Spike Emission Research Articles

The impact of spike timing precision and spike emission reliability on decoding accuracy

Precisely timed and reliably emitted spikes are hypothesized to serve multiple functions, including improving the accuracy and reproducibility of encoding stimuli, memories, or behaviours across trials. When these spikes occur as a repeating sequence, they can be used to encode and decode a potential time series. Here, we show both analytically and in simulations that the error incurred in approximating a time series with precisely timed and reliably emitted spikes decreases linearly with the number of neurons or spikes used in the decoding. This was verified numerically with synthetically generated patterns of spikes. Further, we found that if spikes were imprecise in their timing, or unreliable in their emission, the error incurred in decoding with these spikes would be sub-linear. However, if the spike precision or spike reliability increased with network size, the error incurred in decoding a time-series with sequences of spikes would maintain a linear decrease with network size. The spike precision had to increase linearly with network size, while the probability of spike failure had to decrease with the square-root of the network size. Finally, we identified a candidate circuit to test this scaling relationship: the repeating sequences of spikes with sub-millisecond precision in area HVC (proper name) of the zebra finch. This scaling relationship can be tested using both neural data and song-spectrogram-based recordings while taking advantage of the natural fluctuation in HVC network size due to neurogenesis.

Electric spike emergence and synchronization during wet etching of overlapping low energy light ion microbeam-irradiated polymers

Electric spike emission was observed for the first time for thin polymer foils irradiated with low energy light ion microbeams at overlapping fluences, when placing them in between two vessels filled with etchant and distilled water, respectively, and applying a low sinusoidal transverse voltage. The various shapes of recorded spike types are described and examined. From their emergence that occurs after different derived etching times, and the inter-correlation of different spike parameters, we draw the first conclusions about their formation mechanism, behavior, and usefulness for technical applications.

Solar Radio Spikes and Type IIIb Striae Manifestations of Subsecond Electron Acceleration Triggered by a Coronal Mass Ejection

Understanding electron acceleration associated with magnetic energy release at subsecond scales presents major challenges in solar physics. Solar radio spikes observed as subsecond, narrow-bandwidth bursts with Δf/f ∼ 10−3–10−2 are indicative of a subsecond evolution of the electron distribution. We present a statistical analysis of frequency- and time-resolved imaging of individual spikes and Type IIIb striae associated with a coronal mass ejection (CME). LOFAR imaging reveals that the cotemporal (<2 s) spike and striae intensity contours almost completely overlap. On average, both burst types have a similar source size with a fast expansion at millisecond scales. The radio source centroid velocities are often superluminal and independent of frequency over 30–45 MHz. The CME perturbs the field geometry, leading to increased spike emission likely due to frequent magnetic reconnection. As the field restores itself toward the prior configuration, the observed sky-plane emission locations drift to increased heights over tens of minutes. Combined with previous observations above 1 GHz, the average decay time and source size estimates follow a ∼1/f dependence over three decades in frequency, similar to radio-wave scattering predictions. Both time and spatial characteristics of the bursts between 30 and 70 MHz are consistent with radio-wave scattering with a strong anisotropy of the density fluctuation spectrum. Consequently, the site of the radio-wave emission does not correspond to the observed burst locations and implies acceleration and emission near the CME flank. The bandwidths suggest intrinsic emission source sizes <1″ at 30 MHz and magnetic field strengths a factor of two larger than average in events that produce decameter spikes.

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.

