• 052371 - NEUROENGINEERING [2]

The goal of Neuroengineering [2] is the study of neuroengineering methods and technologies, adopting a transdisciplinary approach. Four specific topics will be addressed: computational neuroscience , rehabilitation robotics, neuroprostheses and optical and electronics technologies for the reading and stimulation of in-vitro neuronal cultures.

For each of the four topics of the Neuroengineering [2] the students are expected to achieve:

- Knowledge and understanding with a crossdisciplinary approach the major assumption for the design, the definition of user requirements, the translation of requirements to technical specifications, the process of proper validation metrics. (lectures)- DD 1

- Implement simple projects tailored to specific research or clinical scenarios, starting from a proper understanding of current state of the art (scientific literature). (lectures and discussion teaching workshops)- DD 2

- Making judgement on the assumptions, their validations, the achieved accuracy, their complexity and their feasibility in different scenarios of applications. (lectures and discussion teaching workshops) – DD 3,5

- Being able to effectively work in group, and communicate by public speaking. (discussion teaching workshops)- DD 4,5

- COMPUTATIONAL NEUROSCIENCE: the design of bioinspired computational models of neurons and of neuronal networks how they are designed and validated against physiopathological data. Brain microcircuit simulations to achieve a better understanding of the physiology and physiopathology of the brain and simulate its information processing functionalities. The example of computational models of the cerebellum will be discussed in details.

- REHABILITATION ROBOTICS: understanding the reason why robots are increasing their importance in the rehabilitation field. Learn the requirements and specification of robot design tailored to rehabilitation applications and study their control strategies. Given some characteristics of the patients (pathology, level of impairment) and the treatment (e.g. home or clinics), the student should be able to design the main requirements of the robot to be used, select the most suitable control strategy, the sensors to be embedded and the type of actuation.

- NEUROPROSTHESES: learn what neuroprostheses are, the rationale of using them for patients affected by neurological diseases. Learn the current technologies and their limits. Given some characteristics of the patients and the treatment, the student should be able to design the main requirements of the NP to be used, select the most suitable control strategy and configuration.

- NEUROENGINEERING FOR BIOLOGY AND PHARMACOLOGY: the students learn the most used optical and electrical solutions for interfacing in vitro neuronal cultures, both aiming at reading electrical activity and stimulating it. Signal processing methods to detect neuronal activity from extracellular electrical recording. Given a neuronal mechanism to be studied, the students should be able to select the best method/technology to be used, justifying the selection.

The course is organized as follows:

• Phase 1: The professor of Neuroengineering [1] will provide frontal classes and practical lessons to introduce the students to the contents of its part (about 35-40 Hours)
• Phase 2: The professor of Neuroengineering [2] will provide frontal classes and seminars to introduce the students to the contents of its part. Step-by-step evaluations are additionally proposed (optional for students), their average can substitute the written exam of this part. (about 35-40 Hours)

• Phase 3: Discussion teaching workshops(about 30 Hours) INNOVATIVE TEACHING CREDIT

All the students are organized into small groups (about 8 students), led by a tutor (usually a PhD student or a Post-doc). The two professors propose an even number of topics. Each students choses a topic (or if necessary, is assigned to a topic to assure roughly uniform numbers on each topic). All the students on one topic form a team (about 8-10 students), led by a tutor (usually a PhD student or a Post-doc).

Each team deeply studies the chosen research topic, starting from a couple of scientific papers proposed by the tutor.

Meeting 1: the team, facilitated by the tutor, discusses the state of the art and defines the goal of a project to improve what presented in the literature.

Meeting 2: Tailored on the goal of the project proposed by the first meeting, the tutor prepares a lab experience, in order to provide the group of the best available practical knowledge to solve the goal.

Autonomous team work:

Each group is then divided by the professor into subgroups of about 3 students, which from now on compete in a challenge on the same topic. All subgroups work independently to implement a proposed solution of the goal. Each subgroup can meet twice with the tutor.

Final presentation:

In the final week of the course, all subgroups present their solution and a board composed by all tutors and professors evaluated the proposed solutions and give a mark to each subgroup, considering multiple aspects such as: originality of the idea, feasibility, communication skills. Prizes will be awarded.

If there are exceptional sensible reasons not to participate to the discussion teaching workshop, custom solutions will be defined in order to achieve the learning outcomes.

Basic knowledge of neurophysiology, control systems and programming

A written test is used to assess the acquired knowledge of the topics, the ability to analyse use cases scenarios, design proper solutions including assumptions, requirements, specifications and validations methods. The written test of Neuroengineering [2] can be substituted by the average evaluation of the step-by-step tests done in Phase 2 (see above).

The final score is composed by the average of the three evaluations: the two written tests of  Neuroengineering [1] and [2] (this latter can be substituted by the step-by-step tests) and The Discussion teaching workshops (each weights 1/3 of the final score).

If there are exceptional reasons not to participate to the discussion teaching workshop, custom solutions will be defined in order to verify the achievement of specific objectives.

Slides of the lectures, with notes Supporting material (chapters of books; scientific reviews; papers) provided on the Beep platform by the teachers Example of assessments. Matlab codes for laboratories activities.