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Sample records for sars-cov spike protein-based

  1. Receptor-binding domain of SARS-Cov spike protein: Soluble expression in purification and functional characterization

    Institute of Scientific and Technical Information of China (English)

    Jing Chen; Lin Miao; Jia-Ming Li; Yan-Ying Li; Qing-Yu Zhu; Chang-Lin Zhou; Hong-Qing Fang; Hui-Peng Chen

    2005-01-01

    AIM: To find a soluble and functional recombinant receptor-binding domain of severe acute respiratory syndrome-associated coronavirus (SARS-Cov), and to analyze its receptor binding ability.METHODS: Three fusion tags (glutathione S-transferase,GST; thioredoxin, Trx; maltose-binding protein, MBP),which preferably contributes to increasing solubility and to facilitating the proper folding of heteroprotein, were used to acquire the soluble and functional expression of RBD protein in Escherichia coli( BL21( DE3 ) and Rosetta-gamiB(DE3) strains). The receptor binding ability of the purified soluble RBD protein was then detected by ELISA and flow cytometry assay.RESULTS: RBD of SARS-Cov spike protein was expressed as inclusion body when fused as TrxA tag form in both BL21 (DE3) and Rosetta-gamiB (DE3) under many different cultures and induction conditions. And there was no visible expression band on SDS-PAGE when RBD was expressed as MBP tagged form. Only GST tagged RBD was soluble expressed in BL21(DE3), and the protein was purified by AKTA Prime Chromatography system. The ELISA data anti-RBD mouse monoclonal antibody 1A5. Further flow cytometry assay demonstrated the high efficiency of RBD's binding ability to ACE2 (angiotensin-converting enzyme 2)positive Vero E6 cell. And ACE2 was proved as a cellular receptor that meditated an initial-affinity interaction with SARS-Cov spike protein. The geometrical mean of GST and respectively.CONCLUSION: In this paper, we get sufficient soluble N terminal GST tagged RBD protein expressed in E. coli BL21(DE3); data from ELISA and flow cytometry assay demonstrate that the recombinant protein is functional and binding to ACE2 positive Vero E6 cell efficiently. And the recombinant RBD derived from E. coli can be used to developing subunit vaccine to block S protein binding with receptor and to neutralizing SARS-Cov infection.

  2. CLONING SEGMENT SPIKE PROTEIN GENE OF SARS-COV AND ITS EXPRESSION IN ESCHERICHIA COLI

    Institute of Scientific and Technical Information of China (English)

    刘中华; 许文波; 毛乃颖; 张燕; 朱贞; 崔爱利; 杨建国; 胡海涛

    2004-01-01

    Objective Expressing and purifying the segment of SARS-CoV spike protein in E.Coli. Methods The target gene was obtained by RT-PCR. The PCR product was cloned into pEGM- T Easy Vector, sequencing and double restriction digestion ( BamHⅠ,PstⅠ) were performed. The target gene was subcloned into PQE30 expression vector. The gene was expressed in the E.coli strain M15 cells induced by IPTG. The protein was purified with a nickel HiTrap chelating metal affinity column. Results The recombinant expression plasmid was successfully constructed and the protein was well expressed in E. coli strain M15 cells. The ideal pure protein was obtained by purification. Western blotting analysis suggested the protein could act with the convalescent sera of lab confirmed SARS patients. Conclusion The segment of SARS-CoV spike protein was well expressed and purified, and can be applied in diagnosis and immunological research of SARS.

  3. Reconstruction of the most recent common ancestor sequences of SARS-Cov S gene and detection of adaptive evolution in the spike protein

    Institute of Scientific and Technical Information of China (English)

    ZHANG Yuan; ZHENG Nan; HAO Pei; ZHONG Yang

    2004-01-01

    @@ The genome organization and expression strategy of severe acute respiratory syndrome coronavirus (SARSCoV) have been described extensivelyl1- 10]. As a structural glycoprotein on the virion surface, the spike protein is responsible for binding to host cellular receptors and for the fusion between the viral envelope and the cellular membrane. It also induces neutralizing antibodies in the host and mediates cellular immunity[11]. Previous studies suggested that amino acid replacements in the spike protein could dramatically alter the pathogenesis and virulence of some coronaviruses[11]. It is therefore reasonable to test the hypothesis that radical amino acid replacements in the spike protein, favored by environmental selective pressure during the process of SARS-CoV interspecific transmission[10], might make this pathogen adapt to a new host. In this study, we investigated a total of 108complete sequences of the SARS-CoV S gene from GenBank (until March 23, 2004). After omission of those records containing frame-shift mutations or low quality sequences, e.g. ZJ01, and selection of one sequence for identical records, an alignment of 42 sequences was obtained using the program Clustal-X[13]. Then, we reconstructed the most recent common ancestor (MRCA) sequences of the SARS-CoV S gene and detected the adaptive evolution in the spike protein.

  4. Bioinformatics analysis of SARS-Cov M protein provides information for vaccine development

    Institute of Scientific and Technical Information of China (English)

    LIU Wanli; LU Yun; CHEN Yinghua

    2003-01-01

    The pathogen causing severe acute respiratory syndrome (SARS) is identified to be SARS-Cov. It is urgent to know more about SARS-Cov for developing an efficient SARS vaccine to prevent this epidemic disease. In this report, the homology of SARS-Cov M protein to other members of coronavirus is illustrated, and all amino acid changes in both S and M proteins among all available SARS-Cov isolates in GenBank are described. Furthermore, one topological trans-membrane secondary structure model of M protein is proposed, which is corresponded well with the accepted topology model of M proteins of other members of coronavirus. Hydrophilic profile analysis indicated that one region (aa150~210) on the cytoplasmic domain is fairly hydrophilic, suggesting its property of antigenicity. Based on the fact that cytoplasmic domain of the M protein of some other coronavirus could induce protective activities against virus infection, this region might be one potential target for SARS vaccine development.

  5. DNA Vaccine of SARS-Cov S Gene Induces Antibody Response in Mice

    Institute of Scientific and Technical Information of China (English)

    PingZHAO; Jin-ShanKE; Zhao-LinQIN; HaoREN; Lan-JuanZHAO; Jian-GuoYU

    2004-01-01

    The spike (S) protein, a main surface antigen of SARS-coronavirus (SARS-CoV), is one of the most important antigen candidates for vaccine design. In the present study, three fragments of the truncated S protein were expressed in E.coli, and analyzed with pooled sera of convalescence phase of SARS patients.The full length S gene DNA vaccine was constructed and used to immunize BALB/c mice. The mouse serum IgG antibody against SARS-CoV was measured by ELISA with E.coli expressed truncated S protein or SARS-CoV lysate as diagnostic antigen. The results showed that all the three fragments of S protein expressed by E.coli was able to react with sera of SARS patients and the S gene DNA candidate vaccine could induce the production of specific IgG antibody against SARS-CoV efficiently in mice with seroconversion ratio of 75% after 3 times of immunization. These findings lay some foundations for further understanding the immunology of SARS-CoV and developing SARS vaccines.

  6. DNA Vaccine of SARS-Cov S Gene Induces Antibody Response in Mice

    Institute of Scientific and Technical Information of China (English)

    Ping ZHAO; Jin-Shan KE; Zhao-Lin QIN; Hao REN; Lan-Juan ZHAO; Jian-Guo YU; Jun GAO; Shi-Ying ZHU; Zhong-Tian QI

    2004-01-01

    The spike (S) protein, a main surface antigen of SARS-coronavirus (SARS-CoV), is one of the most important antigen candidates for vaccine design. In the present study, three fragments of the truncated S protein were expressed in E. Coli, and analyzed with pooled sera of convalescence phase of SARS patients.The full length S gene DNA vaccine was constructed and used to immunize BALB/c mice. The mouse serum IgG antibody against SARS-CoV was measured by ELISA with E. Coli expressed truncated S protein or SARS-CoV lysate as diagnostic antigen. The results showed that all the three fragments of S protein expressed by E. Coli was able to react with sera of SARS patients and the S gene DNA candidate vaccine could induce the production of specific IgG antibody against SARS-CoV efficiently in mice with seroconversion ratio of 75% after 3 times of immunization. These findings lay some foundations for further understanding the immunology of SARS-CoV and developing SARS vaccines.

  7. [Prokaryotic expression of S2 extracellular domain of SARS coronavirus spike protein and its fusion with Hela cell membrane].

    Science.gov (United States)

    Liu, Yun; Liu, Ai-Hua; Deng, Peng; Wu, Xiang-Ling; Li, Tao; Liu, Ya-Wei; Xu, Jia; Jiang, Yong

    2009-03-01

    To construct the expression plasmid of S2 extracellular domain (S2ED) of SARS-coronavirus (SARS- Cov) spike protein (S protein) and enhanced green fluorescent protein (EGFP) to obtain the fusion protein expressed in prokaryotic cells. S2ED based on bioinformatics prediction and EGFP sequence were amplified by PCR and inserted into pET-14b plasmid. The recombinant protein His-S2ED-EGFP was expressed in E. coli by IPTG induction. After purification by Ni-NTA agarose beads, the soluble fractions of the fusion protein were collected and identified by SDS-PAGE and Western blotting. The fusion of S2ED with Hela cell membranes was observed with fluorescent microscope. The pET-14b-S2ED-EGFP plasmid was correctly constructed and highly expressed in BL21 (DE3). When incubated with Hela cells, the purified protein could not internalize through membrane fusion. The expression plasmid containing S2ED of SARS-Cov S protein and EGFP sequence is constructed successfully. Although the recombinant protein obtained has not shown the expected fusion effect with Hela cell membrane, this work may enrich the understanding of the process of membrane fusion mediated by S2 protein and lay the foundation for future study of targeting cell transport system based on cell-specific binding peptide.

  8. Monitoring spike train synchrony

    CERN Document Server

    Kreuz, Thomas; Houghton, Conor; Andrzejak, Ralph G; Mormann, Florian

    2012-01-01

    Recently, the SPIKE-distance has been proposed as a parameter-free and time-scale independent measure of spike train synchrony. This measure is time-resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for event-like firing patterns. Here we present a substantial improvement of this measure which eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real-time. We demonstrate that these methods are capable of extracting valuable information from ...

  9. Monitoring spike train synchrony.

    Science.gov (United States)

    Kreuz, Thomas; Chicharro, Daniel; Houghton, Conor; Andrzejak, Ralph G; Mormann, Florian

    2013-03-01

    Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings.

  10. Dynamical laser spike processing

    CERN Document Server

    Shastri, Bhavin J; Tait, Alexander N; Rodriguez, Alejandro W; Wu, Ben; Prucnal, Paul R

    2015-01-01

    Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved "spiking" of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate that graphene-coupled laser systems offer a unified low-level spike optical processing paradigm that goes well beyond previously studied laser dynamics. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation---fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system, but the addit...

  11. Solar Decameter Spikes

    Science.gov (United States)

    Melnik, V. N.; Shevchuk, N. V.; Konovalenko, A. A.; Rucker, H. O.; Dorovskyy, V. V.; Poedts, S.; Lecacheux, A.

    2014-05-01

    We analyze and discuss the properties of decameter spikes observed in July - August 2002 by the UTR-2 radio telescope. These bursts have a short duration (about one second) and occur in a narrow frequency bandwidth (50 - 70 kHz). They are chaotically located in the dynamic spectrum. Decameter spikes are weak bursts: their fluxes do not exceed 200 - 300 s.f.u. An interesting feature of these spikes is the observed linear increase of the frequency bandwidth with frequency. This dependence can be explained in the framework of the plasma mechanism that causes the radio emission, taking into account that Langmuir waves are generated by fast electrons within a narrow angle θ≈13∘ - 18∘ along the direction of the electron propagation. In the present article we consider the problem of the short lifetime of decameter spikes and discuss why electrons generate plasma waves in limited regions.

  12. Response Features Determining Spike Times

    Directory of Open Access Journals (Sweden)

    Barry J. Richmond

    1999-01-01

    redundant with that carried by the coarse structure. Thus, the existence of precisely timed spike patterns carrying stimulus-related information does not imply control of spike timing at precise time scales.

  13. Protein-based composite materials

    Directory of Open Access Journals (Sweden)

    Xiao Hu

    2012-05-01

    Full Text Available Protein-based composite biomaterials have been actively pursued as they can encompass a range of physical properties to accommodate a broader spectrum of functional requirements, such as elasticity to support diverse tissues. By optimizing molecular interfaces between structural proteins, useful composite materials can be fabricated as films, gels, particles, and fibers, as well as for electrical and optical devices. Such systems provide analogies to more traditional synthetic polymers yet with expanded utility due to the material's tunability, mechanical properties, degradability, biocompatibility, and functionalization, such as for drug delivery, biosensors, and tissue regeneration.

  14. Radioxenon spiked air.

    Science.gov (United States)

    Watrous, Matthew G; Delmore, James E; Hague, Robert K; Houghton, Tracy P; Jenson, Douglas D; Mann, Nick R

    2015-12-01

    Four of the radioactive xenon isotopes ((131m)Xe, (133m)Xe, (133)Xe and (135)Xe) with half-lives ranging from 9 h to 12 days are produced from nuclear fission and can be detected from days to weeks following their production and release. Being inert gases, they are readily transported through the atmosphere. Sources for release of radioactive xenon isotopes include operating nuclear reactors via leaks in fuel rods, medical isotope production facilities, and nuclear weapons' detonations. They are not normally released from fuel reprocessing due to the short half-lives. The Comprehensive Nuclear-Test-Ban Treaty has led to creation of the International Monitoring System. The International Monitoring System, when fully implemented, will consist of one component with 40 stations monitoring radioactive xenon around the globe. Monitoring these radioactive xenon isotopes is important to the Comprehensive Nuclear-Test-Ban Treaty in determining whether a seismically detected event is or is not a nuclear detonation. A variety of radioactive xenon quality control check standards, quantitatively spiked into various gas matrices, could be used to demonstrate that these stations are operating on the same basis in order to bolster defensibility of data across the International Monitoring System. This paper focuses on Idaho National Laboratory's capability to produce three of the xenon isotopes in pure form and the use of the four xenon isotopes in various combinations to produce radioactive xenon spiked air samples that could be subsequently distributed to participating facilities.

  15. Coronavirus spike-receptor interactions

    NARCIS (Netherlands)

    Mou, H.

    2015-01-01

    Coronaviruses cause important diseases in humans and animals. Coronavirus infection starts with the virus binding with its spike proteins to molecules present on the surface of host cells that act as receptors. This spike-receptor interaction is highly specific and determines the virus’ cell, tissue

  16. Mapping spikes to sensations

    Directory of Open Access Journals (Sweden)

    Maik Christopher Stüttgen

    2011-11-01

    Full Text Available Single-unit recordings conducted during perceptual decision-making tasks have yielded tremendous insights into the neural coding of sensory stimuli. In such experiments, detection or discrimination behavior (the psychometric data is observed in parallel with spike trains in sensory neurons (the neurometric data. Frequently, candidate neural codes for information read-out are pitted against each other by transforming the neurometric data in some way and asking which code’s performance most closely approximates the psychometric performance. The code that matches the psychometric performance best is retained as a viable candidate and the others are rejected. In following this strategy, psychometric data is often considered to provide an unbiased measure of perceptual sensitivity. It is rarely acknowledged that psychometric data result from a complex interplay of sensory and non-sensory processes and that neglect of these processes may result in misestimating psychophysical sensitivity. This again may lead to erroneous conclusions regarding the adequacy of neural candidate codes. In this review, we first discuss requirements on the neural data for a subsequent neurometric-psychometric comparison. We then focus on different psychophysical tasks for the assessment of detection and discrimination performance and the cognitive processes that may underlie their execution. We discuss further factors that may compromise psychometric performance and how they can be detected or avoided. We believe that these considerations point to shortcomings in our understanding of the processes underlying perceptual decisions, and therefore offer potential for future research.

  17. Rayleigh--Taylor spike evaporation

    Energy Technology Data Exchange (ETDEWEB)

    Schappert, G. T.; Batha, S. H.; Klare, K. A.; Hollowell, D. E.; Mason, R. J.

    2001-09-01

    Laser-based experiments have shown that Rayleigh--Taylor (RT) growth in thin, perturbed copper foils leads to a phase dominated by narrow spikes between thin bubbles. These experiments were well modeled and diagnosed until this '' spike'' phase, but not into this spike phase. Experiments were designed, modeled, and performed on the OMEGA laser [T. R. Boehly, D. L. Brown, R. S. Craxton , Opt. Commun. 133, 495 (1997)] to study the late-time spike phase. To simulate the conditions and evolution of late time RT, a copper target was fabricated consisting of a series of thin ridges (spikes in cross section) 150 {mu}m apart on a thin flat copper backing. The target was placed on the side of a scale-1.2 hohlraum with the ridges pointing into the hohlraum, which was heated to 190 eV. Side-on radiography imaged the evolution of the ridges and flat copper backing into the typical RT bubble and spike structure including the '' mushroom-like feet'' on the tips of the spikes. RAGE computer models [R. M. Baltrusaitis, M. L. Gittings, R. P. Weaver, R. F. Benjamin, and J. M. Budzinski, Phys. Fluids 8, 2471 (1996)] show the formation of the '' mushrooms,'' as well as how the backing material converges to lengthen the spike. The computer predictions of evolving spike and bubble lengths match measurements fairly well for the thicker backing targets but not for the thinner backings.

  18. Wavelet analysis of epileptic spikes

    CERN Document Server

    Latka, M; Kozik, A; West, B J; Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.

    2003-01-01

    Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous, pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.

  19. Wavelet analysis of epileptic spikes

    Science.gov (United States)

    Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.

    2003-05-01

    Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.

  20. Learning Precise Spike Train to Spike Train Transformations in Multilayer Feedforward Neuronal Networks

    OpenAIRE

    2014-01-01

    We derive a synaptic weight update rule for learning temporally precise spike train to spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing and avoids invoking concepts pertaining to spike rates or probabilistic models of spiking. The derivation is founded on two innovations. First, an error functional is proposed th...

  1. Wavelet transform of neural spike trains

    Science.gov (United States)

    Kim, Youngtae; Jung, Min Whan; Kim, Yunbok

    2000-02-01

    Wavelet transform of neural spike trains recorded with a tetrode in the rat primary somatosensory cortex is described. Continuous wavelet transform (CWT) of the spike train clearly shows singularities hidden in the noisy or chaotic spike trains. A multiresolution analysis of the spike train is also carried out using discrete wavelet transform (DWT) for denoising and approximating at different time scales. Results suggest that this multiscale shape analysis can be a useful tool for classifying the spike trains.

  2. Computing with Spiking Neuron Networks

    NARCIS (Netherlands)

    Paugam-Moisy, H.; Bohte, S.M.; Rozenberg, G.; Baeck, T.H.W.; Kok, J.N.

    2012-01-01

    Abstract Spiking Neuron Networks (SNNs) are often referred to as the 3rd gener- ation of neural networks. Highly inspired from natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an ac- curate modeling of synaptic interactions between neu

  3. Automatic spike sorting using tuning information.

    Science.gov (United States)

    Ventura, Valérie

    2009-09-01

    Current spike sorting methods focus on clustering neurons' characteristic spike waveforms. The resulting spike-sorted data are typically used to estimate how covariates of interest modulate the firing rates of neurons. However, when these covariates do modulate the firing rates, they provide information about spikes' identities, which thus far have been ignored for the purpose of spike sorting. This letter describes a novel approach to spike sorting, which incorporates both waveform information and tuning information obtained from the modulation of firing rates. Because it efficiently uses all the available information, this spike sorter yields lower spike misclassification rates than traditional automatic spike sorters. This theoretical result is verified empirically on several examples. The proposed method does not require additional assumptions; only its implementation is different. It essentially consists of performing spike sorting and tuning estimation simultaneously rather than sequentially, as is currently done. We used an expectation-maximization maximum likelihood algorithm to implement the new spike sorter. We present the general form of this algorithm and provide a detailed implementable version under the assumptions that neurons are independent and spike according to Poisson processes. Finally, we uncover a systematic flaw of spike sorting based on waveform information only.

  4. A simple algorithm for averaging spike trains.

    Science.gov (United States)

    Julienne, Hannah; Houghton, Conor

    2013-02-25

    Although spike trains are the principal channel of communication between neurons, a single stimulus will elicit different spike trains from trial to trial. This variability, in both spike timings and spike number can obscure the temporal structure of spike trains and often means that computations need to be run on numerous spike trains in order to extract features common across all the responses to a particular stimulus. This can increase the computational burden and obscure analytical results. As a consequence, it is useful to consider how to calculate a central spike train that summarizes a set of trials. Indeed, averaging responses over trials is routine for other signal types. Here, a simple method for finding a central spike train is described. The spike trains are first mapped to functions, these functions are averaged, and a greedy algorithm is then used to map the average function back to a spike train. The central spike trains are tested for a large data set. Their performance on a classification-based test is considerably better than the performance of the medoid spike trains.

  5. Epileptiform spike detection via convolutional neural networks

    DEFF Research Database (Denmark)

    Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz

    2016-01-01

    The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated fash...

  6. Automatic spikes detection in seismogram

    Institute of Scientific and Technical Information of China (English)

    王海军; 靳平; 刘贵忠

    2003-01-01

    @@ Data processing for seismic network is very complex and fussy, because a lot of data is recorded in seismic network every day, which make it impossible to process these data all by manual work. Therefore, seismic data should be processed automatically to produce a initial results about events detection and location. Afterwards, these results are reviewed and modified by analyst. In automatic processing data quality checking is important. There are three main problem data thatexist in real seismic records, which include: spike, repeated data and dropouts. Spike is defined as isolated large amplitude point; the other two problem datahave the same features that amplitude of sample points are uniform in a interval. In data quality checking, the first step is to detect and statistic problem data in a data segment, if percent of problem data exceed a threshold, then the whole data segment is masked and not be processed in the later process.

  7. Prospective Coding by Spiking Neurons.

    Directory of Open Access Journals (Sweden)

    Johanni Brea

    2016-06-01

    Full Text Available Animals learn to make predictions, such as associating the sound of a bell with upcoming feeding or predicting a movement that a motor command is eliciting. How predictions are realized on the neuronal level and what plasticity rule underlies their learning is not well understood. Here we propose a biologically plausible synaptic plasticity rule to learn predictions on a single neuron level on a timescale of seconds. The learning rule allows a spiking two-compartment neuron to match its current firing rate to its own expected future discounted firing rate. For instance, if an originally neutral event is repeatedly followed by an event that elevates the firing rate of a neuron, the originally neutral event will eventually also elevate the neuron's firing rate. The plasticity rule is a form of spike timing dependent plasticity in which a presynaptic spike followed by a postsynaptic spike leads to potentiation. Even if the plasticity window has a width of 20 milliseconds, associations on the time scale of seconds can be learned. We illustrate prospective coding with three examples: learning to predict a time varying input, learning to predict the next stimulus in a delayed paired-associate task and learning with a recurrent network to reproduce a temporally compressed version of a sequence. We discuss the potential role of the learning mechanism in classical trace conditioning. In the special case that the signal to be predicted encodes reward, the neuron learns to predict the discounted future reward and learning is closely related to the temporal difference learning algorithm TD(λ.

  8. Measuring multiple spike train synchrony.

    Science.gov (United States)

    Kreuz, Thomas; Chicharro, Daniel; Andrzejak, Ralph G; Haas, Julie S; Abarbanel, Henry D I

    2009-10-15

    Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods.

  9. Protein-Based Drug-Delivery Materials

    Directory of Open Access Journals (Sweden)

    Dave Jao

    2017-05-01

    Full Text Available There is a pressing need for long-term, controlled drug release for sustained treatment of chronic or persistent medical conditions and diseases. Guided drug delivery is difficult because therapeutic compounds need to survive numerous transport barriers and binding targets throughout the body. Nanoscale protein-based polymers are increasingly used for drug and vaccine delivery to cross these biological barriers and through blood circulation to their molecular site of action. Protein-based polymers compared to synthetic polymers have the advantages of good biocompatibility, biodegradability, environmental sustainability, cost effectiveness and availability. This review addresses the sources of protein-based polymers, compares the similarity and differences, and highlights characteristic properties and functionality of these protein materials for sustained and controlled drug release. Targeted drug delivery using highly functional multicomponent protein composites to guide active drugs to the site of interest will also be discussed. A systematical elucidation of drug-delivery efficiency in the case of molecular weight, particle size, shape, morphology, and porosity of materials will then be demonstrated to achieve increased drug absorption. Finally, several important biomedical applications of protein-based materials with drug-delivery function—including bone healing, antibiotic release, wound healing, and corneal regeneration, as well as diabetes, neuroinflammation and cancer treatments—are summarized at the end of this review.

  10. Reliability of Spike Timing in Neocortical Neurons

    Science.gov (United States)

    Mainen, Zachary F.; Sejnowski, Terrence J.

    1995-06-01

    It is not known whether the variability of neural activity in the cerebral cortex carries information or reflects noisy underlying mechanisms. In an examination of the reliability of spike generation using recordings from neurons in rat neocortical slices, the precision of spike timing was found to depend on stimulus transients. Constant stimuli led to imprecise spike trains, whereas stimuli with fluctuations resembling synaptic activity produced spike trains with timing reproducible to less than 1 millisecond. These data suggest a low intrinsic noise level in spike generation, which could allow cortical neurons to accurately transform synaptic input into spike sequences, supporting a possible role for spike timing in the processing of cortical information by the neocortex.

  11. Multiscale analysis of neural spike trains.

    Science.gov (United States)

    Ramezan, Reza; Marriott, Paul; Chenouri, Shojaeddin

    2014-01-30

    This paper studies the multiscale analysis of neural spike trains, through both graphical and Poisson process approaches. We introduce the interspike interval plot, which simultaneously visualizes characteristics of neural spiking activity at different time scales. Using an inhomogeneous Poisson process framework, we discuss multiscale estimates of the intensity functions of spike trains. We also introduce the windowing effect for two multiscale methods. Using quasi-likelihood, we develop bootstrap confidence intervals for the multiscale intensity function. We provide a cross-validation scheme, to choose the tuning parameters, and study its unbiasedness. Studying the relationship between the spike rate and the stimulus signal, we observe that adjusting for the first spike latency is important in cross-validation. We show, through examples, that the correlation between spike trains and spike count variability can be multiscale phenomena. Furthermore, we address the modeling of the periodicity of the spike trains caused by a stimulus signal or by brain rhythms. Within the multiscale framework, we introduce intensity functions for spike trains with multiplicative and additive periodic components. Analyzing a dataset from the retinogeniculate synapse, we compare the fit of these models with the Bayesian adaptive regression splines method and discuss the limitations of the methodology. Computational efficiency, which is usually a challenge in the analysis of spike trains, is one of the highlights of these new models. In an example, we show that the reconstruction quality of a complex intensity function demonstrates the ability of the multiscale methodology to crack the neural code.

