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 (WFs) 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 (DCAE) architecture. DCAEs are deep neural networks that can learn a latent state representation of their inputs. The main advantage of DCAE 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 DCAE-based online spike sorting algorithm achieves over 90% accuracy at sorting previously-unseen spike waveforms. Moreover, our approach produces superior results compared to several state-of-the-art offline spike-sorting procedures.

Categories are reflected in the spiking activity of neurons. However, how neurons form ensembles for categories is unclear. To address this, we simultaneously recorded spiking and local field potential (LFP) activity in the lateral prefrontal cortex (lPFC) of monkeys performing a delayed match to category task with two independent category sets (Animals: Cats vs Dogs; Cars: Sports Cars vs Sedans). We found stimulus and category information in alpha and beta band oscillations. Different category distinctions engaged different frequencies. There was greater spike field coherence (SFC) in alpha (~8-14 Hz) for Cats and in beta (~16-22 Hz) for Dogs. Cars showed similar differences, albeit less pronounced: greater alpha SFC for Sedans and greater beta SFC for Sports Cars. Thus, oscillatory rhythms can help coordinate neurons into different ensembles.Engagement of different frequencies may help differentiate the categories.peer-reviewed)