A nonparametric approach for detection of bursts in spike trains

Spiking Laguerre Volterra networks-predicting neuronal activity from local field potentials

Objective.Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations.Approach.Here, we fill this gap by proposing novel spiking Laguerre-Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics.Main results.The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features.Significance.These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

Neural processing in the primary auditory cortex following cholinergic lesions of the basal forebrain in ferrets

Cortical acetylcholine (ACh) release has been linked to various cognitive functions, including perceptual learning. We have previously shown that cortical cholinergic innervation is necessary for accurate sound localization in ferrets, as well as for their ability to adapt with training to altered spatial cues. To explore whether these behavioral deficits are associated with changes in the response properties of cortical neurons, we recorded neural activity in the primary auditory cortex (A1) of anesthetized ferrets in which cholinergic inputs had been reduced by making bilateral injections of the immunotoxin ME20.4-SAP in the nucleus basalis (NB) prior to training the animals. The pattern of spontaneous activity of A1 units recorded in the ferrets with cholinergic lesions (NB ACh-) was similar to that in controls, although the proportion of burst-type units was significantly lower. Depletion of ACh also resulted in more synchronous activity in A1. No changes in thresholds, frequency tuning or in the distribution of characteristic frequencies were found in these animals. When tested with normal acoustic inputs, the spatial sensitivity of A1 neurons in the NB ACh- ferrets and the distribution of their preferred interaural level differences also closely resembled those found in control animals, indicating that these properties had not been altered by sound localization training with one ear occluded. Simulating the animals' previous experience with a virtual earplug in one ear reduced the contralateral preference of A1 units in both groups, but caused azimuth sensitivity to change in slightly different ways, which may reflect the modest adaptation observed in the NB ACh- group. These results show that while ACh is required for behavioral adaptation to altered spatial cues, it is not required for maintenance of the spectral and spatial response properties of A1 neurons.

Theta and gamma rhythmic coding through two spike output modes in the hippocampus during spatial navigation

Hippocampal CA1 neurons generate single spikes and stereotyped bursts of spikes. However, it is unclear how individual neurons dynamically switch between these output modes and whether these two spiking outputs relay distinct information. We performed extracellular recordings in spatially navigating rats and cellular voltage imaging and optogenetics in awake mice. We found that spike bursts are preferentially linked to cellular and network theta rhythms (3-12 Hz) and encode an animal's position via theta phase precession, particularly as animals are entering a place field. In contrast, single spikes exhibit additional coupling to gamma rhythms (30-100 Hz), particularly as animals leave a place field. Biophysical modeling suggests that intracellular properties alone are sufficient to explain the observed input frequency-dependent spike coding. Thus, hippocampal neurons regulate the generation of bursts and single spikes according to frequency-specific network and intracellular dynamics, suggesting that these spiking modes perform distinct computations to support spatial behavior.

Volitional control of individual neurons in the human brain

Brain-machine interfaces allow neuroscientists to causally link specific neural activity patterns to a particular behaviour. Thus, in addition to their current clinical applications, brain-machine interfaces can also be used as a tool to investigate neural mechanisms of learning and plasticity in the brain. Decades of research using such brain-machine interfaces have shown that animals (non-human primates and rodents) can be operantly conditioned to self-regulate neural activity in various motor-related structures of the brain. Here, we ask whether the human brain, a complex interconnected structure of over 80 billion neurons, can learn to control itself at the most elemental scale-a single neuron. We used the unique opportunity to record single units in 11 individuals with epilepsy to explore whether the firing rate of a single (direct) neuron in limbic and other memory-related brain structures can be brought under volitional control. To do this, we developed a visual neurofeedback task in which participants were trained to move a block on a screen by modulating the activity of an arbitrarily selected neuron from their brain. Remarkably, participants were able to volitionally modulate the firing rate of the direct neuron in these previously uninvestigated structures. We found that a subset of participants (learners), were able to improve their performance within a single training session. Successful learning was characterized by (i) highly specific modulation of the direct neuron (demonstrated by significantly increased firing rates and burst frequency); (ii) a simultaneous decorrelation of the activity of the direct neuron from the neighbouring neurons; and (iii) robust phase-locking of the direct neuron to local alpha/beta-frequency oscillations, which may provide some insights in to the potential neural mechanisms that facilitate this type of learning. Volitional control of neuronal activity in mnemonic structures may provide new ways of probing the function and plasticity of human memory without exogenous stimulation. Furthermore, self-regulation of neural activity in these brain regions may provide an avenue for the development of novel neuroprosthetics for the treatment of neurological conditions that are commonly associated with pathological activity in these brain structures, such as medically refractory epilepsy.

Impulsivity Markers in Parkinsonian Subthalamic Single-Unit Activity

BACKGROUND

Impulsive-compulsive behaviors are common in Parkinson's disease (PD) patients. However, the basal ganglia dysfunctions associated with high impulsivity have not been fully characterized. The objective of this study was to identify the features associated with impulsive-compulsive behaviors in single neurons of the subthalamic nucleus (STN).

METHODS

We compared temporal and spectral features of 412 subthalamic neurons from 12 PD patients with impulsive-compulsive behaviors and 330 neurons from 12 PD patients without. Single-unit activities were extracted from exploratory microrecordings performed during deep brain stimulation (DBS) implant surgery in an OFF medication state.

RESULTS

Patients with impulsive-compulsive behaviors displayed decreased firing frequency during bursts and a larger fraction of tonic neurons combined with weaker beta coherence. Information carried by these features led to the identification of patients with impulsive-compulsive behaviors with an accuracy greater than 80%.

CONCLUSIONS

Impulsive-compulsive behaviors in PD patients are associated with decreased bursts in STN neurons in the OFF medication state. © 2021 International Parkinson and Movement Disorder Society.

Functional Alteration of Cerebello-Cerebral Coupling in an Experimental Mouse Model of Parkinson's Disease

In Parkinson's disease, the degeneration of the midbrain dopaminergic neurons is consistently associated with modified metabolic activity in the cerebellum. Here we examined the functional reorganization taking place in the cerebello-cerebral circuit in a murine model of Parkinson's disease with 6-OHDA lesion of midbrain dopaminergic neurons. Cerebellar optogenetic stimulations evoked similar movements in control and lesioned mice, suggesting a normal coupling of cerebellum to the motor effectors after the lesion. In freely moving animals, the firing rate in the primary motor cortex was decreased after the lesion, while cerebellar nuclei neurons showed an increased firing rate. This increase may result from reduced inhibitory Purkinje cells inputs, since a population of slow and irregular Purkinje cells was observed in the cerebellar hemispheres of lesioned animals. Moreover, cerebellar stimulations generated smaller electrocortical responses in the motor cortex of lesioned animals suggesting a weaker cerebello-cerebral coupling. Overall these results indicate the presence of functional changes in the cerebello-cerebral circuit, but their ability to correct cortical dysfunction may be limited due to functional uncoupling between the cerebellum and cerebral cortex.

