Introduction to modelling of chemical computation in the brain

Conference presentation on computationally demanding studies of synaptic plasticity on the molecular level

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.

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

The probability of a hypothesis, given data.

Why math is useful in data science.

Why statistics are useful for data science.

Statistics is exploring data.

Graphical data exploration

Numerical data exploration

Simple description of statistical data.

Basics of hypothesis testing.