Subthreshold Oscillations Research Articles

Effect of Calcium on the Characteristics of Action Potential Under Different Electrical Stimuli

This study investigates the role of calcium ions in the release of action potentials by comparing two models based on the framework: the standard HH model and a HH + Ca model that incorporates calcium ion channels. Purkinje cells’ responses to four types of electrical current stimuli—constant direct current, step current, square wave current, and sine current—were simulated to analyze the impact of calcium on action potential characteristics. The results indicate that, under the constant direct current stimulation, the action potential firing frequency of both models increased with the escalating current intensity, while the delay time of the first action potential decreased. However, when the current intensity exceeded a specific threshold, the peak amplitude of the action potential gradually diminished. The HH + Ca model exhibited a longer delay in the first action potential compared to the HH model but maintained an action potential release under stronger currents. In response to the step current, both models showed an increased action potential frequency with a higher current, but the HH + Ca model generated subthreshold oscillations under weak currents. With the square wave current, the action potential frequency increased, though the HH + Ca model experienced suppression under high-frequency weak currents. Under the sine current, the action potential frequency rose, with the HH + Ca model showing less depression near the sine peak due to calcium’s role in modulating membrane potential. These findings suggest that calcium ions contribute to a more stable action potential release under varying stimuli.

Interplay of Nav1.8 and Nav1.7 channels drives neuronal hyperexcitability in neuropathic pain.

While voltage-gated sodium channels Nav1.7 and Nav1.8 both contribute to electrogenesis in dorsal root ganglion (DRG) neurons, details of their interactions have remained unexplored. Here, we studied the functional contribution of Nav1.8 in DRG neurons using a dynamic clamp to express Nav1.7L848H, a gain-of-function Nav1.7 mutation that causes inherited erythromelalgia (IEM), a human genetic model of neuropathic pain, and demonstrate a profound functional interaction of Nav1.8 with Nav1.7 close to the threshold for AP generation. At the voltage threshold of -21.9 mV, we observed that Nav1.8 channel open-probability exceeded Nav1.7WT channel open-probability ninefold. Using a kinetic model of Nav1.8, we showed that a reduction of Nav1.8 current by even 25-50% increases rheobase and reduces firing probability in small DRG neurons expressing Nav1.7L848H. Nav1.8 subtraction also reduces the amplitudes of subthreshold membrane potential oscillations in these cells. Our results show that within DRG neurons that express peripheral sodium channel Nav1.7, the Nav1.8 channel amplifies excitability at a broad range of membrane voltages with a predominant effect close to the AP voltage threshold, while Nav1.7 plays a major role at voltages closer to resting membrane potential. Our data show that dynamic-clamp reduction of Nav1.8 conductance by 25-50% can reverse hyperexcitability of DRG neurons expressing a gain-of-function Nav1.7 mutation that causes pain in humans and suggests, more generally, that full inhibition of Nav1.8 may not be required for relief of pain due to DRG neuron hyperexcitability.

Neural coding of space by time.

The intertwining of space and time poses a significant scientific challenge, transcending disciplines from philosophy and physics to neuroscience. Deciphering neural coding, marked by its inherent spatial and temporal dimensions, has proven to be a complex task. In this paper, we present insights into temporal and spatial modes of neural coding and their intricate interplay, drawn from neuroscientific findings. We illustrate the conversion of a purely spatial input into the temporal form of a singular spike train, demonstrating storage, transmission to remote locations, and recall through spike bursts corresponding to Sharp Wave Ripples. Moreover, the converted temporal representation can be transformed back into a spatiotemporal pattern. The principles of the transformation process are illustrated using a simple feed-forward spiking neural network. The frequencies and phases of Subthreshold Membrane potential Oscillations play a pivotal role in this framework. The model offers insights into information multiplexing and phenomena such as stretching or compressing time of spike patterns.

Phase-Dependent Response to Electrical Stimulation of Cortical Networks during Recurrent Epileptiform Short Discharge Generation In Vitro.

