Tonic Spiking Research Articles

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.

Posit and floating-point based Izhikevich neuron: A Comparison of arithmetic

Reduced precision number formats have become increasingly popular in various fields of computational science, as they offer the potential to enhance energy efficiency, reduce silicon area, and improve processing speed. However, this is often at the expense of introducing arithmetic errors that can impact the accuracy of a system. The optimal balance must be struck, judiciously choosing a number format using as few bits as possible, while minimising accuracy loss.In this study, we examine one such format, posit arithmetic as a replacement for floating-point when conducting spiking neuron simulations, specifically using the Izhikevich neuron model. This model is capable of simulating complex neural firing behaviours, 20 of which were originally identified by Izhikevich and are used in this study. We compare the accuracy, spike count, and spike timing of the two arithmetic systems at different bit-depths against a 64-bit floating-point gold-standard. Additionally, we test a rescaled set of Izhikevich equations to mitigate against arithmetic errors by taking advantage of posit arithmetic’s tapered accuracy.Our findings indicate that there is no difference in performance between 32-bit posit, 32-bit floating-point, and our 64-bit reference for all but one of the tested firing types. However, at 16-bit, both arithmetic systems diverge from the 64-bit reference, albeit in different ways. For example, 16-bit posit demonstrates an 18× improvement in accumulated spike timing error over a 1000ms simulation compared to 16-bit floating-point when simulating regular (tonic) spiking. This finding holds particular importance given the prevalence of this particular firing type in specific regions of the brain. Furthermore, when we rescale the neuron equations, this error is eliminated altogether. Although current Posit Arithmetic Units are no smaller than Floating Point Units of the same bit-width, our results demonstrate that 64-bit floating-point can be replaced with 16-bit posit which could enable significant area savings in future systems.

Firing feature-driven neural circuits with scalable memristive neurons for robotic obstacle avoidance

Neural circuits with specific structures and diverse neuronal firing features are the foundation for supporting intelligent tasks in biology and are regarded as the driver for catalyzing next-generation artificial intelligence. Emulating neural circuits in hardware underpins engineering highly efficient neuromorphic chips, however, implementing a firing features-driven functional neural circuit is still an open question. In this work, inspired by avoidance neural circuits of crickets, we construct a spiking feature-driven sensorimotor control neural circuit consisting of three memristive Hodgkin-Huxley neurons. The ascending neurons exhibit mixed tonic spiking and bursting features, which are used for encoding sensing input. Additionally, we innovatively introduce a selective communication scheme in biology to decode mixed firing features using two descending neurons. We proceed to integrate such a neural circuit with a robot for avoidance control and achieve lower latency than conventional platforms. These results provide a foundation for implementing real brain-like systems driven by firing features with memristive neurons and put constructing high-order intelligent machines on the agenda.

Design of a New Neuro-Generator with a Neuronal Module to Produce Pseudorandom and Perfectly Pseudorandom Sequences

This paper presents the design of a new neuro-generator of pseudorandom number type PRNG Pseudorandom Number Generator, which produces complex sequences with an adequate bit distribution. The circuit is connected to a neuronal module with six impulse neurons with different behaviors: spike frequency adaptation, phasic spiking, mixed mode, phasic bursting, tonic bursting and tonic spiking. This module aims to generate a non-periodic signal that becomes the clock signal for one of the LFSRs Linear Feedback Shift Register that the neuro-generator has. To verify its correct operation, the neuro-generator was subjected to a series of tests where the frequencies of the impulse neurons were modified. This modification allows the generation of a greater number of pulses at the output of the neuronal module, to obtain sequences with different characteristics that pass different NIST statistical tests (National Institute of Standards and Technology of U.S.). The results show that the new neuro-generator maintains pseudo-randomness in the sequences obtained with different frequencies and it can be implemented on a reconfigurable FPGA Field Programmable Gate Array Virtex 7 xc7vx485t-2ffg1761 device. Therefore, it can be used for applications such as biological systems.

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

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

Optimal input reverberation and homeostatic self-organization toward the edge of synchronization.