  12. Effects of phase on homeostatic spike rates.

    Science.gov (United States)

    Fisher, Nicholas; Talathi, Sachin S; Carney, Paul R; Ditto, William L

    2010-05-01

    Recent experimental results by Talathi et al. (Neurosci Lett 455:145-149, 2009) showed a divergence in the spike rates of two types of population spike events, representing the putative activity of the excitatory and inhibitory neurons in the CA1 area of an animal model for temporal lobe epilepsy. The divergence in the spike rate was accompanied by a shift in the phase of oscillations between these spike rates leading to a spontaneous epileptic seizure. In this study, we propose a model of homeostatic synaptic plasticity which assumes that the target spike rate of populations of excitatory and inhibitory neurons in the brain is a function of the phase difference between the excitatory and inhibitory spike rates. With this model of homeostatic synaptic plasticity, we are able to simulate the spike rate dynamics seen experimentally by Talathi et al. in a large network of interacting excitatory and inhibitory neurons using two different spiking neuron models. A drift analysis of the spike rates resulting from the homeostatic synaptic plasticity update rule allowed us to determine the type of synapse that may be primarily involved in the spike rate imbalance in the experimental observation by Talathi et al. We find excitatory neurons, particularly those in which the excitatory neuron is presynaptic, have the most influence in producing the diverging spike rates and causing the spike rates to be anti-phase. Our analysis suggests that the excitatory neuronal population, more specifically the excitatory to excitatory synaptic connections, could be implicated in a methodology designed to control epileptic seizures.

  13. Rate-adjusted spike-LFP coherence comparisons from spike-train statistics.

    Science.gov (United States)

    Aoi, Mikio C; Lepage, Kyle Q; Kramer, Mark A; Eden, Uri T

    2015-01-30

    Coherence is a fundamental tool in the analysis of neuronal data and for studying multiscale interactions of single and multiunit spikes with local field potentials. However, when the coherence is used to estimate rhythmic synchrony between spiking and any other time series, the magnitude of the coherence is dependent upon the spike rate. This property is not a statistical bias, but a feature of the coherence function. This dependence confounds cross-condition comparisons of spike-field and spike-spike coherence in electrophysiological experiments. Taking inspiration from correction methods that adjust the spike rate of a recording with bootstrapping ('thinning'), we propose a method of estimating a correction factor for the spike-field and spike-spike coherence that adjusts the coherence to account for this rate dependence. We demonstrate that the proposed rate adjustment is accurate under standard assumptions and derive distributional properties of the estimator. The reduced estimation variance serves to provide a more powerful test of cross-condition differences in spike-LFP coherence than the thinning method and does not require repeated Monte Carlo trials. We also demonstrate some of the negative consequences of failing to account for rate dependence. The proposed spike-field coherence estimator accurately adjusts the spike-field coherence with respect to rate and has well-defined distributional properties that endow the estimator with lower estimation variance than the existing adjustment method.

  14. Multineuronal Spike Sequences Repeat with Millisecond Precision

    Directory of Open Access Journals (Sweden)

    Koki eMatsumoto

    2013-06-01

    Full Text Available Cortical microcircuits are nonrandomly wired by neurons. As a natural consequence, spikes emitted by microcircuits are also nonrandomly patterned in time and space. One of the prominent spike organizations is a repetition of fixed patterns of spike series across multiple neurons. However, several questions remain unsolved, including how precisely spike sequences repeat, how the sequences are spatially organized, how many neurons participate in sequences, and how different sequences are functionally linked. To address these questions, we monitored spontaneous spikes of hippocampal CA3 neurons ex vivo using a high-speed functional multineuron calcium imaging technique that allowed us to monitor spikes with millisecond resolution and to record the location of spiking and nonspiking neurons. Multineuronal spike sequences were overrepresented in spontaneous activity compared to the statistical chance level. Approximately 75% of neurons participated in at least one sequence during our observation period. The participants were sparsely dispersed and did not show specific spatial organization. The number of sequences relative to the chance level decreased when larger time frames were used to detect sequences. Thus, sequences were precise at the millisecond level. Sequences often shared common spikes with other sequences; parts of sequences were subsequently relayed by following sequences, generating complex chains of multiple sequences.

  15. Supervised Learning in Multilayer Spiking Neural Networks

    CERN Document Server

    Sporea, Ioana

    2012-01-01

    The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.

  16. Robust spike-train learning in spike-event based weight update.

    Science.gov (United States)

    Shrestha, Sumit Bam; Song, Qing

    2017-09-12

    Supervised learning algorithms in a spiking neural network either learn a spike-train pattern for a single neuron receiving input spike-train from multiple input synapses or learn to output the first spike time in a feedforward network setting. In this paper, we build upon spike-event based weight update strategy to learn continuous spike-train in a spiking neural network with a hidden layer using a dead zone on-off based adaptive learning rate rule which ensures convergence of the learning process in the sense of weight convergence and robustness of the learning process to external disturbances. Based on different benchmark problems, we compare this new method with other relevant spike-train learning algorithms. The results show that the speed of learning is much improved and the rate of successful learning is also greatly improved. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Rate-adjusted spike-LFP coherence comparisons from spike-train statistics

    OpenAIRE

    2014-01-01

    Coherence is a fundamental tool in the analysis of neuronal data and for studying multiscale interactions of single and multiunit spikes with local field potentials. However, when the coherence is used to estimate rhythmic synchrony between spiking and any other time series, the magnitude of the coherence is dependent upon the spike rate. This property is not a statistical bias, but a feature of the coherence function. This dependence confounds cross-condition comparisons of spike-field and s...

  18. Spiking neuron network Helmholtz machine.

    Science.gov (United States)

    Sountsov, Pavel; Miller, Paul

    2015-01-01

    An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule.

  19. Learning Precise Spike Train-to-Spike Train Transformations in Multilayer Feedforward Neuronal Networks.

    Science.gov (United States)

    Banerjee, Arunava

    2016-05-01

    We derive a synaptic weight update rule for learning temporally precise spike train-to-spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing and avoids invoking concepts pertaining to spike rates or probabilistic models of spiking. The derivation is founded on two innovations. First, an error functional is proposed that compares the spike train emitted by the output neuron of the network to the desired spike train by way of their putative impact on a virtual postsynaptic neuron. This formulation sidesteps the need for spike alignment and leads to closed-form solutions for all quantities of interest. Second, virtual assignment of weights to spikes rather than synapses enables a perturbation analysis of individual spike times and synaptic weights of the output, as well as all intermediate neurons in the network, which yields the gradients of the error functional with respect to the said entities. Learning proceeds via a gradient descent mechanism that leverages these quantities. Simulation experiments demonstrate the efficacy of the proposed learning framework. The experiments also highlight asymmetries between synapses on excitatory and inhibitory neurons.

  20. Statistical properties of superimposed stationary spike trains.

    Science.gov (United States)

    Deger, Moritz; Helias, Moritz; Boucsein, Clemens; Rotter, Stefan

    2012-06-01

    The Poisson process is an often employed model for the activity of neuronal populations. It is known, though, that superpositions of realistic, non- Poisson spike trains are not in general Poisson processes, not even for large numbers of superimposed processes. Here we construct superimposed spike trains from intracellular in vivo recordings from rat neocortex neurons and compare their statistics to specific point process models. The constructed superimposed spike trains reveal strong deviations from the Poisson model. We find that superpositions of model spike trains that take the effective refractoriness of the neurons into account yield a much better description. A minimal model of this kind is the Poisson process with dead-time (PPD). For this process, and for superpositions thereof, we obtain analytical expressions for some second-order statistical quantities-like the count variability, inter-spike interval (ISI) variability and ISI correlations-and demonstrate the match with the in vivo data. We conclude that effective refractoriness is the key property that shapes the statistical properties of the superposition spike trains. We present new, efficient algorithms to generate superpositions of PPDs and of gamma processes that can be used to provide more realistic background input in simulations of networks of spiking neurons. Using these generators, we show in simulations that neurons which receive superimposed spike trains as input are highly sensitive for the statistical effects induced by neuronal refractoriness.

  1. Linking investment spikes and productivity growth

    NARCIS (Netherlands)

    Geylani, P.C.; Stefanou, S.E.

    2013-01-01

    We investigate the relationship between productivity growth and investment spikes using Census Bureau’s plant-level dataset for the U.S. food manufacturing industry. There are differences in productivity growth and investment spike patterns across different sub-industries and food manufacturing indu

  2. Motor control by precisely timed spike patterns

    DEFF Research Database (Denmark)

    Srivastava, Kyle H; Holmes, Caroline M; Vellema, Michiel

    2017-01-01

    A fundamental problem in neuroscience is understanding how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditional theories assume that this information is conveyed by the total number of spikes fired within a specified time...

  3. What causes a neuron to spike?

    CERN Document Server

    Arcas, B A; Arcas, Blaise Aguera y; Fairhall, Adrienne

    2003-01-01

    The computation performed by a neuron can be formulated as a combination of dimensional reduction in stimulus space and the nonlinearity inherent in a spiking output. White noise stimulus and reverse correlation (the spike-triggered average and spike-triggered covariance) are often used in experimental neuroscience to `ask' neurons which dimensions in stimulus space they are sensitive to, and to characterize the nonlinearity of the response. In this paper, we apply reverse correlation to the simplest model neuron with temporal dynamics--the leaky integrate-and-fire model--and find that even for this simple case standard techniques do not recover the known neural computation. To overcome this, we develop novel reverse correlation techniques by selectively analyzing only `isolated' spikes, and taking explicit account of the extended silences that precede these isolated spikes. We discuss the implications of our methods to the characterization of neural adaptation. Although these methods are developed in the con...

  4. Stochastic Variational Learning in Recurrent Spiking Networks

    Directory of Open Access Journals (Sweden)

    Danilo eJimenez Rezende

    2014-04-01

    Full Text Available The ability to learn and perform statistical inference with biologically plausible recurrent network of spiking neurons is an important step towards understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators conveying information about ``novelty on a statistically rigorous ground.Simulations show that our model is able to learn bothstationary and non-stationary patterns of spike trains.We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

  5. Stochastic variational learning in recurrent spiking networks.

    Science.gov (United States)

    Jimenez Rezende, Danilo; Gerstner, Wulfram

    2014-01-01

    The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent spiking networks with hidden neurons, combining principles from variational learning and reinforcement learning. Our network defines a generative model over spike train histories and the derived learning rule has the form of a local Spike Timing Dependent Plasticity rule modulated by global factors (neuromodulators) conveying information about "novelty" on a statistically rigorous ground. Simulations show that our model is able to learn both stationary and non-stationary patterns of spike trains. We also propose one experiment that could potentially be performed with animals in order to test the dynamics of the predicted novelty signal.

  6. Spiking neural P systems with multiple channels.

    Science.gov (United States)

    Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian

    2017-11-01

    Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. PySpike - A Python library for analyzing spike train synchrony

    CERN Document Server

    Mulansky, Mario

    2016-01-01

    Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains) plays a crucial role in addressing this problem. Here, the PySpike library is introduced, a Python package for spike train analysis providing parameter-free and time-scale independent measures of spike train synchrony. It allows to compute bi- and multivariate dissimilarity profiles, averaged values and bivariate matrices. Although mainly focusing on neuroscience, PySpike can also be applied in other contexts like climate research or social sciences. The package is available as Open Source on Github and PyPI.

  8. Spike generation estimated from stationary spike trains in a variety of neurons in vivo.

    Science.gov (United States)

    Spanne, Anton; Geborek, Pontus; Bengtsson, Fredrik; Jörntell, Henrik

    2014-01-01

    To any model of brain function, the variability of neuronal spike firing is a problem that needs to be taken into account. Whereas the synaptic integration can be described in terms of the original Hodgkin-Huxley (H-H) formulations of conductance-based electrical signaling, the transformation of the resulting membrane potential into patterns of spike output is subjected to stochasticity that may not be captured with standard single neuron H-H models. The dynamics of the spike output is dependent on the normal background synaptic noise present in vivo, but the neuronal spike firing variability in vivo is not well studied. In the present study, we made long-term whole cell patch clamp recordings of stationary spike firing states across a range of membrane potentials from a variety of subcortical neurons in the non-anesthetized, decerebrated state in vivo. Based on the data, we formulated a simple, phenomenological model of the properties of the spike generation in each neuron that accurately captured the stationary spike firing statistics across all membrane potentials. The model consists of a parametric relationship between the mean and standard deviation of the inter-spike intervals, where the parameter is linearly related to the injected current over the membrane. This enabled it to generate accurate approximations of spike firing also under inhomogeneous conditions with input that varies over time. The parameters describing the spike firing statistics for different neuron types overlapped extensively, suggesting that the spike generation had similar properties across neurons.

  9. Spiking neural P systems with anti-spikes and without annihilating priority as number acceptors

    Institute of Scientific and Technical Information of China (English)

    Gangjun Tan,Tao Song,; Zhihua Chen

    2014-01-01

    Spiking neural P systems with anti-spikes (ASN P sys-tems) are variant forms of spiking neural P systems, which are inspired by inhibitory impulses/spikes or inhibitory synapses. The typical feature of ASN P systems is when a neuron contains both spikes and anti-spikes, spikes and anti-spikes wil immediately an-nihilate each other in a maximal way. In this paper, a restricted variant of ASN P systems, cal ed ASN P systems without anni-hilating priority, is considered, where the annihilating rule is used as the standard rule, i.e., it is not obligatory to use in the neuron associated with both spikes and anti-spikes. If the annihilating rule is used in a neuron, the annihilation wil consume one time unit. As a result, such systems using two categories of spiking rules (iden-tified by (a, a) and (a, ¯a)) can achieve Turing completeness as number accepting devices.

  10. Spike generation estimated from stationary spike trains in a variety of neurons in vivo

    Directory of Open Access Journals (Sweden)

    Anton eSpanne

    2014-07-01

    Full Text Available To any model of brain function, the variability of neuronal spike firing is a problem that needs to be taken into account. Whereas the synaptic integration can be described in terms of the original Hodgkin-Huxley (H-H formulations of conductance-based electrical signaling, the transformation of the resulting membrane potential into patterns of spike output is subjected to stochasticity that may not be captured with standard single neuron H-H models. The dynamics of the spike output is dependent on the normal background synaptic noise present in vivo, but the neuronal spike firing variability in vivo is not well studied. In the present study, we made long-term whole cell patch clamp recordings of stationary spike firing states across a range of membrane potentials from a variety of subcortical neurons in the non-anesthetized, decerebrated state in vivo. Based on the data, we formulated a simple, phenomenological model of the properties of the spike generation in each neuron that accurately captured the stationary spike firing statistics across all membrane potentials. The model consists of a parametric relationship between the mean and standard deviation of the inter-spike intervals, where the parameter is linearly related to the injected current over the membrane. This enabled it to generate accurate approximations of spike firing also under inhomogeneous conditions with input that varies over time. The parameters describing the spike firing statistics for different neuron types overlapped extensively, suggesting that the spike generation had similar properties across neurons.

  11. Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events.

    Science.gov (United States)

    Shahi, Mina; van Vreeswijk, Carl; Pipa, Gordon

    2016-01-01

    Detecting the existence of temporally coordinated spiking activity, and its role in information processing in the cortex, has remained a major challenge for neuroscience research. Different methods and approaches have been suggested to test whether the observed synchronized events are significantly different from those expected by chance. To analyze the simultaneous spike trains for precise spike correlation, these methods typically model the spike trains as a Poisson process implying that the generation of each spike is independent of all the other spikes. However, studies have shown that neural spike trains exhibit dependence among spike sequences, such as the absolute and relative refractory periods which govern the spike probability of the oncoming action potential based on the time of the last spike, or the bursting behavior, which is characterized by short epochs of rapid action potentials, followed by longer episodes of silence. Here we investigate non-renewal processes with the inter-spike interval distribution model that incorporates spike-history dependence of individual neurons. For that, we use the Monte Carlo method to estimate the full shape of the coincidence count distribution and to generate false positives for coincidence detection. The results show that compared to the distributions based on homogeneous Poisson processes, and also non-Poisson processes, the width of the distribution of joint spike events changes. Non-renewal processes can lead to both heavy tailed or narrow coincidence distribution. We conclude that small differences in the exact autostructure of the point process can cause large differences in the width of a coincidence distribution. Therefore, manipulations of the autostructure for the estimation of significance of joint spike events seem to be inadequate.

  12. Memristors Empower Spiking Neurons With Stochasticity

    KAUST Repository

    Al-Shedivat, Maruan

    2015-06-01

    Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms. © 2011 IEEE.

  13. Fitting Neuron Models to Spike Trains

    Science.gov (United States)

    Rossant, Cyrille; Goodman, Dan F. M.; Fontaine, Bertrand; Platkiewicz, Jonathan; Magnusson, Anna K.; Brette, Romain

    2011-01-01

    Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input–output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model. PMID:21415925

  14. Frequency of Rolandic Spikes in ADHD

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2003-10-01

    Full Text Available The frequency of rolandic spikes in nonepileptic children with attention deficit hyperactivity disorder (ADHD was compared with a control group of normal school-aged children in a study at the University of Frankfurt, Germany.

  15. Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding

    CERN Document Server

    Grüning, André

    2011-01-01

    Few algorithms for supervised training of spiking neural networks exist that can deal with patterns of multiple spikes, and their computational properties are largely unexplored. We demonstrate in a set of simulations that the ReSuMe learning algorithm can be successfully applied to layered neural networks. Input and output patterns are encoded as spike trains of multiple precisely timed spikes, and the network learns to transform the input trains into target output trains. This is done by combining the ReSuMe learning algorithm with multiplicative scaling of the connections of downstream neurons. We show in particular that layered networks with one hidden layer can learn the basic logical operations, including Exclusive-Or, while networks without hidden layer cannot, mirroring an analogous result for layered networks of rate neurons. While supervised learning in spiking neural networks is not yet fit for technical purposes, exploring computational properties of spiking neural networks advances our understand...

  16. Noise-induced interspike interval correlations and spike train regularization in spike-triggered adapting neurons

    CERN Document Server

    Urdapilleta, Eugenio

    2016-01-01

    Spike generation in neurons produces a temporal point process, whose statistics is governed by intrinsic phenomena and the external incoming inputs to be coded. In particular, spike-evoked adaptation currents support a slow temporal process that conditions spiking probability at the present time according to past activity. In this work, we study the statistics of interspike interval correlations arising in such non-renewal spike trains, for a neuron model that reproduces different spike modes in a small adaptation scenario. We found that correlations are stronger as the neuron fires at a particular firing rate, which is defined by the adaptation process. When set in a subthreshold regime, the neuron may sustain this particular firing rate, and thus induce correlations, by noise. Given that, in this regime, interspike intervals are negatively correlated at any lag, this effect surprisingly implies a reduction in the variability of the spike count statistics at a finite noise intensity.

  17. Noise-induced interspike interval correlations and spike train regularization in spike-triggered adapting neurons

    Science.gov (United States)

    Urdapilleta, Eugenio

    2016-09-01

    Spike generation in neurons produces a temporal point process, whose statistics is governed by intrinsic phenomena and the external incoming inputs to be coded. In particular, spike-evoked adaptation currents support a slow temporal process that conditions spiking probability at the present time according to past activity. In this work, we study the statistics of interspike interval correlations arising in such non-renewal spike trains, for a neuron model that reproduces different spike modes in a small adaptation scenario. We found that correlations are stronger as the neuron fires at a particular firing rate, which is defined by the adaptation process. When set in a subthreshold regime, the neuron may sustain this particular firing rate, and thus induce correlations, by noise. Given that, in this regime, interspike intervals are negatively correlated at any lag, this effect surprisingly implies a reduction in the variability of the spike count statistics at a finite noise intensity.

  18. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  19. Training Deep Spiking Neural Networks using Backpropagation

    Directory of Open Access Journals (Sweden)

    Jun Haeng Lee

    2016-11-01

    Full Text Available Deep spiking neural networks (SNNs hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  20. Spike Detection Based on Normalized Correlation with Automatic Template Generation

    Directory of Open Access Journals (Sweden)

    Wen-Jyi Hwang

    2014-06-01

    Full Text Available A novel feedback-based spike detection algorithm for noisy spike trains is presented in this paper. It uses the information extracted from the results of spike classification for the enhancement of spike detection. The algorithm performs template matching for spike detection by a normalized correlator. The detected spikes are then sorted by the OSortalgorithm. The mean of spikes of each cluster produced by the OSort algorithm is used as the template of the normalized correlator for subsequent detection. The automatic generation and updating of templates enhance the robustness of the spike detection to input trains with various spike waveforms and noise levels. Experimental results show that the proposed algorithm operating in conjunction with OSort is an efficient design for attaining high detection and classification accuracy for spike sorting.

  1. Spiking Neurons for Analysis of Patterns

    Science.gov (United States)

    Huntsberger, Terrance

    2008-01-01

    Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological

  2. Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

    Science.gov (United States)

    Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

    2016-01-01

    In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision. PMID:27857680

  3. Nonsmooth dynamics in spiking neuron models

    Science.gov (United States)

    Coombes, S.; Thul, R.; Wedgwood, K. C. A.

    2012-11-01

    Large scale studies of spiking neural networks are a key part of modern approaches to understanding the dynamics of biological neural tissue. One approach in computational neuroscience has been to consider the detailed electrophysiological properties of neurons and build vast computational compartmental models. An alternative has been to develop minimal models of spiking neurons with a reduction in the dimensionality of both parameter and variable space that facilitates more effective simulation studies. In this latter case the single neuron model of choice is often a variant of the classic integrate-and-fire model, which is described by a nonsmooth dynamical system. In this paper we review some of the more popular spiking models of this class and describe the types of spiking pattern that they can generate (ranging from tonic to burst firing). We show that a number of techniques originally developed for the study of impact oscillators are directly relevant to their analysis, particularly those for treating grazing bifurcations. Importantly we highlight one particular single neuron model, capable of generating realistic spike trains, that is both computationally cheap and analytically tractable. This is a planar nonlinear integrate-and-fire model with a piecewise linear vector field and a state dependent reset upon spiking. We call this the PWL-IF model and analyse it at both the single neuron and network level. The techniques and terminology of nonsmooth dynamical systems are used to flesh out the bifurcation structure of the single neuron model, as well as to develop the notion of Lyapunov exponents. We also show how to construct the phase response curve for this system, emphasising that techniques in mathematical neuroscience may also translate back to the field of nonsmooth dynamical systems. The stability of periodic spiking orbits is assessed using a linear stability analysis of spiking times. At the network level we consider linear coupling between voltage

  4. Wavelet analysis of solar decimeter spikes

    Science.gov (United States)

    Fernandes, F. C. R.; Bolzan, M. J. A.; Rosa, R. R.; Prestes, A.; Cecatto, J. R.; Sawant, H. S.

    2012-04-01

    The Brazilian Solar Spectroscope (BSS), in operation at INPE, Brazil, have recorded a group of radio spikes observed on June 24, 1999 (16:53 - 16: 56 UT), in the decimeter frequency range of 1000-2500 MHz, with high temporal resolution of 100 ms and spectral resolution of 10 MHz. This event presents distinct clusters of spikes with intermittent behavior. The spectral and temporal behaviors of the observed groups of spikes of radio were investigated, applying wavelet techniques, which permits to determine the cadence of the clusters. From the dynamic spectra, the following observational parameters were determined: the total duration of the event and of each cluster, the total frequency band and cluster bands, in the case of the harmonic structures. The wavelet analysis shows an average periodicity of about 17 seconds (and harmonics) for the cadence of intermittent clusters of spikes. The analysis also suggested a semi-harmonic frequency ratio of 1:1.2. The same methodology of analysis is being applied to other selected groups of spikes recorded by BSS. These results will be presented and discussed.

  5. Spike Code Flow in Cultured Neuronal Networks.

    Science.gov (United States)

    Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime; Kamimura, Takuya; Yagi, Yasushi; Mizuno-Matsumoto, Yuko; Chen, Yen-Wei

    2016-01-01

    We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of "1101" and "1011," which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the "maximum cross-correlations" among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network.

  6. Spiking models for level-invariant encoding

    Directory of Open Access Journals (Sweden)

    Romain eBrette

    2012-01-01

    Full Text Available Levels of ecological sounds vary over several orders of magnitude,but the firing rate and membrane potential of a neuron are much more limited in range.In binaural neurons of the barn owl, tuning to interaural delays is independent oflevel differences. Yet a monaural neuron with a fixed threshold should fire earlier in responseto louder sounds, which would disrupt the tuning of these neurons. %, resulting in shifts in delay tuning for interaural level differences.How could spike timing be independent of input level?Here I derive theoretical conditions for a spiking model tobe insensitive to input level.The key property is a dynamic change in spike threshold.I then show how level invariance can be physiologically implemented,with specific ionic channel properties.It appears that these ingredients are indeed present inmonaural neurons of the sound localization pathway of birds and mammals.

  7. Filter based phase distortions in extracellular spikes.

    Science.gov (United States)

    Yael, Dorin; Bar-Gad, Izhar

    2017-01-01

    Extracellular recordings are the primary tool for extracting neuronal spike trains in-vivo. One of the crucial pre-processing stages of this signal is the high-pass filtration used to isolate neuronal spiking activity. Filters are characterized by changes in the magnitude and phase of different frequencies. While filters are typically chosen for their effect on magnitudes, little attention has been paid to the impact of these filters on the phase of each frequency. In this study we show that in the case of nonlinear phase shifts generated by most online and offline filters, the signal is severely distorted, resulting in an alteration of the spike waveform. This distortion leads to a shape that deviates from the original waveform as a function of its constituent frequencies, and a dramatic reduction in the SNR of the waveform that disrupts spike detectability. Currently, the vast majority of articles utilizing extracellular data are subject to these distortions since most commercial and academic hardware and software utilize nonlinear phase filters. We show that this severe problem can be avoided by recording wide-band signals followed by zero phase filtering, or alternatively corrected by reversed filtering of a narrow-band filtered, and in some cases even segmented signals. Implementation of either zero phase filtering or phase correction of the nonlinear phase filtering reproduces the original spike waveforms and increases the spike detection rates while reducing the number of false negative and positive errors. This process, in turn, helps eliminate subsequent errors in downstream analyses and misinterpretations of the results.

  8. Filter based phase distortions in extracellular spikes

    Science.gov (United States)

    Yael, Dorin

    2017-01-01

    Extracellular recordings are the primary tool for extracting neuronal spike trains in-vivo. One of the crucial pre-processing stages of this signal is the high-pass filtration used to isolate neuronal spiking activity. Filters are characterized by changes in the magnitude and phase of different frequencies. While filters are typically chosen for their effect on magnitudes, little attention has been paid to the impact of these filters on the phase of each frequency. In this study we show that in the case of nonlinear phase shifts generated by most online and offline filters, the signal is severely distorted, resulting in an alteration of the spike waveform. This distortion leads to a shape that deviates from the original waveform as a function of its constituent frequencies, and a dramatic reduction in the SNR of the waveform that disrupts spike detectability. Currently, the vast majority of articles utilizing extracellular data are subject to these distortions since most commercial and academic hardware and software utilize nonlinear phase filters. We show that this severe problem can be avoided by recording wide-band signals followed by zero phase filtering, or alternatively corrected by reversed filtering of a narrow-band filtered, and in some cases even segmented signals. Implementation of either zero phase filtering or phase correction of the nonlinear phase filtering reproduces the original spike waveforms and increases the spike detection rates while reducing the number of false negative and positive errors. This process, in turn, helps eliminate subsequent errors in downstream analyses and misinterpretations of the results. PMID:28358895

  9. Implementing Signature Neural Networks with Spiking Neurons.

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

  10. Implementing Signature Neural Networks with Spiking Neurons

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

    Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm—i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data—to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the

  11. Evoking prescribed spike times in stochastic neurons

    Science.gov (United States)

    Doose, Jens; Lindner, Benjamin

    2017-09-01

    Single cell stimulation in vivo is a powerful tool to investigate the properties of single neurons and their functionality in neural networks. We present a method to determine a cell-specific stimulus that reliably evokes a prescribed spike train with high temporal precision of action potentials. We test the performance of this stimulus in simulations for two different stochastic neuron models. For a broad range of parameters and a neuron firing with intermediate firing rates (20-40 Hz) the reliability in evoking the prescribed spike train is close to its theoretical maximum that is mainly determined by the level of intrinsic noise.

  12. Improved SpikeProp for Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Falah Y. H. Ahmed

    2013-01-01

    Full Text Available A spiking neurons network encodes information in the timing of individual spike times. A novel supervised learning rule for SpikeProp is derived to overcome the discontinuities introduced by the spiking thresholding. This algorithm is based on an error-backpropagation learning rule suited for supervised learning of spiking neurons that use exact spike time coding. The SpikeProp is able to demonstrate the spiking neurons that can perform complex nonlinear classification in fast temporal coding. This study proposes enhancements of SpikeProp learning algorithm for supervised training of spiking networks which can deal with complex patterns. The proposed methods include the SpikeProp particle swarm optimization (PSO and angle driven dependency learning rate. These methods are presented to SpikeProp network for multilayer learning enhancement and weights optimization. Input and output patterns are encoded as spike trains of precisely timed spikes, and the network learns to transform the input trains into target output trains. With these enhancements, our proposed methods outperformed other conventional neural network architectures.

  13. Comparison of electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially polluted soil.

    Science.gov (United States)

    Ottosen, Lisbeth M; Lepkova, Katarina; Kubal, Martin

    2006-09-01

    Electrokinetic remediation methods for removal of heavy metals from polluted soils have been subjected for quite intense research during the past years since these methods are well suitable for fine-grained soils where other remediation methods fail. Electrodialytic remediation is an electrokinetic remediation method which is based on applying an electric dc field and the use of ion exchange membranes that ensures the main transport of heavy metals to be out of the pollutes soil. An experimental investigation was made with electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially polluted soil under the same operational conditions (constant current density 0.2 mA/cm(2) and duration 28 days). The results of the present paper show that caution must be taken when generalising results obtained in spiked kaolinite to remediation of industrially polluted soils, as it was shown that the removal rate was higher in kaolinite than in both spiked soil and industrial polluted soil. The duration of spiking was found to be an important factor too, when attempting to relate remediation of spiked soil or kaolinite to remediation of industrially polluted soils. Spiking for 2 days was too short. However, spiking for 30 days resulted in a pattern that was more similar to that of industrially polluted soils with similar compositions both regarding sequential extraction and electrodialytic remediation result, though the remediation still progressed slightly faster in the spiked soil. Generalisation of remediation results to a variety of soil types must on the other hand be done with caution since the remediation results of different industrially polluted soils were very different. In one soil a total of 76% Cu was removed and in another soil no Cu was removed only redistributed within the soil. The factor with the highest influence on removal success was soil pH, which must be low in order to mobilize Cu, and thus the buffering capacity against acidification was

  14. Effect of Music on Interictal Epileptiform Spikes

    OpenAIRE

    J Gordon Millichap

    2004-01-01

    The effect of listening to Mozart’s Sonata for Two Pianos (K448) on the frequency of interictal epileptiform discharges (IEDs) in the EEGs of four children, ages 5 – 9 years, with benign childhood epilepsy with centrotemporal spikes (BCECTS) was studied in a prospective, randomized, crossover, placebo-controlled trial at the Medical University of South Carolina, Charleston, SC.

  15. Effect of Music on Interictal Epileptiform Spikes

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2004-10-01

    Full Text Available The effect of listening to Mozart’s Sonata for Two Pianos (K448 on the frequency of interictal epileptiform discharges (IEDs in the EEGs of four children, ages 5 – 9 years, with benign childhood epilepsy with centrotemporal spikes (BCECTS was studied in a prospective, randomized, crossover, placebo-controlled trial at the Medical University of South Carolina, Charleston, SC.

  16. Spike-timing theory of working memory.

    Directory of Open Access Journals (Sweden)

    Botond Szatmáry

    Full Text Available Working memory (WM is the part of the brain's memory system that provides temporary storage and manipulation of information necessary for cognition. Although WM has limited capacity at any given time, it has vast memory content in the sense that it acts on the brain's nearly infinite repertoire of lifetime long-term memories. Using simulations, we show that large memory content and WM functionality emerge spontaneously if we take the spike-timing nature of neuronal processing into account. Here, memories are represented by extensively overlapping groups of neurons that exhibit stereotypical time-locked spatiotemporal spike-timing patterns, called polychronous patterns; and synapses forming such polychronous neuronal groups (PNGs are subject to associative synaptic plasticity in the form of both long-term and short-term spike-timing dependent plasticity. While long-term potentiation is essential in PNG formation, we show how short-term plasticity can temporarily strengthen the synapses of selected PNGs and lead to an increase in the spontaneous reactivation rate of these PNGs. This increased reactivation rate, consistent with in vivo recordings during WM tasks, results in high interspike interval variability and irregular, yet systematically changing, elevated firing rate profiles within the neurons of the selected PNGs. Additionally, our theory explains the relationship between such slowly changing firing rates and precisely timed spikes, and it reveals a novel relationship between WM and the perception of time on the order of seconds.

  17. Physics of volleyball: Spiking with a purpose

    Science.gov (United States)

    Behroozi, F.

    1998-05-01

    A few weeks ago our volleyball coach telephoned me with a problem: How high should a player jump to "spike" a "set" ball so it would clear the net and land at a known distance on the other side of the net?

  18. Some Applications of Spiking Neural P Systems

    OpenAIRE

    Mihai Ionescu; Dragoş Sburlan

    2012-01-01

    In this paper we investigate some applications of spiking neural P systems regarding their capability to solve some classical computer science problems. In this respect versatility of such systems is studied to simulate a well known parallel computational model, namely the Boolean circuits. In addition, another notorious application -- sorting -- is considered within this framework.

  19. An Unsupervised Online Spike-Sorting Framework.

    Science.gov (United States)

    Knieling, Simeon; Sridharan, Kousik S; Belardinelli, Paolo; Naros, Georgios; Weiss, Daniel; Mormann, Florian; Gharabaghi, Alireza

    2016-08-01

    Extracellular neuronal microelectrode recordings can include action potentials from multiple neurons. To separate spikes from different neurons, they can be sorted according to their shape, a procedure referred to as spike-sorting. Several algorithms have been reported to solve this task. However, when clustering outcomes are unsatisfactory, most of them are difficult to adjust to achieve the desired results. We present an online spike-sorting framework that uses feature normalization and weighting to maximize the distinctiveness between different spike shapes. Furthermore, multiple criteria are applied to either facilitate or prevent cluster fusion, thereby enabling experimenters to fine-tune the sorting process. We compare our method to established unsupervised offline (Wave_Clus (WC)) and online (OSort (OS)) algorithms by examining their performance in sorting various test datasets using two different scoring systems (AMI and the Adamos metric). Furthermore, we evaluate sorting capabilities on intra-operative recordings using established quality metrics. Compared to WC and OS, our algorithm achieved comparable or higher scores on average and produced more convincing sorting results for intra-operative datasets. Thus, the presented framework is suitable for both online and offline analysis and could substantially improve the quality of microelectrode-based data evaluation for research and clinical application.

  20. JFK in Blackface: Spike Lee's "Malcolm X."

    Science.gov (United States)

    Walker, Clarence E.

    1993-01-01

    Discusses the failure of filmmaker Spike Lee to grapple with the real politics of Malcolm X before and after he left the Nation of Islam. Acknowledging the complexity of the man and his context would avoid creating a mythical figure similar to Oliver Stone's movie "JFK." (SLD)

  1. Implementing Signature Neural Networks with Spiking Neurons

    Directory of Open Access Journals (Sweden)

    José Luis Carrillo-Medina

    2016-12-01

    Full Text Available Spiking Neural Networks constitute the most promising approach to develop realistic ArtificialNeural Networks (ANNs. Unlike traditional firing rate-based paradigms, information coding inspiking models is based on the precise timing of individual spikes. Spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition. In recent years, majorbreakthroughs in neuroscience research have discovered new relevant computational principles indifferent living neural systems. Could ANNs benefit from some of these recent findings providingnovel elements of inspiration? This is an intriguing question and the development of spiking ANNsincluding novel bio-inspired information coding and processing strategies is gaining attention. Fromthis perspective, in this work, we adapt the core concepts of the recently proposed SignatureNeural Network paradigm – i.e., neural signatures to identify each unit in the network, localinformation contextualization during the processing and multicoding strategies for informationpropagation regarding the origin and the content of the data – to be employed in a spiking neuralnetwork. To the best of our knowledge, none of these mechanisms have been used yet in thecontext of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicabilityin such networks. Computer simulations show that a simple network model like the discussed hereexhibits complex self-organizing properties. The combination of multiple simultaneous encodingschemes allows the network to generate coexisting spatio-temporal patterns of activity encodinginformation in different spatio-temporal spaces. As a function of the network and/or intra-unitparameters shaping the corresponding encoding modality, different forms of competition amongthe evoked patterns can emerge even in the absence of inhibitory connections. These parametersalso

  2. Effects of visual stimulation on LFPs, spikes, and LFP-spike relations in PRR.

    Science.gov (United States)

    Hwang, Eun Jung; Andersen, Richard A

    2011-04-01

    Local field potentials (LFPs) have shown diverse relations to the spikes across different brain areas and stimulus features, suggesting that LFP-spike relationships are highly specific to the underlying connectivity of a local network. If so, the LFP-spike relationship may vary even within one brain area under the same task condition if neurons have heterogeneous connectivity with the active input sources during the task. Here, we tested this hypothesis in the parietal reach region (PRR), which includes two distinct classes of motor goal planning neurons with different connectivity to the visual input, i.e., visuomotor neurons receive stronger visual input than motor neurons. We predicted that the visual stimulation would render both the spike response and the LFP-spike relationship different between the two neuronal subpopulations. Thus we examined how visual stimulations affect spikes, LFPs, and LFP-spike relationships in PRR by comparing their planning (delay) period activity between two conditions: with or without a visual stimulus at the reach target. Neurons were classified as visuomotor if the visual stimulation increased their firing rate, or as motor otherwise. We found that the visual stimulation increased LFP power in gamma bands >40 Hz for both classes. Moreover, confirming our prediction, the correlation between the LFP gamma power and the firing rate became higher for the visuomotor than motor neurons in the presence of visual stimulation. We conclude that LFPs vary with the stimulation condition and that the LFP-spike relationship depends on a given neuron's connectivity to the dominant input sources in a particular stimulation condition.

  3. study of the strength characteristics of protein-based lightweight ...

    African Journals Online (AJOL)

    user

    Compressive strength test was carried out on the protein-based lightweight foamed concrete produced ... 150 mm were produced using ordinary Portland cement (OPC), fine aggregate, ..... values obtained for the loss on ignition (LOI) and SO3.

  4. Protein-Based Urine Test Predicts Kidney Transplant Outcomes

    Science.gov (United States)

    ... News Releases News Release Thursday, August 22, 2013 Protein-based urine test predicts kidney transplant outcomes NIH- ... supporting development of noninvasive tests. Levels of a protein in the urine of kidney transplant recipients can ...

  5. Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains.

    Directory of Open Access Journals (Sweden)

    Timothée Masquelier

    Full Text Available Experimental studies have observed Long Term synaptic Potentiation (LTP when a presynaptic neuron fires shortly before a postsynaptic neuron, and Long Term Depression (LTD when the presynaptic neuron fires shortly after, a phenomenon known as Spike Timing Dependent Plasticity (STDP. When a neuron is presented successively with discrete volleys of input spikes STDP has been shown to learn 'early spike patterns', that is to concentrate synaptic weights on afferents that consistently fire early, with the result that the postsynaptic spike latency decreases, until it reaches a minimal and stable value. Here, we show that these results still stand in a continuous regime where afferents fire continuously with a constant population rate. As such, STDP is able to solve a very difficult computational problem: to localize a repeating spatio-temporal spike pattern embedded in equally dense 'distractor' spike trains. STDP thus enables some form of temporal coding, even in the absence of an explicit time reference. Given that the mechanism exposed here is simple and cheap it is hard to believe that the brain did not evolve to use it.

  6. Generalized Volterra kernel model identification of spike-timing-dependent plasticity from simulated spiking activity.

    Science.gov (United States)

    Robinson, Brian S; Song, Dong; Berger, Theodore W

    2014-01-01

    This paper presents a methodology to estimate a learning rule that governs activity-dependent plasticity from behaviorally recorded spiking events. To demonstrate this framework, we simulate a probabilistic spiking neuron with spike-timing-dependent plasticity (STDP) and estimate all model parameters from the simulated spiking data. In the neuron model, output spiking activity is generated by the combination of noise, feedback from the output, and an input-feedforward component whose magnitude is modulated by synaptic weight. The synaptic weight is calculated with STDP with the following features: (1) weight change based on the relative timing of input-output spike pairs, (2) prolonged plasticity induction, and (3) considerations for system stability. Estimation of all model parameters is achieved iteratively by formulating the model as a generalized linear model with Volterra kernels and basis function expansion. Successful estimation of all model parameters in this study demonstrates the feasibility of this approach for in-vivo experimental studies. Furthermore, the consideration of system stability and prolonged plasticity induction enhances the ability to capture how STDP affects a neural population's signal transformation properties over a realistic time course. Plasticity characterization with this estimation method could yield insights into functional implications of STDP and be incorporated into a cortical prosthesis.

  7. Laguerre-Volterra identification of spike-timing-dependent plasticity from spiking activity: a simulation study.

    Science.gov (United States)

    Robinson, Brian S; Song, Dong; Berger, Theodore W

    2013-01-01

    This paper presents a Laguerre-Volterra methodology for identifying a plasticity learning rule from spiking neural data with four components: 1) By analyzing input-output spiking data, the effective contribution of an input on the output firing probability can be quantified with weighted Volterra kernels. 2) The weight of these Volterra kernels can be tracked over time using the stochastic state point processing filtering algorithm (SSPPF) 3) Plasticity system Volterra kernels can be estimated by treating the tracked change in weight over time as the plasticity system output and the spike timing data as the input. 4) Laguerre expansion of all Volterra kernels allows for minimization of open parameters during estimation steps. A single input spiking neuron with Spike-timing-dependent plasticity (STDP) and prolonged STDP induction is simulated. Using the spiking data from this simulation, the amplitude of the STDP learning rule and the time course of the induction is accurately estimated. This framework can be applied to identify plasticity for more complicated plasticity paradigms and is applicable to in vivo data.

  8. Detection of bursts in neuronal spike trains by the mean inter-spike interval method

    Institute of Scientific and Technical Information of China (English)

    Lin Chen; Yong Deng; Weihua Luo; Zhen Wang; Shaoqun Zeng

    2009-01-01

    Bursts are electrical spikes firing with a high frequency, which are the most important property in synaptic plasticity and information processing in the central nervous system. However, bursts are difficult to identify because bursting activities or patterns vary with phys-iological conditions or external stimuli. In this paper, a simple method automatically to detect bursts in spike trains is described. This method auto-adaptively sets a parameter (mean inter-spike interval) according to intrinsic properties of the detected burst spike trains, without any arbitrary choices or any operator judgrnent. When the mean value of several successive inter-spike intervals is not larger than the parameter, a burst is identified. By this method, bursts can be automatically extracted from different bursting patterns of cultured neurons on multi-electrode arrays, as accurately as by visual inspection. Furthermore, significant changes of burst variables caused by electrical stimulus have been found in spontaneous activity of neuronal network. These suggest that the mean inter-spike interval method is robust for detecting changes in burst patterns and characteristics induced by environmental alterations.

  9. The Mutation Frequency in Different Spike Categories in Barley

    DEFF Research Database (Denmark)

    Frydenberg, O.; Doll, Hans; Sandfær, J.

    1964-01-01

    After gamma irradiation of barley seeds, a comparison has been made between the chlorophyll-mutant frequencies in X1 spikes that had multicellular bud meristems in the seeds at the time of treatment (denoted as pre-formed spikes) and X1 spikes having no recognizable meristems at the time...

  10. Collision-spike Sputtering of Au Nanoparticles.

    Science.gov (United States)

    Sandoval, Luis; Urbassek, Herbert M

    2015-12-01

    Ion irradiation of nanoparticles leads to enhanced sputter yields if the nanoparticle size is of the order of the ion penetration depth. While this feature is reasonably well understood for collision-cascade sputtering, we explore it in the regime of collision-spike sputtering using molecular-dynamics simulation. For the particular case of 200-keV Xe bombardment of Au particles, we show that collision spikes lead to abundant sputtering with an average yield of 397 ± 121 atoms compared to only 116 ± 48 atoms for a bulk Au target. Only around 31 % of the impact energy remains in the nanoparticles after impact; the remainder is transported away by the transmitted projectile and the ejecta. The sputter yield of supported nanoparticles is estimated to be around 80 % of that of free nanoparticles due to the suppression of forward sputtering.

  11. Spiking neural P systems with weights.

    Science.gov (United States)

    Wang, Jun; Hoogeboom, Hendrik Jan; Pan, Linqiang; Păun, Gheorghe; Pérez-Jiménez, Mario J

    2010-10-01

    A variant of spiking neural P systems with positive or negative weights on synapses is introduced, where the rules of a neuron fire when the potential of that neuron equals a given value. The involved values-weights, firing thresholds, potential consumed by each rule-can be real (computable) numbers, rational numbers, integers, and natural numbers. The power of the obtained systems is investigated. For instance, it is proved that integers (very restricted: 1, -1 for weights, 1 and 2 for firing thresholds, and as parameters in the rules) suffice for computing all Turing computable sets of numbers in both the generative and the accepting modes. When only natural numbers are used, a characterization of the family of semilinear sets of numbers is obtained. It is shown that spiking neural P systems with weights can efficiently solve computationally hard problems in a nondeterministic way. Some open problems and suggestions for further research are formulated.

  12. Hardware implementation of stochastic spiking neural networks.

    Science.gov (United States)

    Rosselló, Josep L; Canals, Vincent; Morro, Antoni; Oliver, Antoni

    2012-08-01

    Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

  13. Spiked Instantons from Intersecting D-branes

    CERN Document Server

    Nekrasov, Nikita

    2016-01-01

    The moduli space of spiked instantons that arises in the context of the BPS/CFT correspondence is realised as the moduli space of classical vacua, i.e. low-energy open string field configurations, of a certain stack of intersecting D1-branes and D5-branes in Type IIB string theory. The presence of a constant B-field induces an interesting dynamics involving the tachyon condensation.

  14. Spiked instantons from intersecting D-branes

    Directory of Open Access Journals (Sweden)

    Nikita Nekrasov

    2017-01-01

    Full Text Available The moduli space of spiked instantons that arises in the context of the BPS/CFT correspondence [22] is realised as the moduli space of classical vacua, i.e. low-energy open string field configurations, of a certain stack of intersecting D1-branes and D5-branes in Type IIB string theory. The presence of a constant B-field induces an interesting dynamics involving the tachyon condensation.

  15. Spiked instantons from intersecting D-branes

    Science.gov (United States)

    Nekrasov, Nikita; Prabhakar, Naveen S.

    2017-01-01

    The moduli space of spiked instantons that arises in the context of the BPS/CFT correspondence [22] is realised as the moduli space of classical vacua, i.e. low-energy open string field configurations, of a certain stack of intersecting D1-branes and D5-branes in Type IIB string theory. The presence of a constant B-field induces an interesting dynamics involving the tachyon condensation.

  16. Stochastic synchronization in finite size spiking networks

    Science.gov (United States)

    Doiron, Brent; Rinzel, John; Reyes, Alex

    2006-09-01

    We study a stochastic synchronization of spiking activity in feedforward networks of integrate-and-fire model neurons. A stochastic mean field analysis shows that synchronization occurs only when the network size is sufficiently small. This gives evidence that the dynamics, and hence processing, of finite size populations can be drastically different from that observed in the infinite size limit. Our results agree with experimentally observed synchrony in cortical networks, and further strengthen the link between synchrony and propagation in cortical systems.

  17. Stochastically gating ion channels enable patterned spike firing through activity-dependent modulation of spike probability.

    Directory of Open Access Journals (Sweden)

    Joshua T Dudman

    2009-02-01

    Full Text Available The transformation of synaptic input into patterns of spike output is a fundamental operation that is determined by the particular complement of ion channels that a neuron expresses. Although it is well established that individual ion channel proteins make stochastic transitions between conducting and non-conducting states, most models of synaptic integration are deterministic, and relatively little is known about the functional consequences of interactions between stochastically gating ion channels. Here, we show that a model of stellate neurons from layer II of the medial entorhinal cortex implemented with either stochastic or deterministically gating ion channels can reproduce the resting membrane properties of stellate neurons, but only the stochastic version of the model can fully account for perithreshold membrane potential fluctuations and clustered patterns of spike output that are recorded from stellate neurons during depolarized states. We demonstrate that the stochastic model implements an example of a general mechanism for patterning of neuronal output through activity-dependent changes in the probability of spike firing. Unlike deterministic mechanisms that generate spike patterns through slow changes in the state of model parameters, this general stochastic mechanism does not require retention of information beyond the duration of a single spike and its associated afterhyperpolarization. Instead, clustered patterns of spikes emerge in the stochastic model of stellate neurons as a result of a transient increase in firing probability driven by activation of HCN channels during recovery from the spike afterhyperpolarization. Using this model, we infer conditions in which stochastic ion channel gating may influence firing patterns in vivo and predict consequences of modifications of HCN channel function for in vivo firing patterns.

  18. Analysis of Neuronal Spike Trains, Deconstructed.

    Science.gov (United States)

    Aljadeff, Johnatan; Lansdell, Benjamin J; Fairhall, Adrienne L; Kleinfeld, David

    2016-07-20

    As information flows through the brain, neuronal firing progresses from encoding the world as sensed by the animal to driving the motor output of subsequent behavior. One of the more tractable goals of quantitative neuroscience is to develop predictive models that relate the sensory or motor streams with neuronal firing. Here we review and contrast analytical tools used to accomplish this task. We focus on classes of models in which the external variable is compared with one or more feature vectors to extract a low-dimensional representation, the history of spiking and other variables are potentially incorporated, and these factors are nonlinearly transformed to predict the occurrences of spikes. We illustrate these techniques in application to datasets of different degrees of complexity. In particular, we address the fitting of models in the presence of strong correlations in the external variable, as occurs in natural sensory stimuli and in movement. Spectral correlation between predicted and measured spike trains is introduced to contrast the relative success of different methods.

  19. Superbubbles, Galactic Dynamos and the Spike Instability

    CERN Document Server

    Kulsrud, Russell M

    2015-01-01

    We draw attention to a problem with the alpha-Omega dynamo when it is applied to the origin of the galactic magnetic field under the assumption of perfect flux freezing. The standard theory involves the expulsion of undesirable flux and, because of flux freezing, the mass anchored on this flux also must be expelled. The strong galactic gravitational field makes this impossible on energetic grounds. It is shown that if only short pieces of the undesirable field lines are expelled, then mass can flow down along these field lines without requiring much energy. This expulsion of only short lines of force can be accomplished by a spike instability associated with gigantic astrophysical superbubbles. The physics of this instability is discussed and the results enable an estimate to be made of the number of spikes in the galaxy. It appears that there are probably enough spikes to cut all the undesirable lines into pieces as short as a couple of kiloparsecs during a dynamo time of a billion years. These cut pieces th...

  20. Millisecond precision spike timing shapes tactile perception.

    Science.gov (United States)

    Mackevicius, Emily L; Best, Matthew D; Saal, Hannes P; Bensmaia, Sliman J

    2012-10-31

    In primates, the sense of touch has traditionally been considered to be a spatial modality, drawing an analogy to the visual system. In this view, stimuli are encoded in spatial patterns of activity over the sheet of receptors embedded in the skin. We propose that the spatial processing mode is complemented by a temporal one. Indeed, the transduction and processing of complex, high-frequency skin vibrations have been shown to play an important role in tactile texture perception, and the frequency composition of vibrations shapes the evoked percept. Mechanoreceptive afferents innervating the glabrous skin exhibit temporal patterning in their responses, but the importance and behavioral relevance of spike timing, particularly for naturalistic stimuli, remains to be elucidated. Based on neurophysiological recordings from Rhesus macaques, we show that spike timing conveys information about the frequency composition of skin vibrations, both for individual afferents and for afferent populations, and that the temporal fidelity varies across afferent class. Furthermore, the perception of skin vibrations, measured in human subjects, is better predicted when spike timing is taken into account, and the resolution that predicts perception best matches the optimal resolution of the respective afferent classes. In light of these results, the peripheral representation of complex skin vibrations draws a powerful analogy with the auditory and vibrissal systems.

  1. Branching Shoots and Spikes from Lateral Meristems in Bread Wheat.

    Directory of Open Access Journals (Sweden)

    Ying Wang

    Full Text Available Wheat grain yield consists of three components: spikes per plant, grains per spike (i.e. head or ear, and grain weight; and the grains per spike can be dissected into two subcomponents: spikelets per spike and grains per spikelet. An increase in any of these components will directly contribute to grain yield. Wheat morphology biology tells that a wheat plant has no lateral meristem that forms any branching shoot or spike. In this study, we report two novel shoot and spike traits that were produced from lateral meristems in bread wheat. One is supernumerary shoot that was developed from an axillary bud at the axil of leaves on the elongated internodes of the main stem. The other is supernumerary spike that was generated from a spikelet meristem on a spike. In addition, supernumerary spikelets were generated on the same rachis node of the spike in the plant that had supernumerary shoot and spikes. All of these supernumerary shoots/spikes/spikelets found in the super wheat plants produced normal fertility and seeds, displaying huge yield potential in bread wheat.

  2. Analyzing multiple spike trains with nonparametric Granger causality.

    Science.gov (United States)

    Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj; Ding, Mingzhou

    2009-08-01

    Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.

  3. Introduction to spiking neural networks: Information processing, learning and applications.

    Science.gov (United States)

    Ponulak, Filip; Kasinski, Andrzej

    2011-01-01

    The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.

  4. Comparison of electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially polluted soil

    DEFF Research Database (Denmark)

    Ottosen, Lisbeth M.; Lepkova, Katarina; Kubal, Martin

    2006-01-01

    Electrokinetic remediation methods for removal of heavy metals from polluted soils have been subjected for quite intense research during the past years since these methods are well suitable for fine-grained soils where other remediation methods fail. Electrodialytic remediation is an electrokinetic...... polluted soil under the same operational conditions (constant current density 0.2 mA/cm2 and duration 28 days). The results of the present paper show that caution must be taken when generalising results obtained in spiked kaolinite to remediation of industrially polluted soils, as it was shown...... remediation method which is based on applying an electric DC field and the use of ion exchange membranes that ensures the main transport of heavy metals to be out of the pollutes soil. An experimental investigation was made with electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially...

  5. Comparison of electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially polluted soil

    DEFF Research Database (Denmark)

    Ottosen, Lisbeth M.; Lepkova, Katarina; Kubal, Martin

    2006-01-01

    Electrokinetic remediation methods for removal of heavy metals from polluted soils have been subjected for quite intense research during the past years since these methods are well suitable for fine-grained soils where other remediation methods fail. Electrodialytic remediation is an electrokinetic...... remediation method which is based on applying an electric DC field and the use of ion exchange membranes that ensures the main transport of heavy metals to be out of the pollutes soil. An experimental investigation was made with electrodialytic removal of Cu from spiked kaolinite, spiked soil and industrially...... polluted soil under the same operational conditions (constant current density 0.2 mA/cm2 and duration 28 days). The results of the present paper show that caution must be taken when generalising results obtained in spiked kaolinite to remediation of industrially polluted soils, as it was shown...

