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Since the spiking neural P system (SN P system) was proposed in 2006, it has become a research hotspot in the field of membrane computing. The SN P system performs computations through the encoding, processing, and transmission of spiking information and can be regarded as a third-generation neural network. As a variant of the SN P system, the global asynchronous numerical spiking neural P system (ANSN P system) is adaptable to a broader range of application scenarios. However, in biological neuroscience, some neurons work synchronously within a community to perform specific functions in the brain. Inspired by this, our work investigates a global asynchronous spiking neural P system (ANSN P system) that incorporates certain local synchronous neuron sets. Within these local synchronous sets, neurons must execute their production functions simultaneously, thereby reducing dependence on thresholds and enhancing control uncertainty in ANSN P systems. By analyzing the ADD, SUB, and FIN modules in the generating mode, as well as the INPUT and ADD modules in the accepting mode, this paper demonstrates the novel system's computational capacity as both a generator and an acceptor. Additionally, this paper compares each module to those in other SN P systems, considering the maximum number of neurons and rules per neuron. The results show that this new ANSN P system is at least as effective as the existing SN P systems.

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In interval reproduction tasks, animals must remember the event starting the interval and anticipate the time of the planned response to terminate the interval. The interval reproduction task thus allows for studying both memory for the past and anticipation of the future. We analyzed previously published recordings from the rodent medial prefrontal cortex [J. Henke et al., eLife10, e71612 (2021)] during an interval reproduction task and identified two cell groups by modeling their temporal receptive fields using hierarchical Bayesian models. The firing in the "past cells" group peaked at the start of the interval and relaxed exponentially back to baseline. The firing in the "future cells" group increased exponentially and peaked right before the planned action at the end of the interval. Contrary to the previous assumption that timing information in the brain has one or two time scales for a given interval, we found strong evidence for a continuous distribution of the exponential rate constants for both past and future cell populations. The real Laplace transformation of time predicts exponential firing with a continuous distribution of rate constants across the population. Therefore, the firing pattern of the past cells can be identified with the Laplace transform of time since the past event while the firing pattern of the future cells can be identified with the Laplace transform of time until the planned future event.

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Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available at neuroir.org.

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Fixational eye movements alter the number and timing of spikes transmitted from the retina to the brain, but whether these changes enhance or degrade the retinal signal is unclear. To quantify this, we developed a Bayesian method for reconstructing natural images from the recorded spikes of hundreds of retinal ganglion cells (RGCs) in the macaque retina (male), combining a likelihood model for RGC light responses with the natural image prior implicitly embedded in an artificial neural network optimized for denoising. The method matched or surpassed the performance of previous reconstruction algorithms, and provides an interpretable framework for characterizing the retinal signal. Reconstructions were improved with artificial stimulus jitter that emulated fixational eye movements, even when the eye movement trajectory was assumed to be unknown and had to be inferred from retinal spikes. Reconstructions were degraded by small artificial perturbations of spike times, revealing more precise temporal encoding than suggested by previous studies. Finally, reconstructions were substantially degraded when derived from a model that ignored cell-to-cell interactions, indicating the importance of stimulus-evoked correlations. Thus, fixational eye movements enhance the precision of the retinal representation.

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Cortical neurons exhibit temporally irregular spiking patterns and heterogeneous firing rates. These features arise in model circuits operating in a 'fluctuation-driven regime', in which fluctuations in membrane potentials emerge from the network dynamics. However, it is still debated whether the cortex operates in such a regime. We evaluated the fluctuation-driven hypothesis by analyzing spiking and sub-threshold membrane potentials of neurons in the frontal cortex of mice performing a decision-making task. We showed that while standard fluctuation-driven models successfully account for spiking statistics, they fall short in capturing the heterogeneity in sub-threshold activity. This limitation is an inevitable outcome of bombarding single-compartment neurons with a large number of pre-synaptic inputs, thereby clamping the voltage of all neurons to more or less the same average voltage. To address this, we effectively incorporated dendritic morphology into the standard models. Inclusion of dendritic morphology in the neuronal models increased neuronal selectivity and reduced error trials, suggesting a functional role for dendrites during decision-making. Our work suggests that, during decision-making, cortical neurons in high-order cortical areas operate in a fluctuation-driven regime.

