Firing Threshold Research Articles

Threshold‐type memristor‐based memory circuit

SummaryMemory is of central importance to the research of high‐level cognitive behaviors of humans, where the plastic synapse contributes to the memory formation. Due to the controllable conductance and high‐density integration, the memristor acts as a promising candidate of artificial synapse. In terms of memory retention time, memory contains short‐term memory (STM) and long‐term memory (LTM). Additionally, the STM can be changed to the LTM via repeated learning, and this process is called memory consolidation. A threshold‐type memristor combined with basic operational circuits is used to design a memory circuit that can emulate various memory hierarchy levels containing the STM, the LTM, and the STM‐to‐LTM transition. Threshold firing behavior can also be emulated because of the threshold characteristic of the memristor. Furthermore, a floating emulator circuit that consists of several off‐the‐shelf devices is developed to mimic the electrical behaviors of the threshold‐type memristor, and its hardware circuit implementation is presented. Finally, both simulation and experimental results validate the feasibility and effectiveness of the proposed memory circuit.

Computational model investigating the effect of magnetic field on neural–astrocyte microcircuit* *Project supported by the National Natural Science Foundation of China (Grant No. 61673158) and the Youth Talent Support Program of Hebei Province, China (Grant No. BJ2019044).

Extremely low-frequency magnetic field is widely used as a noninvasive stimulation method in clinical practice and basic research. Electrical field induced from magnetic pulse can decrease or increase neuronal electrical activity. However, the cellular mechanism underlying the effects of magnetic field is not clear from experimental data. Recent studies have demonstrated that “non-neuronal” cells, especially astrocytes, may be the potential effector for transcranial magnetic stimulation (TMS). In the present study, we implemented a neural–astrocyte microcircuit computational model based on hippocampal architecture to investigate the biological effects of different magnetic field frequencies on cells. The purpose of the present study is to elucidate the main influencing factors of MS to allow a better understanding of its mechanisms. Our model reproduced the basic characteristics of the neuron and astrocyte response to different magnetic stimulation. The results predict that interneurons with lower firing thresholds were more active in magnetic fields by contrast to pyramidal neurons. And the synaptic coupling strength between the connected neurons may be one of the critical factor to affect the effect of magnetic field on cells. In addition, the simulations show that astrocytes can decrease or increase slow inward currents (SICs) to finely tune neuronal excitation, which suggests their key role in excitatory–inhibitory balance. The interaction between neurons and astrocytes may represent a novel target for effective therapeutic strategies involving magnetic stimulation.

The time-course of prediction formation and revision in human visual motion processing

Establishing the real-time position of a moving object poses a challenge to the visual system due to neural processing delays. While sensory information is travelling through the visual hierarchy, the object continues moving and information about its position becomes outdated. By extrapolating the position of a moving object along its trajectory, predictive mechanisms might effectively decrease the processing time associated with these objects. Here, we use time-resolved decoding of electroencephalographic (EEG) data from an apparent motion paradigm to demonstrate the interaction of two separate predictive mechanisms. First, we reveal predictive latency advantages for position representations as soon as the second object in an apparent motion sequence – even before the stimulus contains any physical motion energy. This is consistent with the existence of omni-directional, within-layer waves of sub-threshold activity that bring neurons coding for adjacent positions closer to their firing threshold, thereby reducing the processing time of the second stimulus in one of those positions. Second, we show that an additional direction-specific latency advantage emerges from the third sequence position onward, once the direction of the apparent motion stimulus is uniquely determined. Because the receptive fields of early visual areas are too small to encompass sequential apparent motion positions (as evidenced by the lack of latency modulation for the second stimulus position), this latency advantage most likely arises from descending predictions from higher to lower visual areas through feedback connections. Finally, we reveal that the same predictive activation that facilitates the processing of the object in its expected position needs to be overcome when the object's trajectory unexpectedly reverses, causing an additional latency disadvantage for stimuli that violate predictions. Altogether, our results suggest that two complementary mechanisms interact to form and revise predictions in visual motion processing, modulating the latencies of neural position representations at different levels of visual processing.

Regular spiking in high-conductance states: The essential role of inhibition.