  6. Protein-Based Nanomedicine Platforms for Drug Delivery

    Energy Technology Data Exchange (ETDEWEB)

    Ma Ham, Aihui; Tang, Zhiwen; Wu, Hong; Wang, Jun; Lin, Yuehe

    2009-08-03

    Drug delivery systems have been developed for many years, however some limitations still hurdle the pace of going to clinical phase, for example, poor biodistribution, drug molecule cytotoxicity, tissue damage, quick clearance from the circulation system, solubility and stability of drug molecules. To overcome the limitations of drug delivery, biomaterials have to be developed and applied to drug delivery to protect the drug molecules and to enhance the drug’s efficacy. Protein-based nanomedicine platforms for drug delivery are platforms comprised of naturally self-assembled protein subunits of the same protein or a combination of proteins making up a complete system. They are ideal for drug delivery platforms due to their biocompatibility and biodegradability coupled with low toxicity. A variety of proteins have been used and characterized for drug delivery systems including the ferritin/apoferritin protein cage, plant derived viral capsids, the small Heat shock protein (sHsp) cage, albumin, soy and whey protein, collagen, and gelatin. There are many different types and shapes that have been prepared to deliver drug molecules using protein-based platforms including the various protein cages, microspheres, nanoparticles, hydrogels, films, minirods and minipellets. There are over 30 therapeutic compounds that have been investigated with protein-based drug delivery platforms for the potential treatment of various cancers, infectious diseases, chronic diseases, autoimmune diseases. In protein-based drug delivery platforms, protein cage is the most newly developed biomaterials for drug delivery and therapeutic applications. Their uniform sizes, multifunctions, and biodegradability push them to the frontier for drug delivery. In this review, the recent strategic development of drug delivery has been discussed with a special emphasis upon the polymer based, especially protein-based nanomedicine platforms for drug delivery. The advantages and disadvantages are also

  7. Complementary contributions of spike timing and spike rate to perceptual decisions in rat S1 and S2 cortex.

    Science.gov (United States)

    Zuo, Yanfang; Safaai, Houman; Notaro, Giuseppe; Mazzoni, Alberto; Panzeri, Stefano; Diamond, Mathew E

    2015-02-02

    When a neuron responds to a sensory stimulus, two fundamental codes [1-6] may transmit the information specifying stimulus identity--spike rate (the total number of spikes in the sequence, normalized by time) and spike timing (the detailed millisecond-scale temporal structure of the response). To assess the functional significance of these codes, we recorded neuronal responses in primary (S1) and secondary (S2) somatosensory cortex of five rats as they used their whiskers to identify textured surfaces. From the spike trains evoked during whisker contact with the texture, we computed the information that rate and timing codes carried about texture identity and about the rat's choice. S1 and S2 spike timing carried more information about stimulus and about choice than spike rates; the conjunction of rate and timing carried more information than either code alone. Moreover, on trials when our spike-timing-decoding algorithm extracted faithful texture information, the rat was more likely to choose correctly; when our spike-timing-decoding algorithm extracted misleading texture information, the rat was more likely to err. For spike rate information, the relationship between faithfulness of the message and correct choice was significant but weaker. These results indicate that spike timing makes crucial contributions to tactile perception, complementing and surpassing those made by rate. The language by which somatosensory cortical neurons transmit information, and the readout mechanism used to produce behavior, appears to rely on multiplexed signals from spike rate and timing.

  8. Encoding Chaos in Neural Spike Trains

    Science.gov (United States)

    Richardson, Kristen A.; Imhoff, Thomas T.; Grigg, Peter; Collins, James J.

    1998-03-01

    Recently, it has been shown that interspike interval (ISI) series from driven model neurons can be used to discriminate between chaotic and stochastic inputs. Here we extend this work to in vitro experimental studies with rat cutaneous mechanoreceptors. For each of the neurons tested, we show that a chaotically driven ISI series can be distinguished from a stochastically driven ISI series on the basis of a nonlinear prediction measure. This work demonstrates that dynamical information can be preserved when an analog chaotic signal is converted into a spike train by a sensory neuron.

  9. Solving constraint satisfaction problems with networks of spiking neurons

    Directory of Open Access Journals (Sweden)

    Zeno eJonke

    2016-03-01

    Full Text Available Network of neurons in the brain apply – unlike processors in our current generation ofcomputer hardware – an event-based processing strategy, where short pulses (spikes areemitted sparsely by neurons to signal the occurrence of an event at a particular point intime. Such spike-based computations promise to be substantially more power-efficient thantraditional clocked processing schemes. However it turned out to be surprisingly difficult todesign networks of spiking neurons that can solve difficult computational problems on the levelof single spikes (rather than rates of spikes. We present here a new method for designingnetworks of spiking neurons via an energy function. Furthermore we show how the energyfunction of a network of stochastically firing neurons can be shaped in a quite transparentmanner by composing the networks of simple stereotypical network motifs. We show that thisdesign approach enables networks of spiking neurons to produce approximate solutions todifficult (NP-hard constraint satisfaction problems from the domains of planning/optimizationand verification/logical inference. The resulting networks employ noise as a computationalresource. Nevertheless the timing of spikes (rather than just spike rates plays an essential rolein their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines and Gibbs sampling.

  10. Spike-based population coding and working memory.

    Directory of Open Access Journals (Sweden)

    Martin Boerlin

    2011-02-01

    Full Text Available Compelling behavioral evidence suggests that humans can make optimal decisions despite the uncertainty inherent in perceptual or motor tasks. A key question in neuroscience is how populations of spiking neurons can implement such probabilistic computations. In this article, we develop a comprehensive framework for optimal, spike-based sensory integration and working memory in a dynamic environment. We propose that probability distributions are inferred spike-per-spike in recurrently connected networks of integrate-and-fire neurons. As a result, these networks can combine sensory cues optimally, track the state of a time-varying stimulus and memorize accumulated evidence over periods much longer than the time constant of single neurons. Importantly, we propose that population responses and persistent working memory states represent entire probability distributions and not only single stimulus values. These memories are reflected by sustained, asynchronous patterns of activity which make relevant information available to downstream neurons within their short time window of integration. Model neurons act as predictive encoders, only firing spikes which account for new information that has not yet been signaled. Thus, spike times signal deterministically a prediction error, contrary to rate codes in which spike times are considered to be random samples of an underlying firing rate. As a consequence of this coding scheme, a multitude of spike patterns can reliably encode the same information. This results in weakly correlated, Poisson-like spike trains that are sensitive to initial conditions but robust to even high levels of external neural noise. This spike train variability reproduces the one observed in cortical sensory spike trains, but cannot be equated to noise. On the contrary, it is a consequence of optimal spike-based inference. In contrast, we show that rate-based models perform poorly when implemented with stochastically spiking neurons.

  11. Consensus-Based Sorting of Neuronal Spike Waveforms

    Science.gov (United States)

    Fournier, Julien; Mueller, Christian M.; Shein-Idelson, Mark; Hemberger, Mike

    2016-01-01

    Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data. PMID:27536990

  12. Limits of spiked random matrices I

    CERN Document Server

    Bloemendal, Alex

    2010-01-01

    Given a large, high-dimensional sample from a spiked population, the top sample covariance eigenvalue is known to exhibit a phase transition. We show that the largest eigenvalues have asymptotic distributions near the phase transition in the rank-one spiked real Wishart setting and its general beta analogue, proving a conjecture of Baik, Ben Arous and P\\'ech\\'e. We also treat shifted mean Gaussian orthogonal and beta ensembles. Such results are entirely new in the real case; in the complex case we strengthen existing results by providing optimal scaling assumptions. One obtains the known limiting random Schr\\"odinger operator on the half-line, but the boundary condition now depends on the perturbation. We derive several characterizations of the limit laws in which beta appears as a parameter, including a simple linear boundary value problem. This PDE description recovers known explicit formulas at beta=2,4, yielding in particular a new and simple proof of the Painlev\\'e representations for these Tracy-Widom d...

  13. Phase Diagram of Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Hamed eSeyed-Allaei

    2015-03-01

    Full Text Available In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probablilty of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations. but here, I take a different perspective, inspired by evolution. I simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable by nature. Networks which are configured according to the common values, have the best dynamic range in response to an impulse and their dynamic range is more robust in respect to synaptic weights. In fact, evolution has favored networks of best dynamic range. I present a phase diagram that shows the dynamic ranges of different networks of different parameteres. This phase diagram gives an insight into the space of parameters -- excitatory to inhibitory ratio, sparseness of connections and synaptic weights. It may serve as a guideline to decide about the values of parameters in a simulation of spiking neural network.

  14. Communication through resonance in spiking neuronal networks.

    Science.gov (United States)

    Hahn, Gerald; Bujan, Alejandro F; Frégnac, Yves; Aertsen, Ad; Kumar, Arvind

    2014-08-01

    The cortex processes stimuli through a distributed network of specialized brain areas. This processing requires mechanisms that can route neuronal activity across weakly connected cortical regions. Routing models proposed thus far are either limited to propagation of spiking activity across strongly connected networks or require distinct mechanisms that create local oscillations and establish their coherence between distant cortical areas. Here, we propose a novel mechanism which explains how synchronous spiking activity propagates across weakly connected brain areas supported by oscillations. In our model, oscillatory activity unleashes network resonance that amplifies feeble synchronous signals and promotes their propagation along weak connections ("communication through resonance"). The emergence of coherent oscillations is a natural consequence of synchronous activity propagation and therefore the assumption of different mechanisms that create oscillations and provide coherence is not necessary. Moreover, the phase-locking of oscillations is a side effect of communication rather than its requirement. Finally, we show how the state of ongoing activity could affect the communication through resonance and propose that modulations of the ongoing activity state could influence information processing in distributed cortical networks.

  15. Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks

    Directory of Open Access Journals (Sweden)

    Daniel ede Santos-Sierra

    2015-11-01

    Full Text Available Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions cite{Pyragas}, where the slave neuron is able to anticipate in time the behaviour of the master one. In this paper we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI, one of the main features of the neural response associated to the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

  16. An Overview of Bayesian Methods for Neural Spike Train Analysis

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2013-01-01

    Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

  17. A new supervised learning algorithm for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

    2013-06-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

  18. Geomagnetic spikes on the core-mantle boundary

    Science.gov (United States)

    Davies, Christopher; Constable, Catherine

    2017-05-01

    Extreme variations of Earth's magnetic field occurred in the Levant region around 1000 BC, when the field intensity rapidly rose and fell by a factor of 2. No coherent link currently exists between this intensity spike and the global field produced by the core geodynamo. Here we show that the Levantine spike must span >60° longitude at Earth's surface if it originates from the core-mantle boundary (CMB). Several low intensity data are incompatible with this geometric bound, though age uncertainties suggest these data could have sampled the field before the spike emerged. Models that best satisfy energetic and geometric constraints produce CMB spikes 8-22° wide, peaking at O(100) mT. We suggest that the Levantine spike reflects an intense CMB flux patch that grew in place before migrating northwest, contributing to growth of the dipole field. Estimates of Ohmic heating suggest that diffusive processes likely govern the ultimate decay of geomagnetic spikes.

  19. Cytoplasmic tail of coronavirus spike protein has intracellular targeting signals

    Indian Academy of Sciences (India)

    JIBIN SADASIVAN; MANMEET SINGH; JAYASRI DAS SARMA

    2017-06-01

    Intracellular trafficking and localization studies of spike protein from SARS and OC43 showed that SARS spikeprotein is localized in the ER or ERGIC compartment and OC43 spike protein is predominantly localized in thelysosome. Differential localization can be explained by signal sequence. The sequence alignment using Clustal Wshows that the signal sequence present at the cytoplasmic tail plays an important role in spike protein localization. Aunique GYQEL motif is identified at the cytoplasmic terminal of OC43 spike protein which helps in localization in thelysosome, and a novel KLHYT motif is identified in the cytoplasmic tail of SARS spike protein which helps in ER orERGIC localization. This study sheds some light on the role of cytoplasmic tail of spike protein in cell-to-cell fusion,coronavirus host cell fusion and subsequent pathogenicity.

  20. Stochastic optimal control of single neuron spike trains

    DEFF Research Database (Denmark)

    Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë

    2014-01-01

    Objective. External control of spike times in single neurons can reveal important information about a neuron's sub-threshold dynamics that lead to spiking, and has the potential to improve brain–machine interfaces and neural prostheses. The goal of this paper is the design of optimal electrical...... stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic...... to the spike times (open-loop control). Main results. We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy...

  1. The local field potential reflects surplus spike synchrony

    DEFF Research Database (Denmark)

    Denker, Michael; Roux, Sébastien; Lindén, Henrik;

    2011-01-01

    While oscillations of the local field potential (LFP) are commonly attributed to the synchronization of neuronal firing rate on the same time scale, their relationship to coincident spiking in the millisecond range is unknown. Here, we present experimental evidence to reconcile the notions...... of synchrony at the level of spiking and at the mesoscopic scale. We demonstrate that only in time intervals of significant spike synchrony that cannot be explained on the basis of firing rates, coincident spikes are better phase locked to the LFP than predicted by the locking of the individual spikes....... This effect is enhanced in periods of large LFP amplitudes. A quantitative model explains the LFP dynamics by the orchestrated spiking activity in neuronal groups that contribute the observed surplus synchrony. From the correlation analysis, we infer that neurons participate in different constellations...

  2. A spiking neuron circuit based on a carbon nanotube transistor.

    Science.gov (United States)

    Chen, C-L; Kim, K; Truong, Q; Shen, A; Li, Z; Chen, Y

    2012-07-11

    A spiking neuron circuit based on a carbon nanotube (CNT) transistor is presented in this paper. The spiking neuron circuit has a crossbar architecture in which the transistor gates are connected to its row electrodes and the transistor sources are connected to its column electrodes. An electrochemical cell is incorporated in the gate of the transistor by sandwiching a hydrogen-doped poly(ethylene glycol)methyl ether (PEG) electrolyte between the CNT channel and the top gate electrode. An input spike applied to the gate triggers a dynamic drift of the hydrogen ions in the PEG electrolyte, resulting in a post-synaptic current (PSC) through the CNT channel. Spikes input into the rows trigger PSCs through multiple CNT transistors, and PSCs cumulate in the columns and integrate into a 'soma' circuit to trigger output spikes based on an integrate-and-fire mechanism. The spiking neuron circuit can potentially emulate biological neuron networks and their intelligent functions.

  3. Estimating nonstationary input signals from a single neuronal spike train

    OpenAIRE

    Kim, Hideaki; Shinomoto, Shigeru

    2012-01-01

    Neurons temporally integrate input signals, translating them into timed output spikes. Because neurons nonperiodically emit spikes, examining spike timing can reveal information about input signals, which are determined by activities in the populations of excitatory and inhibitory presynaptic neurons. Although a number of mathematical methods have been developed to estimate such input parameters as the mean and fluctuation of the input current, these techniques are based on the unrealistic as...

  4. The Role of Spike Temporal Latencies in Artificial Olfaction

    Science.gov (United States)

    Polese, D.; Martinelli, E.; Dini, F.; Paolesse, R.; Filippini, D.; Lundström, I.; Di Natale, C.

    2011-09-01

    In this paper we investigate the recognition power of spike time latencies in an artificial olfactory system. For the scope we used a recently introduced platform for artificial olfaction implementing an artificial olfactory epithelium, formed by thousands sensors, and an abstract olfactory bulb1. Results show that correct volatile compounds classification can be achieved considering only the first two spikes of the neural network output evidencing that the latency of the first spikes contains actually enough information for odor identification.

  5. Finding the event structure of neuronal spike trains.

    Science.gov (United States)

    Toups, J Vincent; Fellous, Jean-Marc; Thomas, Peter J; Sejnowski, Terrence J; Tiesinga, Paul H

    2011-09-01

    Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004 ). Finding events in data with realistic spiking statistics is challenging because events belonging to different spike patterns may overlap. We propose a method for finding spiking events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.

  6. Spike Inference from Calcium Imaging using Sequential Monte Carlo Methods

    OpenAIRE

    NeuroData; Paninski, L

    2015-01-01

    Vogelstein JT, Paninski L. Spike Inference from Calcium Imaging using Sequential Monte Carlo Methods. Statistical and Applied Mathematical Sciences Institute (SAMSI) Program on Sequential Monte Carlo Methods, 2008

  7. Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.

    Science.gov (United States)

    Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

    2016-01-01

    Network of neurons in the brain apply-unlike processors in our current generation of computer hardware-an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling.

  8. A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity.

    Science.gov (United States)

    Indiveri, Giacomo; Chicca, Elisabetta; Douglas, Rodney

    2006-01-01

    We present a mixed-mode analog/digital VLSI device comprising an array of leaky integrate-and-fire (I&F) neurons, adaptive synapses with spike-timing dependent plasticity, and an asynchronous event based communication infrastructure that allows the user to (re)configure networks of spiking neurons with arbitrary topologies. The asynchronous communication protocol used by the silicon neurons to transmit spikes (events) off-chip and the silicon synapses to receive spikes from the outside is based on the "address-event representation" (AER). We describe the analog circuits designed to implement the silicon neurons and synapses and present experimental data showing the neuron's response properties and the synapses characteristics, in response to AER input spike trains. Our results indicate that these circuits can be used in massively parallel VLSI networks of I&F neurons to simulate real-time complex spike-based learning algorithms.

  9. Self-control with spiking and non-spiking neural networks playing games.

    Science.gov (United States)

    Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

    2010-01-01

    Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sighted, representing the higher brain. The spiking neural networks are implemented with leaky integrate-and-fire neurons with learning based on stochastic synaptic transmission. The differentiating element between the two brain centres in this implementation is based on the memory of past actions determined by an eligibility trace time constant. As the structure of the self-control problem can be likened to the Iterated Prisoner's Dilemma (IPD) game in that cooperation is to defection what self-control is to impulsiveness or what compromising is to insisting, we implemented the neural networks as two players, learning simultaneously but independently, competing in the IPD game. With a technique resembling the precommitment effect, whereby the

  10. Presynaptic spike broadening reduces junctional potential amplitude.

    Science.gov (United States)

    Spencer, A N; Przysiezniak, J; Acosta-Urquidi, J; Basarsky, T A

    1989-08-24

    Presynaptic modulation of action potential duration may regulate synaptic transmission in both vertebrates and invertebrates. Such synaptic plasticity is brought about by modifications to membrane currents at presynaptic release sites, which, in turn, lead to changes in the concentration of cytosolic calcium available for mediating transmitter release. The 'primitive' neuromuscular junction of the jellyfish Polyorchis penicillatus is a useful model of presynaptic modulation. In this study, we show that the durations of action potentials in the motor neurons of this jellyfish are negatively correlated with the amplitude of excitatory junctional potentials. We present data from in vitro voltage-clamp experiments showing that short duration voltage spikes, which elicit large excitatory junctional potentials in vivo, produce larger and briefer calcium currents than do long duration action potentials, which elicit small excitatory junctional potentials.

  11. A Spiking Neural Learning Classifier System

    CERN Document Server

    Howard, Gerard; Lanzi, Pier-Luca

    2012-01-01

    Learning Classifier Systems (LCS) are population-based reinforcement learners used in a wide variety of applications. This paper presents a LCS where each traditional rule is represented by a spiking neural network, a type of network with dynamic internal state. We employ a constructivist model of growth of both neurons and dendrites that realise flexible learning by evolving structures of sufficient complexity to solve a well-known problem involving continuous, real-valued inputs. Additionally, we extend the system to enable temporal state decomposition. By allowing our LCS to chain together sequences of heterogeneous actions into macro-actions, it is shown to perform optimally in a problem where traditional methods can fail to find a solution in a reasonable amount of time. Our final system is tested on a simulated robotics platform.

  12. [Wide QRS tachycardia preceded by pacemaker spikes].

    Science.gov (United States)

    Romero, M; Aranda, A; Gómez, F J; Jurado, A

    2014-04-01

    The differential diagnosis and therapeutic management of wide QRS tachycardia preceded by pacemaker spike is presented. The pacemaker-mediated tachycardia, tachycardia fibrillo-flutter in patients with pacemakers, and runaway pacemakers, have a similar surface electrocardiogram, but respond to different therapeutic measures. The tachycardia response to the application of a magnet over the pacemaker could help in the differential diagnosis, and in some cases will be therapeutic, as in the case of a tachycardia-mediated pacemaker. Although these conditions are diagnosed and treated in hospitals with catheterization laboratories using the application programmer over the pacemaker, patients presenting in primary care clinic and emergency forced us to make a diagnosis and treat the haemodynamically unstable patient prior to referral. Copyright © 2012 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España. All rights reserved.

  13. A novel spatiotemporal analysis of peri-ictal spiking to probe the relation of spikes and seizures in epilepsy.

    Science.gov (United States)

    Krishnan, Balu; Vlachos, Ioannis; Faith, Aaron; Mullane, Steven; Williams, Korwyn; Alexopoulos, Andreas; Iasemidis, Leonidas

    2014-08-01

    The relation between epileptic spikes and seizures is an important but still unresolved question in epilepsy research. Preclinical and clinical studies have produced inconclusive results on the causality or even on the existence of such a relation. We set to investigate this relation taking in consideration seizure severity and spatial extent of spike rate. We developed a novel automated spike detection algorithm based on morphological filtering techniques and then tested the hypothesis that there is a pre-ictal increase and post-ictal decrease of the spatial extent of spike rate. Peri-ictal (around seizures) spikes were detected from intracranial EEG recordings in 5 patients with temporal lobe epilepsy. The 94 recorded seizures were classified into two classes, based on the percentage of brain sites having higher or lower rate of spikes in the pre-ictal compared to post-ictal periods, with a classification accuracy of 87.4%. This seizure classification showed that seizures with increased pre-ictal spike rate and spatial extent compared to the post-ictal period were mostly (83%) clinical seizures, whereas no such statistically significant (α = 0.05) increase was observed peri-ictally in 93% of sub-clinical seizures. These consistent across patients results show the existence of a causal relation between spikes and clinical seizures, and imply resetting of the preceding spiking process by clinical seizures.

  14. Neuronal spike sorting based on radial basis function neural networks

    Directory of Open Access Journals (Sweden)

    Taghavi Kani M

    2011-02-01

    Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.

  15. Spike sorting for polytrodes: a divide and conquer approach

    Directory of Open Access Journals (Sweden)

    Nicholas V. Swindale

    2014-02-01

    Full Text Available In order to determine patterns of neural activity, spike signals recorded by extracellular electrodes have to be clustered (sorted with the aim of ensuring that each cluster represents all the spikes generated by an individual neuron. Many methods for spike sorting have been proposed but few are easily applicable to recordings from polytrodes which may have 16 or more recording sites. As with tetrodes, these are spaced sufficiently closely that signals from single neurons will usually be recorded on several adjacent sites. Although this offers a better chance of distinguishing neurons with similarly shaped spikes, sorting is difficult in such cases because of the high dimensionality of the space in which the signals must be classified. This report details a method for spike sorting based on a divide and conquer approach. Clusters are initially formed by assigning each event to the channel on which it is largest. Each channel-based cluster is then sub-divided into as many distinct clusters as possible. These are then recombined on the basis of pairwise tests into a final set of clusters. Pairwise tests are also performed to establish how distinct each cluster is from the others. A modified gradient ascent clustering (GAC algorithm is used to do the clustering. The method can sort spikes with minimal user input in times comparable to real time for recordings lasting up to 45 minutes. Our results illustrate some of the difficulties inherent in spike sorting, including changes in spike shape over time. We show that some physiologically distinct units may have very similar spike shapes. We show that RMS measures of spike shape similarity are not sensitive enough to discriminate clusters that can otherwise be separated by principal components analysis. Hence spike sorting based on least-squares matching to templates may be unreliable. Our methods should be applicable to tetrodes and scaleable to larger multi-electrode arrays (MEAs.

  16. Regulation, cell differentiation and protein-based inheritance.

    Science.gov (United States)

    Malagnac, Fabienne; Silar, Philippe

    2006-11-01

    Recent research using fungi as models provide new insight into the ability of regulatory networks to generate cellular states that are sufficiently stable to be faithfully transmitted to daughter cells, thereby generating epigenetic inheritance. Such protein-based inheritance is driven by infectious factors endowed with properties usually displayed by prions. We emphasize the contribution of regulatory networks to the emerging properties displayed by cells.

  17. Clustering predicts memory performance in networks of spiking and non-spiking neurons

    Directory of Open Access Journals (Sweden)

    Weiliang eChen

    2011-03-01

    Full Text Available The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so that results in artificial systems could throw light on real systems. We show that there are efficient patterns of connectivity and that these patterns are effective in models with either spiking or non-spiking neurons. This suggests that there may be some underlying general principles governing good connectivity in such networks. We also show that the clustering of the network, measured by Clustering Coefficient, has a strong linear correlation to the performance of associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network.

  18. Reading spike timing without a clock: intrinsic decoding of spike trains.

    Science.gov (United States)

    Panzeri, Stefano; Ince, Robin A A; Diamond, Mathew E; Kayser, Christoph

    2014-03-05

    The precise timing of action potentials of sensory neurons relative to the time of stimulus presentation carries substantial sensory information that is lost or degraded when these responses are summed over longer time windows. However, it is unclear whether and how downstream networks can access information in precise time-varying neural responses. Here, we review approaches to test the hypothesis that the activity of neural populations provides the temporal reference frames needed to decode temporal spike patterns. These approaches are based on comparing the single-trial stimulus discriminability obtained from neural codes defined with respect to network-intrinsic reference frames to the discriminability obtained from codes defined relative to the experimenter's computer clock. Application of this formalism to auditory, visual and somatosensory data shows that information carried by millisecond-scale spike times can be decoded robustly even with little or no independent external knowledge of stimulus time. In cortex, key components of such intrinsic temporal reference frames include dedicated neural populations that signal stimulus onset with reliable and precise latencies, and low-frequency oscillations that can serve as reference for partitioning extended neuronal responses into informative spike patterns.

  19. Parvalbumin tunes spike-timing and efferent short-term plasticity in striatal fast spiking interneurons.

    Science.gov (United States)

    Orduz, David; Bischop, Don Patrick; Schwaller, Beat; Schiffmann, Serge N; Gall, David

    2013-07-01

      Striatal fast spiking interneurons (FSIs) modulate output of the striatum by synchronizing medium-sized spiny neurons (MSNs). Recent studies have broadened our understanding of FSIs, showing that they are implicated in severe motor disorders such as parkinsonism, dystonia and Tourette syndrome. FSIs are the only striatal neurons to express the calcium-binding protein parvalbumin (PV). This selective expression of PV raises questions about the functional role of this Ca(2+) buffer in controlling FSI Ca(2+) dynamics and, consequently, FSI spiking mode and neurotransmission. To study the functional involvement of FSIs in striatal microcircuit activity and the role of PV in FSI function, we performed perforated patch recordings on enhanced green fluorescent protein-expressing FSIs in brain slices from control and PV-/- mice. Our results revealed that PV-/- FSIs fired more regularly and were more excitable than control FSIs by a mechanism in which Ca(2+) buffering is linked to spiking activity as a result of the activation of small conductance Ca(2+)-dependent K(+) channels. A modelling approach of striatal FSIs supports our experimental results. Furthermore, PV deletion modified frequency-specific short-term plasticity at inhibitory FSI to MSN synapses. Our results therefore reinforce the hypothesis that in FSIs, PV is crucial for fine-tuning of the temporal responses of the FSI network and for the orchestration of MSN populations. This, in turn, may play a direct role in the generation and pathology-related worsening of motor rhythms.

  20. Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms

    Science.gov (United States)

    Matthews, Brett A.; Clements, Mark A.

