A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings

Jonathan W. Pillow is with UT Austin, Jonathon Shlens is with the Salk Institute, E. J. Chichilnisky is with the Salk Institute, and Eero P. Simoncelli is with New York University.We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call “binary pursuit”. The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.This work was supported by: Royal Society Society USA/Canada Research Fellowship (JWP) (http://royalsociety.org/grants/); Center for Perceptual Systems, startup funding (JP) (http://www.utexas.edu/cola/centers/cps/); Sloan Research Fellowship (JWP) (http://www.sloan.org/); Miller Institute for Basic Research in Science (JS) (http://millerinstitute.berkeley.edu/); National Eye Institute (NEI) grant EY018003 (EJC, EPS); National Institutes of Health (NIH) Grant EY017736 (EJC); and Howard Hughes Medical Institute (EPS) (http://www.hhmi.org/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Psycholog

Etiology and immunology of infectious bronchitis virus

Infectious bronchitis virus (IBV) of chickens is currently one of the main diseases associated with respiratory syndrome in domestic poultry, as well as with losses related to egg production. The etiological agent is a coronavirus, which presents structural differences in the field, mainly in the S1 spike protein. The immune response against this virus is complicated by the few similarities among serotypes. Environmental and management factors, as well as the high mutation rate of the virus, render it difficult to control the disease and compromise the efficacy of the available vaccines. Bird immune system capacity to respond to challenges depend on the integrity of the mucosae, as an innate compartment, and on the generation of humoral and cell-mediated adaptive responses, and may affect the health status of breeding stocks in the medium run. Vaccination of day-old chicks in the hatchery on aims at eliciting immune responses, particularly cell-mediated responses that are essential when birds are first challenged. Humoral response (IgY and IgA) are also important for virus clearance in subsequent challenges. The presence of antibodies against the S1 spike protein in 3- to 4-week-old birds is important both in broilers and for immunological memory in layers and breeders