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Alzheimer's disease (AD) leads to progressive memory decline, and alterations in hippocampal function are among the earliest pathological features observed in human and animal studies. GABAergic interneurons (INs) within the hippocampus coordinate network activity, among which type 3 interneuron-specific (I-S3) cells expressing vasoactive intestinal polypeptide and calretinin play a crucial role. These cells provide primarily disinhibition to principal excitatory cells (PCs) in the hippocampal CA1 region, regulating incoming inputs and memory formation. However, it remains unclear whether AD pathology induces changes in the activity of I-S3 cells, impacting the hippocampal network motifs. Here, using young adult 3xTg-AD mice, we found that while the density and morphology of I-S3 cells remain unaffected, there were significant changes in their firing output. Specifically, I-S3 cells displayed elongated action potentials and decreased firing rates, which was associated with a reduced inhibition of CA1 INs and their higher recruitment during spatial decision-making and object exploration tasks. Furthermore, the activation of CA1 PCs was also impacted, signifying early disruptions in CA1 network functionality. These findings suggest that altered firing patterns of I-S3 cells might initiate early-stage dysfunction in hippocampal CA1 circuits, potentially influencing the progression of AD pathology.

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INTRODUCTION: Reliable, noninvasive early diagnostics of neuromuscular function in Bell's palsy, which causes facial paralysis and reduced quality of life, remain to be established. Here, we aimed to evaluate the utility of the motor unit number index (MUNIX) for the quantitative electrophysiological assessment of early-stage Bell's palsy, its correlation with clinical assessments, changes following treatment, and association with clinical prognosis. METHODS: MUNIX measures were recorded from the bilateral zygomaticus, orbicularis oculi, and orbicularis oris muscles of 10 healthy individuals and 64 patients with Bell's palsy. The patients were assessed by two specialist neurologists using the House-Brackmann and Sunnybrook Facial Grading Systems. Repeat assessments were performed on 20 patients with Bell's palsy who received treatment. Additionally, the 64 patients were reassessed using clinical scales after a 1-month interval. RESULTS: The MUNIX values of the main affected muscles on the affected side were lower than those on the healthy side in patients with Bell's palsy (p < .05). The MUNIX measurements significantly correlated with the clinical facial nerve palsy scale scores (p < .05). Significant improvements were observed in the MUNIX values on repeat testing following treatment (p < .05). The baseline motor unit size index (the compound muscle action potential amplitude divided by MUNIX) was positively associated with improved clinical presentation after 1 month (p < .05). CONCLUSION: MUNIX can be used as an electrophysiological biomarker for the quantitative assessment of facial nerve palsy and treatment response, and as a prognostic biomarker, in patients with early Bell's palsy, and is recommended as a complement to conventional neurophysiological examinations.

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Aging is a physiological process that is still poorly understood, especially with respect to effects on the brain. There are open questions about aging that are difficult to answer with an experimental approach. Underlying challenges include the difficulty of recording in vivo single cell and network activity simultaneously with submillisecond resolution, and brain compensatory mechanisms triggered by genetic, pharmacologic, or behavioral manipulations. Mathematical modeling can help address some of these questions by allowing us to fix parameters that cannot be controlled experimentally and investigate neural activity under different conditions. We present a biophysical minimal model of CA1 pyramidal cells (PCs) based on general expressions for transmembrane ion transport derived from thermodynamical principles. The model allows directly varying the contribution of ion channels by changing their number. By analyzing the dynamics of the model, we find parameter ranges that reproduce the variability in electrical activity seen in PCs. In addition, increasing the L-type Ca2+ channel expression in the model reproduces age-related changes in electrical activity that are qualitatively and quantitatively similar to those observed in PCs from aged animals. We also make predictions about age-related changes in PC bursting activity that, to our knowledge, have not been reported previously. We conclude that the model's biophysical nature, flexibility, and computational simplicity make it a potentially powerful complement to experimental studies of aging.