Transient or partial synchronization can be used to do computations, although a fully synchronized network is sometimes related to the onset of epileptic seizures. Here, we propose a homeostatic mechanism that is capable of maintaining a neuronal network at the edge of a synchronization transition, thereby avoiding the harmful consequences of a fully synchronized network. We model neurons by maps since they are dynamically richer than integrate-and-fire models and more computationally efficient than conductance-based approaches. We first describe the synchronization phase transition of a dense network of neurons with different tonic spiking frequencies coupled by gap junctions. We show that at the transition critical point, inputs optimally reverberate through the network activity through transient synchronization. Then, we introduce a local homeostatic dynamic in the synaptic coupling and show that it produces a robust self-organization toward the edge of this phase transition. We discuss the potential biological consequences of this self-organization process, such as its relation to the Brain Criticality hypothesis, its input processing capacity, and how its malfunction could lead to pathological synchronization and the onset of seizure-like activity.

Conditional activity of parabrachial Tac1 neurons turns on rapid breathing

Breathing is regulated automatically to preserve homeostasis. This reflex regulation of breathing is achieved through neural circuits that function as negative feedback loops. Similar to how a thermostat regulates temperature in a room, the activity of these circuits can be adjusted up or down to fine tune breathing and restore a homeostatic set-point. Yet, breathing is also conditionally altered by behaviors and emotions, requiring dissociation of breathing frequency and pattern from its underlying automatic control. Relatively little is known about the circuits that mediate this “state-dependent'' control. We previously found that optogenetic activation of a Tac1, but not Calca, expressing subpopulation of lateral parabrachial (PBN) neurons drives breathing frequencies that far exceed those relevant for the homeostatic control of breathing. Here, we combine cell-type-specific silencing and multi-unit recording approaches to further test the hypothesis that this circuit plays an important role in state-dependent, but not automatic, respiratory control. To render Tac1 PBN neurons non-functional, the PBN of adult Tac1Cre mice was injected bilaterally with an AAV that drives Cre-dependent expression of tetanus light chain. After allowing ~4 weeks for viral expression, plethysmography did not reveal any differences in breathing frequency or pattern compared to mice injected with control AAV. Changes in breathing were also absent during acute optogenetic inhibition of Tac1+; Calca- neurons following injection of AAV that expresses iC++ in neurons that contain Cre but not FlpO in the PBN of Tac1Cre; CalcaFlpO mice. To assess the spiking activity of Tac1 PBN neurons relative to ongoing breathing patterns, the PBN of Tac1Cre; CalcaFlpO mice was injected with an AAV that expresses ChRmine in neurons containing Cre but not FlpO, a cranial window was created dorsal to the PBN and a head bar secured to the skull. After 2 weeks, mice were head-fixed and a high-density neuropixels probe was inserted into the PBN via the cranial window. Multi-unit activity was recorded from awake, freely breathing mice during simultaneous recording of respiratory pattern. Illumination of the cranial window with red light was suffcient for ChRmine mediated deep brain activation of Tac1+;Calca- PBN neurons, producing a robust increase in breathing frequency. Recorded PBN units were then optically tagged by assessing their response latency and fidelity to repeated 10 ms light pulses. Based on our preliminary observations, “optotagged” Tac1+; Calca- neurons were mostly quiescent during resting breathing but switched to tonic spiking during bouts of sniffng/rapid breathing. Thus, we predict that Tac1 PBN neurons do not regulate breathing via thermostat-like changes in activity similar to breathing reflexes. Instead, Tac1 PBN neurons represent a switch-like mechanism where these neurons are primarily inactive with minimal respiratory role under normal conditions, but when activated they turn on rapid breathing. R01HL166317. 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.

Effect of size on expression of bistability in mouse spinal motoneurons.

Bistability in spinal motoneurons supports tonic spike activity in the absence of excitatory drive. Earlier work in adult preparations suggested that smaller motoneurons innervating slow antigravity muscle fibers are more likely to generate bistability for postural maintenance. However, whether large motoneurons innervating fast-fatigable muscle fibers display bistability is still controversial. To address this, we examined the relationship between soma size and bistability in lumbar (L4-L5) ventrolateral α-motoneurons of choline acetyltransferase (ChAT)-green fluorescent protein (GFP) and Hb9-GFP mice during the first 4 wk of life. We found that as neuron size increases, the prevalence of bistability rises. Smaller α-motoneurons lack bistability, whereas larger fast α-motoneurons [matrix metalloproteinase-9 (MMP-9)+/Hb9+] with a soma area ≥ 400 µm2 exhibit significantly higher bistability. Ionic currents associated with bistability, including the persistent Nav1.6 current, the thermosensitive Trpm5 Ca2+-activated Na+ current, and the slowly inactivating Kv1.2 current, also scale with cell size. Serotonin evokes full bistability in large motoneurons with partial bistable properties but not in small motoneurons. Our study provides important insights into the neural mechanisms underlying bistability and how motoneuron size correlates with bistability in mice.NEW & NOTEWORTHY Bistability is not a common feature of all mouse spinal motoneurons. It is absent in small, slow motoneurons but present in most large, fast motoneurons. This difference results from differential expression of ionic currents that enable bistability, which are highly expressed in large motoneurons but small or absent in small motoneurons. These results support a possible role for fast motoneurons in maintenance of tonic posture in addition to their known roles in fast movements.

Conversion of spikers to bursters in pituitary cell networks: Is it better to disperse for maximum exposure or circle the wagons?

The endocrine cells of the pituitary gland are electrically active, and in vivo they form small networks where the bidirectional cell-cell coupling is through gap junctions. Numerous studies of dispersed pituitary cells have shown that typical behaviors are tonic spiking and bursting, the latter being more effective at evoking secretion. In this article, we use mathematical modeling to examine the dynamics of small networks of spiking and bursting pituitary cells. We demonstrate that intrinsic bursting cells are capable of converting intrinsic spikers into bursters, and perform a fast/slow analysis to show why this occurs. We then demonstrate the sensitivity of network dynamics to the placement of bursting cells within the network, and demonstrate strategies that are most effective at maximizing secretion from the population of cells. This study provides insights into the in vivo behavior of cells such as the stress-hormone-secreting pituitary corticotrophs that are switched from spiking to bursting by hypothalamic neurohormones. While much is known about the electrical properties of these cells when isolated from the pituitary, how they behave when part of an electrically coupled network has been largely unstudied.

Noise-Induced Toroidal Bursting Oscillations and Coherence Resonance in the Morris–Lecar–Terman Model

We study the three-dimensional Morris–Lecar–Terman neuron model in the parametric region of tonic spiking oscillations close to the bifurcation of the torus birth. It is shown that in this region random disturbances can induce switching to the toroidal bursting mode from the tonic spiking regime. We approximate the probability of such switches as well as analyze temporal characteristics of produced oscillations. Moreover, we indicate a coherence resonance in the stochastic bursting regime and reveal the underlying reasons of these noise-induced phenomena.

Cold-Temperature Coding with Bursting and Spiking Based on TRP Channel Dynamics in Drosophila Larva Sensory Neurons.

Temperature sensation involves thermosensitive TRP (thermoTRP) and non-TRP channels. Drosophila larval Class III (CIII) neurons serve as the primary cold nociceptors and express a suite of thermoTRP channels implicated in noxious cold sensation. How CIII neurons code temperature remains unclear. We combined computational and electrophysiological methods to address this question. In electrophysiological experiments, we identified two basic cold-evoked patterns of CIII neurons: bursting and spiking. In response to a fast temperature drop to noxious cold, CIII neurons distinctly mark different phases of the stimulus. Bursts frequently occurred along with the fast temperature drop, forming a peak in the spiking rate and likely coding the high rate of the temperature change. Single spikes dominated at a steady temperature and exhibited frequency adaptation following the peak. When temperature decreased slowly to the same value, mainly spiking activity was observed, with bursts occurring sporadically throughout the stimulation. The spike and the burst frequencies positively correlated with the rate of the temperature drop. Using a computational model, we explain the distinction in the occurrence of the two CIII cold-evoked patterns bursting and spiking using the dynamics of a thermoTRP current. A two-parameter activity map (Temperature, constant TRP current conductance) marks parameters that support silent, spiking, and bursting regimes. Projecting on the map the instantaneous TRP conductance, governed by activation and inactivation processes, reflects temperature coding responses as a path across silent, spiking, or bursting domains on the map. The map sheds light on how various parameter sets for TRP kinetics represent various types of cold-evoked responses. Together, our results indicate that bursting detects the high rate of temperature change, whereas tonic spiking could reflect both the rate of change and magnitude of steady cold temperature.

Dynamical behaviors of vertical-cavity surface-emitting lasers with an embedded saturable absorber subjected to an on-off keying (OOK) current modulation

Abstract We numerically study the dynamics of VCSEL-SA when modulated by an analog on-off keying (OOK) current. It is found that the laser gives a pulse-like response when operated below the threshold current. After the threshold current, which is in the excitable phase, the laser generates single spikes (phasic spiking) and a series of spikes (tonic spiking) wherein the amplitude of the spikes varies as the form of the current. Complex dynamics such as bursting oscillations and chaotic dynamics are successfully generated at low frequencies (few MHz). After this excitable phase, the laser copies the OOK current with underdamped oscillations generated during the OFF phase of the OOK current.

Canard Mechanism and Rhythm Dynamics of Neuron Models

Canards are a type of transient dynamics that occur in singularly perturbed systems, and they are specific types of solutions with varied dynamic behaviours at the boundary region. This paper introduces the emergence and development of canard phenomena in a neuron model. The singular perturbation system of a general neuron model is investigated, and the link between the transient transition from a neuron model to a canard is summarised. First, the relationship between the folded saddle-type canard and the parabolic burster, as well as the firing-threshold manifold, is established. Moreover, the association between the mixed-mode oscillation and the folded node type is unique. Furthermore, the connection between the mixed-mode oscillation and the limit-cycle canard (singular Hopf bifurcation) is stated. In addition, the link between the torus canard and the transition from tonic spiking to bursting is illustrated. Finally, the specific manifestations of these canard phenomena in the neuron model are demonstrated, such as the singular Hopf bifurcation, the folded-node canard, the torus canard, and the “blue sky catastrophe”. The summary and outlook of this paper point to the realistic possibility of canards, which have not yet been discovered in the neuron model.

Solid State Neuroscience: Spiking Neural Networks as Time Matter

We aim at building a bridge between to a priori disconnected fields: Neuroscience and Material Science. We construct an analogy based on identifying spikes events in time with the positions of particles of matter. We show that one may think of the dynamical states of spiking neurons and spiking neural networks as time-matter. Namely, a structure of spike-events in time having analogue properties to that of ordinary matter. We can define for neural systems notions equivalent to the equations of state, phase diagrams and their phase transitions. For instance, the familiar Ideal Gas Law relation (Pυ = constant) emerges as analogue of the Ideal Integrate and Fire neuron model relation (Iin ISI = constant). We define the neural analogue of the spatial structure correlation function, that can characterize spiking states with temporal long-range order, such as regular tonic spiking. We also define the “neuro-compressibility” response function in analogy to the lattice compressibility. We show that similarly to the case of ordinary matter, the anomalous behavior of the neuro-compressibility is a precursor effect that signals the onset of changes in spiking states. We propose that the notion of neuro-compressibility may open the way to develop novel medical tools for the early diagnose of diseases. It may allow to predict impending anomalous neural states, such as Parkinson’s tremors, epileptic seizures, electric cardiopathies, and perhaps may even serve as a predictor of the likelihood of regaining consciousness.

Transformations of spike and burst oscillations in the stochastic Rulkov model

We study the problem of modeling and analysis of complex oscillatory regimes in neuron dynamics on the basis of the two-dimensional Rulkov model. We focus on the parameter zone in which sharp changes in the type of oscillatory activity occur: burst oscillations with an active phase of a triangular shape are transformed into bimodal bursts combining triangular and trapezoidal phases. Mechanisms of such transformations are clarified geometrically by the technique of critical curves. For the stochastically forced model, we study two phenomena: (i) noise-induced splitting of tonic spiking; (ii) noise-induced transitions from the triangular mono-bursting to the more complicated bi-bursting. The phenomena of coherence and anti-coherence resonance are revealed and discussed.

Population spiking and bursting in next-generation neural masses with spike-frequency adaptation.

