We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human intervention is minimized and streamlined via a graphical interface. We illustrate our method on data from a mammalian retina preparation and document its performance on simulated data consisting of spikes added to experimentally measured background noise. The algorithm is highly accurate.
University of Edinburgh（爱丁堡大学）；MIT–IBM Watson AI Lab（麻省理工-IBM沃森人工智能实验室）；University of Oslo（奥斯陆大学）
Determining the positions of neurons in an extracellular recording is useful for investigating functional properties of the underlying neural circuitry. In this work, we present a Bayesian modelling approach for localizing the source of individual spikes on high-density, microelectrode arrays. To allow for scalable inference, we implement our model as a variational autoencoder and perform amortized variational inference. We evaluate our method on both biophysically realistic simulated and real extracellular datasets, demonstrating that it is more accurate than and can improve spike sorting per…
In electrophysiology, microelectrodes are the primary source for recording neural data of single neurons (single unit activity). These microelectrodes can be implanted individually, or in the form of microelectrodes arrays, consisting of hundreds of electrodes. During recordings, some channels capture the activity of neurons, which is usually contaminated with external artifacts and noise. Another considerable fraction of channels does not record any neural data, but external artifacts and noise. Therefore, an automatic identification and tracking of channels containing neural data is of great…
Multi-electrode arrays (MEAs) can record extracellular action potentials (also known as 'spikes') from hundreds or thousands of neurons simultaneously. Inference of a functional network from a spike train is a fundamental and formidable computational task in neuroscience. With the advancement of MEA technology, it has become increasingly crucial to develop statistical tools for analyzing multiple neuronal activity as a network. In this paper, we propose a scalable Bayesian framework for inference of functional networks from MEA data. Our framework makes use of the hierarchical structure of net…
Multi-electrode arrays (MEA) are increasingly used to investigate spontaneous neuronal network activity. The recorded signals comprise several distinct components: Apart from artefacts without biological significance, one can distinguish between spikes (action potentials) and subthreshold fluctuations (local fields potentials). Here we aim to develop a theoretical model that allows for a compact and robust characterization of subthreshold fluctuations in terms of a Gaussian statistical field theory in two spatial and one temporal dimension. What is usually referred to as the driving noise in t…
Objective. Recent advancements in electrode designs and micro-fabrication technology has allowed existence of microelectrode arrays with hundreds of channels for single-cell recordings. In such electrophysiological recordings, each implanted micro-electrode can record the activities of more than one neuron in its vicinity. Recording the activities of multiple neurons may also be referred to as multiple unit activity. However, for any further analysis, the main goal is to isolate the activity of each recorded neuron and thus called single-unit activity. This process may also be referred to as s…
A common way of studying the relationship between neural activity and behavior is through the analysis of neuronal spike trains that are recorded using one or more electrodes implanted in the brain. Each spike train typically contains spikes generated by multiple neurons. A natural question that arises is "what is the number of neurons $ν$ generating the spike train?"; This article proposes a method-of-moments technique for estimating $ν$. This technique estimates the noise nonparametrically using data from the silent region of the spike train and it applies to isolated spikes with a possibly …
Spike sorting refers to the problem of assigning action potentials observed in extra-cellular recordings of neural activity to the neuron(s) from which they originate. We cast this problem as one of learning a convolutional dictionary from raw multi-electrode waveform data, subject to sparsity constraints. In this context, sparsity refers to the number of neurons that are allowed to spike simultaneously. The convolutional dictionary setting, along with its assumptions (e.g. refractoriness) that are motivated by the spike-sorting problem, let us give theoretical bounds on the sample complexity …
A new learning scheme called time divergence-convergence (TDC) is proposed for two-layer dynamic synapse neural networks (DSNN). DSNN is an artificial neural network model, in which the synaptic transmission is modeled by a dynamic process and the information between neurons are transmitted through spike timing. In TDC, the intra-layer neurons of a DSNN are trained to map input spike trains to a higher dimension of spike trains called a feature-domain, and the output neurons are trained to build the desired spike trains by processing the spike timing of intralayer neurons. The DSNN performance…
One of the main current issues in Neurobiology concerns the understanding of interrelated spiking activity among multineuronal ensembles and differences between stimulus-driven and spontaneous activity in neurophysiological experiments. Multi electrode array recordings that are now commonly used monitor neuronal activity in the form of spike trains from many well identified neurons. A basic question when analyzing such data is the identification of the directed graph describing "synaptic coupling" between neurons. In this article we deal with this matter working with a high quality multielectr…
Over the past two decades, there has been a radical transformation in our ability to extract useful biological signals from the surface electromyogram (EMG). Advances in EMG electrode design and signal processing techniques have resulted in an extraordinary capacity to identify motor unit spike trains from the surface of a muscle. These EMG grid, or high-density surface EMG (HDsEMG), recordings now provide accurate depictions of as many as 20-30 motor unit spike trains simultaneously during isometric contractions, even at high forces. Such multi-unit recordings often display an unexpected feat…
Cross-Modal Representational Knowledge Distillation for Enhanced Spike-Informed LFP Modeling
Ming Hsieh Department of Electrical and Computer Engineering（明希斯电气与计算机工程系）；Thomas Lord Department of Computer Science（托马斯·劳德计算机科学系）；Alfred E. Mann Department of Biomedical Engineering（阿尔弗雷德·E·曼生物医学工程系）；Neuroscience Graduate Program University of Southern California（神经科学研究生项目美国南加州大学）
Functional Classification of Spiking Signal Data Using Artificial Intelligence Techniques: A Review
Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University（迪肯大学智能系统研究与创新研究所）；CSIRO Health and Biosecurity, Australian Animal Health Laboratory（澳大利亚联邦科学与工业研究组织健康与生物安全部，澳大利亚动物健康实验室）；Technical and Vocational University (TVU)（技术职业大学）；University of Birjand（比尔詹德大学）；Islamic Azad University（伊斯兰阿扎德大学）；University of Bologna（博洛尼亚大学）；National Heart Centre Singapore（新加坡国家心脏中心）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；University of Southern Queensland（南昆士兰大学）
Scalable Construction of Spiking Neural Networks using up to thousands of GPUs
University of Cagliari（卡利亚里大学）；Istituto Nazionale di Fisica Nucleare (INFN)（意大利国家核物理研究所）；Jülich Research Centre（于利希研究中心）；RWTH Aachen University（亚琛工业大学）
Complexity in the temporal organization of neural systems may be a reflection of the diversity of its neural constituents. These constituents, excitatory and inhibitory neurons, comprise an invariant ratio in vivo and form the substrate for rhythmic oscillatory activity. To begin to elucidate the dynamical mechanisms that underlie this balance, we construct novel neural circuits not ordinarily found in nature. We culture several networks of neurons composed of excitatory and inhibitory cells and use a multi-electrode array to study their temporal dynamics as the balance is modulated. We use th…
