Lectures related to Morphological Profiles

Introduces nerve cells' electrical activity, focusing on spikes and synaptic potentials.

Covers statistical analysis and visualization of morphometric data, including discrimination between tree types and numpy calculations.

Covers computational neuroscience basics, single neuron models, synapses, and neural variability.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

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

Explores neuroscience data analysis, focusing on K+ and Na+ currents, Ca²+ currents, and data reproducibility.

Covers the main concepts of neural networks, including the Perceptron model and training algorithms.

Explores the reconstruction of a full-scale mouse striatal cellular level model to integrate and interpret striatal data.