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How many neurons can we see with current spike sorting algorithms?

ABSTRACT: Recent studies highlighted the disagreement between the typical number of neurons observed with extracellular recordings and the ones to be expected based on anatomical and physiological considerations. This disagreement has been mainly attributed to the presence of sparsely firing neurons. However, it is also possible that this is due to limitations of the spike sorting algorithms used to process the data. To address this issue, we used realistic simulations of extracellular recordings and found a relatively poor spike sorting performance for simulations containing a large number of neurons. In fact, the number of correctly identified neurons for single-channel recordings showed an asymptotic behavior saturating at about 8-10 units, when up to 20 units were present in the data. This performance was significantly poorer for neurons with low firing rates, as these units were twice more likely to be missed than the ones with high firing rates in simulations containing many neurons. These results uncover one of the main reasons for the relatively low number of neurons found in extracellular recording and also stress the importance of further developments of spike sorting algorithms.

SUBMITTER: Pedreira C

PROVIDER: S-EPMC3657693 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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How many neurons can we see with current spike sorting algorithms?

Pedreira Carlos C Martinez Juan J Ison Matias J MJ Quian Quiroga Rodrigo R

Journal of neuroscience methods 20120725 1

Recent studies highlighted the disagreement between the typical number of neurons observed with extracellular recordings and the ones to be expected based on anatomical and physiological considerations. This disagreement has been mainly attributed to the presence of sparsely firing neurons. However, it is also possible that this is due to limitations of the spike sorting algorithms used to process the data. To address this issue, we used realistic simulations of extracellular recordings and foun ...[more]

PMID: 22841630

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Project description:MotivationAdvancements in high-throughput genomic sequencing are delivering genomic pathogen data at an unprecedented rate, positioning statistical phylogenetics as a critical tool to monitor infectious diseases globally. This rapid growth spurs the need for efficient inference techniques, such as Hamiltonian Monte Carlo (HMC) in a Bayesian framework, to estimate parameters of these phylogenetic models where the dimensions of the parameters increase with the number of sequences N. HMC requires repeated calculation of the gradient of the data log-likelihood with respect to (wrt) all branch-length-specific (BLS) parameters that traditionally takes O(N2) operations using the standard pruning algorithm. A recent study proposes an approach to calculate this gradient in O(N), enabling researchers to take advantage of gradient-based samplers such as HMC. The CPU implementation of this approach makes the calculation of the gradient computationally tractable for nucleotide-based models but falls short in performance for larger state-space size models, such as Markov-modulated and codon models. Here, we describe novel massively parallel algorithms to calculate the gradient of the log-likelihood wrt all BLS parameters that take advantage of graphics processing units (GPUs) and result in many fold higher speedups over previous CPU implementations.ResultsWe benchmark these GPU algorithms on three computing systems using three evolutionary inference examples exploring complete genomes from 997 dengue viruses, 62 carnivore mitochondria and 49 yeasts, and observe a >128-fold speedup over the CPU implementation for codon-based models and >8-fold speedup for nucleotide-based models. As a practical demonstration, we also estimate the timing of the first introduction of West Nile virus into the continental Unites States under a codon model with a relaxed molecular clock from 104 full viral genomes, an inference task previously intractable.Availability and implementationWe provide an implementation of our GPU algorithms in BEAGLE v4.0.0 (https://github.com/beagle-dev/beagle-lib), an open-source library for statistical phylogenetics that enables parallel calculations on multi-core CPUs and GPUs. We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (https://github.com/beast-dev/beast-mcmc).

Project description:High-density microelectrode arrays can be used to record extracellular action potentials from hundreds to thousands of neurons simultaneously. Efficient spike sorters must be developed to cope with such large data volumes. Most existing spike sorting methods for single electrodes or small multielectrodes, however, suffer from the "curse of dimensionality" and cannot be directly applied to recordings with hundreds of electrodes. This holds particularly true for the standard reference spike sorting algorithm, principal component analysis-based feature extraction, followed by k-means or expectation maximization clustering, against which most spike sorters are evaluated. We present a spike sorting algorithm that circumvents the dimensionality problem by sorting local groups of electrodes independently with classical spike sorting approaches. It is scalable to any number of recording electrodes and well suited for parallel computing. The combination of data prewhitening before the principal component analysis-based extraction and a parameter-free clustering algorithm obviated the need for parameter adjustments. We evaluated its performance using surrogate data in which we systematically varied spike amplitudes and spike rates and that were generated by inserting template spikes into the voltage traces of real recordings. In a direct comparison, our algorithm could compete with existing state-of-the-art spike sorters in terms of sensitivity and precision, while parameter adjustment or manual cluster curation was not required. NEW & NOTEWORTHY We present an automatic spike sorting algorithm that combines three strategies to scale classical spike sorting techniques for high-density microelectrode arrays: 1) splitting the recording electrodes into small groups and sorting them independently; 2) clustering a subset of spikes and classifying the rest to limit computation time; and 3) prewhitening the spike waveforms to enable the use of parameter-free clustering. Finally, we combined these strategies into an automatic spike sorter that is competitive with state-of-the-art spike sorters.

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How many neurons can we see with current spike sorting algorithms?

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How many neurons can we see with current spike sorting algorithms?

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