Neural Spike Research Articles

The impact of D-cycloserine and sarcosine on in vivo frontal neural activity in a schizophrenia-like model

BackgroundN-methyl-D-aspartate receptor (NMDAR) hypofunction has been proposed to underlie the pathogenesis of schizophrenia. Specifically, reduced function of NMDARs leads to altered balance between excitation and inhibition which further drives neural network malfunctions. Clinical studies suggested that NMDAR modulators (glycine, D-serine, D-cycloserine and glycine transporter inhibitors) may be beneficial in treating schizophrenia patients. Preclinical evidence also suggested that these NMDAR modulators may enhance synaptic NMDAR function and synaptic plasticity in brain slices. However, an important issue that has not been addressed is whether these NMDAR modulators modulate neural activity/spiking in vivo.MethodsBy using in vivo calcium imaging and single unit recording, we tested the effect of D-cycloserine, sarcosine (glycine transporter 1 inhibitor) and glycine, on schizophrenia-like model mice.ResultsIn vivo neural activity is significantly higher in the schizophrenia-like model mice, compared to control mice. D-cycloserine and sarcosine showed no significant effect on neural activity in the schizophrenia-like model mice. Glycine induced a large reduction in movement in home cage and reduced in vivo brain activity in control mice which prevented further analysis of its effect in schizophrenia-like model mice.ConclusionsWe conclude that there is no significant impact of the tested NMDAR modulators on neural spiking in the schizophrenia-like model mice.

Modeling stimulus-dependent variability improves decoding of population neural responses

Objective. Neural responses to repeated presentations of an identical stimulus often show substantial trial-to-trial variability. How the mean firing rate varies in response to different stimuli or during different movements (tuning curves) has been extensively modeled in a wide variety of neural systems. However, the variability of neural responses can also have clear tuning independent of the tuning in the mean firing rate. This suggests that the variability could contain information regarding the stimulus/movement beyond what is encoded in the mean firing rate. Here we demonstrate how taking variability into account can improve neural decoding. Approach. In a typical neural coding model spike counts are assumed to be Poisson with the mean response depending on an external variable, such as a stimulus or movement. Bayesian decoding methods then use the probabilities under these Poisson tuning models (the likelihood) to estimate the probability of each stimulus given the spikes on a given trial (the posterior). However, under the Poisson model, spike count variability is always exactly equal to the mean (Fano factor = 1). Here we use two alternative models—the Conway–Maxwell–Poisson (CMP) model and negative binomial (NB) model—to more flexibly characterize how neural variability depends on external stimuli. These models both contain the Poisson distribution as a special case but have an additional parameter that allows the variance to be greater than the mean (Fano factor > 1) or, for the CMP model, less than the mean (Fano factor < 1). Main results. We find that neural responses in primary motor (M1), visual (V1), and auditory (A1) cortices have diverse tuning in both their mean firing rates and response variability. Across cortical areas, we find that Bayesian decoders using the CMP or NB models improve stimulus/movement estimation accuracy by 4%–12% compared to the Poisson model. Significance. Moreover, the uncertainty of the non-Poisson decoders more accurately reflects the magnitude of estimation errors. In addition to tuning curves that reflect average neural responses, stimulus-dependent response variability may be an important aspect of the neural code. Modeling this structure could, potentially, lead to improvements in brain machine interfaces.

Reading-out task variables as a low-dimensional reconstruction of neural spike trains in single trials.

We propose a new model of the read-out of spike trains that exploits the multivariate structure of responses of neural ensembles. Assuming the point of view of a read-out neuron that receives synaptic inputs from a population of projecting neurons, synaptic inputs are weighted with a heterogeneous set of weights. We propose that synaptic weights reflect the role of each neuron within the population for the computational task that the network has to solve. In our case, the computational task is discrimination of binary classes of stimuli, and weights are such as to maximize the discrimination capacity of the network. We compute synaptic weights as the feature weights of an optimal linear classifier. Once weights have been learned, they weight spike trains and allow to compute the post-synaptic current that modulates the spiking probability of the read-out unit in real time. We apply the model on parallel spike trains from V1 and V4 areas in the behaving monkey macaca mulatta, while the animal is engaged in a visual discrimination task with binary classes of stimuli. The read-out of spike trains with our model allows to discriminate the two classes of stimuli, while population PSTH entirely fails to do so. Splitting neurons in two subpopulations according to the sign of the weight, we show that population signals of the two functional subnetworks are negatively correlated. Disentangling the superficial, the middle and the deep layer of the cortex, we show that in both V1 and V4, superficial layers are the most important in discriminating binary classes of stimuli.

