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Buccino AP, Damart T, Bartram J, Mandge D, Xue X, Zbili M, Gänswein T, Jaquier A, Emmenegger V, Markram H, Hierlemann A, Van Geit W. A Multimodal Fitting Approach to Construct Single-Neuron Models With Patch Clamp and High-Density Microelectrode Arrays. Neural Comput 2024; 36:1286-1331. [PMID: 38776965 DOI: 10.1162/neco_a_01672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 02/20/2024] [Indexed: 05/25/2024]
Abstract
In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of nonsomatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density microelectrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at subcellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multimodal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and provide the field with neuronal models that are more realistic and can be better validated.
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Affiliation(s)
- Alessio Paolo Buccino
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland
| | - Tanguy Damart
- Blue Brain Project, École polytechnique fédérale de Lausanne, Campus Biotech, 1202 Geneva, Switzerland
| | - Julian Bartram
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland
| | - Darshan Mandge
- Blue Brain Project, École polytechnique fédérale de Lausanne, Campus Biotech, 1202 Geneva, Switzerland
| | - Xiaohan Xue
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland
| | - Mickael Zbili
- Blue Brain Project, École polytechnique fédérale de Lausanne, Campus Biotech, 1202 Geneva, Switzerland
| | - Tobias Gänswein
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland
| | - Aurélien Jaquier
- Blue Brain Project, École polytechnique fédérale de Lausanne, Campus Biotech, 1202 Geneva, Switzerland
| | - Vishalini Emmenegger
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland
| | - Henry Markram
- Blue Brain Project, École polytechnique fédérale de Lausanne, Campus Biotech, 1202 Geneva, Switzerland
| | - Andreas Hierlemann
- Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland
| | - Werner Van Geit
- Blue Brain Project, École polytechnique fédérale de Lausanne, Campus Biotech, 1202 Geneva, Switzerland Present address: Foundation for Research on Information Technologies in Society (IT'IS), Zurich 8004, Switzerland
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2
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Meyer LM, Samann F, Schanze T. DualSort: online spike sorting with a running neural network. J Neural Eng 2023; 20:056031. [PMID: 37795548 DOI: 10.1088/1741-2552/acfb3a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/19/2023] [Indexed: 10/06/2023]
Abstract
Objective.Spike sorting, i.e. the detection and separation of measured action potentials from different extracellularly recorded neurons, remains one of the bottlenecks in deciphering the brain. In recent years, the application of neural networks (NNs) for spike sorting has garnered significant attention. Most methods focus on specific sub-problems within the conventional spike sorting pipeline, such as spike detection or feature extraction, and attempt to solve them with complex network architectures. This paper presents DualSort, a simple NN that gets combined with downstream post-processing for real-time spike sorting. It shows high efficiency, low complexity, and requires a comparatively small amount of human interaction.Approach.Synthetic and experimentally obtained extracellular single-channel recordings were utilized to train and evaluate the proposed NN. For training, spike waveforms were labeled with respect to their associated neuron and position in the signal, allowing the detection and categorization of spikes in unison. DualSort classifies a single spike multiple times in succession, as it runs over the signal in a step-by-step manner and uses a post-processing algorithm that transmits the network output into spike trains. Main results.With the used datasets, DualSort was able to detect and distinguish different spike waveforms and separate them from background activity. The post-processing algorithm significantly strengthened the overall performance of the model, making the system more robust as a whole. Although DualSort is an end-to-end solution that efficiently transforms filtered signals into spike trains, it competes with contemporary state-of-the-art technologies that exclusively target single sub-problems in the conventional spike sorting pipeline.Significance.This work demonstrates that even under high noise levels, complex NNs are not necessary by any means to achieve high performance in spike detection and sorting. The utilization of data augmentation on a limited quantity of spikes could substantially decrease hand-labeling compared to other studies. Furthermore, the proposed framework can be utilized without human interaction when combined with an unsupervised technique that provides pseudo labels for DualSort. Due to the low complexity of our network, it works efficiently and enables real-time processing on basic hardware. The proposed approach is not limited to spike sorting, as it may also be used to process different signals, such as electroencephalogram (EEG), which needs to be investigated in future research.
