scholarly journals Complexity Optimization and High-Throughput Low-Latency Hardware Implementation of a Multi-Electrode Spike-Sorting Algorithm

2015 ◽  
Vol 23 (2) ◽  
pp. 149-158 ◽  
Author(s):  
Jelena Dragas ◽  
David Jackel ◽  
Andreas Hierlemann ◽  
Felix Franke
Keyword(s):  
High Throughput ◽  
Hardware Implementation ◽  
Low Latency ◽  
Sorting Algorithm ◽  
Spike Sorting

scholarly journals PASER for automated analysis of neural signals recorded in pulsating magnetic fields

2019 ◽  
Author(s):  
Terence Brouns ◽  
Tansu Celikel
Keyword(s):  
Magnetic Field ◽  
Magnetic Fields ◽  
High Throughput ◽  
Neural Activity ◽  
Computational Cost ◽  
Field Analysis ◽  
Sorting Algorithm ◽  
Spike Sorting ◽  
Neural Recordings ◽  
Wide Range

AbstractThanks to the advancements in multichannel intracranial neural recordings, magnetic neuroimaging and magnetic neurostimulation techniques (including magnetogenetics), it is now possible to perform large-scale high-throughput neural recordings while imaging or controlling neural activity in a magnetic field. Analysis of neural recordings performed in a switching magnetic field, however, is not a trivial task as gradient and pulse artefacts interfere with the unit isolation. Here we introduce a toolbox called PASER, Processing and Analysis Schemes for Extracellular Recordings, that performs automated denoising, artefact removal, quality control of electrical recordings, unit classification and visualization. PASER is written in MATLAB and modular by design. The current version integrates with third party applications to provide additional functionality, including data import, spike sorting and the analysis of local field potentials. After the description of the toolbox, we evaluate 9 different spike sorting algorithms based on computational cost, unit yield, unit quality and clustering reliability across varying conditions including self-blurring and noise-reversal. Implementation of the best performing spike sorting algorithm (KiloSort) in the default version of the PASER provides the end user with a fully automated pipeline for quantitative analysis of broadband extracellular signals. PASER can be integrated with any established pipeline that sample neural activity with intracranial electrodes. Unlike the existing algorithmic solutions, PASER provides an end-to-end solution for neural recordings made in switching magnetic fields independent from the number of electrodes and the duration of recordings, thus enables high-throughput analysis of neural activity in a wide range of electro-magnetic recording conditions.

scholarly journals Orthros: A Low-Latency PRF

2021 ◽  
pp. 37-77
Author(s):  
Subhadeep Banik ◽  
Takanori Isobe ◽  
Fukang Liu ◽  
Kazuhiko Minematsu ◽  
Kosei Sakamoto
Keyword(s):  
Hardware Implementation ◽  
Block Cipher ◽  
State Of The Art ◽  
Security Analysis ◽  
Parallel Structure ◽  
Low Latency ◽  
Round Function ◽  
Minimum Latency ◽  
Primary Focus ◽  
Pseudorandom Function

We present Orthros, a 128-bit block pseudorandom function. It is designed with primary focus on latency of fully unrolled circuits. For this purpose, we adopt a parallel structure comprising two keyed permutations. The round function of each permutation is similar to Midori, a low-energy block cipher, however we thoroughly revise it to reduce latency, and introduce different rounds to significantly improve cryptographic strength in a small number of rounds. We provide a comprehensive, dedicated security analysis. For hardware implementation, Orthros achieves the lowest latency among the state-of-the-art low-latency primitives. For example, using the STM 90nm library, Orthros achieves a minimum latency of around 2.4 ns, while other constructions like PRINCE, Midori-128 and QARMA9-128- σ0 achieve 2.56 ns, 4.10 ns, 4.38 ns respectively.

scholarly journals Automatic spike sorting for high-density microelectrode arrays

2018 ◽  
Vol 120 (6) ◽  
pp. 3155-3171 ◽  
Author(s):  
Roland Diggelmann ◽  
Michele Fiscella ◽  
Andreas Hierlemann ◽  
Felix Franke
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Microelectrode Arrays ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Sorting Algorithm ◽  
Spike Sorting ◽  
Recording Electrodes

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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