Identifying Data 2022/23
Subject (*) Biological and Computational Models of Knowledge Representation Code 610490017
Study programme
Mestrado Universitario en Neurociencia (Plan 2011)
Descriptors Cycle Period Year Type Credits
Official Master's Degree 2nd four-month period
 First Optional 3
Language
Spanish
Teaching method Face-to-face
Prerequisites
Department Ciencias da Computación e Tecnoloxías da Información
Computación
Coordinador
Pazos Sierra, Alejandro
 E-mail
alejandro.pazos@udc.es
Lecturers
Pazos Sierra, Alejandro
 E-mail
alejandro.pazos@udc.es
Web http://www.usc.gal/es/estudios/masteres/ciencias-salud/master-universitario-neurociencia
General description Dar a coñecer aos alumnos algunhas das técnicas de representación do coñecemento en Sistemas Intelixentes. Por outra parte, ver un exemplo de representación do coñecemento distribuído compatible e baseado con algún sistema biolóxico para a representación do coñecemento.

Study programme competencies
Code Study programme competences

Learning aims
Learning outcomes Study programme competences
To study the fundamental process of modeling an adaptive system
To study the fundamental process of modeling an adaptive system
To understand the characteristics of natural knowledge and its representation and to know the mode of reasoning of the adaptive systems and of the different methods for their learning
To understand the characteristics of natural knowledge and its representation and to know the mode of reasoning of the adaptive systems and of the different methods for their learning
Understand the neurobiological basis on which adaptive systems are based, from which they derive their structure and functionalities
Understand the neurobiological basis on which adaptive systems are based, from which they derive their structure and functionalities
Understand the neurobiological basis on which adaptive systems are based, from which they derive their structure and functionalities
Understand the neurobiological basis on which adaptive systems are based, from which they derive their structure and functionalities
To understand the characteristics of natural knowledge and its representation and to know the mode of reasoning of the adaptive systems and of the different methods for their learning
To understand the characteristics of natural knowledge and its representation and to know the mode of reasoning of the adaptive systems and of the different methods for their learning
To study the fundamental process of modeling an adaptive system
To study the fundamental process of modeling an adaptive system

Contents
Topic Sub-topic
1. HISTORICAL AND BASIC CONCEPTS OF ADAPTATIVE SYSTEMS 1.1 Evolución histórica e precursores.
1.2 Nacemento.
2. MODELOS 2.1 Proceso de Modelización.
2.2 Comparación entre o elemento biolóxico e o formal.
3. O COÑECEMENTO NATURAL E A SÚA REPRESENTACIÓN. 3.1 Características do coñecemento do mundo real.
3.2 Formas de representación do coñecemento.
4. RAZOAMENTO E APRENDIZAXE. 4.1 Modos de Razoamento.
4.2 Tipos de Aprendizaxe.
5. METODOLOXÍA EN SISTEMAS ADAPTATIVOS 5.1 Introducción.
5.2 Etapas da Metodoloxía.
6. APLICACIONS BÁSICAS DOS SISTEMAS CONEXIONISTAS 6.1 Consideracións previas.
6.2 Aplicacións.

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Guest lecture / keynote speech 10 20 30
Collaborative learning 10 10 20
Supervised projects 5 20 25
 
Personalized attention 0 0
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Guest lecture / keynote speech Content of the subject
Collaborative learning Comments on scientific articles and practical exercises
Supervised projects Carrying out a paper on one of the themes of the subject

Personalized attention
Methodologies
Collaborative learning
Supervised projects
Description
Atencíon nas horas de tutoría para guiar a elaboración dos traballos en grupo.

Assessment
Methodologies Competencies Description Qualification
Collaborative learning Debates and comments in class about the contents of theory 20
Guest lecture / keynote speech Assessment by examination of short or development questions 50
Supervised projects Works to increase knowledge about the contents of the subject 30
 
Assessment comments

Sources of information
Basic

Arbib M.A.: "Cerebros, Máquinas y Matemáticas". Ed. Alianza Universidad. Madrid. 1987.

Arbib, M.A.: “The handbook of brain theory and neural networks”. Cambridge, Massachusetts. MIT Press. 1995.

Grossberg, S.: "Neural Networks and Natural Inteligence". Editor: MIT Press, 1988.

Hertz, J., Krogh, A. & Palmer, R.: "Introduction to the Theory of Neural Computation". Santa Fe Institute, Addison-Wesley Editores 1991.

Hinton, G.E.: “How Neural Networks Learn from Experience”. Scientific American, 267, 144-151. 1992.

McCulloch, W. S., and Pitts, W.: "A Logical Calculus of the Ideas Inmanent in the Neural Nets". Buletin of Mathematical Biophysics, vol. 5, pp. 115-137. 1943.

McCulloch, W.S., Arbib, M.A. & Cowan, J.D. "Neurological Models and Integrative Processes". In Yacovits, Jacobi and Goldstein. Ed. Selft-Organizing Systems.Spartan bocks. Washington. 1969.

Minsky, M. & Papert, S.: "Perceptrons". Cambridge, MIT Press. 1988.

Ramón y Cajal, S.: "Textura del Sistema Nervioso del Hombre y los Vertebrados". tomo I. Ed. Alianza. 1989.

Rosenblueth, A., Wiener, N, and Bigelow, J.: "Behavior, Purpose and Teleology". Phylosophy of Science nº10, pp. 18-24. 1943.

Rumelhart, D.E., Widrow, B. & Lehr, M. A.: "The basic ideas in neural networks". Comm. ACM. Num 37. pp 87-92. 1994.
Complementary


Recommendations
Subjects that it is recommended to have taken before

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus

Other comments


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.