The impact of spike timing precision and spike emission reliability on decoding accuracy.

Precisely timed and reliably emitted spikes are hypothesized to serve multiple functions, including improving the accuracy and reproducibility of encoding stimuli, memories, or behaviours across trials. When these spikes occur as a repeating sequence, they can be used to encode and decode a potential time series. Here, we show both analytically and in simulations that the error incurred in approximating a time series with precisely timed and reliably emitted spikes decreases linearly with the number of neurons or spikes used in the decoding. This was verified numerically with synthetically generated patterns of spikes. Further, we found that if spikes were imprecise in their timing, or unreliable in their emission, the error incurred in decoding with these spikes would be sub-linear. However, if the spike precision or spike reliability increased with network size, the error incurred in decoding a time-series with sequences of spikes would maintain a linear decrease with network size. The spike precision had to increase linearly with network size, while the probability of spike failure had to decrease with the square-root of the network size. Finally, we identified a candidate circuit to test this scaling relationship: the repeating sequences of spikes with sub-millisecond precision in area HVC (proper name) of the zebra finch. This scaling relationship can be tested using both neural data and song-spectrogram-based recordings while taking advantage of the natural fluctuation in HVC network size due to neurogenesis.

Anti-Hebbian plasticity drives sequence learning in striatum.

Spatio-temporal activity patterns have been observed in a variety of brain areas in spontaneous activity, prior to or during action, or in response to stimuli. Biological mechanisms endowing neurons with the ability to distinguish between different sequences remain largely unknown. Learning sequences of spikes raises multiple challenges, such as maintaining in memory spike history and discriminating partially overlapping sequences. Here, we show that anti-Hebbian spike-timing dependent plasticity (STDP), as observed at cortico-striatal synapses, can naturally lead to learning spike sequences. We design a spiking model of the striatal output neuron receiving spike patterns defined as sequential input from a fixed set of cortical neurons. We use a simple synaptic plasticity rule that combines anti-Hebbian STDP and non-associative potentiation for a subset of the presented patterns called rewarded patterns. We study the ability of striatal output neurons to discriminate rewarded from non-rewarded patterns by firing only after the presentation of a rewarded pattern. In particular, we show that two biological properties of striatal networks, spiking latency and collateral inhibition, contribute to an increase in accuracy, by allowing a better discrimination of partially overlapping sequences. These results suggest that anti-Hebbian STDP may serve as a biological substrate for learning sequences of spikes.

Spiking activity in the visual thalamus is coupled to pupil dynamics across temporal scales.

The processing of sensory information, even at early stages, is influenced by the internal state of the animal. Internal states, such as arousal, are often characterized by relating neural activity to a single "level" of arousal, defined by a behavioral indicator such as pupil size. In this study, we expand the understanding of arousal-related modulations in sensory systems by uncovering multiple timescales of pupil dynamics and their relationship to neural activity. Specifically, we observed a robust coupling between spiking activity in the mouse dorsolateral geniculate nucleus (dLGN) of the thalamus and pupil dynamics across timescales spanning a few seconds to several minutes. Throughout all these timescales, 2 distinct spiking modes-individual tonic spikes and tightly clustered bursts of spikes-preferred opposite phases of pupil dynamics. This multi-scale coupling reveals modulations distinct from those captured by pupil size per se, locomotion, and eye movements. Furthermore, coupling persisted even during viewing of a naturalistic movie, where it contributed to differences in the encoding of visual information. We conclude that dLGN spiking activity is under the simultaneous influence of multiple arousal-related processes associated with pupil dynamics occurring over a broad range of timescales.

Interventricular septal thickness on cardiac computed tomography as a novel risk factor for conduction disturbances in patients undergoing transcatheter aortic valve replacement.