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Affiliation(s)
- L M Meyer
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
| | - F Samann
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
- Department of Biomedical Engineering, University of Duhok, Kurdistan Region, Iraq
| | - T Schanze
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
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3
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Meszéna D, Barlay A, Boldog P, Furuglyás K, Cserpán D, Wittner L, Ulbert I, Somogyvári Z. Seeing beyond the spikes: reconstructing the complete spatiotemporal membrane potential distribution from paired intra- and extracellular recordings. J Physiol 2023; 601:3351-3376. [PMID: 36511176 DOI: 10.1113/jp283550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 11/28/2022] [Indexed: 12/15/2022] Open
Abstract
Although electrophysiologists have been recording intracellular neural activity routinely ever since the ground-breaking work of Hodgkin and Huxley, and extracellular multichannel electrodes have also been used frequently and extensively, a practical experimental method to track changes in membrane potential along a complete single neuron is still lacking. Instead of obtaining multiple intracellular measurements on the same neuron, we propose an alternative method by combining single-channel somatic patch-clamp and multichannel extracellular potential recordings. In this work, we show that it is possible to reconstruct the complete spatiotemporal distribution of the membrane potential of a single neuron with the spatial resolution of an extracellular probe during action potential generation. Moreover, the reconstruction of the membrane potential allows us to distinguish between the two major but previously hidden components of the current source density (CSD) distribution: the resistive and the capacitive currents. This distinction provides a clue to the clear interpretation of the CSD analysis, because the resistive component corresponds to transmembrane ionic currents (all the synaptic, voltage-sensitive and passive currents), whereas capacitive currents are considered to be the main contributors of counter-currents. We validate our model-based reconstruction approach on simulations and demonstrate its application to experimental data obtained in vitro via paired extracellular and intracellular recordings from a single pyramidal cell of the rat hippocampus. In perspective, the estimation of the spatial distribution of resistive membrane currents makes it possible to distiguish between active and passive sinks and sources of the CSD map and the localization of the synaptic input currents, which make the neuron fire. KEY POINTS: A new computational method is introduced to calculate the unbiased current source density distribution on a single neuron with known morphology. The relationship between extracellular and intracellular electric potential is determined via mathematical formalism, and a new reconstruction method is applied to reveal the full spatiotemporal distribution of the membrane potential and the resistive and capacitive current components. The new reconstruction method was validated on simulations. Simultaneous and colocalized whole-cell patch-clamp and multichannel silicon probe recordings were performed from the same pyramidal neuron in the rat hippocampal CA1 region, in vitro. The method was applied in experimental measurements and returned precise and distinctive characteristics of various intracellular phenomena, such as action potential generation, signal back-propagation and the initial dendritic depolarization preceding the somatic action potential.
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Affiliation(s)
- Domokos Meszéna
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Anna Barlay
- Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Center for Physics, Budapest, Hungary
- Bolyai Institute, University of Szeged, Szeged, Hungary
| | - Péter Boldog
- Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Center for Physics, Budapest, Hungary
- Bolyai Institute, University of Szeged, Szeged, Hungary
| | - Kristóf Furuglyás
- Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Center for Physics, Budapest, Hungary
- Doctoral School of Physics, Faculty of Science, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Dorottya Cserpán
- Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Center for Physics, Budapest, Hungary
| | - Lucia Wittner
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- National Institute of Clinical Neurosciences, Budapest, Hungary
| | - István Ulbert
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Zoltán Somogyvári
- Theoretical Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Center for Physics, Budapest, Hungary
- Axoncord LLC, Budapest, Hungary
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Letner JG, Patel PR, Hsieh JC, Smith Flores IM, della Valle E, Walker LA, Weiland JD, Chestek CA, Cai D. Post-explant profiling of subcellular-scale carbon fiber intracortical electrodes and surrounding neurons enables modeling of recorded electrophysiology. J Neural Eng 2023; 20:026019. [PMID: 36848679 PMCID: PMC10022369 DOI: 10.1088/1741-2552/acbf78] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 01/12/2023] [Accepted: 02/27/2023] [Indexed: 03/01/2023]
