Firing Threshold Research Articles

DT-SCNN: dual-threshold spiking convolutional neural network with fewer operations and memory access for edge applications.

The spiking convolutional neural network (SCNN) is a kind of spiking neural network (SNN) with high accuracy for visual tasks and power efficiency on neuromorphic hardware, which is attractive for edge applications. However, it is challenging to implement SCNNs on resource-constrained edge devices because of the large number of convolutional operations and membrane potential (Vm) storage needed. Previous works have focused on timestep reduction, network pruning, and network quantization to realize SCNN implementation on edge devices. However, they overlooked similarities between spiking feature maps (SFmaps), which contain significant redundancy and cause unnecessary computation and storage. This work proposes a dual-threshold spiking convolutional neural network (DT-SCNN) to decrease the number of operations and memory access by utilizing similarities between SFmaps. The DT-SCNN employs dual firing thresholds to derive two similar SFmaps from one Vm map, reducing the number of convolutional operations and decreasing the volume of Vms and convolutional weights by half. We propose a variant spatio-temporal back propagation (STBP) training method with a two-stage strategy to train DT-SCNNs to decrease the inference timestep to 1. The experimental results show that the dual-thresholds mechanism achieves a 50% reduction in operations and data storage for the convolutional layers compared to conventional SCNNs while achieving not more than a 0.4% accuracy loss on the CIFAR10, MNIST, and Fashion MNIST datasets. Due to the lightweight network and single timestep inference, the DT-SCNN has the least number of operations compared to previous works, paving the way for low-latency and power-efficient edge applications.

Identification of forest fire points under clear sky conditions with Himawari-8 satellite data

ABSTRACT Since forest fire is one of the most dangerous natural disasters and presents serious threats to the local ecological environment, economic growth, and public safety, it is essential to carry out accurate and real-time forest fire monitoring. In this study, a forest fire monitoring method that combines a threshold-based algorithm and random forest (RF) model using Himawari-8 data is proposed. The threshold-based algorithm employs the solar zenith angle to adaptively determine the potential fire point judgement threshold to extract possible fire points. The RF model constructed with spectral features and spatio-temporal information is subsequently utilized to eliminate pseudo-fire points from the results of the threshold-based algorithm. To eliminate fire points in non-forest areas, post-processing is performed using land cover data. Five fire occurrence moments in the research area are selected to verify the identification accuracy. The results reveal that the overall accuracy and the overall comprehensive evaluation values are 97.36% and 0.913, respectively, which demonstrates that the proposed method is capable of accurately identifying forest fire points and providing an effective means for forest fire monitoring.

Thresholds and alternative states in a Neotropical dry forest in response to fire severity.

Neotropical xerophytic forest ecosystems evolved with fires that shaped their resilience to disturbance events. However, it is unknown whether forest resilience to fires persists under a new fire regime influenced by anthropogenic disturbance and climate change. We asked whether there was evidence for a fire severity threshold causing an abrupt transition from a forest to an alternative shrub thicket state in the presence of typical postfire management. We studied a heterogeneous wildfire event to assess medium-term effects (11 years) of varying fire severity in a xerophytic Caldén forest in central Argentina. We conducted vegetation surveys in patches that were exposed to low (LFS), medium (MFS), and high (HFS) fire severities but had similar prefire woody canopy cover. Satellite images were used to quantify fire severity using a delta Normalized Burning Ratio (dNBR) and to map prefire canopy cover. Postfire total woody canopy cover was higher in low and medium than high severity patches, but the understory woody component was highest in HFS patches. The density of woody plants was over three times higher under HFS than MFS and LFS due to the contribution of small woody plants to the total density. Unlike LFS and MFS patches, the small plants in HFS patches were persistent, multistem shrubs that resulted from the resprouting of top-killed Prosopis caldenia trees and, more importantly, from young shrubs that probably established after the wildfire. Our results suggest that the Caldén forest is resilient to fires of low to moderate severities but not to high-severity fires. Fire severities with dNBR values > ~600 triggered an abrupt transition to a shrub thicket state. Postfire grazing and controlled-fire treatments likely contributed to shrub dominance after high-severity wildfire. Forest to shrub thicket transitions enable recurring high-severity fire events. We propose that repeated fires combined with grazing can trap the system in a shrub thicket state. Further studies are needed to determine whether the relationships between fire and vegetation structure examined in this case study represent general mechanisms of irreversible state changes across the Caldenal forest region and whether analogous threshold relationships exist in other fire-prone woodland ecosystems.

