Computational Cognitive Neuroscience: An Introduction
A selected organization of the content here can be used as a textbook for teaching a course on computational cognitive neuroscience, as a update to the CCN book that has been available online since 2012, and was originally published by MIT Press as O’Reilly & Munakata (2000), based on the Leabra framework.
You can read the following pages in order to obtain a sensible progression of information:
- 1. Overview of the field and approach: Computational Cognitive Neuroscience.
- 2. Neurons and what they do: neuron, neuron detector, neuron simulation, detector simulation.
- 3. Networks of neurons: neocortex, categorization, bidirectional connectivity, inhibition.
- 4. Learning: synaptic plasticity, temporal derivative, predictive learning, kinase algorithm.
- 5. Brain structure and function.
- 6. Perception
- 7. Memory
- 8. Reinforcement learning and basal ganglia.
- 9. Prefrontal cortex and working memory.
- 10. Language.
Linear static media
If you prefer traditional “printed” media, a PDF generated from the above content is available. (TODO)