Learning the relationship between biosonar pulse trains and peripheral dynamics in hipposiderid bats

The biosonar system of hipposiderid bats is characterized by a peripheral dynamics consisting of variable noseleaf and pinna motions as well as temporal variability in the emitted pulse trains. It may be hypothesized that a well-integrated biosonar emission system should coordinate noseleaf and pinna motions with the biosonar pulse trains if this leads to synergistic effects. To investigate whether a relationship between these two sources of variability in the emission system of hipposiderid bats exists, a data set with high-speed stereo video recordings of noseleaf and pinna motions that were synchronized with ultrasonic recordings of the biosonar pulses has been analyzed. Different similarity metrics that were developed for analyzing neural spike sequences have been tested for comparing the individual biosonar pulse trains. Based on these metrics, pulse trains were clustered and the results were compared to the different categories of noseleaf and pinna motions that were found in the video recordings. The results obtained so far for 80 recorded sequences show that noseleaf/pinna motions and pulse trains can be organized into joint clusters, i.e., certain categories of peripheral motions cluster with specific types of biosonar pulse trains. These links could indicate a synergistic relationship between noseleaf/pinna motions and pulse timings.The biosonar system of hipposiderid bats is characterized by a peripheral dynamics consisting of variable noseleaf and pinna motions as well as temporal variability in the emitted pulse trains. It may be hypothesized that a well-integrated biosonar emission system should coordinate noseleaf and pinna motions with the biosonar pulse trains if this leads to synergistic effects. To investigate whether a relationship between these two sources of variability in the emission system of hipposiderid bats exists, a data set with high-speed stereo video recordings of noseleaf and pinna motions that were synchronized with ultrasonic recordings of the biosonar pulses has been analyzed. Different similarity metrics that were developed for analyzing neural spike sequences have been tested for comparing the individual biosonar pulse trains. Based on these metrics, pulse trains were clustered and the results were compared to the different categories of noseleaf and pinna motions that were found in the video recordings. T...

Paradoxical Rules of Spike Train Decoding Revealed at the Sensitivity Limit of Vision

All sensory information is encoded in neural spike trains. It is unknown how the brain utilizes this neural code to drive behavior. Here, we unravel the decoding rules of the brain at the most elementary level by linking behavioral decisions to retinal output signals in a single-photon detection task. A transgenic mouse line allowed us to separate the two primary retinal outputs, ON and OFF pathways, carrying information about photon absorptions as increases and decreases in spiking, respectively. We measured the sensitivity limit of rods and the most sensitive ONand OFF ganglion cells and correlated these results with visually guided behavior using markerless head and eye tracking. We show that behavior relies only on the ON pathway even when the OFF pathway would allow higher sensitivity. Paradoxically, behavior does not rely on the spike code with maximal information but instead relies on a decoding strategy based on increases in spiking.

Multifunctional neural probe integrated with push–pull microfluidic channels and microelectrode and biosensors for real-time monitoring of neurochemicals with neural spikes

Multifunctional neural probe integrated with push–pull microfluidic channels and microelectrode and biosensors for real-time monitoring of neurochemicals with neural spikes

Neural spike classification via deep neural network

Neural spike classification via deep neural network

The use of a double-layer platinum black-conducting polymer coating for improvement of neural recording and mitigation of photoelectric artifact