Abnormal patterns of sleep and waking behaviors are accompanied by neocortical oscillation disturbances in an Ank3 mouse model of epilepsy-bipolar disorder comorbidity

ANK3 is a leading bipolar disorder (BD) candidate gene in humans and provides a unique opportunity for studying epilepsy-BD comorbidity. Previous studies showed that deletion of Ank3-1b, a BD-associated variant of Ank3 in mice leads to increased firing threshold and diminished action potential dynamic range of parvalbumin (PV) interneurons and absence epilepsy, thus providing a biological mechanism linking epilepsy and BD. To explore the behavioral overlap of these disorders, we characterized behavioral patterns of Ank3-1b KO mice during overnight home-cage activity and examined network activity during these behaviors using paired video and EEG recordings. Since PV interneurons contribute to the generation of high-frequency gamma oscillations, we anticipated changes in the power of neocortical EEG signals in the gamma frequency range (> 25 Hz) during behavioral states related to human BD symptoms, including abnormal sleep, hyperactivity, and repetitive behaviors. Ank3-1b KO mice exhibited an overall increase in slow gamma (~25-45 Hz) power compared to controls, and slow gamma power correlated with seizure phenotype severity across behaviors. During sleep, increased slow gamma power correlated with decreased time spent in the rapid eye movement (REM) stage of sleep. Seizures were more common during REM sleep compared to non-REM (NREM) sleep. We also found that Ank3-1b KO mice were hyperactive and exhibited a repetitive behavior phenotype that co-occurred with increased slow gamma power. Our results identify a novel EEG biomarker associating Ank3 genetic variation with BD and epilepsy and suggest modulation of gamma oscillations as a potential therapeutic target.

Valproic acid ameliorates cauda equina injury by suppressing HDAC2-mediated ferroptosis.

Persistent neuroinflammatory response after cauda equina injury (CEI) lowers nociceptor firing thresholds, accompanied by pathological pain and decreasing extremity dysfunction. Histone deacetylation has been considered a key regulator of immunity, inflammation, and neurological dysfunction. Our previous study suggested that valproic acid (VPA), a histone deacetylase inhibitor, exhibited neuroprotective effects in rat models of CEI, although the underlying mechanism remains elusive. The cauda equina compression surgery was performed to establish the CEI model. The Basso, Beattie, Bresnahan score, and the von Frey filament test were carried out to measure the animal behavior. Immunofluorescence staining of myelin basic protein and GPX4 was carried out. In addition, transmission electron microscope analysis was used to assess the effect of VPA on the morphological changes of mitochondria. RNA-sequencing was conducted to clarify the underlying mechanism of VPA on CEI protection. In this current study, we revealed that the expression level of HDAC1 and HDAC2 was elevated after cauda equina compression model but was reversed by VPA treatment. Meanwhile, HDAC2 knockdown resulted in the improvement of motor functions and pathologic pain, similar to treatment with VPA. Histology analysis also showed that knockdown of histone deacetylase (HDAC)-2, but not HDAC1, remarkably alleviated cauda equina injury and demyelinating lesions. The potential mechanism may be related to lowering oxidative stress and inflammatory response in the injured region. Notably, the transcriptome sequencing indicated that the therapeutic effect of VPA may depend on HDAC2-mediated ferroptosis. Ferroptosis-related genes were analyzed invivo and DRG cells further validated the reliability of RNA-sequencing results, suggesting HDAC2-H4K12ac axis participated in epigenetic modulation of ferroptosis-related genes. HDAC2 is critically involved in the ferroptosis and neuroinflammation in cauda equina injury, and VPA ameliorated cauda equina injury by suppressing HDAC2-mediated ferroptosis.

