium channel inactivation
Adaptation time scales
Bursting
Intrinsic bursting
Network-induced bursting
Burst mechanisms
Tonic Firing
Regular spiking
Fast spiking
Chattering
Bifurcation analysis
Saddle-node bifurcations
Hopf bifurcations
Parameter space exploration
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5. Synaptic Plasticity and Learning