Abstract
Objective.Characterizing the relationship between neuron spiking and the signals that electrodes record is vital to defining the neural circuits driving brain function and informing clinical brain-machine interface design. However, high electrode biocompatibility and precisely localizing neurons around the electrodes are critical to defining this relationship.Approach.Here, we demonstrate consistent localization of the recording site tips of subcellular-scale (6.8µm diameter) carbon fiber electrodes and the positions of surrounding neurons. We implanted male rats with carbon fiber electrode arrays for 6 or 12+ weeks targeting layer V motor cortex. After explanting the arrays, we immunostained the implant site and localized putative recording site tips with subcellular-cellular resolution. We then 3D segmented neuron somata within a 50µm radius from implanted tips to measure neuron positions and health and compare to healthy cortex with symmetric stereotaxic coordinates.Main results.Immunostaining of astrocyte, microglia, and neuron markers confirmed that overall tissue health was indicative of high biocompatibility near the tips. While neurons near implanted carbon fibers were stretched, their number and distribution were similar to hypothetical fibers placed in healthy contralateral brain. Such similar neuron distributions suggest that these minimally invasive electrodes demonstrate the potential to sample naturalistic neural populations. This motivated the prediction of spikes produced by nearby neurons using a simple point source model fit using recorded electrophysiology and the mean positions of the nearest neurons observed in histology. Comparing spike amplitudes suggests that the radius at which single units can be distinguished from others is near the fourth closest neuron (30.7 ± 4.6µm,X-± S) in layer V motor cortex.Significance.Collectively, these data and simulations provide the first direct evidence that neuron placement in the immediate vicinity of the recording site influences how many spike clusters can be reliably identified by spike sorting.
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Affiliation(s)
- Joseph G Letner
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Paras R Patel
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Jung-Chien Hsieh
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Israel M Smith Flores
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Elena della Valle
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Logan A Walker
- Biophysics Program, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Computational Medicine and Bioinformatics, Michigan Medicine, Ann Arbor, MI 48109, United States of America
| | - James D Weiland
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, Ann Arbor, MI 48105, United States of America
| | - Cynthia A Chestek
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI 48109, United States of America
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States of America
- Robotics Department, University of Michigan, Ann Arbor, MI 48109, United States of America
| | - Dawen Cai
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, United States of America
- Biophysics Program, University of Michigan, Ann Arbor, MI 48109, United States of America
- Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI 48109, United States of America
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5
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Edge computing on TPU for brain implant signal analysis. Neural Netw 2023; 162:212-224. [PMID: 36921432 DOI: 10.1016/j.neunet.2023.02.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/18/2023] [Accepted: 02/23/2023] [Indexed: 03/04/2023]
Abstract
The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision offline evaluation and separate solutions for embedded systems where computational resources are more limited. We propose a deep learning-based spike sorting system, that utilizes both unsupervised and supervised paradigms to learn a general feature embedding space and detect neural activity in raw data as well as predict the feature vectors for sorting. The unsupervised component uses contrastive learning to extract features from individual waveforms, while the supervised component is based on the MobileNetV2 architecture. One of the key advantages of our system is that it can be trained on multiple, diverse datasets simultaneously, resulting in greater generalizability than previous deep learning-based models. We demonstrate that the proposed model does not only reaches the accuracy of current state-of-art offline spike sorting methods but has the unique potential to run on edge Tensor Processing Units (TPUs), specialized chips designed for artificial intelligence and edge computing. We compare our model performance with state of art solutions on paired datasets as well as on hybrid recordings as well. The herein demonstrated system paves the way to the integration of deep learning-based spike sorting algorithms into wearable electronic devices, which will be a crucial element of high-end brain-computer interfaces.
