# Duke Institute for Brain Sciences Methods Meetings

Workshops and tutorials on methods, statistics, and models in neuroscience.

## Category programming

#### Experimentalists often find themselves needing to present carefully controlled stimuli to participants, control and catalog stimulus conditions, responses, reaction times, and other empirically variables of interest. While tools such as...

#### This tutorial introduces the computational cluster at the Duke Brain Imaging and Analysis Center (BIAC) and presents a few simple use cases. Hopefully it will make your experience handling/analyzing neural...

#### In this tutorial we’re going to talk about what probabilistic programming is and how we can use it for statistical modeling. If you aren’t familiar at all with Bayesian stats,...

#### Today we’re going to tackle a common problem faced by graduate students: you brainstormed to design an experiment with your PI, toiled away programming the task, put it out into...

#### As human beings continue to march through the information age, we produce ever-larger quantities of data every second. This is especially relevant to us as scientists, as data gathering and...

#### Hello! I hope you’re doing well. Today we have a tutorial on ~ neural networks ~

#### A too-brief overview of this powerful and (usually) intuitive approach
to statistical computing.

#### There are lots of powerful things you can do with the Markdown editor. If you’ve gotten pretty comfortable with writing in Markdown, then you may enjoy some more advanced tips...

#### Why git? git is a simple but highly flexible system for keeping track of stuff on one or more computers. But, everyone probably already has some way of doing the...

## Category professional development

#### with Jekyll and GitHub pages!

#### If you’re making a blog post on R-related content, you’re probably going to do it using R markdown (Rmd). However, you may have noticed that our website runs on GitHub...

#### There are lots of powerful things you can do with the Markdown editor. If you’ve gotten pretty comfortable with writing in Markdown, then you may enjoy some more advanced tips...

## Category statistics

#### You’ve probably come across linear regression from time to time in your research, or in reading papers – but how does it work? What is linear regression? What are the...

#### If you have ever tried to analyze time series data, you know that time series present all kinds of statistical challenges. Probably the most challenging aspect of time series data...

#### Signal detection in math and psychology

#### So, you’re designing an experiment and you’re faced with answering the age-old question: How many participants do I need for this experiment to work? Probably, your advisor sent you down...

#### As human beings continue to march through the information age, we produce ever-larger quantities of data every second. This is especially relevant to us as scientists, as data gathering and...

#### Have you ever ran a regression and wondered where the coefficients come from or what they mean? Or perhaps you’ve tried the same analysis with different coding schemes, and the...

#### Bayesian statistics are gaining a whole lot of traction in psychology, neuroscience, and a whole lot of other fields. But, since most psychology departments don’t teach Bayesian statistics, you probably...

#### A summary and some questions for group discussion.

## Category journal club

#### A summary and some questions for group discussion.

## Category machine learning

#### Topics/Organization: Some geometric intuition for SVMs Introducing slack variables (Soft-Margin SVMs) The SVM loss function (primal and dual forms) SVMs and Kernels Some coding examples of the above (for fun!)...

#### Hello! I hope you’re doing well. Today we have a tutorial on ~ neural networks ~

## Category math

#### The beauty of the Fourier series and Fourier transform

#### Finding structure The whole goal of our experiments is to uncover some structure in behavior. In some cases we can make this easier for ourselves by simplifying the data we...

#### If you have ever tried to analyze time series data, you know that time series present all kinds of statistical challenges. Probably the most challenging aspect of time series data...

#### If you’ve worked with any kind of neural data, convolutions were involved at some point. Having an intuition for convolutions is quite useful in thinking about what your data actually...

#### If you’ve ever wondered how R or python gives you regression coefficients, the answer is linear algebra! Linear algebra operations are essential to almost all modern methods for analyzing or...

## Category psychology

## Category neuroscience

#### If you’ve worked with any kind of neural data, convolutions were involved at some point. Having an intuition for convolutions is quite useful in thinking about what your data actually...

## Category modeling

#### Topics/Organization: Some geometric intuition for SVMs Introducing slack variables (Soft-Margin SVMs) The SVM loss function (primal and dual forms) SVMs and Kernels Some coding examples of the above (for fun!)...

#### Finding structure The whole goal of our experiments is to uncover some structure in behavior. In some cases we can make this easier for ourselves by simplifying the data we...

#### If you have ever tried to analyze time series data, you know that time series present all kinds of statistical challenges. Probably the most challenging aspect of time series data...

## Category tutorial

#### Analyzing data with repeated observations for a particular participant, stimulus, or other group is one of the most common things you need to do in psychology & neuroscience, like most...

#### Today I am going to present on an alternative way to analyze Likert scale data by using ordinal regression instead of linear regression. But first, why is it even a...

#### Last week, Raphael presented a fantastic conceptual introduction to drift diffusion models, which are an extension of signal detection models over time. Here I’ll be talking about what model fitting...

## Category signal processing

#### The beauty of the Fourier series and Fourier transform