AIMS: We examined whether thickness of the basal muscular interventricular septum (IVS), as measured by pre-procedural computed tomography (CT), could be used to identify the risk of conduction disturbances following transcatheter aortic valve replacement (TAVR). The IVS is a pivotal region of the electrical conduction system of the heart where the atrioventricular conduction axis is located. METHODS AND RESULTS: Included were 78 patients with severe aortic stenosis who underwent CT imaging prior to TAVR. The thickness of muscular IVS was measured in the coronal view, in systolic phases, at 1, 2, 5, and 10 mm below the membranous septum (MS). The primary endpoint was a composite of conduction disturbance following TAVR. Conduction disturbances occurred in 24 out of 78 patients (30.8%). Those with conduction disturbances were significantly more likely to have a thinner IVS than those without conduction disturbances at every measured IVS level (2.98 ± 0.52 mm vs. 3.38 ± 0.52 mm, 4.10 ± 1.02 mm vs. 4.65 ± 0.78 mm, 6.11 ± 1.12 mm vs. 6.88 ± 1.03 mm, and 9.72 ± 1.95 mm vs. 10.70 ± 1.55 mm for 1, 2, 5 and 10 mm below MS, respectively, P < 0.05 for all). Multivariable logistic regression analysis showed that pre-procedural IVS thickness (<4 mm at 2 mm below the MS) was a significant independent predictor of post-procedural conduction disturbance (adjOR 7.387, 95% CI: 2.003-27.244, P = 0.003). CONCLUSION: Pre-procedural CT assessment of basal IVS thickness is a novel predictive marker for the risk of conduction disturbances following TAVR. The IVS thickness potentially acts as an anatomical barrier protecting the underlying conduction system from mechanical compression during TAVR.

The quality and complexity of pairwise maximum entropy models for large cortical populations.

We investigate the ability of the pairwise maximum entropy (PME) model to describe the spiking activity of large populations of neurons recorded from the visual, auditory, motor, and somatosensory cortices. To quantify this performance, we use (1) Kullback-Leibler (KL) divergences, (2) the extent to which the pairwise model predicts third-order correlations, and (3) its ability to predict the probability that multiple neurons are simultaneously active. We compare these with the performance of a model with independent neurons and study the relationship between the different performance measures, while varying the population size, mean firing rate of the chosen population, and the bin size used for binarizing the data. We confirm the previously reported excellent performance of the PME model for small population sizes N < 20. But we also find that larger mean firing rates and bin sizes generally decreases performance. The performance for larger populations were generally not as good. For large populations, pairwise models may be good in terms of predicting third-order correlations and the probability of multiple neurons being active, but still significantly worse than small populations in terms of their improvement over the independent model in KL-divergence. We show that these results are independent of the cortical area and of whether approximate methods or Boltzmann learning are used for inferring the pairwise couplings. We compared the scaling of the inferred couplings with N and find it to be well explained by the Sherrington-Kirkpatrick (SK) model, whose strong coupling regime shows a complex phase with many metastable states. We find that, up to the maximum population size studied here, the fitted PME model remains outside its complex phase. However, the standard deviation of the couplings compared to their mean increases, and the model gets closer to the boundary of the complex phase as the population size grows.

Drift of neural ensembles driven by slow fluctuations of intrinsic excitability.

Representational drift refers to the dynamic nature of neural representations in the brain despite the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The model predicts that drift is induced by co-activation of previously active neurons along with neurons with high excitability which leads to remodeling of the recurrent weights. Consistent with previous experimental works, the drifting ensemble is informative about its temporal history. Crucially, we show that the gradual nature of the drift is necessary for decoding temporal information from the activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an output neuron having plastic synapses with the main region.

Spike timing-based coding in neuromimetic tactile system enables dynamic object classification.

Rapid processing of tactile information is essential to human haptic exploration and dexterous object manipulation. Conventional electronic skins generate frames of tactile signals upon interaction with objects. Unfortunately, they are generally ill-suited for efficient coding of temporal information and rapid feature extraction. In this work, we report a neuromorphic tactile system that uses spike timing, especially the first-spike timing, to code dynamic tactile information about touch and grasp. This strategy enables the system to seamlessly code highly dynamic information with millisecond temporal resolution on par with the biological nervous system, yielding dynamic extraction of tactile features. Upon interaction with objects, the system rapidly classifies them in the initial phase of touch and grasp, thus paving the way to fast tactile feedback desired for neuro-robotics and neuro-prosthetics.

