FIND -- a unified framework for neural data analysis

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BMC Neuroscience

BioMed Central

Open Access

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FIND -- a unified framework for neural data analysis Ad Aertsen*1,2, Christian Garbers1, Antje Kilias1, Ralph Meier1, Martin P Nawrot1,3,4, Karl-Heinz Boven1,5 and Ulrich Egert1,6 Address: 1Bernstein Center for Computational Neuroscience, Albert-Ludwig University, 79104 Freiburg, Germany, 2Neurobiology and Biophysics, Faculty of Biology, Albert-Ludwig University, 79104 Freiburg, Germany, 3Bernstein Center for Computational Neuroscience, 10115 Berlin, Germany, 4Neuroinformatics and Theoretical Neuroscience, Institute of Biology, Freie Universität, 14195 Berlin, Germany, 5Multi Channel Systems, 72770 Reutlingen, Germany and 6Dept. Microsystems Engineering, Faculty of Technical Sciences, Albert-Ludwig University, 79110 Freiburg, Germany Email: Ad Aertsen* - [email protected] * Corresponding author

from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Berlin, Germany. 18-23 July 2009 Published: 29 September 2009 BMC Neuroscience 2009, 10(Suppl 1):S1


Eighteenth Annual Computational Neuroscience Meeting: CNS*2009 Don H Johnson Meeting abstracts - A single PDF containing all abstracts in this Supplement is available here.

This abstract is available from: © 2009 Aertsen et al; licensee BioMed Central Ltd.

The complexity of neurophysiology data has increased tremendously over the last years, especially due to the widespread availability of multi-channel recording techniques. With adequate computing power, the current limit for computational neuroscience is the effort and time it takes for scientists to translate their ideas into working code. Advanced analysis methods are complex and often lack reproducibility on the basis of published descriptions. To overcome this limitation we developed FIND (Finding Information in Neural Data; [1]) as a platform-independent, open-source framework for the analysis of neuronal activity data based on Matlab (Mathworks).

mental data[3,4], both from in vitro and in vivo recordings, and of recording data from simulated network models [5,6].

Here, we outline the structure of the FIND framework and describe its functionality, our measures of quality control, and the policies for developers and users [2]. Within FIND, we have developed a unified data import from various proprietary formats, simplifying standardized interfacing with tools for analysis and simulation. The toolbox FIND covers a steadily increasing number of tools. These analysis tools address various types of neural activity data, including discrete series of spike events, continuous time series and imaging data. Additionally, the toolbox provides solutions for the simulation of parallel stochastic point processes to model multi-channel spiking activity. We will illustrate the functioning of FIND by presenting examples of its application to different types of experi-


Acknowledgments The FIND framework is supported in parts by the German Federal Ministry of Education and Research (BMBF grant 01GQ0420 to the BCCN Freiburg and 01GQ0421 to Multi Channel Systems), and the 6th RFP of the EU (grant no. 15879-FACETS and 012788-NEURO). The contribution of M.N. is funded by the BMBF grant 01GQ0413 to BCCN Berlin.

References 1.

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5. 6.

FIND - Finding Information in Neural Data [http://] Meier R, Egert U, Aertsen A, Nawrot MP: FIND - A unified framework for neural data analysis. Neural Networks 2008, 21:1085-1093. Boucsein C, Tetzlaff T, Meier R, Aertsen A, Naundorf B: Dynamical response properties of neocortical neuron ensembles: Multiplicative versus additive noise. J Neurosci 2009, 29:1006-1010. Nawrot MP, Schnepel P, Aertsen A, Boucsein C: Precisely timed signal transmission in neocortical networks with reliable intermediate-range projections. Frontiers in Neural Circuits 2009, 3:1-11. Kumar A, Schrader S, Aertsen A, Rotter S: The high-conductance state of cortical networks. Neural Computation 2008, 20:1-43. Kumar A, Rotter S, Aertsen A: Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model. J Neurosci 2008, 28:5268-5280.

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