Dynamics and Bifurcation Structure of a Mean-Field Model of Adaptive Exponential Integrate-and-Fire Networks.

Motor unit firing rates of the first dorsal interosseous differ between male and female children aged 8–10 years

Objective:This study examined motor unit (MU) firing rates during a prolonged isometric contraction of the vastus lateralis (VL) for females and males.Methods:Surface electromyographic (sEMG) signals were recorded from the VL for eleven females and twelve males during a 45-second isometric trapezoid muscle actions at 40% of maximal voluntary contraction (MVC). For each MU, mean firing rate (MFR) was calculated for the initial and final 10-second epochs of the steady torque segment and regressed against recruitment threshold (RT, expressed as %MVC), as well as time at recruitment (TREC, seconds). MFR was also averaged for each subject.Results:Significant differences existed across epochs for the y-intercepts (P=0.009) of the MFR vs. TREC relationship, as well as the grouped MFR analysis (P<0.001); no differences were observed between epochs for the MFR vs. RT relationship. Significant differences existed between sexes for the grouped MFR analysis (P=0.049), but no differences were observed for the MFR vs. TREC or MFR vs. RT relationships.Conclusion:Analysis method may impact interpretation of firing rate behavior; increases in MU firing rates across a prolonged isometric contraction were observed in the MFR vs. TREC relationship and the grouped MFR analysis.

An Optimal Decision Population Code that Accounts for Correlated Variability Unambiguously Predicts a Subject’s Choice

Learned Timing of Motor Behavior in the Smooth Eye Movement Region of the Frontal Eye Fields

Single-Neuron Correlates of Atypical Face Processing in Autism

A comprehensive understanding of the neural mechanisms of cognitive function requires an understanding of how neural representations are transformed across different scales of neural organization: from within local microcircuits to across different brain areas. However, the neural transformations within the local microcircuits are poorly understood. Particularly, the role that two main cell classes of neurons in cortical microcircuits (i.e. pyramidal neurons and interneurons) have in auditory behaviour and cognition remains unknown. In this study, we tested the hypothesis that pyramidal cells and interneurons in the auditory cortex play a differential role in auditory categorization. To test this hypothesis, we recorded single-unit activity from the auditory cortex of rhesus monkeys while they categorized speech sounds. Based on the spike-waveform shape, a neuron was classified as either a narrow-spiking putative interneuron or a broad-spiking putative pyramidal neuron. We found that putative interneurons and pyramidal neurons in the auditory cortex differentially coded category information: interneurons were more selective for auditory categories than pyramidal neurons. These differences between cell classes may be an essential property of the neural computations underlying auditory categorization within the microcircuitry of the auditory cortex.

Sleep homeostasis manifests as a relative constancy of its daily amount and intensity. Theoretical descriptions define ‘Process S’, a variable with dynamics dependent on global sleep-wake history, and reflected in electroencephalogram (EEG) slow wave activity (SWA, 0.5–4 Hz) during sleep. The notion of sleep as a local, activity-dependent process suggests that activity history must be integrated to determine the dynamics of global Process S. Here, we developed novel mathematical models of Process S based on cortical activity recorded in freely behaving mice, describing local Process S as a function of the deviation of neuronal firing rates from a locally defined set-point, independent of global sleep-wake state. Averaging locally derived Processes S and their rate parameters yielded values resembling those obtained from EEG SWA and global vigilance states. We conclude that local Process S dynamics reflects neuronal activity integrated over time, and global Process S reflects local processes integrated over space.

Previous investigations report no changes in motor unit (MU) firing rates during submaximal contractions following resistance training. These investigations did not account for MU recruitment or examine firing rates as a function of recruitment threshold (REC). Therefore, MU recruitment and firing rates in chronically resistance-trained (RT) and physically active controls (CON) were examined. Surface electromyography signals were collected from the first dorsal interosseous during isometric muscle actions at 40% and 70% maximal voluntary contraction (MVC). For each MU, force at REC, mean firing rate (MFR) during the steady force, and MU action potential amplitude (MUAPAMP) were analyzed. For each individual and contraction, the MFRs were linearly regressed against REC, whereas, exponential models were applied to the MFR versus MUAPAMP and MUAPAMP versus REC relationships with the y-intercepts and slopes (linear) and A and B terms (exponential) calculated. For the 40% MVC, the RT had less negative slopes (p = 0.001) and lower y-intercepts (p = 0.006) of the MFR versus REC relationships and lower B terms (p = 0.011) of the MUAPAMP versus REC relationships. There were no differences in either relationship between groups for the 70% MVC. During the 40% MVC, the RT had a smaller range of MFRs and MUAPAMPS in comparison with the CON, likely because of reduced MU recruitment. The RT had lower MFRs and recruitment during the 40% MVC, which may indicate a leftward shift in the force-frequency relationship, and thus require less excitation to the motoneuron pool to match the same relative force.

