Population model (evolutionary algorithm)

Related lectures (9)

Covers tools for constraining neuron models using eFEL and BluePyOpt.

Explores heuristic optimization methods to find the global optimum efficiently.

Introduces eFEL and BluePyOpt for optimizing neuron models with experimental data, using evolutionary algorithms.

Explores bio-inspired learning with neural networks and genetic algorithms, covering structure, training, and practical applications.

Explores optimization-driven design, differentiable simulation, control strategies, and the evolution of virtual creatures and soft machines.

Explores optimization in neuron modeling, addressing underconstrained parameters, fitness functions, and successful fitting of firing patterns.

Delves into Pareto optimization techniques for generating system configurations and making decisions efficiently.

Explores the application of deep generative models in drug discovery, focusing on designing small molecules and optimizing molecular structures.

Explores genetic association study methods, challenges, and limitations in analyzing genetic data.

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