KP.3.1.1 became a dominant successor to JN.1 by the second half of 2024 but the intrinsic pathogenicity and virological feature of KP.3.1.1 remain incompletely understood. Here, we comprehensively evaluated the pathogenesis and characteristics of KP.3.1.1 in comparison to JN.1 and other JN.1-derived variants including JN.1.7, KP.2, and KP.3. The unique S31del mutation on KP.3.1.1 spike confers further evasion to the clinically authorized mAb Pemivibart and reduces convalescent serum neutralization efficiency. Structural analysis indicates that S31del induces novel glycosylation sites that facilitates evasion of neutralizing antibodies. We further reveal that S31del significantly enhances pseudovirus entry efficiency in all evaluated cell types including the human primary nasal epithelial cells. Nevertheless, the intrinsic pathogenicity of KP.3.1.1 is similar to JN.1 and KP.3, and higher than that of JN.1.7 and KP.2 in a male hamster model. Interestingly, the increased virus infectivity conferred by S31del in KP.3.1.1 spike is counterbalanced by the NSP10 S33C mutation. Overall, our study indicates that a single spike mutation can confer both enhanced immune escape and increased viral infectivity. The opposing effects of spike and non-spike mutations highlight the complex interplay of viral genomic elements in shaping their overall fitness, and reveal the high plasticity of coronavirus evolution.

Standard x-ray free-electron lasers (FELs) produce pulses with total durations of tens of femtoseconds and with time and spectral profiles consisting of multiple randomly distributed spikes. Strongly compressing an electron beam is a typical approach to produce shorter and coherent FEL pulses. We have advanced this method by starting at the injector of the FEL facility with electron beams with lower charges and shorter durations than in standard configurations. This leads to shorter electron beams with reduced energy spread after full compression and, consequently, to shorter and higher-quality FEL pulses. By operating with electron beams at the injector with charges of a few pC and rms durations of 360 fs, we show the generation of hard x-ray FEL radiation with practically all pulses having a single spike and a duration estimated from spectral measurements of about 300 as (full-width-at-half-maximum values). The demonstration was conducted at SwissFEL, the FEL at the Paul Scherrer Institute in Switzerland. Our work represents a simple way to enhance the production of single-spike events in x-ray FEL facilities, paving the way to achieve fully coherent hard x-ray FEL pulses with unprecedented durations.

Western equine encephalitis virus (WEEV), a group of encephalitic alphaviruses that cause severe diseases in humans and equids, historically used the very-low-density lipoprotein receptor (VLDLR) as a receptor during infection. However, current epidemic strains no longer use VLDLR as a receptor. In this study, we identify that LA1, LA2, LA3, and LA5 of VLDLR can directly interact with WEEV. Using cryo-electron microscopy, we investigate the structures of complexes formed between WEEV and VLDLR-LBD and VLDLR fragments. Our findings show that LA1 and LA2 insert into a cleft formed by two adjacent E2-E1 heterodimers within a single trimeric spike, while LA3 and LA5 interact with the DIII region of WEEV E1. Among VLDLR concatemers, the LA1-5 exhibits the strongest binding affinity for WEEV. Additionally, we find that a single polymorphism in the E2 glycoprotein determines WEEV's receptor tropism. Mutations E2E181K or E2E81K in the nonpathogenic strain Imperial-181 enhanced its ability to enter via VLDLR. These results enhance our understanding of alphavirus receptor recognition and receptor usage shifts, providing insights for the development of antiviral therapies.