Cardiac function is regulated by the sodium-dependent inhibition of the sodium-calcium exchanger NCX1.

The Na+-Ca2+ exchanger (NCX1) is the dominant Ca2+ extrusion mechanism in cardiac myocytes. NCX1 activity is inhibited by intracellular Na+ via a process known as Na+-dependent inactivation. A central question is whether this inactivation plays a physiological role in heart function. Using CRISPR/Cas9, we inserted the K229Q mutation in the gene (Slc8a1) encoding for NCX1. This mutation removes the Na+-dependent inactivation while preserving transport properties and other allosteric regulations. NCX1 mRNA levels, protein expression, and protein localization are unchanged in K229Q male mice. However, they exhibit reduced left ventricular ejection fraction and fractional shortening, while displaying a prolonged QT interval. K229Q ventricular myocytes show enhanced NCX1 activity, resulting in action potential prolongation, higher incidence of aberrant action potentials, a faster decline of Ca2+ transients, and depressed cell shortening. The results demonstrate that NCX1 Na+-dependent inactivation plays an essential role in heart function by affecting both cardiac excitability and contractility.

An artificial visual neuron with multiplexed rate and time-to-first-spike coding.

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware.

Spiking Neural Membrane Systems with Adaptive Synaptic Time Delay.

Spiking neural membrane systems (or spiking neural P systems, SNP systems) are a new type of computation model which have attracted the attention of plentiful scholars for parallelism, time encoding, interpretability and extensibility. The original SNP systems only consider the time delay caused by the execution of rules within neurons, but not caused by the transmission of spikes via synapses between neurons and its adaptive adjustment. In view of the importance of time delay for SNP systems, which are a time encoding computation model, this study proposes SNP systems with adaptive synaptic time delay (ADSNP systems) based on the dynamic regulation mechanism of synaptic transmission delay in neural systems. In ADSNP systems, besides neurons, astrocytes that can generate adenosine triphosphate (ATP) are introduced. After receiving spikes, astrocytes convert spikes into ATP and send ATP to the synapses controlled by them to change the synaptic time delays. The Turing universality of ADSNP systems in number generating and accepting modes is proved. In addition, a small universal ADSNP system using 93 neurons and astrocytes is given. The superiority of the ADSNP system is demonstrated by comparison with the six variants. Finally, an ADSNP system is constructed for credit card fraud detection, which verifies the feasibility of the ADSNP system for solving real-world problems. By considering the adaptive synaptic delay, ADSNP systems better restore the process of information transmission in biological neural networks, and enhance the adaptability of SNP systems, making the control of time more accurate.

Assessing Rat Diaphragm Motor Unit Connectivity Outcome Measures as Quantitative Biomarkers of Phrenic Motor Neuron Degeneration and Compensation.

Loss of ventilatory muscle function is a consequence of motor neuron injury and neurodegeneration (e.g., cervical spinal cord injury and amyotrophic lateral sclerosis, respectively). Phrenic motor neurons are the final link between the central nervous system and muscle, and their respective motor units (groups of muscle fibers innervated by a single motor neuron) represent the smallest functional unit of the neuromuscular ventilatory system. Compound muscle action potential (CMAP), single motor unit potential (SMUP), and motor unit number estimation (MUNE) are established electrophysiological approaches that enable the longitudinal assessment of motor unit integrity in animal models over time but have mostly been applied to limb muscles. Therefore, the objectives of this study are to describe an approach in preclinical rodent studies that can be used longitudinally to quantify the phrenic MUNE, motor unit size (represented as SMUP), and CMAP, and then to demonstrate the utility of these approaches in a motor neuron loss model. Sensitive, objective, and translationally relevant biomarkers for neuronal injury, degeneration, and regeneration in motor neuron injury and diseases can significantly aid and accelerate experimental research discoveries to clinical testing.

