Mean Interspike Interval Research Articles

Comparison of modulation efficiency between normal and degenerated primate retina.

With electrical stimulation, retinal prostheses bypass dysfunctional photoreceptors and activate the surviving bipolar or retinal ganglion cells (RGCs). Therefore, the effective modulation of RGCs is crucial for developing retinal prostheses. Substantial research has been performed on the ability of an electrical stimulus to generate a reliable RGC response. However, different experimental conditions show varying levels of how well the electrical stimulation evokes RGC spikes. Therefore, in this study, we attempted to extract an indicator to understand how the electrical stimulation effectively evokes RGC spikes. Six cynomolgus monkeys were used: three as controls and three as an N-methyl-N-nitrosourea (MNU)-induced retinal degeneration model. The retinal recordings were performed using 8 × 8 multi-electrode arrays (MEAs). Electrical stimulation consisted of symmetrical biphasic pulses of varying amplitudes and durations. The number of stimulation conditions that resulted in significantly higher post-stimulation firing rates than pre-stimulus firing rates was defined as the modulation efficiency ratio (MER). The MER was significantly lower in degenerated retinas than in normal retinas. We investigated the relationship between the variables and the MER in normal and degenerated primate RGCs. External variables, such as duration and inter-electrode distance, and internal variables, such as average firing rates and statistics (mean, standard deviation, and coefficient of variation [CV]) of inter-spike intervals (ISIs) of spontaneous spikes, were used. External variables had similar effects on MER in normal and degenerated RGCs. In contrast, internal variables affected MER differently in normal and degenerated RGCs. While in normal RGCs, they were not related to MER, in degenerated RGCs, the mean ISIs were positively correlated with MER, and the CV of ISIs was negatively correlated with MER. The most important variable affecting MER was the mean ISI. A shorter ISI indicates hyperactive firing in the degenerated retina, which prevents electrical stimulation from evoking more RGCs. We believe that this hyperactivity in degenerated retinas results in a lower MER than that in the normal retina. Our findings can be used to optimize the selection of stimulation channels for in vitro MEA experiments and practical calibration methods to achieve higher efficiency when testing retinal prostheses.

The Essential Oil of Citrus lumia Risso and Poit. 'Pyriformis' Shows Promising Antioxidant, Anti-Inflammatory, and Neuromodulatory Effects.

Citrus lumia Risso and Poit. 'Pyriformis' are horticultural varieties of Citrus lumia Risso. The fruit is very fragrant and pear-shaped, with a bitter juice, a floral flavor, and a very thick rind. The flavedo shows enlarged (0.74 × 1.16 mm), spherical and ellipsoidal secretory cavities containing the essential oil (EO), visible using light microscopy, and more evident using scanning electron microscopy. The GC-FID and GC-MS analyses of the EO showed a phytochemical profile characterized by the predominance of D-limonene (93.67%). The EO showed interesting antioxidant and anti-inflammatory activities (IC50 0.07-2.06 mg/mL), as evaluated by the in vitro cell-free enzymatic and non-enzymatic assays. To evaluate the effect on the neuronal functional activity, the embryonic cortical neuronal networks grown on multi-electrode array chips were exposed to non-cytotoxic concentrations of the EO (5-200 µg/mL). The spontaneous neuronal activity was recorded and the mean firing rate, mean burst rate, percentage of spikes in a burst, mean burst durations and inter-spike intervals within a burst parameter were calculated. The EO induced strong and concentration-dependent neuroinhibitory effects, with IC50 ranging between 11.4-31.1 µg/mL. Furthermore, it showed an acetylcholinesterase inhibitory activity (IC50 0.19 mg/mL), which is promising for controlling some of the key symptoms of neurodegenerative diseases such as memory and cognitive concerns.

Failure to improve task performance after visuomotor training with error reduction feedback for young adults.

Visual feedback that reinforces accurate movements may motivate skill acquisition by promoting self-confidence. This study investigated neuromuscular adaptations to visuomotor training with visual feedback with virtual error reduction. Twenty-eight young adults (24.6 ± 1.6years) were assigned to error reduction (ER) (n = 14) and control (n = 14) groups to train on a bi-rhythmic force task. The ER group received visual feedback and the displayed errors were 50% of the real errors in size. The control group was trained with visual feedback with no reduction in errors. Training-related differences in task accuracy, force behaviors, and motor unit discharge were contrasted between the two groups. The tracking error of the control group progressively declined, whereas the tracking error of the ER group was not evidently reduced in the practice sessions. In the post-test, only the control group exhibited significant task improvements with smaller error size (p = .015) and force enhancement at the target frequencies (p = .001). The motor unit discharge of the control group was training-modulated, as indicated by a reduction of the mean inter-spike interval (p = .018) and smaller low-frequency discharge fluctuations (p = .017) with enhanced firing at the target frequencies of the force task (p = .002). In contrast, the ER group showed no training-related modulation of motor unit behaviors. In conclusion, for young adults, ER feedback does not induce neuromuscular adaptations to the trained visuomotor task, which is conceptually attributable to intrinsic error dead-zones.

A dynamic spike threshold with correlated noise predicts observed patterns of negative interval correlations in neuronal spike trains

Negative correlations in the sequential evolution of interspike intervals (ISIs) are a signature of memory in neuronal spike-trains. They provide coding benefits including firing-rate stabilization, improved detectability of weak sensory signals, and enhanced transmission of information by improving signal-to-noise ratio. Primary electrosensory afferent spike-trains in weakly electric fish fall into two categories based on the pattern of ISI correlations: non-bursting units have negative correlations which remain negative but decay to zero with increasing lags (Type I ISI correlations), and bursting units have oscillatory (alternating sign) correlation which damp to zero with increasing lags (Type II ISI correlations). Here, we predict and match observed ISI correlations in these afferents using a stochastic dynamic threshold model. We determine the ISI correlation function as a function of an arbitrary discrete noise correlation function {{,mathrm{mathbf {R}},}}_k, where k is a multiple of the mean ISI. The function permits forward and inverse calculations of the correlation function. Both types of correlation functions can be generated by adding colored noise to the spike threshold with Type I correlations generated with slow noise and Type II correlations generated with fast noise. A first-order autoregressive (AR) process with a single parameter is sufficient to predict and accurately match both types of afferent ISI correlation functions, with the type being determined by the sign of the AR parameter. The predicted and experimentally observed correlations are in geometric progression. The theory predicts that the limiting sum of ISI correlations is -0.5 yielding a perfect DC-block in the power spectrum of the spike train. Observed ISI correlations from afferents have a limiting sum that is slightly larger at -0.475 pm 0.04 (text {mean} pm text {s.d.}). We conclude that the underlying process for generating ISIs may be a simple combination of low-order AR and moving average processes and discuss the results from the perspective of optimal coding.

A Stochastic Leaky-Integrate-and-Fire Neuron Model With Floating Gate-Based Technology for Fast and Accurate Population Coding

An analytical model has been developed for stochastic leaky-integrate-and-fire (LIF) neurons with floating gate (FG) technology. The stochastic behaviors have been modeled extensively for both individual neurons and populations of neurons. In the FG LIF neurons, the electron injection is governed by the tunneling process through the gate oxide, leading to the exponential distributions of the injection time and inter spike interval (ISI) stochasticity. The concept of the population coding is demonstrated by simulating the stochastic behaviors of the populations of the FG LIF neurons. The ISI stochasticity enables encoding of the input signals to the population outputs. Spike-to-spike stochasticity improves the signal-to-noise ratio of the population outputs. Moreover, the shape of the ISI distribution can be controlled by adjusting the number of electrons to spike (NES). Exponential-like ISI distributions are realized by reducing the NES. With the exponential-like ISI distributions, the population of fast spiking neurons increases significantly (more than 10% of neurons spiking twice faster than the mean ISI), potentially contributing to the fast computation. Finally, step-by-step procedures have been proposed to design the FG LIF neurons exhibiting the desired neuron characteristics including operation voltage (0.5 V to 3 V), leaky time constant <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$( { &lt; } 1~ {\mathrm {\mu }}\text{s}$ </tex-math></inline-formula> to >10 ms), ISI mean (in the range of 6 orders of magnitude) and stochasticity ~0 % to ~60 %) as well as the type of the distribution (exponential-like to Gaussian-like).

First-recruited motor units adopt a faster phenotype in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disorder of motor neurons, carrying a short survival. High-density motor unit recordings permit analysis of motor unit size (amplitude) and firing behaviour (afterhyperpolarization duration and muscle fibre conduction velocity). Serial recordings from biceps brachii indicated that motor units fired faster and with greater amplitude as disease progressed. First-recruited motor units in the latter stages of ALS developed characteristics akin to fast-twitch motor units, possibly as a compensatory mechanism for the selective loss of this motor unit subset. This process may become maladaptive, highlighting a novel therapeutic target to reduce motor unit vulnerability. Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder with a median survival of 3 years. We employed serial high-density surface electromyography (HDSEMG) to characterize voluntary and ectopic patterns of motor unit (MU) firing at different stages of disease. By distinguishing MU subtypes with variable vulnerability to disease, we aimed to evaluate compensatory neuronal adaptations that accompany disease progression. Twenty patients with ALS and five patients with benign fasciculation syndrome (BFS) underwent 1-7 assessments each. HDSEMG measurements comprised 30min of resting muscle and 1min of light voluntary activity from biceps brachii bilaterally. MU decomposition was performed by the progressive FastICA peel-off technique. Inter-spike interval, firing pattern, MU potential area, afterhyperpolarization duration and muscle fibre conduction velocity were determined. In total, 373 MUs (ALS=287; BFS=86) were identified from 182 recordings. Weak ALS muscles demonstrated a lower mean inter-spike interval (82.7ms) than strong ALS muscles (96.0ms; P=0.00919) and BFS muscles (95.3ms; P=0.0039). Mean MU potential area (area under the curve: 487.5 vs. 98.7μV ms; P<0.0001) and muscle fibre conduction velocity (6.2 vs. 5.1m/s; P=0.0292) were greater in weak ALS muscles than in BFS muscles. Purely fasciculating MUs had a greater mean MU potential area than MUs also under voluntary command (area under the curve: 679.6 vs. 232.4 μV ms; P=0.00144). These results suggest that first-recruited MUs develop a faster phenotype in the latter stages of ALS, likely driven by the preferential loss of vulnerable fast-twitch MUs. Inhibition of this potentially maladaptive phenotypic drift may protect the longevity of the MU pool, stimulating a novel therapeutic avenue.

Estimation of Synaptic Activity during Neuronal Oscillations

In the study of brain connectivity, an accessible and convenient way to unveil local functional structures is to infer the time trace of synaptic conductances received by a neuron by using exclusively information about its membrane potential (or voltage). Mathematically speaking, it constitutes a challenging inverse problem: it consists in inferring time-dependent parameters (synaptic conductances) departing from the solutions of a dynamical system that models the neuron’s membrane voltage. Several solutions have been proposed to perform these estimations when the neuron fluctuates mildly within the subthreshold regime, but very few methods exist for the spiking regime as large amplitude oscillations (revealing the activation of complex nonlinear dynamics) hinder the adaptability of subthreshold-based computational strategies (mostly linear). In a previous work, we presented a mathematical proof-of-concept that exploits the analytical knowledge of the period function of the model. Inspired by the relevance of the period function, in this paper we generalize it by providing a computational strategy that can potentially adapt to a variety of models as well as to experimental data. We base our proposal on the frequency versus synaptic conductance curve (f−gsyn), derived from an analytical study of a base model, to infer the actual synaptic conductance from the interspike intervals of the recorded voltage trace. Our results show that, when the conductances do not change abruptly on a time-scale smaller than the mean interspike interval, the time course of the synaptic conductances is well estimated. When no base model can be cast to the data, our strategy can be applied provided that a suitable f−gsyn table can be experimentally constructed. Altogether, this work opens new avenues to unveil local brain connectivity in spiking (nonlinear) regimes.

