Cognitive and Integrative Neurobiology

Course Number: 
Course Type: 
Currently Offered: 
Instructor (MCB Faculty): 
Phillips, Paul
Class Size: 
Course Description: 

The course covers topics in cognitive and integrative systems neuroscience at the graduate level. We use a problem-oriented approach to study selected topics: sensorimotor integration, attention, visual perception, decision making, control of complex movement, computational neuroscience, language, bird song, reward expectation, learning and memory.

Learning Objectives: 

The student will be able to critically analyze the literature pertaining to the neurobiology of cognitive and higher-order functions, and will be able to design well controlled experiments that probe this function.

The course assumes background knowledge of neuroanatomy, cellular neurophysiology and introductory systems neuroscience (sensory and motor systems) at the level covered in NEUBEH 502.
Course requirements, examinations and grading: 

We expect students to master all relevant material in the reading assignments and to delve more deeply into the issues discussed in class.  The course grade is based mainly on the problem sets, which are designed to cover a mixture of factual material and analysis. Questions will draw upon material covered in class as well as background reading assignments. The problems are intended to foster critical thinking and to challenge you to integrate facts, methodology and the interpretation of experimental data. Students will be asked to calculate, graph and evaluate hypotheses using statistical methods.  The course grade will also reflect participation in Thursday “workshops”.  There is no final examination.

Course web site: (UW NETID required)

Sample Schedule:
Week Mon Weds Thurs (workshop) Fri
  1 Introduction to course; Methods in systems neuroscience Methods in systems neuroscience Statistics for neuroscience

Methods in systems neuroscience

  2 Non-associative learning in Aplysia Non-associative learning in Aplysia Associative learning

Candidate learning mechanisms in mammals

  3 Dopamine and neuromodulation  Motivation and hedonia Discuss Problem Set 1

Computational models of reinforcement learning

  4 Dopamine and reinforcement learning What information is conveyed by the afferent pathways to dopamine neurons? Discuss Problem Set 2; Volume transmission

Causal tests of dopamine and reinforcement learning

  5 Signal detection and decision making Signal detection and decision making Discuss Problem Set 3; Economic decision making

Signal detection and decision making

  6 Human imaging Human imaging Human imaging workshop

Human imaging

  7 Vocal learning Vocal learning Discuss Problem Set 4

Vocal learning

  8 Motor control Motor control Motor control workshop

Motor control

  9   Motor control Motor control workshop

Motor control

 10 Motor control Spatial cognition Discuss Problem Set 6, Consciousness workshop

Spatial cognition

Areas of Interest: