Spiking Behavior Research Articles

Exploring phase control in a quantum dot light-emitting diode

We report phase control in a periodically driven chaotic nanosystem consisting of a quantum dot light-emitting diode. Such a dynamical system is a class C laser, whence the charactering features are intrinsically chaotic. Phase control relies on the addition of small parametric harmonic perturbations with adjustable phase. Phase control is demonstrated by changing both frequency and strength of the controlling perturbations. Our results show that phase control has two crucial effects on a quantum dot light-emitting diode. First, it can enhance the spiking behavior in either regular or chaotic regimes; second, it is able to turn periodic behavior to chaotic behavior with a minimal perturbation when a resonance condition at half of the driving frequency is achieved.

Performance Enhancement of a Dielectric Barrier Discharge Vacuum-Ultraviolet Photon Source Using Short-Pulsed Electrical Excitation

We have studied the electrical and optical characteristics of an air-cooled argon excimer vacuum-ultraviolet lamp ( $\lambda \sim 126$ nm) excited by a dielectric barrier discharge powered by: 1) pulsed or 2) sinusoidal high-voltage drivers from 32 to 100 kHz. Compared to sinusoidal excitation, pulsed excitation gives nearly $\sim 2\times $ higher vacuum-ultraviolet (VUV) output and electrical-to-VUV conversion efficiency at high pressure (800–900 mbar). Visually, the pulse-driven plasma is spatially homogeneous, whereas for sinusoidal excitation the plasma becomes filamentary at higher pressure and/or frequency. Spectral emission is highly monochromatic with most of the output in the desired VUV band ( $\lambda = 115$ –140 nm). With the lamp running at pressure >700 mbar and power loadings >1.6 W/cm3, a sharp spike in VUV output was consistently seen at turn-on. We believe that transient phenomena or favorable initial conditions may be partly responsible for this VUV spike, although the equilibrium VUV output appears to be limited due to thermal dissipation, gas heating, and associated loss of gas from the active region. We propose that we may be observing the same intrinsic VUV spiking phenomena as reported in liquid nitrogen-cooled Xe, Kr, and Ar excimer lamps by Gerasimov et al. More importantly, we believe ours is the first such observation reported for an excimer VUV lamp operating near room temperature. This VUV spiking behavior raises the prospect that designs with improved thermal management may achieve even higher VUV power and efficiency.

Stable Propagation of Inhibited Spiking Dynamics in Vertical-Cavity Surface-Emitting Lasers for Neuromorphic Photonic Networks

We investigate experimentally and theoretically the communication of inhibited spiking dynamics between two interlinked photonic neurons based upon the vertical-cavity surface-emitting lasers (VCSELs). We show that the sub-nanosecond speed spiking dynamics fired by a Transmitter-VCSEL (T-VCSEL) can be inhibited under the arrival of suitable external stimuli and that the inhibited spiking behaviors are propagated into another Receiver-VCSEL (R-VCSEL). Both VCSELs exhibit analogous inhibited spiking dynamics in response to stimuli with different temporal durations and strength. In addition, a very good agreement is found between theoretical simulations and experiments. These results offer greater prospects for future networks of VCSEL-based photonic neurons for neuromorphic computing platforms.

A Rate-Reduced Neuron Model for Complex Spiking Behavior

We present a simple rate-reduced neuron model that captures a wide range of complex, biologically plausible, and physiologically relevant spiking behavior. This includes spike-frequency adaptation, postinhibitory rebound, phasic spiking and accommodation, first-spike latency, and inhibition-induced spiking. Furthermore, the model can mimic different neuronal filter properties. It can be used to extend existing neural field models, adding more biological realism and yielding a richer dynamical structure. The model is based on a slight variation of the Rulkov map.

Optimization of binding B-lymphocytes in a microfluidic channel: surface modification, stasis time and shear response