Mean-reverting neuronal model based on two alternating patterns

A neuronal action potential model based on the generalised two-state Ornstein–Uhlenbeck process is studied. The model well describes all phases of a neuronal spike cycle, and the intrinsic parameters of the model have clear specification. Laplace transforms of a firing time are obtained explicitly. Formulae for the mean interspike intervals and their variances, as well as for the average duration of the relative refractory period are also obtained.

Measuring the accuracies of motor unit firing times and action potential waveforms derived from surface electromyographic decomposition

This study included spike trigger averaging (STA) procedures to examine the acceptability of the Precision Decomposition (PD) III derived motor unit action potential (MUAP) trains that met the >90% accuracy criteria from the reconstruct-and-test. MUs met the >90% accuracy criteria from the reconstruct-and-test with STA procedures then applied. Y-intercepts and slopes were calculated for the firing rate- and MUAP amplitude-recruitment threshold relationships. Gaussian noise (1% of the SD of the mean interspike interval) was added to the firing times with the changes in MUAPs quantified. A total of 455 MUs were decomposed with 155 MUs removed as a result of the reconstruct-and-test. Five additional MUs were excluded via the STA criteria. The MUAP waveforms deteriorated with the inclusion of Gaussian noise. There were differences in the derived action potentials amplitudes of higher-threshold MUs between the PD III algorithm and the STA procedure. There was excellent agreement among the slopes and y-intercepts between the relationships that included or excluded MUs that did not meet the STA criteria. There was good agreement between the MUAP amplitude-recruitment threshold relationships derived from the PD III and STA procedure. The addition of the STA procedures did not alter the MU-derived relationships.

Acute physiological responses to combined blood flow restriction and low-level laser.

Blood flow restriction (BFR) is an innovation in fitness to train muscles with low loads at low oxygen levels. Low-level laser therapy (LLLT) is a bio-energetic approach to alleviate muscle fatigue during resistance training. This study investigated the immediate effect of LLLT pre-conditioning on BFR that accelerates muscle fatigue due to ischemia. Fifteen young adults participated in this study of a crossover randomized design. They completed a low-load contraction with various pre-conditioning (blood flow restriction with low-level laser therapy (LLLT + BFR), blood flow restriction with sham low-level laser therapy (BFR), and control). Force fluctuation dynamics, muscle oxygen saturation of hemoglobin and myoglobin (SmO2), and discharge patterns of motor units (MU) were compared. Normalized SmO2 during low-load contractions significantly varied with the pre-contraction protocols (Control (83.6 ± 3.0%) > LLLT + BFR (70.3 ± 2.8%) > BFR (55.4 ± 2.4%). Also, force fluctuations and MU discharge varied with the pre-contraction protocols. Multi-scale entropy and mean frequency of force fluctuations were greater in the LLLT + BFR condition (31.95 ± 0.67) than in the BFR condition (29.47 ± 0.73). The mean inter-spike interval of MUs was greater in the LLLT + BFR condition (53.32 ± 2.70ms) than in the BFR condition (45.04 ± 1.08ms). In particular, MUs with higher recruitment thresholds exhibited greater LLLT-related discharge complexity (LLLT + BFR (0.201 ± 0.012) > BFR (0.154 ± 0.006)). LLLT pre-conditioning can minimize the BFR-related decline in muscle oxygen saturation, leading to force gradation and MU discharge in a cost-effective and complex manner.

Compact Graphene-Based Spiking Neural Network With Unsupervised Learning Capabilities

To fully unleash the potential of graphene-based devices for neuromorphic computing, we propose a graphene synapse and a graphene neuron that form together a basic Spiking Neural Network (SNN) unit, which can potentially be utilized to implement complex SNNs. Specifically, the proposed synapse enables two fundamental synaptic functionalities, i.e., Spike-Timing-Dependent Plasticity (STDP) and Long-Term Plasticity, and both Long-Term Potentiation (LTP) and Long-Term Depression (LTD) can be emulated with the same structure by properly adjusting its bias. The proposed neuron captures the essential Leaky Integrate and Fire spiking neuron behavior with post firing refractory interval. We demonstrate the proper operation of the graphene SNN unit by relying on a mixed simulation approach that embeds the high accuracy of atomistic level simulation of graphene structures conductance within the SPICE framework. Subsequently, we analyze the way graphene synaptic plasticity affects the behavior of a 2-layer SNN example consisting of 6 neurons and demonstrate that LTP significantly increases the number of firing events while LTD is diminishing them, as expected. To assess the plausibility of the graphene SNN reaction to input stimuli we simulate its behavior by means of both SPICE and NEST, a well established SNN simulation framework, and demonstrate that the obtained reactions, characterized in terms of total number of firing events and mean Inter-Spike Interval (ISI) length, are in close agreement, which clearly suggests that the proposed design exhibits a proper behavior. Further, we prove the unsupervised learning capabilities of the proposed design by considering a 2-layer SNN consisting of 30 neurons meant to recognize the characters “A,” “E,” “I,” “O,” and “U,” represented with a 5 by 5 black and white pixel matrix. The SPICE simulation results indicate that the graphene SNN is able to perform unsupervised character recognition associated learning and that its recognition ability is robust to input character variations. Finally, we note that our proposal results in a small real-estate footprint (max. 30 nm $^2$ are required by one graphene-based device) and operates at 200 mV supply voltage, which suggest its suitability for the design of large-scale energy-efficient computing systems.

Multiplicative noise is beneficial for the transmission of sensory signals in simple neuron models

We study simple integrate-and-fire type models with multiplicative noise and consider the transmission of a weak and slow signal, i.e. a signal that evokes a small modulation of the instantaneous firing rate on time scales that are much larger than the membrane time scale and the mean interspike interval. The specific question of interest is whether and how the state-dependence of the noise can be optimized with respect to information transmission. First, in a simple model in which the noise intensity varies linearly with the state variable, we show analytically that multiplicative fluctuations may benefit the signal transfer and we elucidate the mechanism for this improvement. In a conductance-based integrate-and-fire model with synaptically filtered shot-noise input, we show by means of extended numerical simulations that also in a biophysically more relevant situation, multiplicative noise can enhance the signal-to-noise ratio. Our results shed light on a so far unexplored aspect of stochastic signal transmission in neural systems.

The effect of visuospatial resolution on discharge variability among motor units and force-discharge relation.

Although force steadiness varies with visuospatial information, accountable motor unit (MU) behaviors are not fully understood. This study investigated the modulation of MU discharges and force-discharge relation due to variations in the spatial resolution of visual feedback, with a particular focus on discharge variability among MUs. Fourteen young adults produced isometric force at 10% of maximal voluntary contraction (MVC) through index abduction, under the conditions of force trajectory displayed with low visual gain (LVG) and high visual gain (HVG). Together with smaller and more complex force fluctuations, HVG resulted in greater variabilities of the mean interspike interval and discharge irregularity among MUs than LVG did. Estimated via smoothening of a cumulative spike train of all MUs, global discharge rate was tuned to visual gain, with a more complex global discharge rate and a lower force-discharge relation in the HVG condition. These higher discharge variabilities were linked to larger variance of the common drive received by MUs for regulation of muscle force with higher visuospatial information. In summary, higher visuospatial information improves force steadiness with more complex force fluctuations, underlying joint effects of low-pass filter property of the musculotendon complex and central modulation of discharge variability among MUs.