Parkinsonism Differently Affects the Single Neuronal Activity in the Primary and Supplementary Motor Areas in Monkeys: An Investigation in Linear and Nonlinear Domains

The changes in neuronal firing activity in the primary motor cortex (M1) and supplementary motor area (SMA) were compared in monkeys rendered parkinsonian by treatment with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. The neuronal dynamic was characterized using mathematical tools defined in different frameworks (rate, oscillations or complex patterns). Then, and for each cortical area, multivariate and discriminate analyses were further performed on these features to identify those important to differentiate between the normal and the pathological neuronal activity. Our results show a different order in the importance of the features to discriminate the pathological state in each cortical area which suggests that the M1 and the SMA exhibit dissimilarities in their neuronal alterations induced by parkinsonism. Our findings highlight the need for multiple mathematical frameworks to best characterize the pathological neuronal activity related to parkinsonism. Future translational studies are warranted to investigate the causal relationships between cortical region-specificities, dominant pathological hallmarks and symptoms.

GABAA receptors contribute more to rate than temporal coding in the IC of awake mice

Speech is our most important form of communication, yet we have a poor understanding of how communication sounds are processed by the brain. Mice make great model organisms to study neural processing of communication sounds because of their rich repertoire of social vocalizations and because they have brain structures analogous to humans, such as the auditory midbrain nucleus inferior colliculus (IC). Although the combined roles of GABAergic and glycinergic inhibition on vocalization selectivity in the IC have been studied to a limited degree, the discrete contributions of GABAergic inhibition have only rarely been examined. In this study, we examined how GABAergic inhibition contributes to shaping responses to pure tones as well as selectivity to complex sounds in the IC of awake mice. In our set of long-latency neurons, we found that GABAergic inhibition extends the evoked firing rate range of IC neurons by lowering the baseline firing rate but maintaining the highest probability of firing rate. GABAergic inhibition also prevented IC neurons from bursting in a spontaneous state. Finally, we found that although GABAergic inhibition shaped the spectrotemporal response to vocalizations in a nonlinear fashion, it did not affect the neural code needed to discriminate vocalizations, based either on spiking patterns or on firing rate. Overall, our results emphasize that even if GABAergic inhibition generally decreases the firing rate, it does so while maintaining or extending the abilities of neurons in the IC to code the wide variety of sounds that mammals are exposed to in their daily lives.NEW & NOTEWORTHY GABAergic inhibition adds nonlinearity to neuronal response curves. This increases the neuronal range of evoked firing rate by reducing baseline firing. GABAergic inhibition prevents bursting responses from neurons in a spontaneous state, reducing noise in the temporal coding of the neuron. This could result in improved signal transmission to the cortex.

Chronic Stress Induces Activity, Synaptic, and Transcriptional Remodeling of the Lateral Habenula Associated with Deficits in Motivated Behaviors

Chronic stress (CS) is a major risk factor for the development of depression. Here, we demonstrate that CS-induced hyperactivity in ventral tegmental area (VTA)-projecting lateral habenula (LHb) neurons is associated with increased passive coping (PC), but not anxiety or anhedonia. LHb→VTA neurons in mice with increased PC show increased burst and tonic firing as well as synaptic adaptations in excitatory inputs from the entopeduncular nucleus (EP). In vivo manipulations of EP→LHb or LHb→VTA neurons selectively alter PC and effort-related motivation. Conversely, dorsal raphe (DR)-projecting LHb neurons do not show CS-induced hyperactivity and are targeted indirectly by the EP. Using single-cell transcriptomics, we reveal a set of genes that can collectively serve as biomarkers to identify mice with increased PC and differentiate LHb→VTA from LHb→DR neurons. Together, we provide a set of biological markers at the level of genes, synapses, cells, and circuits that define a distinctive CS-induced behavioral phenotype.

Spatio-temporal structure of single neuron subthalamic activity identifies DBS target for anesthetized Tourette syndrome patients

OBJECTIVE

Deep brain stimulation (DBS) of basal ganglia effectively tackles motor symptoms of movement disorders such as Tourette syndrome (TS). The precise location of target stimulation site determines the range of clinical outcome in DBS patients, and the occurrence of side-effects of DBS. DBS implant procedures currently localize stimulation target relying on a combination of pre-surgical imaging, standardized brain atlases and on-the-spot clinical tests. Here we show that temporal structure of single unit activity in subthalamic nucleus (STN) of patients affected by pure TS can contribute to identify the optimal target location of DBS.

APPROACH

Neural activity was recorded at different depths within STN with microelectrodes during DBS implant surgery. Depth specific neural features were extracted and correlated with the optimal depth for tic control.

MAIN RESULTS

We describe for the first time temporal spike patterns of single neurons from sensorimotor STN of anesthetized TS patients. A large fraction of units (31.2%) displayed intense bursting in the delta band (<4 Hz). The highest firing irregularity and hence the higher density of bursting units (42%) were found at the optimal spot for tic control. Discharge patterns irregularity and dominant oscillations frequency (but not firing rate) carried significant information on optimal target.

SIGNIFICANCE

We found single unit activity features in the STN of TS patients reliably associated to optimal DBS target site for tic control. In future works measures of firing irregularity could be integrated with current target localization methods leading to a more effective and safer DBS for TS patients.

