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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
Mathematical and Physical Foundations
Linear Algebra
Vectors
Vector operations
Vector spaces
Basis vectors
Matrices
Matrix representation
Matrix algebra
Matrix operations
Addition and multiplication
Transpose and inverse
Eigenvalues
Characteristic equation
Eigenvalue problems
Eigenvectors
Eigenvector computation
Principal components
Diagonalization
Similarity transformations
Jordan normal form
Singular value decomposition
SVD applications
Dimensionality reduction
Calculus
Derivatives
Partial derivatives
Chain rule
Gradient vectors
Integrals
Definite integrals
Multiple integrals
Vector calculus
Divergence and curl
Line and surface integrals
Differential Equations
Ordinary Differential Equations
First-order ODEs
Separable equations
Linear equations
Higher-order ODEs
Second-order linear equations
Systems of ODEs
Phase plane analysis
Nullclines
Fixed points
Stability analysis
Partial Differential Equations
Diffusion equation
Heat equation
Cable equation
Wave equation
Laplace equation
Numerical Integration Methods
Euler Method
Forward Euler
Backward Euler
Runge-Kutta Methods
Fourth-order Runge-Kutta
Adaptive step size
Stability and accuracy
Numerical stability
Convergence analysis
Probability and Statistics
Probability Distributions
Discrete distributions
Binomial distribution
Poisson distribution
Continuous distributions
Uniform distribution
Gaussian distribution
Exponential distribution
Bayesian Inference
Bayes' theorem
Prior distributions
Likelihood functions
Posterior distributions
Bayesian estimation
Model comparison
Stochastic Processes
Markov processes
Markov chains
Transition matrices
Random walks
Brownian motion
Diffusion processes
Poisson processes
Point processes
Renewal processes
Statistical Inference
Hypothesis testing
Confidence intervals
Maximum likelihood estimation
Information Theory
Entropy
Shannon entropy
Differential entropy
Conditional entropy
Mutual Information
Information gain
Redundancy
Information transmission in neurons
Channel capacity
Noise and information
Coding theory
Optimal codes
Error correction
Physics of Electrical Circuits
Basic Circuit Elements
Resistors
Ohm's law
Power dissipation
Capacitors
Capacitance
Energy storage
Inductors
Inductance
Magnetic energy
Batteries
Voltage sources
Current sources
Circuit Laws
Ohm's Law
Kirchhoff's Laws
Kirchhoff's current law
Kirchhoff's voltage law
Circuit Analysis
RC circuits
Charging and discharging
Time constants
RLC circuits
Resonance
Damping
AC circuit analysis
Impedance
Phase relationships
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2. Foundations in Neuroscience
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4. Modeling Single Neurons