Sorting Overlapping Spike Waveforms from Electrode and Tetrode Recordings

Abstract Background Salmonids return to the river where they were born in a phenomenon known as mother-river migration. The underpinning of migration has been extensively examined, particularly regarding the behavioral correlations of external environmental cues such as the scent of the mother-river and geomagnetic compass. However, neuronal underpinning remains elusive, as there have been no biologging techniques suited to monitor neuronal activity in the brain of large free-swimming fish. In this study, we developed a wireless biologging system to record extracellular neuronal activity in the brains of free-swimming salmonids. Results Using this system, we recorded multiple neuronal activities from the telencephalon of trout swimming in a rectangular water tank. As proof of principle, we examined the activity statistics for extracellular spike waveforms and timing. We found cells firing maximally in response to a specific head direction, similar to the head direction cells found in the rodent brain. The results of our study suggest that the recorded signals originate from neurons. Conclusions We anticipate that our biologging system will facilitate a more detailed investigation into the neural underpinning of fish movement using internally generated information, including responses to external cues.

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Spike sorting with Gaussian mixture models

10.1101/248864 ◽

2018 ◽

Cited By ~ 2

Author(s):

Bryan C. Souza ◽

Vítor Lopes-dos-Santos ◽

João Bacelo ◽

Adriano B. L. Tort

Keyword(s):

Mixture Models ◽

Principal Components ◽

Gaussian Mixture Models ◽

Gaussian Mixture ◽

Spike Sorting ◽

Main Challenge ◽

Manual Adjustment ◽

Cluster Properties ◽

Spike Waveforms ◽

Friendly Graphical User Interface

AbstractThe shape of extracellularly recorded action potentials is a product of several variables, such as the biophysical and anatomical properties of the neuron and the relative position of the electrode. This allows for isolating spikes of different neurons recorded in the same channel into clusters based on waveform features. However, correctly classifying spike waveforms into their underlying neuronal sources remains a main challenge. This process, called spike sorting, typically consists of two steps: (1) extracting relevant waveform features (e.g., height, width), and (2) clustering them into non-overlapping groups believed to correspond to different neurons. In this study, we explored the performance of Gaussian mixture models (GMMs) in these two steps. We extracted relevant waveform features using a combination of common techniques (e.g., principal components and wavelets) and GMM fitting parameters (e.g., standard deviations and peak distances). Then, we developed an approach to perform unsupervised clustering using GMMs, which estimates cluster properties in a data-driven way. Our results show that the proposed GMM-based framework outperforms previously established methods when using realistic simulations of extracellular spikes and actual extracellular recordings to evaluate sorting performance. We also discuss potentially better techniques for feature extraction than the widely used principal components. Finally, we provide a friendly graphical user interface in MATLAB to run our algorithm, which allows for manual adjustment of the automatic results.

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ANN-Based System for Sorting Spike Waveforms Employing Refractory Periods

Artificial Neural Networks: Biological Inspirations – ICANN 2005 - Lecture Notes in Computer Science ◽

10.1007/11550822_20 ◽

2005 ◽

pp. 121-126 ◽

Cited By ~ 1

Author(s):

Thomas Hermle ◽

Martin Bogdan ◽

Cornelius Schwarz ◽

Wolfgang Rosenstiel

Keyword(s):

Refractory Periods ◽

Spike Waveforms

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KCC2 antagonism increases neuronal network excitability but disrupts ictogenesis in vitro

Journal of Neurophysiology ◽

10.1152/jn.00266.2019 ◽

2019 ◽

Vol 122 (3) ◽

pp. 1163-1173 ◽

Cited By ~ 2

Author(s):

Li-Yuan Chen ◽

Maxime Lévesque ◽

Massimo Avoli

Keyword(s):