    2014-01-01

    This paper details a novel probabilistic method for automatic neural spike sorting which uses stochastic point process models of neural spike trains and parameterized action potential waveforms. A novel likelihood model for observed firing times as the aggregation of hidden neural spike trains is derived, as well as an iterative procedure for clustering the data and finding the parameters that maximize the likelihood. The method is executed and evaluated on both a fully labeled semiartificial dataset and a partially labeled real dataset of extracellular electric traces from rat hippocampus. In conditions of relatively high difficulty (i.e., with additive noise and with similar action potential waveform shapes for distinct neurons) the method achieves significant improvements in clustering performance over a baseline waveform-only Gaussian mixture model (GMM) clustering on the semiartificial set (1.98% reduction in error rate) and outperforms both the GMM and a state-of-the-art method on the real dataset (5.04% reduction in false positive + false negative errors). Finally, an empirical study of two free parameters for our method is performed on the semiartificial dataset. PMID:24829568

  1. Reliability of spike and burst firing in thalamocortical relay cells

    NARCIS (Netherlands)

    Zeldenrust, F.; Chameau, P.J.P.; Wadman, W.J.

    2013-01-01

    The reliability and precision of the timing of spikes in a spike train is an important aspect of neuronal coding. We investigated reliability in thalamocortical relay (TCR) cells in the acute slice and also in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimpose

  2. A new class of metrics for spike trains.

    Science.gov (United States)

    Rusu, Cătălin V; Florian, Răzvan V

    2014-02-01

    The distance between a pair of spike trains, quantifying the differences between them, can be measured using various metrics. Here we introduce a new class of spike train metrics, inspired by the Pompeiu-Hausdorff distance, and compare them with existing metrics. Some of our new metrics (the modulus-metric and the max-metric) have characteristics that are qualitatively different from those of classical metrics like the van Rossum distance or the Victor and Purpura distance. The modulus-metric and the max-metric are particularly suitable for measuring distances between spike trains where information is encoded in bursts, but the number and the timing of spikes inside a burst do not carry information. The modulus-metric does not depend on any parameters and can be computed using a fast algorithm whose time depends linearly on the number of spikes in the two spike trains. We also introduce localized versions of the new metrics, which could have the biologically relevant interpretation of measuring the differences between spike trains as they are perceived at a particular moment in time by a neuron receiving these spike trains.

  3. No WIMP mini-spikes in dwarf spheroidal galaxies

    NARCIS (Netherlands)

    Wanders, M.; Bertone, G.; Volonteri, M.; Weniger, C.

    2015-01-01

    The formation of black holes inevitably affects the distribution of dark and baryonic matter in their vicinity, leading to an enhancement of the dark matter density, called spike, and if dark matter is made of WIMPs, to a strong enhancement of the dark matter annihilation rate. Spikes at the center

  4. Spectral components of cytosolic [Ca2+] spiking in neurons

    DEFF Research Database (Denmark)

    Kardos, J; Szilágyi, N; Juhász, G

    1998-01-01

    . Delayed complex responses of large [Ca2+]c spiking observed in cells from a different set of cultures were synthesized by a set of frequencies within the range 0.018-0.117 Hz. Differential frequency patterns are suggested as characteristics of the [Ca2+]c spiking responses of neurons under different...

  5. Spike wave location and density disturb sleep slow waves in patients with CSWS (continuous spike waves during sleep).

    Science.gov (United States)

    Bölsterli Heinzle, Bigna K; Fattinger, Sara; Kurth, Salomé; Lebourgeois, Monique K; Ringli, Maya; Bast, Thomas; Critelli, Hanne; Schmitt, Bernhard; Huber, Reto

    2014-04-01

    In CSWS (continuous spike waves during sleep) activation of spike waves during slow wave sleep has been causally linked to neuropsychological deficits, but the pathophysiologic mechanisms are still unknown. In healthy subjects, the overnight decrease of the slope of slow waves in NREM (non-rapid eye movement) sleep has been linked to brain recovery to regain optimal cognitive performance. Here, we investigated whether the electrophysiologic hallmark of CSWS, the spike waves during sleep, is related to an alteration in the overnight decrease of the slope, and if this alteration is linked to location and density of spike waves. In a retrospective study, the slope of slow waves (0.5-2 Hz) in the first hour and last hour of sleep (19 electroencephalography [EEG] electrodes) of 14 patients with CSWS (3.1-13.5 years) was calculated. The spike wave "focus" was determined as the location of highest spike amplitude and the density of spike waves as spike wave index (SWI). There was no overnight change of the slope of slow waves in the "focus." Instead, in "nonfocal" regions, the slope decreased significantly. This difference in the overnight course resulted in a steeper slope in the "focus" compared to "nonfocal" electrodes during the last hour of sleep. Spike wave density was correlated with the impairment of the overnight slope decrease: The higher the SWI, the more hampered the slope decrease. Location and density of spike waves are related to an alteration of the physiologic overnight decrease of the slow wave slope. This overnight decrease of the slope was shown to be closely related to the recovery function of sleep. Such recovery is necessary for optimal cognitive performance during wakefulness. Therefore we propose the impairment of this process by spike waves as a potential mechanism leading to neuropsychological deficits in CSWS. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here. Wiley Periodicals

  6. Reliability of SEMG spike parameters during concentric contractions.

    Science.gov (United States)

    Gabriel, D A

    2000-01-01

    This study examined the reliability of four surface electromyographic (SEMG) spike parameters during concentric (isotonic) contractions: mean spike amplitude, mean spike frequency, mean spike slope, and the mean number of peaks per spike. Eighteen subjects performed rapid elbow flexion on a horizontal angular displacement device that was used to measure joint torque. The SEMG activity of the biceps brachii was monitored with Beckman Ag/AgCl electrodes. The testing schedule consisted of four hundred trials distributed equally over four sessions. The stability of the means across sessions and the consistency of scores within subjects was determined for the first five (1-5) and last five (96-100) trials of each session to examine the possible influence of a "warm up" effect. All measures exhibited a significant (p contractions enhanced the ability to recruit more fast-twitch motor units across test days.

  7. Wicket spikes: clinical correlates of a previously undescribed EEG pattern.

    Science.gov (United States)

    Reiher, J; Lebel, M

    1977-02-01

    From an analysis of the electroencephalograms of 4,458 patients who underwent recording during both wakefulness ans sleep, through the years 1969 to 1975, wicket spikes-- recorded in 39 patients-- may be described as follows: They were found during both wakefulness ans sleep, almost exclusively in adults. Their cardinal feature is a changing mode of occurrence through any single recording: from intermittent trains of more or less sustained, arciform, discharges resembling mu rhythm, to sporadic, urinary, single spikes. When occurring singly, wicket spikes can be mistaken for anterior or middle temporal spikes, since they predominate in eith er area, and since they share with them other characteristics such as amplitude (60 to 210 microvolts), polarity (surface negative) duration, and configuration. Wicket spikes should not be considered interictal abnormalities; they do nor correlate with epilepsy or with any particular symptom complex.

  8. Stochastic optimal control of single neuron spike trains

    DEFF Research Database (Denmark)

    Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë

    2014-01-01

    Objective. External control of spike times in single neurons can reveal important information about a neuron's sub-threshold dynamics that lead to spiking, and has the potential to improve brain–machine interfaces and neural prostheses. The goal of this paper is the design of optimal electrical...... stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic...... of control degrades with increasing intensity of the noise. Simulations show that our algorithms produce the desired results for the LIF model, but also for the case where the neuron dynamics are given by more complex models than the LIF model. This is illustrated explicitly using the Morris–Lecar spiking...

  9. Unsupervised Spike Sorting Based on Discriminative Subspace Learning

    CERN Document Server

    Keshtkaran, Mohammad Reza

    2014-01-01

    Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. In this paper, we present two unsupervised spike sorting algorithms based on discriminative subspace learning. The first algorithm simultaneously learns the discriminative feature subspace and performs clustering. It uses histogram of features in the most discriminative projection to detect the number of neurons. The second algorithm performs hierarchical divisive clustering that learns a discriminative 1-dimensional subspace for clustering in each level of the hierarchy until achieving almost unimodal distribution in the subspace. The algorithms are tested on synthetic and in-vivo data, and are compared against two widely used spike sorting methods. The comparative results demonstrate that our spike sorting methods can achieve substantially higher accuracy in lower dimensional feature space, and they are highly robust to noise. Moreover, they provide significantly better cluster separab...

  10. Gamma Oscillations of Spiking Neural Populations Enhance Signal Discrimination

    Science.gov (United States)

    Masuda, Naoki; Doiron, Brent

    2007-01-01

    Selective attention is an important filter for complex environments where distractions compete with signals. Attention increases both the gamma-band power of cortical local field potentials and the spike-field coherence within the receptive field of an attended object. However, the mechanisms by which gamma-band activity enhances, if at all, the encoding of input signals are not well understood. We propose that gamma oscillations induce binomial-like spike-count statistics across noisy neural populations. Using simplified models of spiking neurons, we show how the discrimination of static signals based on the population spike-count response is improved with gamma induced binomial statistics. These results give an important mechanistic link between the neural correlates of attention and the discrimination tasks where attention is known to enhance performance. Further, they show how a rhythmicity of spike responses can enhance coding schemes that are not temporally sensitive. PMID:18052541

  11. Gamma oscillations of spiking neural populations enhance signal discrimination.

    Directory of Open Access Journals (Sweden)

    Naoki Masuda

    2007-11-01

    Full Text Available Selective attention is an important filter for complex environments where distractions compete with signals. Attention increases both the gamma-band power of cortical local field potentials and the spike-field coherence within the receptive field of an attended object. However, the mechanisms by which gamma-band activity enhances, if at all, the encoding of input signals are not well understood. We propose that gamma oscillations induce binomial-like spike-count statistics across noisy neural populations. Using simplified models of spiking neurons, we show how the discrimination of static signals based on the population spike-count response is improved with gamma induced binomial statistics. These results give an important mechanistic link between the neural correlates of attention and the discrimination tasks where attention is known to enhance performance. Further, they show how a rhythmicity of spike responses can enhance coding schemes that are not temporally sensitive.

  12. Property Analysis and Suppression Method of Real Measured Sea Spikes

    Directory of Open Access Journals (Sweden)

    Huang Yong

    2015-06-01

    Full Text Available In case of high-resolution, low grazing angle, high sea state, and horizontal transmitting, horizontal receiving polarization, the radar returns are strengthened, resulting in sea spikes. The sea spikes have the characteristics of high amplitudes, nonstationary, and non-Gaussian, which have a strong impact on the radar detection of weak marine moving targets. This study proposes a method for sea clutter suppression. Firstly, based on the sea spikes identification and selection method, the amplitude, temporal correlation, Doppler spectrum, and fractional power spectrum properties of sea spikes are analyzed. Secondly, the data to be detected are chosen by selecting the background clutter with minimum mean power, which can also eliminate the sea spikes. Correspondingly, sea clutter is suppressed with improved Signal-to-Clutter Ratio (SCR. Finally, the results of experiment with real radar data verify the effectiveness of the proposed method.

  13. Limits of spiked random matrices II

    CERN Document Server

    Bloemendal, Alex

    2011-01-01

    The top eigenvalues of rank r spiked real Wishart matrices and additively perturbed Gaussian orthogonal ensembles are known to exhibit a phase transition in the large size limit. We show that they have limiting distributions for near-critical perturbations, fully resolving the conjecture of Baik, Ben Arous and P\\'ech\\'e (2005). The starting point is a new (2r+1)-diagonal form that is algebraically natural to the problem; for both models it converges to a certain random Schr\\"odinger operator on the half-line with r x r matrix-valued potential. The perturbation determines the boundary condition, and the low-lying eigenvalues describe the limit jointly over all perturbations in a fixed subspace. We treat the real, complex and quaternion (beta = 1,2,4) cases simultaneously. We also characterize the limit laws in terms of a diffusion related to Dyson's Brownian motion, and further in terms of a linear parabolic PDE; here beta is simply a parameter. At beta = 2 the PDE appears to reconcile with known Painlev\\'e fo...

  14. Recurrent Spiking Networks Solve Planning Tasks

    Science.gov (United States)

    Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan

    2016-02-01

    A recurrent spiking neural network is proposed that implements planning as probabilistic inference for finite and infinite horizon tasks. The architecture splits this problem into two parts: The stochastic transient firing of the network embodies the dynamics of the planning task. With appropriate injected input this dynamics is shaped to generate high-reward state trajectories. A general class of reward-modulated plasticity rules for these afferent synapses is presented. The updates optimize the likelihood of getting a reward through a variant of an Expectation Maximization algorithm and learning is guaranteed to convergence to a local maximum. We find that the network dynamics are qualitatively similar to transient firing patterns during planning and foraging in the hippocampus of awake behaving rats. The model extends classical attractor models and provides a testable prediction on identifying modulating contextual information. In a real robot arm reaching and obstacle avoidance task the ability to represent multiple task solutions is investigated. The neural planning method with its local update rules provides the basis for future neuromorphic hardware implementations with promising potentials like large data processing abilities and early initiation of strategies to avoid dangerous situations in robot co-worker scenarios.

  15. Memory recall and spike-frequency adaptation

    Science.gov (United States)

    Roach, James P.; Sander, Leonard M.; Zochowski, Michal R.

    2016-05-01

    The brain can reproduce memories from partial data; this ability is critical for memory recall. The process of memory recall has been studied using autoassociative networks such as the Hopfield model. This kind of model reliably converges to stored patterns that contain the memory. However, it is unclear how the behavior is controlled by the brain so that after convergence to one configuration, it can proceed with recognition of another one. In the Hopfield model, this happens only through unrealistic changes of an effective global temperature that destabilizes all stored configurations. Here we show that spike-frequency adaptation (SFA), a common mechanism affecting neuron activation in the brain, can provide state-dependent control of pattern retrieval. We demonstrate this in a Hopfield network modified to include SFA, and also in a model network of biophysical neurons. In both cases, SFA allows for selective stabilization of attractors with different basins of attraction, and also for temporal dynamics of attractor switching that is not possible in standard autoassociative schemes. The dynamics of our models give a plausible account of different sorts of memory retrieval.

  16. Asynchronous Rate Chaos in Spiking Neuronal Circuits

    Science.gov (United States)

    Harish, Omri; Hansel, David

    2015-01-01

    The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI) spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results. PMID:26230679

  17. Studying a Chaotic Spiking Neural Model

    Directory of Open Access Journals (Sweden)

    Mohammad Alhawarat

    2013-09-01

    Full Text Available Dynamics of a chaotic spiking neuron model are being studied mathematically and experimentally. The Nonlinear Dynamic State neuron (NDS is analysed to further understand the model and improve it.Chaos has many interesting properties such as sensitivity to initial conditions, space filling, control and synchronization. As suggested by biologists, these properties may be exploited and play vital role in carrying out computational tasks in human brain. The NDS model has some limitations; in thus paper the model is investigated to overcomesome of these limitations in order to enhance the model. Therefore,the model’s parameters are tuned and the resulted dynamics are studied. Also, the discretization method of the model is considered. Moreover, a mathematical analysis is carried out to reveal the underlying dynamics of the model after tuning of its parameters. The results of the aforementioned methods revealedsome facts regarding the NDS attractor and suggest the stabilization of a large number of unstable periodic orbits (UPOs which might correspond to memories in phase space

  18. Uroguanylin induces electroencephalographic spikes in rats

    Directory of Open Access Journals (Sweden)

    MDA. Teixeira

    Full Text Available Uroguanylin (UGN is an endogenous peptide that acts on membrane-bound guanylate cyclase receptors of intestinal and renal cells increasing cGMP production and regulating electrolyte and water epithelial transport. Recent research works demonstrate the expression of this peptide and its receptor in the central nervous system. The current work was undertaken in order to evaluate modifications of electroencephalographic spectra (EEG in anesthetized Wistar rats, submitted to intracisternal infusion of uroguanylin (0.0125 nmoles/min or 0.04 nmoles/min. The current observations demonstrate that 0.0125 nmoles/min and 0.04 nmoles/min intracisternal infusion of UGN significantly enhances amplitude and frequency of sharp waves and evoked spikes (p = 0.03. No statistical significance was observed on absolute alpha and theta spectra amplitude. The present data suggest that UGN acts on bioelectrogenesis of cortical cells by inducing hypersynchronic firing of neurons. This effect is blocked by nedocromil, suggesting that UGN acts by increasing the activity of chloride channels.

  19. Uroguanylin induces electroencephalographic spikes in rats.

    Science.gov (United States)

    Teixeira, M D A; Nascimento, N R F; Fonteles, M C; Vale, O C

    2013-08-01

    Uroguanylin (UGN) is an endogenous peptide that acts on membrane-bound guanylate cyclase receptors of intestinal and renal cells increasing cGMP production and regulating electrolyte and water epithelial transport. Recent research works demonstrate the expression of this peptide and its receptor in the central nervous system. The current work was undertaken in order to evaluate modifications of electroencephalographic spectra (EEG) in anesthetized Wistar rats, submitted to intracisternal infusion of uroguanylin (0.0125 nmoles/min or 0.04 nmoles/min). The current observations demonstrate that 0.0125 nmoles/min and 0.04 nmoles/min intracisternal infusion of UGN significantly enhances amplitude and frequency of sharp waves and evoked spikes (p = 0.03). No statistical significance was observed on absolute alpha and theta spectra amplitude. The present data suggest that UGN acts on bioelectrogenesis of cortical cells by inducing hypersynchronic firing of neurons. This effect is blocked by nedocromil, suggesting that UGN acts by increasing the activity of chloride channels.

  20. STUDYING A CHAOTIC SPIKING NEURAL MODEL

    Directory of Open Access Journals (Sweden)

    Mohammad Alhawarat

    2013-09-01

    Full Text Available Dynamics of a chaotic spiking neuron model are being studied mathematically and experimentally. The Nonlinear Dynamic State neuron (NDS is analysed to further understand the model and improve it. Chaos has many interesting properties such as sensitivity to initial conditions, space filling, control and synchronization. As suggested by biologists, these properties may be exploited and play vital role in carrying out computational tasks in human brain. The NDS model has some limitations; in thus paper the model is investigated to overcome some of these limitations in order to enhance the model. Therefore, the model’s parameters are tuned and the resulted dynamics are studied. Also, the discretization method of the model is considered. Moreover, a mathematical analysis is carried out to reveal the underlying dynamics of the model after tuning of its parameters. The results of the aforementioned methods revealed some facts regarding the NDS attractor and suggest the stabilization of a large number of unstable periodic orbits (UPOs which might correspond to memories in phase space.

  1. Current protein-based anti-angiogenic therapeutics.

    Science.gov (United States)

    Chakrabarti, Sanjukta; Barrow, Colin J; Kanwar, Rupinder K; Ramana, Venkata; Kanwar, Jagat R

    2014-01-01

    Angiogenesis is a multistep process for the formation of new blood vessels. Interactions between several cellular factors including growth factors, cytokines and hematopoietic factors lead to activation of various cellular pathways finally resulting in the extracellular matrix (ECM) degradation, endothelial cell proliferation, survival and migration. Normally, angiogenesis is an essential requirement for vascular development in growing embryos as well as in adult tissues where this process depends on the intricate balance between the activities of the pro- and anti-angiogenic factors. Abnormal angiogenesis results in aberrant vasculature leading to various pathological conditions. The most important factor implicated in angiogenic processes is vascular endothelial growth factor (VEGF) and its family of ligands and receptors. Several anti-angiogenic drugs have been developed and many more are currently in different phases of clinical trials, which target various angiogenesis-inducing agents including VEGF, VEGF receptors, angiopoietins and ECM components such as integrins. Anti-angiogenic therapy can be divided into gene-based therapy and protein-based therapy. Gene-based therapies include the use of antisense oligonucleotides, siRNA, aptamers, catalytic oligonucleotides including ribozymes and DNAzymes and transcription decoys. Protein-based therapeutics includes monoclonal antibodies, peptidomimetics, fusion proteins and decoy receptors. The later class of therapeutics has several advantages over gene-based and small molecule drugs, including specificity and complexity in functions, better tolerability, less interference with normal biological processes and lesser adverse effects due to decreased immune response by virtue of being mostly body's natural proteins. This review provides a comprehensive overview of angiogenesis and on the current protein-based anti-angiogenic therapeutics under research and in the clinic.

  2. Spiking irregularity and frequency modulate the behavioral report of single-neuron stimulation.

    Science.gov (United States)

    Doron, Guy; von Heimendahl, Moritz; Schlattmann, Peter; Houweling, Arthur R; Brecht, Michael

    2014-02-01

    The action potential activity of single cortical neurons can evoke measurable sensory effects, but it is not known how spiking parameters and neuronal subtypes affect the evoked sensations. Here, we examined the effects of spike train irregularity, spike frequency, and spike number on the detectability of single-neuron stimulation in rat somatosensory cortex. For regular-spiking, putative excitatory neurons, detectability increased with spike train irregularity and decreasing spike frequencies but was not affected by spike number. Stimulation of single, fast-spiking, putative inhibitory neurons led to a larger sensory effect compared to regular-spiking neurons, and the effect size depended only on spike irregularity. An ideal-observer analysis suggests that, under our experimental conditions, rats were using integration windows of a few hundred milliseconds or more. Our data imply that the behaving animal is sensitive to single neurons' spikes and even to their temporal patterning.

  3. A method for decoding the neurophysiological spike-response transform.

    Science.gov (United States)

    Stern, Estee; García-Crescioni, Keyla; Miller, Mark W; Peskin, Charles S; Brezina, Vladimir

    2009-11-15

    Many physiological responses elicited by neuronal spikes-intracellular calcium transients, synaptic potentials, muscle contractions-are built up of discrete, elementary responses to each spike. However, the spikes occur in trains of arbitrary temporal complexity, and each elementary response not only sums with previous ones, but can itself be modified by the previous history of the activity. A basic goal in system identification is to characterize the spike-response transform in terms of a small number of functions-the elementary response kernel and additional kernels or functions that describe the dependence on previous history-that will predict the response to any arbitrary spike train. Here we do this by developing further and generalizing the "synaptic decoding" approach of Sen et al. (1996). Given the spike times in a train and the observed overall response, we use least-squares minimization to construct the best estimated response and at the same time best estimates of the elementary response kernel and the other functions that characterize the spike-response transform. We avoid the need for any specific initial assumptions about these functions by using techniques of mathematical analysis and linear algebra that allow us to solve simultaneously for all of the numerical function values treated as independent parameters. The functions are such that they may be interpreted mechanistically. We examine the performance of the method as applied to synthetic data. We then use the method to decode real synaptic and muscle contraction transforms.

  4. Cytosolic pro-apoptotic SPIKE induces mitochondrial apoptosis in cancer.

    Science.gov (United States)

    Nikolic, Ivana; Kastratovic, Tatjana; Zelen, Ivanka; Zivanovic, Aleksandar; Arsenijevic, Slobodan; Mitrovic, Marina

    2010-04-30

    Proteins of the BCL-2 family are important regulators of apoptosis. The BCL-2 family includes three main subgroups: the anti-apoptotic group, such as BCL-2, BCL-XL, BCL-W, and MCL-1; multi-domain pro-apoptotic BAX, BAK; and pro-apoptotic "BH3-only" BIK, PUMA, NOXA, BID, BAD, and SPIKE. SPIKE, a rare pro-apoptotic protein, is highly conserved throughout the evolution, including Caenorhabditis elegans, whose expression is downregulated in certain tumors, including kidney, lung, and breast. In the literature, SPIKE was proposed to interact with BAP31 and prevent BCL-XL from binding to BAP31. Here, we utilized the Position Weight Matrix method to identify SPIKE to be a BH3-only pro-apoptotic protein mainly localized in the cytosol of all cancer cell lines tested. Overexpression of SPIKE weakly induced apoptosis in comparison to the known BH3-only pro-apoptotic protein BIK. SPIKE promoted mitochondrial cytochrome c release, the activation of caspase 3, and the caspase cleavage of caspase's downstream substrates BAP31 and p130CAS. Although the informatics analysis of SPIKE implicates this protein as a member of the BH3-only BCL-2 subfamily, its role in apoptosis remains to be elucidated.

  5. Statistical technique for analysing functional connectivity of multiple spike trains.

    Science.gov (United States)

    Masud, Mohammad Shahed; Borisyuk, Roman

    2011-03-15

    A new statistical technique, the Cox method, used for analysing functional connectivity of simultaneously recorded multiple spike trains is presented. This method is based on the theory of modulated renewal processes and it estimates a vector of influence strengths from multiple spike trains (called reference trains) to the selected (target) spike train. Selecting another target spike train and repeating the calculation of the influence strengths from the reference spike trains enables researchers to find all functional connections among multiple spike trains. In order to study functional connectivity an "influence function" is identified. This function recognises the specificity of neuronal interactions and reflects the dynamics of postsynaptic potential. In comparison to existing techniques, the Cox method has the following advantages: it does not use bins (binless method); it is applicable to cases where the sample size is small; it is sufficiently sensitive such that it estimates weak influences; it supports the simultaneous analysis of multiple influences; it is able to identify a correct connectivity scheme in difficult cases of "common source" or "indirect" connectivity. The Cox method has been thoroughly tested using multiple sets of data generated by the neural network model of the leaky integrate and fire neurons with a prescribed architecture of connections. The results suggest that this method is highly successful for analysing functional connectivity of simultaneously recorded multiple spike trains.

  6. Controlling chaos in balanced neural circuits with input spike trains

    Science.gov (United States)

    Engelken, Rainer; Wolf, Fred

    The cerebral cortex can be seen as a system of neural circuits driving each other with spike trains. Here we study how the statistics of these spike trains affects chaos in balanced target circuits.Earlier studies of chaos in balanced neural circuits either used a fixed input [van Vreeswijk, Sompolinsky 1996, Monteforte, Wolf 2010] or white noise [Lajoie et al. 2014]. We study dynamical stability of balanced networks driven by input spike trains with variable statistics. The analytically obtained Jacobian enables us to calculate the complete Lyapunov spectrum. We solved the dynamics in event-based simulations and calculated Lyapunov spectra, entropy production rate and attractor dimension. We vary correlations, irregularity, coupling strength and spike rate of the input and action potential onset rapidness of recurrent neurons.We generally find a suppression of chaos by input spike trains. This is strengthened by bursty and correlated input spike trains and increased action potential onset rapidness. We find a link between response reliability and the Lyapunov spectrum. Our study extends findings in chaotic rate models [Molgedey et al. 1992] to spiking neuron models and opens a novel avenue to study the role of projections in shaping the dynamics of large neural circuits.

  7. Spectral components of cytosolic [Ca2+] spiking in neurons

    DEFF Research Database (Denmark)

    Kardos, J; Szilágyi, N; Juhász, G;

    1998-01-01

    into evolutionary spectra of a characteristic set of frequencies. Non-delayed small spikes on top of sustained [Ca2+]c were synthesized by a main component frequency, 0.132+/-0.012 Hz, showing its maximal amplitude in phase with the start of depolarization (25 mM KCI) combined with caffeine (10 mM) application....... Delayed complex responses of large [Ca2+]c spiking observed in cells from a different set of cultures were synthesized by a set of frequencies within the range 0.018-0.117 Hz. Differential frequency patterns are suggested as characteristics of the [Ca2+]c spiking responses of neurons under different...

