Advanced topics in Cognitive Neuroscience

Recommended Prerequisites

Portuguese and English.

Teaching Methods

Theoretical classes will be based on the lectures by the instructor. Students are encouraged to take part and intervene in class, in response to class questions and mandatory readings. Practical classes will require students to do bibliographic research, and to present content and experimental data related to one or more scientific articles.

Learning Outcomes

The goal of this UC is to guide students through the major topics of cognitive neuroscience, and specifically through the major modes of brain functioning. Importantly, the UC will present examples of these modes of operation drawn from major cognitive processes. The UC will also bring students advanced and innovative themes in cognitive neuroscience.

Importantly, this UC also aims to increase neuroscientific literacy, and the knowledge of neuroimaging methodology in psychology students. Finally, students have the opportunity to have some laboratory experience in advanced aspects in cognitiveneuroscience, through structured activities in the lab of the instructor.

Work Placement(s)

No

Syllabus

1. Introduction to the UC and Advanced Topics in Cognitive Neuroscience

2. Advanced methods in neuroimaging – multivariate-pattern analysis and machine learning

3. Advanced methods in neuroimaging - representational similarity analysis

4. Computational neuroscience - deep learning and others

5. Multimodality and abstract representations

6. Resting-state Neural networks

7. Neuronal Organization I - Segregation

8. Neuronal Organization II - Topography and Maps

9. Neuronal Organization III - Neural networks/small worldness and connectivity constraints.

Head Lecturer(s)

Jorge Manuel Castelo Branco de Albuquerque Almeida

Assessment Methods

Assessment
Resolution Problems: 20.0%
Synthesis work: 35.0%
Frequency: 45.0%

Bibliography

Bibliografia principal:

Milner, A.D. (2017). How do the two visual streams interact with each other? Experimental Brain Research, 235, 1297–1308.

Wandell, B.A., Dumoulin, S.O. & Brewer, A.A. (2007). Visual field maps in human cortex. Neuron, 56(2), 366-383.

Sporns, O. & Betzel, R.F. (2016). Modular Brain Networks. Annual Review of Psychology, 67, 613–640.

Raichle, M.E. (2015). The Brain's Default Mode Network. Annual Review of Neuroscience, 38, 433-447.

Cohen, J., Daw, N., Engelhardt, B., Hasson, U., Li, K. et al. (2017). Computational approaches to fMRI analysis. Nature Neuroscience, 20, 304–313.

Wallace, M.T., Woynaroski, T.G. & Stevenson, R.A. (2020). Multisensory Integration as a Window into Orderly and Disrupted Cognition and Communication. Annual Review of Psychology, 71, 193-219.

Outros artigos específicos dos exemplos serão dados aos alunos.