The impedance of electrode and photostimulation artifacts (short-duration and high-amplitude spikes) are still hindering the employment of silicon-based neural probe in optogenetics. A fiber-based optrode modified with a double-layer platinum black-poly (3,4ethylenedioxythiophene) PEDOT/poly (4-styrenesulfonate) PSS (Pt-PP) coating has been developed for improvement of neural recording quality and mitigation of photoelectric artifact simultaneously. The Pt-PP coating was made by layer-by-layer electrochemical deposition followed by the ultrasonication and Cyclic Voltammetry (CV) scanning to verify its mechanical and electrochemical stability. Both in-vitro and in-vivo experiments demonstrated that Pt-PP coated optrode had outstanding recording performance (high signal-to-noise ratio about 9.64) and low photoelectric amplitude (850 μV). The artifact recovery time of Pt-PP coated optrode (0.3 ms) after photostimulation was significantly decreased when compared to platinum black (6 ms) or PEDOT/PSS (0.7 ms) coated one which has potential to retain high-quality neural signals in animal experiments. At last, the optogenetics experiments revealed the capability of Pt-PP coated optrode to record the change in neural spike rate with certain spatial resolution and shorter artifact recovery time. These results suggest that Pt-PP coating has great potential for neural electrodes in the application of neuroscience.

Biologically-inspired image processing in computational retina models

Retinal Prosthesis (RP) is an approach to restore vision, using an implanted device to electrically stimulate the retina. A fundamental problem in RP is to translate the visual scene to retina neural spike patterns, mimicking the computations normally done by retina neural circuits. Towards the perspective of improved RP interventions, we propose a Computer Vision (CV) image preprocessing method based on Retinal Ganglion Cells functions and then use the method to reproduce retina output with a standard Generalized Integrate & Fire (GIF) neuron model. “Virtual Retina” simulation software is used to provide the stimulus-retina response data to train and test our model. We use a sequence of natural images as model input and show that models using the proposed CV image preprocessing outperform models using raw image intensity (interspike-interval distance 0.17 vs 0.27). This result is aligned with our hypothesis that raw image intensity is an improper image representation for Retinal Ganglion Cells response prediction.

Serotonin 5-HT1A, 5-HT2A and dopamine D2 receptors strongly influence prefronto-hippocampal neural networks in alert mice: Contribution to the actions of risperidone

Atypical antipsychotic drugs (APDs) used to treat positive and negative symptoms in schizophrenia block serotonin receptors 5-HT2AR and dopamine receptors D2R and stimulate 5-HT1AR directly or indirectly. However, the exact cellular mechanisms mediating their therapeutic actions remain unresolved. We recorded neural activity in the prefrontal cortex (PFC) and hippocampus (HPC) of freely-moving mice before and after acute administration of 5-HT1AR, 5-HT2AR and D2R selective agonists and antagonists and atypical APD risperidone. We then investigated the contribution of the three receptors to the actions of risperidone on brain activity via statistical modeling and pharmacological reversal (risperidone + 5-HT1AR antagonist WAY-100635, risperidone + 5-HT2A/2CR agonist DOI, risperidone + D2R agonist quinpirole). Risperidone, 5-HT1AR agonism with 8-OH-DPAT, 5-HT2AR antagonism with M100907, and D2R antagonism with haloperidol reduced locomotor activity of mice that correlated with a suppression of neural spiking, power of theta and gamma oscillations in PFC and HPC, and reduction of PFC-HPC theta phase synchronization. By contrast, activation of 5-HT2AR with DOI enhanced high-gamma oscillations in PFC and PFC-HPC high gamma functional connectivity, likely related to its hallucinogenic effects. Together, power changes, regression modeling and pharmacological reversals suggest an important role of 5-HT1AR agonism and 5-HT2AR antagonism in risperidone-induced alterations of delta, beta and gamma oscillations, while D2R antagonism may contribute to risperidone-mediated changes in delta oscillations. This study provides novel insight into the neural mechanisms for widely prescribed psychiatric medication targeting the serotonin and dopamine systems in two regions involved in the pathophysiology of schizophrenia.

Platinum/Graphene Oxide Coated Microfabricated Arrays for Multinucleus Neural Activities Detection in the Rat Models of Parkinson's Disease Treated by Apomorphine.