The influence of common oscillatory inputs to the motoneuron pool on correlated patterns of motor unit discharge was examined using model simulations. Motor unit synchronization, in-phase fluctuations in mean firing rates known as 'common drive', and the coefficient of variation of the muscle force were examined as the frequency and amplitude of common oscillatory inputs to the motoneuron pool were varied. The amount of synchronization, the peak correlation between mean firing rates and the coefficient of variation of the force varied with both the frequency and amplitude of the common input signal. Values for 'common drive' and the force coefficient of variation were highest for oscillatory inputs at frequencies less than 5 Hz, while synchronization reached a maximum when the frequency of the common input was close to the average motor unit firing rate. The frequency of the common input signal for which the highest levels of synchronization were observed increased as motoneuron firing rates increased in response to higher target force levels. The simulation results suggest that common low-frequency oscillations in motor unit firing rates and short-term synchronization result from oscillatory activity in different bands of the frequency spectrum of shared motoneuron inputs. The results also indicate that the amount of synchronization between motor unit discharges depends not only on the amplitude of the shared input signal, but also on its frequency in relation to the present firing rates of the individual motor units.

PURPOSE: Differences in motor unit (MU) behavior as a result of chronic training have previously been reported. It is hypothesized that MU behavior is regulated by the physical properties of a muscle rather than the central nervous system. Thus, it has been suggested that differences in MU firing rates between training statuses were likely due to differences in the physical properties of the MU, such as, percent myosin heavy chain [%MHC] expression. No study has correlated MU control strategies during a voluntary contraction with MHC expression in vivo. METHODS: Twelve individuals (age = 20.91 ± 2.30 yrs, weight = 70.76 ± 14.47 kg) volunteered for this investigation. Participants performed 3 isometric maximal voluntary contractions of the leg extensors on an isokinetic dynamometer followed by an isometric trapezoid muscle action at 40% MVC. An electromyographic (EMG) sensor was placed over the vastus lateralis (VL). EMG signals were decomposed to extract action potentials and firing events of single MUs. Only MUs with > 90% accuracies were used for further analysis. Recruitment (REC) thresholds and mean firing rates (MFR) were calculated for each MU. MFR was calculated as the average value of the MFR trajectory during steady force. Subjects gave a muscle biopsy of the VL. Type I %MHC expression was determined by SDS-PAGE. Linear regressions were performed to determine the slopes and y-intercepts of the MFR versus REC relationships. Pearson product-moment correlations were used to determine the relationship between type I %MHC expression with the slopes and y-intercepts. Alpha was set at 0.05. RESULTS: Pearson’s product moment correlations were significant between the type I %MHC expression and the slopes (P = 0.001, r = 0.844) from the MFR versus REC relationships, but not the y-intercepts (P = 0.826, r = -0.071). CONCLUSION: Individuals with a greater percentage of type I %MHC expression had greater firing rates of the higher-threshold MUs at the targeted force level than individuals with a lower percentage of type I %MHC expression. It is plausible that the firing rates of the higher-threshold MUs are lower in individuals with greater percentages of type II MHC isoform content as a result of greater twitch forces. This study supported the hypothesis that the MU control scheme is regulated by the physical properties of the muscle.

In this paper, we mainly discuss effects of partial time delay on temporal dynamics of Watts–Strogatz (WS) small-world neuronal networks by controlling two parameters. One is the time delay [Formula: see text] and the other is the probability of partial time delay [Formula: see text]. Temporal dynamics of WS small-world neuronal networks are discussed with the aid of temporal coherence and mean firing rate. With the obtained simulation results, it is revealed that for small time delay [Formula: see text], the probability [Formula: see text] could weaken temporal coherence and increase mean firing rate of neuronal networks, which indicates that it could improve neuronal firings of the neuronal networks while destroying firing regularity. For large time delay [Formula: see text], temporal coherence and mean firing rate do not have great changes with respect to [Formula: see text]. Time delay [Formula: see text] always has great influence on both temporal coherence and mean firing rate no matter what is the value of [Formula: see text]. Moreover, with the analysis of spike trains and histograms of interspike intervals of neurons inside neuronal networks, it is found that the effects of partial time delays on temporal coherence and mean firing rate could be the result of locking between the period of neuronal firing activities and the value of time delay [Formula: see text]. In brief, partial time delay could have great influence on temporal dynamics of the neuronal networks.