Strong inhibitory input to neurons, which occurs in balanced states of neural networks, increases synaptic current fluctuations. This has led to the assumption that inhibition contributes to the high spike-firing irregularity observed in vivo. We used single compartment neuronal models with time-correlated (due to synaptic filtering) and state-dependent (due to reversal potentials) input to demonstrate that inhibitory input acts to decrease membrane potential fluctuations, a result that cannot be achieved with simplified neural input models. To clarify the effects on spike-firing regularity, we used models with different spike-firing adaptation mechanisms, and we observed that the addition of inhibition increased firing regularity in models with dynamic firing thresholds and decreased firing regularity if spike-firing adaptation was implemented through ionic currents or not at all. This fluctuation-stabilization mechanism provides an alternative perspective on the importance of strong inhibitory inputs observed in balanced states of neural networks, and it highlights the key roles of biologically plausible inputs and specific adaptation mechanisms in neuronal modeling.

A new recursive least squares-based learning algorithm for spiking neurons

Spiking neural networks (SNNs) are regarded as effective models for processing spatio-temporal information. However, their inherent complexity of temporal coding makes it an arduous task to put forward an effective supervised learning algorithm, which still puzzles researchers in this area. In this paper, we propose a Recursive Least Squares-Based Learning Rule (RLSBLR) for SNN to generate the desired spatio-temporal spike train. During the learning process of our method, the weight update is driven by the cost function defined by the difference between the membrane potential and the firing threshold. The amount of weight modification depends not only on the impact of the current error function, but also on the previous error functions which are evaluated by current weights. In order to improve the learning performance, we integrate a modified synaptic delay learning to the proposed RLSBLR. We conduct experiments in different settings, such as spiking lengths, number of inputs, firing rates, noises and learning parameters, to thoroughly investigate the performance of this learning algorithm. The proposed RLSBLR is compared with competitive algorithms of Perceptron-Based Spiking Neuron Learning Rule (PBSNLR) and Remote Supervised Method (ReSuMe). Experimental results demonstrate that the proposed RLSBLR can achieve higher learning accuracy, higher efficiency and better robustness against different types of noise. In addition, we apply the proposed RLSBLR to open source database TIDIGITS, and the results show that our algorithm has a good practical application performance.

Incorporating Structural Plasticity Approaches in Spiking Neural Networks for EEG Modelling

Structural Plasticity (SP) in the brain is a process that allows structural neuronal changes, in response to learning. Spiking Neural Networks (SNN) are an emerging form of artificial neural networks that use brain-inspired techniques to learn. However, the application of SP in SNNs, its impact on overall learning, and network behaviour is rarely explored. In the present study, we use an SNN with a single hidden layer, to apply SP in classifying Electroencephalography (EEG) signals of two publicly available datasets. We considered classification accuracy as the learning capability and applied metaheuristics to derive the optimised number of neurons for the hidden layer along with other hyperparameters of the network. The optimised structure was then compared with overgrown and undergrown structures to compare the accuracy, stability, and behaviour of the network properties. Networks with SP yielded ~94% and ~92% accuracies in classifying wrist positions and mental states(stressed vs relaxed) respectively. The same SNN developed for mental stress classification produced ~77% and ~73% accuracies in classifying arousal and valence states. Moreover, the networks with SP demonstrated superior performance stability during iterative random initiations. Interestingly, these networks had a smaller number of inactive neurons and a preference for lowered neuron firing thresholds. This research highlights the importance of systematically selecting the hidden layer neurons over arbitrary settings, particularly for SNNs using Spike Time Dependent Plasticity learning and provides potential findings that may lead to the development of SP learning algorithms for SNNs.

Mechanisms of Self-Organized Quasicriticality in Neuronal Network Models

The critical brain hypothesis states that there are information processing advantages for neuronal networks working close to the critical region of a phase transition. If this is true, we must ask how the networks achieve and maintain this critical state. Here, we review several proposed biological mechanisms that turn the critical region into an attractor of a dynamics in network parameters like synapses, neuronal gains, and firing thresholds. Since neuronal networks (biological and models) are not conservative but dissipative, we expect not exact criticality but self-organized quasicriticality, where the system hovers around the critical point.

Neuromodulatory Interventions for Traumatic Brain Injury.

Neuromodulatory Interventions for Traumatic Brain Injury.

Development of a fire detection and suppression system for a smart air cargo container

ABSTRACTThis study investigates and proposes a fire detection and suppression system for a smart air cargo container. A series of smoke spread and fire evolution numerical models are executed to assess the performance of container-based fire detection in various fire scenarios. This is to identify the worst case and optimise the location and threshold setting of fire detection sensors, achieving the shortest detection time. It is found that the fire detection threshold (reduction in light transmission = 12%/ft) for a container-based system can be set at three times the standard activation threshold for a cargo-based fire detection system, which can reduce the number of false alarms by three orders of magnitude. Moreover, effectiveness analysis of passive fire protection for the glass fibre-reinforced polymer-made smart container indicates an allowable leakage size of 0.01m2. The risk of internal overpressure has been found to be negligible for the leakage size required by aircraft pressure equalisation.