Chronic pain enhances excitability of corticotropin-releasing factor-expressing neurons in the oval part of the bed nucleus of the stria terminalis.

We previously reported that enhanced corticotropin-releasing factor (CRF) signaling in the bed nucleus of the stria terminalis (BNST) caused the aversive responses during acute pain and suppressed the brain reward system during chronic pain. However, it remains to be examined whether chronic pain alters the excitability of CRF neurons in the BNST. In this study we investigated the chronic pain-induced changes in excitability of CRF-expressing neurons in the oval part of the BNST (ovBNSTCRF neurons) by whole-cell patch-clamp electrophysiology. CRF-Cre; Ai14 mice were used to visualize CRF neurons by tdTomato. Electrophysiological recordings from brain slices prepared from a mouse model of neuropathic pain revealed that rheobase and firing threshold were significantly decreased in the chronic pain group compared with the sham-operated control group. Firing rate of the chronic pain group was higher than that of the control group. These data indicate that chronic pain elevated neuronal excitability of ovBNSTCRF neurons.

Subthalamic nucleus synchronization between beta band local field potential and single-unit activity in Parkinson's disease.

Local field potential (LFP) oscillations in the beta band (13-30 Hz) in the subthalamic nucleus (STN) of Parkinson's disease patients have been implicated in disease severity and treatment response. The relationship between single-neuron activity in the STN and regional beta power changes remains unclear. We used spike-triggered average (STA) to assess beta synchronization in STN. Beta power and STA magnitude at the beta frequency range were compared in three conditions: STN versus other subcortical structures, dorsal versus ventral STN, and high versus low beta power STN recordings. Magnitude of STA-LFP was greater within the STN compared to extra-STN structures along the trajectory path, despite no difference in percentage of the total power. Within the STN, there was a higher percent beta power in dorsal compared to ventral STN but no difference in STA-LFP magnitude. Further refining the comparison to high versus low beta peak power recordings inside the STN to evaluate if single-unit activity synchronized more strongly with beta band activity in areas of high beta power resulted in a significantly higher STA magnitude for areas of high beta power. Overall, these results suggest that STN single units strongly synchronize to beta activity, particularly units in areas of high beta power.

Modeling the effect of magnetoelectric nanoparticles on neuronal electrical activity: An analog circuit approach.

This paper introduces a physical neuron model that incorporates magnetoelectric nanoparticles (MENPs) as an essential electrical circuit component to wirelessly control local neural activity. Availability of such a model is important as MENPs, due to their magnetoelectric effect, can wirelessly and noninvasively modulate neural activity, which, in turn, has implications for both finding cures for neurological diseases and creating a wireless noninvasive high-resolution brain-machine interface. When placed on a neuronal membrane, MENPs act as magnetic-field-controlled finite-size electric dipoles that generate local electric fields across the membrane in response to magnetic fields, thus allowing to controllably activate local ion channels and locally initiate an action potential. Herein, the neuronal electrical characteristic description is based on ion channel activation and inhibition mechanisms. A MENP-based memristive Hodgkin-Huxley circuit model is extracted by combining the Hodgkin-Huxley model and an equivalent circuit model for a single MENP. In this model, each MENP becomes an integral part of the neuron, thus enabling wireless local control of the neuron's electric circuit itself. Furthermore, the model is expanded to include multiple MENPs to describe collective effects in neural systems.

Role of tactile stimulation during the preweaning period on the development of the peripheral sensory sural (SU) nerve in adult artificially reared female rat.

