- Poster presentation
- Open Access
Inferring computational function of neuronal networks from multi-electrode array recordings: an evolutionary approach
© Kiehl; licensee BioMed Central Ltd. 2011
- Published: 18 July 2011
- Neuronal Network
- Computational Function
- Microelectrode Array
- Evolutionary Computing
- Specific Inference
The availability of microelectrode array systems (MEA’s) has increased dramatically in recent years. Along with this increase in availability, these systems have also grown in capability. Modern systems stimulate and record in-vitro neuronal networks on an increasing number of channels. Closed-loop capabilities further expand the functionality of these systems. Future miniaturization of these systems and commensurate increased resolution promises a continued rise in the volume of data being produced in this domain.
It is difficult to determine if analysis tools are keeping pace with data generation. As of a few years ago Brown et al. saw a distinct need for investment in this vein. It seems likely that data acquisition will continue to outstrip analysis just as it has in other bioinformatics domains. In 2006 Waganaar et al made available an extensive data set or recorded microelectrode array activity . This data set contained both spontaneous and stimulated activity recorded regularly, from 58 unique cultures, of varying cell density, over a period of five weeks. While this study is referenced by hundreds of presenters and researchers, only a few groups have published analyses of this data. Patnaik et al stand out in that very small cohort as they sought to infer network structure from this data set based on the last 5 days of recordings from 6 cultures. It is likely that these cultures were more static at this stage and were no longer developing connections at the same rate one may have observed in earlier time points.
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