Binding and maintaining cells inside microfluidic channels is a challenging task due to the potential release of cells from the channels with the flow and accompanying shear stress. In this work we optimized the binding of human B-lymphocyte cells (HR1K) inside a microfluidic channel and determined the strength of this binding under shear stress of flowing liquid. In order to determine the parameters required for a live/dead test in microfluidic devices, populations of both living and dead cells were tested separately. Channels were prepared in glass-polydimethylsiloxane hybrid chips, with a self-assembled monolayer of 3-(glycidyloxypropyl)trimethoxysilane (GPTMS) before covalently immobilizing anti-CD20 antibody. Without GPTMS linker, ∼90% of the CD20-expressing cells detached at 200 μl min−1 (the highest flow rate studied). With GPTMS linker, the bonding method proved critical for sustained immobilization of HR1K cells under flow. Masking the channel area during plasma bonding preserves the antibody functionality; the masked surface gives 15% cell detachment at 200 μl min−1 compared with 80% for an unmasked surface. Sealing the chip via clamping (without plasma treatment) was similar to masked plasma treatment (20% detachment) and allowing a post-adhesion stasis time (30 min) did not significantly change the relative cell detachment for the flow rates studied. Membrane integrity and calcium spiking behaviour were measured fluorescently, and demonstrated that the live cells retained comparable functionality to unanchored cells for the duration of the flow experiments. Non-viable HR1K cells were found to detach more readily, exhibiting only 20% cell retention at 200 μl min−1 compared with >80% for live cells.

Integrate-and-fire models with an almost periodic input function

We investigate leaky integrate-and-fire models (LIF models for short) driven by Stepanov and μ-almost periodic functions. Special attention is paid to the properties of the firing map and its displacement, which give information about the spiking behavior of the considered system. We provide conditions under which such maps are well-defined and are uniformly continuous. We show that the LIF models with Stepanov almost periodic inputs have uniformly almost periodic displacements. We also show that in the case of μ-almost periodic drives it may happen that the displacement map is uniformly continuous, but is not μ-almost periodic (and thus cannot be Stepanov or uniformly almost periodic). By allowing discontinuous inputs, we extend some previous results, showing, for example, that the firing rate for the LIF models with Stepanov almost periodic input exists and is unique. This is a starting point for the investigation of the dynamics of almost-periodically driven integrate-and-fire systems.

Rate, not selectivity, determines neuronal population coding accuracy in auditory cortex.

The notion that neurons with higher selectivity carry more information about external sensory inputs is widely accepted in neuroscience. High-selectivity neurons respond to a narrow range of sensory inputs, and thus would be considered highly informative by rejecting a large proportion of possible inputs. In auditory cortex, neuronal responses are less selective immediately after the onset of a sound and then become highly selective in the following sustained response epoch. These 2 temporal response epochs have thus been interpreted to encode first the presence and then the content of a sound input. Contrary to predictions from that prevailing theory, however, we found that the neural population conveys similar information about sound input across the 2 epochs in spite of the neuronal selectivity differences. The amount of information encoded turns out to be almost completely dependent upon the total number of population spikes in the read-out window for this system. Moreover, inhomogeneous Poisson spiking behavior is sufficient to account for this property. These results imply a novel principle of sensory encoding that is potentially shared widely among multiple sensory systems.

Identification of Persistent and Resurgent Sodium Currents in Spiral Ganglion Neurons Cultured from the Mouse Cochlea.

In spiral ganglion neurons (SGNs), the afferent single units of the auditory nerve, high spontaneous and evoked firing rates ensure preservation of the temporal code describing the key features of incoming sound. During postnatal development, the spatiotemporal distribution of ion channel subtypes contributes to the maturation of action potential generation in SGNs, and to their ability to generate spike patterns that follow rapidly changing inputs. Here we describe tetrodotoxin (TTX)-sensitive Na+ currents in SGNs cultured from mice, whose properties may support this fast spiking behavior. A subthreshold persistent Na+ current (INaP) and a resurgent Na+ current (INaR) both emerged prior to the onset of hearing and became more prevalent as hearing matured. Navβ4 subunits, which are proposed to play a key role in mediating INaR elsewhere in the nervous system, were immunolocalized to the first heminode where spikes are generated in the auditory nerve, and to perisomatic nodes of Ranvier. ATX-II, a sea anemone toxin that slows classical Na+ channel inactivation selectively, enhanced INaP five-fold and INaR three-fold in voltage clamp recordings. In rapidly-adapting SGNs under current clamp, ATX-II increased the likelihood of firing additional action potentials. The data identify INaP and INaR as novel regulators of excitability in SGNs, and consistent with their roles in other neuronal types, we suggest that these nonclassical Na+ currents may contribute to the control of refractoriness in the auditory nerve.