Three-dimensional neuron-astrocyte construction on matrigel enhances establishment of functional voltage-gated sodium channels.

This study aimed to investigate and compare cell growth manners and functional differences of primary cortical neurons cultured on either poly-d-lysine (PDL) and or Matrigel, to delineate the role of extracellular matrix on providing resemblance to in vivo cellular interactions in nervous tissue. Primary cortical neurons, obtained from embryonic day 15 mice pups, seeded either on PDL- or Matrigel-coated culture ware were investigated by DIC/bright field and fluorescence/confocal microscopy for their morphology, 2D and 3D structure, and distribution patterns. Patch clamp, western blot, and RT-PCR studies were performed to investigate neuronal firing thresholds and sodium channel subtypes Nav1.2 and Nav1.6 expression. Cortical neurons cultured on PDL coating possessed a 2D structure composed of a few numbers of branched and tortuous neurites that contacted with each other in one to one manner, however, neurons on Matrigel coating showed a more complicated dimensional network that depicted tight, linear axonal bundles forming a 3D interacted neuron-astrocyte construction. This difference in growth patterns also showed a significant alteration in neuronal firing threshold which was recorded between 80<Iinj>120pA on PDL and 2<Iinj>160pA on Matrigel. Neurons grown up on Matrigel showed increased levels of sodium channel protein expression of Nav1.2 and Nav1.6 compared to neurons on PDL. These results have demonstrated that a 3D interacted neuron-astrocyte construction on Matrigel enhances the development of Nav1.2 and Nav1.6 in vitro and decreases neuronal firing threshold by 40 times compared to conventional PDL, resembling in vivo neuronal networks and hence would be a better in vitro model of adult neurons.

Variant-specific changes in persistent or resurgent sodium current in SCN8A-related epilepsy patient-derived neurons.

Missense variants in the SCN8A voltage-gated sodium channel gene are linked to early-infantile epileptic encephalopathy type 13, also known as SCN8A-related epilepsy. These patients exhibit a wide spectrum of intractable seizure types, severe developmental delay, movement disorders, and elevated risk of sudden unexpected death in epilepsy. The mechanisms by which SCN8A variants lead to epilepsy are poorly understood, although heterologous expression systems and mouse models have demonstrated altered sodium current properties. To investigate these mechanisms using a patient-specific model, we generated induced pluripotent stem cells from three patients with missense variants in SCN8A: p.R1872>L (Patient 1); p.V1592>L (Patient 2); and p.N1759>S (Patient 3). Using small molecule differentiation into excitatory neurons, induced pluripotent stem cell-derived neurons from all three patients displayed altered sodium currents. Patients 1 and 2 had elevated persistent current, while Patient 3 had increased resurgent current compared to controls. Neurons from all three patients displayed shorter axon initial segment lengths compared to controls. Further analyses focused on one of the patients with increased persistent sodium current (Patient 1) and the patient with increased resurgent current (Patient 3). Excitatory cortical neurons from both patients had prolonged action potential repolarization. Using doxycycline-inducible expression of the neuronal transcription factors neurogenin 1 and 2 to synchronize differentiation of induced excitatory cortical-like neurons, we investigated network activity and response to pharmacotherapies. Both small molecule differentiated and induced patient neurons displayed similar abnormalities in action potential repolarization. Patient induced neurons showed increased burstiness that was sensitive to phenytoin, currently a standard treatment for SCN8A-related epilepsy patients, or riluzole, an FDA-approved drug used in amyotrophic lateral sclerosis and known to block persistent and resurgent sodium currents, at pharmacologically relevant concentrations. Patch-clamp recordings showed that riluzole suppressed spontaneous firing and increased the action potential firing threshold of patient-derived neurons to more depolarized potentials. Two of the patients in this study were prescribed riluzole off-label. Patient 1 had a 50% reduction in seizure frequency. Patient 3 experienced an immediate and dramatic seizure reduction with months of seizure freedom. An additional patient with a SCN8A variant in domain IV of Nav1.6 (p.V1757>I) had a dramatic reduction in seizure frequency for several months after starting riluzole treatment, but then seizures recurred. Our results indicate that patient-specific neurons are useful for modelling SCN8A-related epilepsy and demonstrate SCN8A variant-specific mechanisms. Moreover, these findings suggest that patient-specific neuronal disease modelling offers a useful platform for discovering precision epilepsy therapies.