Motor Unit Discharge Characteristics And Walking Performance Of Individuals With Multiple Sclerosis.

PURPOSE: To examine the associations between strength, force steadiness, and motor unit discharge characteristics of lower leg muscle with assessments of walking performance and disability status in individuals with multiple sclerosis (MS). METHODS: Persons with MS (n=23, 9 men, 53 ± 7 yrs) attended 1 to 3 evaluation sessions that were performed before, immediately after, and 4 wks after a clinical trial of neuromuscular electrical stimulation. Outcomes included 3 questionnaires (Patient Determined Disease Steps [PDDS], Modified Fatigue Impact Scale [MFIS], and MS Walking Scale-12 [MSWS-12]), 25-ft walk test, 6-min walk test, and maximal torque and force steadiness (10 and 20% MVC) for lower leg muscles. High-density surface EMG recordings were obtained from the tibialis anterior (TA), soleus (Sol), and medial gastrocnemius (MG) during the steady contractions and decomposed into discharge times of motor unit action potentials. RESULTS: Greater levels of self-reported disability (PDDS, MFIS, MSWS-12) were associated with less distance walked in 6 min and longer times to walk 25 ft. Mean interspike intervals (ISI) were 110 ± 23 ms and 102 ± 22 ms (10% and 20% MVC force, respectively) for TA motor units (n= 1,634), 138 ± 36 ms and 135 ± 34 ms for MG motor units (n=696), and 150 ± 33 ms and 145 ± 34 ms for Sol motor units (n = 902). A multiple regression model explained 40% (P=0.001) of the variance in 6-min distance with two predictor variables: mean ISI of MG (partial r = -0.48) and MVC dorsiflexor torque of the more affected leg (partial r=0.37). Similarly, another multiple regression model explained 47% of the variance (P<0.001) in 25-ft time with three predictor variables: mean ISI of soleus (partial r=0.51), MVC dorsiflexor torque of the more affected leg (partial r = -0.43), and force steadiness of the plantar flexors (partial r = 0.39). CONCLUSION: Moderate amounts of the variance in two walking tests for persons with MS were explained by mean discharge times of action potentials by motor units in plantar flexor muscles during steady submaximal contractions, the strength of the dorsiflexor muscles in the more affected leg, and force steadiness during an isometric contraction. This research was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the NIH (R03HD079508).

Motor unit discharge characteristics and walking performance of individuals with multiple sclerosis.

Walking performance of persons with multiple sclerosis (MS) is strongly influenced by the activation signals received by lower leg muscles. We examined the associations between force steadiness and motor unit discharge characteristics of lower leg muscles during submaximal isometric contractions with tests of walking performance and disability status in individuals who self-reported walking difficulties due to MS. We expected that worse walking performance would be associated with weaker plantar flexor muscles, worse force steadiness, and slower motor unit discharge times. Twenty-three individuals with relapsing-remitting MS (56 ± 7 yr) participated in the study. Participants completed one to three evaluation sessions that involved two walking tests (25-ft walk and 6-min walk), a manual dexterity test (grooved pegboard), health-related questionnaires, and measurement of strength, force steadiness, and motor unit discharge characteristics of lower leg muscles. Multiple regression analyses were used to construct models to explain the variance in measures of walking performance. There were statistically significant differences (effect sizes: 0.21-0.60) between the three muscles in mean interspike interval (ISI) and ISI distributions during steady submaximal contractions with the plantar flexor and dorsiflexor muscles. The regression models explained 40% of the variance in 6-min walk distance and 47% of the variance in 25-ft walk time with two or three variables that included mean ISI for one of the plantar flexor muscles, dorsiflexor strength, and force steadiness. Walking speed and endurance in persons with relapsing-remitting MS were reduced in individuals with longer ISIs, weaker dorsiflexors, and worse plantar flexor force steadiness. NEW & NOTEWORTHY The walking endurance and gait speed of persons with relapsing-remitting multiple sclerosis (MS) were worse in individuals who had weaker dorsiflexor muscles and greater force fluctuations and longer times between action potentials discharged by motor units in plantar flexor muscles during steady isometric contractions. These findings indicate that the control of motor unit activity in lower leg muscles of individuals with MS is associated with their walking ability.