Cascadable Neuron-Like Spiking Dynamics in Coupled VCSELs Subject to Orthogonally Polarized Optical Pulse Injection

The cascadability of spiking dynamics in coupled vertical-cavity surface-emitting lasers (VCSELs) subject to orthogonally polarized optical pulse injection (OPOPI) is investigated numerically based on the well-known spin-flip model. For the coupled VCSELs systems, different connection topologies are considered. The effects of stimuli strength, coupling strength, frequency detuning between two coupled VCSELs, and the pump current on the cascadable spiking dynamics are examined by extensive numerical bifurcation analysis. It is found that, the phasic spike generated in one VCSEL with OPOPI can also induce a similar phasic spike in another VCSEL in three considered connection topologies. Besides, such a cascadable spiking behavior can be achieved in wide range of stimuli strength, coupling strength, and frequency detuning. However, failure transfer of the phasic spike between two VCSELs may be caused when the pump currents are relatively large, and hence, low pump currents are desirable for both VCSELs to allow for successful cascadable spiking dynamics. This work is interesting and valuable for constructing reliable optical spiking information processing platform based on polarization dynamics of VCSELs.

Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference

Probabilistic inference from real-time input data is becoming increasingly popular and may be one of the potential pathways at enabling cognitive intelligence. As a matter of fact, preliminary research has revealed that stochastic functionalities also underlie the spiking behavior of neurons in cortical microcircuits of the human brain. In tune with such observations, neuromorphic and other unconventional computing platforms have recently started adopting the usage of computational units that generate outputs probabilistically, depending on the magnitude of the input stimulus. In this work, we experimentally demonstrate a spintronic device that offers a direct mapping to the functionality of such a controllable stochastic switching element. We show that the probabilistic switching of Ta/CoFeB/MgO heterostructures in presence of spin-orbit torque and thermal noise can be harnessed to enable probabilistic inference in a plethora of unconventional computing scenarios. This work can potentially pave the way for hardware that directly mimics the computational units of Bayesian inference.

Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models.

A key problem in computational neuroscience is to find simple, tractable models that are nevertheless flexible enough to capture the response properties of real neurons. Here we examine the capabilities of recurrent point process models known as Poisson generalized linear models (GLMs). These models are defined by a set of linear filters and a point nonlinearity and are conditionally Poisson spiking. They have desirable statistical properties for fitting and have been widely used to analyze spike trains from electrophysiological recordings. However, the dynamical repertoire of GLMs has not been systematically compared to that of real neurons. Here we show that GLMs can reproduce a comprehensive suite of canonical neural response behaviors, including tonic and phasic spiking, bursting, spike rate adaptation, type I and type II excitation, and two forms of bistability. GLMs can also capture stimulus-dependent changes in spike timing precision and reliability that mimic those observed in real neurons, and can exhibit varying degrees of stochasticity, from virtually deterministic responses to greater-than-Poisson variability. These results show that Poisson GLMs can exhibit a wide range of dynamic spiking behaviors found in real neurons, making them well suited for qualitative dynamical as well as quantitative statistical studies of single-neuron and population response properties.

Stability properties of a two-dimensional system involving one Caputo derivative and applications to the investigation of a fractional-order Morris–Lecar neuronal model

Necessary and sufficient conditions are given for the asymptotic stability and instability of a two-dimensional incommensurate order autonomous linear system, which consists of a differential equation with a Caputo-type fractional-order derivative and a classical first-order differential equation. These conditions are expressed in terms of the elements of the system’s matrix, as well as of the fractional order of the Caputo derivative. In this setting, we obtain a generalization of the well-known Routh–Hurwitz conditions. These theoretical results are then applied to the analysis of a two-dimensional fractional-order Morris–Lecar neuronal model, focusing on stability and instability properties. This fractional-order model is built up taking into account the dimensional consistency of the resulting system of differential equations. The occurrence of Hopf bifurcations is also discussed. Numerical simulations exemplify the theoretical results, revealing rich spiking behavior. The obtained results are also compared to similar ones obtained for the classical integer-order Morris–Lecar neuronal model.