Superconducting Nanowire Spiking Element for Neural Networks.

As the limits of traditional von Neumann computing come into view, the brain's ability to communicate vast quantities of information using low-power spikes has become an increasing source of inspiration for alternative architectures. Key to the success of these largescale neural networks is a power-efficient spiking element that is scalable and easily interfaced with traditional control electronics. In this work, we present a spiking element fabricated from superconducting nanowires that has pulse energies on the order of ∼10 aJ. We demonstrate that the device reproduces essential characteristics of biological neurons, such as a refractory period and a firing threshold. Through simulations using experimentally measured device parameters, we show how nanowire-based networks may be used for inference in image recognition and that the probabilistic nature of nanowire switching may be exploited for modeling biological processes and for applications that rely on stochasticity.

Effects of tDCS on spontaneous spike activity in a healthy ambulatory rat model

BackgroundThe neurophysiological effects of transcranial direct current stimulation (tDCS) are typically described with respect to changes in cortical excitability, defined by using transcranial magnetic stimulation pulses to determine changes in motor evoked potentials. However, how individual cortical neurons change firing patterns under the influence of tDCS is largely unknown. While the relatively weak currents produced in the brain by tDCS may not be adequate to directly depolarize neuronal membranes, ongoing neuronal activity, combined with subthreshold changes in membrane polarization might be sufficient to alter the threshold for neural firing. ObjectivesThe purpose of this study was to determine the effects of tDCS on neurophysiological activity in motor cortex of freely moving, healthy rats. MethodsIn nine healthy, ambulatory rats, each studied under six different stimulation conditions varying in current intensity (maximum current density = 39.8 A/m2 at 0.4 mA) and polarity (anodal or cathodal), neural activity was analyzed in response to 20 min of tDCS applied through bone screws insulated from the overlying scalp. ResultsAfter analysis of 480 multi-unit channels that satisfied a rigid set of neurophysiological criteria, we found no systematic effect of tDCS stimulation condition on firing rate or firing pattern. Restricting the analysis to the most responsive units, subtle, but statistically significant changes occurred only in the highest intensity anodal condition. ConclusionsThese results confirm that at current densities typically used in human or animal tDCS studies, observed effects of tDCS are likely to occur via mechanisms other than direct neuronal depolarization.

Functional changes in the temporomandibular joint mechanoreceptors associated with experimentally induced condylar resorption in rats.

To evaluate the influence of experimentally induced progressive condylar resorption (PCR) on temporomandibular joint (TMJ) mechanoreception. Twenty 13-week-old male albino Wistar rats were divided equally into control and PCR groups. A compressive force was loaded on the left TMJ of PCR group rats to induce condylar resorption. Single-unit activities of TMJ mechanoreceptors were also induced through passive jaw movement. Recording was performed for the left Gasserian ganglion at 3 days and 1 week after the establishment of PCR group. The effects of PCR on TMJ units were assessed by measuring the firing threshold, maximum instantaneous firing frequency, and average firing frequency. Compared with the control group, there were no significant differences in the firing threshold of the PCR group after 3 days. The thresholds were significantly higher 1 week after compressive force loading on the condyle. The maximum instantaneous firing frequencies and the average firing frequencies showed no significant differences after 3 days. However, these were significantly lower 1 week after compressive force loading. The findings suggest that compressive force loading on the condyle may influence the function of TMJ mechanoreceptors.

A Single Transistor Neuron With Independently Accessed Double-Gate for Excitatory-Inhibitory Function and Tunable Firing Threshold Voltage

For the first time, a single transistor neuron (1T-neuron) with an independently accessed double-gate structure is demonstrated for excitatory-inhibitory-function, and control of the firing threshold voltage (VT,firing). The double-gate is composed of a primary gate and a secondary gate. The former is to determine an excitatory and inhibitory state as a toggle switch and the latter is to dynamically adjust VT,firing for homeostasis to sustain reliable neuromorphic operation. In addition to its conventional excitation function, the newly implemented inhibition function improves the energy efficiency of the neuromorphic network. The tunable threshold voltage for firing maintains homeostasis, even for high or low transmitted signals that are out of a desired spiking frequency range.

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 threshold and many firms are near their firing threshold. Policies that target the job destruction margin, such as employment protection subsidies, are particularly effective at such times.