Online Detection of Multiple Stimulus Changes Based on Single Neuron Interspike Intervals

Nervous systems need to detect stimulus changes based on their neuronal responses without using any additional information on the number, times, and types of stimulus changes. Here, two relatively simple, biologically realistic change point detection methods are compared with two common analysis methods. The four methods are applied to intra- and extracellularly recorded responses of a single cricket interneuron (AN2) to acoustic simulation. Solely based on these recorded responses, the methods should detect an unknown number of different types of sound intensity in- and decreases shortly after their occurrences. For this task, the methods rely on calculating an adjusting interspike interval (ISI). Both simple methods try to separate responses to intensity in- or decreases from activity during constant stimulation. The Pure-ISI method performs this task based on the distribution of the ISI, while the ISI-Ratio method uses the ratio of actual and previous ISI. These methods are compared to the frequently used Moving-Average method, which calculates mean and standard deviation of the instantaneous spike rate in a moving interval. Additionally, a classification method provides the upper limit of the change point detection performance that can be expected for the cricket interneuron responses. The classification learns the statistical properties of the actual and previous ISI during stimulus changes and constant stimulation from a training data set. The main results are: (1) The Moving-Average method requires a stable activity in a long interval to estimate the previous activity, which was not always given in our data set. (2) The Pure-ISI method can reliably detect stimulus intensity increases when the neuron bursts, but it fails to identify intensity decreases. (3) The ISI-Ratio method detects stimulus in- and decreases well, if the spike train is not too noisy. (4) The classification method shows good performance for the detection of stimulus in- and decreases. But due to the statistical learning, this method tends to confuse responses to constant stimulation with responses triggered by a stimulus change. Our results suggest that stimulus change detection does not require computationally costly mechanisms. Simple nervous systems like the cricket's could effectively apply ISI-Ratios to solve this fundamental task.

Discrimination of bursts and tonic activity in multifunctional sensorimotor neural network using the extended hill-valley method

Individual neurons can exhibit a wide range of activity, including spontaneous spiking, tonic spiking, bursting, or spike-frequency adaptation, and can also transition between these activity types. Manual identification of these activity patterns can be subjective and inconsistent. The extended hill-valley (EHV) analysis discriminates tonic spiking and bursts in a spike train by detecting fluctuations in a local, history-dependent analysis signal derived from the spike train. Consequently, the EHV method is not susceptible to changes in baseline firing rate and can identify different types of activity patterns. In addition, output from the EHV method can be used to identify more complex activity patterns such as phasotonic bursting, in which a burst is immediately followed by a period of tonic spiking.NEW & NOTEWORTHY Neurons exhibit diverse spiking patterns, but automated activity classification has focused mainly on detecting bursts. The novel extended hill-valley algorithm uses a smoothed, history-dependent signal to discriminate different types of activity, such as bursts and tonic spiking.

Fluctuation Scaling of Neuronal Firing and Bursting in Spontaneously Active Brain Circuits

We employed high-density microelectrode arrays to investigate spontaneous firing patterns of neurons in brain circuits of the primary somatosensory cortex (S1) in mice. We recorded from over 150 neurons for 10min in each of eight different experiments, identified their location in S1, sorted their action potentials (spikes), and computed their power spectra and inter-spike interval (ISI) statistics. Of all persistently active neurons, 92% fired with a single dominant frequency - regularly firing neurons (RNs) - from 1 to 8Hz while 8% fired in burst with two dominant frequencies - bursting neurons (BNs) - corresponding to the inter-burst (2-6Hz) and intra-burst intervals (20-160Hz). RNs were predominantly located in layers 2/3 and 5/6 while BNs localized to layers 4 and 5. Across neurons, the standard deviation of ISI was a power law of its mean, a property known as fluctuation scaling, with a power law exponent of 1 for RNs and 1.25 for BNs. The power law implies that firing and bursting patterns are scale invariant: the firing pattern of a given RN or BN resembles that of another RN or BN, respectively, after a time contraction or dilation. An explanation for this scale invariance is discussed in the context of previous computational studies as well as its potential role in information processing.

In Vitro Screening for Seizure Liability Using Microelectrode Array Technology

Drug-induced seizure liabilities produce significant compound attrition during drug discovery. Currently available in vitro cytotoxicity assays cannot predict all toxicity mechanisms due to the failure of these assays to predict sublethal target-specific electrophysiological liabilities. Identification of seizurogenic and other electrophysiological effects at early stages of the drug development process is important to ensure that safe candidate compounds can be developed while chemical design is taking place, long before these liabilities are discovered in costly preclinical in vivo studies. The development of a high throughput and reliable in vitro assay to screen compounds for seizure liabilities would de-risk compounds significantly earlier in the drug discovery process and with greater dependability. Here we describe a method for screening compounds that utilizes rat cortical neurons plated onto multiwell microelectrode array plates to identify compounds that cause neurophysiological disruptions. Changes in 12 electrophysiological parameters (spike train descriptors) were measured after application of known seizurogenic compounds and the response pattern was mapped relative to negative controls, vehicle control and neurotoxic controls. Twenty chemicals with a variety of therapeutic indications and targets, including GABAA antagonists, glycine receptor antagonists, ion channel blockers, muscarinic agonist, δ-opioid receptor agonist, dopaminergic D2/adrenergic receptor blocker and nonsteroidal anti-inflammatory drugs, were tested to assess this system. Sixteen of the seventeen seizurogenic/neurotoxic compounds tested positive for seizure liability or neurotoxicity, moreover, different endpoint response patterns for firing rate, burst characteristics and synchrony that distinguished the chemicals into groups relating to target and seizurogenic response emerged from the data. The negative and vehicle control compounds had no effect on neural activity. In conclusion, the multiwell microelectrode array platform using cryopreserved rat cortical neurons is a highly effective high throughput method for reliably screening seizure liabilities within an early de-risking drug development paradigm.

Methods for the Detection of Seizure Bursts in Epilepsy

Background: Seizure clusters and “bursts” are of clinical importance. Clusters are reported to be a marker of antiepileptic drug resistance. Additionally, seizure clustering has been found to be associated with increased morbidity and mortality. However, there are no statistical methods described in the literature to delineate bursting phenomenon in epileptic seizures.

Methods: We present three automatic burst detection methods referred to as precision constrained grouping (PCG), burst duration constrained grouping (BCG), and interseizure interval constrained grouping (ICG). Concordance correlation coefficients were used to confirm the pairwise agreement between common bursts isolated using these three automatic burst detection procedures. Additionally, three graphical methods were employed to demonstrate seizure bursts: modified scatter plots, staircase plots, and dropline plots. Burst detection procedures are demonstrated on data from continuous intracranial ambulatory EEG monitoring in a patient diagnosed with drug-refractory focal epilepsy.

Results: We analyzed 1,569 seizures, from our assigned index patient, captured on ambulatory intracranial EEG monitoring. A total of 31, 32, and 32 seizure bursts were detected by the three quantitative methods (BCG, ICG, and PCG), respectively. The concordance correlation coefficient was ≥0.99 signifying considerably stronger than chance burst detector agreements with one another.

Conclusions: Bursting is a quantifiable temporal phenomenon in epilepsy and seizure bursts can be reliably detected using our methodology.

