Skip to content

Advertisement

  • Oral presentation
  • Open Access

FIND -- a unified framework for neural data analysis

  • 1, 2Email author,
  • 1,
  • 1,
  • 1,
  • 1, 3, 4,
  • 1, 5 and
  • 1, 6
BMC Neuroscience200910 (Suppl 1) :S1

https://doi.org/10.1186/1471-2202-10-S1-S1

  • Published:

Keywords

  • Point Process
  • Spike Activity
  • Discrete Series
  • Widespread Availability
  • Recording Technique

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).

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

Declarations

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.

Authors’ Affiliations

(1)
Bernstein Center for Computational Neuroscience, Albert-Ludwig University, 79104 Freiburg, Germany
(2)
Neurobiology and Biophysics, Faculty of Biology, Albert-Ludwig University, 79104 Freiburg, Germany
(3)
Bernstein Center for Computational Neuroscience, 10115 Berlin, Germany
(4)
Neuroinformatics and Theoretical Neuroscience, Institute of Biology, Freie Universität, 14195 Berlin, Germany
(5)
Multi Channel Systems, 72770 Reutlingen, Germany
(6)
Dept. Microsystems Engineering, Faculty of Technical Sciences, Albert-Ludwig University, 79110 Freiburg, Germany

References

  1. FIND - Finding Information in Neural Data. [http://find.bccn.uni-freiburg.de]
  2. Meier R, Egert U, Aertsen A, Nawrot MP: FIND - A unified framework for neural data analysis. Neural Networks. 2008, 21: 1085-1093. 10.1016/j.neunet.2008.06.019.PubMedView ArticleGoogle Scholar
  3. 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. 10.1523/JNEUROSCI.3424-08.2009.PubMedView ArticleGoogle Scholar
  4. 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. 10.3389/neuro.04.001.2009.PubMed CentralPubMedView ArticleGoogle Scholar
  5. Kumar A, Schrader S, Aertsen A, Rotter S: The high-conductance state of cortical networks. Neural Computation. 2008, 20: 1-43. 10.1162/neco.2008.20.1.1.PubMedView ArticleGoogle Scholar
  6. 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. 10.1523/JNEUROSCI.2542-07.2008.PubMedView ArticleGoogle Scholar

Copyright

© Aertsen et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd.

Advertisement