Computational Cognitive Neuroscience

Students in Computational Cognitive Neuroscience (CCN) complete a programming course, two neuroscience courses, and three advanced courses that also count toward the three breadth courses required as part of the common graduate curriculum. 

Table of CCN requirements

One programming course. Students take PSYC 43030 Introduction to Python Programming in the Behavioral Sciences; this requirement may be waived if the student has sufficient programming experience.  

Two neuroscience courses. For students entering in Autumn 2025 and later, students complete two courses from the five options below:
                  NURB 30600 Neuroscience Core Sequence: Cellular Neuroscience
                  NURB 30700 Neuroscience Core Sequence: Behavioral Neuroscience
                  NURB 30800 Neuroscience Core Sequence: Systems Neuroscience
                  CPNS 34231 Methods in Computational Neuroscience
                  PSYC 42350 Advanced Topics in Human Neuroimaging

For students entering in Autumn 2024 and earlier, students may complete two of the courses listed above or two courses from the four previous offerings of the Neuroscience courses:
                  CPNS 30000 Cellular Neurobiology
                  CPNS 30107 Behavioral Neuroscience
                  CPNS 30116 Survey of Systems Neuroscience
                  CPNS 34231 Methods in Computational Neuroscience

Three area courses. CCN students are required to take three advanced courses, one of which is required to be a breadth course outside of the student's main discipline. These courses will also fulfill the breadth courses required as part of the common graduate curriculum. Eligible courses will include all graduate level seminars taught by faculty in the Psychology Department, as well as a list of courses in other departments that are deemed relevant for the computational cognitive neuroscience curriculum. These outside courses will provide additional opportunities for computational and analytic training.

Advanced courses taught in Psychology (note this is not a complete list):

  • PSYC 31900 The Neuroscience of Narratives. (Leong)
  • PSYC 32680 Computational Approaches to Social and Affective Neuroscience (Leong)
  • PSYC 33910 Hormones, Brains, and Behavior (Prendergast)
  • PSYC 34810 Neuroeconomics. (Bakkour)
  • PSYC 36200 Deep Learning Models as Model Systems for Cognitive Neuroscience (Leong)
  • PSYC 37400 Long Term Memory.  (Gallo)
  • PSYC 37250 Foundations of Neuroscience: Historical Perspectives.  (Kay)
  • PSYC 41135 Electrophysiological Studies of Hierarchical Memory Representations (Awh)
  • PSYC 42350 Advanced Topics in Human Neuroimaging.  (Bainbridge, Rosenberg)
  • PSYC 42570 Integrating the Real World into Perception and Memory.  (Bainbridge)
  • PSYC 42950 Memory and Decision Making.  (Bakkour)
  • PSYC 43110 Affective Neuroscience. (Norman)
  • PSYC 43130 Stress and the Social Brain. (Norman)
  • PSYC 43780 Basics of Conducting EEG and ERP Research.  (Vogel)
  • PSYC 43921 Current Topics in Working Memory.  (Awh)
  • PSYC 44250 EEG Measures of Memory (Vogel)
  • PSYC 45500 Cognitive and Social Neuroscience of Aging.  (Gallo)
  • PSYC 46050 Principles of Data Science and Engineering for Laboratory Research. (Yu)
  • PSYC 46662 Advanced Topics in Genes and Behavior (London)
  • PSYC 48880 Neural and Cognitive Studies of Goal-Driven Behavior

  Other computational courses not taught in Psychology (not a complete list):

  • MACS 30000 - Introduction to Computational Social Science
  • MACS 30121 - Accelerated Python Programming with Social Science Applications
  • MACS 30123 - Accelerated Large-Scale Computing for the Social Sciences
  • MACS 30500 - Computing for the Social Sciences
  • MACS 30124 - Computational Analysis of Social Processes
  • MACS 33002 - Introduction to Machine Learning
  • MACS 40100 - Big Data & Society
  • MACS 40400 - Computation and the Identification of Cultural Patterns
  • MACS 40800 - Unsupervised Machine Learning
  • MACS 51000 - Introduction to Causal Inference
  • MACS 31300 - AI Applications in the Social Sciences
  • MACS 37000 - Thinking with Deep Learning for Complex Social & Cultural Data Analysis
  • MACS 40101 - Social Network Analysis
  • CMSC 30900: Computers for Learning
  • CMSC 33281: Topics in Human Robot Interaction
  • CMSC 35300: Mathematical Foundations of Machine Learning
  • CMSC 35200-1: Deep Learning Systems
  • TTIC 31210: Advanced Natural Language Processing
  • TTIC 31230: Fundamentals of Deep Learning
  • TTIC 31220 - Unsupervised Learning and Data Analysis
  • TTIC 31020 - Introduction to Machine Learning
  • TTIC 31250: Introduction to the Theory of Machine
  • TTIC 31040: Introduction to Computer Vision
  • CPNS 34231 Methods in Computational Neuroscience 
  • CPNS 32111 Modeling and Signal Analysis for Neuroscientists
  • PSYC 36210 Mathematical Methods for Biological Sciences 1
  • PSYC 36211 Mathematical Methods for Biological Sciences 2