Burst Detection Methods

'Bursting', defined as periods of high-frequency firing of a neuron separated by periods of quiescence, has been observed in various neuronal systems, both in vitro and in vivo. It has been associated with a range of neuronal processes, including efficient information transfer and the formation of functional networks during development, and has been shown to be sensitive to genetic and pharmacological manipulations. Accurate detection of periods of bursting activity is thus an important aspect of characterising both spontaneous and evoked neuronal network activity. A wide variety of computational methods have been developed to detect periods of bursting in spike trains recorded from neuronal networks. In this chapter, we review several of the most popular and successful of these methods.

Detection of synchronized burst firing in cultured human induced pluripotent stem cell-derived neurons using a 4-step method

Human induced pluripotent stem cell-derived neurons are promising for use in toxicity evaluations in nonclinical studies. The multi-electrode array (MEA) assay is used in such evaluation systems because it can measure the electrophysiological function of a neural network noninvasively and with high throughput. Synchronized burst firing (SBF) is the main analytic parameter of pharmacological effects in MEA data, but an accurate method for detecting SBFs has not been established. In this study, we present a 4-step method that accurately detects a target SBF confirmed by the researcher's interpretation of a raster plot. This method calculates one set parameter per step, in the following order: the inter-spike interval (ISI), the number of spikes in an SBF, the inter-SBF interval, and the number of spikes in an SBF again. We found that the 4-step method is advantageous over the conventional method because it determines the preferable duration of an SBF, accurately distinguishes continuous SBFs, detects weak SBFs, and avoids false detection of SBFs. We found also that pharmacological evaluations involving SBF analysis may differ depending on whether the 4-step or conventional threshold method is used. This 4-step method may contribute to improving the accuracy of drug toxicity and efficacy evaluations using human induced pluripotent stem cell-derived neurons.

Distance-based permutation of inter-meal differences as a sensitive test of temporomandibular joint nociception in rats

Purpose

Compare non-parametric permutation method using intr-meal rate as endpoint to existing ANOVA method that uses average daily meal duration as an endpoint for detection of chronic pain in Sprague-Dawley rats.

Methods

Nociception following bilateral temporomandibular joint (TMJ) injection of high-dose of Complete Freunds Adjuvant (CFA, 250 μg/50 μL per side) could be detected in young adult male Sprague-Dawley rats using average daily meal durations as a measure of nociception for up to 19 days (Kramer, Kerins, Schneiderman, & Bellinger, 2010) using ANOVA and multiple comparisons range tests. In this study, we reanalyzed the data using a non-parametric permutation procedure based on absolute differences between intra-meal feeding rate curves. In addition, to that experiment, we injected bilaterally the TMJ of naive rats with either a low-dose CFA (15 μg/50 μL per side, n=6) or saline (50 μL of 0.9%, n=4) and monitored the animals for 7 days.

Results

The permutation test of the intra-meal feeding rate detected the presence of nociception in the high-dose CFA treatment group for up to 40 days or twice as long as when using ANOVA on average daily meal durations. The permutation method also detected the low-dose CFA induced nociception with ten-times lower p-values and for several days longer than ANOVA of changes in meal durations. CFA-induced injury resulted in even reduction of intra-meal feeding rate and lengthening of the meals in both high- and low-dose CFA-injected animals. The rate analysis also showed when the rats first started a meal they were experiencing the same level of nociception as at the end of the meal. This demonstrated that intra-meal chewing itself did not alter the level of nociception.

Conclusions

These results suggest that permutation tests based on differences in intra-meal feeding rates can be used as a sensitive test to determine and study the temporal patterns of TMJ nociception.

Ventral Tegmental Area Dopamine Neurons Firing Model Reveals Prenatal Nicotine Induced Alterations

The dopamine (DA) neurons found in the ventral tegmental area (VTA) are widely involved in the addiction and natural reward circuitry of the brain. Their firing patterns were shown to be important modulators of dopamine release and repetitive burst-like firing activity was highlighted as a major firing pattern of DA neurons in the VTA. In the present study we use a state space model to characterize the DA neurons firing patterns, and trace transitions of neural activity through bursting and non-bursting states. The hidden semi-Markov model (HSMM) framework, which we use, offers a statistically principled inference of bursting states and considers VTA DA firing patterns to be generated according to a Gamma process. Additionally, the explicit Gamma-based modeling of state durations allows efficient decoding of underlying neural information. Consequently, we decode and segment our single unit recordings from DA neurons in VTA according to the sequence of statistically discriminated HSMM states. The segmentation is used to study bursting state characteristics in data recorded from rats prenatally exposed to nicotine (6 mg/kg/day starting with gestational day 3) and rats from saline treated dams. Our results indicate that prenatal nicotine exposure significantly alters burst firing patterns of a subset of DA neurons in adolescent rats, suggesting nicotine exposure during gestation may induce severe effects on the neural networks involved in addiction and reward.

Human striatal recordings reveal abnormal discharge of projection neurons in Parkinson's disease

Circuitry models of Parkinson's disease (PD) are based on striatal dopamine loss and aberrant striatal inputs into the basal ganglia network. However, extrastriatal mechanisms have increasingly been the focus of attention, whereas the status of striatal discharges in the parkinsonian human brain remains conjectural. We now report the activity pattern of striatal projection neurons (SPNs) in patients with PD undergoing deep brain stimulation surgery, compared with patients with essential tremor (ET) and isolated dystonia (ID). The SPN activity in ET was very low (2.1 ± 0.1 Hz) and reminiscent of that found in normal animals. In contrast, SPNs in PD fired at much higher frequency (30.2 ± 1.2 Hz) and with abundant spike bursts. The difference between PD and ET was reproduced between 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-treated and normal nonhuman primates. The SPN activity was also increased in ID, but to a lower level compared with the hyperactivity observed in PD. These results provide direct evidence that the striatum contributes significantly altered signals to the network in patients with PD.

The Gamma renewal process as an output of the diffusion leaky integrate-and-fire neuronal model

Statistical properties of spike trains as well as other neurophysiological data suggest a number of mathematical models of neurons. These models range from entirely descriptive ones to those deduced from the properties of the real neurons. One of them, the diffusion leaky integrate-and-fire neuronal model, which is based on the Ornstein-Uhlenbeck (OU) stochastic process that is restricted by an absorbing barrier, can describe a wide range of neuronal activity in terms of its parameters. These parameters are readily associated with known physiological mechanisms. The other model is descriptive, Gamma renewal process, and its parameters only reflect the observed experimental data or assumed theoretical properties. Both of these commonly used models are related here. We show under which conditions the Gamma model is an output from the diffusion OU model. In some cases, we can see that the Gamma distribution is unrealistic to be achieved for the employed parameters of the OU process.