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Lesion studies have historically been instrumental for establishing causal connections between brain and behavior. They stand to provide additional insight if integrated with multielectrode techniques common in systems neuroscience. Here, we present and test a platform for creating electrolytic lesions through chronically implanted, intracortical multielectrode probes without compromising the ability to acquire neuroelectrophysiology. A custom-built current source provides stable current and allows for controlled, repeatable lesions in awake-behaving animals. Performance of this novel lesioning technique was validated using histology from ex vivo and in vivo testing, current and voltage traces from the device, and measurements of spiking activity before and after lesioning. This electrolytic lesioning method avoids disruptive procedures, provides millimeter precision over the extent and submillimeter precision over the location of the injury, and permits electrophysiological recording of single-unit activity from the remaining neuronal population after lesioning. This technique can be used in many areas of cortex, in several species, and theoretically with any multielectrode probe. The low-cost, external lesioning device can also easily be adopted into an existing electrophysiology recording setup. This technique is expected to enable future causal investigations of the recorded neuronal population's role in neuronal circuit function, while simultaneously providing new insight into local reorganization after neuron loss.

Over the past three decades, the field of neuroscience has made significant leaps in understanding how the brain works. This is largely thanks to microelectrode arrays, devices which are surgically implanted into the outermost layer of the brain known as the cortex. Once inserted, these devices can precisely monitor the electrical activity of a few hundred neurons while also stimulating neurons to reversibly modulate their activity. However, current microelectrode arrays are missing a key function: they cannot irreversibly inactivate neurons over long-time scales. This ability would allow researchers to understand how networks of neurons adapt and re-organize after injury or during neurodegenerative diseases where brain cells are progressively lost. To address this limitation, Bray, Clarke, et al. developed a device capable of creating consistent amounts of neuron loss, while retaining the crucial ability to record electrical activity following a lesion. Calibration tests in sheep and pigs provided the necessary parameters for this custom circuit, which was then verified as safe in non-human primates. These experiments demonstrated that the device could effectively cause neuron loss without compromising the recording capabilities of the microelectrode array. By seamlessly integrating neuron inactivation with monitoring of neuronal activity, scientists can now investigate the direct effects of such damage and subsequent neural reorganization. This device could help neuroscientists to explore neural repair and rehabilitation after brain cell loss, which may lead to better treatments for neurodegenerative diseases. In addition, this technique could offer insights into the interactions between neural circuits that drive behavior, enhancing our understanding of the complex mechanisms underlying how the brain works.

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Objective: To investigate the action potential firing patterns of neurons in the visual sensory layers of the superior colliculus in early postnatal mice and the electrophysiological characteristics of neurons with different firing patterns. Methods: This experimental study utilized whole-cell patch-clamp recordings performed on neurons in the visual sensory layers of the superior colliculus using brain slices from 57 healthy male C57BL/6J mice aged 14 to 20 days (weighing 5.0 to 8.9 g) using brain slices. In current-clamp mode, action potential characteristics were analyzed based on the first action potential generated by depolarizing current, and the firing patterns of neurons were recorded using step depolarizing currents. Neuronal firing patterns were analyzed using hierarchical clustering, and the active electrical properties of neurons with different firing patterns were compared. Results: A total of 135 neurons from the visual sensory layers of the superior colliculus were successfully recorded. Cluster analysis of the neuronal firing patterns identified three types of firing patterns: tonic firing (97, 72%), phasic firing (26, 19%), and single firing (12, 9%). The number of action potentials for each firing pattern was 13.30±7.38, 3.73±3.61, and 0.83±0.39, respectively, with significant differences (P<0.001). There was no significant difference in the membrane potential response to step currents among the three firing pattern types (P>0.05). The action potential amplitudes were (60.45±12.22), (53.67±13.20), and (44.04± 12.92) mV, and the afterhyperpolarization amplitudes were (13.45±13.79), (12.02±13.11), and (20.75±2.85) mV, respectively. The maximum rising slopes were (171.29±77.46), (130.14±61.83), and (78.89±37.08) V/s, and the maximum falling slopes were (-76.33±33.61), (-68.17±31.65), and (-47.97±13.92) V/s, respectively, with all differences being statistically significant (all P<0.05). There were no significant differences in the resting membrane potential, action potential threshold, half-width, and afterhyperpolarization duration among the three firing pattern types (all P>0.05). Conclusions: In the early postnatal mice, neurons in the visual sensory layers of the superior colliculus exhibit three distinct firing patterns: tonic, phasic, and single firing. These firing pattern types show significant differences in action potential amplitude, afterhyperpolarization amplitude, maximum rising slopes, and maximum falling slopes.

