Cajal Course in Computational Neuroscience

10 parts

The CAJAL computational neuroscience courses are held at the Champalimaud Centre for the Unknown, Lisbon, Portugal. They are part of the CAJAL Advanced Neuroscience Training Programme, which offers state-of-the-art hands-on training courses in neuroscience.

This three-weeks school teaches the central ideas, methods, and practice of modern computational neuroscience through a combination of lectures and hands-on project work. Each morning is devoted to lectures given by distinguished international faculty on topics across the breadth of experimental and computational neuroscience. During the rest of the day, students work on research projects in teams of 2-3 people under the close supervision of expert tutors and faculty. Research projects will be proposed by faculty before the course, and will include the modeling of neurons, neural systems, and behavior, the analysis of state-of-the-art neural data (behavioral data, multi-electrode recordings, calcium imaging data, connectomics data, etc.), and the development of theories to explain experimental observations.

The course is designed for graduate students and postdoctoral fellows from a variety of disciplines, including neuroscience, physics, electrical engineering, computer science, mathematics, and psychology. Students are expected to have a keen interest and basic background in neurobiology, a solid foundation in mathematics, as well as some computer experience."

Models and theory

Introduction to the role of models in theoretical neuroscience. Speaker: Jakob Macke

Biophysical models of single neurons

Different types of models, model complexity, and how to choose an appropriate model. Speaker: Astrid Prinz.

Tightly and loosely coupled networks

Balanced E-I networks, stability and gain modulation. Speaker: Kenneth Miller.

Dimensionality reduction of large-scale neural recordings

Methods for dimensionality reduction of data, with focus on factor analysis. Speaker: Byron Yu.

Dynamics of rate-based and spiking balanced random networks

Rate and spiking Bayes populations. What are the mathematical techniques we can use to understand networks of neurons? Speaker: Julijana Gjorgjieva.

Theory of network dynamics

Spiking neuron networks and linear response models. Speaker: Tatjana Tchumatchenko.

Neural data analysis: The Bayesics

Bayesian neuron models and parameter estimation. Speaker: Jakob Macke.

Bayesian models of perception, cognition and learning

Bayesian memory and learning, how to go from observations to latent variables. Speaker: Máté Lengyel.

Constraints on information processing

Constraints can help us understand how the brain works. Speaker: Simon Laughlin.

Evolution and Brain Computation

Approaching neural systems from an evolutionary perspective. Speaker: Gilles Laurent