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6
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Shomali SR, Rasuli SN, Ahmadabadi MN, Shimazaki H. Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons. Commun Biol 2023; 6:169. [PMID: 36792689 PMCID: PMC9932086 DOI: 10.1038/s42003-023-04511-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 01/20/2023] [Indexed: 02/17/2023] Open
Abstract
Identifying network architecture from observed neural activities is crucial in neuroscience studies. A key requirement is knowledge of the statistical input-output relation of single neurons in vivo. By utilizing an exact analytical solution of the spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near the threshold, we construct a framework that links synaptic type, strength, and spiking nonlinearity with the statistics of neuronal population activity. The framework explains structured pairwise and higher-order interactions of neurons receiving common inputs under different architectures. We compared the theoretical predictions with the activity of monkey and mouse V1 neurons and found that excitatory inputs given to pairs explained the observed sparse activity characterized by strong negative triple-wise interactions, thereby ruling out the alternative explanation by shared inhibition. Moreover, we showed that the strong interactions are a signature of excitatory rather than inhibitory inputs whenever the spontaneous rate is low. We present a guide map of neural interactions that help researchers to specify the hidden neuronal motifs underlying observed interactions found in empirical data.
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Affiliation(s)
- Safura Rashid Shomali
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, 19395-5746, Iran.
| | - Seyyed Nader Rasuli
- grid.418744.a0000 0000 8841 7951School of Physics, Institute for Research in Fundamental Sciences (IPM), Tehran, 19395-5531 Iran ,grid.411872.90000 0001 2087 2250Department of Physics, University of Guilan, Rasht, 41335-1914 Iran
| | - Majid Nili Ahmadabadi
- grid.46072.370000 0004 0612 7950Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 14395-515 Iran
| | - Hideaki Shimazaki
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan. .,Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN), Hokkaido University, Hokkaido, 060-0812, Japan.
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7
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Shabestari PS, Buccino AP, Kumar SS, Pedrocchi A, Hierlemann A. A modulated template-matching approach to improve spike sorting of bursting neurons. IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE : HEALTHCARE TECHNOLOGY : [PROCEEDINGS]. IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE 2021; 2021:9644995. [PMID: 35018369 PMCID: PMC7612198 DOI: 10.1109/biocas49922.2021.9644995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In extracellular neural electrophysiology, individual spikes have to be assigned to their cell of origin in a procedure called "spike sorting". Spike sorting is an unsupervised problem, since no ground-truth information is generally available. Here, we focus on improving spike sorting performance, particularly during periods of high synchronous activity or so-called "bursting". Bursting entails systematic changes in spike shapes and amplitudes and remains a challenge for current spike sorting schemes. We use realistic simulated bursting recordings of high-density micro-electrode arrays (HD-MEAs) and we present a fully automated algorithm based on template matching with a focus on recovering missed spikes during bursts. To compare and benchmark spike-sorting performance after applying our method, we used ground-truth information of simulated recordings. We show that our approach can be effective in improving spike sorting performance during bursting. Further validation with experimental recordings is necessary.
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Affiliation(s)
- Payam S. Shabestari
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- Department of Electronics, Informatics, and Biomedical Engineering, Politecnico di Milano, Milan, Italy
| | - Alessio P. Buccino
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Sreedhar S. Kumar
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Alessandra Pedrocchi
- Department of Electronics, Informatics, and Biomedical Engineering, Politecnico di Milano, Milan, Italy
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
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8
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Wouters J, Kloosterman F, Bertrand A. SHYBRID: A Graphical Tool for Generating Hybrid Ground-Truth Spiking Data for Evaluating Spike Sorting Performance. Neuroinformatics 2021; 19:141-158. [PMID: 32617751 DOI: 10.1007/s12021-020-09474-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Spike sorting is the process of retrieving the spike times of individual neurons that are present in an extracellular neural recording. Over the last decades, many spike sorting algorithms have been published. In an effort to guide a user towards a specific spike sorting algorithm, given a specific recording setting (i.e., brain region and recording device), we provide an open-source graphical tool for the generation of hybrid ground-truth data in Python. Hybrid ground-truth data is a data-driven modelling paradigm in which spikes from a single unit are moved to a different location on the recording probe, thereby generating a virtual unit of which the spike times are known. The tool enables a user to efficiently generate hybrid ground-truth datasets and make informed decisions between spike sorting algorithms, fine-tune the algorithm parameters towards the used recording setting, or get a deeper understanding of those algorithms.
