B8836 - Universal Semantics (1) (LM)

Learning outcomes

At the end of the course, the student knows and can apply abstraction methods to interpret any kind of signal from a physical, social or cognitive environment, either inductively (pattern discovery) or deductively (pattern finding). The methods will leverage theoretical or experimental findings from philosophy, cognitive science, semiotics, and computer science.

Course contents

The course covers fundamental principles and techniques of biologically constrained neurocomputational models of cognitive functions. The initial part of the course provides a brief review and introduction to general concepts and basic methods of brain-inspired computational modelling.

The core of the module focusses on theoretical aspects, motivations, and methods of physiologically and anatomically constrained computational modelling of cognitive and higher-order brain functions, dwelling on current open issues and recent developments in the field. Existing architectures modelling specific higher-order brain functions are looked at, including:

• perception;
• attention;
• working memory;
• semantic and episodic memory;
• decision making;
• language and reasoning.
  

On the basis of these examples, the conceptual building blocks and methodologies of biologically constrained computational modelling in cognitive neuroscience are explained, while general strengths and limitations identified.

Readings/Bibliography

• Randall C. O’Reilly & Yuko Munakata (c2024) Computational Explorations in Cognitive Neuroscience. The MIT Press, Cambridge (MA), London (England). 5^th Edition.
  
  
• Britt Anderson (2014) Computational Neuroscience and Cognitive Modelling – A student’s introduction to methods and procedures. SAGE Publications Inc., London, England.

Teaching methods

The course consists of two face-to-face lectures a week (2 hours each), complemented by weekly hands-on laboratory sessions (2 hours).

Assessment methods

The assessment will be based on establishing whether, and to which extent, students are able to:

1. Explain the principles and motivations underlying brain-inspired computational modelling of cognitive functions and critically identify strengths and weaknesses of different computational modelling approaches to experimental cognitive neuroscience;
2. Identify a suitable level of modelling abstraction for a given cognitive neuroscience research question;
3. Critically evaluate a given modelling approach or computational architecture (identify key underlying assumptions, unsupported claims, strengths and potential improvements);
4. Describe and critically assess the key computational mechanisms and assumptions underlying some of the existing architectures simulating specific higher-order brain functions (e.g., vision, attention, memory, language, etc.);
5. Discuss open issues in the field of brain-inspired cognitive modelling and possible approaches to tackle them in practice.

 

Students with disabilities and Specific Learning Disorders (SLD)

Students with disabilities or SLD are entitled to special accommodations according to their condition, following evaluation by the University Service for Students with Disabilities and SLD.

Please do not contact the course coordinator directly, but get in touch with the Service to schedule an appointment. The Service will determine which accommodations - if any - are appropriate.

More information available at:

https://site.unibo.it/studenti-con-disabilita-e-dsa/it/per-studenti

Office hours

See the website of Massimiliano Garagnani