A Novel Nociceptor Functional Circuit for Tactile Applications

In this research, the main functional characteristics of nociceptors are considered for designing a novel neuromorphic circuit in TSMC 180 nm CMOS technology with a 1.8 V supply voltage and 780 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power consumption. Indeed, the current study is the first analog circuit realization of the functional model of nociceptors. The proposed circuit includes three spiking neurons. The first and second neurons determine the pressure level and the active area of the stimulus, respectively. The pressure to active area ratio is sensed by the third neuron and helps for better discrimination of the input stimuli. To evaluate the performance of the proposed nociceptor circuit, we perform numerical simulations and robotic experiments. In this way, the 3D-printed objects with different sharpness are touched by a custom-made tactile sensor and the spiking responses from the nociceptor neuromorphic circuit are then collected. Next, machine learning algorithms are applied offline to classify objects based on the rate and time of the spike responses. The results show that by analyzing the spike patterns obtained from the bio-inspired circuit, it is possible to recognize stimuli after the emission of a few spikes. The proposed approach is the proof of concept that circuit implementation of the functional model of the nociceptors expands the range of object recognition and hence facilitates the fabrication of novel tactile sensory systems for bio-robotic and prosthetic applications.

Long-Term Synaptic Plasticity Tunes the Gain of Information Channels through the Cerebellum Granular Layer.

A central hypothesis on brain functioning is that long-term potentiation (LTP) and depression (LTD) regulate the signals transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, granule cells have been shown to control the gain of signals transmitted through the mossy fiber pathway by exploiting synaptic inhibition in the glomeruli. However, the way LTP and LTD control signal transformation at the single-cell level in the space, time and frequency domains remains unclear. Here, the impact of LTP and LTD on incoming activity patterns was analyzed by combining patch-clamp recordings in acute cerebellar slices and mathematical modeling. LTP reduced the delay, increased the gain and broadened the frequency bandwidth of mossy fiber burst transmission, while LTD caused opposite changes. These properties, by exploiting NMDA subthreshold integration, emerged from microscopic changes in spike generation in individual granule cells such that LTP anticipated the emission of spikes and increased their number and precision, while LTD sorted the opposite effects. Thus, akin with the expansion recoding process theoretically attributed to the cerebellum granular layer, LTP and LTD could implement selective filtering lines channeling information toward the molecular and Purkinje cell layers for further processing.

High-efficiency and low-energy ship recognition strategy based on spiking neural network in SAR images.

Ship recognition using synthetic aperture radar (SAR) images has important applications in the military and civilian fields. Aiming at the problems of the many model parameters and high-energy losses in the traditional deep learning methods for the target recognition in the SAR images, this study has proposed a high-efficiency and low-energy ship recognition strategy based on the spiking neural network (SNN) in the SAR images. First, the visual attention mechanism is used to extract the visual saliency map from the SAR image, and then the Poisson encoder is used to encode it into a spike train, which can suppress the background noise while retaining the visual saliency feature of the SAR image. Besides, an SNN model integrating the time-series information is constructed by combining the leaked and integrated firing spiking neurons with the convolutional neural network (CNN), which can use the firing frequency of the spiking neurons to realize the ship recognition in SAR images. Finally, to solve the problem that SNN model is difficult to train, the arctangent function is used as the surrogate gradient function of the spike emission function during the backpropagation. Hence, applying this backpropagation method to the training process can optimize the SNN model. The experimental results show the following: (1) the proposed strategy can more accurately recognize the ship in the SAR image, and the F1 score can reach 98.55%, which has a better recognition performance than the other traditional deep learning methods; (2) the proposed strategy has the least amount of model parameters (only 3.11MB), which is far less than the model parameters of the other traditional deep learning methods; (3) the proposed strategy has fewer operations (only 17.97M) and can reach 1/30 time of operands of the other traditional deep learning methods, which shows the high efficiency of the proposed strategy using the spike emission signals; (4) the proposed strategy has the energy loss of 1.38 × 10−6J, which can achieve the low energy advantage of nearly three orders of the magnitude compared to the other traditional deep learning methods, indicating that the proposed strategy has a significant energy efficiency.

