Minds, Brains and Machines

• Teachers
• Weekly hours
• Competences
• Objectives
• Contents
• Activities
• Teaching methodology
• Evaluation methodology
• Bibliography
• Previous capacities

Teachers

Person in charge

• Alfredo Vellido Alcacena ( )
• Ruth De Diego Balaguer ( )

Others

• Ignasi Cos Aguilera ( )

Weekly hours

Competences

Generic Technical Competences

Generic

• CG1 - Capability to plan, design and implement products, processes, services and facilities in all areas of Artificial Intelligence.

Technical Competences of each Specialization

Academic

• CEA3 - Capability to understand the basic operation principles of Machine Learning main techniques, and to know how to use on the environment of an intelligent system or service.
• CEA4 - Capability to understand the basic operation principles of Computational Intelligence main techniques, and to know how to use in the environment of an intelligent system or service.
• CEA8 - Capability to research in new techniques, methodologies, architectures, services or systems in the area of ??Artificial Intelligence.
• CEA11 - Capability to understand the advanced techniques of Computational Intelligence, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.

Professional

• CEP5 - Capability to design new tools and new techniques of Artificial Intelligence in professional practice.

Transversal Competences

Teamwork

• CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.

Information literacy

• CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

Appropiate attitude towards work

• CT5 - Capability to be motivated for professional development, to meet new challenges and for continuous improvement. Capability to work in situations with lack of information.

Reasoning

• CT6 - Capability to evaluate and analyze on a reasoned and critical way about situations, projects, proposals, reports and scientific-technical surveys. Capability to argue the reasons that explain or justify such situations, proposals, etc..

Analisis y sintesis

• CT7 - Capability to analyze and solve complex technical problems.

Basic

• CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
• CB8 - Capability to communicate their conclusions, and the knowledge and rationale underpinning these, to both skilled and unskilled public in a clear and unambiguous way.
• CB9 - Possession of the learning skills that enable the students to continue studying in a way that will be mainly self-directed or autonomous.

Objectives

1. Understanding some Neuroscience basics
   Related competences: CT4, CB6, CB8, CB9,
2. Understanding some Neuroimaging basics as a basis for Neuroscience
   Related competences: CT4, CT6, CT7, CB6, CB8, CB9,
3. Understanding some basics of Computational Neuroscience
   Related competences: CT4, CT5, CT6, CT7, CB6, CB8, CB9,
4. Application of Machine Learning and Computational Intelligence to Computational Neuroscience
   Related competences: CEA3, CEA4, CEP5, CT3, CT4, CT5, CT6, CT7, CB6, CB8, CB9,
5. Reward processing as a Computational Neuroscience problem
   Related competences: CT4, CT6, CT7, CB6, CB8, CB9,
6. Computational Neuroscience of vision
   Related competences: CEA8, CEA11, CG1, CT3, CT4, CB6, CB8, CB9,

Contents

1. Basic concepts of brain function
   Basic concepts of brain function
2. Introduction to Neuroimage Techniques in Neuroscience
   Introduction to Neuroimage Techniques in Neuroscience
3. Brain functions in brain networks and their connectivity
   Brain functions in brain networks and their connectivity
4. Basics of Computational Intelligence
   Basics of Computational Intelligence
5. Decoding neurocognitive states with neural networks
   Decoding neurocognitive states with neural networks
6. Reward processing and reinforcement learning
   Reward processing and reinforcement learning
7. Computational Intelligence of Vision
   Computational Intelligence of Vision

Activities

Activity Evaluation act

Teaching methodology

Evaluation methodology

Bibliography

Basic:

• The computational brain - Churchland, P.S.; Sejnowski, T.J, The MIT Press, 1992. ISBN: 9780262531207
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991000733519706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Theoretical neuroscience: computational and mathematical modeling of neural systems - Dayan, P.; Abbott. L.F, The MIT Press, 2001. ISBN: 0262041995
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991002427149706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Handbook of functional neuroimaging of cognition - Cabeza, R.; Kingstone, A. (eds.), The MIT Press, 2005. ISBN: 0262033445
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991004001319706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Computational maps in the visual cortex - Miikkulainen, R. [et al.], Springer, 2005. ISBN: 9780387220246
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003184099706711&context=L&vid=34CSUC_UPC:VU1&lang=ca

Previous capacities