Modules

Each Module includes:

1. Theory and hands-on learning: up to 3 hours of digital video lessons, divided into sub-sections, accessible offline at any time.
2. Insight learning: an optional 1-hour online session, to deepen the studied materials; occasionally, in-person sessions can also be organised, in coincidence with events and initiatives promoted by ETHICS.
3. Attendance test: an assessment based on an oral test organised by the Module Instructor, which students can choose to take to earn an official Certificate. Each Certificate provides them with the opportunity to prove their attendance and involvement in one education Module. Each Module is equivalent to 0.5 ECTS (4h of digital learning + 8.5h of individual study).

Module Title	Module Contents
Electrophysiological feature extraction Principal Investigator: Rosanna Migliore Instructor: Luca Leonardo Bologna Affiliation: CNR- Institute of Biophysics- IBFC	The Module focuses on understanding the meaning of the most common features of electrophysiological signals and learning the NeuroFeatureExtract feature extraction process. It is composed of four videos:  – Explanation and role of electrophysiological features and feature extraction in neuroscience – The Electrophys Feature Extraction Library (eFEL) for feature extraction in Python  – How to use the EBRAINS NeuroFeatureExtract (NFE) web application for online feature extraction – Exercises on feature extraction through the EBRAINS NFE
Single cell data-driven model building Principal Investigator: Michele Migliore Instructor: Carmen Alina Lupascu (theory) Instructor: Paola Vitale (hands-on) Affiliation: CNR- Institute of Biophysics- IBFC	This Module focuses on the construction and optimisation of biologically realistic single-cell models, utilising state-of-the-art online resources, tools, and services. Participants will learn how to access and integrate these resources through EBRAINS interactive workflows for cellular-level modeling and the Hippocampus Hub. Through theoretical lessons and hands-on exercises, the Module will guide participants in building, refining, and optimising single-cell models, ensuring accuracy and biological relevance in neural simulations.
Computational Neuropsychology of higher-order conscious cognition: neuro-inspired computational models of healthy, neurological, and psychiatric conditions Principal Investigator: Gianluca Baldassarre Instructor: Giovanni Granato Affiliation: CNR-Institute of Cognitive Science and Technologies- ISTC	The Module will provide a brief introduction to the computational neuropsychology of higher-order conscious cognition, with a specific focus on executive functions and the Wisconsin Card Sorting Test (WCST), a gold standard test for measuring cognitive flexibility. Then, it will introduce a neuro-inspired computational model, in particular, its architecture and submodules (e.g., discriminative and generative processes of visual cortical hierarchies, attention and selection processes supported by higher-order cortices and basal ganglia, and working memory supported by prefrontal cortices). Subsequently, the Module will introduce the model code and a graphical user interface (GUI), which supports the simulation/fitting of different groups of healthy human participants and patients (brain lesions, neurodegenerative disorders, etc.). The Module will finally introduce hands-on activities (data fitting, virtual lesion/virtual therapy approaches), at the end of which the student will produce a short scientific report (in case of promising results, a paper draft could follow the completion of the module). Overall, the theoretical content and practical activities will illustrate model-based methods for assessing higher-order cognitive differences in human health and clinical conditions.
Realistic modelling of brain microcircuits  Principal Investigators: Egidio D’Angelo and Claudia Casellato Instructor: Dimitri Rodarie   Affiliation: Dept. of Brain and Behavioral Sciences – University of Pavia	This Module provides a comprehensive introduction to the reconstruction, simulation, and validation of brain microcircuit models. Participants will learn key concepts, tools, and workflows essential for building biologically realistic neural circuits. The course will cover: – The Brain Scaffold Builder (BSB): Theory and workflow – Data Integration: Importing data from brain atlases – Microcircuit Construction: Cell placement and connectivity – Simulation Techniques: Running simulations with NEURON and NEST – Practical Application: A case study using the cerebellar cortex microcircuit – Model Validation: Principles and methodologies for assessing model accuracy Through theoretical insights and hands-on exercises, participants will develop the skills needed to reconstruct and analyse brain microcircuits using state-of-the-art computational tools.
Advancing continual learning for robotic applications Principal Investigator: Egidio Falotico Instructor: Nilay Kushawaha Affiliation: SSSA – Sant’Anna school of advanced studies	Humans excel at lifelong learning, adapting to ever-changing environments. However, for deep learning algorithms, learning sequentially or from a non-stationary data stream without forgetting previously acquired knowledge (catastrophic forgetting) remains a significant challenge. In this Module, we will explore the concept of continual learning, which is essential for developing autonomous agents capable of learning in an open-ended and progressive manner. We will cover strategies for exploring the environment, acquiring new knowledge, and retaining previously learned information. Additionally, we will introduce and analyse some of the most advanced state-of-the-art algorithms for continual learning.
Spiking Neural Network 101 Principal Investigator: Alessandra Pedrocchi  Instructor: Alberto Antonietti Affiliation: POLIMI-Politecnico Milano	The Module introduces participants to spiking neural networks (SNNs) as a tool for developing bioinspired models of brain circuits. Students will explore different single-neuron models, ranging in complexity from: Integrate-and-fire models Current-based and conductance-based models Hodgkin-Huxley models (Extended) Generalized integrate-and-fire models Then, students will learn how to choose between different synapse models, explaining how short-term plasticity and long-term plasticity can be included.