  8. Recent advances in recombinant protein-based malaria vaccines.

    Science.gov (United States)

    Draper, Simon J; Angov, Evelina; Horii, Toshihiro; Miller, Louis H; Srinivasan, Prakash; Theisen, Michael; Biswas, Sumi

    2015-12-22

    Plasmodium parasites are the causative agent of human malaria, and the development of a highly effective vaccine against infection, disease and transmission remains a key priority. It is widely established that multiple stages of the parasite's complex lifecycle within the human host and mosquito vector are susceptible to vaccine-induced antibodies. The mainstay approach to antibody induction by subunit vaccination has been the delivery of protein antigen formulated in adjuvant. Extensive efforts have been made in this endeavor with respect to malaria vaccine development, especially with regard to target antigen discovery, protein expression platforms, adjuvant testing, and development of soluble and virus-like particle (VLP) delivery platforms. The breadth of approaches to protein-based vaccines is continuing to expand as innovative new concepts in next-generation subunit design are explored, with the prospects for the development of a highly effective multi-component/multi-stage/multi-antigen formulation seeming ever more likely. This review will focus on recent progress in protein vaccine design, development and/or clinical testing for a number of leading malaria antigens from the sporozoite-, merozoite- and sexual-stages of the parasite's lifecycle-including PfCelTOS, PfMSP1, PfAMA1, PfRH5, PfSERA5, PfGLURP, PfMSP3, Pfs48/45 and Pfs25. Future prospects and challenges for the development, production, human delivery and assessment of protein-based malaria vaccines are discussed.

  9. Chromatographic Studies of Protein-Based Chiral Separations

    Science.gov (United States)

    Bi, Cong; Zheng, Xiwei; Azaria, Shiden; Beeram, Sandya; Li, Zhao; Hage, David S.

    2016-01-01

    The development of separation methods for the analysis and resolution of chiral drugs and solutes has been an area of ongoing interest in pharmaceutical research. The use of proteins as chiral binding agents in high-performance liquid chromatography (HPLC) has been an approach that has received particular attention in such work. This report provides an overview of proteins that have been used as binding agents to create chiral stationary phases (CSPs) and in the use of chromatographic methods to study these materials and protein-based chiral separations. The supports and methods that have been employed to prepare protein-based CSPs will also be discussed and compared. Specific types of CSPs that are considered include those that employ serum transport proteins (e.g., human serum albumin, bovine serum albumin, and alpha1-acid glycoprotein), enzymes (e.g., penicillin G acylase, cellobiohydrolases, and α-chymotrypsin) or other types of proteins (e.g., ovomucoid, antibodies, and avidin or streptavidin). The properties and applications for each type of protein and CSP will also be discussed in terms of their use in chromatography and chiral separations.

  10. Contrast Adaptation Decreases Complexity in Retinal Ganglion Cell Spike Train

    Institute of Scientific and Technical Information of China (English)

    WANG Guang-Li; HUANG Shi-Yong; ZHANG Ying-Ying; LIANG Pei-Ji

    2007-01-01

    @@ The difference in temporal structures of retinal ganglion cell spike trains between spontaneous activity and firing activity after contrast adaptation is investigated. The Lempel-Ziv complexity analysis reveals that the complexity of the neural spike train decreases after contrast adaptation. This implies that the behaviour of the neuron becomes ordered, which may carry relevant information about the external stimulus. Thus, during the neuron activity after contrast adaptation, external information could be encoded in forms of some certain patterns in the temporal structure of spike train that is significantly different, compared to that of the spike train during spontaneous activity, although the firing rates in spontaneous activity and firing activity after contrast adaptation are sometime similar.

  11. Timing intervals using population synchrony and spike timing dependent plasticity

    Directory of Open Access Journals (Sweden)

    Wei Xu

    2016-12-01

    Full Text Available We present a computational model by which ensembles of regularly spiking neurons can encode different time intervals through synchronous firing. We show that a neuron responding to a large population of convergent inputs has the potential to learn to produce an appropriately-timed output via spike-time dependent plasticity. We explain why temporal variability of this population synchrony increases with increasing time intervals. We also show that the scalar property of timing and its violation at short intervals can be explained by the spike-wise accumulation of jitter in the inter-spike intervals of timing neurons. We explore how the challenge of encoding longer time intervals can be overcome and conclude that this may involve a switch to a different population of neurons with lower firing rate, with the added effect of producing an earlier bias in response. Experimental data on human timing performance show features in agreement with the model’s output.

  12. Efficiently passing messages in distributed spiking neural network simulation.

    Science.gov (United States)

    Thibeault, Corey M; Minkovich, Kirill; O'Brien, Michael J; Harris, Frederick C; Srinivasa, Narayan

    2013-01-01

    Efficiently passing spiking messages in a neural model is an important aspect of high-performance simulation. As the scale of networks has increased so has the size of the computing systems required to simulate them. In addition, the information exchange of these resources has become more of an impediment to performance. In this paper we explore spike message passing using different mechanisms provided by the Message Passing Interface (MPI). A specific implementation, MVAPICH, designed for high-performance clusters with Infiniband hardware is employed. The focus is on providing information about these mechanisms for users of commodity high-performance spiking simulators. In addition, a novel hybrid method for spike exchange was implemented and benchmarked.

  13. SPIKY: A graphical user interface for monitoring spike train synchrony

    CERN Document Server

    Bozanic, Nebojsa

    2014-01-01

    Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface which facilitates the applicati...

  14. Preterm Birth Risk Spikes in Mothers with Sleep Disorders

    Science.gov (United States)

    ... https://medlineplus.gov/news/fullstory_167666.html Preterm Birth Risk Spikes in Mothers With Sleep Disorders Pregnant ... during pregnancy may increase the risk of preterm birth, a new study finds. The California research looked ...

  15. Benchmarking Spike Rate Inference in Population Calcium Imaging.

    Science.gov (United States)

    Theis, Lucas; Berens, Philipp; Froudarakis, Emmanouil; Reimer, Jacob; Román Rosón, Miroslav; Baden, Tom; Euler, Thomas; Tolias, Andreas S; Bethge, Matthias

    2016-05-01

    A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset (>100,000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience.

  16. Hardware/Software Co-Design for Spike Based Recognition

    CERN Document Server

    Ghani, Arfan; Maguire, Liam; Harkin, Jim

    2008-01-01

    The practical applications based on recurrent spiking neurons are limited due to their non-trivial learning algorithms. The temporal nature of spiking neurons is more favorable for hardware implementation where signals can be represented in binary form and communication can be done through the use of spikes. This work investigates the potential of recurrent spiking neurons implementations on reconfigurable platforms and their applicability in temporal based applications. A theoretical framework of reservoir computing is investigated for hardware/software implementation. In this framework, only readout neurons are trained which overcomes the burden of training at the network level. These recurrent neural networks are termed as microcircuits which are viewed as basic computational units in cortical computation. This paper investigates the potential of recurrent neural reservoirs and presents a novel hardware/software strategy for their implementation on FPGAs. The design is implemented and the functionality is ...

  17. Balanced synaptic input shapes the correlation between neural spike trains.

    Science.gov (United States)

    Litwin-Kumar, Ashok; Oswald, Anne-Marie M; Urban, Nathaniel N; Doiron, Brent

    2011-12-01

    Stimulus properties, attention, and behavioral context influence correlations between the spike times produced by a pair of neurons. However, the biophysical mechanisms that modulate these correlations are poorly understood. With a combined theoretical and experimental approach, we show that the rate of balanced excitatory and inhibitory synaptic input modulates the magnitude and timescale of pairwise spike train correlation. High rate synaptic inputs promote spike time synchrony rather than long timescale spike rate correlations, while low rate synaptic inputs produce opposite results. This correlation shaping is due to a combination of enhanced high frequency input transfer and reduced firing rate gain in the high input rate state compared to the low state. Our study extends neural modulation from single neuron responses to population activity, a necessary step in understanding how the dynamics and processing of neural activity change across distinct brain states.

  18. Balanced synaptic input shapes the correlation between neural spike trains.

    Directory of Open Access Journals (Sweden)

    Ashok Litwin-Kumar

    2011-12-01

    Full Text Available Stimulus properties, attention, and behavioral context influence correlations between the spike times produced by a pair of neurons. However, the biophysical mechanisms that modulate these correlations are poorly understood. With a combined theoretical and experimental approach, we show that the rate of balanced excitatory and inhibitory synaptic input modulates the magnitude and timescale of pairwise spike train correlation. High rate synaptic inputs promote spike time synchrony rather than long timescale spike rate correlations, while low rate synaptic inputs produce opposite results. This correlation shaping is due to a combination of enhanced high frequency input transfer and reduced firing rate gain in the high input rate state compared to the low state. Our study extends neural modulation from single neuron responses to population activity, a necessary step in understanding how the dynamics and processing of neural activity change across distinct brain states.

  19. Higher Order Spike Synchrony in Prefrontal Cortex during visual memory

    Directory of Open Access Journals (Sweden)

    Gordon ePipa

    2011-06-01

    Full Text Available Precise temporal synchrony of spike firing has been postulated as an important neuronal mechanism for signal integration and the induction of plasticity in neocortex. As prefrontal cortex plays an important role in organizing memory and executive functions, the convergence of multiple visual pathways onto PFC predicts that neurons should preferentially synchronize their spiking when stimulus information is processed. Furthermore, synchronous spike firing should intensify if memory processes require the induction of neuronal plasticity, even if this is only for short-term. Here we show with multiple simultaneously recorded units in ventral prefrontal cortex that neurons participate in 3 ms precise synchronous discharges distributed across multiple sites separated by at least 500 µm. The frequency of synchronous firing is modulated by behavioral performance and is specific for the memorized visual stimuli. In particular, during the memory period in which activity is not stimulus driven, larger groups of up to 7 sites exhibit performance dependent modulation of their spike synchronization.

  20. Efficiently passing messages in distributed spiking neural network simulation

    Directory of Open Access Journals (Sweden)

    Corey Michael Thibeault

    2013-06-01

    Full Text Available Efficiently passing spiking messages in a neural model is an important aspect of high-performance simulation. As the scale of networks has increased so has the size of the computing systems required to simulate them. In addition, the information exchange of these resources has become more of an impediment to performance. In this paper we explore spike message passing using different mechanisms provided by the Message Passing Interface (MPI. A specific implementation, MVAPICH, designed for high-performance clusters with Infiniband hardware is employed. The focus is on providing information about these mechanisms for users of commodity high-performance spiking simulators. In addition, a novel hybrid-method for spike exchange was implemented and benchmarked.

  1. Upper Limb Biomechanics During the Volleyball Serve and Spike

    OpenAIRE

    Reeser, Jonathan C.; Fleisig, Glenn S.; Bolt, Becky; Ruan, Mianfang

    2010-01-01

    Background: The shoulder is the third-most commonly injured body part in volleyball, with the majority of shoulder problems resulting from chronic overuse. Hypothesis: Significant kinetic differences exist among specific types of volleyball serves and spikes. Study Design: Controlled laboratory study. Methods: Fourteen healthy female collegiate volleyball players performed 5 successful trials of 4 skills: 2 directional spikes, an off-speed roll shot, and the float serve. Volunteers who were c...

  2. Membrane currents of spiking cells isolated from turtle retina.

    Science.gov (United States)

    Lasater, E M; Witkovsky, P

    1990-05-01

    We examined the membrane properties of spiking neurons isolated from the turtle (Pseudemys scripta) retina. The cells were maintained in culture for 1-7 days and were studied with the whole cell patch clamp technique. We utilized cells whose perikaryal diameters were greater than 15 microns since Kolb (1982) reported that ganglion cell perikarya in Pseudemys retina are 13-25 microns, whereas amacrine perikarya are less than 14 microns in diameter. We identified 5 currents in the studied cells: (1) a transient sodium current (INa) blocked by TTX, (2) a sustained calcium current (ICa) blocked by cobalt and enhanced by Bay-K 8644, (3) a calcium-dependent potassium current (IK(Ca)), (4) an A-type transient potassium current (IA) somewhat more sensitive to 4-AP than TEA, (5) a sustained potassium current (IK) more sensitive to TEA than 4-AP. The estimated average input resistance of the cells at -70 mV was 720 +/- 440 M omega. When all active currents were blocked, the membrane resistance between -130 and +20 mV was 2.5 G omega. When examined under current clamp, some cells produced multiple spikes to depolarizing steps of 0.1-0.3 nA, whereas other cells produced only a single spike irrespective of the strength of the current pulse. Most single spikers had an outward current that rose to a peak relatively slowly, whereas multiple spikers tend to have a more rapidly activating outward current. Under current clamp, 4-AP slowed the repolarization phase of the spike thus broadening it, but did not always abolish the ability to produce multiple spikes. TEA induced a depolarized plateau following the initial spike which precluded further spikes. It thus appears that the spiking patterns of the retinal cells are shaped primarily by the kinetics of INa, IK and IA and to a lesser extent by IK(Ca).

  3. Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-01

    Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

  4. Learning probabilistic models of connectivity from multiple spike train data

    OpenAIRE

    2010-01-01

    Neuronal circuits or cell assemblies carry out brain function through complex coordinated firing patterns [1]. Inferring topology of neuronal circuits from simultaneously recorded spike train data is a challenging problem in neuroscience. In this work we present a new class of dynamic Bayesian networks to infer polysynaptic excitatory connectivity between spiking cortical neurons [2]. The emphasis on excitatory networks allows us to learn connectivity models by exploiting fast data mining alg...

  5. SPIKY: a graphical user interface for monitoring spike train synchrony.

    Science.gov (United States)

    Kreuz, Thomas; Mulansky, Mario; Bozanic, Nebojsa

    2015-05-01

    Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels. Copyright © 2015 the American Physiological Society.

  6. Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition

    OpenAIRE

    Wu, Xinyu; Saxena, Vishal; Zhu, Kehan

    2015-01-01

    A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog CMOS-memristor approaches required extensive CMOS circuitry for training, and thus eliminated most of the density advantages gained by the adoption of memristor synapses. Further, they used different waveforms for pre and post-synaptic spikes that added undesirable...

  7. The Omega-Infinity Limit of Single Spikes

    CERN Document Server

    Axenides, Minos; Linardopoulos, Georgios

    2016-01-01

    A new infinite-size limit of strings in RxS2 is presented. The limit is obtained from single spike strings by letting their angular velocity omega become infinite. We derive the energy-momenta relation of omega-infinity single spikes as their linear velocity v-->1 and their angular momentum J-->1. Generally, the v-->1, J-->1 limit of single spikes is singular and has to be excluded from the spectrum and be studied separately. We discover that the dispersion relation of omega-infinity single spikes contains logarithms in the limit J-->1. This result is somewhat surprising, since the logarithmic behavior in the string spectra is typically associated with their motion in non-compact spaces such as AdS. Omega-infinity single spikes seem to completely cover the surface of the 2-sphere they occupy, so that they may essentially be viewed as some sort of "brany strings". A proof of the sphere-filling property of omega-infinity single spikes is given in the appendix.

  8. Neuronal spike initiation modulated by extracellular electric fields.

    Directory of Open Access Journals (Sweden)

    Guo-Sheng Yi

    Full Text Available Based on a reduced two-compartment model, the dynamical and biophysical mechanism underlying the spike initiation of the neuron to extracellular electric fields is investigated in this paper. With stability and phase plane analysis, we first investigate in detail the dynamical properties of neuronal spike initiation induced by geometric parameter and internal coupling conductance. The geometric parameter is the ratio between soma area and total membrane area, which describes the proportion of area occupied by somatic chamber. It is found that varying it could qualitatively alter the bifurcation structures of equilibrium as well as neuronal phase portraits, which remain unchanged when varying internal coupling conductance. By analyzing the activating properties of somatic membrane currents at subthreshold potentials, we explore the relevant biophysical basis of spike initiation dynamics induced by these two parameters. It is observed that increasing geometric parameter could greatly decrease the intensity of the internal current flowing from soma to dendrite, which switches spike initiation dynamics from Hopf bifurcation to SNIC bifurcation; increasing internal coupling conductance could lead to the increase of this outward internal current, whereas the increasing range is so small that it could not qualitatively alter the spike initiation dynamics. These results highlight that neuronal geometric parameter is a crucial factor in determining the spike initiation dynamics to electric fields. The finding is useful to interpret the functional significance of neuronal biophysical properties in their encoding dynamics, which could contribute to uncovering how neuron encodes electric field signals.

  9. Estimating nonstationary input signals from a single neuronal spike train.

    Science.gov (United States)

    Kim, Hideaki; Shinomoto, Shigeru

    2012-11-01

    Neurons temporally integrate input signals, translating them into timed output spikes. Because neurons nonperiodically emit spikes, examining spike timing can reveal information about input signals, which are determined by activities in the populations of excitatory and inhibitory presynaptic neurons. Although a number of mathematical methods have been developed to estimate such input parameters as the mean and fluctuation of the input current, these techniques are based on the unrealistic assumption that presynaptic activity is constant over time. Here, we propose tracking temporal variations in input parameters with a two-step analysis method. First, nonstationary firing characteristics comprising the firing rate and non-Poisson irregularity are estimated from a spike train using a computationally feasible state-space algorithm. Then, information about the firing characteristics is converted into likely input parameters over time using a transformation formula, which was constructed by inverting the neuronal forward transformation of the input current to output spikes. By analyzing spike trains recorded in vivo, we found that neuronal input parameters are similar in the primary visual cortex V1 and middle temporal area, whereas parameters in the lateral geniculate nucleus of the thalamus were markedly different.

  10. Automatic fitting of spiking neuron models to electrophysiological recordings

    Directory of Open Access Journals (Sweden)

    Cyrille Rossant

    2010-03-01

    Full Text Available Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains that can run in parallel on graphics processing units (GPUs. The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models.

  11. Studying spike trains using a van Rossum metric with a synapse-like filter.

    Science.gov (United States)

    Houghton, Conor

    2009-02-01

    Spike trains are unreliable. For example, in the primary sensory areas, spike patterns and precise spike times will vary between responses to the same stimulus. Nonetheless, information about sensory inputs is communicated in the form of spike trains. A challenge in understanding spike trains is to assess the significance of individual spikes in encoding information. One approach is to define a spike train metric, allowing a distance to be calculated between pairs of spike trains. In a good metric, this distance will depend on the information the spike trains encode. This method has been used previously to calculate the timescale over which the precision of spike times is significant. Here, a new metric is constructed based on a simple model of synaptic conductances which includes binding site depletion. Including binding site depletion in the metric means that a given individual spike has a smaller effect on the distance if it occurs soon after other spikes. The metric proves effective at classifying neuronal responses by stimuli in the sample data set of electro-physiological recordings from the primary auditory area of the zebra finch fore-brain. This shows that this is an effective metric for these spike trains suggesting that in these spike trains the significance of a spike is modulated by its proximity to previous spikes. This modulation is a putative information-coding property of spike trains.

  12. Recent advances in recombinant protein-based malaria vaccines

    DEFF Research Database (Denmark)

    Draper, Simon J; Angov, Evelina; Horii, Toshihiro

    2015-01-01

    Plasmodium parasites are the causative agent of human malaria, and the development of a highly effective vaccine against infection, disease and transmission remains a key priority. It is widely established that multiple stages of the parasite's complex lifecycle within the human host and mosquito...... vector are susceptible to vaccine-induced antibodies. The mainstay approach to antibody induction by subunit vaccination has been the delivery of protein antigen formulated in adjuvant. Extensive efforts have been made in this endeavor with respect to malaria vaccine development, especially with regard...... to target antigen discovery, protein expression platforms, adjuvant testing, and development of soluble and virus-like particle (VLP) delivery platforms. The breadth of approaches to protein-based vaccines is continuing to expand as innovative new concepts in next-generation subunit design are explored...

  13. Novel blood protein based scaffolds for cardiovascular tissue engineering

    Directory of Open Access Journals (Sweden)

    Kuhn Antonia I.

    2016-09-01

    Full Text Available A major challenge in cardiovascular tissue engineering is the fabrication of scaffolds, which provide appropriate morphological and mechanical properties while avoiding undesirable immune reactions. In this study electrospinning was used to fabricate scaffolds out of blood proteins for cardiovascular tissue engineering. Lyophilised porcine plasma was dissolved in deionised water at a final concentration of 7.5% m/v and blended with 3.7% m/v PEO. Electrospinning resulted in homogeneous fibre morphologies with a mean fibre diameter of 151 nm, which could be adapted to create macroscopic shapes (mats, tubes. Cross-linking with glutaraldehyde vapour improved the long-term stability of protein based scaffolds in comparison to untreated scaffolds, resulting in a mass loss of 41% and 96% after 28 days of incubation in aqueous solution, respectively.

  14. Spike Pattern Structure Influences Synaptic Efficacy Variability under STDP and Synaptic Homeostasis. II: Spike Shuffling Methods on LIF Networks

    Science.gov (United States)

    Bi, Zedong; Zhou, Changsong

    2016-01-01

    Synapses may undergo variable changes during plasticity because of the variability of spike patterns such as temporal stochasticity and spatial randomness. Here, we call the variability of synaptic weight changes during plasticity to be efficacy variability. In this paper, we investigate how four aspects of spike pattern statistics (i.e., synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations) influence the efficacy variability under pair-wise additive spike-timing dependent plasticity (STDP) and synaptic homeostasis (the mean strength of plastic synapses into a neuron is bounded), by implementing spike shuffling methods onto spike patterns self-organized by a network of excitatory and inhibitory leaky integrate-and-fire (LIF) neurons. With the increase of the decay time scale of the inhibitory synaptic currents, the LIF network undergoes a transition from asynchronous state to weak synchronous state and then to synchronous bursting state. We first shuffle these spike patterns using a variety of methods, each designed to evidently change a specific pattern statistics; and then investigate the change of efficacy variability of the synapses under STDP and synaptic homeostasis, when the neurons in the network fire according to the spike patterns before and after being treated by a shuffling method. In this way, we can understand how the change of pattern statistics may cause the change of efficacy variability. Our results are consistent with those of our previous study which implements spike-generating models on converging motifs. We also find that burstiness/regularity is important to determine the efficacy variability under asynchronous states, while heterogeneity of cross-correlations is the main factor to cause efficacy variability when the network moves into synchronous bursting states (the states observed in epilepsy). PMID:27555816

  15. Sums of Spike Waveform Features for Motor Decoding

    Directory of Open Access Journals (Sweden)

    Jie Li

    2017-07-01

    Full Text Available Traditionally, the key step before decoding motor intentions from cortical recordings is spike sorting, the process of identifying which neuron was responsible for an action potential. Recently, researchers have started investigating approaches to decoding which omit the spike sorting step, by directly using information about action potentials' waveform shapes in the decoder, though this approach is not yet widespread. Particularly, one recent approach involves computing the moments of waveform features and using these moment values as inputs to decoders. This computationally inexpensive approach was shown to be comparable in accuracy to traditional spike sorting. In this study, we use offline data recorded from two Rhesus monkeys to further validate this approach. We also modify this approach by using sums of exponentiated features of spikes, rather than moments. Our results show that using waveform feature sums facilitates significantly higher hand movement reconstruction accuracy than using waveform feature moments, though the magnitudes of differences are small. We find that using the sums of one simple feature, the spike amplitude, allows better offline decoding accuracy than traditional spike sorting by expert (correlation of 0.767, 0.785 vs. 0.744, 0.738, respectively, for two monkeys, average 16% reduction in mean-squared-error, as well as unsorted threshold crossings (0.746, 0.776; average 9% reduction in mean-squared-error. Our results suggest that the sums-of-features framework has potential as an alternative to both spike sorting and using unsorted threshold crossings, if developed further. Also, we present data comparing sorted vs. unsorted spike counts in terms of offline decoding accuracy. Traditional sorted spike counts do not include waveforms that do not match any template (“hash”, but threshold crossing counts do include this hash. On our data and in previous work, hash contributes to decoding accuracy. Thus, using the

  16. Aspartic acid aminotransferase activity is increased in actively spiking compared with non-spiking human epileptic cortex.

    Science.gov (United States)

    Kish, S J; Dixon, L M; Sherwin, A L

    1988-01-01

    Increased concentration of the excitatory neurotransmitter aspartic acid in actively spiking human epileptic cerebral cortex was recently described. In order to further characterise changes in the aspartergic system in epileptic brain, the behaviour of aspartic acid aminotransferase (AAT), a key enzyme involved in aspartic acid metabolism has now been examined. Electrocorticography performed during surgery was employed to identify cortical epileptic spike foci in 16 patients undergoing temporal lobectomy for intractable seizures. Patients with spontaneously spiking lateral temporal cortex (n = 8) were compared with a non-spiking control group (n = 8) of patients in whom the epileptic lesions were confined to the hippocampus sparing the temporal convexity. Mean activity of AAT in spiking cortex was significantly elevated by 16-18%, with aspartic acid concentration increased by 28%. Possible explanations for the enhanced AAT activity include increased proliferation of cortical AAT-containing astrocytes at the spiking focus and/or a generalised increase in neuronal or extraneuronal metabolism consequent to the ongoing epileptic discharge. It is suggested that the data provide additional support for a disturbance of central excitatory aspartic acid mechanisms in human epileptic brain. PMID:2898010

  17. Fixed latency on-chip interconnect for hardware spiking neural network architectures

    NARCIS (Netherlands)

    Pande, Sandeep; Morgan, Fearghal; Smit, Gerard; Bruintjes, Tom; Rutgers, Jochem; Cawley, Seamus; Harkin, Jim; McDaid, Liam

    2013-01-01

    Information in a Spiking Neural Network (SNN) is encoded as the relative timing between spikes. Distortion in spike timings can impact the accuracy of SNN operation by modifying the precise firing time of neurons within the SNN. Maintaining the integrity of spike timings is crucial for reliable oper

  18. Dual roles for spike signaling in cortical neural populations

    Directory of Open Access Journals (Sweden)

    Dana eBallard

    2011-06-01

    Full Text Available A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning post-stimulus histograms and exponential interval histograms. In addition it makes testable predictions that follow from the γ latency coding.

  19. Unbiased estimation of precise temporal correlations between spike trains.

    Science.gov (United States)

    Stark, Eran; Abeles, Moshe

    2009-04-30

    A key issue in systems neuroscience is the contribution of precise temporal inter-neuronal interactions to information processing in the brain, and the main analytical tool used for studying pair-wise interactions is the cross-correlation histogram (CCH). Although simple to generate, a CCH is influenced by multiple factors in addition to precise temporal correlations between two spike trains, thus complicating its interpretation. A Monte-Carlo-based technique, the jittering method, has been suggested to isolate the contribution of precise temporal interactions to neural information processing. Here, we show that jittering spike trains is equivalent to convolving the CCH derived from the original trains with a finite window and using a Poisson distribution to estimate probabilities. Both procedures over-fit the original spike trains and therefore the resulting statistical tests are biased and have low power. We devise an alternative method, based on convolving the CCH with a partially hollowed window, and illustrate its utility using artificial and real spike trains. The modified convolution method is unbiased, has high power, and is computationally fast. We recommend caution in the use of the jittering method and in the interpretation of results based on it, and suggest using the modified convolution method for detecting precise temporal correlations between spike trains.

  20. Neuronal spike train entropy estimation by history clustering.

    Science.gov (United States)

    Watters, Nicholas; Reeke, George N

    2014-09-01

    Neurons send signals to each other by means of sequences of action potentials (spikes). Ignoring variations in spike amplitude and shape that are probably not meaningful to a receiving cell, the information content, or entropy of the signal depends on only the timing of action potentials, and because there is no external clock, only the interspike intervals, and not the absolute spike times, are significant. Estimating spike train entropy is a difficult task, particularly with small data sets, and many methods of entropy estimation have been proposed. Here we present two related model-based methods for estimating the entropy of neural signals and compare them to existing methods. One of the methods is fast and reasonably accurate, and it converges well with short spike time records; the other is impractically time-consuming but apparently very accurate, relying on generating artificial data that are a statistical match to the experimental data. Using the slow, accurate method to generate a best-estimate entropy value, we find that the faster estimator converges to this value more closely and with smaller data sets than many existing entropy estimators.