(Invited) Low-Resistance Ohmic Contacts to Al0.45Ga0.55n/ Al0.3Ga0.7n HEMTS

RF amplifiers and power electronics transistors in III-N semiconductors are increasingly important in emerging applications, as are photo-transistors for the purpose of UV light detection. Increasing the Al fraction in AlGaN-based HEMT devices enables transistors with higher breakdown voltages and a potentially favorable breakdown - specific on-resistance tradeoff. However, the advantages of higher aluminum content comes with a major challenge: Ohmic contacts with acceptable contact resistance become increasingly problematic with increasing Al content in the channel and barrier layer. We report sub 10-4 W-cm2 specific contact resistivity (rc) for a 30% channel, improving on the best prior result of approximately 2x10-4 W-cm2for AlGaN channels of at least 30% Al. We investigated five different Ohmic contact metallizations consisting of X/Al/Y/Au where X = Ti, Nb/Ti, Zr, or V, and Y = Ni, Mo, or V (Table 1) as function of alloying conditions. The MOCVD-grown structure (Figure 1) is on sapphire substrates and consists of a 1.6 µm AlN buffer layer, a 4.15 µm thick Al0.3Ga0.7N channel layer and a 50 nm thick Al0.45Ga0.55N barrier with Rsheet of 3500 Ω/□ and ns= 5x1012/cm2. Representative current-voltage plots measured on circular TLM structures are shown in Figure 2. Ohmic contacts with average specific contact resistance (pc) of 7x10-5 Ω-cm2 (3.3x10-5 Ω-cm2) extracted from device (TLM) data were achieved with a conventional Ti/Al/Ni/Au metal stack and a multi-temperature stepped-anneal process. Structural characterization was carried out on the Ti/Al/Ni/Au contacts, to compare Ohmic and Schottky conditions. TEM was used to characterize the interaction between the contact metals and the AlGaN, revealing an interface consisting primarily of Ti-Au with limited indication of spiking behavior. Using the optimized Ohmic contact process, HEMTs with 10 µm source-to-drain spacing and 2 µm gate lengths were fabricated; current densities of 78 mA/mm were achieved, and parasitic loading of RON was reduced. This work was supported by the Laboratory Directed Research and Development (LDRD) program at Sandia. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. Figure 1

Temperature Control for Proton Exchange Membrane Fuel Cell based on Current Constraint with Consideration of Limited Cooling Capacity

AbstractTemperature is one of the key factors to ensure reliable and efficient performance of proton exchange membrane fuel cells. Whereas, due to the limited cooling capacity in automotive application, conventional strategies may fail to remove the excessive heat when the cooling actuator saturates. Hence, three temperature control strategies based on current constraint are developed in this study. Firstly, a dynamic thermal model based on energy conservation is presented. Then a duty ratio split control strategy is proposed to calculate the reduced current from the excess of duty ratio of radiator, yet spike behavior occurs. Hence, a current governor control strategy and a constrained model predictive control strategy are derived to trade off the power demand and cooling capacity, where the maximum load current is calculated by current governor in real time and optimized under the physical constraints of duty ratio and current change rate, respectively. The performances of all the strategies are demonstrated. The current governor control strategy is easy to implement with less computational burden. The constrained model predictive control strategy has fast response and can take into account various constraints explicitly. Both of them have satisfying control performances regardless of limited cooling capacity.

Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation

The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation. This approach, however, leads to a model with an infinite-dimensional state space and non-standard boundary conditions. Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated. We evaluate the reduced models for a wide range of biologically plausible input statistics and find that both approximation approaches lead to spike rate models that accurately reproduce the spiking behavior of the underlying adaptive integrate-and-fire population. Particularly the cascade-based models are overall most accurate and robust, especially in the sensitive region of rapidly changing input. For the mean-driven regime, when input fluctuations are not too strong and fast, however, the best performing model is based on the spectral decomposition. The low-dimensional models also well reproduce stable oscillatory spike rate dynamics that are generated either by recurrent synaptic excitation and neuronal adaptation or through delayed inhibitory synaptic feedback. The computational demands of the reduced models are very low but the implementation complexity differs between the different model variants. Therefore we have made available implementations that allow to numerically integrate the low-dimensional spike rate models as well as the Fokker-Planck partial differential equation in efficient ways for arbitrary model parametrizations as open source software. The derived spike rate descriptions retain a direct link to the properties of single neurons, allow for convenient mathematical analyses of network states, and are well suited for application in neural mass/mean-field based brain network models.

Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making.

A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject's decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a “coding duality” in which there are accumulation and consensus formation processes distinguished by different timescales.

Oscillatory signatures underlie growth regimes in Arabidopsis pollen tubes: computational methods to estimate tip location, periodicity, and synchronization in growing cells.

Oscillations in pollen tubes have been reported for many cellular processes, including growth, extracellular ion fluxes, and cytosolic ion concentrations. However, there is a shortage of quantitative methods to measure and characterize the different dynamic regimes observed. Herein, a suite of open-source computational methods and original algorithms were integrated into an automated analysis pipeline that we employed to characterize specific oscillatory signatures in pollen tubes of Arabidopsis thaliana (Col-0). Importantly, it enabled us to detect and quantify a Ca2+ spiking behaviour upon growth arrest and synchronized oscillations involving growth, extracellular H+ fluxes, and cytosolic Ca2+, providing the basis for novel hypotheses. Our computational approach includes a new tip detection method with subpixel resolution using linear regression, showing improved ability to detect oscillations when compared to currently available methods. We named this data analysis pipeline 'Computational Heuristics for Understanding Kymographs and aNalysis of Oscillations Relying on Regression and Improved Statistics', or CHUKNORRIS. It can integrate diverse data types (imaging, electrophysiology), extract quantitative and time-explicit estimates of oscillatory characteristics from isolated time series (period and amplitude) or pairs (phase relationships and delays), and evaluate their synchronization state. Here, its performance is tested with ratiometric and single channel kymographs, ion flux data, and growth rate analysis.

