Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation
Tim Zhang, Corey Lammie, Mostafa Rahimi Azghadi, Amirali Amirsoleimani, Majid Ahmadi, Roman Genov
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/timothyzh/mwscas2022spikesortingcriteriaOfficialIn paperpytorch★ 2
Abstract
Spike sorting algorithms are used to separate extracellular recordings of neuronal populations into single-unit spike activities. The development of customized hardware implementing spike sorting algorithms is burgeoning. However, there is a lack of a systematic approach and a set of standardized evaluation criteria to facilitate direct comparison of both software and hardware implementations. In this paper, we formalize a set of standardized criteria and a publicly available synthetic dataset entitled Synthetic Simulations Of Extracellular Recordings (SSOER), which was constructed by aggregating existing synthetic datasets with varying Signal-To-Noise Ratios (SNRs). Furthermore, we present a benchmark for future comparison, and use our criteria to evaluate a simulated Resistive Random-Access Memory (RRAM) In-Memory Computing (IMC) system using the Discrete Wavelet Transform (DWT) for feature extraction. Our system consumes approximately (per channel) 10.72mW and occupies an area of 0.66mm^2 in a 22nm FDSOI Complementary Metal-Oxide-Semiconductor (CMOS) process.