Interval Histograms Research Articles

Digital Heart-Rate Variability Parameter Monitoring and Assessment ASIC

This paper describes experimental results for an application-specific integrated circuit (ASIC), designed for digital heart rate variability (HRV) parameter monitoring and assessment. This ASIC chip measures beat-to-beat (RR) intervals and stores HRV parameters into its internal memory in real time. A wide range of short-term and long-term ECG signals obtained from Physionet was used for testing. The system detects R peaks with millisecond accuracy, and stores up to 2 min of continuous RR interval data and up to 4 min of RR interval histogram. The prototype chip was fabricated in a 0.5 ¿m complementary metal-oxide semiconductor technology on a 3×3 mm(2) die area, with a measured dynamic power consumption of 10 ¿W and measured leakage current of 2.62 nA. The HRV monitoring system including this HRV ASIC, an analog-to-digital converter, and a low complexity microcontroller was estimated to consume 32.5 ¿V, which is seven times lower power than a stand-alone microcontroller performing the same functions. Compact size, low cost, and low power consumption make this chip suitable for a miniaturized portable HRV monitoring system.

Emergence of Local Ca Oscillators in Cardiac Pacemaker Cells: 2d Ca Dynamics Measurements, An Analytical Theory, and Complex Systems Numerical Modeling

The sarcoplasmic reticulum (SR) ofsanoatrial node cells (SANC) is capable of generating roughly periodic spontaneous localized Ca2+releases (LCRs), recently dubbed as “Ca2+clock”. The LCRs interact with sarcolemma electrogenic molecules and are critically important for cardiac impulse initiation and pacemaker rate regulation. Mechanisms of emergence of the local Ca2+ oscillators by stochastically gated release channels remain unknown. Methods: We explored the emergence of rhythmic LCRs in rabbit SANC using a fast 2-D camera, an analytical theory, and complex systems numerical modeling. Results: Spontaneously beating SANC exhibit action potential-induced Ca2+ transients that are shortly preceded by multiple wavelet-like LCRs throughout the cell. The LCRs persisted in KCl-depolarized SANC and were recorded for 30-60 seconds. Autocorrelation analysis and histograms of intervals between persisting releases show that Ca2+dynamics are roughly periodic in each spontaneously active cell location. The histograms exhibit a gap followed by a skewed peak that was interpreted in terms of an analytical theory that considers Ca2+releases to have a restitution time followed by a Poisson process. The experimentally measured Ca2+releases were closely reproduced by a complex systems numerical 2D-model featuring an array of stochastic but diffusively coupled Ca2+release units (CRUs) with fixed restitution times. As the amplitude of the CRU releases increase from 0.5 to 1.25 pA in the model, the CRUs strongly interact via diffusion and Ca2+induced Ca2+release (CICR), resulting in a larger Ca2+release size (between sparks and global waves), and a higher rhythmicity of release occurrence. Conclusions: SANC SR generates roughly periodic LCRs. The emergence of the local Ca2+oscillators is an inherent fundamental property of an ensemble of diffusively interacting, stochastic CRUs having time-dependent restitution. The rhythmicity of the releases increases as interactions of CRUs enhance.

The effects of stochastic and deterministic adaptation currents on interspike interval histograms and correlations