So Many Choices: What Computational Models Reveal about Decision-Making Mechanisms

Magnocellular oxytocin neurones are proposed as a suitable system for studying the mechanisms involved in the regulation of neuronal bursting activity. They display high frequency (50 sp./s) bursts of spikes (approximately every 300 s), in response to specific stimuli, which are superimposed on a variable level of basal activity and are tightly co-ordinated as a result of network interactions. The relationship between the strength of the bursting activity (as quantified by burst amplitude and interburst interval) and the characteristics of the interburst basal activity were assessed. During control conditions, mean basal activity and variability of firing increased just before bursts. During experimental conditions leading to burst facilitation, burst amplitude increased and interburst interval decreased while a sustained increase in mean firing rate occurred. Variability of firing (measured by both the standard deviation of firing rate, and the index of dispersion which corrected this standard deviation for differences in mean firing rate), increased demonstrating an increase in spike clustering greater than expected as a result of increased basal activity. When bursting was restrained (i.e. interburst interval increased), mean basal activity increased substantially, but index of dispersion decreased. A narrowing of the interspike interval distribution occurred, indicating increased regularity of firing. The aspect of basal activity most strongly correlated with bursting was variability of firing rate. The strongest correlate of burst amplitude was the standard deviation of mean firing rate, whereas the strongest and most consistent correlate of interburst interval was the index of dispersion. In conclusion, bursting behaviour is most strongly related to the irregularity rather than the level of basal activity.

For emerging respiratory infectious diseases like COVID-19, non-pharmaceutical interventions such as isolation are crucial for controlling the spread. From the perspective of network transmission, non-pharmaceutical interventions like isolation alter the degree distribution and other topological structures of the network, thereby controlling the spread of the infectious disease. In this paper, we establish a SEIR mean-field propagation dynamics model for the synchronous evolution of dynamic networks caused by propagation and tracing isolation. We employ the reducing-dimension method to convert the mean-field model in networks into an equivalent and simpler low-dimension model, and then calculate the exact expression of the final size. In addition, we get the differential equations of the degree distribution over time in dynamic networks under tracing isolation and the relationships between the first and second moment of the dynamic network. While the degree of a node remains constant regardless of its state in many previous studies, this paper takes into account that the degree of each node changes over time whatever its state under the disease spread and intervention measures.

Despite ample evidence that females are weaker and possess smaller muscle cross-sectional areas (CSAs) compared to males, it remains unclear if there are sex-related differences in the properties of motor units (MU). Eleven males (age 22 ± 3years) and 12 females (age 21 ± 1years) performed isometric trapezoid muscle actions at 10% and 70% of maximal voluntary contraction (MVC). Surface electromyography signals were recorded and decomposed into MU action potential (AP) waveforms and firing instances. Average MUAP amplitudes (MUAPAMPS), mean firing rates (MFRs), initial firing rates (IFRs), and recruitment thresholds (RT) were calculated for the 10% MVC, while MUAPAMPS, IFRs, and MFRs were regressed against RT for the 70% MVC. Ultrasonography was used to measure CSA of the first dorsal interosseous (FDI). Males had greater CSAs (p < 0.001; males 2.34 ± 0.28cm2, females 1.82 ± 0.18cm2) and MVC strength (p < 0.001; males 25.9 ± 5.5N, females 16.44 ± 2.5N). No differences existed for MUAPAMPS, IFRs, MFRs, or RTs (p > 0.05) during the 10% MVC. For the 70% MVC, the y-intercepts from the MUAPAMPS vs. RT relationships were greater (p < 0.05) for the males (males - 0.19 ± 0.53mV; females - 0.78 ± 0.75mV), while the inverse was true for the MFR vs. RT relationships (males 31.55 ± 6.92pps, females 38.65 ± 6.71pps) with no differences (p > 0.05) in the slopes. Therefore, smaller CSAs and weaker MVCs are likely the result of smaller higher-threshold MUs for females.