Apomorphine (APO) is often used to treat Parkinson's disease (PD) while the mechanism in PD treatment remains unclear. In this study, a four-shank microelectrode array (MEA) was fabricated to monitor cortex (two shorter shanks) and caudate putamen (CPU) (two longer shanks) signals simultaneously. The dopamine (DA) concentration and neural spike firing in the bilateral targets were recorded in the unilateral 6-OHDA PD rats treated by APO. Electroplating platinum black and reduced graphene oxide nanocomposites onto the electrode surface significantly improved their performance with higher sensitivity, selectivity, and linearity and lower impedance. The DA level and neural spike firing rate were regulated to the normal level. On the lesioned side, the mean DA concentration increased, and the firing rate decreased significantly in the cortex and CPU. Synchronous spike oscillation and typical periodic firing appeared on the lesioned side but were significantly reduced after APO treatment. This represents a substantial contribution to PD treatment and effective recovery of movement. The nanotechnology-enhanced MEA biosensing interface provides an elaborate platform for detecting neuron activities and helps to reveal the abnormal signals between neurons.

A Physiologically Inspired Model for Solving the Cocktail Party Problem

At a cocktail party, we can broadly monitor the entire acoustic scene to detect important cues (e.g., our names being called, or the fire alarm going off), or selectively listen to a target sound source (e.g., a conversation partner). It has recently been observed that individual neurons in the avian field L (analog to the mammalian auditory cortex) can display broad spatial tuning to single targets and selective tuning to a target embedded in spatially distributed sound mixtures. Here, we describe a model inspired by these experimental observations and apply it to process mixtures of human speech sentences. This processing is realized in the neural spiking domain. It converts binaural acoustic inputs into cortical spike trains using a multi-stage model composed of a cochlear filter-bank, a midbrain spatial-localization network, and a cortical network. The output spike trains of the cortical network are then converted back into an acoustic waveform, using a stimulus reconstruction technique. The intelligibility of the reconstructed output is quantified using an objective measure of speech intelligibility. We apply the algorithm to single and multi-talker speech to demonstrate that the physiologically inspired algorithm is able to achieve intelligible reconstruction of an “attended” target sentence embedded in two other non-attended masker sentences. The algorithm is also robust to masker level and displays performance trends comparable to humans. The ideas from this work may help improve the performance of hearing assistive devices (e.g., hearing aids and cochlear implants), speech-recognition technology, and computational algorithms for processing natural scenes cluttered with spatially distributed acoustic objects.

Computationally inexpensive enhanced growing neural gas algorithm for real-time adaptive neural spike clustering

Objective. Real-time closed-loop neural feedback control requires the analysis of action potential traces within several milliseconds after they have been recorded from the brain. The current generation of spike clustering algorithms were mostly designed for off-line use and also require a significant amount of computational resources. A new spike clustering algorithm, termed ‘enhanced growing neural gas (EGNG)’, was therefore developed that is computationally lightweight and memory conserving. The EGNG algorithm can adapt to changes of the electrophysiological recording environment and can classify both pre-recorded and streaming action potentials. Approach. The algorithm only uses a small number of EGNG nodes and edges to learn the neural spike distributions which eliminates the need of retaining the neural data in the system memory to conserve computational resources. Most of the computations revolve around calculating Euclidian distances, which is computationally inexpensive and can be implemented in parallel using digital circuit technology. Main results. EGNG was evaluated off-line using both synthetic and pre-recorded neural spikes. Streaming synthetic neural spikes were also used to evaluate the ability of EGNG to classify action potentials in real-time. The algorithm was also implemented in hardware with a Field Programming Gate Array (FPGA) chip, and the worst-case clustering latency was 3.10 µs, allowing a minimum of 322 580 neural spikes to be clustered per second. Significance. The EGNG algorithm provides a viable solution to classification of neural spikes in real-time and can be implemented with limited computational resources as a front-end spike clustering unit for future tethered-free and miniaturized closed-loop neural feedback systems.

Temporal Coding of Reward Value in Monkey Ventral Striatal Tonically Active Neurons.

