Stream-based Hebbian Eigenfilter for real-time neuronal spike discrimination
Background Principal component analysis (PCA) has been widely employed for automatic neuronal spike sorting. Calculating principal components (PCs) is computationally expensive, and requires complex numerical operations and large memory resources. Substantial hardware resources are therefore needed...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BioMed Central Ltd
2012
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| Online Access: | http://hdl.handle.net/1721.1/71767 |
| _version_ | 1826201027925245952 |
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| author | Yu, Bo Mak, Terrence Li, Xiangyu Smith, Leslie Sun, Yihe Poon, Chi-Sang |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Yu, Bo Mak, Terrence Li, Xiangyu Smith, Leslie Sun, Yihe Poon, Chi-Sang |
| author_sort | Yu, Bo |
| collection | MIT |
| description | Background Principal component analysis (PCA) has been widely employed for automatic neuronal spike sorting. Calculating principal components (PCs) is computationally expensive, and requires complex numerical operations and large memory resources. Substantial hardware resources are therefore needed for hardware implementations of PCA. General Hebbian algorithm (GHA) has been proposed for calculating PCs of neuronal spikes in our previous work, which eliminates the needs of computationally expensive covariance analysis and eigenvalue decomposition in conventional PCA algorithms. However, large memory resources are still inherently required for storing a large volume of aligned spikes for training PCs. The large size memory will consume large hardware resources and contribute significant power dissipation, which make GHA difficult to be implemented in portable or implantable multi-channel recording micro-systems. Method In this paper, we present a new algorithm for PCA-based spike sorting based on GHA, namely stream-based Hebbian eigenfilter, which eliminates the inherent memory requirements of GHA while keeping the accuracy of spike sorting by utilizing the pseudo-stationarity of neuronal spikes. Because of the reduction of large hardware storage requirements, the proposed algorithm can lead to ultra-low hardware resources and power consumption of hardware implementations, which is critical for the future multi-channel micro-systems. Both clinical and synthetic neural recording data sets were employed for evaluating the accuracy of the stream-based Hebbian eigenfilter. The performance of spike sorting using stream-based eigenfilter and the computational complexity of the eigenfilter were rigorously evaluated and compared with conventional PCA algorithms. Field programmable logic arrays (FPGAs) were employed to implement the proposed algorithm, evaluate the hardware implementations and demonstrate the reduction in both power consumption and hardware memories achieved by the streaming computing Results and discussion Results demonstrate that the stream-based eigenfilter can achieve the same accuracy and is 10 times more computationally efficient when compared with conventional PCA algorithms. Hardware evaluations show that 90.3% logic resources, 95.1% power consumption and 86.8% computing latency can be reduced by the stream-based eigenfilter when compared with PCA hardware. By utilizing the streaming method, 92% memory resources and 67% power consumption can be saved when compared with the direct implementation of GHA. Conclusion Stream-based Hebbian eigenfilter presents a novel approach to enable real-time spike sorting with reduced computational complexity and hardware costs. This new design can be further utilized for multi-channel neuro-physiological experiments or chronic implants. |
| first_indexed | 2024-09-23T11:45:30Z |
| format | Article |
| id | mit-1721.1/71767 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:45:30Z |
| publishDate | 2012 |
| publisher | BioMed Central Ltd |
| record_format | dspace |