Potassium Chloride ◽

Entorhinal Cortex ◽

Single Unit ◽

Neuronal Excitability ◽

Cell Activity ◽

Principal Cell ◽

Cell Firing ◽

Interictal Discharges ◽

Tetrode Recordings

The potassium-chloride cotransporter 2 (KCC2) plays a role in epileptiform synchronization, but it remains unclear how it influences such a process. Here, we used tetrode recordings in the in vitro rat entorhinal cortex (EC) to analyze the effects of the KCC2 antagonist VU0463271 on 4-aminopyridine (4AP)-induced ictal and interictal activity. During 4AP application, ictal events were associated with significant increases in interneurons and principal cells activities. VU0463271 application transformed ictal discharges to shorter ictal-like events that were not accompanied by significant increases in interneuron or principal cell firing. Interictal events persisted during VU0463271 application at an accelerated frequency of occurrence with significant increases in interneuron and principal cell activity. Further analysis revealed that interneuron and principal cell firing rate during 4AP-induced interictal events were increased after VU0463271 application without changes in synchronicity. Overall, our results demonstrate that in the EC, KCC2 antagonism enhances both interneuron and principal cell excitability, while paradoxically decreasing the ability of neuronal networks to generate structured ictal events. NEW & NOTEWORTHY We are the first to use tetrode recordings in the entorhinal cortex to demonstrate that antagonizing potassium-chloride cotransporter 2 (KCC2) function abolishes ictal discharges and the associated, dynamic changes in single-unit firing in the in vitro 4-aminopyrine model of epileptiform synchronization. Interictal discharges were, however, shorter and more frequent during KCC2 antagonism, while the associated single-unit activity increased, suggesting augmented neuronal excitability. Our findings highlight the complex role of KCC2 in disease pathology.

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Automatic Spike Sorting Using Tuning Information

Neural Computation ◽

10.1162/neco.2009.12-07-669 ◽

2009 ◽

Vol 21 (9) ◽

pp. 2466-2501 ◽

Cited By ~ 21

Author(s):

Valérie Ventura

Keyword(s):

Maximum Likelihood ◽

Expectation Maximization ◽

Poisson Processes ◽

Spike Sorting ◽

Maximum Likelihood Algorithm ◽

Firing Rates ◽

Novel Approach ◽

Misclassification Rates ◽

Available Information ◽

Spike Waveforms

Current spike sorting methods focus on clustering neurons' characteristic spike waveforms. The resulting spike-sorted data are typically used to estimate how covariates of interest modulate the firing rates of neurons. However, when these covariates do modulate the firing rates, they provide information about spikes' identities, which thus far have been ignored for the purpose of spike sorting. This letter describes a novel approach to spike sorting, which incorporates both waveform information and tuning information obtained from the modulation of firing rates. Because it efficiently uses all the available information, this spike sorter yields lower spike misclassification rates than traditional automatic spike sorters. This theoretical result is verified empirically on several examples. The proposed method does not require additional assumptions; only its implementation is different. It essentially consists of performing spike sorting and tuning estimation simultaneously rather than sequentially, as is currently done. We used an expectation-maximization maximum likelihood algorithm to implement the new spike sorter. We present the general form of this algorithm and provide a detailed implementable version under the assumptions that neurons are independent and spike according to Poisson processes. Finally, we uncover a systematic flaw of spike sorting based on waveform information only.

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Representations of Conspecific Song by Starling Secondary Forebrain Auditory Neurons: Toward a Hierarchical Framework

Journal of Neurophysiology ◽

10.1152/jn.00464.2009 ◽

2010 ◽

Vol 103 (3) ◽

pp. 1195-1208 ◽

Cited By ~ 25

Author(s):

C. Daniel Meliza ◽

Zhiyi Chi ◽

Daniel Margoliash

Keyword(s):

