Comparison of burst detectors for spike trains

Accurate identification of bursting activity is an essential element in the characterization of neuronal network activity. Despite this, no one technique for identifying bursts in spike trains has been widely adopted. Instead, many methods have been developed for the analysis of bursting activity, often on an ad hoc basis. Here we provide an unbiased assessment of the effectiveness of eight of these methods at detecting bursts in a range of spike trains. We suggest a list of features that an ideal burst detection technique should possess and use synthetic data to assess each method in regard to these properties. We further employ each of the methods to reanalyze microelectrode array (MEA) recordings from mouse retinal ganglion cells and examine their coherence with bursts detected by a human observer. We show that several common burst detection techniques perform poorly at analyzing spike trains with a variety of properties. We identify four promising burst detection techniques, which are then applied to MEA recordings of networks of human induced pluripotent stem cell-derived neurons and used to describe the ontogeny of bursting activity in these networks over several months of development. We conclude that no current method can provide "perfect" burst detection results across a range of spike trains; however, two burst detection techniques, the MaxInterval and logISI methods, outperform compared with others. We provide recommendations for the robust analysis of bursting activity in experimental recordings using current techniques.Experimental data collection was supported by the BBSRC (PC, OP, grant number BB/H008608/1). EC was supported by a Wellcome Trust PhD Studentship and NIHR Cambridge Biomedical Research Centre Studentship. CWT was supported by a bursary from the Bridgwater Summer Undergraduate Research programme.This is the final version of the article. It first appeared from the American Physiological Society via https://doi.org/10.1152/jn.00093.201

Discrete- and Continuous-Time Probabilistic Models and Algorithms for Inferring Neuronal UP and DOWN States

UP and DOWN states, the periodic fluctuations between increased and decreased spiking activity of a neuronal population, are a fundamental feature of cortical circuits. Understanding UP-DOWN state dynamics is important for understanding how these circuits represent and transmit information in the brain. To date, limited work has been done on characterizing the stochastic properties of UP-DOWN state dynamics. We present a set of Markov and semi-Markov discrete- and continuous-time probability models for estimating UP and DOWN states from multiunit neural spiking activity. We model multiunit neural spiking activity as a stochastic point process, modulated by the hidden (UP and DOWN) states and the ensemble spiking history. We estimate jointly the hidden states and the model parameters by maximum likelihood using an expectation-maximization (EM) algorithm and a Monte Carlo EM algorithm that uses reversible-jump Markov chain Monte Carlo sampling in the E-step. We apply our models and algorithms in the analysis of both simulated multiunit spiking activity and actual multi- unit spiking activity recorded from primary somatosensory cortex in a behaving rat during slow-wave sleep. Our approach provides a statistical characterization of UP-DOWN state dynamics that can serve as a basis for verifying and refining mechanistic descriptions of this process.National Institutes of Health (U.S.) (Grant R01-DA015644)National Institutes of Health (U.S.) (Director Pioneer Award DP1- OD003646)National Institutes of Health (U.S.) (NIH/NHLBI grant R01-HL084502)National Institutes of Health (U.S.) (NIH institutional NRSA grant T32 HL07901

Bursti-tunnistusmenetelmät ihmisperäisistä monikykyisistä kantasoluista erilaistetuille hermosoluverkostoille