Spike-frequency adaptation (SFA) is a fundamental neuronal mechanism taking into account the fatigue due to spike emissions and the consequent reduction of the firing activity. We have studied the effect of this adaptation mechanism on the macroscopic dynamics of excitatory and inhibitory networks of quadratic integrate-and-fire (QIF) neurons coupled via exponentially decaying post-synaptic potentials. In particular, we have studied the population activities by employing an exact mean-field reduction, which gives rise to next-generation neural mass models. This low-dimensional reduction allows for the derivation of bifurcation diagrams and the identification of the possible macroscopic regimes emerging both in a single and in two identically coupled neural masses. In single populations SFA favors the emergence of population bursts in excitatory networks, while it hinders tonic population spiking for inhibitory ones. The symmetric coupling of two neural masses, in absence of adaptation, leads to the emergence of macroscopic solutions with broken symmetry, namely, chimera-like solutions in the inhibitory case and antiphase population spikes in the excitatory one. The addition of SFA leads to new collective dynamical regimes exhibiting cross-frequency coupling (CFC) among the fast synaptic timescale and the slow adaptation one, ranging from antiphase slow-fast nested oscillations to symmetric and asymmetric bursting phenomena. The analysis of these CFC rhythms in the θ-γ range has revealed that a reduction of SFA leads to an increase of the θ frequency joined to a decrease of the γ one. This is analogous to what has been reported experimentally for the hippocampus and the olfactory cortex of rodents under cholinergic modulation, which is known to reduce SFA.

Mechanisms leading to bursting oscillations in the system of predator–prey communities coupled by migrations

The purpose is to study the periodic regimes of the dynamics for two non-identical predator– prey communities coupled by migrations, associated with the partial synchronization of fluctuations in the abundance of communities. The combination of fluctuations in neighboring sites leads to the regimes that include both fast bursts (bursting oscillations) and slow oscillations (tonic spiking). These types of activity are characterized by a different ratio of synchronous and non-synchronous dynamics of communities in certain periods of time. In this paper, we describe scenarios of the transition between different types of burst activity. These types of dynamics differ from each other not so much in size, shape, and number of spikes in a burst, but in the order of these bursts relative to the slow-fast cycle. Methods. To study the proposed model, we use the bifurcation analysis methods of dynamic systems, as well as geometric methods based on the division of the full system into fast and slow equations (subsystems). Results. We showed that the dynamics of the first subsystem with a slow-fast limit cycle directly determines the dynamics of the second one with burst activity through a smooth dependence of regime on the number of predators and a non-smooth dependence on the number of prey. We constructed the invariant manifolds on which there are parts of dynamics with tonic (slow manifold) and burst (fast manifold) activity of the full system. Conclusion. We described the scenario for bursting with different waveforms, which are determined by the appearance of the fast invariant manifold and the location of its parts relative to the slow-fast cycle. The transitions between different types of burst are accompanied by a change in the oscillation period, the degree of synchronization, and, as a result, the dynamics becomes quasi-periodic when both communities are not synchronous with each other.

How noise transforms spiking into bursting in a neuron model having the Lukyanov–Shilnikov bifurcation

Motivated by an attractive biophysical problem related to revealing the underlying mechanisms of complex oscillatory forms of neural activity, we study a three-dimensional model having the Lukyanov–Shilnikov bifurcation in the parameter region where the system simulates tonic spiking oscillations. We reveal that additive white noise can transit this system from the spiking regime to the bursting. Moreover, it is found that this phenomenon is accompanied by the coherence resonance in generated oscillations and followed by transition to chaos. We investigate these noise-driven effects using both direct numerical simulation method with statistical post-processing of results as well as the theoretical stochastic sensitivity analysis and the methods of confidence domains and Mahalanobis metrics.

Characterization and modeling of spiking and bursting in experimental NbO x neuron

Hardware spiking neural networks hold the promise of realizing artificial intelligence with high energy efficiency. In this context, solid-state and scalable memristors can be used to mimic biological neuron characteristics. However, these devices show limited neuronal behaviors and have to be integrated in more complex circuits to implement the rich dynamics of biological neurons. Here we studied a NbO x memristor neuron that is capable of emulating numerous neuronal dynamics, including tonic spiking, stochastic spiking, leaky-integrate-and-fire features, spike latency, temporal integration. The device also exhibits phasic bursting, a property that has scarcely been observed and studied in solid-state nano-neurons. We show that we can reproduce and understand this particular response through simulations using non-linear dynamics. These results show that a single NbO x device is sufficient to emulate a collection of rich neuronal dynamics that paves a path forward for realizing scalable and energy-efficient neuromorphic computing paradigms.

Spike timing-based regulation of thalamocortical signaling

For decades, thalamic burst and tonic spiking modes have been theorized to regulate sensory signaling in the thalamocortical circuit. In this issue of Neuron, Borden etal. demonstrate a timing-based mechanism by which thalamic spiking mode controls sensory responses in the awake cortex.