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Neural system prediction and identification challenge.

Ioannis Vlachos¹, Yury V Zaytsev², Sebastian Spreizer¹

¹Faculty of Biology, Bernstein Center Freiburg, University of Freiburg Freiburg im Breisgau, Germany.

Frontiers in Neuroinformatics

|January 9, 2014

Summary

This summary is machine-generated.

Researchers developed the Neural Systems Identification and Prediction Challenge (nuSPIC) to determine if neuronal activity and connectivity can reveal biological neural network (BNN) functions. This crowdsourcing challenge uses the NEST simulator to explore computational inference of BNN functions.

Keywords:

NEST network function network simulation nuSPIC spiking neural network

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Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Understanding biological neural networks (BNNs) is central to neuroscience.
Current methods focus on recording neuronal activity and connectivity, but theoretical proof of function inference is lacking.

Purpose of the Study:

To computationally investigate if neuronal activity and connectivity data are sufficient to infer BNN functions.
To introduce the Neural Systems Identification and Prediction Challenge (nuSPIC) as a platform for this investigation.

Main Methods:

The nuSPIC challenge provides BNN connectivity and activity data.
Participants use the NEST simulator via a web interface to infer implemented functions and create new ones.
The challenge employs a crowdsourcing model and requires no programming knowledge.

Main Results:

The study aims to identify which neural functions are inferable from systematic recordings.
Results will guide the design of experimental paradigms for discovering neural functions.

Conclusions:

The nuSPIC challenge provides a computational framework to address fundamental questions in neuroscience.
This approach facilitates understanding the relationship between neural structure, activity, and function.