Effects of Voice Therapy on Laryngeal Motor Units During Phonation in Chronic Superior Laryngeal Nerve Paresis Dysphonia

Injury to the superior laryngeal nerve can result in dysphonia, and in particular, loss of vocal range. It can be an especially difficult problem to address with either voice therapy or surgical intervention. Some clinicians and scientists suggest that combining vocal exercises with adjunctive neuromuscular electrical stimulation may enhance the positive effects of voice therapy for superior laryngeal nerve paresis (SLNP). However, the effects of voice therapy without neuromuscular electrical stimulation are unknown. The purpose of this retrospective study was to demonstrate the clinical effectiveness of voice therapy for rehabilitating chronic SLNP dysphonia in two subjects, using interspike interval (ISI) variability of laryngeal motor units by laryngeal electromyography (LEMG). Both patients underwent LEMG and were diagnosed with having 70% recruitment of the cricothyroid muscle, and 70% recruitment of the cricothyroid and thyroarytenoid muscles, respectively. Both patients received voice therapy for 3 months. Grade, roughness, breathiness, asthenia, and strain (GRBAS) scale, stroboscopic examination, aerodynamic assessment, acoustic analysis, and Voice Handicap Index-10 were performed before and after voice therapy. Mean ISI variability during steady phonation was also assessed. After voice therapy, both patients showed improvement in vocal assessments by acoustic, aerodynamic, GRBAS, and Voice Handicap Index-10 analysis. LEMG indicated shortened ISIs in both cases. This study suggests that voice therapy for chronic SLNP dysphonia can be useful for improving SLNP and voice quality.

P279 Distribution of single unit activity patterns in subthalamic nucleus of Parkinsonian patients

Objectives Subthalamic nucleus (STN) is the main target for surgery treatment of Parkinson’s disease (PD) patients. The aim of the study was to characterize spontaneous single unit activity in subthalamic nucleus of PD patients and to search for the possible correlations between characteristics of neuronal activity and clinical phenotype. Methods We have analyzed single unit activity of 493 cells registered in subthalamic nucleus of 9 patients (3 males and 6 females) with PD (56.8 ± 4.8 yrs, UPDRS III score in off-state 52.0 ± 12.2), that were undergoing bilateral DBS surgery at Burdenko Neurosurgery Center in 2015–2017. For each neuron we determined several characteristics: firing rate, coefficient of variation (CV), mean and median interspike interval (ISI), asymmetry index (AI), burst characteristics. We also have performed spectral analysis of oscillations in the spiketrains. Results We have identified three different patterns of neuronal activity: tonic regular activity (AI > 0.7, CV 0.7, CV > 0.85)? in 5–25% of neurons, burst-like activity (AI Significance The unevenness of neuronal activity patterns and oscillations of STN could be used for choosing optimal location of DBS electrodes in PD patients.

Effect of dietary salt intake on epithelial Na+ channels (ENaC) in vasopressin magnocellular neurosecretory neurons in the rat supraoptic nucleus.