| spelling | mit-1721.1/717672022-10-01T05:46:05Z Stream-based Hebbian Eigenfilter for real-time neuronal spike discrimination Yu, Bo Mak, Terrence Li, Xiangyu Smith, Leslie Sun, Yihe Poon, Chi-Sang Harvard University--MIT Division of Health Sciences and Technology Poon, Chi-Sang Background Principal component analysis (PCA) has been widely employed for automatic neuronal spike sorting. Calculating principal components (PCs) is computationally expensive, and requires complex numerical operations and large memory resources. Substantial hardware resources are therefore needed for hardware implementations of PCA. General Hebbian algorithm (GHA) has been proposed for calculating PCs of neuronal spikes in our previous work, which eliminates the needs of computationally expensive covariance analysis and eigenvalue decomposition in conventional PCA algorithms. However, large memory resources are still inherently required for storing a large volume of aligned spikes for training PCs. The large size memory will consume large hardware resources and contribute significant power dissipation, which make GHA difficult to be implemented in portable or implantable multi-channel recording micro-systems. Method In this paper, we present a new algorithm for PCA-based spike sorting based on GHA, namely stream-based Hebbian eigenfilter, which eliminates the inherent memory requirements of GHA while keeping the accuracy of spike sorting by utilizing the pseudo-stationarity of neuronal spikes. Because of the reduction of large hardware storage requirements, the proposed algorithm can lead to ultra-low hardware resources and power consumption of hardware implementations, which is critical for the future multi-channel micro-systems. Both clinical and synthetic neural recording data sets were employed for evaluating the accuracy of the stream-based Hebbian eigenfilter. The performance of spike sorting using stream-based eigenfilter and the computational complexity of the eigenfilter were rigorously evaluated and compared with conventional PCA algorithms. Field programmable logic arrays (FPGAs) were employed to implement the proposed algorithm, evaluate the hardware implementations and demonstrate the reduction in both power consumption and hardware memories achieved by the streaming computing Results and discussion Results demonstrate that the stream-based eigenfilter can achieve the same accuracy and is 10 times more computationally efficient when compared with conventional PCA algorithms. Hardware evaluations show that 90.3% logic resources, 95.1% power consumption and 86.8% computing latency can be reduced by the stream-based eigenfilter when compared with PCA hardware. By utilizing the streaming method, 92% memory resources and 67% power consumption can be saved when compared with the direct implementation of GHA. Conclusion Stream-based Hebbian eigenfilter presents a novel approach to enable real-time spike sorting with reduced computational complexity and hardware costs. This new design can be further utilized for multi-channel neuro-physiological experiments or chronic implants. Engineering and Physical Sciences Research Council (EPSRC grant EP/E044662/1) National Natural Science Foundation (China) ( grant 61006021) National Natural Science Foundation (China) (grant 4112029) National Institutes of Health (U.S.) (Grant HL067966) National Institutes of Health (U.S.) (Grant RR028241) 2012-07-23T20:36:28Z 2012-07-23T20:36:28Z 2012-04 2011-12 2012-05-15T15:03:46Z Article http://purl.org/eprint/type/JournalArticle 1475-925X http://hdl.handle.net/1721.1/71767 Yu, Bo et al. “Stream-based Hebbian Eigenfilter for Real-time Neuronal Spike Discrimination.” BioMedical Engineering OnLine 11.1 (2012): 18. Web. en http://dx.doi.org/10.1186/1475-925X-11-18 BioMedical Engineering OnLine Creative Commons Attribution http://creativecommons.org/licenses/by/2.0 Yu et al.; licensee BioMed Central Ltd. application/pdf BioMed Central Ltd BioMed Central Ltd |
| spellingShingle | Yu, Bo Mak, Terrence Li, Xiangyu Smith, Leslie Sun, Yihe Poon, Chi-Sang Stream-based Hebbian Eigenfilter for real-time neuronal spike discrimination |
| title | Stream-based Hebbian Eigenfilter for real-time neuronal spike discrimination |
| title_full | Stream-based Hebbian Eigenfilter for real-time neuronal spike discrimination |
| title_fullStr | Stream-based Hebbian Eigenfilter for real-time neuronal spike discrimination |
| title_full_unstemmed | Stream-based Hebbian Eigenfilter for real-time neuronal spike discrimination |
| title_short | Stream-based Hebbian Eigenfilter for real-time neuronal spike discrimination |
| title_sort | stream based hebbian eigenfilter for real time neuronal spike discrimination |
| url | http://hdl.handle.net/1721.1/71767 |
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