Symplectic integrator

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Covers the fundamentals of computational neuroscience, focusing on biophysics and modeling.

Explores molecular dynamics, integrating Newton's equations to make atoms move according to interatomic forces.

Covers the basics of numerical analysis and computational methods using Python, focusing on algorithms and practical applications in mathematics.

Explores scientific computing in neuroscience, emphasizing the simulation of neurons and networks using tools like NEURON, NEST, and BRIAN.

Covers Monte Carlo simulations, ensemble properties, and numerical integration techniques.

Explores classical molecular dynamics simulations, integration algorithms, and trajectory accuracy.

Explores the importance of step size in determining the finesse of partitions.

Discusses accuracy and stability in computational algorithms, focusing on the Verlet and Gear algorithms and comparing their performance.

Explores Poincaré sections, chaos, and strange attractors in nonlinear physics.

Explains centroids of plane areas and normal stresses in beams.