High-resolution observations with ARTEMIS/JLS and the NRH

Context.Narrowband bursts (spikes) are very small duration and bandwidth bursts which appear on dynamic spectra from microwave to decametric frequencies. They are believed to be manifestations of small-scale energy release through magnetic reconnection.Aims.We study the position of the spike-like structures relative to the front of type-II bursts and their role in the burst emission.Methods.We used high-sensitivity, low-noise dynamic spectra obtained with the acousto-optic analyzer (SAO) of the ARTEMIS-JLS solar radiospectrograph, in conjunction with high-time-resolution images from the Nançay Radioheliograph (NRH) in order to study spike-like bursts near the front of a type-II radio burst recorded at the west limb during the November 3, 2003 extreme solar event. The spike-like emission in the dynamic spectrum was enhanced by means of high-pass-time filtering.Results.We identified a number of spikes in the NRH images. Due to the lower temporal resolution of the NRH, multiple spikes detected in the dynamic spectrum appeared as single structures in the images. These spikes had an average size of ≈200″ and their observed brightness temperature was 1.4 to 5.6 × 109K, providing a significant contribution to the emission of the type-II burst front. At variance with a previous study on the type-IV associated spikes, we found no systematic displacement between the spike emission and the emission between spikes. At 327.0 MHz, the type II emission was located about 0.3R⊙above the pre-existing continuum emission, which, in turn, was located 0.1R⊙above the western limb.Conclusions.This study, combined with our previous results, indicates that the spike-like chains aligned along the type II burst MHD shock front are not a perturbation of the type II emission, as in the case of type IV spikes, but a manifestation of the type II emission itself. The preponderance of these chains, together with the lack of isolated structures or irregular clusters, points towards some form of small-scale magnetic reconnection, organized along the type-II propagating front.

First Frequency-time-resolved Imaging Spectroscopy Observations of Solar Radio Spikes

Abstract Solar radio spikes are short duration and narrow bandwidth fine structures in dynamic spectra observed from the GHz to tens of MHz range. Their very short duration and narrow frequency bandwidth are indicative of subsecond small-scale energy release in the solar corona, yet their origin is not understood. Using the LOw Frequency ARray, we present spatially, frequency, and time resolved observations of individual radio spikes associated with a coronal mass ejection. Individual radio spike imaging demonstrates that the observed area is increasing in time and the centroid positions of the individual spikes move superluminally parallel to the solar limb. Comparison of spike characteristics with that of individual Type IIIb striae observed in the same event show similarities in duration, bandwidth, drift rate, polarization, and observed area, as well the spike and striae motion in the image plane suggesting fundamental plasma emission with the spike emission region on the order of ∼108 cm, with brightness temperature as high as 1013 K. The observed spatial, spectral, and temporal properties of the individual spike bursts are also suggestive of the radiation responsible for spikes escaping through anisotropic density turbulence in closed loop structures with scattering preferentially along the guiding magnetic field oriented parallel to the limb in the scattering region. The dominance of scattering on the observed time profile suggests the energy release time is likely to be shorter than what is often assumed. The observations also imply that the density turbulence anisotropy along closed magnetic field lines is higher than along open field lines.

Synchronization, Stochasticity, and Phase Waves in Neuronal Networks With Spatially-Structured Connectivity.

Oscillations in the beta/low gamma range (10–45 Hz) are recorded in diverse neural structures. They have successfully been modeled as sparsely synchronized oscillations arising from reciprocal interactions between randomly connected excitatory (E) pyramidal cells and local interneurons (I). The synchronization of spatially distant oscillatory spiking E–I modules has been well-studied in the rate model framework but less so for modules of spiking neurons. Here, we first show that previously proposed modifications of rate models provide a quantitative description of spiking E–I modules of Exponential Integrate-and-Fire (EIF) neurons. This allows us to analyze the dynamical regimes of sparsely synchronized oscillatory E–I modules connected by long-range excitatory interactions, for two modules, as well as for a chain of such modules. For modules with a large number of neurons (> 105), we obtain results similar to previously obtained ones based on the classic deterministic Wilson-Cowan rate model, with the added bonus that the results quantitatively describe simulations of spiking EIF neurons. However, for modules with a moderate (~ 104) number of neurons, stochastic variations in the spike emission of neurons are important and need to be taken into account. On the one hand, they modify the oscillations in a way that tends to promote synchronization between different modules. On the other hand, independent fluctuations on different modules tend to disrupt synchronization. The correlations between distant oscillatory modules can be described by stochastic equations for the oscillator phases that have been intensely studied in other contexts. On shorter distances, we develop a description that also takes into account amplitude modes and that quantitatively accounts for our simulation data. Stochastic dephasing of neighboring modules produces transient phase gradients and the transient appearance of phase waves. We propose that these stochastically-induced phase waves provide an explanative framework for the observations of traveling waves in the cortex during beta oscillations.