The rostromedioventral striatum is critical for behavior dependent on evaluating rewards. We asked what contribution tonically active neurons (TANs), the putative striatal cholinergic interneurons, make in coding reward value in this part of the striatum. Two female monkeys were given the option to accept or reject an offered reward in each trial, the value of which was signaled by a visual cue. Forty-five percent of the TANs use temporally modulated activity to encode information about discounted value. These responses were significantly better represented using principal component analysis than by just counting spikes. The temporal coding is straightforward: the spikes are distributed according to a sinusoidal envelope of activity that changes gain, ranging from positive to negative according to discounted value. Our results show that the information about the relative value of an offered reward is temporally encoded in neural spike trains of TANs. This temporal coding may allow well tuned, coordinated behavior to emerge.SIGNIFICANCE STATEMENT Ever since the discovery that neurons use trains of pulses to transmit information, it seemed self-evident that information would be encoded into the pattern of the spikes. However, there is not much evidence that spike patterns encode cognitive information. We find that a set of interneurons, the tonically active neurons (TANs) in monkeys' striatum, use temporal patterns of response to encode information about the discounted value of offered rewards. The code seems straightforward: a sinusoidal envelope that changes gain according to the discounted value of the offer, describes the rate of spiking across time. This temporal modulation may provide a means to synchronize these interneurons and the activity of other neural elements including principal output neurons.

SpikeDeeptector: a deep-learning based method for detection of neural spiking activity

Objective. In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain–computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas. Approach. We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise. Main results. We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs. Significance. The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training. Clinical trial registration number. The clinical trial registration number for patients implanted with the Utah array is NCT 01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation.

Compact and Low-Power Neural Spike Compression Using Undercomplete Autoencoders.

Implantable microsystems that collect and transmit neural data are becoming very useful entities in the field of neuroscience. Limited by high data rates, on-chip compression is often required to transmit the recorded data without causing power dissipation at levels that would damage sensitive brain tissue. This paper presents a data compression system designed for brain-computer interfaces (BCIs) based on undercomplete autoencoders. To the best of our knowledge, the proposed system is the first to achieve an average spike reconstruction quality of 14-dB signal-to-noise-and-distortion ratio (SNDR) at a 32× compression ratio (CR), 18-dB SNDR at a 16× CR, 22-dB SNDR at an 8× CR, and 35-dB SNDR at a 4× CR of neural spikes. The spike detection and autoencoder-based compression modules are designed and implemented in a standard 45-nm CMOS process. The post-synthesis simulation results report that the compression module consumes between 1.4 and 222.5 [Formula: see text] of power per channel and takes between 0.018 and 0.082mm2 of silicon area, depending on the desired CR and number of channels.

Computationally efficient fully-automatic online neural spike detection and sorting in presence of multi-unit activity for implantable circuits

BackgroundSpike sorting is a basic step for implantable neural interfaces. With the growing number of channels, the process should be computationally efficient, automatic,robust and applicable on implantable circuits. New MethodThe proposed method is a combination of fully-automatic offline and online processes. It introduces a novel method for automatically determining a data-aware spike detection threshold, computationally efficient spike feature extraction, automatic optimal cluster number evaluation and verification coupled with Self-Organizing Maps to accurately determine cluster centroids. The system has the ability of unsupervised online operation after initial fully-automatic offline training. The prime focus of this paper is to fully-automate the complete spike detection and sorting pipeline, while keeping the accuracy high. ResultsThe proposed system is simulated on two well-known datasets. The automatic threshold improves detection accuracies significantly( > 15%) as compared to the most common detector. The system is able to effectively handle background multi-unit activity with improved performance. ComparisonMost of the existing methods are not fully-automatic; they require supervision and expert intervention at various stages of the pipeline. Secondly, existing works focus on foreground neural activity. Recent research has highlighted importance of background multi-unit activity, and this work is amongst the first efforts that proposes and verifies an automatic methodology to effectively handle them as well. ConclusionThis paper proposes a fully-automatic, computationally efficient system for spike sorting for both single-unit and multi-unit spikes. Although the scope of this work is design and verification through computer simulations, the system has been designed to be easily transferable into an integrated hardware form.

