DOI: 10.25377/sussex.26177569.v1
title: Validation data for paper "SSSort 2.0: A semi-automated spike detection and sorting system for single sensillum recordings"

description: <b>Research data for paper published in Journal of Neuroscience Methods, Volume 415, March 2025.</b>This data was used to validate the sorting accuracy of SSSort 2.0 and other spike sorting methods used for comparisons in this paper. This data includes the SSR trace recordings, as well as the 'ground truth' spike times used in this analysis.Ground truth data sets were generated using the Spike2 data acquisition and analysis package from Cambridge Electronic Design, Ltd. (https://ced.co.uk/). In order to improve the usefulness of this data, we have included both the original Spike2 file formats (smrx and smr), as well as the complete datasets in a widely compatible format (txt) and the individual test files in a Python readable format (dll).<b>Data & File Overview</b><b>File List:</b>Synthetic 'ground truth' data in 64bit Spike2 format (smrx):· SSSort_doubleAB.smrx· SSSort_singleA.smrx· SSSort_singleB.smrxSynthetic 'ground truth' in spreadsheet text format (txt):· SSSort_doubleAB.txt· SSSort_singleA.txt· SSSort_singleB.txtMerged data files in 32bit Spike2 format (smr):· SSSort_doubleAB_asymXX.smr· SSSort_singleA_asymXX.smr· SSSort_singleB_asymXX.smrMerged data files in Python readable format (dll):· SSSort_doubleAB_asymXX.dll· SSSort_singleA_asymXX.dll· SSSort_singleB_asymXX.dll<b>Data-specific Information for: ‘SSSort_doubleAB.smrx’, ‘SSSort_singleA.smrx’ & ‘SSSort_singleB.smrx’</b>These data files each contain seven Waveform channels and two Event+ channels:· Channel 1: Waveform of A spiking data· Channel 2: Waveform of B spikes· Channel 3: Event+ of A spike events· Channel 4: Event+ of B spiking data· Channels 5-9: Waveforms of merged synthetic SSR data with 0.3 to 0.7 B:A asymmetries<b>Data-specific Information for: ‘SSSort_doubleAB.txt, ‘SSSort_singleA.txt’ & ‘SSSort_singleB.txt’</b>These data files each contain the same data as the above ‘.smrx’ files, but in a more openly readable format.<b>Data-specific Information for: 'SSSort_doubleAB_asymXX.smr', ‘SSSort_singleA_asymXX.smr' & ‘SSSort_singleA_asymXX.smr'</b>These data files each contain a single Waveform channel of merged synthetic SSR data at XX B:A asymmetry. This format can be converted to dll with the 'smr2dill.py' provided on the SSSort GitHub repository.<b>Data-specific Information for: 'SSSort_doubleAB_asymXX.dll, ‘SSSort_singleA_asymXX.dll' & ‘SSSort_singleA_asymXX.dll'</b>These data files each contain the same single Waveform channel data as the above ‘.smr’ files and are suitable for direct analysis in SSSort 2.0.<b>Abstract</b>Single-sensillum recordings are a valuable tool for sensory research which, by their nature, access extra-cellular signals typically reflecting the combined activity of several co-housed sensory neurons. However, isolating the contribution of an individual neuron through spike-sorting has remained a major challenge due to firing rate-dependent changes in spike shape and the overlap of co-occurring spikes from several neurons. These challenges have so far made it close to impossible to investigate the responses to more complex, mixed odour stimuli. Here we present SSSort 2.0, a method and software addressing both problems through automated and semi-automated signal processing. We have also developed a method for more objective validation of spike sorting methods based on generating surrogate ground truth data and we have tested the practical effectiveness of our software in a user study. We find that SSSort 2.0 typically matches or exceeds the performance of expert manual spike sorting. We further demonstrate that, for novices, accuracy is much better with SSSort 2.0 under most conditions. Overall, we have demonstrated that spike-sorting with SSSort 2.0 software can automate data processing of SSRs with accuracy levels comparable to, or above, expert manual performance.