Key points A growing body of evidence suggests that epithelial Na+ channels (ENaCs) in the brain play a significant role in the regulation of blood pressure; however, the brain structures that mediate the effect are not well understood.Because vasopressin (VP) neurons play a pivotal role in coordinating neuroendocrine and autonomic responses to maintain cardiovascular homeostasis, a basic understanding of the regulation and activity of ENaC in VP neurons is of great interest.We show that high dietary salt intake caused an increase in the expression and activity of ENaC which resulted in the steady state depolarization of VP neurons.The results help us understand one of the mechanisms underlying how dietary salt intake affects the activity of VP neurons via ENaC activity. All three epithelial Na+ channel (ENaC) subunits (α, β and γ) are located in vasopressin (VP) magnocellular neurons in the hypothalamic supraoptic (SON) and paraventricular nuclei. Our previous study demonstrated that ENaC mediates a Na+ leak current that affects the steady state membrane potential in VP neurons. In the present study, we evaluated the effect of dietary salt intake on ENaC regulation and activity in VP neurons. High dietary salt intake for 7 days caused an increase in expression of β‐ and γENaC subunits in the SON and the translocation of αENaC immunoreactivity towards the plasma membrane. Patch clamp experiments on hypothalamic slices showed that the mean amplitude of the putative ENaC currents was significantly greater in VP neurons from animals that were fed a high salt diet compared with controls. The enhanced ENaC current contributed to the more depolarized basal membrane potential observed in VP neurons in the high salt diet group. These findings indicate that high dietary NaCl intake enhances the expression and activity of ENaCs, which augments synaptic drive by depolarizing the basal membrane potential close to the action potential threshold during hormonal demand. However, ENaCs appear to have only a minor role in the regulation of the firing activity of VP neurons in the absence of synaptic inputs as neither the mean intraburst frequency, burst duration, nor interspike interval variability of phasic bursting activity was affected. Moreover, ENaC activity did not affect the initiation, sustention, or termination of the phasic bursting generated in an intrinsic manner without synaptic inputs.

Synchronization of bursting neurons with a slowly varying d. c. current

Bursting of neuronal firing is an interesting dynamical consequences depending on fast/slow dynamics. Certain cells in different brain regions produce spike-burst activity. We study such firing activity and its transitions to synchronization using identical as well as non-identical coupled bursting Morris-Lecar (M-L) neurons. Synchronization of different firing activity is a multi-time-scale phenomenon and burst synchronization presents the precursor to spike synchronization. Chemical synapses are one of the dynamical means of information processing between neurons. Electrical synapses play a major role for synchronous activity in a certain network of neurons. Synaptically coupled neural cells exhibit different types of synchronization such as in phase or anti-phase depending on the nature and strength of coupling functions and the synchronization regimes are analyzed by similarity functions. The sequential transitions to synchronization regime are examined by the maximum transverse Lyapunov exponents. Synchronization of voltage traces of two types of planar bursting mechanisms is explored for both kind of synapses under realistic conditions. The noisy influence effects on the transmission of signals and strongly acts to the firing activity (such as periodic firing and bursting) and integration of signals for a network. It has been examined using the mean interspike interval analysis. The transition to synchronization states of coupled and a network of bursting neurons may be useful for further research in information processing and even the origins of certain neurological disorders.

Output Stream of Leaky Integrate-and-Fire Neuron Without Diffusion Approximation

Probability density function (pdf) of output interspike intervals (ISI) as well as mean ISI is found in exact form for leaky integrate-and-fire (LIF) neuron stimulated with Poisson stream. The diffusion approximation is not used. The whole range of possible ISI values is represented as infinite union of disjoint intervals: \(]0;\infty [ = ]0;T_2] + \sum _{m=0}^\infty ]T_2+m\,T_3;T_2+(m+1)T_3]\), where \(T_2\) and \(T_3\) are defined by the LIF’s physical parameters. Exact expression for the obtained pdf is different on different intervals and is given as finite sum of multiple integrals. For the first three intervals the integrals are taken which brings about exact expressions with polylogarithm functions. The found distribution can be bimodal for some values of parameters. Conditions, which ensure bimodality are briefly analyzed.