Long-term real-time imaging of a voltage sensitive dye in cultured hippocampal neurons using the silver plasmonic dish

We have demonstrated the real-time voltage sensitive dye (VSD) imaging of a neuronal network using Di-4-ANEPPS at a 1 ms-time resolution on a silver (Ag) plasmonic dish to improve the signals to noise (S/N) ratio. The plasmonic dish is composed of a culture dish and a metal-coated substrate with a wavelength-order periodical structure on its surface that can enhance excitation field due to the surface plasmon resonance (SPR)-field. In our group, spontaneous neural spikes that are undetectable on a conventional culture dish were frequently detected on a gold (Au) plasmonic dish during real-time VSD imaging. In the present study, Ag and Au plasmonic chips were prepared and tested. Larger fluorescence enhancement of the VSD was obtained on the Ag plasmonic dish compared with on the Au. Number of neural spikes were frequently detected on the Ag plasmonic dish compared to the Au plasmonic dish in the control neurons and after application picrotoxin (PTX), because of the photobleaching rate was suppressed and the noise level decreased by the reduction of illumination light on the Ag surface. Therefore, the use of an Ag plasmonic dish was considered to be suitable for the long-term and real-time observation by VSD imaging.

Microelectrode Arrays Modified with Nanocomposites for Monitoring Dopamine and Spike Firings under Deep Brain Stimulation in Rat Models of Parkinson's Disease.

Little is known about the efficacy of deep brain stimulation (DBS) as an effective treatment for Parkinson's Disease (PD) because of the lack of multichannel neural electrical and chemical detection techniques at the cellular level. In this study, a 7-mm-long and 250-μm-wide microelectrode array (MEA) was fabricated to provide real-time monitoring of dopamine (DA) concentration and neural spike firings in the caudate putamen (CPU) of rats with PD. Platinumn nanoparticles and reduced graphene oxide nanocomposites (Pt/rGO) were modified onto the sensitive microelectrode sites. The detection limit (50 nM) and sensitivity (8.251 pA/μM) met the specific requirements for DA detection in vivo. A single neural spike was isolated due to the high signal-to-noise ratio of the MEA. DBS was applied in the affected side of the globus pallidus internal (GPi) in PD rats. After DBS, the concentration of DA in the bilateral CPU increased markedly. The mean increment of the ipsilateral DA was 7.33 μM (increasing from 0.54 μM to 7.87 μM), which was 2.2-fold higher than the increment in the contralateral side. The mean amplitude of neural spikes in the bilateral CPU decreased more than 10%, and was more obvious in the ipsilateral side where the spike amplitude changed from 169 μV to 134 μV. Spike firing rate decreased by 65% (ipsilateral side) and 51% (contralateral side). The power of the local field potential decreased to 940 μW (ipsilateral side) and 530 μW (contralateral side) in 0-30 Hz. Collectively, our data show that the GPi-DBS plays a significant regulatory role in the bilateral CPU in terms of DA concentration, spike firing, and power; furthermore, the ipsilateral variations of the dual mode signals were more significant than those in the contralateral side. These results provide new detection and stimulation technology for understanding the mechanisms underlying Parkinson's disease and should, therefore, represent a useful resource for the design of future treatments.

Spontaneous spike signals originated from redox-active molecules functionalised on carbon nanotubes

Biological computing has always been an interesting and promising field due to its high optimisation and energy frugality in problem-solving. Frequency and timing of the neural spike signal define how the information processing works in biological neural networks. Here, we demonstrate that the addition of redox-active polyoxometalate (POM) molecules to carbon nanotube (CNT) parallel multi-junctions provides an interesting spike signal. The redox state of POM molecules at the junction between CNTs and metal electrodes contributes to the generation of spike signal merely by the use of small DC bias. However, the spike signal from the device was frequently disrupted by the phase transition in the output current. It was elucidated that the lifetime of redox state exhibited by POM is the principal determinant of spike signal generation in a CNT/POM network.