Design of spiking neural network architecture based on dendritic computation principles

The paper presents the hardware architecture design of a spiking neural network (SNN) based on dendritic computation principles. The integration of active dendritic properties into the neuronal structure of SNN aims to minimize the number of functional blocks required for hardware implementation, including synaptic connections and neurons. The available memory on the neuromorphic architecture imposes limitations on implementation, hence the need to reduce the number of functional blocks. As a test task for the SNN based on dendritic computations, we selected the image classification of eight symbols, consisting of digits one through eight. These symbols are depicted as 3×7 pixel, 1-bit images. Active dendritic properties were analyzed using the “delay plasticity” [1] principle, which introduces the mechanism of adjusting input signal delays in spiking neuron inputs. We designed an SNN model with complementary delay inputs, referred to as the active dendrite SNN, as a principle implementation. Input spikes arriving at the primary inputs are duplicated to the delay inputs after a modifiable time delay. For convenience, each delay input was set at a single value. The input images were scanned sequentially. The neural network received three direct and three inverse inputs from the six main inputs that were coded with spikes corresponding to three pixels of a string. An “on” pixel was coded with a spike arriving at a direct input, while an “off” pixel was coded with a spike arriving at the corresponding inverse input. The line scanning time was 10 μs, input width was 1 μs, and delay time was 5 μs. The optimization of spiking neuron parameters was performed through a stochastic search algorithm based on simulated annealing. The parameters optimized for the Leaky-Integrate-and-Fire (LIF) neurons included the leakage time constant (22.8 μs), firing threshold (1150 arbitrary units), and refractory period (1 μs). The active dendrite SNN training employed the tempotron learning rule [2]. The training optimized the following parameters: the maximum change in synaptic weight on potentiation and depression (0.7 and –3 arbitrary units, respectively) and the synaptic weight’s upper bound (195 arbitrary units). Complementary delayed inputs facilitated the learning of the order in which input patterns arrived for SNN neurons during training. The paper compares an SNN architecture based on dendritic computations to our previously designed two-layer SNN with a hidden perceptron layer and an output layer consisting of LIF neurons [3]. Using the same LIF neuron design, input image coding, and LIF neuron layer structure as in the proposed architecture, our two-layer SNN with a hidden perceptron layer and output layer of LIF neurons successfully recognized 3×5 images of three symbols with only 10 neurons and 63 synapses. Alternatively, the active dendrite SNN was able to recognize 3×7 images of eight symbols with four neurons and 48 synaptic weights. In conclusion, incorporating active dendrite properties into the SNN architecture for image recognition resulted in optimized functional block usage, lowering the number of neurons and synapses used by 60 and 24%, respectively.

Halide Perovskites-Based Diffusive Memristors for Artificial Mechano-Nociceptive System.

Numerous efforts for emulating organ systems comprised of multiple functional units have driven substantial advancements in bio-realistic electronics and systems. The resistance change behavior observed in diffusive memristors shares similarities with the potential change in biological neurons. Here, the diffusive threshold switching phenomenon in Ag-incorporated organometallic halide perovskites is utilized to demonstrate the functions of afferent neurons. Halide perovskites-based diffusive memristors show a low threshold voltage of ≈0.2V with little variation, attributed to the facile migration of Ag ions uniformly dispersed within the halide matrix. Based on the reversible and reliable volatile threshold switching, the memristors successfully demonstrate fundamental nociceptive functions including threshold firing, relaxation, and sensitization. Furthermore, to replicate the biological mechano-nociceptive phenomenon at a system level, an artificial mechano-nociceptive system is built by integrating a diffusive memristor with a force-sensing resistor. The presented system is capable of detecting and discerning the detrimental impact caused by a heavy steel ball, effectively exhibiting the corresponding sensitization response. By further extending the single nociceptive system into a 5 × 5 array, successful stereoscopic nociception of uneven impulses is achieved in the artificial skin system through array-scale sensitization. These results represent significant progress in the field of bio-inspired electronics and systems.

