Spring 2019

Computational Neuroscience

15-686 Neural Computation: 12 units

  • Instructor: Tai Sing Lee
  • Date/Time: Mon & Wed 1:30 PM – 2:50 PM
  • Location: Wean Hall 5302

Computational neuroscience is an interdisciplinary science that seeks to understand how the brain computes to achieve natural intelligence. It seeks to understand the computational principles and mechanisms of intelligent behaviors and mental abilities — such as perception, language, motor control, and learning — by building artificial systems and computational models with the same capabilities. This course explores how neurons encode and process information, adapt and learn, communicate, cooperate, compete and compute at the individual level as well as at the levels of networks and systems. It will introduce basic concepts in computational modeling, information theory, signal processing, system analysis, statistical and probabilistic inference. Concrete examples will be drawn from the visual system and the motor systems, and studied from computational, psychological and biological perspectives. Students will learn to perform computational experiments using Matlab and quantitative studies of neurons and neuronal networks.

18-698/42-632 Neural Signal Processing: 12 units

  • Instructor: Byron Yu
  • Date/Time: Tues & Thurs 1:30 PM – 2:50 PM
  • Location: Baker Hall A53

The brain is among the most complex systems ever studied. Underlying the brain’s ability to process sensory information and drive motor actions is a network of roughly 10^11 neurons, each making 10^3 connections with other neurons. Modern statistical and machine learning tools are needed to interpret the plethora of neural data being collected, both for (1) furthering our understanding of how the brain works, and (2) designing biomedical devices that interface with the brain. This course will cover a range of statistical methods and their application to neural data analysis. The statistical topics include latent variable models, dynamical systems, point processes, dimensionality reduction, Bayesian inference, and spectral analysis. The neuroscience applications include neural decoding, firing rate estimation, neural system characterization, sensorimotor control, spike sorting, and field potential analysis. Prerequisites: 18-290; 36-217, or equivalent introductory probability theory and random variables course; an introductory linear algebra course; senior or graduate standing. No prior knowledge of neuroscience is needed.

36-759 Statistical Models of the Brain: 12 units

  • Instructor: Rob Kass & Brent Doiron
  • Location: Mellon Institute 130
  • Days/Times: W/F 1:30PM – 2:50PM

This introductory course in computational neuroscience is intended for a broad range of CNBC students, with backgrounds that may be either technical (math, engineering, statistics, etc.) or non-technical (biology, neuroscience, etc.). The course is co-taught by Brent Doiron and Rob Kass. Pitt students should register in MATH 3375; CMU students may register in 36-759. The two instructors settled on “statistical models” as a unifying theme for the many kinds of models discussed, ranging from those that describe the physiology of neurons to those that describe human behavior. Statistical ideas have been part of neurophysiology since the first probabilistic descriptions of spike trains, and the quantal hypothesis of neurotransmitter release, more than 50 years ago; they have been part of experimental psychology even longer. In broad stroke, this course will examine a few of the most important methods and claims that have come from applying statistical thinking to the brain. However, some of the topics involve tools typically taught in statistics courses, while other topics involve tools taught in math courses. Even at an intuitive level, a single course can not provide a comprehensive view of computational neuroscience; the field is too broad. Instead, by studying a series of examples, many of them very influential, students will come away with a sense of the way that computational methods contribute to contemporary understanding of neuroscience.

85-719 Introduction to Parallel Distributed Processing: 12 units

  • Instructor: David Plaut
  • Date/Time: Tues & Thurs 10:30 – 11:50 AM
  • Location: Scaife Hall 214

This course provides an overview of Parallel-Distributed-Processing/neural-network models of perception, memory, language, knowledge representation, and learning. The course consists of lectures describing the theory behind the models as well as their implementation, and their application to specific empirical domains. Students get hands-on experience developing and running simulation models.

Systems Neuroscience

03-763 Advanced Systems Neuroscience: 12 units

  • Instructors: Sandra Kuhlman
  • Date/Time/Location: Tues & Thurs 9:00 AM – 10:20 AM (Hamerschlag Hall B131), Thurs 4:30 PM – 5:50 PM (Mellon Institute 355)

Modern neuroscience is an interdisciplinary field that seeks to understand the function of the brain and nervous system. This course provides a comprehensive survey of systems neuroscience, a rapidly growing scientific field that seeks to link the structure and function of brain circuitry to perception and behavior. This course will explore brain systems through a combination of classical, Nobel prize-winning data and cutting edge primary literature. Topics will include sensory systems, motor function, animal behavior and human behavior in health and disease. Lectures will provide fundamental information as well as a detailed understanding of experimental designs that enabled discoveries. Finally, students will learn to interpret and critique the diverse and multimodal data that drives systems neuroscience. This course is a graduate version of 03-363. Students will attend the same lectures as the students in 03-363, plus an additional once weekly meeting. In this meeting, topics covered in the lectures will be addressed in greater depth, often through discussions of papers from the primary literature. Students will read and be expected to have an in depth understanding of several classic papers from the literature as well as current papers that illustrate cutting edge approaches to systems neuroscience or important new concepts. Use of animals as research model systems will also be discussed. Performance in this portion of the class will be assessed by supplemental exam questions as well as by additional homework assignments.