Temporal evolution of spatial optical emission characteristics by inductively-coupled impulse sputtering (ICIS) using high power-burst 155kHz-ICP

A copper target was negatively-biased and was sputtered by bombardment of argon ion species produced 155 kHz-inductively-coupled plasma (ICP) in a burst-pulse mode for a duration of 800 μs. This system was called ICIS (Inductively-coupled impulse sputtering). The plasma density was on the order of 1019 m−3 in the ICP region. The target current density and the power density were about 0.8 A/cm2 and about 0.3 kW/cm2, respectively. Temporal evolution of the optical emission of the plasma species was discussed. The observed species were argon ions, excited argon and excited copper. The entire region consisted of the ICP, a bulk plasma and a target plasma. Argon ions produced in the ICP were transported by an ambipolar diffusion toward the target and excited argon and excited copper species were mainly produced in the target plasma. A plateau emission by argon ions and spike emission by energetic electrons which were originated from secondary electrons were seen. Gas rarefaction of argon gas also influenced the temporary evolution particularly near at the target. This was discussed using a carbon target.

Reconfigurable Computation in Spiking Neural Networks

The computation of rank ordering plays a fundamental role in cognitive tasks and offers a basic building block for computing arbitrary digital functions. Spiking neural networks have been demonstrated to be capable of identifying the largest k out of N analog input signals through their collective nonlinear dynamics. By finding partial rank orderings, they perform k-winners-take-all computations. Yet, for any given study so far, the value of k is fixed, often to k equal one. Here we present a concept for spiking neural networks that are capable of (re)configurable computation by choosing k via one global system parameter. The spiking network acts via pulse-suppression induced by inhibitory pulse-couplings. Couplings are proportional to each units' state variable (neuron voltage), constituting an uncommon but straightforward type of leaky integrate-and-fire neural network. The result of a computation is encoded as a stable periodic orbit with k units spiking at some frequency and others at lower frequency or not at all. Orbit stability makes the resulting analog-to-digital computation robust to sufficiently small variations of both, parameters and signals. Moreover, the computation is completed quickly within a few spike emissions per neuron. These results indicate how reconfigurable k-winners-take-all computations may be implemented and effectively exploited in simple hardware relying only on basic dynamical units and spike interactions resembling simple current leakages to a common ground.

Dimensional reduction in networks of non-Markovian spiking neurons: Equivalence of synaptic filtering and heterogeneous propagation delays.

Message passing between components of a distributed physical system is non-instantaneous and contributes to determine the time scales of the emerging collective dynamics. In biological neuron networks this is due in part to local synaptic filtering of exchanged spikes, and in part to the distribution of the axonal transmission delays. How differently these two kinds of communication protocols affect the network dynamics is still an open issue due to the difficulties in dealing with the non-Markovian nature of synaptic transmission. Here, we develop a mean-field dimensional reduction yielding to an effective Markovian dynamics of the population density of the neuronal membrane potential, valid under the hypothesis of small fluctuations of the synaptic current. Within this limit, the resulting theory allows us to prove the formal equivalence between the two transmission mechanisms, holding for any synaptic time scale, integrate-and-fire neuron model, spike emission regimes and for different network states even when the neuron number is finite. The equivalence holds even for larger fluctuations of the synaptic input, if white noise currents are incorporated to model other possible biological features such as ionic channel stochasticity.