Event Abstract Back to Event The effects of stochastic and deterministic adaptation currents on interspike interval histograms and correlations Tilo Schwalger1*, Karin Fisch2, Jan Benda2 and Benjamin Lindner1 1 MPI for the Physics of Complex Systems, MPI, Germany 2 Ludwig-Maximilians-Universität München, Division of Neurobiology, Department Biology II, Germany The spiking patterns of neurons can be often highly variable. In many cases, the origin of this neural noise is not known and might be difficult to access directly. Here, we explore the possibility to distinguish between two different kinds of intrinsic noise solely by measuring the interspike interval (ISI) statistics of a neuron. Specifically, we consider adaptive neuron models in which fluctuations (channel noise) are either associated with fast ionic currents or with slow adaptation currents that are observed in many neurons. We show by means of analytical techniques and extensive numerical simulations that the shape of the ISI histograms and the ISI correlations are markedly different in both cases: For fast current fluctuations and deterministic adaptation, the ISI density is well approximated by an inverse Gaussian and the ISI correlations are negative. In contrast, for stochastic adaptation currents, the density is more peaked and has a heavier tail than an inverse Gaussian density and the serial correlations are positive. For the theoretical analysis we use an analytically tractable integrate-and-fire model. The results are qualitatively confirmed by simulations of a biophysically more realistic Hodgkin-Huxley type model. Our results could be used to infer the dominant source of noise in neurons from their ISI statistics Keywords: computational neuroscience Conference: Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010. Presentation Type: Poster Abstract Topic: Bernstein Conference on Computational Neuroscience Citation: Schwalger T, Fisch K, Benda J and Lindner B (2010). The effects of stochastic and deterministic adaptation currents on interspike interval histograms and correlations. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00016 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 22 Sep 2010; Published Online: 23 Sep 2010. * Correspondence: Dr. Tilo Schwalger, MPI for the Physics of Complex Systems, MPI, Dresden, Germany, tilo.schwalger@epfl.ch Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Tilo Schwalger Karin Fisch Jan Benda Benjamin Lindner Google Tilo Schwalger Karin Fisch Jan Benda Benjamin Lindner Google Scholar Tilo Schwalger Karin Fisch Jan Benda Benjamin Lindner PubMed Tilo Schwalger Karin Fisch Jan Benda Benjamin Lindner Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Response of a Hodgkin-Huxley neuron to a high-frequency input

We study the response of a Hodgkin-Huxley neuron stimulated by a periodic sequence of conductance pulses arriving through the synapse in the high-frequency regime. In addition to the usual excitation threshold there is a smooth crossover from the firing to the silent regime for increasing pulse amplitude gsyn. The amplitude of the voltage spikes decreases approximately linearly with gsyn. In some regions of parameter space the response is irregular, probably chaotic. In the chaotic regime between the mode-locked regions 3:1 and 2:1 near the lower excitation threshold, the output interspike interval histogram (ISIH) undergoes a sharp transition. If the driving period is below the critical value, T<T*, the output histogram contains only odd multiples of Ti. For Ti>T* even multiples of Ti also appear in the histogram, starting from the largest values. Near T* the ISIH scales logarithmically on both sides of the transition. The coefficient of variation of ISIH has a cusp singularity at T*. The average response period has a maximum slightly above T*. Near the excitation threshold in the chaotic regime the average firing rate rises sublinearly from frequencies of order 1 Hz.

A distinctly bimodal distribution pattern in the RR interval histogram predicts early recurrence of atrial fibrillation after electrical cardioversion

Abnormal AV nodal behaviour could contribute to recurrence of Atrial Fibrillation (AF) after electrical cardioversion (ECV). We constructed RR interval histograms from 24-hour ECGs recorded before ECV in 98 patients with persistent AF. RR histograms were classified as unimodal or bimodal, and bimodal histograms classified into distinctly or indistinctly bimodal. At 1 week after ECV, 52 patients (53%) were in AF and at one-month 66 (67%) were in AF. A bimodal RR interval distribution during AF was found in 17 patients (18%), a distinctly bimodal RR histogram in 8 of these (47%). Compared to those with indistinct bimodality, patients with a distinctly bimodal RR histogram were more likely to have AF at one-week and one-month (88 vs. 33%, 100 vs. 33%, p=0.01, p=0.009, respectively).

A self-adapting approach for the detection of bursts and network bursts in neuronal cultures

Dissociated networks of neurons typically exhibit bursting behavior, whose features are strongly influenced by the age of the culture, by chemical/electrical stimulation or by environmental conditions. To help the experimenter in identifying the changes possibly induced by specific protocols, we developed a self-adapting method for detecting both bursts and network bursts from electrophysiological activity recorded by means of micro-electrode arrays. The algorithm is based on the computation of the logarithmic inter-spike interval histogram and automatically detects the best threshold to distinguish between inter- and intra-burst inter-spike intervals for each recording channel of the array. An analogous procedure is followed for the detection of network bursts, looking for sequences of closely spaced single-channel bursts. We tested our algorithm on recordings of spontaneous as well as chemically stimulated activity, comparing its performance to other methods available in the literature.

