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University of California at Berkeley: The Neural Prediction Challenge (RRID:SCR_001920)Copy Citation Copied
URL: http://neuralprediction.berkeley.edu/
Proper Citation: University of California at Berkeley: The Neural Prediction Challenge (RRID:SCR_001920)
Description: The aim of the Neural Prediction Challenge is to accelerate the development of predictive models and to provide computational neuroscientists an opportunity to test their models objectively. The challenge is really quite simple: you will be given 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. Current data consist of recordings from visual and auditory neurons during naturalistic stimulation. Data are provided in simple ascii files that are easily readable in Matlab (or by any other modern programming language). Details on data formatting are provided with each data set. Predictions will be evaluated continuously as they are received and results will be posted in aggregate form. Individuals' names, prediction scores and models will not be posted without prior permission (though we may contact participants directly, see official rules). Please note that this is an academic research project, it is not a traditional contest. There is no real ending date, and there is nothing to win. Sponsors: This project is supported by the NIH Human Brain Project.
Synonyms: UCB Neural Challenge
Resource Type: data or information resource, portal, topical portal
Keywords: auditory stimulus, challenge, computational neuroscientist, development, neural, neuron, neuronal, predictive model, response, visual stimulus
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