Grassland Resilience to Woody Encroachment in North America and the Effectiveness of Using Fire in National Parks

The grasslands of North America are threatened by woody encroachment. Restoring historical fire regimes has been used to manage brush encroachment. However, fire management may be insufficient due to the nonlinear and hysteretic responses of vegetation recovery following encroachment and the social–political constraints affecting fire management. We synthesized the fire thresholds required to control woody encroachment by typical encroaching species in North America, especially the Great Plains region, and identified the social–political constraints facing fire management in selected grassland national parks. Our synthesis revealed the resistance, hysteresis, and irreversibility of encroached grasslands using fire and emphasized the need for a combination of brush management methods if the impacts of climate change are to be addressed. Frequent fires alone may maintain grassland states, reflecting resistance. However, high-intensity fires exceeding fire-mortality thresholds are required to exclude non-resprouting shrubs and trees, indicating hysteresis. Fire alone may be insufficient to reverse encroachment by resprouting species, exhibiting reversibility. In practice, appropriate fire management may restore resistant grassland states. However, social–political constraints have restricted the use of frequent and high-intensity fires, thereby reducing the effectiveness of management actions to control woody encroachment of grasslands in national parks. This research proposes a resilience-based framework to manage woody encroachment in grassland national parks and similar protected areas.

Research on transformer fire alarm threshold based on multi-sensor data analysis

Based on literature, on-site investigation and accident case analysis, this paper selects multi-sensor for transformer fire alarm, builds the transformer fire test platform, and carries out the transformer fire test and interference test to obtain the training and test data of neural network data fusion. This paper determines the fire alarm threshold of multi-sensor signal. It provides a benchmark for comparing the alarm results of multi-sensor data simulation fusion with those of single sensor. This paper studies the preprocessing multi-sensor data, compiles the corresponding algorithm, and obtains the multi-sensor preprocessed data after dimension reduction and noise reduction. It provides data support for multi-sensor data fusion.

Optically-triggered deterministic spiking regimes in nanostructure resonant tunnelling diode-photodetectors

This work reports a nanostructure resonant tunnelling diode-photodetector (RTD-PD) device and demonstrates its operation as a controllable, optically-triggered excitable spike generator. The top contact layer of the device is designed with a nanopillar structure (500 nm in diameter) to restrain the injection current, yielding therefore lower energy operation for spike generation. We demonstrate experimentally the deterministic optical triggering of controllable and repeatable neuron-like spike patterns in the nanostructure RTD-PDs. Moreover, we show the device’s ability to deliver spiking responses when biased in either of the two regions adjacent to the negative differential conductance region, the so-called ‘peak’ and ‘valley’ points of the current–voltage (I–V) characteristic. This work also demonstrates experimentally key neuron-like dynamical features in the nanostructure RTD-PD, such as a well-defined threshold (in input optical intensity) for spike firing, as well as the presence of spike firing refractory time. The optoelectronic and chip-scale character of the proposed system together with the deterministic, repeatable and well controllable nature of the optically-elicited spiking responses render this nanostructure RTD-PD element as a highly promising solution for high-speed, energy-efficient optoelectronic artificial spiking neurons for novel light-enabled neuromorphic computing hardware.

TissueGene-C induces long-term analgesic effects through regulation of pain mediators and neuronal sensitization in a rat monoiodoacetate-induced model of osteoarthritis pain

TissueGene-C (TG-C), a combination of human allogeneic chondrocytes and irradiated GP2-293 cells engineered to overexpress transforming growth factor-β1 (TGF-β1), has been developed as a novel cell-based gene therapy and a candidate for disease modifying osteoarthritis drug (DMOAD). We aim to investigate analgesic mechanism of TG-C in a pre-clinical animal model with monoiodoacetate (MIA)-induced pain. We used a rat MIA model of osteoarthritis (OA) pain. We examined that TG-C can regulate pain by inhibiting the upregulation of various pain mediators in both knee joint tissue and dorsal root ganglia (DRG) (n=112) and alleviating pain behavior (n=41) and neuronal hyperexcitability in DRG (n=60), afferent nerve fiber (n=24), and spinal cord (n=35). TG-C significantly alleviated pain-related behavior by restoring altered dynamic weight bearing and reduced mechanical threshold of the affected hindlimb. TG-C significantly suppressed the expression of nerve growth factor (NGF) and calcitonin gene-related peptide (CGRP) in inflamed joint tissue. TG-C significantly suppressed the upregulation of tropomyosin receptor kinase A (TrkA) and nerve injury/regeneration protein (GAP43) and activation of Iba1-positive microglial cells in DRG. TG-C significantly recovered neuronal hyperexcitability by restoring RMP and firing threshold and frequency of DRG neurons, attenuating firing rates of mechanosensitive C- or Aδ-nerve fiber innervating knee joint, and lowering increased miniature and evoked excitatory postsynaptic currents (mEPSCs and eEPSCs) in the spinal cord. Our results demonstrated that TG-C exerted potent analgesic effects in a rat MIA model of OA pain by inhibiting the upregulation of pain mediators and modulating neuronal sensitization.