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Cortical processing of auditory information can be affected by interspecies differences as well as brain states. Here we compare multifeature spectro-temporal receptive fields (STRFs) and associated input/output functions or nonlinearities (NLs) of neurons in primary auditory cortex (AC) of four mammalian species. Single-unit recordings were performed in awake animals (female squirrel monkeys, female, and male mice) and anesthetized animals (female squirrel monkeys, rats, and cats). Neuronal responses were modeled as consisting of two STRFs and their associated NLs. The NLs for the STRF with the highest information content show a broad distribution between linear and quadratic forms. In awake animals, we find a higher percentage of quadratic-like NLs as opposed to more linear NLs in anesthetized animals. Moderate sex differences of the shape of NLs were observed between male and female unanesthetized mice. This indicates that the core AC possesses a rich variety of potential computations, particularly in awake animals, suggesting that multiple computational algorithms are at play to enable the auditory system's robust recognition of auditory events.

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Revealing the relationship between neural network structure and function is one central theme of neuroscience. In the context of working memory (WM), anatomical data suggested that the topological structure of microcircuits within WM gradient network may differ, and the impact of such structural heterogeneity on WM activity remains unknown. Here, we proposed a spiking neural network model that can replicate the fundamental characteristics of WM: delay-period neural activity involves association cortex but not sensory cortex. First, experimentally observed receptor expression gradient along the WM gradient network is reproduced by our network model. Second, by analyzing the correlation between different local structures and duration of WM activity, we demonstrated that small-worldness, excitation-inhibition balance, and cycle structures play crucial roles in sustaining WM-related activity. To elucidate the relationship between the structure and functionality of neural networks, structural circuit gradients in brain should also be subject to further measurement. Finally, combining anatomical data, we simulated the duration of WM activity across different brain regions, its maintenance relies on the interaction between local and distributed networks. Overall, network structural gradient and interaction between local and distributed networks are of great significance for WM.

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Neural activity in the cortex exhibits a wide range of firing variability and rich correlation structures. Studies on neural coding indicate that correlated neural variability can influence the quality of neural codes, either beneficially or adversely. However, the mechanisms by which correlated neural variability is transformed and processed across neural populations to achieve meaningful computation remain largely unclear. Here we propose a theory of covariance computation with spiking neurons which offers a unifying perspective on neural representation and computation with correlated noise. We employ a recently proposed computational framework known as the moment neural network to resolve the nonlinear coupling of correlated neural variability with a task-driven approach to constructing neural network models for performing covariance-based perceptual tasks. In particular, we demonstrate how perceptual information initially encoded entirely within the covariance of upstream neurons' spiking activity can be passed, in a near-lossless manner, to the mean firing rate of downstream neurons, which in turn can be used to inform inference. The proposed theory of covariance computation addresses an important question of how the brain extracts perceptual information from noisy sensory stimuli to generate a stable perceptual whole and indicates a more direct role that correlated variability plays in cortical information processing.

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Prior studies have documented the role of the striatum and its dopaminergic input in time processing, but the contribution of local striatal cholinergic innervation has not been specifically investigated. To address this issue, we recorded the activity of tonically active neurons (TANs), thought to be cholinergic interneurons in the striatum, in two male macaques performing self-initiated movements after specified intervals in the seconds range have elapsed. The behavioral data showed that movement timing was adjusted according to the temporal requirements. About one-third of all recorded TANs displayed brief depressions in firing in response to the cue that indicates the interval duration, and the strength of these modulations was, in some instances, related to the timing of movement. The rewarding outcome of actions also impacted TAN activity, as reflected by stronger responses to the cue paralleled by weaker responses to reward when monkeys performed correctly timed movements over consecutive trials. It therefore appears that TAN responses may act as a start signal for keeping track of time and reward prediction could be incorporated in this signaling function. We conclude that the role of the striatal cholinergic TAN system in time processing is embedded in predicting rewarding outcomes during timing behavior.