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Affiliation(s)
- Jasper Wouters
- Department of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing, and Data Analytics, KU Leuven, Leuven, Belgium.
| | - Fabian Kloosterman
- Neuro-Electronics Research Flanders (NERF), Leuven, Belgium
- Brain & Cognition Research Unit, KU Leuven, Leuven, Belgium
- VIB, Leuven, Belgium
| | - Alexander Bertrand
- Department of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing, and Data Analytics, KU Leuven, Leuven, Belgium
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9
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Buccino AP, Einevoll GT. MEArec: A Fast and Customizable Testbench Simulator for Ground-truth Extracellular Spiking Activity. Neuroinformatics 2021; 19:185-204. [PMID: 32648042 PMCID: PMC7782412 DOI: 10.1007/s12021-020-09467-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
When recording neural activity from extracellular electrodes, both in vivo and in vitro, spike sorting is a required and very important processing step that allows for identification of single neurons’ activity. Spike sorting is a complex algorithmic procedure, and in recent years many groups have attempted to tackle this problem, resulting in numerous methods and software packages. However, validation of spike sorting techniques is complicated. It is an inherently unsupervised problem and it is hard to find universal metrics to evaluate performance. Simultaneous recordings that combine extracellular and patch-clamp or juxtacellular techniques can provide ground-truth data to evaluate spike sorting methods. However, their utility is limited by the fact that only a few cells can be measured at the same time. Simulated ground-truth recordings can provide a powerful alternative mean to rank the performance of spike sorters. We present here MEArec, a Python-based software which permits flexible and fast simulation of extracellular recordings. MEArec allows users to generate extracellular signals on various customizable electrode designs and can replicate various problematic aspects for spike sorting, such as bursting, spatio-temporal overlapping events, and drifts. We expect MEArec will provide a common testbench for spike sorting development and evaluation, in which spike sorting developers can rapidly generate and evaluate the performance of their algorithms.
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Affiliation(s)
- Alessio Paolo Buccino
- Centre for Integrative Neuroplasticity (CINPLA), University of Oslo, Oslo, Norway. .,Bio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zürich, Zürich, Switzerland.
| | - Gaute Tomas Einevoll
- Centre for Integrative Neuroplasticity (CINPLA), University of Oslo, Oslo, Norway.,Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
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10
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Rynes ML, Ghanbari L, Schulman DS, Linn S, Laroque M, Dominguez J, Navabi ZS, Sherman P, Kodandaramaiah SB. Assembly and operation of an open-source, computer numerical controlled (CNC) robot for performing cranial microsurgical procedures. Nat Protoc 2020; 15:1992-2023. [PMID: 32405052 DOI: 10.1038/s41596-020-0318-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 03/03/2020] [Indexed: 12/14/2022]
Abstract
Cranial microsurgery is an essential procedure for accessing the brain through the skull that can be used to introduce neural probes that measure and manipulate neural activity. Neuroscientists have typically used tools such as high-speed drills adapted from dentistry to perform these procedures. As the number of technologies available for neuroscientists has increased, the corresponding cranial microsurgery procedures to deploy them have become more complex. Using a robotic tool that automatically performs these procedures could standardize cranial microsurgeries across neuroscience laboratories and democratize the more challenging procedures. We have recently engineered a robotic surgery platform that utilizes principles of computer numerical control (CNC) machining to perform a wide variety of automated cranial procedures. Here, we describe how to adapt, configure and use an inexpensive desktop CNC mill equipped with a custom-built surface profiler for performing CNC-guided microsurgery on mice. Detailed instructions are provided to utilize this 'Craniobot' for performing circular craniotomies for coverslip implantation, large craniotomies for implanting transparent polymer skulls for cortex-wide imaging access and skull thinning for intact skull imaging. The Craniobot can be set up in <2 weeks using parts that cost <$1,500, and we anticipate that the Craniobot could be easily adapted for use in other small animals.
