Introduction to stability analysis of neural models
Oscillations and bursting
Weakly coupled oscillators
Continuation of coupled oscillators
Firing rate models.
Pattern generation in visual system hallucinations.
Introduction to the role of models in theoretical neuroscience
Different types of models, model complexity, and how to choose an appropriate model.
Balanced E-I networks, stability and gain modulation
Methods for dimensionality reduction of data, with focus on factor analysis.
Spiking neuron networks and linear response models.
Bayesian neuron models and parameter estimation.
Bayesian memory and learning, how to go from observations to latent variables.
Constraints can help us understand how the brain works.
Approaching neural systems from an evolutionary perspective
This lecture describes non-spiking simple neuron models used in artificial neural networks and machine learning.
This lecture focuses on where and how Jupyter notebooks can be used most effectively for education