Abstract for Data-Modeling Match:
We have developed a statistical model, algorithm, and corresponding software to estimate the times at which a neuron spiked on the basis of calcium imaging data. We are also in the process of developing a model and algorithm and software to perform inference on these estimated spike times, i.e. to obtain a p-value quantifying how likely it is to have observed such a large increase in fluorescence in the absence of a true spike. Our software is implemented in python and R and is described here: https://jewellsean.github.io/fast-spike-deconvolution/.
What is the analytical tool you have developed:
We have developed a model for spike estimation on the basis of calcium imaging data, and are currently developing an approach to conduct inference on the estimated spikes. The model is implemented in R and python, and is available at https://jewellsean.github.io/fast-spike-deconvolution/.
What input do you need?
These methods require calcium imaging data as input. For each neuron, the fluorescence trace should be DF/F transformed.
What are the questions you can answer?
On the basis of calcium imaging data, we can answer the question of "When did the neuron spike?" We are also working to answer the question of "What is the probability of observing such a large increase in fluorescence in the absence of a spike?" (The latter amounts to computing a p-value associated with each spike.)
What are the data specifications needed for your TMM tool?
The TMM tool requires DF/F derived from calcium imaging data, for a single neuron. It can be applied repeatedly to a large number of neurons. So far we have applied it to data from the visual cortex of mice, from the Allen Brain Observatory. We are in the process of applying it to dopamine neurons.