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.

Scientific programming: A crash course

Basic coding skills are essential in science, but, unfortunately, many students do not receive formal training in programming and are often left to figure things out for themselves. The aim of this short crash course is cover enough of the core principles that you can feel confident in continuing your coding journey through self-directed learning and discovery. By the end of the course, you should be able to script a basic experiment, handle data, and plot graphs.

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

Cognitive and non-cognitive factors influencing academic and life outcomes, and normal and pathological aging and the role of intelligence

Raffaella I. Rumiati, SISSA office 341,,

The course will be held always online and sometimes also in presence (SISSA, room 139)

Data Modelling for Human Cognition Research

In this course we will go together over a set of data from a fairly standard behavioural experiment in human cognition research, trying to extract reliable inferences from it. In doing this, we'll go over a number of basic and not-so-basic statistical issues, which includes graphical data exploration, data manipulation (e.g., standardisation, centering), regression, mixed-effect modelling, predictor collinearity, non linearity, and (perhaps) bootstrap

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.

Introduction to Systems and Computational Neuroscience: Evolution of Neural Computation

The course delineates the evolution of the vertebrate nervous systems, with a particular focus on mammals and among them on the human lineage.

Introduction to Systems and Computational Neuroscience: Tactile Perception

This course focuses on the basic principles of organization of the sensory pathways and their target regions of cerebral cortex.

Introduction to Systems and Computational Neuroscience: Visual Perception

The course focuses on the structure and functions of the mammalian visual systems, with a special emphasis on shape processing and object recognition.

Language, Reading and the Brain

This course offers an introduction to how the brain deals with language and reading. We will focus particularly on questions like -- is the relationship between sounds/letters and meaning arbitrary? What kind of information, then, is encoded in the language signal? How does the brain extract this information?