Spiking Behavior Research Articles

Interfacial capacitance in lithium disilicate glass: Experimental factors and charge carrier density

AbstractThe formation of an electric double‐layer (EDL) is an important phenomenon for many research areas, including energy storage technology. Although EDL is well‐known in electrochemistry, most of the studies involve the characterization of liquid electrolyte/electrode interfaces, and only a limited number of studies in solid‐solid contacts, such as solid electrolyte/electrode interface are available. This paper employed electrochemical impedance spectroscopy (EIS) to systematically investigate the influence of experimental factors in the interfacial capacitance arising from the electrode polarization in a lithium disilicate glass/gold electrode interface. It analyzed the influence of a.c. input voltage amplitude, samples' roughness (mechanical and chemomechanical polishing) and thickness, range of applied frequency and temperature, and the number of impedance cycles. In short, it was found that an input voltage range of 15–60 mV is indicated to minimize potential electrochemical processes during electrode polarization, where the data is reproducible from the second measurement cycle onward. Smoother surfaces closely approximated ideal electrode spike behavior, with surface treatment exhibiting influence on interfacial capacitance values. Moreover, as expected, we observed an increase in relative permittivity values with increasing thickness, accompanied by decreased capacitance values. Finally, by employing optimal experimental conditions and analyzing the inflection frequency () of the versus log() curve, we determined that the ratio between effective charge carriers () and the total number of charge carriers () falls within the range of 5–12% between 130°C and 280°C.

The Effects of Omeprazole on the Neuron-like Spiking of the Electrical Potential of Proteinoid Microspheres.

This study examines a new approach to hybrid neuromorphic devices by studying the impact of omeprazole-proteinoid complexes on Izhikevich neuron models. We investigate the influence of these metabolic structures on five specific patterns of neuronal firing: accommodation, chattering, triggered spiking, phasic spiking, and tonic spiking. By combining omeprazole, a proton pump inhibitor, with proteinoids, we create a unique substrate that interfaces with neuromorphic models. The Izhikevich neuron model is used because it is computationally efficient and can accurately simulate the various behaviours of cortical neurons. The results of our simulations show that omeprazole-proteinoid complexes have the ability to affect neuronal dynamics in different ways. This suggests that they could be used as adjustable components in bio-inspired computer systems. We noticed a notable alteration in the frequency of spikes, patterns of bursts, and rates of adaptation, especially in chattering and triggered spiking behaviours. The findings indicate that omeprazole-proteinoid complexes have the potential to serve as adaptable elements in neuromorphic systems, presenting novel opportunities for information processing and computation that have origins in neurobiological principles. This study makes a valuable contribution to the expanding field of biochemical neuromorphic devices and establishes a basis for the development of hybrid bio-synthetic computational systems.

Spiking activities in small neural networks induced by external forcing.

Neurons in an excitable mode do not show spiking activity and, therefore, do not contribute to information transfer transmission and its processing. However, some external influences, coupling, or time delay can lead to the appearance of oscillations in individual systems or networks. The main goal of this paper is to uncover the connection parameters and parameters of external influences that lead to the arising of spiking behavior in a small network of locally coupled FitzHugh-Nagumo oscillators. In this study, we analyze the dynamics of a small network in the absence and presence of several types of external influences. First, we consider the impact of periodic-pulse exposure generated as a periodic sequence of Gaussian pulses. Second, we show what behavior can be induced by far less regular pulsed influence (Lévy noise) and its special case called white Gaussian noise. For all types of influences, we have identified the appropriate parameters (local coupling strength, intensity, and frequency) that induce spiking activity in the small network.

Diversity, Stability, and the Forecast Challenge in Forest Lepidopteran Predictive Ecology: Are Multi-Scale Plant–Insect Interactions the Key to Increased Forecast Precision?