A burst is a set of subsequent action potentials that are fired at a high frequency. Although bursts are a fundamental part of electrical activity of neuronal networks in vitro, no standardized method exists for burst detection. Visual identification of bursts is a widely accepted method, but it is not objective nor time-efficient. Therefore, various algorithms have been developed for burst detection. Burst detection algorithms are typically developed and verified only on one specific type of data. This can be problematic because the bursting activity is highly variable between different cell types. Consequently, the applicability of the algorithms is restricted to a narrow range of activity types. Especially applicability to human neuronal networks is questionable because the algorithms are often developed on rodent neuronal networks, which display distinct activity patterns in comparison to human networks. The aim of this thesis was to produce a test data set, which would well represent bursting and non-bursting activity observed in human pluripotent stem cell (hPSC)-derived neuronal networks, and to identify a single algorithm with optimal parameters that would successfully detect the bursts in this test data set. As rodent neuronal networks are also widely used in neuroscience, the algorithm was desired to function also on activity derived from rodent cultures. To achieve these goals, hESCs were differentiated into functional neuronal networks and cultured on microelectrode array (MEA). Primary rat cortical neurons were similarly cultured on MEA. Electrical activity of the developing networks was recorded twice a week until synchronized bursting emerged. At this point, pharmacological assays were performed in order to record modulated activity. On the MEA recordings, distinct activity patterns were identified, and short recordings representative of the distinct patterns were included to the test data set. The performance of four contemporary burst detection algorithms was evaluated on the test data set. The evaluation was based on visual identification of bursts from the raw MEA signal. For each algorithm, a performance score was determined and sensitivity and specificity were computed. The evaluation was performed in two runs using either 3 or 5 as minimum number of spikes required for a burst. Other algorithm parameters were set to default values suggested by the original authors. The optimization possibilities were not encouraging for other algorithms but logISI, which also provided the highest performance and the most balanced sensitivity and specificity values. Parameters of logISI were optimized for the test data set, which significantly improved its performance. As a result, logISI displayed good or excellent performance on the test data obtained from human and rat neuronal networks during spontaneous and pharmacologically modulated activity. Based on these results, logISI could have the potential to become a standard burst detection algorithm in the field.Bursti (engl. burst) on peräkkäisten korkealla taajuudella esiintyvien toimintapotentiaalien ryhmä. Vaikka burstit ovat olennainen osa maljalla kasvatettujen hermosoluverkostojen sähköistä aktiivisuutta, ei niiden tunnistukseen ole standardimenetelmää. Visuaalinen bursti-tunnistus on laajasti hyväksytty menetelmä, mutta se ei ole objektiivinen eikä ajallisesti tehokas. Tästä syystä bursti-tunnistukseen on kehitetty useita algoritmeja. Tyypillisesti nämä algoritmit on kehitetty ja niiden toiminta on varmennettu vain tietyn tyyppisellä datalla. Tämä voi olla ongelmallista, koska bursti-aktiivisuus eri solutyyppien välillä on vaihtelevaa. Näin ollen algoritmien soveltaminen on rajoitettu vain pieneen osaan aktiivisuustyyppejä. Erityisesti algoritmien soveltaminen ihmisperäisiin hermosoluverkostoihin on kyseenalaista, sillä algoritmit on usein kehitetty jyrsijäperäisillä hermosoluverkostoilla, joiden aktiivisuustyypit eroavat ihmisperäisisten hermosoluverkostojen aktiivisuustyypeistä. Tämän työn tavoitteena oli kerätä testiaineisto, joka sisältäisi monikykyisistä ihmisen kantasoluista erilaistetuissa hermosoluverkostoissa havaittavat burstaavat ja ei-burstaavat aktiviisuustyypit, sekä löytää tällä testiaineistolla toimiva algoritmi ja optimaaliset arvot sen muuttujille. Koska jyrsijäperäiset hermosoluverkostot ovat neurotieteissä paljon käytettyjä, valitun algoritmin haluttiin toimivan myös niistä peräisin olevalla aineistolla. Tavoitteen saavuttamiseksi ihmisperäisistä alkion kantasoluista erilaistettiin toiminnallisia hermosoluverkostoja, joita viljeltiin mikroelektrodihilan (engl. microelectrode array, MEA) päällä. Rotan eristettyjä aivokuoren hermosoluja viljeltiin samoin MEA:lla. Hermosoluverkostojen sähköistä aktiivisuutta mitattiin niiden kehityksen aikana kahdesti viikossa, kunnes havaittiin synkronista bursti-aktiivisuutta. Synkronisen bursti-aktiivisuuden ilmaannuttua suoritettiin farmakologiset testit ja mitattiin näin muunneltua aktiivisuutta. Saaduista MEA-mittauksista etsittiin erilaisia aktiivisuustyyppejä, joista muodostettiin testiaineisto. Neljän nykyaikaisen algoritmin toimintaa arvioitiin tässä testiaineistossa. Arviointi tehtiin vertailemalla algoritmien tuloksia raakasignaalista tehdyn visuaalisen bursti-tunnistuksen tuloksiin. Jokaisen algoritmin suoritus pisteytettiin ja niiden herkkyys ja tarkkuus laskettiin. Suoritusta arvioitiin kahdesti siten, että burstin vähimmäispiikkimäärä asetettiin ensin kolmeen ja sitten viiteen. Muiden muuttujien arvot asetettiin algoritmien kehittäjien alkuperäisten suositusten mukaisesti. Optimointi- mahdollisuudet olivat lupaavat vain logISI-algoritmille, joka myös suoriutui parhaiten ja jonka herkkyys ja tarkkuus olivat parhaassa tasapainossa. LogISI:n muuttujat optimoitiin testiaineistolle, mikä huomattavasti paransi sen suoritusta kyseisessä aineistossa. Optimoidulla logISI:llä saatiin joko hyvä tai erinomainen tulos koko testiaineistolla, joka oli saatu mittaamalla spontaania ja farmakologisesti muunneltua aktiivisuutta sekä ihmisperäisistä kantasoluista erilaistetuista että rotan aivokuoresta eristetyistä hermosolu-verkostoista. Näiden tulosten perusteella logISI on potentiaalinen vaihtoehto bursti-tunnistuksen standardimetodiksi