Single-Unit Stability Using Chronically Implanted Multielectrode Arrays

The use of chronic intracortical multielectrode arrays has become increasingly prevalent in neurophysiological experiments. However, it is not obvious whether neuronal signals obtained over multiple recording sessions come from the same or different neurons. Here, we develop a criterion to assess single-unit stability by measuring the similarity of 1) average spike waveforms and 2) interspike interval histograms (ISIHs). Neuronal activity was recorded from four Utah arrays implanted in primary motor and premotor cortices in three rhesus macaque monkeys during 10 recording sessions over a 15- to 17-day period. A unit was defined as stable through a given day if the stability criterion was satisfied on all recordings leading up to that day. We found that 57% of the original units were stable through 7 days, 43% were stable through 10 days, and 39% were stable through 15 days. Moreover, stable units were more likely to remain stable in subsequent recording sessions (i.e., 89% of the neurons that were stable through four sessions remained stable on the fifth). Using both waveform and ISIH data instead of just waveforms improved performance by reducing the number of false positives. We also demonstrate that this method can be used to track neurons across days, even during adaptation to a visuomotor rotation. Identifying a stable subset of neurons should allow the study of long-term learning effects across days and has practical implications for pooling of behavioral data across days and for increasing the effectiveness of brain-machine interfaces.

Response properties of cochlear nucleus neurons in monkeys

Much of what is known about how the cochlear nuclei participate in mammalian hearing comes from studies of non-primate mammalian species. To determine to what extent the cochlear nuclei of primates resemble those of other mammalian orders, we have recorded responses to sound in three primate species: marmosets, cynomolgus macaques, and squirrel monkeys. These recordings show that the same types of temporal firing patterns are found in primates that have been described in other mammals. Responses to tones of neurons in the ventral cochlear nucleus have similar tuning, latencies, post-stimulus time and interspike interval histograms as those recorded in non-primate cochlear nucleus neurons. In the dorsal cochlear nucleus, too, responses were similar. From these results it is evident that insights gained from non-primate studies can be applied to the peripheral auditory system of primates.

A FEATURE SELECTION APPROACH FOR AUTOMATIC MUSIC GENRE CLASSIFICATION

In this paper we present an analysis of the suitability of four different feature sets which are currently employed to represent music signals in the context of the automatic music genre classification. To such an aim, feature selection is carried out through genetic algorithms, and it is applied to multiple feature vectors generated from different segments of the music signal. The feature sets used in this paper, which encompass time-domain and frequency-domain characteristics of the music signal, comprise: short-time Fourier transform, Mel frequency cepstral coefficient, beat-related features, pitch-related features, inter-onset interval histogram coefficients, rhythm histograms and statistical spectrum descriptors. The classification is based on the use of multiple feature vectors and an ensemble approach, according to time and space decomposition strategies. Feature vectors are extracted from music segments from the beginning, middle and end parts of the music signal (time-decomposition). Despite music genre classification being a multi-class problem, we accomplish the task using a combination of binary classifiers, whose results are merged to produce the final music genre label (space decomposition). Experiments were carried out on two databases: the Latin Music Database, which contains 3,227 music pieces categorized into ten musical genres; the ISMIR'2004 genre contest database which contains 1,458 music pieces categorized into six popular western musical genres. The experimental results have shown that the feature sets have different importance according to the part of the music signal from where the feature vectors are extracted. Furthermore, the ensemble approach provides better results than the individual segments in most cases. For high-dimensional feature sets, the feature selection provides a compact but discriminative feature subset which has an interesting trade-off between classification accuracy and computational effort.

Spike Train Analysis Toolkit: Enabling Wider Application of Information-Theoretic Techniques to Neurophysiology

Conventional methods widely available for the analysis of spike trains and related neural data include various time- and frequency-domain analyses, such as peri-event and interspike interval histograms, spectral measures, and probability distributions. Information theoretic methods are increasingly recognized as significant tools for the analysis of spike train data. However, developing robust implementations of these methods can be time-consuming, and determining applicability to neural recordings can require expertise. In order to facilitate more widespread adoption of these informative methods by the neuroscience community, we have developed the Spike Train Analysis Toolkit. STAToolkit is a software package which implements, documents, and guides application of several information-theoretic spike train analysis techniques, thus minimizing the effort needed to adopt and use them. This implementation behaves like a typical Matlab toolbox, but the underlying computations are coded in C for portability, optimized for efficiency, and interfaced with Matlab via the MEX framework. STAToolkit runs on any of three major platforms: Windows, Mac OS, and Linux. The toolkit reads input from files with an easy-to-generate text-based, platform-independent format. STAToolkit, including full documentation and test cases, is freely available open source via http://neuroanalysis.org , maintained as a resource for the computational neuroscience and neuroinformatics communities. Use cases drawn from somatosensory and gustatory neurophysiology, and community use of STAToolkit, demonstrate its utility and scope.