The closed-loop control of pathological brain activity is a challenging task. In this study, we investigated the sensitivity of continuous epileptiform short discharge generation to electrical stimulation applied at different phases between the discharges using an in vitro 4-AP-based model of epilepsy in rat hippocampal slices. As a measure of stimulation effectiveness, we introduced a sensitivity function, which we then measured in experiments and analyzed with different biophysical and abstract mathematical models, namely, (i) the two-order subsystem of our previous Epileptor-2 model, describing short discharge generation governed by synaptic resource dynamics; (ii) a similar model governed by shunting conductance dynamics (Epileptor-2B); (iii) the stochastic leaky integrate-and-fire (LIF)-like model applied for the network; (iv) the LIF model with potassium M-channels (LIF+KM), belonging to Class II of excitability; and (v) the Epileptor-2B model with after-spike depolarization. A semi-analytic method was proposed for calculating the interspike interval (ISI) distribution and the sensitivity function in LIF and LIF+KM models, which provided parametric analysis. Sensitivity was found to increase with phase for all models except the last one. The Epileptor-2B model is favored over other models for subthreshold oscillations in the presence of large noise, based on the comparison of ISI statistics and sensitivity functions with experimental data. This study also emphasizes the stochastic nature of epileptiform discharge generation and the greater effectiveness of closed-loop stimulation in later phases of ISIs.

Thalamocortical Dynamics during Rapid Eye Movement Sleep in the Mouse Somatosensory Pathway.

Rapid eye movement (REM) sleep, also referred to as paradoxical sleep for the striking resemblance of its electroencephalogram (EEG) to the one observed in wakefulness, is characterized by the occurrence of transient events such as limb twitches or facial and rapid eye movements. Here, we investigated the local activity of the primary somatosensory or barrel cortex (S1) in naturally sleeping head-fixed male mice during REM. Through local field potential recordings, we uncovered local appearances of spindle waves in the barrel cortex during REM concomitant with strong delta power, challenging the view of a wakefulness-like activity in REM. We further performed extra- and intracellular recordings of thalamic cells in head-fixed mice. Our data show high-frequency thalamic bursts of spikes and subthreshold spindle oscillations in approximately half of the neurons of the ventral posterior medial nucleus which further confirmed the thalamic origin of local cortical spindles in S1 in REM. Cortical spindle oscillations were suppressed, while thalamus spike firing increased, associated with rapid mouse whisker movements and S1 cortical activity transitioned to an activated state. During REM, the sensory thalamus and barrel cortex therefore alternate between high (wake-like) and low (non-REM sleep-like) activation states, potentially providing a neuronal substrate for mnemonic processes occurring during this paradoxical sleep stage.

Modelling and analysis of cAMP-induced mixed-mode oscillations in cortical neurons: Critical roles of HCN and M-type potassium channels.

Cyclic AMP controls neuronal ion channel activity. For example hyperpolarization-activated cyclic nucleotide-gated (HCN) and M-type K+ channels are activated by cAMP. These effects have been suggested to be involved in astrocyte control of neuronal activity, for example, by controlling the action potential firing frequency. In cortical neurons, cAMP can induce mixed-mode oscillations (MMOs) consisting of small-amplitude, subthreshold oscillations separating complete action potentials, which lowers the firing frequency greatly. We extend a model of neuronal activity by including HCN and M channels, and show that it can reproduce a series of experimental results under various conditions involving and inferring with cAMP-induced activation of HCN and M channels. In particular, we find that the model can exhibit MMOs as found experimentally, and argue that both HCN and M channels are crucial for reproducing these patterns. To understand how M and HCN channels contribute to produce MMOs, we exploit the fact that the model is a three-time scale dynamical system with one fast, two slow, and two super-slow variables. We show that the MMO mechanism does not rely on the super-slow dynamics of HCN and M channel gating variables, since the model is able to produce MMOs even when HCN and M channel activity is kept constant. In other words, the cAMP-induced increase in the average activity of HCN and M channels allows MMOs to be produced by the slow-fast subsystem alone. We show that the slow-fast subsystem MMOs are due to a folded node singularity, a geometrical structure well known to be involved in the generation of MMOs in slow-fast systems. Besides raising new mathematical questions for multiple-timescale systems, our work is a starting point for future research on how cAMP signalling, for example resulting from interactions between neurons and glial cells, affects neuronal activity via HCN and M channels.