A comparison of computational methods for detecting bursts in neuronal spike trains and their application to human stem cell-derived neuronal networks

We provide an unbiased quantitative assessment of eight existing methods for identifying bursts in neuronal spike trains. We reveal limitations in a number of commonly used burst detection techniques and provide recommendations for the best practice for accurate identification of bursts using existing techniques. An analysis of the ontogeny of bursting activity in a novel data set of recordings from human induced pluripotent stem cell-derived neuronal networks, using the highest-performing burst detectors from our study, is also presented.

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.

Development of a spontaneously active dorsal root ganglia assay using multiwell multielectrode arrays

In vitro phenotypic assays of sensory neuron activity are important tools for identifying potential analgesic compounds. These assays are typically characterized by hyperexcitable and/or abnormally, spontaneously active cells. Whereas manual electrophysiology experiments provide high-resolution biophysical data to characterize both in vitro models and potential therapeutic modalities (e.g., action potential characteristics, the role of specific ion channels, and receptors), these techniques are hampered by their low throughput. We have established a spontaneously active dorsal root ganglia (DRG) platform using multiwell multielectrode arrays (MEAs) that greatly increase the ability to evaluate the effects of multiple compounds and conditions on DRG excitability within the context of a cellular network. We show that spontaneous DRG firing can be attenuated with selective Na(+) and Ca(2+) channel blockers, as well as enhanced with K(+) channel blockers. In addition, spontaneous activity can be augmented with both the transient receptor potential cation channel subfamily V member 1 agonist capsaicin and the peptide bradykinin and completely blocked with neurokinin receptor antagonists. Finally, we validated the use of this assay by demonstrating that commonly used neuropathic pain therapeutics suppress DRG spontaneous activity. Overall, we have optimized primary rat DRG cells on a multiwell MEA platform to generate and characterize spontaneously active cultures that have the potential to be used as an in vitro phenotypic assay to evaluate potential therapeutics in rodent models of pain.

Rational design of DNA-actuated enzyme nanoreactors guided by single molecule analysis

The control of enzymatic reactions using nanoscale DNA devices offers a powerful application of DNA nanotechnology uniquely derived from actuation. However, previous characterization of enzymatic reaction rates using bulk biochemical assays reported suboptimal function of DNA devices such as tweezers. To gain mechanistic insight into this deficiency and to identify design rules to improve their function, here we exploit the synergy of single molecule imaging and computational modeling to characterize the three-dimensional structures and catalytic functions of DNA tweezer-actuated nanoreactors. Our analysis revealed two important deficiencies--incomplete closure upon actuation and conformational heterogeneity. Upon rational redesign of the Holliday junctions located at their hinge and arms, we found that the DNA tweezers could be more completely and uniformly closed. A novel single molecule enzyme assay was developed to demonstrate that our design improvements yield significant, independent enhancements in the fraction of active enzyme nanoreactors and their individual substrate turnover frequencies. The sequence-level design strategies explored here may aid more broadly in improving the performance of DNA-based nanodevices including biological and chemical sensors.

The Shine-Dalgarno sequence of riboswitch-regulated single mRNAs shows ligand-dependent accessibility bursts

In response to intracellular signals in Gram-negative bacteria, translational riboswitches—commonly embedded in messenger RNAs (mRNAs)—regulate gene expression through inhibition of translation initiation. It is generally thought that this regulation originates from occlusion of the Shine-Dalgarno (SD) sequence upon ligand binding; however, little direct evidence exists. Here we develop Single Molecule Kinetic Analysis of RNA Transient Structure (SiM-KARTS) to investigate the ligand-dependent accessibility of the SD sequence of an mRNA hosting the 7-aminomethyl-7-deazaguanine (preQ₁)-sensing riboswitch. Spike train analysis reveals that individual mRNA molecules alternate between two conformational states, distinguished by ‘bursts' of probe binding associated with increased SD sequence accessibility. Addition of preQ₁ decreases the lifetime of the SD's high-accessibility (bursting) state and prolongs the time between bursts. In addition, ligand-jump experiments reveal imperfect riboswitching of single mRNA molecules. Such complex ligand sensing by individual mRNA molecules rationalizes the nuanced ligand response observed during bulk mRNA translation.

In response to intracellular signals, bacterial translational riboswitches embedded in mRNAs can regulate gene expression through inhibition of translation initiation. Here, the authors describe SiM-KARTS, a novel approach for detecting changes in the structure of single RNA molecules in response to a ligand.

Bursting activity of substantia nigra pars reticulata neurons in mouse parkinsonism in awake and anesthetized states

Electrophysiological changes in basal ganglia neurons are hypothesized to underlie motor dysfunction in Parkinson's disease (PD). Previous results in head-restrained MPTP-treated non-human primates have suggested that increased bursting within the basal ganglia and related thalamic and cortical areas may be a hallmark of pathophysiological activity. In this study, we investigated whether there is increased bursting in substantia nigra pars reticulata (SNpr) output neurons in anesthetized and awake, head-restrained unilaterally lesioned 6-OHDA mice when compared to control mice. Confirming previous studies, we show that there are significant changes in the firing rate and pattern in SNpr neuron activity under urethane anesthesia. The regular firing pattern of control urethane-anesthetized SNpr neurons was not present in the 6-OHDA-lesioned group, as the latter neurons instead became phase locked with cortical slow wave activity (SWA). Next, we examined whether such robust electrophysiological changes between groups carried over to the awake state. SNpr neurons from both groups fired at much higher frequencies in the awake state than in the anesthetized state and surprisingly showed only modest changes between awake control and 6-OHDA groups. While there were no differences in firing rate between groups in the awake state, an increase in the coefficient of variation (CV) was observed in the 6-OHDA group. Contrary to the bursting hypothesis, this increased CV was not due to changes in bursting but was instead due to a mild increase in pausing. Together, these results suggest that differences in SNpr activity between control and 6-OHDA lesioned mice may be strongly influenced by changes in network activity during different arousal and behavioral states.