The new method of calculation sum and difference histogram for quantized data

This article describes the new method of two-sample-sum and two-sample-difference of quantized data histogram calculation. The method minimizes the influence of quantizer nonlinearity and quantization on the histogram that is being obtained. The authors concentrated on applying this method for time-interval histogram calculation. Two different kinds of tests that confirm the usefulness of described method and correctness of its implementation have been performed. The first test relates to synthetically generated data, while the second one has been made for physically measured time-stamps.

Brain activity during bilateral rapid alternate finger tapping measured with magnetoencephalography

Using magnetoencephalography (MEG), brain regions involved in an alternate bimanual tapping task by index fingers triggered with spontaneous timing were investigated. The tapping mode in which both index fingers moved simultaneously was interlaced during the task. The groups of the alternate tapping (AL mode) and the simultaneous tapping (SI mode) were extracted from the successive alternating taps with a histogram of intervals between the right and left index fingers. MEG signals in each mode were averaged separately before and after the tapping initiation of the dominant index finger. The activities of the contralateral sensorimotor cortex before and after the tapping initiation in the AL mode were larger than that in the SI mode. The result indicates that the activity of the contralateral sensorimotor cortex depends on the degree of achievement in the difficult motor task such as the voluntary alternate tapping movements.

A model of auditory spiral ganglion neurons

Event Abstract Back to Event A model of auditory spiral ganglion neurons Paul Wilhelm-Bade1, Marek Rudnicki1* and Werner Hemmert2, 3 1 Technische Universitat Munich, Fakultat fur Elektrotechnik und Informationstechnik, Germany 2 Bernstein Center for Computational Neuroscience, Germany 3 Technische Universitat Munich, Institute for Medical Engineering, Germany Our study focuses on biophysical modeling of the auditory periphery and initial stages of neural processing. We examined in detail synaptic excitation between inner hair cells and spiral ganglion type I neurons. Spiral ganglion neurons encode and convey information about sound to the central nervous system in the form of action potentials. For the purpose of our study we utilized a biophysical model of the auditory periphery proposed by Sumner (2002). It consists of outer/middle ear filters, a basilar membrane filter bank, an inner hair cell model coupled with complex vesicle pool dynamics at the presynaptic membrane. Finally, fusion of vesicles, modelled with a probabilistic function, releases neurotransmitter into the synaptic cleft. Response of auditory nerve fibers is modeled with a spike generator. The absolute refractory period is set to 0.75 ms and the relative refractory period is modelled with an exponentially decaying function. In our approach we substituted the artificial spike generation and refraction model with a more realistic spiral ganglion neuron model with Hodgkin-Huxley type ion channels proposed by Negm and Bruce (2008). The model included several channels also found in cochlear nucleus neurons (K_A, K_ht, K_lt). Our model consisted of the postsynaptic bouton (1.5x1.7µm) from high-spontaneous rate fibers. We coupled the model of the synapse with the spiral ganglion neuron using a synaptic excitation model fitted to results from Glowatzki and Fuchs' (2002) experiments, who conducted patch clamp measurements at the afferent postsynapse. We verified our hybrid model against various experiments, mostly pure tone stimulation. Rate intensity functions fitted experimental data well, rates varied from about 40 spikes/s to a maximum of 260 spikes/s. Adaptation properties were investigated with peri-stimulus time histograms (PSTH). As adaptation is mainly governed by vesicle pool dynamics, only small changes occurred compared with the statistical spike generation model and adaptation was consistent with experiments. Interestingly, Hodgkin-Huxley models of spiral ganglion neurons exhibited a notch visible in the PSTH after rapid adaptation that could also be observed in experiments. This was not revealed by the statistical spike generator. The fiber's refractory period was investigated using inter-spike interval histograms. The refractory period varied with simulus intensity from 1ms (spontaneous activity) to 0.7ms (84dB_SPL). We also analyzed phase locking with the synchronization index. It was slightly lower compared to the statistical spike generator. By varying the density of K_lt and K_A channels, we could replicate heterogenity of auditory nerve fibers as shown by Adamson et al. (2002). In summary, replacing the statistical spike generation model with a more realistic model of the postsynaptic membrane obsoletes the introduction of non-physiologic parameters for absolute and relative refraction. It improves the refractory behaviour and provides more realistic spike trains of the auditory nerve. Acknowledgments:Supported by within the Munich Bernstein Center for Computational Neuroscience by the German Federal Ministry of Education and Research (reference numbers 01GQ0441 and 01GQ0443). Conference: Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009. Presentation Type: Poster Presentation Topic: Sensory processing Citation: Wilhelm-Bade P, Rudnicki M and Hemmert W (2009). A model of auditory spiral ganglion neurons. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.neuro.10.2009.14.131 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 27 Aug 2009; Published Online: 27 Aug 2009. * Correspondence: Marek Rudnicki, Technische Universitat Munich, Fakultat fur Elektrotechnik und Informationstechnik, Munich, Germany, marek.rudnicki@tum.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Paul Wilhelm-Bade Marek Rudnicki Werner Hemmert Google Paul Wilhelm-Bade Marek Rudnicki Werner Hemmert Google Scholar Paul Wilhelm-Bade Marek Rudnicki Werner Hemmert PubMed Paul Wilhelm-Bade Marek Rudnicki Werner Hemmert Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Neural Signal Manager: a collection of classical and innovative tools for multi‐channel spike train analysis

AbstractRecent developments in the neuroengineering field and the widespread use of the micro electrode arrays (MEAs) for electrophysiological investigations made available new approaches for studying the dynamics of dissociated neuronal networks as well as acute/organotypic slices maintained ex vivo. Importantly, the extraction of relevant parameters from these neural populations is likely to involve long‐term measurements, lasting from a few hours to entire days. The processing of huge amounts of electrophysiological data, in terms of computational time and automation of the procedures, is actually one of the major bottlenecks for both in vivo and in vitro recordings. In this paper we present a collection of algorithms implemented within a new software package, named the Neural Signal Manager (NSM), aimed at analyzing a huge quantity of data recorded by means of MEAs in a fast and efficient way. The NSM offers different approaches for both spike and burst analysis, and integrates state‐of‐the‐art statistical algorithms, such as the inter‐spike interval histogram or the post stimulus time histogram, with some recent ones, such as the burst detection and its related statistics. In order to show the potentialities of the software, the application of the developed algorithms to a set of spontaneous activity recordings from dissociated cultures at different ages is presented in the Results section. Copyright © 2008 John Wiley &amp; Sons, Ltd.

Information transmission in oscillatory neural activity

Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al. in Spikes: exploring the neural code. MIT Press, Cambridge, 1999; Brenner et al. in Neural Comput 12(7):1531-1552, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.

PoincarÉ Surface Profiles of RR Intervals: A Novel Noninvasive Method for the Evaluation of Preferential AV Nodal Conduction During Atrial Fibrillation

The ventricular response during atrial fibrillation (AF) presents particular characteristics that may play a relevant role in the selection of the most appropriate treatment. Using different ECG signal processing techniques such as RR histogram analysis or histographic Poincaré plots (PPs) (so-called 3-D PPs), clusters of RR intervals due to preferential atrioventricular (AV) node conduction can be observed. However, these methods are limited by the need for visual inspection and subjective interpretation of analysis results. The objective of this paper is to develop a method to automatically detect and quantify preferential clusters of RR intervals. This novel method, the Poincaré surface profile (PSP), uses the information of histographic PPs to filter part of the AV node memory effects. PSP detected all RR populations present in RR interval histograms in 55 patients with persistent AF and also 67% additional RR populations. In addition, a reduction of beat-to-beat dependencies allowed a more accurate location of RR populations. This novel Poincaré-plot-based analysis also allows monitoring of short-term variations of preferential conductions. We illustrate the capability of this short-time monitoring technique to evaluate the effects of rate control drugs on each preferential conduction.

Spontaneous discharge characteristic of neurons in the ventromedial nucleus of the rat hypothalamus in vivo