Switching dynamics of a Filippov memristive Hindmash–Rose neuron model with time delay

Considering the existence of magnetic induction effect with different intensities in the process of subthreshold and suprathreshold oscillations of bioelectrical activities, a non-smooth feedback strategy for memristive current with time delay is proposed, and then a four-dimensional Filippov Hindmarsh–Rose (HR) neuron model is established. The local stability and bifurcation patterns of delayed subsystems are qualitatively analyzed. Accordingly, the discriminant formula for the direction and stability of periodic solutions generated by Hopf bifurcation is obtained on the center manifold. Importantly, the stability of subsystems has switching behavior, which is accompanied by abundant hidden electrical activities under the effect of time delay. The theoretical analysis clarifies that the proposed feedback strategy leads to complex sliding mode dynamics, including sliding segments, various equilibrium points and sliding bifurcations. Meanwhile, the analytical conditions for motions of grazing, sliding, and crossing are developed and verified based on the flow switching theory. Moreover, the mechanism and evolutive rule of the self-excited and hidden sliding electrical activities are revealed by the fast-slow variable dissection method. Finally, it is verified that the time delay can not only induce bistable structures composed of the quiescent state and periodic bursting, but also eliminate the hidden sliding dynamics.

Calcium-Sensitive Subthreshold Oscillations and Electrical Coupling in Principal Cells of Mouse Dorsal Cochlear Nucleus.

In higher sensory brain regions, slow oscillations (0.5-5 Hz) associated with quiet wakefulness and attention modulate multisensory integration, predictive coding, and perception. Although often assumed to originate via thalamocortical mechanisms, the extent to which subcortical sensory pathways are independently capable of slow oscillatory activity is unclear. We find that in the first station for auditory processing, the cochlear nucleus, fusiform cells from juvenile mice (of either sex) generate robust 1-2 Hz oscillations in membrane potential and exhibit electrical resonance. Such oscillations were absent prior to the onset of hearing, intrinsically generated by hyperpolarization-activated cyclic nucleotide-gated (HCN) and persistent Na+ conductances (NaP) interacting with passive membrane properties, and reflected the intrinsic resonance properties of fusiform cells. Cx36-containing gap junctions facilitated oscillation strength and promoted pairwise synchrony of oscillations between neighboring neurons. The strength of oscillations were strikingly sensitive to external Ca2+, disappearing at concentrations >1.7 mM, due in part to the shunting effect of small-conductance calcium-activated potassium (SK) channels. This effect explains their apparent absence in previous in vitro studies of cochlear nucleus which routinely employed high-Ca2+ extracellular solution. In contrast, oscillations were amplified in reduced Ca2+ solutions, due to relief of suppression by Ca2+ of Na+ channel gating. Our results thus reveal mechanisms for synchronous oscillatory activity in auditory brainstem, suggesting that slow oscillations, and by extension their perceptual effects, may originate at the earliest stages of sensory processing.

Ih current stabilizes excitability in rodent DRG neurons and reverses hyperexcitability in a nociceptive neuron model of inherited neuropathic pain.

