title: Complexity optimization and high-throughput low-latency hardware implementation of a multi-electrode spike-sorting algorithm.
abstract: Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction.
authors.forename: Jelena
authors.initials: J
authors.lastname: Dragas
authors.forename: David
authors.initials: D
authors.lastname: Jackel
authors.forename: Andreas
authors.initials: A
authors.lastname: Hierlemann
authors.forename: Felix
authors.initials: F
authors.lastname: Franke
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D004562:Electrocardiography / Q000295:instrumentation* / Q000379:methods; D004566:Electrodes; D004867:Equipment Design; D019544:Equipment Failure Analysis; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*