Maternal deprivation, as a result of the artificial rearing (AR) paradigm, disturbs electrophysiological and histological characteristics of the peripheral sensory sural (SU) nerve of infant and adult male rats. Such changes are prevented by providing tactile or social stimulation during isolation. AR also affects the female rat's brain and behavior; however, it is unknown whether this early adverse experience also alters their SU nerve development or if tactile stimulation might prevent these possible developmental effects. To assess these possibilities, the electrophysiological and histological characteristics of the SU nerve from adult diestrus AR female rats that: (i) received no tactile stimulation (AR group), (ii) received tactile stimulation in the anogenital and body area (AR-Tactile group), or (iii) were mother reared (MR group) were determined. We found that the amplitude, but not the area, of the evoked compound action potential response in SU nerves of AR rats was lower than those of SU nerves of MR female rats. Tactile stimulation prevented these effects. Additionally, we found a reduction in the outer diameter and myelin thickness of axons, as well as a large proportion of axons with low myelin thickness in nerves of AR rats compared to the nerves of the MR and AR-Tactile groups of rats; however, tactile stimulation only partially prevented these effects. Our data indicate that maternal deprivation disturbs the development of sensory SU nerves in female rats, whereas tactile stimulation partially prevents the changes generated by AR. Considering that our previous studies have shown more severe effects of AR on male SU nerve development, we suggest that sex-associated factors may be involved in these processes.

Interdependence of cellular and network properties in respiratory rhythm generation.

How breathing is generated by the preBötzinger complex (preBötC) remains divided between two ideological frameworks, and a persistent sodium current (INaP) lies at the heart of this debate. Although INaP is widely expressed, the pacemaker hypothesis considers it essential because it endows a small subset of neurons with intrinsic bursting or "pacemaker" activity. In contrast, burstlet theory considers INaP dispensable because rhythm emerges from "preinspiratory" spiking activity driven by feed-forward network interactions. Using computational modeling, we find that small changes in spike shape can dissociate INaP from intrinsic bursting. Consistent with many experimental benchmarks, conditional effects on spike shape during simulated changes in oxygenation, development, extracellular potassium, and temperature alter the prevalence of intrinsic bursting and preinspiratory spiking without altering the role of INaP. Our results support a unifying hypothesis where INaP and excitatory network interactions, but not intrinsic bursting or preinspiratory spiking, are critical interdependent features of preBötC rhythmogenesis.

Use of three-dimensional electroanatomic mapping for epicardial access: needle tracking, electrographic characteristics, and clinical application.

AIMS: Pericardiocentesis is usually completed under fluoroscopy. The electroanatomic mapping (EAM) system allows visualizing puncture needle tip (NT) while displaying the electrogram recorded from NT, making it possible to obtain epicardial access (EA) independent of fluoroscopy. This study was designed to establish and validate a technique by which EA is obtained under guidance of three-dimensional (3D) EAM combined with NT electrogram. METHODS AND RESULTS: 3D shell of the heart was generated, and the NT was made trackable in the EAM system. Unipolar NT electrogram was continuously monitored. Penetration into pericardial sac was determined by an increase in NT potential amplitude and an injury current. A long guidewire of which the tip was also visible in the EAM system was advanced to confirm EA. Epicardial access was successfully obtained without complication in 13 pigs and 22 patients. In the animals, NT potential amplitude was 3.2 ± 1.0 mV when it was located in mediastinum, 5.2 ± 1.6 mV when in contact with fibrous pericardium, and 9.8 ± 2.8 mV after penetrating into pericardial sac (all P ≤ 0.001). In human subjects, it measured 1.54 ± 0.40 mV, 3.61 ± 1.08 mV, and 7.15 ± 2.88 mV, respectively (all P < 0.001). Fluoroscopy time decreased in every 4-5 cases (64 ± 15, 23 ± 17, and 0 s for animals 1-4, 5-8, 9-13, respectively, P = 0.01; 44 ± 23, 31 ± 18, 4±7 s for patients 1-7, 8-14, 15-22, respectively, P < 0.001). In five pigs and seven patients, EA was obtained without X-ray exposure. CONCLUSION: By tracking NT in the 3D EAM system and continuously monitoring the NT electrogram, it is feasible and safe to obtain EA with minimum or no fluoroscopic guidance.

Morphological and electrophysiological characterization of a novel displaced astrocyte in the mouse retina.

