Stream-based Hebbian eigenfilter for real-time neuronal spike discrimination.
Stream-based Hebbian eigenfilter for real-time neuronal spike discrimination.
复制标题
基于流的 Hebbian 特征滤波器，用于实时神经元尖峰辨别
DOI：
10.1186/1475-925x-11-18
复制
发表时间：
2012-04-10
影响因子：
3.9
通讯作者：
Poon CS
中科院分区：
文献类型：
--
作者：
Yu B;Mak T;Li X;Smith L;Sun Y;Poon CS
背景主成分分析（PCA）已被广泛应用于神经元放电的自动分选.计算主成分（PC）在计算上是昂贵的，并且需要复杂的数值运算和大量的存储器资源。因此，PCA的硬件实现需要大量的硬件资源。在我们以前的工作中，已经提出了通用Hebbian算法（GHA）来计算神经元棘波的PC，它消除了传统PCA算法中计算昂贵的协方差分析和特征值分解的需要。然而，仍然固有地需要大的存储器资源来存储用于训练PC的大量对齐尖峰。大容量的存储器会消耗大量的硬件资源和功耗，这使得GHA难以在便携式或植入式多通道记录微系统中实现。其消除了GHA的固有记忆要求，同时通过利用神经元尖峰的伪平稳性来保持尖峰分类的准确性。由于减少了大量的硬件存储需求，所提出的算法可以导致超低的硬件资源和功耗的硬件实现，这对未来的多通道微系统是至关重要的。临床和合成神经记录数据集被用来评估基于流的赫布特征滤波器的准确性。严格评估了基于流的特征滤波器的尖峰排序性能和特征滤波器的计算复杂度，并与传统的PCA算法进行了比较。现场可编程逻辑阵列（FPGA）被用来实现所提出的算法，评估的硬件实现，并展示了减少功耗和硬件内存实现的流式计算结果和discussionResults表明，基于流的特征滤波器可以达到相同的精度，是10倍以上的计算效率相比，传统的PCA算法。硬件评估表明，90.3%的逻辑资源，95.1%的功耗和86.8%的计算延迟可以减少基于流的特征滤波器相比PCA硬件。通过利用流的方法，92%的内存资源和67%的功耗可以节省时相比，直接实现GHA.ConclusionStream-based赫布特征滤波器提供了一种新的方法，使实时尖峰排序，降低计算复杂度和硬件成本。这种新的设计可以进一步用于多通道神经生理实验或慢性植入。
BackgroundPrincipal 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.MethodIn 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 computingResults and discussionResults 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.ConclusionStream-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.