We show here that hyperpolarization-activated current (Ih ) unexpectedly acts to inhibit the activity of dorsal root ganglion (DRG) neurons expressing WT Nav1.7, the largest inward current and primary driver of DRG neuronal firing, and hyperexcitable DRG neurons expressing a gain-of-function Nav1.7 mutation that causes inherited erythromelalgia (IEM), a human genetic model of neuropathic pain. In this study we created a kinetic model of Ih and used it, in combination with dynamic-clamp, to study Ih function in DRG neurons. We show, for the first time, that Ih increases rheobase and reduces the firing probability in small DRG neurons, and demonstrate that the amplitude of subthreshold oscillations is reduced by Ih . Our results show that Ih , due to slow gating, is not deactivated during action potentials (APs) and has a striking damping action, which reverses from depolarizing to hyperpolarizing, close to the threshold for AP generation. Moreover, we show that Ih reverses the hyperexcitability of DRG neurons expressing a gain-of-function Nav1.7 mutation that causes IEM. In the aggregate, our results show that Ih unexpectedly has strikingly different effects in DRG neurons as compared to previously- and well-studied cardiac cells. Within DRG neurons where Nav1.7 is present, Ih reduces depolarizing sodium current inflow due to enhancement of Nav1.7 channel fast inactivation and creates additional damping action by reversal of Ih direction from depolarizing to hyperpolarizing close to the threshold for AP generation. These actions of Ih limit the firing of DRG neurons expressing WT Nav1.7 and reverse the hyperexcitability of DRG neurons expressing a gain-of-function Nav1.7 mutation that causes IEM. KEY POINTS: Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, the molecular determinants of hyperpolarization-activated current (Ih ) have been characterized as a 'pain pacemaker', and thus considered to be a potential molecular target for pain therapeutics. Dorsal root ganglion (DRG) neurons express Nav1.7, a channel that is not present in central neurons or cardiac tissue. Gain-of-function mutations (GOF) of Nav1.7 identified in inherited erythromelalgia (IEM), a human genetic model of neuropathic pain, produce DRG neuron hyperexcitability, which in turn produces severe pain. We found that Ih increases rheobase and reduces firing probability in small DRG neurons expressing WT Nav1.7, and demonstrate that the amplitude of subthreshold oscillations is reduced by Ih . We also demonstrate that Ih reverses the hyperexcitability of DRG neurons expressing a GOF Nav1.7 mutation (L858H) that causes IEM. Our results show that, in contrast to cardiac cells and CNS neurons, Ih acts to stabilize DRG neuron excitability and prevents excessive firing.

Cardiac behaviors and chaotic arrhythmias in the Hindmarsh–Rose model

The Hindmarsh–Rose is one of the best-known models of computational neuroscience. Despite its popularity as a neuron model, we demonstrate that it is also a complete cardiac model. We employ a method based on bifurcations of the interspike interval to redraw its phase diagram and reveal a cardiac region. This diagram bears great resemblance to that of the map-based model for neurons and cardiac cells, the KTz model. Both phase diagrams are compared, showing a very similar placement of behaviors in the parameter space. Adjusting the Hindmarsh–Rose parameters allows us to obtain behaviors similar to atrial, ventricular and pacemaker cells. We also report the neuronal behavior of sustained subthreshold oscillations in the diagram, also unknown in the model. We demonstrate the existence of periodic and chaotic early afterdepolarizations, a behavior linked to life-threatening arrhythmias. In a second phase diagram, we find a chaotic region with early afterdepolarizations and self-organized periodic structures known as shrimps. We also propose a new and simple method to calculate the electrocardiogram using the membrane potential of a point cell and demonstrate its use for the study of QT syndromes using the Hindmarsh–Rose model. Given the smaller number of equations and parameters than detailed conductance models and richer dynamics than other general models, this work presents the Hindmarsh–Rose as a promising alternative for computational cardiology studies.

Analysis of Underlap Tri-Gate FinFET and Its Capacitance Effects for Analog/Radio Frequency Applications

Manufacturing ultra-scaled FinFET devices has become a massive obstacle for device engineers. The critical challenge experienced Multi-Gate FETs is process variation; Consequently, devices’ performances are impacted and analyzed for device performance losses due to misalignments of gate locations close to sources and drain edgess (lower regions). FinFET is examined using a 3D mathematical model, the impact of base gate areas on variables such as electric fields, surface channel potentials, subthreshold oscillations, threshold voltages, and drainage-induced barrier reductions and effects beneath coating. 3D simulators validate the outcomes yielded by the model. The advantage of underlap FinFET of streamlining investigates the spacer dielectric material (low k and high k) and its underlapped Gate length using the TCAD simulator.

How inhibitory and excitatory inputs gate output of the inferior olive.