Time Resolution Dependence of Information Measures for Spiking Neurons: Atoms, Scaling, and Universality

The mutual information between stimulus and spike-train response is commonly used to monitor neural coding efficiency, but neuronal computation broadly conceived requires more refined and targeted information measures of input-output joint processes. A first step towards that larger goal is to develop information measures for individual output processes, including information generation (entropy rate), stored information (statistical complexity), predictable information (excess entropy), and active information accumulation (bound information rate). We calculate these for spike trains generated by a variety of noise-driven integrate-and-fire neurons as a function of time resolution and for alternating renewal processes. We show that their time-resolution dependence reveals coarse-grained structural properties of interspike interval statistics; e.g., $\tau$-entropy rates that diverge less quickly than the firing rate indicate interspike interval correlations. We also find evidence that the excess entropy and regularized statistical complexity of different types of integrate-and-fire neurons are universal in the continuous-time limit in the sense that they do not depend on mechanism details. This suggests a surprising simplicity in the spike trains generated by these model neurons. Interestingly, neurons with gamma-distributed ISIs and neurons whose spike trains are alternating renewal processes do not fall into the same universality class. These results lead to two conclusions. First, the dependence of information measures on time resolution reveals mechanistic details about spike train generation. Second, information measures can be used as model selection tools for analyzing spike train processes.Comment: 20 pages, 6 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/trdctim.ht

Variability of grid-cell activity

Action potentials of grid cells in the entorhinal cortex of navigating rodents occur every two seconds on average. If one considers the precise temporal sequence of these events, however, it can be seen that they rarely occur in isolation. In fact, the intervals between successive action potentials can be on the order of a few milliseconds. Mapped to the trajectory of the animal, a clear clustering of the action potentials in space can be observed as well. The places where the density of such events is particularly high are called firing fields and are arranged in a hexagonal grid. Regardless of the cell characteristics, the number of spikes observed on different crossings of a field varies strongly. The time between subsequent field crossings is on the order of seconds. We found out that one cause of spike-count variability is that the exact position of the firing fields is not stable over time. In addition, the shifts of the fields were correlated across simultaneously recorded cells. This kind of non-stationarity in the grid-cell network allows conclusions to be drawn about the functioning of this system. Furthermore, dynamic field locations imply that common methods for data analysis of grid-cell recordings can be problematic. Furthermore, we found out that a subset of grid cells, which have particularly high firing rates when crossing a field, can be associated with a peculiarity in the shape of their action potentials: The spikes of some cells are followed by a short afterdepolarization (DAP). At the same time, we discovered cells with even smaller and extremely stereotypical intervals between their spikes. This group of neurons, however, exhibited less pronounced DAPs. Cells with and without DAP did not differ in their spatial firing behavior. Our results imply that different burst behaviors are not directly related to different types of spatial coding. In addition, we suggest that bursting of grid cells could be altered via the mechanisms of DAP formation. In summary, this work shows how details of neuronal activity on two different time scales provide fundamental insights into the processes of spatial navigation. Untethered firing fields and intermittent silences: Why grid‐cell discharge is so variable - Grid cells in medial entorhinal cortex are notoriously variable in their responses, despite the striking hexagonal arrangement of their spatial firing fields. Indeed, when the animal moves through a firing field, grid cells often fire much more vigorously than predicted or do not fire at all. The source of this trial‐to‐trial variability is not completely understood. By analyzing grid‐cell spike trains from mice running in open arenas and on linear tracks, we characterize the phenomenon of “missed” firing fields using the statistical theory of zero inflation. We find that one major cause of grid‐cell variability lies in the spatial representation itself: firing fields are not as strongly anchored to spatial location as the averaged grid suggests. In addition, grid fields from different cells drift together from trial to trial, regardless of whether the environment is real or virtual, or whether the animal moves in light or darkness. Spatial realignment across trials sharpens the grid representation, yielding firing fields that are more pronounced and significantly narrower. These findings indicate that ensembles of grid cells encode relative position more reliably than absolute position. Spike Afterpotentials Shape the In Vivo Burst Activity of Principal Cells in Medial Entorhinal Cortex - Principal neurons in rodent medial entorhinal cortex (MEC) generate high-frequency bursts during natural behavior. While in vitro studies point to potential mechanisms that could support such burst sequences, it remains unclear whether these mechanisms are effective under in vivo conditions. In this study, we focused on the membrane-potential dynamics immediately following action potentials (APs), as measured in whole-cell recordings from male mice running in virtual corridors (Domnisoru et al., 2013). These afterpotentials consisted either of a hyperpolarization, an extended ramp-like shoulder, or a depolarization reminiscent of depolarizing afterpotentials (DAPs) recorded in vitro in MEC principal neurons. Next, we correlated the afterpotentials with the cells' propensity to fire bursts. All DAP cells with known location resided in Layer II, generated bursts, and their interspike intervals (ISIs) were typically between 5 and 15 ms. The ISI distributions of Layer-II cells without DAPs peaked sharply at around 4 ms and varied only minimally across that group. This dichotomy in burst behavior is explained by cell-group-specific DAP dynamics. The same two groups of bursting neurons also emerged when we clustered extracellular spike-train autocorrelations measured in real 2D arenas (Latuske et al., 2015). Apart from slight variations in grid spacing, no difference in the spatial coding properties of the grid cells across all three groups was discernible. Layer III neurons were only sparsely bursting (SB) and had no DAPs. As various mechanisms for modulating ion-channels underlying DAPs exist, our results suggest that temporal features of MEC activity can be altered while maintaining the cells' overall spatial tuning characteristics

