A Multi-Channel Spike Sorting Processor With Accurate Clustering Algorithm Using Convolutional Autoencoder

Seong, Changyu

doi:10.1109/TBCAS.2021.3134660

A Multi-Channel Spike Sorting Processor With Accurate Clustering Algorithm Using Convolutional Autoencoder

2021-12-01

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DOI：10.1109/TBCAS.2021.3134660

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This paper presents a spike sorting processor based on an accurate spike clustering algorithm. The proposed spike sorting algorithm employs an L2-normalized convolutional autoencoder to extract features from the input, where the autoencoder is trained using the proposed spike sorting-aware loss. In addition, we propose a similarity-based K-means clustering algorithm that conditionally updates the means by observing the cosine similarity. The modified K-means algorithm exhibits better convergence and enables online clustering with higher classification accuracy. We implement a spike sorting processor based on the proposed algorithm using an efficient time-multiplexed hardware architecture in a 40-nm CMOS process. Experimental results show that the processor consumes 224.75 mu W/mm(2) when processing 16 input channels at 7.68 MHz and 0.55 V. Our design achieves 95.54% clustering accuracy, outperforming prior spike sorting processor designs.

Keywords:

Clustering algorithms

Feature extraction

Sorting

Training

Image reconstruction

Real-time systems

Autoencoder

neural recording

real-time recording

spike sorting

A Multi-Channel Spike Sorting Processor With Accurate Clustering Algorithm Using Convolutional Autoencoder

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