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Neural Networks
Volume 21, Issue 8, October 2008, Pages 1085-1093
Neuroinformatics, Neuroinformatics
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doi:10.1016/j.neunet.2008.06.019    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2008 Elsevier Ltd All rights reserved.

2008 Special Issue

FIND — A unified framework for neural data analysis

Ralph Meiera, Corresponding Author Contact Information, E-mail The Corresponding Author, Ulrich Egerta, b, Ad Aertsena, E-mail The Corresponding Author and Martin P. Nawrotc, d, a

aBernstein Center for Computational Neuroscience, Albert-Ludwigs-University, Freiburg, Germany bDepartment of Microsystems Engineering, Faculty of Applied Sciences, Albert-Ludwigs-University, Freiburg, Germany cNeuroinformatics and Theoretical Neuroscience, Institute of Biology–Neurobiology, Freie Universität Berlin, Germany dBernstein Center for Computational Neuroscience, Berlin, Germany

Received 2 November 2007; 
revised 7 June 2008; 
accepted 17 June 2008. 
Available online 3 July 2008.


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 develop FIND (Finding Information in Neural Data) 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. 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 illustrate two examples of complex analyses with FIND tools: First, we present a time-resolved characterization of the spiking irregularity in an in vivo extracellular recording from a mushroom-body extrinsic neuron in the honeybee during odor stimulation. Second, we describe layer specific input dynamics in the rat primary visual cortex in vivo in response to visual flash stimulation on the basis of multi-channel spiking activity.

Keywords: Coefficient of variation; Gamma process; Point process simulation; Spike train analysis; Neural activity data analysis; Open source toolbox; Mushroom body; Visual cortex; Rat; Honey bee

Article Outline

1. Motivation
2. Concept
3. Design and community interaction
3.1. Data import and internal data representation
3.2. External data representation and storage
3.3. Addressing the need for different programming languages by using plug-ins
3.3.1. Application of tools
3.3.2. Tool development and integration
3.3.3. Quality management
3.3.4. Teaching and training
4. Functionality
5. Examples of analysis with FIND
5.1. Odor-response dynamics of spiking irregularity in a mushroom-body extrinsic neuron of the honeybee
5.1.1. Experiments
5.1.2. Rate estimation
5.1.3. Time warp
5.1.4. Time-resolved estimation of CV
5.1.5. Dynamic changes of the gamma order
5.1.6. Conclusion and interpretation
5.2. Layer-specific stimulus response latency in the rat visual cortex
5.2.1. Experiments
5.2.2. Estimation of firing rates and their derivatives
5.2.3. Estimation of temporal delays between channels
5.2.4. Estimation of the absolute latencies
5.2.5. Conclusion and interpretation
6. Conclusions, discussion and outlook

Neural Networks
Volume 21, Issue 8, October 2008, Pages 1085-1093
Neuroinformatics, Neuroinformatics
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