I report on long-term patterns of outbreak cycling in four study systems across Canada and illustrate how forecasting in these systems is highly imprecise because of complexity in the cycling and a lack of spatial synchrony amongst sample locations. I describe how a range of bottom-up effects could be generating complexity in these otherwise periodic systems. (1) The spruce budworm in Québec exhibits aperiodic and asynchronous behavior at fast time-scales, and a slow modulation of cycle peak intensity that varies regionally. (2) The forest tent caterpillar across Canada exhibits eruptive spiking behavior that is aperiodic locally, and asynchronous amongst regions, yet aggregates to produce a pattern of periodic outbreaks. In Québec, forest tent caterpillar cycles differ in the aspen-dominated northwest versus the maple-dominated southeast, with opposing patterns of cycle intensity between the two regions. (3) In Alberta, forest tent caterpillar outbreak cycles resist synchronization across a forest landscape gradient, even at very fine spatial scales, resulting in a complex pattern of cycling that defies simple forecasting techniques. (4) In the Border Lakes region of Ontario and Minnesota, where the two insect species coexist in a mixedwood landscape of hardwood and conifers, outbreak cycle intensity in each species varies spatially and temporally in response to host forest landscape structure. Much attention has been given to the effect of top-down agents in driving synchronizable population cycles. However, foliage loss, tree death, and forest succession at stem, stand, and landscape scales affect larval and adult dispersal success, and may serve to override regulatory processes that cause otherwise top-down-driven periodic, synchronized, and predictable population oscillations to become aperiodic, asynchronous, and unpredictable. Incorporating bottom-up effects at multiple spatial and temporal scales may be the key to making significant improvements in forest insect outbreak forecasting.

Micro-electrode array recording of extracellular electrical potentials of liquid static surface fermented Hericium erinaceus

Hericium erinaceus is a basidiomycetes fungus with previously uncharacterised extracellular electrophysiology. Here, we present results of recordings of the electrical potentials of fungal biofilms of this species using microelectrode arrays (MEAs). In particular, we focused on modelling the temporal and spatial progression of the low frequency (≤ 1 Hz) potentials. Culture media control studies showed that the electrical potential activity results from the growth and subsequent spiking behaviours of the mycelium extracellular matrices. An antifungal assay using nystatin suspension, 10,000 unit/mL in DPBS, provided evidence for the biological origin of electrical potentials due to targeting of the selective permeability of the cell membrane and subsequent cessation of electrical activity. Conversely, injection of L-glutamic acid increased the combined multi-channel mean firing rate from 0.04 Hz to 0.1 Hz. Analysis of bursting and spatial propagation of the extracellular signals are also presented.

Firing patterns and fast–slow dynamics in an N-type LAM-based FitzHugh–Nagumo circuit

The diversity of firing patterns and their bifurcation mechanisms of a neuron circuit are crucial for exploiting bionic applications. This paper constructs an N-type locally active memristor (LAM)-based FitzHugh–Nagumo (FHN) circuit by replacing the tunnel-diode in the original FHN circuit. Numerical simulations and hardware measurements reveal that the N-type LAM-based FHN circuit can reproduce rich neuromorphic firing patterns of quasi-periodic/periodic bursting behaviors and chaotic/periodic spiking behaviors. The bursting and spiking behaviors are triggered by a low-frequency stimulus and a high-frequency one, respectively. Besides, the fold and Hopf bifurcation sets are depicted. Then, the bifurcation mechanisms for the quasi-periodic and periodic bursting behaviors via Hopf/Hopf and Hopf/fold bifurcations are theoretically deduced by time-domain waveform and equilibrium trajectory. The numerical results and hardware experiments exhibit the effectiveness of the proposed memristive FHN circuit in reproducing abundant firing patterns of bursting and spiking behaviors.

Adaptive spiking, itinerancy, and quantum effects in artificial neuron circuit hardware with niobium–hafnium oxide-niobium memristor devices inserted

To improve artificial intelligence/autonomous systems and help with treating neurological conditions, there is a requirement for the discovery and design of artificial neuron hardware that mimics the advanced functionality and operation of the neural networks available in biological organisms. We examine experimental artificial neuron circuits that we designed and built in hardware with memristor devices using 4.2 nm of hafnium oxide and niobium metal inserted in the positive and negative feedback of an oscillator. At room temperature, these artificial neurons have adaptive a spiking behavior and hybrid non-chaotic/chaotic modes. When networked, they output with strong itinerancy, and we demonstrate a four-neuron learning network and modulation of signals. The superconducting state at 8.1 K results in Josephson tunneling with signs that the hafnium oxide ionic states are influenced by quantum control effects in accordance with quantum master equation calculations of the expectation values and correlation functions with a calibrated time-dependent Hamiltonian. These results are of importance to continue advancing neuromorphic hardware technologies that integrate memristors and other memory devices for many biological-inspired applications and beyond that can function with adaptive-itinerant spiking and quantum effects in their principles of operation.