Spike sorting is one of the cornerstones of extracellular electrophysiology. By leveraging advanced signal processing and data analysis techniques, spike sorting makes it possible to detect, isolate, and map single neuron spiking activity from both in vivo and in vitro extracellular electrophysiological recordings. A crucial step of any spike sorting pipeline is to reduce the dimensionality of the recorded spike waveform data. Reducing the dimensionality of the processed data is a near-universal practice, fundamentally motivated by the use of clustering algorithms responsible to detect, isolate, and sort the recorded putative neurons. In this paper, we propose and illustrate on both synthetic and experimental data that employing the nonlinear dimensionality reduction technique Uniform Manifold Approximation and Projection (UMAP) can drastically improve the performance, efficiency, robustness, and scalability of spike sorting pipelines without increasing their computational cost. We show how replacing the linear or ad hoc, expert-defined, supervised nonlinear dimensionality reduction methods commonly used in spike sorting pipelines by the unsupervised, mathematically grounded, nonlinear dimensionality reduction method provided by UMAP drastically increases the number of correctly sorted neurons, makes the identification of quieter, seldom spiking neurons more reliable, enables deeper and more precise explorations and analysis of the neural code, and paves new ways toward more efficient and end-to-end automatable spike sorting pipelines of large-scale extracellular neural recording as those produced by high-density multielectrode arrays.

Spike sorting is one of the cornerstones of extracellular electrophysiology. By leveraging advanced signal processing and data analysis techniques, spike sorting makes it possible to detect, isolate, and map single neuron spiking activity from both in vivo and in vitro extracellular electrophysiological recordings. A crucial step of any spike sorting pipeline is to reduce the dimensionality of the recorded spike waveform data. Reducing the dimensionality of the processed data is a near-universal practice, fundamentally motivated by the use of clustering algorithms responsible to detect, isolate, and sort the recorded putative neurons. In this paper, we propose and illustrate on both synthetic and experimental data that employing the nonlinear dimensionality reduction technique Uniform Manifold Approximation and Projection (UMAP) can drastically improve the performance, efficiency, robustness, and scalability of spike sorting pipelines without increasing their computational cost. We show how replacing the linear or ad hoc, expert-defined, supervised nonlinear dimensionality reduction methods commonly used in spike sorting pipelines by the unsupervised, mathematically grounded, nonlinear dimensionality reduction method provided by UMAP drastically increases the number of correctly sorted neurons, makes the identification of quieter, seldom spiking neurons more reliable, enables deeper and more precise explorations and analysis of the neural code, and paves new ways toward more efficient and end-to-end automatable spike sorting pipelines of large-scale extracellular neural recording as those produced by high-density multielectrode arrays.

The timing of spikes dictates a neuron’s impact on downstream circuits and behavior, and spike timing is determined by the membrane potential (Vm). However, due to technical challenges, it has been impossible to analyze the relative timing of Vm dynamics between neurons during behavior. Using large scale membrane voltage imaging, we simultaneously recorded Vm from many individual hippocampal neurons in animals engaged in a virtual spatial task. We found that relative phase of Vm theta oscillations across neurons exhibit gradual or discrete shifts depending on spatial position. This finding extends beyond previous studies showing Vm dynamics in single neurons or spiking activity in multiple neurons, revealing previously unknown evidence for consistent coding of space by spike-independent relative phase of Vm theta dynamics between neurons.

Uncertainty is omnipresent. While humans and other animals take uncertainty into account during decision making, it remains unclear how it is represented in cortex. Recent theoretical work on uncertainty computation in cortical neurons predicts a stimulus-triggered decrease of the within-trial membrane potential variability. Yet, testing this prediction in experimental data is uniquely challenging as it would require a large number of intracellular recordings in vivo. We thus leverage simulation-based inference to gain insights about the membrane potential statistics underlying single unit spiking activity. This allows us to investigate the effect of stimulus reliability on membrane potential variability in posterior parietal cortex neurons while male mice performed a multisensory change detection task. The inferred membrane potential statistics show that neurons decrease their membrane potential variability in response to task-relevant stimuli. In particular, more perceptually reliable stimuli lead to larger decreases in membrane potential variability than less reliable ones, in line with theoretical predictions. These findings suggest that cortical neurons track uncertainty, providing Bayesian benefits for downstream computations.

