Another Neural Prediction Challenge!

In a previous post, I noted that Jack Gallant had issued a Neural Prediction Challenge. Now, we have another challenge, this time from the Gerstner lab:

Here is our Challenge, open to everybody in in neural modeling, machine learning, or similar fields:

- Is it possible to predict the timing
of every spike that a neuron emits with 2 ms precision?
- Is it possible to predict the subthreshold membrane potential
with a precision of 2 mV for arbitrary input?

Annotated training data and test stimuli from several
cells under different stimulation conditions are available
at
http://icwww.epfl.ch/~gerstner/QuantNeuronMod2007/challenge.html

Important dates
* Data set available by March 16.
* Participants must submit their prediction by June 1st.
* Winner announced around June 10 .
* Winning results will be presented at the workshop June 25/26
Quantitative Neuron Modeling: Predicting every spike?
http://icwww.epfl.ch/~gerstner/QuantNeuronMod2007/

Competition and Prizes
The competition is organized in several categories, called A,B,C,D.
Participants may run in one or several categories

* 1st prize :

o 4 nights of hotel in Lausanne at the Lake of Geneva,
June 23-27.
o Free participation in the Quantitative Neuron Modeling
workshop June 25/26
o 35-minute-slot for talk as an
Invited Speaker at the workshop.

* 2nd prize:

o Free participation in the
Quantitative Neuron Modeling workshop June 25/26
o Poster presentation and poster spotlight in the workshop.


Methods and Models:
The only aspect that counts for us is the quality of the prediction
on the test set. In terms of methods, anything goes
(Machine learning, compartmental model, integrate-and-fire model,
systems identification etc)

For details
http://icwww.epfl.ch/~gerstner/QuantNeuronMod2007/challenge.html


We hope that many people will take up the challenge.
Let the best model win!

Neural Prediction Challenge

The gauntlet has been thrown. Accept the Neural Prediction Challenge if you dare!

The challenge is really quite simple: We will give you some (visual and/or auditory) stimuli and corresponding neural responses, and you must try to predict responses to other stimuli. Each data set will be divided in to two subsets: a fit set (90% of the data) that includes both the stimuli and the corresponding neuronal responses; and a validation set (10% of the data) that includes only stimuli (no responses). Your job is to use the fit set to fit your model and then to generate predicted responses based on the stimuli provided in the validation set. Once you have the predictions you should return them to us. We will compare your predicted responses to the responses actually observed in the validation set.

Kudos to Jack Gallant and other people responsible for coming up with this idea, collecting and sharing data, and putting together the site.