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Affiliation(s)
- Mathew L Rynes
- Department of Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Leila Ghanbari
- Department of Mechanical Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Daniel Sousa Schulman
- Department of Mechanical Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Samantha Linn
- Department of Mechanical Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Michael Laroque
- Schools of Medicine, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Judith Dominguez
- Department of Mechanical Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Zahra S Navabi
- Department of Mechanical Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Peter Sherman
- Department of Mechanical Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Suhasa B Kodandaramaiah
- Department of Biomedical Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA. .,Department of Mechanical Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA.
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11
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Magland J, Jun JJ, Lovero E, Morley AJ, Hurwitz CL, Buccino AP, Garcia S, Barnett AH. SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters. eLife 2020; 9:e55167. [PMID: 32427564 PMCID: PMC7237210 DOI: 10.7554/elife.55167] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 05/05/2020] [Indexed: 01/03/2023] Open
Abstract
Spike sorting is a crucial step in electrophysiological studies of neuronal activity. While many spike sorting packages are available, there is little consensus about which are most accurate under different experimental conditions. SpikeForest is an open-source and reproducible software suite that benchmarks the performance of automated spike sorting algorithms across an extensive, curated database of ground-truth electrophysiological recordings, displaying results interactively on a continuously-updating website. With contributions from eleven laboratories, our database currently comprises 650 recordings (1.3 TB total size) with around 35,000 ground-truth units. These data include paired intracellular/extracellular recordings and state-of-the-art simulated recordings. Ten of the most popular spike sorting codes are wrapped in a Python package and evaluated on a compute cluster using an automated pipeline. SpikeForest documents community progress in automated spike sorting, and guides neuroscientists to an optimal choice of sorter and parameters for a wide range of probes and brain regions.
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Affiliation(s)
- Jeremy Magland
- Center for Computational Mathematics, Flatiron InstituteNew YorkUnited States
| | - James J Jun
- Center for Computational Mathematics, Flatiron InstituteNew YorkUnited States
| | - Elizabeth Lovero
- Scientific Computing Core, Flatiron InstituteNew YorkUnited States
| | - Alexander J Morley
- Medical Research Council Brain Network Dynamics Unit, University of OxfordOxfordUnited Kingdom
| | - Cole Lincoln Hurwitz
- Institute for Adaptive and Neural Computation Informatics, University of EdinburghEdinburghUnited Kingdom
| | | | - Samuel Garcia
- Centre de Recherche en Neuroscience de Lyon, Université de LyonLyonFrance
| | - Alex H Barnett
- Center for Computational Mathematics, Flatiron InstituteNew YorkUnited States
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Zanoci C, Dehghani N, Tegmark M. Ensemble inhibition and excitation in the human cortex: An Ising-model analysis with uncertainties. Phys Rev E 2019; 99:032408. [PMID: 30999501 DOI: 10.1103/physreve.99.032408] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Indexed: 11/07/2022]
Abstract
The pairwise maximum entropy model, also known as the Ising model, has been widely used to analyze the collective activity of neurons. However, controversy persists in the literature about seemingly inconsistent findings, whose significance is unclear due to lack of reliable error estimates. We therefore develop a method for accurately estimating parameter uncertainty based on random walks in parameter space using adaptive Markov-chain Monte Carlo after the convergence of the main optimization algorithm. We apply our method to the activity patterns of excitatory and inhibitory neurons recorded with multielectrode arrays in the human temporal cortex during the wake-sleep cycle. Our analysis shows that the Ising model captures neuronal collective behavior much better than the independent model during wakefulness, light sleep, and deep sleep when both excitatory (E) and inhibitory (I) neurons are modeled; ignoring the inhibitory effects of I neurons dramatically overestimates synchrony among E neurons. Furthermore, information-theoretic measures reveal that the Ising model explains about 80-95% of the correlations, depending on sleep state and neuron type. Thermodynamic measures show signatures of criticality, although we take this with a grain of salt as it may be merely a reflection of long-range neural correlations.
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Affiliation(s)
- Cristian Zanoci
- Department of Physics and Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Nima Dehghani
- Department of Physics and Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Max Tegmark
- Department of Physics and Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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