BCSC 511 Behavioral Methods in Cognitive Science, or research experience

This course will cover a variety of behavioral techniques and analyses, with the goal of developing technical and analytical skills in thinking about experimental psychology. After several classes to introduce behavioral research and experimentation, we will focus on three prior experiments in detail: time series data from language comprehension, psychophysics, and animal behavior.Tools: Work will use a variety of free/open/replicable analysis tools in R and RStudio, including: Version control, backup, and data sharing: github and RStudio integration Data wrangling: tidyverse, magrittr, dplyr, broom, purrr Data visualization: ggplot2, plotly Data and code documentation: R markdown (good coding: styler, lintr, assertthat, here) Setting up code for reuse and sharing (devtools, roxygen2) Data analysis: lme4, lmerTest, gamm4 Data and model tables: stargazer, sjPlot Optionally Bayesian data analysis: brms (requires rstan, stan, and c-compiler to be installed)

BCSC 512 Computational Methods in Cognitive Science, or research experience

Deep neural networks (DNNs) have become very important modeling tools in cognitive science and neuroscience. This course focuses on: (1) the mathematical foundations of deep neural networks (DNNs); (2) knowledge of how to implement DNNs using the Python programming language and the Keras library; and (3) the uses of DNNs in the cognitive science and neuroscience literatures.

BCSC 513 Intro to fMRI, or research experience

Prerequisite: Prior programming experience (esp. Matlab) recommended. The core focus of the course will be on how fMRI can be used to ask questions about neural representations and cognitive and perceptual information processing. Some of the questions that the course will address include: 1) The basic fMRI signal just shows activation in different parts of the brain. How can we get from that to addressing questions about neural representations and neural information processing? 2) Ways of relating neural activation to behavioral performance. Can fMRI provide information over and above what can be obtained from behavior alone? 3) Standard fMRI analysis using the General Linear Model, including preprocessing steps. 4) Multivariate fMRI analysis using machine learning approaches. There will also be a component, about 20% of the class, on the big-picture aspects of MRI physics and physiology which make fMRI possible.

BCSC 515 Applied Introduction to Signal and Systems in BCS, or research experience

Why should I care about vector spaces and orthonormal bases?  Or that a matrix defines a linear transformation? How should I properly acquire a biological signal? How are linear time-invariant systems useful to my research? What is a power spectrum and how do I properly estimate it?
These are examples of the questions that will be addressed in this course. Students in BCS come from a variety of programs, some of which do not emphasize quantitative training. The goal of the course is to provide an introduction to the analysis of signals and systems and establish a background foundation by covering fundamental mathematical concepts that are essential for conducting rigorous research. To do so, the course will follow an applied approach, in which individual concepts are first introduced by means of scientific articles in the BCS-relevant literature, covered in depth in class, and then reviewed by means of additional articles or case-study problems. Rather than simply surveying topics at an introductory level, the course aims to reach a sufficiently advanced level for each technique introduced, that the student should be able to use that technique in his/her research and in other graduate courses. To give a common introductory framework to the materials, the course will focus on linear methods, but we will also discuss deviations from linearity. Covered topics, all introduced with the immediate goal of grasping why they are critically important to the student's research, include fundamental concepts from linear algebra, system analysis, and signal processing.

BCSC 547 Introduction to Computational Neuroscience, or research experience

Computational neuroscience studies how the brain can be understood in terms of computations implemented by neural circuits, and in terms of using computational methods to analyze neural and behavioral data. This course for advanced undergraduates and graduate students starts with models of individuals neurons before moving on to networks of neurons and behavior. It provides both a classic signal processing, and a probabilistic perspective on how neurons support the brain’s computations. While primarily lecture-based, an important part of the course are exercises that typically consist on implementing (programming) a model discussed in the class and analyze its behavior. The course also provides the opportunity for a final project but this is not required. The material mostly considers the sensory system and perceptual decision-making.

Programming experience and a minimal background in linear algebra (vectors and matrices) and analysis (basic ordinary differential equations) are essential. At the beginning, there will be a very brief introduction to the key biological concepts necessary for the course.

BCSC 557 Advanced Computational Neuroscience

This is a seminar-style course for advanced undergraduate and graduate students covering multiple areas of computational neuroscience by weekly readings and student presentations. Many of the topics are deeper explorations of topics covered in BCSC 547 Introduction to Computational Neuroscience, focusing on the sensory system, decision-making, action selection and active inference, especially from a probabilistic and normative perspective. The reading list is somewhat flexible and adaptable to student interest. There is an opportunity for a final project but this is not required

Additional Required Courses

BCSC 582 Grant Writing in Brain and Cognitive Sciences

A workshop in which students will write a proposal for either a pre-doctoral or post-doctoral NRSA fellowship from NIH. Students will review old NRSA proposals, both successful and unsuccessful and analyze the components of a successful proposal. Through process of peer review and discussion, students will write and revise the main sections of an NRSA proposal, culminating in a penultimate proposal that will be reviewed by two mock study sections – one in the class and one by faculty in BCS and CVS. Reviews from these study sections will be returned a week before the deadline for NRSA proposals at NIH. Students are encouraged to use the class to prepare real proposals that they can submit to NIH.

BCSC 599 Professional Development and Career Planning

The purpose of this 1-credit course is to provide first- and second-year graduate students with a set of guiding principles for optimizing their progression through the PhD program. The following topics will be discussed: fulfilling program requirements, advising and mentoring, time management, conference presentations and journal publications, writing skills for journals and grants, how to juggle, persist, drop, and collaborate in your research projects, the post-PhD job market and qualifications required for success.

Ethics: Option IND 501 or NSF Responsible Conduct of Research Training

*see below

BCSC 595: PhD Research

Variable

BCSC 598: Supervised Teaching Assistant

Teaching Assistant for undergraduate courses in Department of Brain and Cognitive Sciences – required for all BCS graduate students to TA 3 times

Qualifying exam:

covering the areas of Language and Cognition, Perception and Action and Behavioral Neuroscience – required for all BCS graduate students

Doctoral Dissertation

including oral defense

Total:

90 credit hours