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000141627 1001_ $$0P:(DE-HGF)0$$aVlachos, Ioannis$$b0
000141627 245__ $$aNeural system prediction and identification challenge
000141627 260__ $$aLausanne$$bFrontiers Research Foundation$$c2013
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000141627 520__ $$aCan we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.
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000141627 7001_ $$0P:(DE-Juel1)151167$$aZaytsev, Yury$$b1$$ufzj
000141627 7001_ $$0P:(DE-HGF)0$$aSpreizer, Sebastian$$b2
000141627 7001_ $$0P:(DE-HGF)0$$aAertsen, Ad$$b3
000141627 7001_ $$0P:(DE-HGF)0$$aKumar, Arvind$$b4$$eCorresponding author
000141627 773__ $$0PERI:(DE-600)2452979-5$$a10.3389/fninf.2013.00043$$gVol. 7$$n43$$p1-10$$tFrontiers in neuroinformatics$$v7$$x1662-5196$$y2013
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