Generalized Bio-Plausible Neuron Model with Refractoriness, Frequency Adaptation and Dynamic Threshold

Real biological neurons are dynamical systems that can process a nonlinear, arbitrary input signal with a dynamic threshold voltage, exhibit refractory time, and have a smooth [Formula: see text]–[Formula: see text] curve. This paper presents a generalized bio-plausible neuron model with refractoriness, frequency adaptation, and dynamic threshold. The spiking characteristics of the generalized neuron are enhanced with less circuitry and fewer parameters compared to older circuits. The proposed silicon neuron can generate all known spiking behaviors, just like a biological neuron. The circuit is designed using a 180[Formula: see text]nm standard CMOS process. The entire design uses 22 transistors with a total power consumption of only 2[Formula: see text]nW for a 1[Formula: see text]nA step current at a spike frequency of 122[Formula: see text]Hz. The power consumption and spike frequency rate depend on the input current. The proposed neuron is also capable of displaying both Type I and Type II frequency response characteristics. In addition, our neuron model underwent Monte Carlo simulation with 1000 sample runs to assess its robustness and stability against process variations and uncertainties. The results indicate that the average energy consumption of the proposed neuron model falls within the range of 16[Formula: see text]pJ to 68[Formula: see text]pJ.

An Ion-Mediated Spiking Chemical Neuron based on Mott Memristor.

Artificial spiking neurons capable of interpreting ionic information into electrical spikes are critical to mimic biological signaling systems. Mott memristors are attractive for constructing artificial spiking neurons due to their simple structure, low energy consumption, and rich neural dynamics. However, challenges remain in achieving ion-mediated spiking and biohybrid-interfacing in Mott neurons. Here, a biomimetic spiking chemical neuron (SCN) utilizing an NbOx Mott memristor and oxide field-effect transistor-type chemical sensor is introduced. The SCN exhibits both excitation and inhibition spiking behaviors toward ionic concentrations akin to biological neural systems. It demonstrates spiking responses across physiological and pathological Na+ concentrations (1-200× 10-3 m). The Na+-mediated SCN enables both frequency encoding and time-to-first-spike coding schemes, illustrating the rich neural dynamics of Mott neuron. In addition, the SCN interfaced with L929 cells facilitates real-time modulation of ion-mediated spiking under both normal and salty cellular microenvironments.

Dynamics of Leaky Integrate‐and‐Fire Neurons Based on Oxyvanite Memristors for Spiking Neural Networks

Neuromorphic computing implemented with spiking neural networks (SNNs) based on volatile threshold switching is an energy‐efficient computing paradigm that may overcome future limitations of the von Neumann architecture. Herein, threshold switching in oxyvanite (V3O5) memristors and their application as a leaky integrate‐and‐fire (LIF) neuron are explored. The spiking response of individual neurons is examined as a function of circuit parameters, input pulse train, and temperature and reveals a pulse height‐dependent spike rate in which devices exhibit excitatory spiking behavior under low input voltages and protective inhibition spiking under high voltages. Resistively coupled LIF neurons are shown to exhibit additional neural functionalities (i.e., phasic, regular and adaptation, etc.) depending on the input voltage and circuit parameters. The behavior of both individual and coupled neurons is shown to be described by a physics‐based lumped element circuit model, which therefore provides a solid foundation for exploring more complex systems. Finally, the performance of a perceptron SNN employing these LIF neurons is assessed by simulating the classification of image recognition algorithm. These results advance the development of robust solid‐state neurons with low power consumption for neuromorphic computing.