The inferior olive provides the climbing fibers to Purkinje cells in the cerebellar cortex, where they elicit all-or-none complex spikes and control major forms of plasticity. Given their important role in both short-term and long-term coordination of cerebellum-dependent behaviors, it is paramount to understand the factors that determine the output of olivary neurons. Here, we use mouse models to investigate how the inhibitory and excitatory inputs to the olivary neurons interact with each other, generating spiking patterns of olivary neurons that align with their intrinsic oscillations. Using dual color optogenetic stimulation and whole-cell recordings, we demonstrate how intervals between the inhibitory input from the cerebellar nuclei and excitatory input from the mesodiencephalic junction affect phase and gain of the olivary output at both the sub- and suprathreshold level. When the excitatory input is activated shortly (~50 ms) after the inhibitory input, the phase of the intrinsic oscillations becomes remarkably unstable and the excitatory input can hardly generate any olivary spike. Instead, when the excitatory input is activated one cycle (~150 ms) after the inhibitory input, the excitatory input can optimally drive olivary spiking, riding on top of the first cycle of the subthreshold oscillations that have been powerfully reset by the preceding inhibitory input. Simulations of a large-scale network model of the inferior olive highlight to what extent the synaptic interactions penetrate in the neuropil, generating quasi-oscillatory spiking patterns in large parts of the olivary subnuclei, the size of which also depends on the relative timing of the inhibitory and excitatory inputs.

Licochalcone Mediates the Pain Relief by Targeting the Voltage-Gated Sodium Channel.

Licorice is a traditional Chinese medicine and recorded to have pain relief effects in national pharmacopoeia, but the mechanisms behind these effects have not been fully explored. Among the hundreds of compounds in licorice, licochalcone A (LCA) and licochalcone B (LCB) are two important components belonging to the chalcone family. In this study, we compared the analgesic effects of these two licochalcones and the molecular mechanisms. LCA and LCB were applied in cultured dorsal root ganglion (DRG) neurons, and the voltage-gated sodium (NaV) currents and action potentials were recorded. The electrophysiological experiments showed that LCA can inhibit NaV currents and dampen excitabilities of DRG neurons, whereas LCB did not show inhibition effect on NaV currents. Because the NaV1.7 channel can modulate Subthreshold membrane potential oscillations in DRG neuron, which can palliate neuropathic pain, HEK293T cells were transfected with NaV1.7 channel and recorded with whole-cell patch clamp. LCA can also inhibit NaV1.7 channels exogenously expressed in HEK293T cells. We further explored the analgesic effects of LCA and LCB on formalin-induced pain animal models. The animal behavior tests revealed that LCA can inhibit the pain responses during phase 1 and phase 2 of formalin test, and LCB can inhibit the pain responses during phase 2. The differences of the effects on NaV currents between LCA and LCB provide us with the basis for developing NaV channel inhibitors, and the novel findings of analgesic effects indicate that licochalcones can be developed into effective analgesic medicines. SIGNIFICANCE STATEMENT: This study found that licochalcone A (LCA) can inhibit voltage-gated sodium (NaV) currents, dampen excitabilities of dorsal root ganglion neurons, and inhibit the NaV1.7 channels exogenously expressed in HEK293T cells. Animal behavior tests showed that LCA can inhibit the pain responses during phase 1 and phase 2 of formalin test, whereas licochalcone B can inhibit the pain responses during phase 2. These findings indicate that licochalcones could be the leading compounds for developing NaV channel inhibitors and effective analgesic medicines.

Sodium-mediated plateau potentials in an identified decisional neuron contribute to feeding-related motor pattern genesis in Aplysia.

Motivated behaviors such as feeding depend on the functional properties of decision neurons to provide the flexibility required for behavioral adaptation. Here, we analyzed the ionic basis of the endogenous membrane properties of an identified decision neuron (B63) that drive radula biting cycles underlying food-seeking behavior in Aplysia. Each spontaneous bite cycle arises from the irregular triggering of a plateau-like potential and resultant bursting by rhythmic subthreshold oscillations in B63's membrane potential. In isolated buccal ganglion preparations, and after synaptic isolation, the expression of B63's plateau potentials persisted after removal of extracellular calcium, but was completely suppressed in a tetrodotoxin (TTX)- containing bath solution, thereby indicating the contribution of a transmembrane Na+ influx. Potassium outward efflux through tetraethylammonium (TEA)- and calcium-sensitive channels was found to contribute to each plateau's active termination. This intrinsic plateauing capability, in contrast to B63's membrane potential oscillation, was blocked by the calcium-activated non-specific cationic current (ICAN) blocker flufenamic acid (FFA). Conversely, the SERCA blocker cyclopianozic acid (CPA), which abolished the neuron's oscillation, did not prevent the expression of experimentally evoked plateau potentials. These results therefore indicate that the dynamic properties of the decision neuron B63 rely on two distinct mechanisms involving different sub-populations of ionic conductances.