Spiking neural networks modelled as timed automata: with parameter learning

In this paper we address the issue of automatically learning parameters of spiking neural networks. Biological neurons are formalized as timed automata and synaptical connections are represented as shared channels among these automata. Such a formalism allows us to take into account several time-related aspects, such as the influence of past inputs in the computation of the potential value of each neuron, or the presence of the refractory period, a lapse of time immediately following the spike emission in which the neuron cannot emit. The proposed model is then formally validated: more precisely, we ensure that some relevant properties expressed as temporal logical formulae hold in the model. Once the validation step is accomplished, we take advantage of the proposed model to write an algorithm for learning synaptical weight values such that an expected behavior can be displayed. The technique we present takes inspiration from supervised learning ones: we compare the effective output of the network to the expected one and backpropagate proper corrective actions in the network. We develop several case studies including a mutual inhibition network.

Harmonics of Solar Radio Spikes at Metric Wavelengths

This paper presents the latest observations from the newly-built solar radio spectrograph at the \emph{Chashan Solar Observatory}. On July 18 2016, the spectrograph records a solar spike burst event, which has several episodes showing harmonic structures, with the second, third, and fourth harmonics. The lower harmonic radio spike emissions are observed later than the higher harmonic bands, and the temporal delay of the second (third) harmonic relative to the fourth harmonic is about 30\ --\ 40 (10) ms. Based on the electron cyclotron maser emission mechanism, we analyze possible causes of the temporal delay and further infer relevant coronal parameters, such as the magnetic field strength and the electron density at the radio source.

Ion track etching revisited: II. Electronic properties of aged tracks in polymers

ABSTRACTWe compile here electronic ion track etching effects, such as capacitive-type currents, current spike emission, phase shift, rectification and background currents that eventually emerge upon application of sinusoidal alternating voltages across thin, aged swift heavy ion-irradiated polymer foils during etching. Both capacitive-type currents and current spike emission occur as long as obstacles still prevent a smooth continuous charge carrier passage across the foils. In the case of sufficiently high applied electric fields, these obstacles are overcome by spike emission. These effects vanish upon etchant breakthrough. Subsequent transmitted currents are usually of Ohmic type, but shortly after breakthrough (during the track’ core etching) often still exhibit deviations such as strong positive phase shifts. They stem from very slow charge carrier mobility across the etched ion tracks due to retarding trapping/detrapping processes. Upon etching the track’s penumbra, one occasionally observes a split-up into two transmitted current components, one with positive and another one with negative phase shifts. Usually, these phase shifts vanish when bulk etching starts. Current rectification upon track etching is a very frequent phenomenon. Rectification uses to inverse when core etching ends and penumbra etching begins. When the latter ends, rectification largely vanishes. Occasionally, some residual rectification remains which we attribute to the aged polymeric bulk itself. Last not least, we still consider background currents which often emerge transiently during track etching. We could assign them clearly to differences in the electrochemical potential of the liquids on both sides of the etched polymer foils. Transient relaxation effects during the track etching cause their eventually chaotic behaviour.

Unique probe of dark matter in the core of M87 with the Event Horizon Telescope

We demonstrate the unprecedented capabilities of the Event Horizon Telescope (EHT) to image the innermost dark matter profile in the vicinity of the supermassive black hole at the center of the M87 radio galaxy. We present the first model of the synchrotron emission induced by dark matter annihilations from a spiky profile in the close vicinity of a supermassive black hole, accounting for strong gravitational lensing effects. Our results show that the EHT should readily resolve dark matter spikes if present. Moreover, the photon ring surrounding the silhouette of the black hole is clearly visible in the spike emission, which introduces observable small-scale structure into the signal. We find that the dark matter-induced emission provides an adequate fit to the existing EHT data, implying that in addition to the jet, a dark matter spike may account for a sizable portion of the millimeter emission from the innermost (subparsec) region of M87. Regardless, our results show that the EHT can probe very weakly annihilating dark matter. Current EHT observations already constrain very small cross sections, typically down to a few 10^{-31} cm^{3} s^{-1} for a 10 GeV candidate, close to characteristic values for p-wave-suppressed annihilation. Future EHT observations will further improve constraints on the DM scenario.