Graded defragmentation of cortical neuronal firing during recovery of consciousness in rats

State-dependent neuronal firing patterns reflect changes in ongoing information processing and cortical function. A disruption of neuronal coordination has been suggested as the neural correlate of anesthesia. Here, we studied the temporal correlation patterns of ongoing spike activity, during a stepwise reduction of the volatile anesthetic desflurane, in the cerebral cortex of freely moving rats. We hypothesized that the recovery of consciousness from general anesthesia is accompanied by specific changes in the spatiotemporal pattern and correlation of neuronal activity. Sixty-four contact microelectrode arrays were chronically implanted in the primary visual cortex (contacts spanning 1.4-mm depth and 1.4-mm width) for recording of extracellular unit activity at four steady-state levels of anesthesia (8-2% desflurane) and wakefulness. Recovery of consciousness was defined as the regaining of the righting reflex (near 4%). High-intensity firing (HI) periods were segmented using a threshold (200-ms) representing the minimum in the neurons' bimodal interspike interval histogram under anesthesia. We found that the HI periods were highly fragmented in deep anesthesia and gradually transformed to a near-continuous firing pattern at wakefulness. As the anesthetic was withdrawn, HI periods became longer and increasingly correlated among the units both locally and across remote recording sites. Paradoxically, in 4 of 8 animals, HI correlation was also high at the deepest level of anesthesia (8%) when local field potentials (LFP) were burst-suppressed. We conclude that recovery from desflurane anesthesia is accompanied by a graded defragmentation of neuronal activity in the cerebral cortex. Hypersynchrony during deep anesthesia is an exception that occurs only with LFP burst suppression.

Estimating the correlation between bursty spike trains and local field potentials

To further understand rhythmic neuronal synchronization, an increasingly useful method is to determine the relationship between the spiking activity of individual neurons and the local field potentials (LFPs) of neural ensembles. Spike field coherence (SFC) is a widely used method for measuring the synchronization between spike trains and LFPs. However, due to the strong dependency of SFC on the burst index, it is not suitable for analyzing the relationship between bursty spike trains and LFPs, particularly in high frequency bands. To address this issue, we developed a method called weighted spike field correlation (WSFC), which uses the first spike in each burst multiple times to estimate the relationship. In the calculation, the number of times that the first spike is used is equal to the spike count per burst. The performance of this method was demonstrated using simulated bursty spike trains and LFPs, which comprised sinusoids with different frequencies, amplitudes, and phases. This method was also used to estimate the correlation between pyramidal cells in the hippocampus and gamma oscillations in rats performing behaviors. Analyses using simulated and real data demonstrated that the WSFC method is a promising measure for estimating the correlation between bursty spike trains and high frequency LFPs.

Abnormal Bursting as a Pathophysiological Mechanism in Parkinson's Disease

Despite remarkable advances in Parkinson's disease (PD) research, the pathophysiological mechanisms causing motor dysfunction remain unclear, possibly delaying the advent of new and improved therapies. Several such mechanisms have been proposed including changes in neuronal firing rates, the emergence of pathological oscillatory activity, increased neural synchronization, and abnormal bursting. This review focuses specifically on the role of abnormal bursting of basal ganglia neurons in PD, where a burst is a physiologically-relevant, transient increase in neuronal firing over some reference period or activity. After reviewing current methods for how bursts are detected and what the functional role of bursts may be under normal conditions, existing studies are reviewed that suggest that bursting is abnormally increased in PD and that this increases with worsening disease. Finally, the influence of therapeutic approaches for PD such as dopamine-replacement therapy with levodopa or dopamine agonists, lesions, or deep brain stimulation on bursting is discussed. Although there is insufficient evidence to conclude that increased bursting causes motor dysfunction in PD, current evidence suggests that targeted investigations into the role of bursting in PD may be warranted.

Parameters for burst detection

Bursts of action potentials within neurons and throughout networks are believed to serve roles in how neurons handle and store information, both in vivo and in vitro. Accurate detection of burst occurrences and durations are therefore crucial for many studies. A number of algorithms have been proposed to do so, but a standard method has not been adopted. This is due, in part, to many algorithms requiring the adjustment of multiple ad-hoc parameters and further post-hoc criteria in order to produce satisfactory results. Here, we broadly catalog existing approaches and present a new approach requiring the selection of only a single parameter: the number of spikes N comprising the smallest burst to consider. A burst was identified if N spikes occurred in less than T ms, where the threshold T was automatically determined from observing a probability distribution of inter-spike-intervals. Performance was compared vs. different classes of detectors on data gathered from in vitro neuronal networks grown over microelectrode arrays. Our approach offered a number of useful features including: a simple implementation, no need for ad-hoc or post-hoc criteria, and precise assignment of burst boundary time points. Unlike existing approaches, detection was not biased toward larger bursts, allowing identification and analysis of a greater range of neuronal and network dynamics.

Method for stationarity-segmentation of spike train data with application to the Pearson cross-correlation

Correlations among neurons are supposed to play an important role in computation and information coding in the nervous system. Empirically, functional interactions between neurons are most commonly assessed by cross-correlation functions. Recent studies have suggested that pairwise correlations may indeed be sufficient to capture most of the information present in neural interactions. Many applications of correlation functions, however, implicitly tend to assume that the underlying processes are stationary. This assumption will usually fail for real neurons recorded in vivo since their activity during behavioral tasks is heavily influenced by stimulus-, movement-, or cognition-related processes as well as by more general processes like slow oscillations or changes in state of alertness. To address the problem of nonstationarity, we introduce a method for assessing stationarity empirically and then “slicing” spike trains into stationary segments according to the statistical definition of weak-sense stationarity. We examine pairwise Pearson cross-correlations (PCCs) under both stationary and nonstationary conditions and identify another source of covariance that can be differentiated from the covariance of the spike times and emerges as a consequence of residual nonstationarities after the slicing process: the covariance of the firing rates defined on each segment. Based on this, a correction of the PCC is introduced that accounts for the effect of segmentation. We probe these methods both on simulated data sets and on in vivo recordings from the prefrontal cortex of behaving rats. Rather than for removing nonstationarities, the present method may also be used for detecting significant events in spike trains.