Band-to-Band Tunneling Based Ultra-Energy-Efficient Silicon Neuron

The human brain comprises about a hundred billion neurons connected through quadrillion synapses. Spiking Neural Networks (SNNs) take inspiration from the brain to model complex cognitive and learning tasks. Neuromorphic engineering implements SNNs in hardware, aspiring to mimic the brain at scale (i.e., 100 billion neurons) with biological area and energy efficiency. The design of ultra-energy efficient and compact neurons is essential for the large-scale implementation of SNNs in hardware. In this work, we have experimentally demonstrated a Partially Depleted (PD) Silicon-On-Insulator (SOI) MOSFET based Leaky-Integrate & Fire (LIF) neuron where energy-and area-efficiency is enabled by two elements of design - first tunneling based operation and second compact sub-threshold SOI control circuit design. Band-to-Band Tunneling (BTBT) induced hole storage in the body is used for the "Integrate" function of the neuron. A compact control circuit "Fires" a spike when the body potential exceeds the firing threshold. The neuron then "Resets" by removing the stored holes from the body contact of the device. Additionally, the control circuit provides "Leakiness" in the neuron which is an essential property of biological neurons. The proposed neuron provides 10x higher area efficiency compared to CMOS design with equivalent energy/spike. Alternatively, it has 10^4x higher energy efficiency at area-equivalent neuron technologies. Biologically comparable energy- and area-efficiency along with CMOS compatibility make the proposed device attractive for large-scale hardware implementation of SNNs.

Information filtering by coincidence detection of synchronous population output: analytical approaches to the coherence function of a two-stage neural system

Information about time-dependent sensory stimuli is encoded in the activity of neural populations; distinct aspects of the stimulus are read out by different types of neurons: while overall information is perceived by integrator cells, so-called coincidence detector cells are driven mainly by the synchronous activity in the population that encodes predominantly high-frequency content of the input signal (high-pass information filtering). Previously, an analytically accessible statistic called the partial synchronous output was introduced as a proxy for the coincidence detector cell’s output in order to approximate its information transmission. In the first part of the current paper, we compare the information filtering properties (specifically, the coherence function) of this proxy to those of a simple coincidence detector neuron. We show that the latter’s coherence function can indeed be well-approximated by the partial synchronous output with a time scale and threshold criterion that are related approximately linearly to the membrane time constant and firing threshold of the coincidence detector cell. In the second part of the paper, we propose an alternative theory for the spectral measures (including the coherence) of the coincidence detector cell that combines linear-response theory for shot-noise driven integrate-and-fire neurons with a novel perturbation ansatz for the spectra of spike-trains driven by colored noise. We demonstrate how the variability of the synaptic weights for connections from the population to the coincidence detector can shape the information transmission of the entire two-stage system.

A simple digital spiking neural network: Synchronization and spike-train approximation

<p style='text-indent:20px;'>This paper studies synchronization phenomena of spike-trains and approximation of target spike-trains in a simple network of digital spiking neurons. Repeating integrate-and-fire behavior between a periodic base signal and constant firing threshold, the neurons can generate various spike-trains. Connecting multiple neurons by cross-firing with delay, the network is constructed. The network can exhibit multi-phase synchronization of various spike-trains. Stability of the synchronization phenomena can be guaranteed theoretically. Applying a simple winner-take-all switching, the network can approximate target spike-trains automatically. In order to evaluate the approximation performance, we present two metrics: spike-position error and spike missing rate. Using the metrics, approximation capability of the network is investigated in typical target signals. Presenting an FPGA based hardware prototype, typical synchronization phenomenon and spike-train approximation are confirmed experimentally.

Economic crisis impact on low carbon transition in economy-ecosystem

To better understand the economic crisis impact on industry economy in low carbon transition, the simplified macroeconomic system combined with a distributed industry-energy ecosystem, namely the economy-ecosystem, was established based on an agent-based modeling approach. A set of behavioral rules for each agent involved was formalized to study the competition between high and low carbon energy under the scheme of ecological life of industrial firms, and periodical economy crisis was emerged by adjusting asymmetric adjustment speed ratio of hiring to firing and default threshold that controls the ratio between total debt and total circulating currency. The impact of the economic crisis on the ecosystem in a distributed energy system and the interaction between the economy and ecosystem in low carbon transition under different conditions of the ecosystem were investigated. Simulation results show that high carbon energy is more resistant to economic crisis than low carbon energy. Moreover, low-carbon transition with economic recession state can be reached in economy-ecosystem. Various policy implications are also given to policymakers with different technical levels and economic status.