Project:
Ming Large-Scale Neural Ensemble Recording
(NIH 1 R21 NS054148-01A1)
PI: Karim Oweiss,
Co-PI: Rong Jin, Department of CSE, Michigan State University
Abstract:
Understanding how neurons act in concert requires observation of the collective activity of large, spatially distributed neuronal aggregates. Largely motivated by the rapid advances in microfabrication technology, high-density implantable electronic interfaces are now enabling the acquisition of large volumes of physiological and behavioral data, triggering concomitant neurobiological discoveries. Nevertheless, advances in the fabrication of high-density microelectrode arrays (MEAs) were not associated with quantum advances in array processing and data analysis techniques in order to unveil the affluent information content in the recorded neural data. As the number of recording channels on a single microprobe device becomes astoundingly large, no discipline is more challenged than signal processing and data mining in accommodating these new advances within the emerging neural engineering arena. There is an intrinsic need to design new algorithms and software tools to optimize array processing and information retrieval from multiple spike train neural data to answer several persistent neuroscience questions. The fundamental objective of this research is to explore and develop an integrated array processing and data mining framework with companion software tools to extract the useful information from large-scale neuronal ensemble recordings through the following broad aims:
1. Develop scalable and adaptive array processing algorithms for processing high-density microelectrode array recordings in short and long-term experimental setups;
2. Develop data analysis and clustering techniques for mining functional interdependency among neural ensembles from the recorded mixtures;
3. Test and demonstrate the efficiency of these techniques and the integrity of the developed software on simulated and experimental data shared by investigators in the field.
Students:
Project Goal:
The fundamental objective of this research is to explore and develop an integrated array processing and data mining framework with companion software tools to extract the useful information from large-scale neuronal ensemble recordings through the following broad aims: (a) develop scalable and adaptive array processing algorithms for processing high-density microelectrode array recordings in short and long-term experimental setups; (b) develop data analysis and clustering techniques for mining functional interdependency among neural ensembles from the recorded mixtures; and (c) test and demonstrate the efficiency of these techniques and the integrity of the developed software on simulated and experimental data shared by investigators in the field.
Current Results:
Develop spectral clustering algorithms to infer functional dependency among neurons
Develop Dynamic Bayesian Network (DBN) algorithms to reconstruct functional neuronal circuits from ensemble recordings
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