Minds, Brains and Machines

Credits
4
Types
Elective
Requirements
This subject has not requirements , but it has got previous capacities
Department
CS;UB
Web
http://postgrau.upc.edu/ai/gimaster/courses/minds-brains-and-machines-ub
Mail
ruth.dediego@ub.edu
How should intelligence be modelled? There seems to be a general agreement within the Cognitive Sciences (Psychology, Neuroscience, Artificial Intelligence) that intelligence is mostly computation. Despite this agreement, these disciplines differ on the adequate level of explanation in which computation should be characterized. Computational Neuroscience, for example, attempts to understand how brains "compute" but it emphasizes descriptions of biologically realistic neurons and their physiology. But, is this an adequate level of explanation?
The aims of the course are to discus these issues and to briefly introduce AI students in the fields of computational neuroscience, neuroscience and psychology to see how these disciplines can enrich each other.

Teachers

Person in charge

Others

Weekly hours

Theory
2.5
Problems
0
Laboratory
0
Guided learning
0.33333334
Autonomous learning
5.5

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


    essay on a topic of Computational Neuroscience

    essay on a topic of Computational Neuroscience
    Objectives: 1 2 3 4 5 6
    Week: 12
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Basic concepts of brain function

    Basic concepts of brain function
    • Theory: Basic concepts of brain function
    • Autonomous learning: Basic concepts of brain function
    Objectives: 1
    Contents:
    Theory
    4h
    Problems
    2h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    12h

    Introduction to Neuroimage Techniques in Neuroscience

    Introduction to Neuroimage Techniques in Neuroscience
    • Theory: Introduction to Neuroimage Techniques in Neuroscience
    • Autonomous learning: Introduction to Neuroimage Techniques in Neuroscience
    Objectives: 2
    Contents:
    Theory
    2h
    Problems
    1h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    6h

    Brain functions in brain networks and their connectivity

    Brain functions in brain networks and their connectivity
    • Theory: Brain functions in brain networks and their connectivity
    • Autonomous learning: Brain functions in brain networks and their connectivity
    Objectives: 1 3
    Contents:
    Theory
    2h
    Problems
    1h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    6h

    Basics of Computational Intelligence

    Basics of Computational Intelligence
    • Autonomous learning: Basics of Computational Intelligence
    Objectives: 3
    Contents:
    Theory
    6h
    Problems
    1h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    14h

    Decoding neurocognitive states with neural networks

    Decoding neurocognitive states with neural networks
    • Theory: Decoding neurocognitive states with neural networks
    • Autonomous learning: Decoding neurocognitive states with neural networks
    Objectives: 3 4
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    9h

    Reward processing and reinforcement learning

    Reward processing and reinforcement learning
    • Theory: Reward processing and reinforcement learning
    • Autonomous learning: Reward processing and reinforcement learning
    Objectives: 5
    Contents:
    Theory
    2h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    6h

    Computational Intelligence of Vision

    Computational Intelligence of Vision
    • Theory: Computational Intelligence of Vision
    • Guided learning: Computational Intelligence of Vision
    • Autonomous learning: Computational Intelligence of Vision
    Objectives: 6
    Contents:
    Theory
    6h
    Problems
    1h
    Laboratory
    0h
    Guided learning
    3h
    Autonomous learning
    11h

    Teaching methodology

    This course will build on different teaching methodology (TM) aspects, including:
    TM1: Expositive seminars
    TM2: Expositive-participative seminars
    TM3: Orientation for individual assignments (essays)
    TM4: Individual tutorization

    Evaluation methodology

    The course will be evaluated through a final essay that will take one of these three modalities:
    1. State of the art on an specific IDA-DM topic
    2. Evaluation of an IDA-DM software tool with original experiments
    3. Pure research essay, with original experimental content

    Bibliography

    Basic

    Web links

    Previous capacities

    Students are expected to have at least some basic background in the area of artificial intelligence and, more specifically, with the areas of Machine Leaning and Computational Intelligence.
    Some basic knowledge of probability theory and statistics, as well as neuroscience would be beneficial, but not essential.
    Other than this, the course is open to students and researchers of all types of background