COSC 494 / 594 — Computational Cognitive Neuroscience

Student Learning Outcomes

Students will:

· Understand differences between different types of neural models.

· Understand membrane potential and generation of action potential.

· Understand neurons as pattern matchers.

· Understand interaction of neurons in networks.

· Understand learning and adaptive mechanisms (LTP, LTD, STDP, XCAL, etc.).

· Explore effects of parameters on learning.

· Be able to locate anatomical brain regions and discuss functions.

· Understand large-scale organization of perceptual systems.

· Understand self-organization in perceptual systems.

· Explore effects of lesions on attention.

· Understand mechanisms of reinforcement learning (dopamine, basal ganglia, temporal difference, PVLV, etc.).

· Understand differences between learning and dynamics in various brain regions (attractor, separator, integrative).

· Model error-driven learning (cerebellum).

· Understand mechanisms of short-term, episodic, procedural, and semantic memory. Understand basic mechanisms of language.

· Understand executive function, reinforcement, and planning.

· Be able to use emergent simulator to run experiments.

· Be able to modify emergent simulations to test new hypotheses.

ABET Student Outcomes Addressed by this Course

	Computing Accreditation Commission Statement of Student Outcomes
Course Outcomes ¯	a	b	c	d	e	f	g	h	i	j	k
Cognitive modeling	X	X							X	X
Neurons & networks	X									X
Learning & adaptation	X									X
Perception & attention
Motor control & reinforcement										X
Learning & memory										X
Language
Executive function