Biologically Realistic Behaviors From a Superconducting Neuron Model

In previous work, we introduced the “Josephson junction neuron,” a superconducting circuit that can reproduce some important properties of biological neurons like action potentials and refractory periods. Circuits of Josephson junction neurons have very fast dynamics and operate fully in parallel, and large simulations would be possible with them that are not feasible using traditional computers. Here, we show that this model reproduces 15 common dynamical behaviors of individual cortical spiking neurons for various choices of the intrinsic circuit parameters, while 4 more behaviors can be produced by also including a sinusoidal bias current, giving a total of 19. Behaviors such as single spike response, tonic (periodic) spiking only when stimulated, bursting, tonic bursting, frequency adaptation, and subthreshold oscillations are some of the common behaviors seen. Even bistability of periodic firing and quiescent modes are possible. This suggests that Josephson junction neurons could be fabricated and used to simulate biologically realistic neural circuits, networks, and perhaps even larger regions of the brain.

The effect of the subthreshold oscillation induced by the neurons' resonance upon the electrical stimulation-dependent instability.

Repetitive electrical nerve stimulation can induce a long-lasting perturbation of the axon's membrane potential, resulting in unstable stimulus-response relationships. Despite being observed in electrophysiology, the precise mechanism underlying electrical stimulation-dependent (ES-dependent) instability is still an open question. This study proposes a model to reveal a facet of this problem: how threshold fluctuation affects electrical nerve stimulations. This study proposes a new method based on a Circuit-Probability theory (C-P theory) to reveal the interlinkages between the subthreshold oscillation induced by neurons' resonance and ES-dependent instability of neural response. Supported by in-vivo studies, this new model predicts several key characteristics of ES-dependent instability and proposes a stimulation method to minimize the instability. This model provides a powerful tool to improve our understanding of the interaction between the external electric field and the complexity of the biophysical characteristics of axons.

Low-dimensional models of single neurons: a review.

The classical Hodgkin-Huxley (HH) point-neuron model of action potential generation is four-dimensional. It consists of four ordinary differential equations describing the dynamics of the membrane potential and three gating variables associated to a transient sodium and a delayed-rectifier potassium ionic currents. Conductance-based models of HH type are higher-dimensional extensions of the classical HH model. They include a number of supplementary state variables associated with other ionic current types, and are able to describe additional phenomena such as subthreshold oscillations, mixed-mode oscillations (subthreshold oscillations interspersed with spikes), clustering and bursting. In this manuscript we discuss biophysically plausible and phenomenological reduced models that preserve the biophysical and/or dynamic description of models of HH type and the ability to produce complex phenomena, but the number of effective dimensions (state variables) is lower. We describe several representative models. We also describe systematic and heuristic methods of deriving reduced models from models of HH type.

