Cosyne 2008 Workshops

March 3-4, 2008

Snow Bird, Utah

Workshop Title

Data sharing and modeling challenges in neuroscience - a first step towards predictive neuron models?


Arnd Roth (UCL)
Wulfram Gerstner (EPFL)
Fritz Sommer (UC Berkeley):


Data sharing in neuroscience is currently pushed by modelers, experimentalists, and public agencies, for achieving various -not always compatible- goals:

  1. to test the predictions of models
  2. to help with parameter settings in large-scale simulations
  3. to overcome the limitations of the traditional "single lab" approach
  4. to expose experimental data to the full spectrum of available analysis methods
  5. to make the results of publicly funded science available to everybody

To achieve these goals, the sharing of raw data as such seems not sufficient. Rather, data sharing in neuroscience requires additional efforts to organize the raw data. For example, it is typically required to link raw data to detailed protocol descriptions, to stimuli or behavioral responses.

A discussion of questions of how to organize neuroscience data for sharing and what to expect from such activities seems timely since public support for data sharing activities has been recently brought into place, for example, a new NSF program for funding efforts in experimental labs to make data publicly available.

One specific topic of this workshop is to discuss recent experiences to organize neuroscience data by defining "modeling challenges". A modeling challenge puts data sets in the context of particular questions to be addressed, for instance by specifying a task that a model for the data should be able to achieve. First steps in this direction have been taken by the Berkeley "Neural Prediction Challenge" or by the Lausanne "Competition: Predicting Single-Neuron Behavior".

This discussion-oriented workshop aims to bring three different groups of people together: theoreticians interested in quantitatively predictive models; experimentalists interested in data sharing; and proponents of data sharing initiatives and challenges.

General questions to be addressed:

  1. How can we quantify the predictive power of current neuron models?
  2. How can we make use of publicly available data?
  3. What type of data would modelers like to see publicly available?
  4. What type of data would experimentalists like to see publicly available?

Format: Relatively short talks (15 minutes) with enough time for discussion after talks and in one or two general discussion sessions.

Workshop program

Morning session
8:00 Introduction
8:10 Walter Senn (University of Bern) Nonlinear response properties of prefrontal pyramidal neurons: is the classical integrate-and-fire model appropriate?
8:35 Shaul Druckmann (Hebrew University) The predictive power of conductance-based neuron models constrained by experimental responses to different input types
9:00 Quentin Huys (Gatsby, UCL) Fitting biophysically detailed models to noisy data
9:25 Jonathan Pillow (Gatsby, UCL) The effects of correlated population activity on single-neuron spike prediction
9:50 Coffee break
10:00 Pablo Achard (Brandeis) Neurofitter: A parameter tuning package for electrophysiological neuron models
10:25 Erik De Schutter (Okinawa Institute of Science and Technology) Using Neurofitter for automated parameter fitting to electrophysiological data
10:50 Wulfram Gerstner (EPFL) Why public data needs a modeling challenge - The Lausanne single-neuron modeling competition, challenges A and B
11:00 Arnd Roth (Gatsby, UCL) The Lausanne single-neuron modeling competition, challenges C and D
11:10 General discussion
Afternoon session
4:30 Konrad Koerding (Northwestern U.) Priors for inferring functional connectivity
4:55 Ken Harris (Rutgers) Fitting and evaluating models of cortical dynamics
5:20 Dario Ringach (UCLA) Natural images for everyone
5:45 Coffee break
5:55 Laurent Itti (USC) Eye movements during free-viewing of natural videos
6:20 Fritz Sommer (UC Berkeley) Data sharing for Computational Neuroscience - the new CRCNS initiative
6:30 Tom Jackson (York U.) CARMEN: e-Science for Neurophysiology
6:40 Jan Bjaalie (INCF Stockholm) Global infrastructure for data sharing
6:50 General discussion

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