1. Chapter 10: The cognitive brain
Fundamentals of
Computational Neuroscience
Chapter 10: The cognitive brain
Dec 09

2. Hierarchical maps and attentive vision

3. Attention in visual search and object recognition
Gustavo Deco

4. Model

5. Example results

6. The interconnecting workspace hypothesis
Stanislas Dehaene, M. Kergsberg, and J.P. Changeux, PNAS 1998

7. Stroop task modelling

8. The anticipating brain
The brain can develop a model of the world, which can be used to anticipate or predict the environment.
The inverse of the model can be used to recognize causes by evoking internal concepts.
Hierarchical representations are essential to capture the richness of the world.
Internal concepts are learned through matching the brain's hypotheses with input from the world.
An agent can learn actively by testing hypothesis through actions.
The temporal domain is an important degree of freedom.

9. Outline

10. Recurrent networks with hidden nodes

11. Training Boltzmann machines

12. The restricted Boltzmann machine

13. Deep generative models

14. Adaptive resonance theory (ART)

15. Further readings