http://gru.stanford.edu/ 2026-05-14T19:33:38-0700 http://gru.stanford.edu/ http://gru.stanford.edu/lib/tpl/gru/images/favicon.ico text/html 2022-08-30T13:40:31-0700 Anonymous (anonymous@undisclosed.example.com) http://gru.stanford.edu/doku.php/tutorials/boldmodels?rev=1661892031&do=diff Introduction The key to understanding analysis of any data including BOLD imaging data is to remember that all analyses are models. One designs and runs an experiment, collects data and then models the results and examines the goodness-of-fit and parameters of the model. If you have a good model of the data, then your goodness-of-fit will be good and you will get fit parameters that can be meaningful interpreted. Models contain multiple assumptions about the processes that generated the data, s… text/html 2022-08-30T13:40:31-0700 Anonymous (anonymous@undisclosed.example.com) http://gru.stanford.edu/doku.php/tutorials/channel?rev=1661892031&do=diff Introduction Encoding models can be a powerful way to analyze data because they can reduce a high-dimensional stimulus space to a lower dimensional representation based on knowledge of how neural system encode stimuli and thus have more power to extract relevant information from cortical measurements. A channel encoding model, first introduced by text/html 2022-08-30T13:40:31-0700 Anonymous (anonymous@undisclosed.example.com) http://gru.stanford.edu/doku.php/tutorials/channel_full_code?rev=1661892031&do=diff Complete code for channel encoding model tutorial %%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Make simulated data %%%%%%%%%%%%%%%%%%%%%%%%%%%%% % some variables iNeuron = 1; orientations = 0:179; k = 10; % loop over each neuron tuning function for orientPreference = 0:2:179 % compute the neural response as a Von Mises function %Note the 2 here which makes it so that our 0 - 180 orientation % space gets mapped to all 360 degrees neuralResponse(iNeuron,:) = exp(k*cos(2*pi*(orientations-orientPref… text/html 2022-08-30T13:40:31-0700 Anonymous (anonymous@undisclosed.example.com) http://gru.stanford.edu/doku.php/tutorials/channel_r?rev=1661892031&do=diff R version of Channel Encoding model tutorial Download R Markdown file here: http://gru.stanford.edu/pub/tutorials/encoding/encoding_tutorial_RNotebook.Rmd HTML of R notebook HTML page text/html 2022-08-30T13:40:31-0700 Anonymous (anonymous@undisclosed.example.com) http://gru.stanford.edu/doku.php/tutorials/diffusion?rev=1661892031&do=diff Diffusion model Signal Detection Theory can be used to get bias-free measures of subject's sensitivity, predict performance on a two alternative forced choice and has been quite useful both in psychophysics and in cognitive neuroscience where it is often used to link physiology to behavior. But, one thing it completely sweeps under the carpet is time. There is no notion of how the decision process evolves over time and so therefore it has nothing to say about reaction times. So, diffusion model… text/html 2022-08-30T13:40:31-0700 Anonymous (anonymous@undisclosed.example.com) http://gru.stanford.edu/doku.php/tutorials/fouriertransform?rev=1661892031&do=diff Fourier Transform The Fourier transform is an amazing mathematical tool for understanding signals, filtering and systems. What is a signal? A signal is typically something that varies in time, like the amplitude of a sound wave or the voltage in a circuit. What do we mean by filtering? Filtering is when you perform an operation called convolution that can change the characteristics of a signal, like reduce noise in an audio signal or remove high pitched sounds and keep low pitched sounds. What … text/html 2026-03-10T16:02:20-0700 Anonymous (anonymous@undisclosed.example.com) http://gru.stanford.edu/doku.php/tutorials/fouriertransformcomputation?rev=1773183740&do=diff Computation of Fourier transform This is a gentle introduction to how the Discrete Fourier transform is computed. See the tutorial on the Fourier transform for a basic introduction to its use. After doing this tutorial you will be able to: Compute the amplitude and phase of each component frequency of a signal. Explain how the Fourier transform is a projection on to cosine and sine axis of different frequencies. Reason about how this is a rotation of the time based axes. See how the complex ex… text/html 2022-08-30T13:40:31-0700 Anonymous (anonymous@undisclosed.example.com) http://gru.stanford.edu/doku.php/tutorials/prf?rev=1661892031&do=diff Introduction This tutorial will take you through fitting a pRF encoding model (see Dumoulin and Wandell (2008) for details) to actual data. You will write all the code to do this - either in Matlab or Python (there are worked examples of code for both Matlab and Python). You can check text/html 2022-10-01T12:24:28-0700 Anonymous (anonymous@undisclosed.example.com) http://gru.stanford.edu/doku.php/tutorials/python?rev=1664652268&do=diff We will be running python through a <https://jupyter.org]jupyter> notebook. If you don't already have Jupiter installed on your computer, one easy way to install is through anaconda. When you have finished downloading, go to your /Applications folder and run Anaconda-Navigator and you should see a screen like the following text/html 2022-08-30T13:40:31-0700 Anonymous (anonymous@undisclosed.example.com) http://gru.stanford.edu/doku.php/tutorials/sdt?rev=1661892031&do=diff Signal detection simulation We're going to simulate a signal detection experiment and an “ideal observer” (an observer who behaves exactly according to signal detection theory). This is always a useful thing to do when trying to understand a decision model, experimental method or an analysis tool. You get to control and play around with the simulation to see what effect it has on the analysis that comes out.