Understory fuel loads and the impact factors of Quercus mongolica natural secondary forest in Hebei Pro-vince, China.

We investigated understory fuel loads of Quercus mongolica natural secondary forests in Hebei Province, China. We analyzed the effects of stand factors, topographic factors, and ground cover factors on the quantity and composition of fuel, established the dynamic models of understory fuel loads, and proposed management measures. The results showed that the understory total fuel load in Q. mongolica natural secondary forests was 11.68 t·hm-2, which exceeded the forest fire potential threshold (10 t·hm-2). The understory dead fuel load was mainly humus, and the understory living fuel load was mainly shrubs. The 1 h time-lag fuel load increased significantly with increasing canopy density, stand density, stand age, and litter thickness. The 10 h time-lag fuel load increased signi-ficantly with increasing stand density, average tree height, and litter thickness. Humus load decreased significantly with increasing altitude and increased significantly with increasing humus thickness. Herb load increased significantly with increasing sunny slope orientation and herbal coverage. Shrub load increased significantly with increasing slope degree, shrub coverage, and humus thickness. Understory total fuel load decreased significantly with increasing altitude, and increased significantly with increasing stand density, humus thickness, and litter thickness. The results of stepwise regression analysis indicated that stand density, humus thickness, and altitude could better predict the understory total fuel load (Radj2=0.775). Therefore, more attention should be paid on the control of stand density of Q. mongolica natural secondary forest in Hebei Province. Cleaning of litters and humus on the ground would help prevent forest fires scientifically and effectively.

Quantitative assessment of fire occurrence Dead Fuel Index threshold and spatio-temporal variation in different grassland types of China-Mongolia border area

Quantitative assessment of fire occurrence Dead Fuel Index threshold and spatio-temporal variation in different grassland types of China-Mongolia border area

DIET-SNN: A Low-Latency Spiking Neural Network With Direct Input Encoding and Leakage and Threshold Optimization.

Bioinspired spiking neural networks (SNNs), operating with asynchronous binary signals (or spikes) distributed over time, can potentially lead to greater computational efficiency on event-driven hardware. The state-of-the-art SNNs suffer from high inference latency, resulting from inefficient input encoding and suboptimal settings of the neuron parameters (firing threshold and membrane leak). We propose DIET-SNN, a low-latency deep spiking network trained with gradient descent to optimize the membrane leak and the firing threshold along with other network parameters (weights). The membrane leak and threshold of each layer are optimized with end-to-end backpropagation to achieve competitive accuracy at reduced latency. The input layer directly processes the analog pixel values of an image without converting it to spike train. The first convolutional layer converts analog inputs into spikes where leaky-integrate-and-fire (LIF) neurons integrate the weighted inputs and generate an output spike when the membrane potential crosses the trained firing threshold. The trained membrane leak selectively attenuates the membrane potential, which increases activation sparsity in the network. The reduced latency combined with high activation sparsity provides massive improvements in computational efficiency. We evaluate DIET-SNN on image classification tasks from CIFAR and ImageNet datasets on VGG and ResNet architectures. We achieve top-1 accuracy of 69% with five timesteps (inference latency) on the ImageNet dataset with 12x less compute energy than an equivalent standard artificial neural network (ANN). In addition, DIET-SNN performs 20-500x faster inference compared to other state-of-the-art SNN models.

Exploring Adversarial Attack in Spiking Neural Networks With Spike-Compatible Gradient.