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EMG filling curve characterizes the EMG filling process and EMG probability density function (PDF) shape change for the entire force range of a muscle. We aim to understand the relation between the physiological and recording variables, and the resulting EMG filling curves. We thereby present an analytical and simulation study to explain how the filling curve patterns relate to specific changes in the motor unit potential (MUP) waveforms and motor unit (MU) firing rates, the two main factors affecting the EMG PDF, but also to recording conditions in terms of noise level. We compare the analytical results with simulated cases verifying a perfect agreement with the analytical model. Finally, we present a set of real EMG filling curves with distinct patterns to explain the information about MUP amplitudes, MU firing rates, and noise level that these patterns provide in the light of the analytical study. Our findings reflect that the filling factor increases when firing rate increases or when newly recruited motor unit have potentials of smaller or equal amplitude than the former ones. On the other hand, the filling factor decreases when newly recruited potentials are larger in amplitude than the previous potentials. Filling curves are shown to be consistent under changes of the MUP waveform, and stretched under MUP amplitude scaling. Our findings also show how additive noise affects the filling curve and can even impede to obtain reliable information from the EMG PDF statistics.

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Inhibitory circuits in the mammalian olfactory bulb (OB) dynamically reformat olfactory information as it propagates from peripheral receptors to downstream cortex. To gain mechanistic insight into how specific OB interneuron types support this sensory processing, we examine unitary synaptic interactions between excitatory mitral and tufted cells (MTCs), the OB projection neurons, and a conserved population of anaxonic external plexiform layer interneurons (EPL-INs) using pair and quartet whole-cell recordings in acute mouse brain slices. Physiological, morphological, neurochemical, and synaptic analyses divide EPL-INs into distinct subtypes and reveal that parvalbumin-expressing fast-spiking EPL-INs (FSIs) perisomatically innervate MTCs with release-competent dendrites and synaptically detonate to mediate fast, short-latency recurrent and lateral inhibition. Sparse MTC synchronization supralinearly increases this high-fidelity inhibition, while sensory afferent activation combined with single-cell silencing reveals that individual FSIs account for a substantial fraction of total network-driven MTC lateral inhibition. OB output is thus powerfully shaped by detonation-driven high-fidelity perisomatic inhibition.

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Skeletal muscle contractions are initiated by action potentials, which are sensed by the voltage-gated calcium channel (CaV1.1) and are conformationally coupled to calcium release from intracellular stores. Notably, CaV1.1 contains four separate voltage-sensing domains (VSDs), which activate channel gating and excitation-contraction (EC-) coupling at different voltages and with distinct kinetics. Here we show that a single VSD of CaV1.1 controls skeletal muscle EC-coupling. Whereas mutations in VSDs I, II and IV affect the current properties but not EC-coupling, only mutations in VSD III alter the voltage-dependence of depolarization-induced calcium release. Molecular dynamics simulations reveal comprehensive, non-canonical state transitions of VSD III in response to membrane depolarization. Identifying the voltage sensor that activates EC-coupling and detecting its unique conformational changes opens the door to unraveling the downstream events linking VSD III motion to the opening of the calcium release channel, and thus resolving the signal transduction mechanism of skeletal muscle EC-coupling.

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Mechanosensitive ensembles of neurons in insects, known as chordotonal organs (COs), function in proprioception, the detection of sound and substrate vibrations. Here, we characterized the mechanical sensitivity of the lateral pentascolopidial CO (lch5) of Drosophila melanogaster larvae to establish its postulated role in proprioception. We developed a physiologically realistic method to replicate proprioceptive input to lch5 by pulling the apodeme (tendon) to which the tips of the neurons attach. We found that lch5 sensory neurons respond transiently with a short latency to the velocity component of stretch displacements and the release of stretch (relaxation). In the mechanosensory mutant inactive, lch5 has a decreased response to mechanical stimuli and a lower overall spontaneous spike rate. Finally, we simulated the input that lch5 receives during crawling and observed spike rate changes of peristaltic body contraction. We provide a characterization of proprioceptive feedback in D. melanogaster larvae and firmly establish the proprioceptive function of lch5 in larval locomotion.