Objective.In this work, we aim to enforce the spiking of the membrane potential of a single neuron or a neuronal network, described by dynamical models, by controlling the current injection in the presence of model uncertainty and synaptic noise.Approach.In this study, we propose Shaping Robust Dynamic Inversion (SRDI) as a robust nonlinear control technique, which uses dynamic inversion of neuronal dynamical systems and shapes the error surface to derive a current control signal that enforces the spiking of membrane potential under model uncertainty.Main results.We apply SRDI to Hodgkin-Huxley model, integrate-and-fire model, and FitzHugh-Nagumo model to achieve controlled neuron spiking. Comparative studies show that SRDI outperforms classical dynamic inversion in robustness and linear model predictive control in computational time.Significance.SRDI enables precise and efficient neural control by shaping error dynamics, handling nonlinearities, and maintaining robustness to noise and model uncertainty, achieving controlled timing for single spikes, spike trains, and small neuronal networks.

In this work, a supervised learning rule based on Temporal Single Spike Coding for Effective Transfer Learning (TS4TL) is presented, an efficient approach for training multilayer fully connected Spiking Neural Networks (SNNs) as classifier blocks within a Transfer Learning (TL) framework. A new target assignment method named as "Absolute Target" is proposed, which utilizes a fixed, non-relative target signal specifically designed for single-spike temporal coding. In this approach, the firing time of the correct output neuron is treated as the target spike time, while no spikes are assigned to the other neurons. Unlike existing relative target strategies, this method minimizes computational complexity, reduces training time, and decreases energy consumption by limiting the number of spikes required for classification, all while ensuring a stable and computationally efficient training process. By seamlessly integrating this learning rule into the TL framework, TS4TL effectively leverages pre-trained feature extractors, demonstrating robust performance even with limited labelled data and varying data distributions. The proposed learning rule scales efficiently across both shallow and deep network architectures while maintaining consistent accuracy and reliability. Extensive evaluations on benchmark datasets highlight the strength of this approach, achieving state-of-the-art accuracies, including 98.91% on Eth80, surpassing previous works, and 91.89% on Fashion-MNIST, outperforming all fully connected structures in the literature. Additionally, high accuracies of 98.45% and 97.75% were recorded on the MNIST and Caltech101-Face/Bike datasets, respectively. Furthermore, TS4TL addresses a critical challenge by effectively reducing neuron misfires, ensuring that neurons respond correctly based on first-spike coding, a significant improvement over manually imposed solutions seen in prior works. These contributions collectively highlight the potential of TS4TL as a scalable and high-performance solution for temporal learning in SNNs.

The expansion of large-scale neural recording capabilities has provided new opportunities to examine multi-scale cortical network activity at single neuron resolution. At the same time, the growing scale and complexity of these datasets introduce new conceptual and technical challenges beyond what can be addressed using traditional analysis techniques. Here, we present the Similarity Networks (SIMNETS) analysis framework: an efficient and scalable pipeline designed to embed simultaneously recorded neurons into low dimensional maps according to the intrinsic relationship between their spike trains, making it possible to identify and visualize groups of neurons performing similar computations. The critical innovation is the use of pairwise spike train similarity (SSIM) matrices to capture the intrinsic relationship between the spike trains emitted by a neuron at different points in time (i.e., different experimental conditions), reflecting how the neuron responds to time-varying internal and external drives and making it possible to easily compare the information processing properties across neuronal populations. We use three publicly available neural population test datasets from the visual, motor, and hippocampal CA1 brain regions to validate the SIMNETS framework and demonstrate how it can be used to identify putative subnetworks (i.e., clusters of neurons with similar computational properties). Our analysis pipeline includes a novel statistical test designed to evaluate the likelihood of detecting spurious neuron clusters to validate network structure results. The SIMNETS framework provides a way to rapidly examine the computational structure of neuronal networks at multiple scales based on the intrinsic structure of single unit spike trains.