Spiking neural network (SNN) is broadly deployed in neuromorphic devices to emulate brain function. In this context, SNN security becomes important while lacking in-depth investigation. To this end, we target the adversarial attack against SNNs and identify several challenges distinct from the artificial neural network (ANN) attack: 1) current adversarial attack is mainly based on gradient information that presents in a spatiotemporal pattern in SNNs, hard to obtain with conventional backpropagation algorithms; 2) the continuous gradient of the input is incompatible with the binary spiking input during gradient accumulation, hindering the generation of spike-based adversarial examples; and 3) the input gradient can be all-zeros (i.e., vanishing) sometimes due to the zero-dominant derivative of the firing function. Recently, backpropagation through time (BPTT)-inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models accurately given spatiotemporal gradient maps. We propose two approaches to address the above challenges of gradient-input incompatibility and gradient vanishing. Specifically, we design a gradient-to-spike (G2S) converter to convert continuous gradients to ternary ones compatible with spike inputs. Then, we design a restricted spike flipper (RSF) to construct ternary gradients that can randomly flip the spike inputs with a controllable turnover rate, when meeting all-zero gradients. Putting these methods together, we build an adversarial attack methodology for SNNs. Moreover, we analyze the influence of the training loss function and the firing threshold of the penultimate layer on the attack effectiveness. Extensive experiments are conducted to validate our solution. Besides the quantitative analysis of the influence factors, we also compare SNNs and ANNs against adversarial attacks under different attack methods. This work can help reveal what happens in SNN attacks and might stimulate more research on the security of SNN models and neuromorphic devices.

Numerical simulation study of firing threshold of STN neuron stimulated by focused ultrasound.

Electroencephalography (EEG) is one of the functional brain imaging techniques to effectively measure neuronal activity, but its low spatial resolution makes it difficult to localize evoked excitatory neurons or areas of abnormal firing. Multimodal imaging techniques are expected to combine the high spatial resolution (mm level) of focused ultrasound (FUS) with the high temporal resolution (ms level) of EEG. The technique must be performed under the premise that ultrasound stimulation does not affect neuronal firing, and there is an urgent need to determine the threshold of this ultrasound stimulation parameter. In this paper, the subthalamic nucleus neuronal firing model and the bilayer sonophore model are combined to numerically simulate the neuronal firing rhythm under the conditions of different stimulation parameters. The correlation and frequency differences of neuronal firing rhythms with and without ultrasound stimulation were compared and used as an index to evaluate the degree of change, and the final range of effective threshold parameters for ultrasound stimulation of neurons but not inducing neuronal firing was obtained. The results showed that the correlation of neuronal firing rhythms in both conditions with and without stimulation decreased and the frequency difference increased with increasing ultrasound parameters such as duty cycle, intensity, center frequency and pulse repetition frequency. An effective range of stimulation threshold parameters can be obtained based on the correlation coefficients and frequency difference matrices under different parameter combinations. The threshold can further promote the safe and effective application of FUS for multimodal electrophysiological imaging.

Theory of the asynchronous state of structured rotator networks and its application to recurrent networks of excitatory and inhibitory units.

Recurrently coupled oscillators that are sufficiently heterogeneous and/or randomly coupled can show an asynchronous activity in which there are no significant correlations among the units of the network. The asynchronous state can nevertheless exhibit a rich temporal correlation statistics that is generally difficult to capture theoretically. For randomly coupled rotator networks, it is possible to derive differential equationsthat determine the autocorrelation functions of the network noise and of the single elements in the network. So far, the theory has been restricted to statistically homogeneous networks, making it difficult to apply this framework to real-world networks, which are structured with respect to the properties of the single units and their connectivity. A particularly striking case are neural networks for which one has to distinguish between excitatory and inhibitory neurons, which drive their target neurons towards or away from the firing threshold. To take into account network structures like that, here we extend the theory for rotator networks to the case of multiple populations. Specifically, we derive a system of differential equationsthat govern the self-consistent autocorrelation functions of the network fluctuations in the respective populations. We then apply this general theory to the special but important case of recurrent networks of excitatory and inhibitory units in the balanced case and compare our theory to numerical simulations. We inspect the effect of the network structure on the noise statistics by comparing our results to the case of an equivalent homogeneous network devoid of internal structure. Our results show that structured connectivity and heterogeneity of the oscillator type can both enhance or reduce the overall strength of the generated network noise and shape its temporal correlations.

