Ham Clustering

Journal of Neuroscience Methods 235 (2014) 145–156

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Journal of Neuroscience Methods

journal homepage: www.elsevier.com/locate/jneumeth

Computational Neuroscience

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting

Sivylla E. Paraskevopoulou a,b,∗ , Di Wu a , Amir Eftekhar a,b , Timothy G. Constandinou a,b

a b

Department of Electrical and Electronic Engineering, Imperial College, London, SW7 2BT, UK Centre for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College, London, SW7 2AZ, UK

h i g h l i g h t s

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We present a novel unsupervised classification method for real-time spike sorting. The classification accuracy achieved is high (comparable to k-means with 10 iterations). Classification accuracy was derived with multiple feature sets and for multiple datasets (both simulated and real neural recordings). The low complexity of the method (quantified in terms of memory and computation requirements) makes it suitable for hardware implementation.

a r t i c l e

i n f o

a b s t r a c t

This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroidbased clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated...