Interspike Interval Research Articles

Spectral resonance in Fitzhugh-Nagumo neuron system: relation with stochastic resonance and its role in EMG signal characterization.

This paper examines the existence of spectral resonance in the Fitzhugh-Nagumo (FHN) system driven by periodical signal and unbounded noise having Gaussian distribution. It is newly revealed that if the inter-spike-interval (ISI) distribution is accumulated on a single cluster, there exists a dual relationship between stochastic resonance and spectral resonance determined by commonly used metric normalized standard deviation of ISI. Furthermore, the ISI distribution is also concentrated on more than one cluster depending on different driving signal frequency. Consequently, the apparent regular spiking behavior is observed to occur at specified driving signal frequencies which result in a local minimum in entropy function indicating spectral resonance. Therefore it is proposed that occurrence of spectral resonance strongly depends on the shape of ISI distribution tuned by the stochastic and deterministic driving signal parameters and conventional metrics may not indicate entire resonance behavior. Correspondingly, the entropy function is utilized in this paper as an alternative metric to enable the detection of the spectral resonance occurrence. The ISI distribution obtained from the FHN system is investigated to relate the real electromyography (EMG) measurements under different conditions such as myokymia and neuromyotonia. It is seen that ISI distribution observed from myokymic EMG exhibits notably close behavior as in the case of spectral resonance generated by FHN whereas a wider distribution is monitored in the case of neuromyotonia. It is contributed that the modeling and parameterization based on ISI distribution can be potentially used to identify different neural activities.

Subthalamic nucleus neuronal activity entropy in the effective stimulation area in patients with Parkinson’s disease

One of the most successful and promising treatments for Parkinson’s disease today is deep brain stimulation (DBS). Accurate localization of the stimulation area is critical for the outcome of surgical DBS electrode implantation in the subthalamic nucleus (STN). To achieve this, microelectrode recording is used during surgery to precisely locate the STN borders [1], while test stimulations with a macroelectrode are widely employed to enhance DBS electrode placement accuracy. Thus, through testing multiple trajectories, the most clinically effective one can be selected. However, a comprehensive and reliable description of the specific single neuron activity associated with successful DBS electrode implantation is currently lacking. A study was conducted using microelectrode recordings (MER) of single neuron activity in the STN of 21 Parkinson’s disease patients (UPDRS III off/on=46.9/12.8) to identify the neuronal activity features associated with the most favorable clinical outcome of test stimulation. A comparative analysis of 29 activity parameters, such as firing rate, oscillatory activity intensity in different frequency ranges (Oscores), coefficient of variation, burst, pause index, among others [2], was conducted for 618 identified neurons. In addition, the approach of computing the entropy of the change in interspike intervals (ISI) of the pulse sequence [3] and the process of identifying patterns of neural activity using hierarchical clustering [4] were implemented. A comparative analysis of single neuron activity indicated notable (p 0.05) variations between disregarded and selected paths for the ultimate implantation of DBS electrode, solely relying on entropy parameters. Trajectories that delivered optimal test stimulation outcomes demonstrated reduced entropy of interspike intervals. Only the patterns of neuronal activity characterized by extended periods of steady firing and brief pauses, referred to as tonic and pause activity, respectively, were found to contribute to the difference in entropy between trajectories. In contrast, there were no statistically significant differences detected in entropy values associated with neurons demonstrating burst activity. Furthermore, we observed a positive and significant correlation between entropy values and the degree of improvement in disease presentation before and after administration of medication. A comparative analysis was conducted on the activity of single neurons along different trajectories with varied test stimulation responses. The results showed a weak effectiveness of linear parameters in determining the optimal electrode insertion path for improved clinical outcomes. However, the use of non-linear activity parameters, specifically entropy, in single neurons effectively differentiated trajectories with significant versus absent/insignificant test stimulation results. Furthermore, the significance of entropy in establishing the basal ganglia functions and describing information transfer processes in movement control is emphasized by interpreting this parameter as a measure of uncertainty or unpredictability of the information system in relation to the findings of other studies [5].

Neural activity of the subthalamic nucleus during voluntary movements in patients with Parkinson’s disease

Microelectrode recording (MER) during electrode implantation for deep brain stimulation (DBS) can determine the boundaries of the subthalamic nucleus (STN), and track neural activity during rest and voluntary movement in Parkinson’s disease (PD) patients. However, the functional role of STN in motor control remains unclear, despite its effectiveness in stimulation. Single unit activity of STN was recorded in 16 patients with PD during implantation of deep brain stimulation electrodes. Electromyograms of forearm muscles and a phonogram featuring verbal commands were simultaneously recorded with MER. The patients were instructed to perform motor tests involving clenching their hand into a fist. Out of the 560 neurons studied, 93 (16.6%) responded to motor tasks. The registered neurons were classified into three patterns using the hierarchical clustering method of histograms of interspike interval density — namely, tonic, burst, and pause patterns. Sensitive neurons were classified according to their responses, with activation (76.9%) and inhibition (23.1%) being the two identified types. In 90% of cases, neuron activation preceded movement. Of the responses, 53.8% were classified as tonic and 46.2% as phasic. Two thirds of inhibitory responses were advanced, occurring 0.2–0.3 seconds before movement onset. One third of neurons were activated after movement initiation with a delay of 0.05–0.2 seconds. 83.3% of the inhibitory neurons responded tonically, while the remaining 16.7% responded phasically. Notably, all phasic reactions occurred before the onset of movement. A comparison of the parameters of sensitive and non-sensitive neurons revealed several differences. The findings suggest a potential physiological distinction between the two neuron types. All three patterns of activity were present in both types of neurons, but sensitive neurons exhibited a wider representation of pause neurons (57.9% vs. 49.5%). Additionally, burst and pause neurons responded to movements that featured significantly lower variance of multiple activity parameters, such as firing rate, burst index, mean burst length, and oscillation scores in the 8–12 and 12–20 Hz range. The distribution of sensitive neurons along the electrode trajectory through the subthalamic nucleus was analyzed. Sensitive neurons were located significantly more dorsally compared to non-sensitive neurons, and no sensitive pause cells were observed in the ventral half of the STN. Based on our findings, it is presumed that the pause pattern plays a critical role in both motor control and its related disorders in Parkinson’s disease. A diverse range of neuronal responses suggests a high degree of heterogeneity among STN neurons implicated in motor control. The presence of both delayed and advanced neural responses suggests that STN involvement in motor control encompasses both the preparation and initiation of movement, as well as the regulation of ongoing movements via afferent feedback. The range of neural responses aligns with the dynamic model of basal ganglia, which suggests that STN can perform distinct functional roles during different stages of movement, including initiation, control, and completion.

Estimation of the instantaneous spike train variability

The variability of neuronal spike trains is usually measured by the Fano factor or the coefficient of variation of interspike intervals, but their estimation is problematic, especially with limited amount of data. In this paper we show that it is in fact possible to estimate a quantity equivalent to the Fano factor and the squared coefficient of variation based on the intervals from only one specific (random) time. This leads to two very simple but precise Fano factor estimators, that can be interpreted as estimators of instantaneous variability. We derive their properties, evaluate their accuracy in various situations and show that they are often more accurate than the standard estimators. The presented estimators are particularly suitable for the case where variability changes rapidly.

The Potential Role of Thyroid Hormone Therapy in Neural Progenitor Cell Differentiation and Its Impact on Neurodevelopmental Disorders.

Thyroid hormone (T3) plays a vital role in brain development and its dysregulation can impact behavior, nervous system function, and cognitive development. Large case-cohort studies have associated abnormal maternal T3 during early pregnancy to epilepsy, autism, and attention deficit hyperactivity disorder (ADHD) in children. Recent experimental findings have also shown T3's influence on the fate of neural precursor cells and raise the question of its convergence with embryonic neural progenitors. Our objective was to investigate how T3 treatment affects neuronal development and functionality at the cellular level. In vitro experiments using neural precursor cells (NPCs) measured cell growth and numbers after exposure to varying T3 concentrations. Time points included week 0 (W0) representing NPCs treated with 100nM T3 for 5days, and differentiated cortical neurons assessed at weeks 3 (W3), 6 (W6), and 8 (W8). Techniques such as single-cell calcium imaging and whole-cell patch clamp were utilized to evaluate neuronal activity and function. IHC staining detected mature neuron markers, and RNA sequencing enabled molecular profiling. W6 and W8 neurons exhibited higher action potential frequencies, with W6 showing increased peak amplitudes and shortened inter-spike intervals by 50%, indicating enhanced activity. Transcriptomic analysis revealed that W6 T3-treated neurons formed a distinct cluster, suggesting accelerated maturation. Comparison with the whole transcriptome further unveiled a correlation between W6 neurons treated with T3 and neuronal regulatory elements associated with autism and ADHD. These findings provide insights into T3's impact on neuronal development and potential mechanisms of T3 dysregulation and neurodevelopmental disorders.

An integrate-and-fire approach to Ca2+ signaling. Part II: Cumulative refractoriness

Inositol 1,4,5-trisphosphate-induced Ca2+ signaling is a second messenger system used by almost all eukaryotic cells. The agonist concentration stimulating Ca2+ signals is encoded in the frequency of a Ca2+ concentration spike sequence. When a cell is stimulated, the interspike intervals (ISIs) often show a distinct transient during which they gradually increase, a system property we refer to as cumulative refractoriness. We extend a previously published stochastic model to include the Ca2+ concentration in the intracellular Ca2+ store as a slow adaptation variable. This model can reproduce both stationary and transient statistics of experimentally observed ISI sequences. We derive approximate expressions for the mean and coefficient of variation of the stationary ISIs. We also consider the response to the onset of a constant stimulus and estimate the length of the transient and the strength of the adaptation of the ISI. We show that the adaptation sets the coefficient of variation in agreement with current ideas derived from experiments. Moreover, we explain why, despite a pronounced transient behavior, ISI correlations can be weak, as often observed in experiments. Finally, we fit our model to reproduce the transient statistics of experimentally observed ISI sequences in stimulated HEK cells. The fitted model is able to qualitatively reproduce the relationship between the stationary interval correlations and the number of transient intervals, as well as the strength of the ISI adaptation. We also find positive correlations in the experimental sequence that cannot be explained by our model.

Altered neural encoding of vowels in noise does not affect behavioral vowel discrimination in gerbils with age-related hearing loss.

Understanding speech in a noisy environment, as opposed to speech in quiet, becomes increasingly more difficult with increasing age. Using the quiet-aged gerbil, we studied the effects of aging on speech-in-noise processing. Specifically, behavioral vowel discrimination and the encoding of these vowels by single auditory-nerve fibers were compared, to elucidate some of the underlying mechanisms of age-related speech-in-noise perception deficits. Young-adult and quiet-aged Mongolian gerbils, of either sex, were trained to discriminate a deviant naturally-spoken vowel in a sequence of vowel standards against a speech-like background noise. In addition, we recorded responses from single auditory-nerve fibers of young-adult and quiet-aged gerbils while presenting the same speech stimuli. Behavioral vowel discrimination was not significantly affected by aging. For both young-adult and quiet-aged gerbils, the behavioral discrimination between /eː/ and /iː/ was more difficult to make than /eː/ vs. /aː/ or /iː/ vs. /aː/, as evidenced by longer response times and lower d' values. In young-adults, spike timing-based vowel discrimination agreed with the behavioral vowel discrimination, while in quiet-aged gerbils it did not. Paradoxically, discrimination between vowels based on temporal responses was enhanced in aged gerbils for all vowel comparisons. Representation schemes, based on the spectrum of the inter-spike interval histogram, revealed stronger encoding of both the fundamental and the lower formant frequencies in fibers of quiet-aged gerbils, but no qualitative changes in vowel encoding. Elevated thresholds in combination with a fixed stimulus level, i.e., lower sensation levels of the stimuli for old individuals, can explain the enhanced temporal coding of the vowels in noise. These results suggest that the altered auditory-nerve discrimination metrics in old gerbils may mask age-related deterioration in the central (auditory) system to the extent that behavioral vowel discrimination matches that of the young adults.

Granule cells perform frequency-dependent pattern separation in a computational model of the dentate gyrus.

Mnemonic discrimination (MD) may be dependent on oscillatory perforant path input frequencies to the hippocampus in a "U"-shaped fashion, where some studies show that slow and fast input frequencies support MD, while other studies show that intermediate frequencies disrupt MD. We hypothesize that pattern separation (PS) underlies frequency-dependent MD performance. We aim to study, in a computational model of the hippocampal dentate gyrus (DG), the network and cellular mechanisms governing this putative "U"-shaped PS relationship. We implemented a biophysical model of the DG that produces the hypothesized "U"-shaped input frequency-PS relationship, and its associated oscillatory electrophysiological signatures. We subsequently evaluated the network's PS ability using an adapted spatiotemporal task. We undertook systematic lesion studies to identify the network-level mechanisms driving the "U"-shaped input frequency-PS relationship. A minimal circuit of a single granule cell (GC) stimulated with oscillatory inputs was also used to study potential cellular-level mechanisms. Lesioning synapses onto GCs did not impact the "U"-shaped input frequency-PS relationship. Furthermore, GC inhibition limits PS performance for fast frequency inputs, while enhancing PS for slow frequency inputs. GC interspike interval was found to be input frequency dependent in a "U"-shaped fashion, paralleling frequency-dependent PS observed at the network level. Additionally, GCs showed an attenuated firing response for fast frequency inputs. We conclude that independent of network-level inhibition, GCs may intrinsically be capable of producing a "U"-shaped input frequency-PS relationship. GCs may preferentially decorrelate slow and fast inputs via spike timing reorganization and high frequency filtering.

Introducing the STReaC (Spike Train Response Classification) toolbox

Background:This work presents a toolbox that implements methodology for automated classification of diverse neural responses to optogenetic stimulation or other changes in conditions, based on spike train recordings. New Method:The toolbox implements what we call the Spike Train Response Classification algorithm (STReaC), which compares measurements of activity during a baseline period with analogous measurements during a subsequent period to identify various responses that might result from an event such as introduction of a sustained stimulus. The analyzed response types span a variety of patterns involving distinct time courses of increased firing, or excitation, decreased firing, or inhibition, or combinations of these. Excitation (inhibition) is identified from a comparative analysis of the spike density function (interspike interval function) for the baseline period relative to the corresponding function for the response period. Results:The STReaC algorithm as implemented in this toolbox provides a user-friendly, tunable, objective methodology that can detect a variety of neuronal response types and associated subtleties. We demonstrate this with single-unit neural recordings of rodent substantia nigra pars reticulata (SNr) during optogenetic stimulation of the globus pallidus externa (GPe). Comparison with existing methods:In several examples, we illustrate how the toolbox classifies responses in situations in which traditional methods (spike counting and visual inspection) either fail to detect a response or provide a false positive. Conclusions:The STReaC toolbox provides a simple, efficient approach for classifying spike trains into a variety of response types defined relative to a period of baseline spiking.

Exploring Filter Banks and Spike Interval Statistics of Level-Crossing ADCs for Fault Diagnosis of Rolling Element Bearings

Nowadays, lots of data are generated in industries using vibration sensors to evaluate the equipment’s working condition and identify faults. A significant challenge is that only a small fraction of data can be transmitted for intelligent fault diagnosis and storage. The edge processing capacity is often insufficient for advanced analysis due to time and resource constraints. The neuromorphic signal encoding scheme efficiently reduces the data rate by encoding relevant signal changes into spike trains while discarding redundant information and noise, enabling energy-efficient neuromorphic processing. Due to the presence of dominant operational features and noise in the original measurements, signal pre-processing is required to extract the relevant features before spike coding and processing. The work investigates the effects of different filter banks (pre-processing methods) on the spike encodings for vibration measurements from bearings. This also includes bearing fault features diagnosis based on statistical analysis of generated spikes. The comparative analysis is made for benchmarking different signal pre-processing methods (e.g., envelope, empirical mode decomposition (EMD), and gammatone filter) on bearing vibration datasets. An event-triggered scheme, i.e., Level-crossing analog-to-digital converters (LC-ADCs) is applied to encode the vibration measurement to spikes. Inter-spike intervals (ISIs) statistics are analysed for fault diagnosis of bearings. The results obtained for CWRU bearing databases indicate a possible fault detection and diagnosis with significant data rate reduction and an opportunity for improved computational efficiency. With the developed approach, the envelope filter is found to be the most efficient of all. This work enables a new approach to improve the energy efficiency of condition monitoring systems and further sets a new course of research development in this area using neuromorphic technologies.

Cortical origin of theta error signals.

A multi-scale approach elucidated the origin of the error-related-negativity (ERN), with its associated theta-rhythm, and the post-error-positivity (Pe) in macaque supplementary eye field (SEF). Using biophysical modeling, synaptic inputs to a subpopulation of layer-3 (L3) and layer-5 (L5) pyramidal cells (PCs) were optimized to reproduce error-related spiking modulation and inter-spike intervals. The intrinsic dynamics of dendrites in L5 but not L3 error PCs generate theta rhythmicity with random phases. Saccades synchronized the phases of the theta-rhythm, which was magnified on errors. Contributions from error PCs to the laminar current source density (CSD) observed in SEF were negligible and could not explain the observed association between error-related spiking modulation in L3 PCs and scalp-EEG. CSD from recorded laminar field potentials in SEF was comprised of multipolar components, with monopoles indicating strong electro-diffusion, dendritic/axonal electrotonic current leakage outside SEF, or violations of the model assumptions. Our results also demonstrate the involvement of secondary cortical regions, in addition to SEF, particularly for the later Pe component. The dipolar component from the observed CSD paralleled the ERN dynamics, while the quadrupolar component paralleled the Pe. These results provide the most advanced explanation to date of the cellular mechanisms generating the ERN.

Dexmedetomidine modulates neuronal activity of horizontal limbs of diagonal band via α2 adrenergic receptor in mice

Background and objectivesDexmedetomidine (DEX) is widely used in clinical sedation which has little effect on cardiopulmonary inhibition, however the mechanism remains to be elucidated. The basal forebrain (BF) is a key nucleus that controls sleep-wake cycle. The horizontal limbs of diagonal bundle (HDB) is one subregions of the BF. The purpose of this study was to examine whether the possible mechanism of DEX is through the α2 adrenergic receptor of BF (HDB).MethodsIn this study, we investigated the effects of DEX on the BF (HDB) by using whole cell patch clamp recordings. The threshold stimulus intensity, the inter-spike-intervals (ISIs) and the frequency of action potential firing in the BF (HDB) neurons were recorded by application of DEX (2 µM) and co-application of a α2 adrenergic receptor antagonist phentolamine (PHEN) (10 µM).ResultsDEX (2 µM) increased the threshold stimulus intensity, inhibited the frequency of action potential firing and enlarged the inter-spike-interval (ISI) in the BF (HDB) neurons. These effects were reversed by co-application of PHEN (10 µM).ConclusionTaken together, our findings revealed DEX decreased the discharge activity of BF (HDB) neuron via α2 adrenergic receptors.

Footedness but not dominance influences force steadiness during isometric dorsiflexion in young men

The aim of the study was to assess the potential influence of footedness and dominance on maximal force, force fluctuations and neural drive during dorsiflexion. Fifteen left-footed (LF) and fifteen right-footed (RF) young adults performed 2 maximal voluntary contractions (MVC) and 3 steady submaximal isometric contractions at five target forces (5, 10, 20, 40 and 60% MVC) with the dorsiflexors of both legs. High-density electromyography (EMG) was used to record the discharge characteristics of motor units (MUs) of Tibialis Anterior. MVC force and EMG amplitude (root mean square) were similar between the two legs and groups (p > 0.05). Force fluctuations (Coefficient of Variation, CoV for force), mean discharge rate of MUs, discharge variability (CoV of interspike interval), and variability in neural drive (standard deviation of filtered cumulative spike train) were greater (p < 0.05) and the input–output gain of the MUs (ΔDR/ΔF) was lower (p < 0.05) for the LF relative to the RF group. The differences in force fluctuations during steady contractions with the dorsiflexors were associated with footedness but not with dominance. They reflect greater variability in motor neuron output, as suggested by coefficient of variation for interspike interval (independent input) and the standard deviation of the smoothed discharge times (common input).

Coexisting firing patterns and circuit design of locally active memristive autapse morris-lecar neuron

A novel bistable locally active memristor is proposed in this paper. A locally active memristive autapse Morris-Lecar neuron model is constructed by using memristor to simulate the autapse of neuron. The equilibrium point and stability of the system are analyzed, and the firing mode and bifurcation characteristics of the neuronal system are revealed by using dynamic analysis methods such as slow-fast dynamics, interspike interval bifurcation diagrams, Lyapunov exponents, phase diagrams and time series diagram. By changing the memristive autapse gain and the initial state of the system, the existence of coexisting firing patterns in the constructed neuron model is confirmed. Finally, to further verify the effectiveness of the numerical simulation, the analog equivalent circuit of the locally active memristive neuron system is designed, which proves that the system is physically realizable.

The Roles of Potassium and Calcium Currents in the Bistable Firing Transition.

Healthy brains display a wide range of firing patterns, from synchronized oscillations during slow-wave sleep to desynchronized firing during movement. These physiological activities coexist with periods of pathological hyperactivity in the epileptic brain, where neurons can fire in synchronized bursts. Most cortical neurons are pyramidal regular spiking (RS) cells with frequency adaptation and do not exhibit bursts in current-clamp experiments (in vitro). In this work, we investigate the transition mechanism of spike-to-burst patterns due to slow potassium and calcium currents, considering a conductance-based model of a cortical RS cell. The joint influence of potassium and calcium ion channels on high synchronous patterns is investigated for different synaptic couplings (gsyn) and external current inputs (I). Our results suggest that slow potassium currents play an important role in the emergence of high-synchronous activities, as well as in the spike-to-burst firing pattern transitions. This transition is related to the bistable dynamics of the neuronal network, where physiological asynchronous states coexist with pathological burst synchronization. The hysteresis curve of the coefficient of variation of the inter-spike interval demonstrates that a burst can be initiated by firing states with neuronal synchronization. Furthermore, we notice that high-threshold (IL) and low-threshold (IT) ion channels play a role in increasing and decreasing the parameter conditions (gsyn and I) in which bistable dynamics occur, respectively. For high values of IL conductance, a synchronous burst appears when neurons are weakly coupled and receive more external input. On the other hand, when the conductance IT increases, higher coupling and lower I are necessary to produce burst synchronization. In light of our results, we suggest that channel subtype-specific pharmacological interactions can be useful to induce transitions from pathological high bursting states to healthy states.

Cardiac behaviors and chaotic arrhythmias in the Hindmarsh–Rose model

The Hindmarsh–Rose is one of the best-known models of computational neuroscience. Despite its popularity as a neuron model, we demonstrate that it is also a complete cardiac model. We employ a method based on bifurcations of the interspike interval to redraw its phase diagram and reveal a cardiac region. This diagram bears great resemblance to that of the map-based model for neurons and cardiac cells, the KTz model. Both phase diagrams are compared, showing a very similar placement of behaviors in the parameter space. Adjusting the Hindmarsh–Rose parameters allows us to obtain behaviors similar to atrial, ventricular and pacemaker cells. We also report the neuronal behavior of sustained subthreshold oscillations in the diagram, also unknown in the model. We demonstrate the existence of periodic and chaotic early afterdepolarizations, a behavior linked to life-threatening arrhythmias. In a second phase diagram, we find a chaotic region with early afterdepolarizations and self-organized periodic structures known as shrimps. We also propose a new and simple method to calculate the electrocardiogram using the membrane potential of a point cell and demonstrate its use for the study of QT syndromes using the Hindmarsh–Rose model. Given the smaller number of equations and parameters than detailed conductance models and richer dynamics than other general models, this work presents the Hindmarsh–Rose as a promising alternative for computational cardiology studies.

Adomian Decomposition, Firing Change Process Analysis and Synchronous Control of Fractional-Order Hindmarsh–Rose Neurons in Electromagnetic Field

In this paper, based on integer-order Hindmarsh–Rose (HR) neurons under an electric field, the fractional-order model is constructed, and the nonlinear term is decomposed by the Adomian decomposition method, and the numerical solution of the system is obtained. The firing behavior of the neuron model is analyzed by using a phase diagram, interspike interval (ISI) bifurcation diagram, sample entropy (SE) complexity, and largest Lyapunov exponent (LLE). Based on the sliding mode control theory, a chaos synchronization controller of the system is designed. Matlab simulation results show that the controller is realizable and effective, and also has the characteristic of fast response, which provides a reference for the control and application of a memristor neural network system.