Hybrid synaptic structure for spiking neural network realization

Neural networks and neuromorphic computing represent fundamental paradigms as alternative approaches to Von-Neumann-based implementations, advancing in the applications of deep learning and machine vision. Nonetheless, conventional semiconductor circuits encounter challenges in achieving ultra-fast processing speed and low power consumption due to their dissipative properties. Conversely, single flux quantum circuits exhibit inherent spiking behavior, showcasing their characteristics as a promising candidate for spiking neural networks (SNNs). In this work, we present a compact hybrid synapse circuit to mimic the biological interconnect functionality, enabling the weighting operations for excitatory and inhibitory impulses. Additionally, the proposed structure facilitates input accumulation, which is performed before the activation function. In the experiments, our synaptic structure interfaces with a soma circuit fabricated using a commercial Nb process, underscoring its compatibility and supporting its potential for integration into efficient neural network architectures. The weight value on the synapse is configurable by utilizing cryo-CMOS circuits, providing adaptability to the inference networks. We’ve successfully designed, fabricated, and partially tested the JJ-Synapse within our cryocooler system, enabling high-speed inference implementation for SNNs.

A Morphologically Distinct Subclass of Somatostatin-expressing Interneurons in the Somatosensory Cortex are Strongly Recruited by Input from the Motor Cortex

The motor cortex (M1) and somatosensory cortex (S1) are strongly interconnected regions, and their interactions are essential for sensory perception and motor execution. However, the mechanisms by which M1 activity modulates sensory processing within S1 are poorly understood. In particular, M1 recruitment of distinct GABAergic inhibitory cells could impact S1 responsiveness. Preliminary work in our lab identified a subpopulation of somatostatin (SOM) expressing inhibitory interneurons in layer 6 (L6) of S1 that are strongly recruited by M1 input. We hypothesize these M1-responding L6 SOM cells are an electrophysiological and morphologically distinct subclass that express the enzyme neuronal nitric oxide synthase (nNOS). To stimulate the M1 to S1 pathway and test this hypothesis, we injected adeno-associated virus (AAV) encoding the light-sensitive cation channel, channelrhodopsin-2 (ChR2), in M1 of postnatal day 21 (+/-1) mice in vivo. After three weeks of expression, we prepared acute coronal brain slices for targeted loose-patch recordings and selective optical stimulation of M1 terminals. We found two groups of L6 SOM cells based on their spiking behavior during photostimulation: responsive (15%) and non-responsive (85%). Whole-cell recordings and neurobiotin injections into responsive and non-responsive SOM cells revealed robust electrophysiological and morphological differences. Initial anatomical reconstructions indicate that the non-responsive SOM cells (n=12) exhibit both Martinotti (with an L1 axonal projection) and non-Martinotti (no L1 axonal projection) morphologies, whereas the responsive SOM cells had axonal arborizations projecting toward the underlying white matter (n=10). The responsive SOM cells had quasi-fast-spiking electrophysiological properties (n=10) and were negative for nNOS (n=9), whereas the non-responsive SOM cells exhibited non-adaptive spiking behavior. Importantly, responsive SOM cells were negative for parvalbumin (PV), a marker for a distinct class of fast-spiking interneurons in the cortex, indicating these cells were not mislabeled PV cells due to the known off-target recombination in the SOM-IRES-Cre mouse line (n=4). In summary, our data show that input from M1 strongly recruits a previously unknown SOM-expressing interneuron in lower L6 with distinct morphological and electrophysiological features that could influence sensory responsiveness in S1. Future studies will focus on the mechanisms of this selective activation and identifying the postsynaptic targets of these cells to further our understanding of this sensorimotor circuit. NIH R01NS117636 (SRC); APS SURF (MKS). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Neuromorphic one-shot learning utilizing a phase-transition material

Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO2), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO2 devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to fourfold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning in neuromorphic hardware.

Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks

We analyze time-averaged experimental data from in vitro activities of neuronal networks. Through a Pairwise Maximum-Entropy method, we identify through an inverse binary Ising-like model the local fields and interaction couplings which best reproduce the average activities of each neuron as well as the statistical correlations between the activities of each pair of neurons in the system. The specific information about the type of neurons is mainly stored in the local fields, while a symmetric distribution of interaction constants seems generic. Our findings demonstrate that, despite not being directly incorporated into the inference approach, the experimentally observed correlations among groups of three neurons are accurately captured by the derived Ising-like model. Within the context of the thermodynamic analogy inherent to the Ising-like models developed in this study, our findings additionally indicate that these models demonstrate characteristics of second-order phase transitions between ferromagnetic and paramagnetic states at temperatures above, but close to, unity. Considering that the operating temperature utilized in the Maximum-Entropy method is To=1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T_{o}=1$$\\end{document}, this observation further expands the thermodynamic conceptual parallelism postulated in this work for the manifestation of criticality in neuronal network behavior.

Intrinsic and Synaptic Contributions to Repetitive Spiking in Dentate Granule Cells.

Repetitive firing of granule cells (GCs) in the dentate gyrus (DG) facilitates synaptic transmission to the CA3 region. This facilitation can gate and amplify the flow of information through the hippocampus. High-frequency bursts in the DG are linked to behavior and plasticity, but GCs do not readily burst. Under normal conditions, a single shock to the perforant path in a hippocampal slice typically drives a GC to fire a single spike, and only occasionally more than one spike is seen. Repetitive spiking in GCs is not robust, and the mechanisms are poorly understood. Here, we used a hybrid genetically encoded voltage sensor to image voltage changes evoked by cortical inputs in many mature GCs simultaneously in hippocampal slices from male and female mice. This enabled us to study relatively infrequent double and triple spikes. We found GCs are relatively homogeneous and their double spiking behavior is cell autonomous. Blockade of GABA type A receptors increased multiple spikes and prolonged the interspike interval, indicating inhibitory interneurons limit repetitive spiking and set the time window for successive spikes. Inhibiting synaptic glutamate release showed that recurrent excitation mediated by hilar mossy cells contributes to, but is not necessary for, multiple spiking. Blockade of T-type Ca2+ channels did not reduce multiple spiking but prolonged interspike intervals. Imaging voltage changes in different GC compartments revealed that second spikes can be initiated in either dendrites or somata. Thus, pharmacological and biophysical experiments reveal roles for both synaptic circuitry and intrinsic excitability in GC repetitive spiking.

In Situ Imaging of Dynamic Current Paths in a Neuromorphic Nanoparticle Network with Critical Spiking Behavior

AbstractIn the strive for energy efficient computing, many different neuromorphic computing and engineering schemes have been introduced. Nanoparticle networks (NPNs) at the percolation threshold have been established as one of the promising candidates, e.g., for reservoir computing because among other useful properties they show self‐organization and brain‐like avalanche dynamics. The dynamic resistance changes trace back to spatio‐temporal reconfigurations in the connectivity upon resistive switching in distributed memristive nano‐junctions and nano‐gaps between neighboring nanoparticles. Until now, however, there has not yet been any direct imaging or monitoring of current paths in NPN. In this study, an NPN comprising of Ag/CxOyHz core/shell and Ag nanoparticles at the percolation threshold is reported. It is shown that this NPN is within a critical regime, exhibiting avalanche dynamics. To monitor in situ the evolving current paths in this NPN, active voltage contrast and resistive contrast imaging are used complementarily. Including simulations, the results provide experimental insight toward understanding the complex current response of the memristive NPN. As such, this study paves the way toward an experimental characterization of dynamic reorganizations in current paths inside NPN, which is highly relevant for validating and improving simulations and finally establishing a deeper understanding of switching dynamics in NPNs.