S4-KD: A single step spiking SiamFC+ + for object tracking with knowledge distillation.

This work offers a comprehensive approach to understanding the phenomena underlying vesicular exocytosis, a process involved in vital functions of living organisms such as neuronal and neuroendocrine signaling. The kinetics of release of most neuromediators that modulate these functions in various ways can be efficiently monitored using single-cell amperometry (SCA). Indeed, SCA at ultramicro- or nanoelectrodes provides the necessary temporal, flux, and nanoscale resolution to accurately report on the shape and intensity of single exocytotic spikes. Rather than characterizing amperometric spikes using standard descriptive parameters (e.g., amplitude and half-width), however, this study summarizes a modeling approach based on the underlying biology and physical chemistry of single exocytotic events. This approach provides deeper insights into intravesicular phenomena that control vesicular release dynamics. The ensuing model's intrinsic parsimony makes it computationally efficient and friendly, enabling the processing of large amperometric traces to gain statistically significant insights.

ABSTRACTInterictal focal paroxysmal fast activity is a pattern seen in scalp electroencephalography recordings of patients with epilepsy, but its clinical relevance remains unclear. High‐frequency oscillations, considered a surrogate marker of epileptogenicity, are being studied in relation to scalp electroencephalography. To explore the importance of paroxysmal fast activity, electroencephalography data from three adults with intractable temporal lobe epilepsy were examined. Ten consecutive samples of paroxysmal fast activity and single spikes, matched in amplitude, were collected from each patient for time‐frequency analysis. High‐frequency oscillations were found to occur in 93.3% of paroxysmal fast activity samples, but only in 10% of single spike samples. This suggests that paroxysmal fast activity is more closely linked to seizure‐related brain activity than single spikes. Monitoring paroxysmal fast activity may help identify epileptic waveforms and improve assessment of intractable epilepsy.

PCDH10 is a newly identified general receptor for Western equine encephalitis virus (WEEV) members, a group of encephalitic alphaviruses that cause severe diseases in humans and equids. While WEEV typically binds PCDH10 as a receptor, nonpathogenic strains have evolved to lose mammalian PCDH10 binding, retaining only avian PCDH10 affinity. Virulent strains also engage VLDLR and ApoER2 as alternative receptors. Here, we determine the structure of WEEV strain 71V1658 virus-like particles (VLPs) in complex with human PCDH10 extracellular cadherin repeats 1-2 (EC1-EC2) by cryo-electron microscopy at 2.99 Å resolution. EC1 inserts into a cleft clamped by two adjacent E2-E1 heterodimers within a single trimeric spike, whereas EC2 maintains no contact with the WEEV VLP. Mutagenesis studies elucidate the impacts of the interacting residues on PCDH10. And residue 153 of E2 is crucial for PCDH10 binding, and the E2Q153L mutation observes in the nonpathogenic strain Imperial-181 restores its ability to bind to PCDH10. Moreover, the arginine residue at position 89 on avian PCDH10 is essential for its interaction with strain Imperial-181. These results advance our understanding of receptor recognition by alphaviruses and the shift in receptor usage, providing insights for the development of antiviral therapies.