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Ventral tegmental area (VTA) dopamine (DA) neurons have been found to substantially associate with post-traumatic stress disorder (PTSD) pathology, however, whether and how these DA neurons affect fear memory management in PTSD individuals remains largely unknown. In this study, we utilized auditory conditioned foot-shock to evaluate the fear memory retrieval and retention characteristics in a single prolonged stress-induced PTSD rat model. We employed chemogenetic technology to specifically activate VTA DA neurons to examine the freezing behaviors responding to the conditioned stimuli. In vivo extracellular electrophysiological analyses were used to identify VTA DA neuronal firing alterations due to the chemogenetic activation. The results demonstrated that PTSD model rats showed comparable fear memory retrieval (Day 2 after the conditioned foot-shock), but significant enhancements in fear memory retention (Day 8 after the conditioned foot-shock), compared to normal control rats. Chemogenetic activation of VTA DA neurons markedly diminished the retention of fear memory in PTSD model rats, which appeared concomitantly with increases in the firing activities of the DA neurons. These findings revealed that PTSD induced the persistence of fear memory, which could be attenuated by activation of VTA DA neurons. It is presumed that VTA dopaminergic signals may serve as a prospective option for PTSD treatment.

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BACKGROUND AND OBJECTIVES: Autoantibodies against the protein leucine-rich glioma inactivated 1 (LGI1) cause the most common subtype of autoimmune encephalitis with predominant involvement of the limbic system, associated with seizures and memory deficits. LGI1 and its receptor ADAM22 are part of a transsynaptic protein complex that includes several proteins involved in presynaptic neurotransmitter release and postsynaptic glutamate sensing. Autoantibodies against LGI1 increase excitatory synaptic strength, but studies that genetically disrupt the LGI1-ADAM22 complex report a reduction in postsynaptic glutamate receptor-mediated responses. Thus, the mechanisms underlying the increased synaptic strength induced by LGI1 autoantibodies remain elusive, and the contributions of presynaptic molecules to the LGI1-transsynaptic complex remain unclear. We therefore investigated the presynaptic mechanisms that mediate autoantibody-induced synaptic strengthening. METHODS: We studied the effects of patient-derived purified polyclonal LGI1 autoantibodies on synaptic structure and function by combining direct patch-clamp recordings from presynaptic boutons and somata of hippocampal neurons with super-resolution light and electron microscopy of hippocampal cultures and brain slices. We also identified the protein domain mediating the presynaptic effect using domain-specific patient-derived monoclonal antibodies. RESULTS: LGI1 autoantibodies dose-dependently increased short-term depression during high-frequency transmission, consistent with increased release probability. The increased neurotransmission was not related to presynaptic calcium channels because presynaptic Cav2.1 channel density, calcium current amplitude, and calcium channel gating were unaffected by LGI1 autoantibodies. By contrast, application of LGI1 autoantibodies homogeneously reduced Kv1.1 and Kv1.2 channel density on the surface of presynaptic boutons. Direct presynaptic patch-clamp recordings revealed that LGI1 autoantibodies cause a pronounced broadening of the presynaptic action potential. Domain-specific effects of LGI1 autoantibodies were analyzed at the neuronal soma. Somatic action potential broadening was induced by polyclonal LGI1 autoantibodies and patient-derived monoclonal autoantibodies targeting the epitempin domain, but not the leucin-rich repeat domain. DISCUSSION: Our results indicate that LGI1 autoantibodies reduce the density of both Kv1.1 and Kv1.2 on presynaptic boutons, without actions on calcium channel density or function, thereby broadening the presynaptic action potential and increasing neurotransmitter release. This study provides a molecular explanation for the neuronal hyperactivity observed in patients with LGI1 autoantibodies.

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