The reward-attention circuit model: Nicotine’s influence on attentional focus and consequences on attention deficit hyperactivity disorder

In this work we investigate the influence that nicotine exerts on attention focusing, modeling the coupling of reward and thalamocortical circuits. Each neuron in the neural networks that replicate such circuits is described by a carefully designed coupled system of nonlinear differential equations that details essential neurophysiological properties.Computer simulations of the reward-attention circuit reflects the spiking behavior of each individual neuron in the network, under the presence or absence of nicotine. It becomes clear that changes in the dopaminergic levels in the reward circuit due to nicotine strongly influences the nigral dopaminergic activity, that modulates the thalamocortical filtering mechanism.Our results indicate that nicotine strengthens the attentional focus on a particular stimulus, decreasing the smoker’s mental flexibility. Also, the simulations highlight aspects of clinical inattention symptoms, particularly in the attention deficit hyperactivity disorder. They show why patients suffering from attentional lability might improve the attentional focus when exposed to nicotine.

Developmental nicotine exposure alters potassium currents in hypoglossal motoneurons of neonatal rat

We previously showed that nicotine exposure in utero and after birth via breast milk [developmental nicotine exposure (DNE)] is associated with many changes in the structure and function of hypoglossal motoneurons (XIIMNs), including a reduction in the size of the dendritic arbor and an increase in cell excitability. Interestingly, the elevated excitability was associated with a reduction in the expression of glutamate receptors on the cell body. Together, these observations are consistent with a homeostatic compensation aimed at restoring cell excitability. Compensation for increased cell excitability could also occur by changing potassium conductance, which plays a critical role in regulating resting potential, spike threshold, and repetitive spiking behavior. Here we test the hypothesis that the previously observed increase in the excitability of XIIMNs from DNE animals is associated with an increase in whole cell potassium currents. Potassium currents were measured in XIIMNs in brain stem slices derived from DNE and control rat pups ranging in age from 0 to 4 days by whole cell patch-clamp electrophysiology. All currents were measured after blockade of action potential-dependent synaptic transmission with tetrodotoxin. Compared with control cells, XIIMNs from DNE animals showed significantly larger transient and sustained potassium currents, but this was observed only under conditions of increased cell and network excitability, which we evoked by raising extracellular potassium from 3 to 9 mM. These observations suggest that the larger potassium currents in nicotine-exposed neurons are an important homeostatic compensation that prevents "runaway" excitability under stressful conditions, when neurons are receiving elevated excitatory synaptic input.NEW & NOTEWORTHY Developmental nicotine exposure is associated with increased cell excitability, which is often accompanied by compensatory changes aimed at normalizing excitability. Here we show that whole cell potassium currents are also increased in hypoglossal motoneurons from nicotine-exposed neonatal rats under conditions of increased cell and network excitability. This is consistent with a compensatory response aimed at preventing instability under conditions in which excitatory synaptic input is high and is compatible with the concept of homeostatic plasticity.

Efficient generation of hPSC-derived midbrain dopaminergic neurons in a fully defined, scalable, 3D biomaterial platform

Pluripotent stem cells (PSCs) have major potential as an unlimited source of functional cells for many biomedical applications; however, the development of cell manufacturing systems to enable this promise faces many challenges. For example, there have been major recent advances in the generation of midbrain dopaminergic (mDA) neurons from stem cells for Parkinson’s Disease (PD) therapy; however, production of these cells typically involves undefined components and difficult to scale 2D culture formats. Here, we used a fully defined, 3D, thermoresponsive biomaterial platform to rapidly generate large numbers of action-potential firing mDA neurons after 25 days of differentiation (~40% tyrosine hydroxylase (TH) positive, maturing into 25% cells exhibiting mDA neuron-like spiking behavior). Importantly, mDA neurons generated in 3D exhibited a 30-fold increase in viability upon implantation into rat striatum compared to neurons generated on 2D, consistent with the elevated expression of survival markers FOXA2 and EN1 in 3D. A defined, scalable, and resource-efficient cell culture platform can thus rapidly generate high quality differentiated cells, both neurons and potentially other cell types, with strong potential to accelerate both basic and translational research.