Online spike sorting via deep contractive autoencoder. (November 2022)

• Record Type: Journal Article
• Authors: Radmanesh, Mohammadreza; Rezaei, Ahmad Asgharian; Jalili, Mahdi; Hashemi, Alireza; Goudarzi, Morteza Moazami
• Is Part Of: Neural networks. Volume 155(2022)
• Page Start: 39
• Page End: 49
• Publication Date: 2022-11
• Subjects: Online spike sorting -- Deep learning -- Clustering -- Optimization -- Denoising autoencoders; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2022.08.001
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Spike sorting – the process of separating spikes from different neurons – is often the first and most critical step in the neural data analysis pipeline. Spike-sorting techniques isolate a single neuron's activity from background electrical noise based on the shapes of the waveforms obtained from extracellular recordings. Despite several advancements in this area, an important remaining challenge in neuroscience is online spike sorting, which has the potential to significantly advance basic neuroscience research and the clinical setting by providing the means to produce real-time perturbations of neurons via closed-loop control. Current approaches to online spike sorting are not fully automated, are computationally expensive and are often outperformed by offline approaches. In this paper, we present a novel algorithm for fast and robust online classification of single neuron activity. This algorithm is based on a deep contractive autoencoder (CAE) architecture. CAEs are neural networks that can learn a latent state representation of their inputs. The main advantage of CAE-based approaches is that they are less sensitive to noise (i.e., small perturbations in their inputs). We therefore reasoned that they can form the basis for robust online spike sorting algorithms. Overall, our deep CAE-based online spike sorting algorithm achieves over 90% accuracy in sorting unseen spike waveforms, outperforming existing models and maintaining a performance close to the offline case. In the offline scenario, our method substantially outperforms the existing models, providing an average improvement of 40% in accuracy over different datasets. Highlights: A novel algorithm for spike sorting based on a Contractive Auto-encoder. Produce representations of spike waveforms that are robust to additive noise. Reliably classify spikes for small and large datasets. Outperform SOTA approaches in various online and offline spike-sorting applications.