Predictive Power ◽

Functional Organization ◽

Receptive Fields ◽

Spike Timing ◽

Auditory Neurons ◽

Linear Summation ◽

European Starlings ◽

Spike Timing Precision ◽

Spectrotemporal Receptive Field ◽

Spike Waveforms

The functional organization giving rise to stimulus selectivity in higher-order auditory neurons remains under active study. We explored the selectivity for motifs, spectrotemporally distinct perceptual units in starling song, recording the responses of 96 caudomedial mesopallium (CMM) neurons in European starlings ( Sturnus vulgaris) under awake-restrained and urethane-anesthetized conditions. A subset of neurons was highly selective between motifs. Selectivity was correlated with low spontaneous firing rates and high spike timing precision, and all but one of the selective neurons had similar spike waveforms. Neurons were further tested with stimuli in which the notes comprising the motifs were manipulated. Responses to most of the isolated notes were similar in amplitude, duration, and temporal pattern to the responses elicited by those notes in the context of the motif. For these neurons, we could accurately predict the responses to motifs from the sum of the responses to notes. Some notes were suppressed by the motif context, such that removing other notes from motifs unmasked additional excitation. Models of linear summation of note responses consistently outperformed spectrotemporal receptive field models in predicting responses to song stimuli. Tests with randomized sequences of notes confirmed the predictive power of these models. Whole notes gave better predictions than did note fragments. Thus in CMM, auditory objects (motifs) can be represented by a linear combination of excitation and suppression elicited by the note components of the object. We hypothesize that the receptive fields arise from selective convergence by inputs responding to specific spectrotemporal features of starling notes.

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Variation of neuronal spike waveforms in long-term extracellular recordings

Journal of Life Support Engineering ◽

10.5136/lifesupport.16.supplement_225 ◽

2004 ◽

Vol 16 (Supplement) ◽

pp. 225-226

Author(s):

S. GO ◽

E. KOBAYASHI ◽

I. SAKUMA ◽

Y. JINMBO

Keyword(s):

Extracellular Recordings ◽

Neuronal Spike ◽

Spike Waveforms

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Joint analysis of extracellular spike waveforms and neuronal network bursts

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2015.11.022 ◽

2016 ◽

Vol 259 ◽

pp. 143-155 ◽

Cited By ~ 7

Author(s):

Fikret Emre Kapucu ◽

Meeri E.-L. Mäkinen ◽

Jarno M.A. Tanskanen ◽

Laura Ylä-Outinen ◽

Susanna Narkilahti ◽

...

Keyword(s):

Neuronal Network ◽

Joint Analysis ◽

Network Bursts ◽

Spike Waveforms

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A Detailed and Fast Model of Extracellular Recordings

Neural Computation ◽

10.1162/neco_a_00433 ◽

2013 ◽

Vol 25 (5) ◽

pp. 1191-1212 ◽

Cited By ~ 39

Author(s):

Luis A. Camuñas-Mesa ◽

Rodrigo Quian Quiroga

Keyword(s):

Action Potentials ◽

Finite Size ◽

Real Data ◽

Recording Electrode ◽

Electrode Diameter ◽

Extracellular Recordings ◽

Novel Method ◽

Tissue Interface ◽

Tetrode Recordings

We present a novel method to generate realistic simulations of extracellular recordings. The simulations were obtained by superimposing the activity of neurons placed randomly in a cube of brain tissue. Detailed models of individual neurons were used to reproduce the extracellular action potentials of close-by neurons. To reduce the computational load, the contributions of neurons further away were simulated using previously recorded spikes with their amplitude normalized by the distance to the recording electrode. For making the simulations more realistic, we also considered a model of a finite-size electrode by averaging the potential along the electrode surface and modeling the electrode-tissue interface with a capacitive filter. This model allowed studying the effect of the electrode diameter on the quality of the recordings and how it affects the number of identified neurons after spike sorting. Given that not all neurons are active at a time, we also generated simulations with different ratios of active neurons and estimated the ratio that matches the signal-to-noise values observed in real data. Finally, we used the model to simulate tetrode recordings.

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