  1. Multiplexed Spike Coding and Adaptation in the Thalamus

    Directory of Open Access Journals (Sweden)

    Rebecca A. Mease

    2017-05-01

    Full Text Available High-frequency “burst” clusters of spikes are a generic output pattern of many neurons. While bursting is a ubiquitous computational feature of different nervous systems across animal species, the encoding of synaptic inputs by bursts is not well understood. We find that bursting neurons in the rodent thalamus employ “multiplexing” to differentially encode low- and high-frequency stimulus features associated with either T-type calcium “low-threshold” or fast sodium spiking events, respectively, and these events adapt differently. Thus, thalamic bursts encode disparate information in three channels: (1 burst size, (2 burst onset time, and (3 precise spike timing within bursts. Strikingly, this latter “intraburst” encoding channel shows millisecond-level feature selectivity and adapts across statistical contexts to maintain stable information encoded per spike. Consequently, calcium events both encode low-frequency stimuli and, in parallel, gate a transient window for high-frequency, adaptive stimulus encoding by sodium spike timing, allowing bursts to efficiently convey fine-scale temporal information.

  2. Financial time series prediction using spiking neural networks.

    Science.gov (United States)

    Reid, David; Hussain, Abir Jaafar; Tawfik, Hissam

    2014-01-01

    In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

  3. Financial time series prediction using spiking neural networks.

    Directory of Open Access Journals (Sweden)

    David Reid

    Full Text Available In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.

  4. Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding.

    Directory of Open Access Journals (Sweden)

    Chao Huang

    2016-06-01

    Full Text Available Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information.

  5. Effects of nicotine stimulation on spikes, theta frequency oscillations, and spike-theta oscillation relationship in rat medial septum diagonal band Broca slices

    Institute of Scientific and Technical Information of China (English)

    Dong WEN; Ce PENG; Gao-xiang OU-YANG; Zainab HENDERSON; Xiao-li LI; Cheng-biao LU

    2013-01-01

    Aim:Spiking activities and neuronal network oscillations in the theta frequency range have been found in many cortical areas during information processing.The aim of this study is to determine whether nicotinic acetylcholine receptors (nAChRs) mediate neuronal network activity in rat medial septum diagonal band Broca (MSDB) slices.Methods:Extracellular field potentials were recorded in the slices using an Axoprobe 1A amplifier.Data analysis was performed offline.Spike sorting and local field potential (LFP) analyses were performed using Spike2 software.The role of spiking activity in the generation of LFP oscillations in the slices was determined by analyzing the phase-time relationship between the spikes and LFP oscillations.Circular statistic analysis based on the Rayleigh test was used to determine the significance of phase relationships between the spikes and LFP oscillations.The timing relationship was examined by quantifying the spike-field coherence (SFC).Results:Application of nicotine (250 nmol/L) induced prominent LFP oscillations in the theta frequency band and both small-and large-amplitude population spiking activity in the slices.These spikes were phase-locked to theta oscillations at specific phases.The Rayleigh test showed a statistically significant relationship in phase-locking between the spikes and theta oscillations.Larger changes in the SFC were observed for large-amplitude spikes,indicating an accurate timing relationship between this type of spike and LFP oscillations.The nicotine-induced spiking activity (large-amplitude population spikes) was suppressed by the nAChR antagonist dihydro-β-erythroidine (0.3 μmol/L).Conclusion:The results demonstrate that large-amplitude spikes are phase-locked to theta oscillations and have a high spike-timing accuracy,which are likely a main contributor to the theta oscillations generated in MSDB during nicotine receptor activation.

  6. Affixing plant sections without protein based adhesives for protease histochemistry.

    Science.gov (United States)

    Jona, R; Griglione, R

    1999-01-01

    To submit a section of plant tissue to histochemical analysis using protease, the protein based adhesives which keep the slices attached to the slides must be replaced because they are attacked by the enzyme and the slices are washed off the slides. We devised a method to keep the slices attached to the slides during histochemical extractions and subsequent staining. Slides are frosted on two lateral zones by spreading on them a fluoride paste composed of 15 g barium sulfate, 15 g ammonium difluoride, 8 g oxalic acid, 40 ml glycerine and 12 ml deionized water using a thin paint brush. After removing the paste with tap water and drying the slides, the sections are placed on the central clear zone of the slide and covered with an ethyl-cellulose film that keeps the slices in place and allows the reagents to act through it. To do this, the slides are dipped into 0.5% ethyl cellulose (ETC) prepared in a 4:1 mixture of toluene and absolute ethanol. The ETC coating is layered three times to improve its firmness and its ability to retain the slices on the slides. To obtain perfect adhesion, the slide should be oven dried (40-50 C for 10-15 min) to remove any trace of humidity before applying each layer of ETC. Subsequently the sections can be extracted and stained without undue loss of material.

  7. Protein-based supramolecular polymers: progress and prospect.

    Science.gov (United States)

    Luo, Quan; Dong, Zeyuan; Hou, Chunxi; Liu, Junqiu

    2014-09-11

    Proteins are naturally evolved macromolecules with highly sophisticated structures and diverse properties. The design and controlled self-assembly of proteins into polymeric architectures via supramolecular interactions offers unique advantages in understanding the spontaneously self-organisational process and fabrication of various bioactive materials. This feature article highlights recent advances and future trends in supramolecular polymers that are directly assembled from the building blocks of proteins. Non-covalent interactions capable of inducing polymerization include aromatic π-π stacking, host-guest interactions, metal coordination, and interprotein interactions combined with site-selective protein modification to explore the dynamic and specific unidirectional aggregation behaviours among protein units. We also discuss some extended supramolecular protein polymers achieved by rational design and fine-tuning the protein-protein interactions, which may help to inspire future design of more complicated polymeric protein assemblies. The protein-based supramolecular polymer system provides a versatile platform for functionalization and thereby shows great potential in the development of novel biomaterials with controlled structures and properties.

  8. Protein-based flexible whispering gallery mode resonators

    Science.gov (United States)

    Yilmaz, Huzeyfe; Pena-Francesch, Abdon; Xu, Linhua; Shreiner, Robert; Jung, Huihun; Huang, Steven H.; Özdemir, Sahin K.; Demirel, Melik C.; Yang, Lan

    2016-02-01

    The idea of creating photonics tools for sensing, imaging and material characterization has long been pursued and many achievements have been made. Approaching the level of solutions provided by nature however is hindered by routine choice of materials. To this end recent years have witnessed a great effort to engineer mechanically flexible photonic devices using polymer substrates. On the other hand, biodegradability and biocompatibility still remains to be incorporated. Hence biomimetics holds the key to overcome the limitations of traditional materials in photonics design. Natural proteins such as sucker ring teeth (SRT) and silk for instance have remarkable mechanical and optical properties that exceed the endeavors of most synthetic and natural polymers. Here we demonstrate for the first time, toroidal whispering gallery mode resonators (WGMR) fabricated entirely from protein structures such as SRT of Loligo vulgaris (European squid) and silk from Bombyx mori. We provide here complete optical and material characterization of proteinaceous WGMRs, revealing high quality factors in microscale and enhancement of Raman signatures by a microcavity. We also present a most simple application of a WGMR as a natural protein add-drop filter, made of SRT protein. Our work shows that with protein-based materials, optical, mechanical and thermal properties can be devised at the molecular level and it lays the groundwork for future eco-friendly, flexible photonics device design.

  9. Glucose Synthesis in a Protein-Based Artificial Photosynthesis System.

    Science.gov (United States)

    Lu, Hao; Yuan, Wenqiao; Zhou, Jack; Chong, Parkson Lee-Gau

    2015-09-01

    The objective of this study was to understand glucose synthesis of a protein-based artificial photosynthesis system affected by operating conditions, including the concentrations of reactants, reaction temperature, and illumination. Results from non-vesicle-based glyceraldehyde-3-phosphate (GAP) and glucose synthesis showed that the initial concentrations of ribulose-1,5-bisphosphate (RuBP) and adenosine triphosphate (ATP), lighting source, and temperature significantly affected glucose synthesis. Higher initial concentrations of RuBP and ATP significantly enhanced GAP synthesis, which was linearly correlated to glucose synthesis, confirming the proper functions of all catalyzing enzymes in the system. White fluorescent light inhibited artificial photosynthesis and reduced glucose synthesis by 79.2 % compared to in the dark. The reaction temperature of 40 °C was optimum, whereas lower or higher temperature reduced glucose synthesis. Glucose synthesis in the vesicle-based artificial photosynthesis system reconstituted with bacteriorhodopsin, F 0 F 1 ATP synthase, and polydimethylsiloxane-methyloxazoline-polydimethylsiloxane triblock copolymer was successfully demonstrated. This system efficiently utilized light-induced ATP to drive glucose synthesis, and 5.2 μg ml(-1) glucose was synthesized in 0.78-ml reaction buffer in 7 h. Light-dependent reactions were found to be the bottleneck of the studied artificial photosynthesis system.

  10. Plasticizing Effects of Polyamines in Protein-Based Films

    Directory of Open Access Journals (Sweden)

    Mohammed Sabbah

    2017-05-01

    Full Text Available Zeta potential and nanoparticle size were determined on film forming solutions of native and heat-denatured proteins of bitter vetch as a function of pH and of different concentrations of the polyamines spermidine and spermine, both in the absence and presence of the plasticizer glycerol. Our results showed that both polyamines decreased the negative zeta potential of all samples under pH 8.0 as a consequence of their ionic interaction with proteins. At the same time, they enhanced the dimension of nanoparticles under pH 8.0 as a result of macromolecular aggregations. By using native protein solutions, handleable films were obtained only from samples containing either a minimum of 33 mM glycerol or 4 mM spermidine, or both compounds together at lower glycerol concentrations. However, 2 mM spermidine was sufficient to obtain handleable film by using heat-treated samples without glycerol. Conversely, brittle materials were obtained by spermine alone, thus indicating that only spermidine was able to act as an ionic plasticizer. Lastly, both polyamines, mainly spermine, were found able to act as “glycerol-like” plasticizers at concentrations higher than 5 mM under experimental conditions at which their amino groups are undissociated. Our findings open new perspectives in obtaining protein-based films by using aliphatic polycations as components.

  11. Mussel-mimetic protein-based adhesive hydrogel.

    Science.gov (United States)

    Kim, Bum Jin; Oh, Dongyeop X; Kim, Sangsik; Seo, Jeong Hyun; Hwang, Dong Soo; Masic, Admir; Han, Dong Keun; Cha, Hyung Joon

    2014-05-12

    Hydrogel systems based on cross-linked polymeric materials which could provide both adhesion and cohesion in wet environment have been considered as a promising formulation of tissue adhesives. Inspired by marine mussel adhesion, many researchers have tried to exploit the 3,4-dihydroxyphenylalanine (DOPA) molecule as a cross-linking mediator of synthetic polymer-based hydrogels which is known to be able to achieve cohesive hardening as well as adhesive bonding with diverse surfaces. Beside DOPA residue, composition of other amino acid residues and structure of mussel adhesive proteins (MAPs) have also been considered important elements for mussel adhesion. Herein, we represent a novel protein-based hydrogel system using DOPA-containing recombinant MAP. Gelation can be achieved using both oxdiation-induced DOPA quinone-mediated covalent and Fe(3+)-mediated coordinative noncovalent cross-linking. Fe(3+)-mediated hydrogels show deformable and self-healing viscoelastic behavior in rheological analysis, which is also well-reflected in bulk adhesion strength measurement. Quinone-mediated hydrogel has higher cohesive strength and can provide sufficient gelation time for easier handling. Collectively, our newly developed MAP hydrogel can potentially be used as tissue adhesive and sealant for future applications.

  12. Food protein-based phytosterol nanoparticles: fabrication and characterization.

    Science.gov (United States)

    Cao, Wen-Jun; Ou, Shi-Yi; Lin, Wei-Feng; Tang, Chuan-He

    2016-09-14

    The development of food-grade (nano)particles as a delivery system for poorly water soluble bioactives has recently attracted increasing attention. This work is an attempt to fabricate food protein-based nanoparticles as delivery systems for improving the water dispersion and bioaccessibility of phytosterols (PS) by an emulsification-evaporation method. The fabricated PS nanoparticles were characterized in terms of particle size, encapsulation efficiency (EE%) and loading amount (LA), and ξ-potential. Among all the test proteins, including soy protein isolate (SPI), whey protein concentrate (WPC) and sodium caseinate (SC), SC was confirmed to be the most suitable protein for the PS nano-formulation. Besides the type of protein, the particle size, EE% and LA of PS in the nanoparticles varied with the applied protein concentration in the aqueous phase and organic volume fraction. The freeze-dried PS nanoparticles with SC exhibited good water re-dispersion behavior and low crystallinity of PS. The LA of PS in the nanoparticles decreased upon storage, especially at high temperatures (e.g., >25 °C). The PS in the fabricated nanoparticles exhibited much better bioaccessibility than free PS. The findings would be of relevance for the fabrication of food-grade colloidal phytosterols, with great potential to be applied in functional food formulations.

  13. Colorful protein-based fluorescent probes for collagen imaging.

    Directory of Open Access Journals (Sweden)

    Stijn J A Aper

    Full Text Available Real-time visualization of collagen is important in studies on tissue formation and remodeling in the research fields of developmental biology and tissue engineering. Our group has previously reported on a fluorescent probe for the specific imaging of collagen in live tissue in situ, consisting of the native collagen binding protein CNA35 labeled with fluorescent dye Oregon Green 488 (CNA35-OG488. The CNA35-OG488 probe has become widely used for collagen imaging. To allow for the use of CNA35-based probes in a broader range of applications, we here present a toolbox of six genetically-encoded collagen probes which are fusions of CNA35 to fluorescent proteins that span the visible spectrum: mTurquoise2, EGFP, mAmetrine, LSSmOrange, tdTomato and mCherry. While CNA35-OG488 requires a chemical conjugation step for labeling with the fluorescent dye, these protein-based probes can be easily produced in high yields by expression in E. coli and purified in one step using Ni2+-affinity chromatography. The probes all bind specifically to collagen, both in vitro and in porcine pericardial tissue. Some first applications of the probes are shown in multicolor imaging of engineered tissue and two-photon imaging of collagen in human skin. The fully-genetic encoding of the new probes makes them easily accessible to all scientists interested in collagen formation and remodeling.

  14. Radioxenon detector calibration spike production and delivery systems

    Energy Technology Data Exchange (ETDEWEB)

    Foxe, Michael P.; Cameron, Ian M.; Cooper, Matthew W.; Haas, Derek A.; Hayes, James C.; Kriss, Aaron A.; Lidey, Lance S.; Mendez, Jennifer M.; Prinke, Amanda M.; Riedmann, Robin A.

    2016-03-01

    Abstract Beta-Gamma coincidence radioxenon detectors must be calibrated for each of the four-radioxenon isotopes (135Xe, 133Xe, 133mXe, and 131mXe). Without a proper calibration, there is potential for the misidentification of the amount of each isotope detected. It is important to accurately determine the amount of each radioxenon isotope, as the ratios can be used to distinguish between an anthropogenic source and a nuclear explosion. We have developed a xenon calibration system (XeCalS) that produces calibration spikes of known activity and pressure for field calibration of detectors. The activity concentrations of these calibration spikes are measured using a beta-gamma coincidence detector and a high purity germanium (HPGe) detector. We will present the results from the development and commissioning of XeCalS, along with the future plans for a portable spike implementation system.

  15. Inferring Synaptic Connectivity from Spatio-Temporal Spike Patterns

    Directory of Open Access Journals (Sweden)

    Frank eVan Bussel

    2011-02-01

    Full Text Available Networks of well-known dynamical units but unknown interaction topology arise across various fields of biology, including genetics, ecology and neuroscience. The collective dynamics of such networks is often sensitive to the presence (or absence of individual interactions, but there is usually no direct way to probe for their existence. Here we present an explicit method for reconstructing interaction networks of leaky integrate-and-fire neurons from the spike patterns they exhibit in response to external driving. Given the dynamical parameters are known, the approach works well for networks in simple collective states but is also applicable to networks exhibiting complex spatio-temporal spike patterns. In particular, stationarity of spiking time series is not required.

  16. Grain price spikes and beggar-thy-neighbor policy responses

    DEFF Research Database (Denmark)

    Boysen, Ole; Jensen, Hans Grinsted

    Upward spikes in the international price of food in recent years led some countries to raise export barriers, thereby exacerbating both the price spike and reducing the terms of trade for food-importing countries (beggaring their neighbors). At the same time, and for similar political......-economy reasons, numerous food-importing countries reduced or suspended their import tariffs, and some even provided food import subsidies -- which also exacerbated the international price spike, thus turning the terms of trade even further against food-importing countries. This issue became a major item...... on the agenda of various international policy fora, including the annual meetings of G20 countries in recent years. For that reason, recent studies have attempted to quantify the extent to which such policy actions contributed to the rise in food prices. A study by Jensen & Anderson (2014) uses the global AGE...

  17. The thermodynamic temperature of a rhythmic spiking network

    CERN Document Server

    Merolla, Paul; Arthur, John

    2010-01-01

    Artificial neural networks built from two-state neurons are powerful computational substrates, whose computational ability is well understood by analogy with statistical mechanics. In this work, we introduce similar analogies in the context of spiking neurons in a fixed time window, where excitatory and inhibitory inputs drawn from a Poisson distribution play the role of temperature. For single neurons with a "bandgap" between their inputs and the spike threshold, this temperature allows for stochastic spiking. By imposing a global inhibitory rhythm over the fixed time windows, we connect neurons into a network that exhibits synchronous, clock-like updating akin to neural networks. We implement a single-layer Boltzmann machine without learning to demonstrate our model.

  18. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan

    2015-04-01

    Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

  19. Stable propagation of synchronous spiking in cortical neural networks

    Science.gov (United States)

    Diesmann, Markus; Gewaltig, Marc-Oliver; Aertsen, Ad

    1999-12-01

    The classical view of neural coding has emphasized the importance of information carried by the rate at which neurons discharge action potentials. More recent proposals that information may be carried by precise spike timing have been challenged by the assumption that these neurons operate in a noisy fashion-presumably reflecting fluctuations in synaptic input-and, thus, incapable of transmitting signals with millisecond fidelity. Here we show that precisely synchronized action potentials can propagate within a model of cortical network activity that recapitulates many of the features of biological systems. An attractor, yielding a stable spiking precision in the (sub)millisecond range, governs the dynamics of synchronization. Our results indicate that a combinatorial neural code, based on rapid associations of groups of neurons co-ordinating their activity at the single spike level, is possible within a cortical-like network.

  20. Entropy-based parametric estimation of spike train statistics

    CERN Document Server

    Vasquez, J C; Viéville, T

    2010-01-01

    We consider the evolution of a network of neurons, focusing on the asymptotic behavior of spikes dynamics instead of membrane potential dynamics. The spike response is not sought as a deterministic response in this context, but as a conditional probability : "Reading out the code" consists of inferring such a probability. This probability is computed from empirical raster plots, by using the framework of thermodynamic formalism in ergodic theory. This gives us a parametric statistical model where the probability has the form of a Gibbs distribution. In this respect, this approach generalizes the seminal and profound work of Schneidman and collaborators. A minimal presentation of the formalism is reviewed here, while a general algorithmic estimation method is proposed yielding fast convergent implementations. It is also made explicit how several spike observables (entropy, rate, synchronizations, correlations) are given in closed-form from the parametric estimation. This paradigm does not only allow us to esti...

  1. A Neuron Model for FPGA Spiking Neuronal Network Implementation

    Directory of Open Access Journals (Sweden)

    BONTEANU, G.

    2011-11-01

    Full Text Available We propose a neuron model, able to reproduce the basic elements of the neuronal dynamics, optimized for digital implementation of Spiking Neural Networks. Its architecture is structured in two major blocks, a datapath and a control unit. The datapath consists of a membrane potential circuit, which emulates the neuronal dynamics at the soma level, and a synaptic circuit used to update the synaptic weight according to the spike timing dependent plasticity (STDP mechanism. The proposed model is implemented into a Cyclone II-Altera FPGA device. Our results indicate the neuron model can be used to build up 1K Spiking Neural Networks on reconfigurable logic suport, to explore various network topologies.

  2. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.

    Science.gov (United States)

    Truccolo, Wilson; Eden, Uri T; Fellows, Matthew R; Donoghue, John P; Brown, Emery N

    2005-02-01

    Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.

  3. Upper limb biomechanics during the volleyball serve and spike.

    Science.gov (United States)

    Reeser, Jonathan C; Fleisig, Glenn S; Bolt, Becky; Ruan, Mianfang

    2010-09-01

    The shoulder is the third-most commonly injured body part in volleyball, with the majority of shoulder problems resulting from chronic overuse. Significant kinetic differences exist among specific types of volleyball serves and spikes. Controlled laboratory study. Fourteen healthy female collegiate volleyball players performed 5 successful trials of 4 skills: 2 directional spikes, an off-speed roll shot, and the float serve. Volunteers who were competent in jump serves (n, 5) performed 5 trials of that skill. A 240-Hz 3-dimensional automatic digitizing system captured each trial. Multivariate analysis of variance and post hoc paired t tests were used to compare kinetic parameters for the shoulder and elbow across all the skills (except the jump serve). A similar statistical analysis was performed for upper extremity kinematics. Forces, torques, and angular velocities at the shoulder and elbow were lowest for the roll shot and second-lowest for the float serve. No differences were detected between the cross-body and straight-ahead spikes. Although there was an insufficient number of participants to statistically analyze the jump serve, the data for it appear similar to those of the cross-body and straight-ahead spikes. Shoulder abduction at the instant of ball contact was approximately 130° for all skills, which is substantially greater than that previously reported for female athletes performing tennis serves or baseball pitches. Because shoulder kinetics were greatest during spiking, the volleyball player with symptoms of shoulder overuse may wish to reduce the number of repetitions performed during practice. Limiting the number of jump serves may also reduce the athlete's risk of overuse-related shoulder dysfunction. Volleyball-specific overhead skills, such as the spike and serve, produce considerable upper extremity force and torque, which may contribute to the risk of shoulder injury.

  4. Information content in cortical spike trains during brain state transitions.

    Science.gov (United States)

    Arnold, Maria M; Szczepanski, Janusz; Montejo, Noelia; Amigó, José M; Wajnryb, Eligiusz; Sanchez-Vives, Maria V

    2013-02-01

    Even in the absence of external stimuli there is ongoing activity in the cerebral cortex as a result of recurrent connectivity. This paper attempts to characterize one aspect of this ongoing activity by examining how the information content carried by specific neurons varies as a function of brain state. We recorded from rats chronically implanted with tetrodes in the primary visual cortex during awake and sleep periods. Electro-encephalogram and spike trains were recorded during 30-min periods, and 2-4 neuronal spikes were isolated per tetrode off-line. All the activity included in the analysis was spontaneous, being recorded from the visual cortex in the absence of visual stimuli. The brain state was determined through a combination of behavior evaluation, electroencephalogram and electromyogram analysis. Information in the spike trains was determined by using Lempel-Ziv Complexity. Complexity was used to estimate the entropy of neural discharges and thus the information content (Amigóet al. Neural Comput., 2004, 16: 717-736). The information content in spike trains (range 4-70 bits s(-1) ) was evaluated during different brain states and particularly during the transition periods. Transitions toward states of deeper sleep coincided with a decrease of information, while transitions to the awake state resulted in an increase in information. Changes in both directions were of the same magnitude, about 30%. Information in spike trains showed a high temporal correlation between neurons, reinforcing the idea of the impact of the brain state in the information content of spike trains.

  5. Bayesian Inference for Structured Spike and Slab Priors

    DEFF Research Database (Denmark)

    Andersen, Michael Riis; Winther, Ole; Hansen, Lars Kai

    2014-01-01

    Sparse signal recovery addresses the problem of solving underdetermined linear inverse problems subject to a sparsity constraint. We propose a novel prior formulation, the structured spike and slab prior, which allows to incorporate a priori knowledge of the sparsity pattern by imposing a spatial...... Gaussian process on the spike and slab probabilities. Thus, prior information on the structure of the sparsity pattern can be encoded using generic covariance functions. Furthermore, we provide a Bayesian inference scheme for the proposed model based on the expectation propagation framework. Using...

  6. Local Variation of Hashtag Spike Trains and Popularity in Twitter.

    Science.gov (United States)

    Sanlı, Ceyda; Lambiotte, Renaud

    2015-01-01

    We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamical properties, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media.

  7. Chaos-based mixed signal implementation of spiking neurons.

    Science.gov (United States)

    Rossello, Josep L; Canals, Vincent; Morro, Antoni; Verd, Jaume

    2009-12-01

    A new design of Spiking Neural Networks is proposed and fabricated using a 0.35 microm CMOS technology. The architecture is based on the use of both digital and analog circuitry. The digital circuitry is dedicated to the inter-neuron communication while the analog part implements the internal non-linear behavior associated to spiking neurons. The main advantages of the proposed system are the small area of integration with respect to digital solutions, its implementation using a standard CMOS process only and the reliability of the inter-neuron communication.

  8. Spikes Synchronization in Neural Networks with Synaptic Plasticity

    CERN Document Server

    Borges, Rafael R; Batista, Antonio M; Caldas, Iberê L; Borges, Fernando S; Lameu, Ewandson L

    2015-01-01

    In this paper, we investigated the neural spikes synchronisation in a neural network with synaptic plasticity and external perturbation. In the simulations the neural dynamics is described by the Hodgkin Huxley model considering chemical synapses (excitatory) among neurons. According to neural spikes synchronisation is expected that a perturbation produce non synchronised regimes. However, in the literature there are works showing that the combination of synaptic plasticity and external perturbation may generate synchronised regime. This article describes the effect of the synaptic plasticity on the synchronisation, where we consider a perturbation with a uniform distribution. This study is relevant to researches of neural disorders control.

  9. Soft X-ray harmonic comb from relativistic electron spikes

    CERN Document Server

    Pirozhkov, A S; Esirkepov, T Zh; Gallegos, P; Ahmed, H; Ragozin, E N; Faenov, A Ya; Pikuz, T A; Kawachi, T; Sagisaka, A; Koga, J K; Coury, M; Green, J; Foster, P; Brenner, C; Dromey, B; Symes, D R; Mori, M; Kawase, K; Kameshima, T; Fukuda, Y; Chen, L; Daito, I; Ogura, K; Hayashi, Y; Kotaki, H; Kiriyama, H; Okada, H; Nishimori, N; Imazono, T; Kondo, K; Kimura, T; Tajima, T; Daido, H; Rajeev, P; McKenna, P; Borghesi, M; Neely, D; Kato, Y; Bulanov, S V

    2012-01-01

    We demonstrate a new high-order harmonic generation mechanism reaching the `water window' spectral region in experiments with multi-terawatt femtosecond lasers irradiating gas jets. A few hundred harmonic orders are resolved, giving uJ/sr pulses. Harmonics are collectively emitted by an oscillating electron spike formed at the joint of the boundaries of a cavity and bow wave created by a relativistically self-focusing laser in underdense plasma. The spike sharpness and stability are explained by catastrophe theory. The mechanism is corroborated by particle-in-cell simulations.

  10. Bayesian Inference for Structured Spike and Slab Priors

    DEFF Research Database (Denmark)

    Andersen, Michael Riis; Winther, Ole; Hansen, Lars Kai

    2014-01-01

    Sparse signal recovery addresses the problem of solving underdetermined linear inverse problems subject to a sparsity constraint. We propose a novel prior formulation, the structured spike and slab prior, which allows to incorporate a priori knowledge of the sparsity pattern by imposing a spatial...... Gaussian process on the spike and slab probabilities. Thus, prior information on the structure of the sparsity pattern can be encoded using generic covariance functions. Furthermore, we provide a Bayesian inference scheme for the proposed model based on the expectation propagation framework. Using...