Determine Neuronal Tuning Curves by Exploring Optimum Firing Rate Distribution for Information Efficiency.

This paper proposed a new method to determine the neuronal tuning curves for maximum information efficiency by computing the optimum firing rate distribution. Firstly, we proposed a general definition for the information efficiency, which is relevant to mutual information and neuronal energy consumption. The energy consumption is composed of two parts: neuronal basic energy consumption and neuronal spike emission energy consumption. A parameter to model the relative importance of energy consumption is introduced in the definition of the information efficiency. Then, we designed a combination of exponential functions to describe the optimum firing rate distribution based on the analysis of the dependency of the mutual information and the energy consumption on the shape of the functions of the firing rate distributions. Furthermore, we developed a rapid algorithm to search the parameter values of the optimum firing rate distribution function. Finally, we found with the rapid algorithm that a combination of two different exponential functions with two free parameters can describe the optimum firing rate distribution accurately. We also found that if the energy consumption is relatively unimportant (important) compared to the mutual information or the neuronal basic energy consumption is relatively large (small), the curve of the optimum firing rate distribution will be relatively flat (steep), and the corresponding optimum tuning curve exhibits a form of sigmoid if the stimuli distribution is normal.

Emergent dynamics of spiking neurons with fluctuating threshold

Role of fluctuating threshold on neuronal dynamics is investigated. The threshold function is assumed to follow a normal probability distribution. Standard deviation of inter-spike interval of the response is computed as an indicator of irregularity in spike emission. It has been observed that, the irregularity in spiking is more if the threshold variation is more. A significant change in modal characteristics of Inter Spike Intervals (ISI) is seen to occur as a function of fluctuation parameter. Investigation is further carried out for coupled system of neurons. Cooperative dynamics of coupled neurons are discussed in view of synchronization. Total and partial synchronization regimes are depicted with the help of contour plots of synchrony measure under various conditions. Results of this investigation may provide a basis for exploring the complexities of neural communication and brain functioning.

Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task.

The computational role of spike time synchronization at millisecond precision among neurons in the cerebral cortex is hotly debated. Studies performed on data of limited size provided experimental evidence that low-order correlations occur in relation to behavior. Advances in electrophysiological technology to record from hundreds of neurons simultaneously provide the opportunity to observe coordinated spiking activity of larger populations of cells. We recently published a method that combines data mining and statistical evaluation to search for significant patterns of synchronous spikes in massively parallel spike trains (Torre et al., 2013). The method solves the computational and multiple testing problems raised by the high dimensionality of the data. In the current study, we used our method on simultaneous recordings from two macaque monkeys engaged in an instructed-delay reach-to-grasp task to determine the emergence of spike synchronization in relation to behavior. We found a multitude of synchronous spike patterns aligned in both monkeys along a preferential mediolateral orientation in brain space. The occurrence of the patterns is highly specific to behavior, indicating that different behaviors are associated with the synchronization of different groups of neurons ("cell assemblies"). However, pooled patterns that overlap in neuronal composition exhibit no specificity, suggesting that exclusive cell assemblies become active during different behaviors, but can recruit partly identical neurons. These findings are consistent across multiple recording sessions analyzed across the two monkeys. Neurons in the brain communicate via electrical impulses called spikes. How spikes are coordinated to process information is still largely unknown. Synchronous spikes are effective in triggering a spike emission in receiving neurons and have been shown to occur in relation to behavior in a number of studies on simultaneous recordings of few neurons. We recently published a method to extend this type of investigation to larger data. Here, we apply it to simultaneous recordings of hundreds of neurons from the motor cortex of macaque monkeys performing a motor task. Our analysis reveals groups of neurons selectively synchronizing their activity in relation to behavior, which sheds new light on the role of synchrony in information processing in the cerebral cortex.