Explicit-Duration Hidden Markov Model Inference of UP-DOWN States from Continuous Signals

Neocortical neurons show UP-DOWN state (UDS) oscillations under a variety of conditions. These UDS have been extensively studied because of the insight they can yield into the functioning of cortical networks, and their proposed role in putative memory formation. A key element in these studies is determining the precise duration and timing of the UDS. These states are typically determined from the membrane potential of one or a small number of cells, which is often not sufficient to reliably estimate the state of an ensemble of neocortical neurons. The local field potential (LFP) provides an attractive method for determining the state of a patch of cortex with high spatio-temporal resolution; however current methods for inferring UDS from LFP signals lack the robustness and flexibility to be applicable when UDS properties may vary substantially within and across experiments. Here we present an explicit-duration hidden Markov model (EDHMM) framework that is sufficiently general to allow statistically principled inference of UDS from different types of signals (membrane potential, LFP, EEG), combinations of signals (e.g., multichannel LFP recordings) and signal features over long recordings where substantial non-stationarities are present. Using cortical LFPs recorded from urethane-anesthetized mice, we demonstrate that the proposed method allows robust inference of UDS. To illustrate the flexibility of the algorithm we show that it performs well on EEG recordings as well. We then validate these results using simultaneous recordings of the LFP and membrane potential (MP) of nearby cortical neurons, showing that our method offers significant improvements over standard methods. These results could be useful for determining functional connectivity of different brain regions, as well as understanding network dynamics

Statistical analysis of neuronal data: Development of quantitative frameworks and application to microelectrode array analysis and cell type classification

With increasing amounts of data being collected in various fields of neuroscience, there is a growing need for robust techniques for the analysis of this information. This thesis focuses on the evaluation and development of quantitative frameworks for the analysis and classification of neuronal data from a variety of contexts. Firstly, I investigate methods for analysing spontaneous neuronal network activity recorded on microelectrode arrays (MEAs). I perform an unbiased evaluation of the existing techniques for detecting ‘bursts’ of neuronal activity in these types of recordings, and provide recommendations for the robust analysis of bursting activity in a range of contexts using both existing and adapted burst detection methods. These techniques are then used to analyse bursting activity in novel recordings of human induced pluripotent stem cell-derived neuronal networks. Results from this review of burst analysis methods are then used to inform the development of a framework for characterising the activity of neuronal networks recorded on MEAs, using properties of bursting as well as other common features of spontaneous activity. Using this framework, I examine the ontogeny of spontaneous network activity in in vitro neuronal networks from various brain regions, recorded on both single and multi-well MEAs. I also develop a framework for classifying these recordings according to their network type, based on quantitative features of their activity patterns. Next, I take a multi-view approach to classifying neuronal cell types using both the morphological and electrophysiological features of cells. I show that a number of multi-view clustering algorithms can more reliably differentiate between neuronal cell types in two existing data sets, compared to single-view clustering techniques applied to either the morphological or electrophysiological ‘view’ of the data, or a concatenation of the two views. To close, I examine the properties of the cell types identified by these methods.Supported by a Wellcome Trust PhD Studentship and a National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre Studentshi