Courses

The teaching offer in the Cognitive Neuroscience PhD at SISSA consists of theoretical and methodological courses. The former are taught by our Faculty, and cover advanced topics in the fields that are investigated in the group (tactile, visual and time perception, language and reading, social and food neuroscience, and neural computation). The latter are taught by our Faculty, external experts and, occasionally, postdocs in the group; and are designed to introduce the students to the tools that they may use during their PhD (coding, information theory, TMS, fMRI, eye tracking, EEG, and scientific writing).

The courses are primarily directed to 1st year students, who must pass all the theoretical exams and the coding, information theory and scientific writing courses in order to be admitted to the second year. Senior students are also more than welcome to attend any of the courses, and take part into the exams if they wish so. The TMS, fMRI, eye tracking and EEG courses will all start with a couple of introductory meetings, which are designed the illustrate the main features of the tool at hand, so that everyone can decide whether they’re interested in attending the remaining classes.

To explore the course list for 2016-2017, please use the menu on the right.

Eye Tracking Course

The 2020 eye tracking course will be taught by Elizabeth Schotter, University of South Florida, from February 10 to February 14. Meetings will happen daily, from 2pm to 4pm, and there might be some extra, hands-on axctivity. More details about the course are in the CNS Calendar page.

All the SISSA PhD students (especially those in the first year) and Trento master students are encouraged to attend.

Signal detection and Information Analyses in Neuroscience

This course provides an introduction to fundamental analyses and methods for understanding computational principles governing various aspects of information processing in the brain, including examples from single sensory neurons to ensemble of neurons, and behavioural and psychological measures.

Syllabus

Introduction to Statistical Models

How the Brain Tells Time

This course will offer an overview of the state of the art of the neuroscience of time, from theoretical models to empirical data. Particular emphasis will be given to the perception of time.

Cognitive Neuroscience - Intelligence

Lecture 1. Introduction to intelligence

Early definitions of intelligence: single factor vs multiple factors
Construct validity and construct reliability
Scientific definitions of intelligence (Francis Galton, James McKeen Cattell, Alfred Binet, Henry Goddard, Lewis Terman, Robert Yerkes)
The origin of the Intelligence Quotient (IQ) tests
The early tests: Binet and Simon’s scales, Stanford-Binet scale, the Alpha and Beta tests

Modelling of Human Cognition Data

In this course we go together over a set of human cognition data, trying to extract reliable inferences from it. If it sounds very general, that's exactly it—you can think of this course as a series of hands-on meetings on what is our everyday task as scientists, that is, transforming experimental data into solid science (which is somewhat correlated to have a publishable paper, or a solid chance to get your PhD ;-)).

Learning, Inference, and Prediction in the Brain

This course is an introduction to the brain as a predictive organ. The brain learns about its environment and infers the state of that environment in order to predict it, which will help ensure its own survival.

Arduino and Microcontrollers Course

This course is intended as an introduction to using arduino and microcontrollers.

Introduction to Electronics

Along this course you will be guided through a rational pathway touching different topics in the field of electronics. By following a theoretical approach with practical examples connected to the scientific laboratory environment, the course is open to everybody who aims to understand and take advantage of electronics as a tool for their research.

Scientific Programming

Programming is quickly becoming a necessary skill for any scientist to have, especially with regards to data collection and data analysis. This course is aimed at building a solid foundation for understanding programming, as well as developing concrete skills in the main areas of data science: collection, organization, visualization, analysis and modelling.

Scientific Writing

The aim of this course is to help improve one’s scientific writing, oral and poster presentations by introducing rules of style to communicate in a concise, clear and convincing way and rules for effective slide and poster preparation and speech delivery.

Cognitive Neuroscience