Differences between spectro-temporal receptive fields derived from artificial and natural stimuli in the auditory cortex

Spectro-temporal properties of auditory cortex neurons have been extensively studied with artificial sounds but it is still unclear whether they help in understanding neuronal responses to communication sounds. Here, we directly compared spectro-temporal receptive fields (STRFs) obtained from the same neurons using both artificial stimuli (dynamic moving ripples, DMRs) and natural stimuli (conspecific vocalizations) that were matched in terms of spectral content, average power and modulation spectrum. On a population of auditory cortex neurons exhibiting reliable tuning curves when tested with pure tones, significant STRFs were obtained for 62% of the cells with vocalizations and 68% with DMR. However, for many cells with significant vocalization-derived STRFs (STRF_voc) and DMR-derived STRFs (STRF_dmr), the BF, latency, bandwidth and global STRFs shape differed more than what would be predicted by spiking responses simulated by a linear model based on a non-homogenous Poisson process. Moreover STRF_voc predicted neural responses to vocalizations more accurately than STRF_dmr predicted neural response to DMRs, despite similar spike-timing reliability for both sets of stimuli. Cortical bursts, which potentially introduce nonlinearities in evoked responses, did not explain the differences between STRF_voc and STRF_dmr. Altogether, these results suggest that the nonlinearity of auditory cortical responses makes it difficult to predict responses to communication sounds from STRFs computed from artificial stimuli.

Population-wide distributions of neural activity during perceptual decision-making

Cortical activity involves large populations of neurons, even when it is limited to functionally coherent areas. Electrophysiological recordings, on the other hand, involve comparatively small neural ensembles, even when modern-day techniques are used. Here we review results which have started to fill the gap between these two scales of inquiry, by shedding light on the statistical distributions of activity in large populations of cells. We put our main focus on data recorded in awake animals that perform simple decision-making tasks and consider statistical distributions of activity throughout cortex, across sensory, associative, and motor areas. We transversally review the complexity of these distributions, from distributions of firing rates and metrics of spike-train structure, through distributions of tuning to stimuli or actions and of choice signals, and finally the dynamical evolution of neural population activity and the distributions of (pairwise) neural interactions. This approach reveals shared patterns of statistical organization across cortex, including: (i) long-tailed distributions of activity, where quasi-silence seems to be the rule for a majority of neurons; that are barely distinguishable between spontaneous and active states; (ii) distributions of tuning parameters for sensory (and motor) variables, which show an extensive extrapolation and fragmentation of their representations in the periphery; and (iii) population-wide dynamics that reveal rotations of internal representations over time, whose traces can be found both in stimulus-driven and internally generated activity. We discuss how these insights are leading us away from the notion of discrete classes of cells, and are acting as powerful constraints on theories and models of cortical organization and population coding.

Detection of bursts and pauses in spike trains

Midbrain dopaminergic neurons in vivo exhibit a wide range of firing patterns. They normally fire constantly at a low rate, and speed up, firing a phasic burst when reward exceeds prediction, or pause when an expected reward does not occur. Therefore, the detection of bursts and pauses from spike train data is a critical problem when studying the role of phasic dopamine (DA) in reward related learning, and other DA dependent behaviors. However, few statistical methods have been developed that can identify bursts and pauses simultaneously. We propose a new statistical method, the Robust Gaussian Surprise (RGS) method, which performs an exhaustive search of bursts and pauses in spike trains simultaneously. We found that the RGS method is adaptable to various patterns of spike trains recorded in vivo, and is not influenced by baseline firing rate, making it applicable to all in vivo spike trains where baseline firing rates vary over time. We compare the performance of the RGS method to other methods of detecting bursts, such as the Poisson Surprise (PS), Rank Surprise (RS), and Template methods. Analysis of data using the RGS method reveals potential mechanisms underlying how bursts and pauses are controlled in DA neurons.

Probing real sensory worlds of receivers with unsupervised clustering

The task of an organism to extract information about the external environment from sensory signals is based entirely on the analysis of ongoing afferent spike activity provided by the sense organs. We investigate the processing of auditory stimuli by an acoustic interneuron of insects. In contrast to most previous work we do this by using stimuli and neurophysiological recordings directly in the nocturnal tropical rainforest, where the insect communicates. Different from typical recordings in sound proof laboratories, strong environmental noise from multiple sound sources interferes with the perception of acoustic signals in these realistic scenarios. We apply a recently developed unsupervised machine learning algorithm based on probabilistic inference to find frequently occurring firing patterns in the response of the acoustic interneuron. We can thus ask how much information the central nervous system of the receiver can extract from bursts without ever being told which type and which variants of bursts are characteristic for particular stimuli. Our results show that the reliability of burst coding in the time domain is so high that identical stimuli lead to extremely similar spike pattern responses, even for different preparations on different dates, and even if one of the preparations is recorded outdoors and the other one in the sound proof lab. Simultaneous recordings in two preparations exposed to the same acoustic environment reveal that characteristics of burst patterns are largely preserved among individuals of the same species. Our study shows that burst coding can provide a reliable mechanism for acoustic insects to classify and discriminate signals under very noisy real-world conditions. This gives new insights into the neural mechanisms potentially used by bushcrickets to discriminate conspecific songs from sounds of predators in similar carrier frequency bands.

Activity-dependent clustering of functional synaptic inputs on developing hippocampal dendrites

During brain development, before sensory systems become functional, neuronal networks spontaneously generate repetitive bursts of neuronal activity, which are typically synchronized across many neurons. Such activity patterns have been described on the level of networks and cells, but the fine-structure of inputs received by an individual neuron during spontaneous network activity has not been studied. Here, we used calcium imaging to record activity at many synapses of hippocampal pyramidal neurons simultaneously to establish the activity patterns in the majority of synapses of an entire cell. Analysis of the spatiotemporal patterns of synaptic activity revealed a fine-scale connectivity rule: neighboring synapses (<16 μm intersynapse distance) are more likely to be coactive than synapses that are farther away from each other. Blocking spiking activity or NMDA receptor activation revealed that the clustering of synaptic inputs required neuronal activity, demonstrating a role of developmentally expressed spontaneous activity for connecting neurons with subcellular precision.

Gray matter loss correlates with mesial temporal lobe neuronal hyperexcitability inside the human seizure-onset zone

PURPOSE

Patient studies have not provided consistent evidence for interictal neuronal hyperexcitability inside the seizure-onset zone (SOZ). We hypothesized that gray matter (GM) loss could have important effects on neuronal firing, and quantifying these effects would reveal significant differences in neuronal firing inside versus outside the SOZ.

METHODS

Magnetic resonance imaging (MRI) and computational unfolding of mesial temporal lobe (MTL) subregions was used to construct anatomic maps to compute GM loss in presurgical patients with medically intractable focal seizures in relation to controls. In patients, these same maps were used to locate the position of microelectrodes that recorded interictal neuronal activity. Single neuron firing and burst rates were evaluated in relation to GM loss and MTL subregions inside and outside the SOZ.