Neural activity of retinal ganglion cells under continuous, dynamically-modulated high frequency electrical stimulation

Objective. Current retinal prosthetics are limited in their ability to precisely control firing patterns of functionally distinct retinal ganglion cell (RGC) types. The aim of this study was to characterise RGC responses to continuous, kilohertz-frequency-varying stimulation to assess its utility in controlling RGC activity. Approach. We used in vitro patch-clamp experiments to assess electrically-evoked ON and OFF RGC responses to frequency-varying pulse train sequences. In each sequence, the stimulation amplitude was kept constant while the stimulation frequency (0.5–10 kHz) was changed every 40 ms, in either a linearly increasing, linearly decreasing or randomised manner. The stimulation amplitude across sequences was increased from 10 to 300 µA. Main results. We found that continuous stimulation without rest periods caused complex and irreproducible stimulus-response relationships, primarily due to strong stimulus-induced response adaptation and influence of the preceding stimulus frequency on the response to a subsequent stimulus. In addition, ON and OFF populations showed different sensitivities to continuous, frequency-varying pulse trains, with OFF cells generally exhibiting more dependency on frequency changes within a sequence. Finally, the ability to maintain spiking behaviour to continuous stimulation in RGCs significantly reduced over longer stimulation durations irrespective of the frequency order. Significance. This study represents an important step in advancing and understanding the utility of continuous frequency modulation in controlling functionally distinct RGCs. Our results indicate that continuous, kHz-frequency-varying stimulation sequences provide very limited control of RGC firing patterns due to inter-dependency between adjacent frequencies and generally, different RGC types do not display different frequency preferences under such stimulation conditions. For future stimulation strategies using kHz frequencies, careful consideration must be given to design appropriate pauses in stimulation, stimulation frequency order and the length of continuous stimulation duration.

Dataset of human-single neuron activity during a Sternberg working memory task

We present a dataset of 1809 single neurons recorded from the human medial temporal lobe (amygdala and hippocampus) and medial frontal lobe (anterior cingulate cortex, pre-supplementary motor area, ventral medial prefrontal cortex) across 41 sessions from 21 patients that underwent seizure monitoring with depth electrodes. Subjects performed a screening task (907 neurons) to identify images for which highly selective cells were present. Subjects then performed a working memory task (902 neurons), in which they were sequentially presented with 1–3 images for which highly selective cells were present and, following a maintenance period, were asked if the probe was identical to one of the maintained images. This Neurodata Without Borders formatted dataset includes spike times, extracellular spike waveforms, stimuli presented, behavior, electrode locations, and subject demographics. As validation, we replicate previous findings on the selectivity of concept cells and their persistent activity during working memory maintenance. This large dataset of rare human single-neuron recordings and behavior enables the investigation of the neural mechanisms of working memory in humans.

Periodic and chaotic spiking behaviors in a simplified memristive Hodgkin-Huxley circuit

The famous Hodgkin-Huxley circuit contains two time-varying resistors to describe the electrophysiological characteristics of sodium and potassium ion channels. But the time-varying resistors are expressed by mixed exponential equations, which are unavailable to directly implement in analog circuit and hinders hardware application of the Hodgkin-Huxley circuit. To hit this issue, a simplified memristive Hodgkin-Huxley (m-HH) circuit is proposed, which only involves two locally active memristors (LAMs) to depict the sodium and potassium ion channels, a capacitor to describe the neuron membrane, a DC current to represent external stimulus, and two DC voltages to express the reversal potentials. MATLAB-based numerical simulations and analog circuit-based hardware measurements display that the simplified m-HH circuit can generate memristor parameter- and DC current-related periodic spiking behaviors with different periodicities and chaotic spiking behavior. This delights that electrophysiological characteristics of ion channels and external stimulus can be employed for regulating these periodic and chaotic spiking behaviors. It is interesting that the simplified m-HH circuit can generate frequency self-adaptation and coexisting firing patterns. The inter-spike interval bifurcation diagram shows that the frequency of the periodic spiking behavior increases with the increase of externally applied DC current. Besides, the hybrid parameter bifurcation diagram displays that the simplified m-HH circuit can generate memristor initial state-related coexisting firing patterns of period-6 with chaos, period-3 with chaos, and period-3 with period-8. These offer us the unique insight into explaining some biological firing patterns. The most significance is that the analog hardware circuit is feasible in reproducing these periodic and chaotic spiking behaviors and benefits for developing spiking-based neuromorphic hardware.

ReLU Function-Based Locally Active Memristor and Its Application in Generating Spiking Behaviors

ReLU Function-Based Locally Active Memristor and Its Application in Generating Spiking Behaviors