Deficits in neuromuscular control of increasing force in patients with chronic lateral epicondylitis.

Objective: This study investigated the neuromuscular control of increasing and releasing force in patients with chronic lateral epicondylitis (CLE). Methods: Fifteen patients with CLE (10 males, 5 females, 46.5 ± 6.3years) and fifteen healthy participants (9 males, 6 females, 45.3 ± 2.5years) participated in this study. In addition to power grip and maximal voluntary contraction (MVC) of wrist extension, force fluctuation dynamics and characteristics of inter-spike intervals (ISI) of motor units (MUs) with various recruitment thresholds in the extensor carpi radialis brevis (ECRB) and extensor carpi radialis longus (ECRL) during a designated force-tracking task with a trapezoidal target (0%-75%-0% MVC) were assessed. Results: Besides a smaller MVC of wrist extension, the patients exhibited significantly greater task errors (p = 0.007) and force fluctuations (p = 0.001) during force increment than the healthy counterparts. Nevertheless, no force variables significantly differed between groups during force release (p > 0.05). During force increment, the amplitudes of the motor unit action potential of the ECRB and ECRL muscles of the patients were smaller than those of the heathy counterparts (p < 0.001). The patient group also exhibited a higher percentage of motor units (MU) with lower recruitment threshold (<5% MVC) in the ECRL/ECRB muscles and a lower percentage of MU with higher recruitment threshold (>40% MVC) in the ECRB muscle, compared to the healthy group. During force increment, the patient group exhibited a higher rate of decrease in inter-spike intervals (ISIs) of motor units with lower recruitment thresholds (<10% MVC) in the ECRB and ECRL muscles, compared to the control group (p < 0.005). Conclusion: The patients with CLE exhibited more pronounced impairment in increasing force than in releasing force. This impairment in increasing force is attributed to deficits in tendon structure and degenerative changes in the larger motor units of the wrist extensors. To compensate for the neuromuscular deficits, the rate of progressive increase in discharge rate of the remaining smaller motor units (MUs) is enhanced to generate force. Significance: The deficits in neuromuscular control observed in CLE with degenerative changes cannot be fully explained by the experimental pain model, which predicts pain-related inhibition on low-threshold motor units.

Frequency-dependent avoidance movement of glass catfish in response to sinusoidal electrical stimulation and associated spiking patterns of electroreceptors.

The glass catfish is a freshwater fish with electroreceptors on its body surface. In this study, we investigated its behavioral response to sinusoidal electrical stimulation with a dipole wider than its body length and the spiking patterns of its electroreceptors. We observed that sinusoidal electric stimulation with a large dipole distance elicited in the glass catfish an avoidance movement whose frequency range is frequency-dependent. The movements were prominent in the frequency range between 10-20 Hz. When the stimulation strength increased, the movements were also found in the low-frequency range. In electrophysiological experiments, periodic interspike intervals of the electroreceptors were modulated by sinusoidal electrical stimuli. The stimulation introduced irregularity in the spiking patterns. The local variability of the spike modulations was significantly higher in the frequency range of 4-40 Hz and was particularly sensitive at 20 Hz. The avoidance movements and an increase in the local variability in the spike patterns were found around 20 Hz. Our results indicate that the glass catfish avoids sinusoidal electrical stimulation in a frequency-dependent manner, and this is associated with local modulations in the spiking patterns of the electroreceptors.

Stochastic generation of oscillations in the 3D-Model of cool-flame combustion of a hydrocarbon mixture

The stochastic model of cool-flame combustion of a hydrocarbon mixture is considered. For this 3D model, noise excitement of large-amplitude oscillations in the parametric zone, where the deterministic model has a single equilibrium attractor, is studied. By statistics of interspike intervals, a phenomenon of anti-coherence resonance is revealed. To estimate threshold intensities of noise that causes excitement, stochastic sensitivity technique and method of principal directions are applied.