Acute binge-like third-trimester-equivalent alcohol exposure (TTAE) causes apoptosic neurodegeneration in brain regions necessary for spatial learning and memory, such as the anterior thalamus (AT), which encodes context-relevant information, including head direction. While we are beginning to understand the behavioral consequences of this exposure, the neural correlates of spatial cognition deficits are underexplored. Thus, we recorded a mixture of neurons from the AT and surrounding brain regions in mice with TTAE while they freely moved within a controlled environment. To model acute binge-like TTAE, C57BL/6J mice received 2 injections of 2.5 g/kg alcohol (or saline) on post-natal day (PND) 7. Subjects were then left undisturbed until the day of surgery as adults (>PND 60), when they were implanted with silicon or multi-wire arrays. Mice were placed in a circular 40 cm diameter arena under dim red light with 2 LED cues on the walls that rotated on a pseudo-random basis while electrophysiological data was recorded. Following data preprocessing, spike and waveform features were extracted from each putative single spiking unit. These features were reduced utilizing uniform manifold approximation and projection (UMAP). Following dimensionality reduction, we used agglomerative hierarchical clustering to find populations of neurons with similar features. Following this, we compared each feature based on treatment and found the features most important to disambiguate TTAE's impact on neural activity. TTAE was associated with decreases in mean firing rate, peak firing rate, rebound index, and tail decay constant, increases in alpha, peak to trough time, and repolarization time, and bidirectional differences as a function of neuronal subtype in burst index and rebound index. This suggests that TTAE produces long-lasting and fundamental differences in spiking features that can be observed in vivo and are amenable to intervention. Together, this dataset provides further clarifying criteria that can be utilized to diagnose and treat FASD.

The orbital motion of water particles under a progressive wave in shallow waters becomes a straight line parallel to the bottom, i.e., oscillatory motion, at the seabed. A new time-dependent boundary layer develops over the seabed for each half-cycle of this motion. The turbulent oscillatory wave boundary layer is of great importance in many engineering applications, especially in coastal engineering. Even though both laminar and turbulent regimes have been considered in oscillatory boundary layers, of particular interest is the transitional regime. The laminar-to-turbulent transition first occurs in the form of tiny turbulent patches close to the wall, called turbulent spots, just before the near-bed flow reversal. These coherent structures are arrowhead-shaped isolated areas where the flow bursts with intense oscillations, in an otherwise laminar boundary-layer flow (Sumer and Fuhrman, 2020). Single or multiple spikes in the bed shear stress signal reaching up to 3 or 4 times the magnitude of the bed shear stress is a good indicator of a turbulent spot (Carstensen et al., 2010). Although much experimental and numerical research has been conducted (e.g., Carstensen et al., 2010; Jensen et al., 1989), there are still many unanswered questions regarding the transition. This study aims to address these questions using the DNS method, which has become very popular in turbulence-related problems (e.g., Mazzuoli et al., 2011; Xiong et al., 2020), by focusing on observing turbulent spots and locating their birthplace, concurrently with the bed shear stress under the spot structure. The present study is being conducted in close collaboration with Professor Liang Cheng and Drs. Chengwang Xiong and Chengjiao Ren of the University of Western Australia. The study is only in the early stages, and some early results will be presented at the meeting.

Under in vivo conditions, CA1 pyramidal cells from the hippocampus display transitions from single spikes to bursts. It is believed that subthreshold hyperpolarization and depolarization, also known as down and up-states, play a pivotal role in these transitions. Nevertheless, a central impediment to correlating suprathreshold (spiking) and subthreshold activity has been the technical difficulties associated this type of recordings, even with widely used calcium imaging or multielectrode recordings. Recent work using voltage imaging with genetically encoded voltage indicators has been able to correlate spiking patterns with subthreshold activity in a variety of CA1 neurons, and recent computational models have been able to capture these transitions. In this work, we used a computational model of a CA1 pyramidal cell to investigate the role of intrinsic conductances and oscillatory patterns in generating down and up-states and their modulation in the transition from single spiking to bursting. Specifically, we observed the emergence of distinct spiking resonances between these two spiking modes that share the same voltage traces in the presence of theta or gamma oscillatory inputs, a phenomenon we call interleaved single and bursting spiking resonance. We noticed that these resonances do not necessarily overlap in frequency or amplitude, underscoring their relevance for providing flexibility to neural processing. We studied the conductance values of three current types that are thought to be critical for the bursting behavior: persistent sodium current (INaP) and its conductance GNaP, delayed rectifier potassium (IKDR) and its conductance GKDR, and hyperpolarization-activated current (Ih) and its conductance Gh. We conclude that the intricate interplay of ionic currents significantly influences the neuronal firing patterns, transitioning from single to burst firing during sustained depolarization. Specifically, the intermediate levels of GNaP and GKDR facilitate spiking resonance at gamma-frequency inputs. The resonance characteristics vary between single and burst firing modes, each displaying distinct amplitudes and resonant frequencies. Furthermore, low GNaP and high GKDR values lock bursting to theta frequencies, while high GNaP and low GKDR values lock single spiking to gamma frequencies. Lastly, the duration of quiet intervals plays a crucial role in determining the likelihood of transitioning to either bursting or single spiking modes. We confirmed that the same features were present in previously recorded in vivo voltage-imaging data. Understanding these dynamics provides valuable insights into the fundamental mechanisms underlying neuronal excitability under in vivo conditions.

