Our projects often develop code for modeling, data analysis, or stimulus presentation. This page provides links to those code repositories. Our public code bases are all available here:

A simple synaptic model for the neuron T4

We released code that shows how a simple conductance model for T4 can explain a wide variety of data in the field. Here it is.

Zavatone-Veth, J. A., Badwan, B. A., Clark, D. A. (2020) “A minimal synaptic model for direction selective neurons in Drosophila”, Journal of Vision, 20(2), 1–22. [Link

Temporal super-resolution averaging for functional imaging

We developed a straightforward method that allows one to extract mean responses (or more complicated measurements) with high temporal resolution from low frame rate imaging. For instance, one may use it to compute means with 100 Hz resolution from 2-photon data acquired at 10 frames per second. The Matlab code is here.

Mano, O., Creamer, M. S., Matulis, C. A., Salazar-Gatzimas, E., Chen, J., Zavatone-Veth, J. A., Clark, D. A. (2019) “Using slow frame rate imaging to extract fast receptive fields”, Nature Communications 10: 4979. [Link]

Panoramic display code

Generate panoramic visual stimuli using a projector and a mirror-based geometry. This code uses Psychtoolbox. The repository is here

Creamer, M. S., Mano, O., Tanaka, R., Clark, D. A. (2019) “A flexible geometry for panoramic visual and optogenetic stimulation during behavior and physiology”, Journal of Neuroscience Methods. [Link]

Modeling for apparent motion

This repository contains models for thinking about apparent motion referenced in the paper below.

Salazar-Gatzimas, E., Agrochao, M., Fitzgerald, J. E., Clark, D. A. (2018) “The neuronal basis of an illusory motion percept is explained by decorrelation of parallel motion pathways”, Current Biology 28: 3748-3760. [Link]

GPU-accelerated code to compute stimulus-weighted response covariance matrices (analogous to spike triggered covariance)

It can be useful to compute the response-weighted stimulus covariance matrix, including for spike-triggered covariance analyses. This code does it in Matlab up to 100x faster than on your CPU.

Mano, O., Clark, D. A. (2017) “Graphics Processing Unit-Accelerated Code for Computing Second-Order Wiener Kernels and Spike-Triggered Covariance”, PLOS ONE 12(1): e0169842. [Link]