The ventromedial nucleus of the hypothalamus (VMN) is one of the main central regulators of two vital behaviours in rat: feeding behaviour and sexual behaviour. To better understand how these behaviours are regulated in the brain requires assessing how physiological stimuli are encoded by the electrical activity of populations of neurons, but there is still little known about the electrical activity of neurons in the VMN, in particular how it is regulated in vivo. Here, we recorded spontaneous firing activity from single VMN neurons in urethane-anaesthetized rats in vivo, and characterized their electrophysiological identities. For each of 271 cells, we constructed hazard functions from interspike interval histograms to show how the excitability of the cell changes with time after a spike. We completed the statistical characterization of each cell by analysis of its mean firing rate and coefficient of variation, and features of its interspike interval distribution, including kurtosis and skew (around the mean and around the mode). We thereby identified nine subpopulations of neurons in the VMN, which we named according to the main features of their firing pattern. One of the subpopulations fires very regularly, another almost randomly and another in intermittent clusters of two-three spikes, but perhaps the most interesting subpopulation are 'oscillatory cells' whose activity seems to be governed by an extrinsic 3-Hz rhythm. Whether these electrophysiologically distinct populations are also functionally and neurochemically distinct has now to be tested.

Structure of Neuronal Impulse Trains of the Rat Inferior Olive under Conditions of Long-Lasting Vibrational Influence

In acute experiments on albino rats anesthetized with Nembutal (40 mg/kg, i.p.), we recorded the background impulse activity (BIA) generated by neurons of the inferior olive in the norm and after 5-, 10-, and 15-daylong vibrational influence (60 Hz, 2 h, daily). We characterized the distributions of neurons according to the regularity of impulse successions, their dynamics, and pattern of histograms of interspike intervals (ISIs); we also calculated the mean frequency of impulsation and the coefficient of variation of ISIs. It was demonstrated that the most significant shifts of the characteristics of BIA generated by neurons of the inferior olive were formed within the first 10 days of the vibrational influence. These shifts were observed mainly in the mean discharge frequency (increased within the initial period) and, to a lesser extent, in the intrinsic structure of impulse trains. Such shifts in the background activity of the inferior olive caused by long-lasting vibrational influence result, perhaps, from intensification of the influences of excitatory cerebellar/mesodiencephalic inputs to olivary neurons within the early periods of action of the above factor and prevalence of GABAergic influences within the later periods. It seems possible that, under such conditions, the characteristics of electrical synapses of the olivary neurons are also subjected to modification.

Laboratory synthesis of industrial noise environments with predetermined statistical properties

High‐level nonGaussion noise is commonly found in a variety of industrial environments. Recent experiments have shown that for a given energy level, the statistical properties of a noise can have a strong effect on the extent of hearing loss produced in exposed individuals. In order to study, in an animal model, the effects on hearing of such noise environments, the statistical properties of the noise as embodied in the kurtosis metric must be under experimental control. For a fixed value of kurtosis and energy level the following four variables will have a strong effect on hearing loss: (1) peak histogram; (2) interval histogram; (3) duration of noise transients; and (4) level of any background Gaussian noise. Simulations have shown that the relations among kurtosis and these variables are nonlinear. However, under certain restricted conditions, these relations may be linear. Accordingly, two strategies for designing controlled industrial noise exposures are presented: (1) the interval‐priority model and (2) the duration‐priority model. Computer simulations and measurements of actual acoustic environments showed that these two models could be effectively used to simulate a wide variety of realistic industrial noises.

Firing pattern of fasciculations in ALS

In amyotrophic lateral sclerosis (ALS), the origin of fasciculations is disputed. We hypothesized that the discharge pattern of fasciculation potentials (FPs) would be different for FPs arising in the motor axon or in the spinal motor neuron. FPs were recorded by high-density surface EMG of the biceps brachii or vastus lateralis muscle for 15 minutes in 10 patients with ALS. Records were decomposed into different FP waveforms and their firing moments. Interspike interval (ISI) histograms were constructed for FPs that fired more than 100 times. Two types of ISI histograms were found. 1) In 23 of 30 different FPs with a total of 8,597 ISIs, the refractory period was 3 to 4 msec. ISIs longer than 15 msec had a Poisson distribution. Five of these 23 FPs discharged doublets with an ISI of approximately 5 msec, indicative of supernormality. This is consistent with the FPs arising in motor axons. 2) In the other 7 FPs, accounting for 11,266 ISIs, the refractory period was 17 to 46 msec. The preferred ISI duration was around 80 msec. Both timing factors are consistent with origin in the spinal motor neuron. Firing pattern analysis, based on high-density surface EMG, can detect fasciculation potentials (FPs) of axonal and neuronal origin in amyotrophic lateral sclerosis. The two FP types coexist within the same muscle. The recognition that clinically identical fasciculations conceal the existence of two types of FP that can be studied in a noninvasive manner will introduce a new aspect in the research of motor neuron disease.