Software Defined Networking (SDN) allows for more flexible network administration than traditional architectures. Software-defined networks (SDNs) efficiently manage data flows and optimize network resources. However, heterogeneity influences the quality of the services. (QoS) needs and network resource demands. They behave differently when traveling to their last point. Currently, numerous data center networks (DCNs) struggle with the unfair use of several network resources by big packets (Elephant flowing) arriving during any instant affecting specific flows (mice flow). Elephant Flows (EF) account for just a small percentage of the entire traffic. Nevertheless, they are considered Long-lasting (LLF) and often burn network resources. Their actions cause congestion and delays in most Mice Flows (MF). Forecasting and categorizing flow traffic is essential for optimal resource usage, QoS provisioning, and reducing network congestion and delays. This paper suggested a third-generation Single Spike Neural Network (SSNN) supervised learning approach using temporal coding to identify heterogeneous traffic. The classifier approach uses three features: flow time, byte rate, and packet rate. The SSNN is then taught to categorize the traffic into two classes. This training classifies two types of traffic: elephant and mice flow. The effectiveness of the algorithm was examined when classifying traffic using many metrics and its efficiency was proven as it was able to reduce the average error and its accuracy reached 99%. The suggested model's usefulness is demonstrated by its efficient training procedure, which provides rapid and accurate results.

Single-fiber electromyography is the most sensitive tool for diagnosing neuromuscular diseases but is limited in differentiating between presynaptic and postsynaptic neuromuscular junction involvement with increased jitter. With rising botulinum toxin (BoNT) use for therapeutic and cosmetic applications, referrals for electromyography because of myasthenia-like symptoms have increased, complicating differential diagnosis. This study examines whether spike count measurements from single-use concentric needle electrodes can distinguish BoNT effects from neuromuscular junction diseases such as myasthenia gravis. We analyzed 49 patients and 26 controls, assessing jitter and spike count with concentric needle electrodes in the frontalis muscle. Groups included those exposed to BoNT (>1 month and <1 month prior) and patients with myasthenia gravis, with normal jitter controls for comparison. Data were analyzed for jitter, spike count, and additional electrophysiologic parameters using standard statistical tests (P < 0.05). Results showed that spike counts were significantly different across the groups. Higher spike counts with lower single spike frequency were found in patients with BoNT exposure >1 month, suggesting a differentiation point from primary neuromuscular involvement. In contrast, patients with myasthenia gravis exhibited increased jitter without increased spike counts. These findings indicate that spike count analysis with concentric needle electrodes may aid in differentiating iatrogenic BoNT effects from primary neuromuscular junction disorders, such as myasthenia gravis. However, further studies with larger sample sizes are necessary to validate these results.

Efficient schemes for sampling from the eigenvalues of the Wishart distribution have recently been described for both the standard Wishart case (where the covariance matrix is the identity) and the spiked Wishart with a single spike (where the covariance matrix differs from the identity in a single entry on the diagonal). Here, we generalize these schemes to the spiked Wishart with an arbitrary number of spikes. This approach also applies to the spiked pseudo-Wishart distribution. We describe how to differentiate this procedure for the purposes of stochastic gradient descent, allowing the fitting of the eigenvalue distribution to some target distribution.