Investigation of chaotic resonance in Type-I and Type-II Morris-Lecar neurons

The signal encoding capacity of the nervous system is significantly related to the spiking regime of neurons, and the firing behaviors of neurons are vibrant due to their complex structure. Chaotic fluctuations emerge at both micro and macro levels in the neuronal medium. It is thought that chaotic oscillations are the beneficial component, rather than disruptive, for cognitive functions of the nervous system. It is found that external chaotic activity at a suitable level enhances the weak signal encoding performance of neurons, particularly when the weak signal frequency is chosen close to that of the chaotic fluctuations-induced sub-threshold oscillations frequencies. This manipulation of the signal encoding performance of neurons by chaotic fluctuations is explained by the “chaotic resonance” phenomenon. In the current work, we systematically examine the impacts of the extrinsic chaotic stimulus from the Lorenz system on the subthreshold periodic signal encoding performance of Type-I and Type-II Morris-Lecar neurons. Our results show that weak signal encoding performance of both Type-I and Type-II Morris-Lecar neurons exhibits resonance behavior and its resonance mainly depends on external chaotic current intensity. Also, we reveal that suitable chaotic current level for the best encoding in Morris-Lecar neurons of the sub-threshold signal changes with signal frequency. In addition, we indicate that there is a frequency range in which Morris-Lecar neurons are sensitive to signals. We also demonstrate the existence of a chaotic current intensity range at these frequencies, enabling neurons to improve weak signal encoding performance. Finally, it is observed that the frequency ranges in which Type-I and Type-II neurons are sensitive to weak signals are quite different. In this regard, our research provides important insights into the possible roles of chaotic resonance in the weak signal encoding of different types of neurons.

Enabling a New Methodology of Neural Coding: Multiplexing Temporal Encoding in Neuromorphic Computing

From rate to temporal encoding, spiking information processing has demonstrated advantages across diverse neuromorphic applications. In the aspects of data capacity and robustness, multiplexing encoding outperforms alternative encoding schemes. In this work, we aim to implement a new class of multiplexing temporal encoders, patterning stimuli in multiple timescales to improve the information processing capability, and robustness of systems deployed in noisy environments. Benefitted by the internal reference frame using subthreshold membrane oscillation (SMO), the encoded spike patterns are less sensitive to the input noise, increasing the encoder’s robustness. Our design results in a tremendous saving on power consumption and silicon area compared with the power-hungry analog-to-digital converters. Furthermore, a working prototype of the multiplexing temporal encoder built based on an interspike interval (ISI) encoding scheme is implemented on a silicon chip using the standard 180-nm CMOS process. To the best of our knowledge, our introduced encoder demonstrates the first integrated circuit (IC) implementation of neural encoding with multiplexing topology. Finally, the accuracy and efficiency of our design are evaluated through standard machine learning benchmarks, including Modified National Institute of Standards and Technology (MNIST), Canadian Institute For Advanced Research (CIFAR)-10, Street View House Number (SVHN), and spectrum sensing in high-speed communication networks. While our multiplexing temporal encoder demonstrates a higher classification accuracy across all the benchmarks, the power consumption and dissipated energy per spike reach merely <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.6~\mu \text {W}$ </tex-math></inline-formula> and 95 fJ/spike, respectively, with an effective frame rate of 300 MHz. Compared with alternative encoding schemes, our multiplexing temporal encoder achieves at most 100% higher data capacity, 11.4% more accurate in classification, and 25% more robust against noise. Compared with the state-of-the-art designs, our work achieves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$105 \times $ </tex-math></inline-formula> power efficiency without significantly increasing the silicon area.

Age-Related Changes in Neurons and Satellite Glial Cells in Mouse Dorsal Root Ganglia.

The effects of aging on the nervous system are well documented. However, most previous studies on this topic were performed on the central nervous system. The present study was carried out on the dorsal root ganglia (DRGs) of mice, and focused on age-related changes in DRG neurons and satellite glial cells (SGCs). Intracellular electrodes were used for dye injection to examine the gap junction-mediated coupling between neurons and SGCs, and for intracellular electrical recordings from the neurons. Tactile sensitivity was assessed with von Frey hairs. We found that 3-23% of DRG neurons were dye-coupled to SGCs surrounding neighboring neurons in 8-24-month (Mo)-old mice, whereas in young adult (3 Mo) mice, the figure was 0%. The threshold current for firing an action potential in sensory neurons was significantly lower in DRGs from 12 Mo mice compared with those from 3 Mo mice. The percentage of neurons with spontaneous subthreshold membrane potential oscillation was greater by two-fold in 12 Mo mice. The withdrawal threshold was lower by 22% in 12 Mo mice compared with 3 Mo ones. These results show that in the aged mice, a proportion of DRG neurons is coupled to SGCs, and that the membrane excitability of the DRG neurons increases with age. We propose that augmented neuron-SGC communications via gap junctions are caused by low-grade inflammation associated with aging, and this may contribute to pain behavior.