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We thank Claudio Alonso for his help with fly experiments and for providing lab support. The work in this paper was funded by a Leverhulme Trust project grant (TN and GK) and the EPSRC (Brains on Board project, Grant Number EP/P006094/1, ActiveAI project, Grant Number EP/S030964/1) (TN) . PID2021-122347NB-I00 (MCIN/AEI and ERDF-"A way of making Europe") (AGP) .

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SSSort 2.0: A semi-automated spike detection and sorting system for single sensillum recordings

Publicado en:JOURNAL OF NEUROSCIENCE METHODS. 415 110351- - 2025-03-01 415(), DOI: 10.1016/j.jneumeth.2024.110351

Autores: Ellison, Lydia; Raiser, Georg; Garrido-Pena, Alicia; Kemenes, Gyorgy; Nowotny, Thomas

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Background: 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. New Method: 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. Results: 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. Conclusion: 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.

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