Astrocytes throughout the central nervous system are heterogeneous in both structure and function. This diversity leads to tissue-specific specialization where morphology is adapted to the surrounding neuronal circuitry, as seen in Bergman glia of the cerebellum and Müller glia of the retina. Because morphology can be a differentiating factor for cellular classification, we recently developed a mouse where glial-fibrillary acidic protein (GFAP)-expressing cells stochastically label for full membranous morphology. Here we utilize this tool to investigate whether morphological and electrophysiological features separate types of mouse retinal astrocytes. In this work, we report on a novel glial population found in the inner plexiform layer and ganglion cell layer which expresses the canonical astrocyte markers GFAP, S100ß, connexin-43, Sox2 and Sox9. Apart from their retinal layer localization, these cells are unique in their radial distribution. They are notably absent from the mid-retina but are heavily concentrated near the optic nerve head, and to a lesser degree the peripheral retina. Additionally, their morphology is distinct from both nerve fiber layer astrocytes and Müller glia, appearing more similar to amacrine cells. Despite this structural similarity, these cells lack protein expression of common neuronal markers. Additionally, they do not exhibit action potentials, but rather resemble astrocytes and Müller glia in their small amplitude, graded depolarization to both light onset and offset. Their structure, protein expression, physiology, and intercellular connections suggest that these cells are astrocytic, displaced from their counterparts in the nerve fiber layer. As such, we refer to these cells as displaced retinal astrocytes.

Neural populations within macaque early vestibular pathways are adapted to encode natural self-motion.

How the activities of large neural populations are integrated in the brain to ensure accurate perception and behavior remains a central problem in systems neuroscience. Here, we investigated population coding of naturalistic self-motion by neurons within early vestibular pathways in rhesus macaques (Macacca mulatta). While vestibular neurons displayed similar dynamic tuning to self-motion, inspection of their spike trains revealed significant heterogeneity. Further analysis revealed that, during natural but not artificial stimulation, heterogeneity resulted primarily from variability across neurons as opposed to trial-to-trial variability. Interestingly, vestibular neurons displayed different correlation structures during naturalistic and artificial self-motion. Specifically, while correlations due to the stimulus (i.e., signal correlations) did not differ, correlations between the trial-to-trial variabilities of neural responses (i.e., noise correlations) were instead significantly positive during naturalistic but not artificial stimulation. Using computational modeling, we show that positive noise correlations during naturalistic stimulation benefits information transmission by heterogeneous vestibular neural populations. Taken together, our results provide evidence that neurons within early vestibular pathways are adapted to the statistics of natural self-motion stimuli at the population level. We suggest that similar adaptations will be found in other systems and species.

GLP-1 modulated the firing activity of nigral dopaminergic neurons in both normal and parkinsonian mice.

The spontaneous firing activity of nigral dopaminergic neurons is associated with some important roles including modulation of dopamine release, expression of tyrosine hydroxylase (TH), as well as neuronal survival. The decreased neuroactivity of nigral dopaminergic neurons has been revealed in Parkinson's disease. Central glucagon-like peptide-1 (GLP-1) functions as a neurotransmitter or neuromodulator to exert multiple brain functions. Although morphological studies revealed the expression of GLP-1 receptors (GLP-1Rs) in the substantia nigra pars compacta, the possible modulation of GLP-1 on spontaneous firing activity of nigral dopaminergic neurons is unknown. The present extracellular in vivo single unit recordings revealed that GLP-1R agonist exendin-4 significantly increased the spontaneous firing rate and decreased the firing regularity of partial nigral dopaminergic neurons of adult male C57BL/6 mice. Blockade of GLP-1Rs by exendin (9-39) decreased the firing rate of nigral dopaminergic neurons suggesting the involvement of endogenous GLP-1 in the modulation of firing activity. Furthermore, the PKA and the transient receptor potential canonical (TRPC) 4/5 channels are involved in activation of GLP-1Rs-induced excitatory effects of nigral dopaminergic neurons. Under parkinsonian state, both the exogenous and endogenous GLP-1 could still induce excitatory effects on the surviving nigral dopaminergic neurons. As the mild excitatory stimuli exert neuroprotective effects on nigral dopaminergic neurons, the present GLP-1-induced excitatory effects may partially contribute to its antiparkinsonian effects.