KEY FINDINGS

MTL GM thickness was reduced inside and outside the SOZ in patients with respect to controls, yet GM loss was associated more strongly with firing and burst rates in several MTL subregions inside the SOZ. Adjusting single neuron firing and burst rates for the effects of GM loss revealed significantly higher firing rates in the subregion consisting of dentate gyrus and CA2 and CA3 (CA23DG), as well as CA1 and entorhinal cortex (EC) inside versus outside the SOZ where normalized MRI GM loss was ≥1.40 mm. Firing rates were higher in subicular cortex inside the SOZ at GM loss ≥1.97 mm, whereas burst rates were higher in CA23DG, CA1, and EC inside than outside the SOZ at similar levels of GM loss.

SIGNIFICANCE

The correlation between GM loss and increased firing and burst rates suggests GM structural alterations in MTL subregions are associated with interictal neuronal hyperexcitability inside the SOZ. Significant differences in firing rates and bursting in areas with GM loss inside compared to outside the SOZ indicate that synaptic reorganization following cell loss could be associated with varying degrees of epileptogenicity in patients with intractable focal seizures.

Measuring burstiness and regularity in oscillatory spike trains

The ability of neurons to emit different firing patterns such as bursts or oscillations is important for information processing in the brain. In dopaminergic neurons, prominent patterns include repetitive, oscillatory bursts, regular pacemakers, and irregular spike trains with nonstationary properties. In order to describe and measure the variability of these patterns, we describe burstiness and regularity in a single model framework. We present a doubly stochastic spike train model in which a background oscillation with independent and normally distributed intervals gives rise to either single spikes or bursty spike events with Gaussian firing intensities. Five easily interpretable parameters allow a classification into bursty or single spike and irregularly or regularly oscillating firing patterns. This classification is based primarily on features of the autocorrelation histogram which are usually studied qualitatively by visual inspection. The present model provides a quantitative and objective classification scheme and relates these features directly to the underlying processes. In addition, confidence intervals visualize the uncertainty of parameter estimation and classification precision. We apply the model to a data set obtained from single dopaminergic substantia nigra neurons recorded extracellularly in vivo. The model is able to represent a high variety of discharge patterns observed empirically, and the classification agrees closely with visual inspection. In addition, changes in the parameters can be studied quantitatively, including also the properties related to bursting behavior. Thus, the proposed model can be used for the description of neuronal firing patterns and the investigation of their dynamical changes with cellular and experimental conditions.

Synergistic Coding by Cortical Neural Ensembles

An essential step towards understanding how the brain orchestrates information processing at the cellular and population levels is to simultaneously observe the spiking activity of cortical neurons that mediate perception, learning, and motor processing. In this paper, we formulate an information theoretic approach to determine whether cooperation among neurons may constitute a governing mechanism of information processing when encoding external covariates. Specifically, we show that conditional independence between neuronal outputs may not provide an optimal encoding strategy when the firing probability of a neuron depends on the history of firing of other neurons connected to it. Rather, cooperation among neurons can provide a "message-passing" mechanism that preserves most of the information in the covariates under specific constraints governing their connectivity structure. Using a biologically plausible statistical learning model, we demonstrate the performance of the proposed approach in synergistically encoding a motor task using a subset of neurons drawn randomly from a large population. We demonstrate its superiority in approximating the joint density of the population from limited data compared to a statistically independent model and a maximum entropy (MaxEnt) model.

Detection of bursts in extracellular spike trains using hidden semi-Markov point process models

Neurons in vitro and in vivo have epochs of bursting or "up state" activity during which firing rates are dramatically elevated. Various methods of detecting bursts in extracellular spike trains have appeared in the literature, the most widely used apparently being Poisson Surprise (PS). A natural description of the phenomenon assumes (1) there are two hidden states, which we label "burst" and "non-burst," (2) the neuron evolves stochastically, switching at random between these two states, and (3) within each state the spike train follows a time-homogeneous point process. If in (2) the transitions from non-burst to burst and burst to non-burst states are memoryless, this becomes a hidden Markov model (HMM). For HMMs, the state transitions follow exponential distributions, and are highly irregular. Because observed bursting may in some cases be fairly regular-exhibiting inter-burst intervals with small variation-we relaxed this assumption. When more general probability distributions are used to describe the state transitions the two-state point process model becomes a hidden semi-Markov model (HSMM). We developed an efficient Bayesian computational scheme to fit HSMMs to spike train data. Numerical simulations indicate the method can perform well, sometimes yielding very different results than those based on PS.

Assessment of bursting activity and interspike intervals variability: a case study for methodological comparison

The detection and characterization of bursting activity remains a topic where no consensual definition has been reached so far. We compare here three different approaches of spike trains variability: statistical characterization (average frequency, coefficient of variation), burst detection (Poisson and rank surprise) and multi-scale analysis (detrended fluctuations analysis). Using both real and simulated data, we show that Poisson surprise provides information closely related to the coefficient of variation and that rank surprise detects significant bursts which are associated with long-range correlations. Since these long-range correlations are only adequately characterized with multi-scale analysis, this study emphasizes the complementarity of these approaches for the complete characterization of spike trains.

Balance of increases and decreases in firing rate of the spontaneous activity of basal ganglia high-frequency discharge neurons

Most neurons in the external and internal segments of the globus pallidus and the substantia nigra pars reticulata (GPe, GPi, and SNr) are characterized by a high-frequency discharge (HFD) rate (50-80 Hz) that, in most GPe neurons, is also interrupted by pauses. Almost all (approximately 90%) of the synaptic inputs to these HFD neurons are GABAergic and inhibitory. Nevertheless, their responses to behavioral events are usually dominated by increases in discharge rate. Additionally, there are no reports of prolonged bursts in the spontaneous activity of these cells that could reflect their disinhibition by GPe pauses. We recorded the spontaneous activity of 385 GPe, GPi, and SNr HFD neurons during a quiet-wakeful state from two monkeys. We developed three complementary methods to quantify the balance of increases and decreases in the spontaneous discharge of HFD neurons and validated them by simulations. Unlike the behavioral evoked responses, the spontaneous activity of pallidal and SNr neurons is not dominated by increases. Moreover, the activity of basal ganglia neurons does not include bursts that could reflect disinhibition by the spontaneous pauses of GPe neurons. These findings suggest that the discharge increase/decrease balance during a quiet-wakeful state better reflects the inhibitory input of the HFD basal ganglia neurons than during responses to behavioral events; however, the GPe pauses are not echoed by comparable bursts either in the GPe or in the output nuclei. Changes in the excitatory drive of these structures (e.g., during behavioral activity) thus may lead to a remarkable change in this balance.