Persistent inflammation selectively activates opioid-sensitive phasic-firing neurons within the vlPAG.

The ventrolateral periaqueductal gray (vlPAG) is a key brain area within the descending pain modulatory pathway and an important target for opioid-induced analgesia. The vlPAG contains heterogeneous neurons with respect to neurotransmitter content, receptor and channel expression, and in vivo response to noxious stimuli. This study characterizes intrinsic membrane properties of vlPAG neurons to identify neuron types that respond to inflammation and determine whether the pain-responsive neurons are inhibited by opioids. Surveying 382 neurons identified four neuron types with distinct intrinsic firing patterns: Phasic (48%), Tonic (33%), Onset (10%), and Random (9%). Mu-opioid receptor (MOR) expression was determined by the ability of a selective MOR agonist (DAMGO) to activate G protein-coupled inwardly rectifying potassium channel (GIRK) currents. Opioid-sensitive neurons were observed within each neuron type. Opioid sensitivity did not correlate with other intrinsic firing features, including low-threshold spiking that has been previously proposed to identify opioid-sensitive GABAergic neurons in the vlPAG of mice. Complete Freund's adjuvant (CFA)-induced acute inflammation (2 h) had no effect on vlPAG neuron firing patterns. However, persistent inflammation (5-7 days) selectively activated Phasic neurons through a significant reduction in their firing threshold. Opioid-sensitive neurons were strongly activated compared with the opioid-insensitive Phasic neurons. Overall, this study provides a framework to further identify neurons activated by persistent inflammation so that they may be targeted for future pain therapies.NEW & NOTEWORTHY Intrinsic firing properties define four distinct vlPAG neuron populations, and a subset of each population expresses MORs coupled to GIRK channels. Persistent, but not acute, inflammation selectively activates opioid-sensitive Phasic vlPAG neurons. Although the vlPAG is known to contribute to the descending inhibition of pain, the activation of a single physiologically defined neuron type in the presence of persistent inflammation represents a mechanism by which the vlPAG participates in descending facilitation of pain.

Influence of amyloid beta on impulse spiking of isolated hippocampal neurons.

One of the signs of Alzheimer's disease (AD) is the formation of β-amyloid plaques, which ultimately lead to the dysfunction of neurons with subsequent neurodegeneration. Although extensive researches have been conducted on the effects of different amyloid conformations such as oligomers and fibrils on neuronal function in isolated cells and circuits, the exact contribution of extracellular beta-amyloid on neurons remains incompletely comprehended. In our experiments, we studied the effect of β-amyloid peptide (Aβ1-42) on the action potential (APs) generation in isolated CA1 hippocampal neurons in perforated patch clamp conditions. Our findings demonstrate that Aβ1-42 affects the generation of APs differently in various hippocampal neurons, albeit with a shared effect of enhancing the firing response of the neurons within a minute of the start of Aβ1-42 application. In the first response type, there was a shift of 20-65% toward smaller values in the firing threshold of action potentials in response to inward current. Conversely, the firing threshold of action potentials was not affected in the second type of response to the application of Aβ1-42. In these neurons, Aβ1-42 caused a moderate increase in the frequency of spiking, up to 15%, with a relatively uniform increase in the frequency of action potentials generation regardless of the level of input current. Obtained data prove the absence of direct short-term negative effect of the Aβ1-42 on APs generation in neurons. Even with increasing the APs generation frequency and lowering the neurons' activation threshold, neurons were functional. Obtained data can suggest that only the long-acting presence of the Aβ1-42 in the cell environment can cause neuronal dysfunction due to a prolonged increase of APs firing and predisposition to this process.

The State-Dependent Effectiveness of Hiring Subsidies

The responsiveness of job creation to shocks is procyclical, while the responsiveness of job destruction is countercyclical. This new finding can be explained by a heterogeneous-firm model in which hiring costs lead to lumpy employment adjustment. The model predicts that policies that aim to stimulate employment by encouraging job creation, such as hiring subsidies, are significantly less effective in recessions: these are times when few firms are near their hiring thresholds and many firms are near their firing thresholds. Policies that target the job destruction margin, such as employment protection subsidies, are particularly effective at such times. (JEL E24, E32, H25, J23, J64)