  11. Toxicity of nickel-spiked freshwater sediments to benthic invertebrates-Spiking methodology, species sensitivity, and nickel bioavailability

    Science.gov (United States)

    Besser, John M.; Brumbaugh, William G.; Kemble, Nile E.; Ivey, Chris D.; Kunz, James L.; Ingersoll, Christopher G.; Rudel, David

    2011-01-01

    This report summarizes data from studies of the toxicity and bioavailability of nickel in nickel-spiked freshwater sediments. The goal of these studies was to generate toxicity and chemistry data to support development of broadly applicable sediment quality guidelines for nickel. The studies were conducted as three tasks, which are presented here as three chapters: Task 1, Development of methods for preparation and toxicity testing of nickel-spiked freshwater sediments; Task 2, Sensitivity of benthic invertebrates to toxicity of nickel-spiked freshwater sediments; and Task 3, Effect of sediment characteristics on nickel bioavailability. Appendices with additional methodological details and raw chemistry and toxicity data for the three tasks are available online at http://pubs.usgs.gov/sir/2011/5225/downloads/.

  12. Differential gating of dendritic spikes by compartmentalized inhibition

    Directory of Open Access Journals (Sweden)

    Katharina Anna Wilmes

    2014-03-01

    Full Text Available Different types of local inhibitory interneurons innervate different dendritic sites of pyramidal neurons in cortex and hippocampus (Klausberger 2009. What could be the functional role of compartmentalized inhibition? Pyramidal cell dendrites support different forms of active signal propagation, which are important not only for dendritic and neuronal signal processing (Smith et al. 2013, but also for synaptic plasticity. While back-propagating action potentials signal post-synaptic activity to synapses in apical oblique and basal dendrites (Markram et al. 1997, Cho et al. 2006, calcium spikes cause plasticity of distal apical tuft synapses (Golding et al. 2002. Suspiciously, the associated regions of the dendrite are targeted by different interneuron populations. Parvalbumin-positive interneurons typically target the proximal dendritic and somatic parts of the neuron, while somatostatin-positive interneurons target the apical dendrite. The matching compartmentalization in terms of dendritic spikes and inhibitory control suggests that inhibition could differentially regulate different dendritic spikes and thereby introduce a compartment-specific modulation of synaptic plasticity. We evaluate this hypothesis in a biophysical multi-compartment model of a pyramidal neuron, receiving shunting inhibition at different locations on the dendrite. The model shows that, first, inhibition can gate dendritic spikes in an all-or-none manner. Second, spatially selective inhibition can individually suppress back-propagating action potentials and calcium spikes, thereby allowing a compartment-specific switch for synaptic plasticity. In our model, proximal inhibition on the apical dendrite eliminated both the back-propagating action potential and the calcium spike, thus influencing plasticity in the whole apical dendrite. Distal apical inhibition could selectively affect calcium spikes and thus distal plasticity, without suppressing back­propagation of action

  13. Identification of pre-spike network in patients with mesial temporal lobe epilepsy

    Directory of Open Access Journals (Sweden)

    Nahla L Faizo

    2014-10-01

    Full Text Available Background: Seizures and inter-ictal spikes in mesial temporal lobe epilepsy (MTLE affect a network of brain regions rather than a single epileptic focus. Simultaneous EEG-fMRI studies have demonstrated a functional network in which hemodynamic changes are time-locked to spikes. However, whether this reflects the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation remains unknown. The functional connectivity changes prior to spikes may provide information about the connectivity changes that lead to the generation of spikes. We used EEG-fMRI to investigate functional connectivity changes immediately prior to the appearance of inter-ictal spikes on EEG in MTLE patients.Methods/principal findings: 15 MTLE patients underwent continuous EEG-fMRI during rest. Spikes were identified on EEG and three 10s epochs were defined relative to spike onset: spike (0s to 10s, pre-spike (-10s to 0s and rest (-20s to -10s, with no previous spikes in the preceding 45s. Significant spike-related activation in the hippocampus ipsilateral to the seizure focus was found compared to the pre-spike and rest epochs. The peak voxel within the hippocampus ipsilateral to the seizure focus was used as a seed region for functional connectivity analysis in the three conditions. A significant change in functional connectivity patterns was observed before the appearance of electrographic spikes. Specifically, there was significant loss of coherence between both hippocampi during the pre-spike period compared to spike and rest states.Conclusion/significance: In keeping with previous findings of abnormal inter-hemispheric hippocampal connectivity in MTLE, our findings specifically link reduced connectivity to the period immediately before spikes. This brief decoupling is consistent with a deficit in mutual (inter-hemispheric hippocampal inhibition that may predispose to spike generation.

  14. Fractal dimension analysis for spike detection in low SNR extracellular signals

    Science.gov (United States)

    Salmasi, Mehrdad; Büttner, Ulrich; Glasauer, Stefan

    2016-06-01

    Objective. Many algorithms have been suggested for detection and sorting of spikes in extracellular recording. Nevertheless, it is still challenging to detect spikes in low signal-to-noise ratios (SNR). We propose a spike detection algorithm that is based on the fractal properties of extracellular signals and can detect spikes in low SNR regimes. Semi-intact spikes are low-amplitude spikes whose shapes are almost preserved. The detection of these spikes can significantly enhance the performance of multi-electrode recording systems. Approach. Semi-intact spikes are simulated by adding three noise components to a spike train: thermal noise, inter-spike noise, and spike-level noise. We show that simulated signals have fractal properties which make them proper candidates for fractal analysis. Then we use fractal dimension as the main core of our spike detection algorithm and call it fractal detector. The performance of the fractal detector is compared with three frequently used spike detectors. Main results. We demonstrate that in low SNR, the fractal detector has the best performance and results in the highest detection probability. It is shown that, in contrast to the other three detectors, the performance of the fractal detector is independent of inter-spike noise power and that variations in spike shape do not alter its performance. Finally, we use the fractal detector for spike detection in experimental data and similar to simulations, it is shown that the fractal detector has the best performance in low SNR regimes. Significance. The detection of low-amplitude spikes provides more information about the neural activity in the vicinity of the recording electrodes. Our results suggest using the fractal detector as a reliable and robust method for detecting semi-intact spikes in low SNR extracellular signals.

  15. Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

    Science.gov (United States)

    Gardner, Brian; Sporea, Ioana; Grüning, André

    2015-12-01

    Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

  16. Synaptic channel model including effects of spike width variation

    OpenAIRE

    2015-01-01

    Synaptic Channel Model Including Effects of Spike Width Variation Hamideh Ramezani Next-generation and Wireless Communications Laboratory (NWCL) Department of Electrical and Electronics Engineering Koc University, Istanbul, Turkey Ozgur B. Akan Next-generation and Wireless Communications Laboratory (NWCL) Department of Electrical and Electronics Engineering Koc University, Istanbul, Turkey ABSTRACT An accu...

  17. Dark Matter Density Spikes around Primordial Black Holes

    CERN Document Server

    Eroshenko, Yu N

    2016-01-01

    We show that density spikes begin to form from dark matter particles around primordial black holes immediately after their formation at the radiation-dominated cosmological stage. This follows from the fact that in the thermal velocity distribution of particles there are particles with low velocities that remain in finite orbits around black holes and are not involved in the cosmological expansion. The accumulation of such particles near black holes gives rise to density spikes. These spikes are considerably denser than those that are formed later by the mechanism of secondary accretion. The density spikes must be bright gamma-ray sources. Comparison of the calculated signal from particle annihilation with the Fermi-LAT data constrains the present-day cosmological density parameter for primordial black holes with masses $M_{\\rm BH}\\geq10^{-8}M_\\odot$ from above by values from $\\Omega_{\\rm BH}\\leq1$ to $\\Omega_{\\rm BH}\\leq10^{-8}$, depending on $M_{\\rm BH}$. These constraints are several orders of magnitude mo...

  18. Learning anticipation via spiking networks: application to navigation control.

    Science.gov (United States)

    Arena, Paolo; Fortuna, Luigi; Frasca, Mattia; Patané, Luca

    2009-02-01

    In this paper, we introduce a network of spiking neurons devoted to navigation control. Three different examples, dealing with stimuli of increasing complexity, are investigated. In the first one, obstacle avoidance in a simulated robot is achieved through a network of spiking neurons. In the second example, a second layer is designed aiming to provide the robot with a target approaching system, making it able to move towards visual targets. Finally, a network of spiking neurons for navigation based on visual cues is introduced. In all cases, the robot was assumed to rely on some a priori known responses to low-level sensors (i.e., to contact sensors in the case of obstacles, to proximity target sensors in the case of visual targets, or to the visual target for navigation with visual cues). Based on their knowledge, the robot has to learn the response to high-level stimuli (i.e., range finder sensors or visual input). The biologically plausible paradigm of spike-timing-dependent plasticity (STDP) is included in the network to make the system able to learn high-level responses that guide navigation through a simple unstructured environment. The learning procedure is based on classical conditioning.

  19. Detection of bursts and pauses in spike trains.

    Science.gov (United States)

    Ko, D; Wilson, C J; Lobb, C J; Paladini, C A

    2012-10-15

    Midbrain dopaminergic neurons in vivo exhibit a wide range of firing patterns. They normally fire constantly at a low rate, and speed up, firing a phasic burst when reward exceeds prediction, or pause when an expected reward does not occur. Therefore, the detection of bursts and pauses from spike train data is a critical problem when studying the role of phasic dopamine (DA) in reward related learning, and other DA dependent behaviors. However, few statistical methods have been developed that can identify bursts and pauses simultaneously. We propose a new statistical method, the Robust Gaussian Surprise (RGS) method, which performs an exhaustive search of bursts and pauses in spike trains simultaneously. We found that the RGS method is adaptable to various patterns of spike trains recorded in vivo, and is not influenced by baseline firing rate, making it applicable to all in vivo spike trains where baseline firing rates vary over time. We compare the performance of the RGS method to other methods of detecting bursts, such as the Poisson Surprise (PS), Rank Surprise (RS), and Template methods. Analysis of data using the RGS method reveals potential mechanisms underlying how bursts and pauses are controlled in DA neurons.

  20. The stochastic properties of input spike trains control neuronal arithmetic.

    Science.gov (United States)

    Bures, Zbynek

    2012-02-01

    In the nervous system, the representation of signals is based predominantly on the rate and timing of neuronal discharges. In most everyday tasks, the brain has to carry out a variety of mathematical operations on the discharge patterns. Recent findings show that even single neurons are capable of performing basic arithmetic on the sequences of spikes. However, the interaction of the two spike trains, and thus the resulting arithmetic operation may be influenced by the stochastic properties of the interacting spike trains. If we represent the individual discharges as events of a random point process, then an arithmetical operation is given by the interaction of two point processes. Employing a probabilistic model based on detection of coincidence of random events and complementary computer simulations, we show that the point process statistics control the arithmetical operation being performed and, particularly, that it is possible to switch from subtraction to division solely by changing the distribution of the inter-event intervals of the processes. Consequences of the model for evaluation of binaural information in the auditory brainstem are demonstrated. The results accentuate the importance of the stochastic properties of neuronal discharge patterns for information processing in the brain; further studies related to neuronal arithmetic should therefore consider the statistics of the interacting spike trains.

  1. Sleep deprivation and spike-wave discharges in epileptic rats

    NARCIS (Netherlands)

    Drinkenburg, W.H.I.M.; Coenen, A.M.L.; Vossen, J.M.H.; Luijtelaar, E.L.J.M. van

    1995-01-01

    The effects of sleep deprivation were studied on the occurrence of spike-wave discharges in the electroencephalogram of rats of the epileptic WAG/Rij strain, a model for absence epilepsy. This was done before, during and after a period of 12 hours of near total sleep deprivation. A substantial incre

  2. Effect of Rolandic Spikes on ADHD Impulsive Behavior

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2007-01-01

    Full Text Available The association of Rolandic spikes with the neuropsychological profile of children with attention deficit hyperactivity disorder (ADHD was studied in a total of 48 patients at JW Goethe-University, Frankfurt/Main; and Central Institute of Mental Health, Mannheim, Germany.

  3. Emergence of spike correlations in periodically forced excitable systems

    Science.gov (United States)

    Reinoso, José A.; Torrent, M. C.; Masoller, Cristina

    2016-09-01

    In sensory neurons the presence of noise can facilitate the detection of weak information-carrying signals, which are encoded and transmitted via correlated sequences of spikes. Here we investigate the relative temporal order in spike sequences induced by a subthreshold periodic input in the presence of white Gaussian noise. To simulate the spikes, we use the FitzHugh-Nagumo model and to investigate the output sequence of interspike intervals (ISIs), we use the symbolic method of ordinal analysis. We find different types of relative temporal order in the form of preferred ordinal patterns that depend on both the strength of the noise and the period of the input signal. We also demonstrate a resonancelike behavior, as certain periods and noise levels enhance temporal ordering in the ISI sequence, maximizing the probability of the preferred patterns. Our findings could be relevant for understanding the mechanisms underlying temporal coding, by which single sensory neurons represent in spike sequences the information about weak periodic stimuli.

  4. Concerning spikes in emission and absorption in the microwave range

    Science.gov (United States)

    Chernov, Gennady P.; Sych, Robert A.; Huang, Guang-Li; Ji, Hai-Sheng; Yan, Yi-Hua; Tan, Cheng-Ming

    2013-01-01

    In some events, weak fast solar bursts (near the level of the quiet Sun) were observed in the background of numerous spikes in emission and absorption. In such a case, the background contains the noise signals of the receiver. In events on 2005 September 16 and 2002 April 14, the solar origin of fast bursts was confirmed by simultaneous recording of the bursts at several remote observatories. The noisy background pixels in emission and absorption can be excluded by subtracting a higher level of continuum when constructing the spectra. The wavelet spectrum, noisy profiles in different polarization channels and a spectrum with continuum level greater than zero demonstrates the noisy character of pixels with the lowest levels of emission and absorption. Thus, in each case, in order to judge the solar origin of all spikes, it is necessary to determine the level of continuum against the background of which the solar bursts are observed. Several models of microwave spikes are discussed. The electron cyclotron maser emission mechanism runs into serious problems with the interpretation of microwave millisecond spikes: the main obstacles are too high values of the magnetic field strength in the source (ωPe shock fronts in the reconnection region.

  5. Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights.

    Science.gov (United States)

    Samadi, Arash; Lillicrap, Timothy P; Tweed, Douglas B

    2017-03-01

    Recent work in computer science has shown the power of deep learning driven by the backpropagation algorithm in networks of artificial neurons. But real neurons in the brain are different from most of these artificial ones in at least three crucial ways: they emit spikes rather than graded outputs, their inputs and outputs are related dynamically rather than by piecewise-smooth functions, and they have no known way to coordinate arrays of synapses in separate forward and feedback pathways so that they change simultaneously and identically, as they do in backpropagation. Given these differences, it is unlikely that current deep learning algorithms can operate in the brain, but we that show these problems can be solved by two simple devices: learning rules can approximate dynamic input-output relations with piecewise-smooth functions, and a variation on the feedback alignment algorithm can train deep networks without having to coordinate forward and feedback synapses. Our results also show that deep spiking networks learn much better if each neuron computes an intracellular teaching signal that reflects that cell's nonlinearity. With this mechanism, networks of spiking neurons show useful learning in synapses at least nine layers upstream from the output cells and perform well compared to other spiking networks in the literature on the MNIST digit recognition task.

  6. Analyzing the impact of investment spikes on dynamic productivity growth

    NARCIS (Netherlands)

    Kapelko, Magdalena; Oude Lansink, Alfons; Stefanou, S.E.

    2015-01-01

    Firm-level data usually show that a large portion of firm-level investment takes place in a few investment episodes. This paper assesses productivity growth and its components in production framework that accounts for the dynamics of capital adjustment and relates this to investment spikes using

  7. Sleep deprivation and spike-wave discharges in epileptic rats

    NARCIS (Netherlands)

    Drinkenburg, W.H.I.M.; Coenen, A.M.L.; Vossen, J.M.H.; Luijtelaar, E.L.J.M. van

    1995-01-01

    The effects of sleep deprivation were studied on the occurrence of spike-wave discharges in the electroencephalogram of rats of the epileptic WAG/Rij strain, a model for absence epilepsy. This was done before, during and after a period of 12 hours of near total sleep deprivation. A substantial

  8. Do spikes persist in a quantum treatment of spacetime singularities?

    Science.gov (United States)

    Czuchry, Ewa; Garfinkle, David; Klauder, John R.; Piechocki, Włodzimierz

    2017-01-01

    The classical approach to spacetime singularities leads to a simplified dynamics in which spatial derivatives become unimportant compared to time derivatives, and thus each spatial point essentially becomes uncoupled from its neighbors. This uncoupled dynamics leads to sharp features (called "spikes") as follows: particular spatial points follow an exceptional dynamical path that differs from that of their neighbors, with the consequence that, in the neighborhood of these exceptional points, the spatial profile becomes ever more sharp. Spikes are consequences of the BKL-type oscillatory evolution towards generic singularities of spacetime. Do spikes persist when the spacetime dynamics is treated using quantum mechanics? To address this question, we treat a Hamiltonian system that describes the dynamics of the approach to the singularity and consider how to quantize that system. We argue that this particular system is best treated using an affine quantization approach (rather than the more familiar methods of canonical quantization), and we set up the formalism needed for this treatment. Our investigation, based on this affine approach, shows the nonexistence of quantum spikes.

  9. A memristive spiking neuron with firing rate coding

    Directory of Open Access Journals (Sweden)

    Marina eIgnatov

    2015-10-01

    Full Text Available Perception, decisions, and sensations are all encoded into trains of action potentials in the brain. The relation between stimulus strength and all-or-nothing spiking of neurons is widely believed to be the basis of this coding. This initiated the development of spiking neuron models; one of today's most powerful conceptual tool for the analysis and emulation of neural dynamics. The success of electronic circuit models and their physical realization within silicon field-effect transistor circuits lead to elegant technical approaches. Recently, the spectrum of electronic devices for neural computing has been extended by memristive devices, mainly used to emulate static synaptic functionality. Their capabilities for emulations of neural activity were recently demonstrated using a memristive neuristor circuit, while a memristive neuron circuit has so far been elusive. Here, a spiking neuron model is experimentally realized in a compact circuit comprising memristive and memcapacitive devices based on the strongly correlated electron material vanadium dioxide (VO2 and on the chemical electromigration cell Ag/TiO2-x/Al. The circuit can emulate dynamical spiking patterns in response to an external stimulus including adaptation, which is at the heart of firing rate coding as first observed by E.D. Adrian in 1926.

  10. Kids' Use of Artificial Sweeteners Spiked in Recent Years

    Science.gov (United States)

    ... gov/news/fullstory_163047.html Kids' Use of Artificial Sweeteners Spiked in Recent Years Some studies suggest a ... FRIDAY, Jan. 13, 2017 (HealthDay News) -- Use of artificial sugar by American ... low-calorie sweeteners such as aspartame, sucralose and saccharin rose 200 ...

  11. Spike-timing-based computation in sound localization.

    Directory of Open Access Journals (Sweden)

    Dan F M Goodman

    2010-11-01

    Full Text Available Spike timing is precise in the auditory system and it has been argued that it conveys information about auditory stimuli, in particular about the location of a sound source. However, beyond simple time differences, the way in which neurons might extract this information is unclear and the potential computational advantages are unknown. The computational difficulty of this task for an animal is to locate the source of an unexpected sound from two monaural signals that are highly dependent on the unknown source signal. In neuron models consisting of spectro-temporal filtering and spiking nonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that depend on source location rather than on source signal. Location-specific synchrony patterns would then result in the activation of location-specific assemblies of postsynaptic neurons. We designed a spiking neuron model which exploited this principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related transfer functions. The model was able to accurately estimate the location of previously unknown sounds in both azimuth and elevation (including front/back discrimination in a known acoustic environment. We found that multiple representations of different acoustic environments could coexist as sets of overlapping neural assemblies which could be associated with spatial locations by Hebbian learning. The model demonstrates the computational relevance of relative spike timing to extract spatial information about sources independently of the source signal.

  12. Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems

    Directory of Open Access Journals (Sweden)

    Emre eNeftci

    2014-01-01

    Full Text Available Restricted Boltzmann Machines (RBMs and Deep Belief Networks have been demonstrated to perform efficiently in variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The reverberating activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP carries out the weight updates in an online, asynchronous fashion.We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

  13. Event-driven contrastive divergence for spiking neuromorphic systems.

    Science.gov (United States)

    Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

    2013-01-01

    Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

  14. Composting of swine manure spiked with sulfadiazine, chlortetracycline and ciprofloxacin.

    Science.gov (United States)

    Selvam, Ammaiyappan; Zhao, Zhenyong; Wong, Jonathan W C

    2012-12-01

    The fate of chlortetracycline (CTC), sulfadiazine (SDZ) and ciprofloxacin (CIP) during composting of swine manure and their effect on composting process were investigated. Swine manure was spiked with antibiotics, mixed with saw dust (1:1 on DW basis) and composted for 56 d. Antibiotics were spiked to a final concentration of 50 mg/kg CTC+10 mg/kg SDZ+10 mg/kg CIP (High-level) or 5 mg/kg CTC+1 mg/kg SDZ+1 mg/kg CIP (Low-level), and a control without antibiotics. Antibiotics at high concentrations delayed the initial decomposition that also affected the nitrogen mineralization. CTC and SDZ were completely removed from the composting mass within 21 and 3d, respectively; whereas, 17-31% of the spiked CIP remained in the composting mass. Therefore, composting could effectively remove the CTC and SDZ spiked even at high concentrations, but the removal of ciprofloxacin (belonging to fluoroquinolone) needs to be improved, indicating this antibiotic may get into the ecosystem through land application of livestock compost. Copyright © 2011. Published by Elsevier Ltd.

  15. Concerning spikes in emission and absorption in the microwave range

    Institute of Scientific and Technical Information of China (English)

    Gennady P.Chernov; Robert A.Sych; Guang-Li Huang; Hai-Sheng Ji; Yi-Hua Yan; Cheng-Ming Tan

    2013-01-01

    In some events,weak fast solar bursts (near the level of the quiet Sun)were observed in the background of numerous spikes in emission and absorption.In such a case,the background contains the noise signals of the receiver.In events on 2005 September 16 and 2002 April 14,the solar origin of fast bursts was confirmed by simultaneous recording of the bursts at several remote observatories.The noisy background pixels in emission and absorption can be excluded by subtracting a higher level of continuum when constructing the spectra.The wavelet spectrum,noisy profiles in different polarization channels and a spectrum with continuum level greater than zero demonstrates the noisy character of pixels with the lowest levels of emission and absorption.Thus,in each case,in order to judge the solar origin of all spikes,it is necessary to determine the level of continuum against the background of which the solar bursts are observed.Several models of microwave spikes are discussed.The electron cyclotron maser emission mechanism runs into serious problems with the interpretation of microwave millisecond spikes:the main obstacles are too high values of the magnetic field strength in the source (ωPe (<) ωBe).The probable mechanism is the interaction of plasma Langmuir waves with ion-sound waves (l + s → t) in a source related to shock fronts in the reconnection region.

  16. Quantifying Spike Train Oscillations: Biases, Distortions and Solutions

    Science.gov (United States)

    Matzner, Ayala; Bar-Gad, Izhar

    2015-01-01

    Estimation of the power spectrum is a common method for identifying oscillatory changes in neuronal activity. However, the stochastic nature of neuronal activity leads to severe biases in the estimation of these oscillations in single unit spike trains. Different biological and experimental factors cause the spike train to differentially reflect its underlying oscillatory rate function. We analyzed the effect of factors, such as the mean firing rate and the recording duration, on the detectability of oscillations and their significance, and tested these theoretical results on experimental data recorded in Parkinsonian non-human primates. The effect of these factors is dramatic, such that in some conditions, the detection of existing oscillations is impossible. Moreover, these biases impede the comparison of oscillations across brain regions, neuronal types, behavioral states and separate recordings with different underlying parameters, and lead inevitably to a gross misinterpretation of experimental results. We introduce a novel objective measure, the "modulation index", which overcomes these biases, and enables reliable detection of oscillations from spike trains and a direct estimation of the oscillation magnitude. The modulation index detects a high percentage of oscillations over a wide range of parameters, compared to classical spectral analysis methods, and enables an unbiased comparison between spike trains recorded from different neurons and using different experimental protocols. PMID:25909328

  17. Uncovering representations of sleep-associated hippocampal ensemble spike activity

    Science.gov (United States)

    Chen, Zhe; Grosmark, Andres D.; Penagos, Hector; Wilson, Matthew A.

    2016-08-01

    Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness.

  18. Neural spike sorting with spatio-temporal features

    NARCIS (Netherlands)

    Archer, Claude; Hochstenbach, Michiel; Hoede, C.; Meijer, Hil Gaétan Ellart; Ali Salah, Albert; Swist, Tomasz; Zyprych, Joanna; Bokhove, Onno; Bokhove, O.; Hurink, J.L.; Hurink, Johann L.; Meinsma, G.; Meinsma, Gjerrit; Stolk, C.C.; Vellekoop, M.H.

    2008-01-01

    The paper analyses signals that have been measured by brain probes during surgery. First background noise is removed from the signals. The remaining signals are a superposition of spike trains which are subsequently assigned to different families. For this two techniques are used: classic PCA and

  19. Neural spike sorting with spatio-temporal features

    NARCIS (Netherlands)

    Archer, C.; Hochstenbach, M.; Hoede, K.; Meinsma, G.; Meijer, H.; Salah, A.A.; Stolk, C.C.; Swist, T.; Zyprych, J.; Bokhove, O.; Hurink, J.; Meinsma, G.; Stolk, C.; Vellekoop, M.

    2009-01-01

    The paper analyses signals that have been measured by brain probes during surgery. First background noise is removed from the signals. The remaining signals are a superposition of spike trains which are subsequently assigned to different families. For this two techniques are used: classic PCA and

  20. Analysis of spike waves in epilepsy using Hilbert-Huang transform.

    Science.gov (United States)

    Zhu, Jin-De; Lin, Chin-Feng; Chang, Shun-Hsyung; Wang, Jung-Hua; Peng, Tsung-Ii; Chien, Yu-Yi

    2015-01-01

    In this paper, we used the Hilbert-Huang transform (HHT) analysis method to examine the time-frequency characteristics of spike waves for detecting epilepsy symptoms. We obtained a sample of spike waves and nonspike waves for HHT decomposition by using numerous intrinsic mode functions (IMFs) of the Hilbert transform (HT) to determine the instantaneous, marginal, and Hilbert energy spectra. The Pearson correlation coefficients of the IMFs, and energy-IMF distributions for the electroencephalogram (EEG) signal without spike waves, Spike I, Spike II and Spike III sample waves were determined. The analysis results showed that the ratios of the referred wave and Spike III wave to the referred total energy for IMF1, IMF2, and the residual function exceeded 10%. Furthermore, the energy ratios for IMF1, IMF2, IMF3 and the residual function of Spike I, Spike II to their total energy exceeded 10%. The Pearson correlation coefficients of the IMF3 of the EEG signal without spike waves and Spike I wave, EEG signal without spike waves and Spike II wave, EEG signal without spike waves and Spike III wave, Spike I and II waves, Spike I and III waves, and Spike II and III waves were 0.002, 0.06, 0.01, 0.17, 0.03, and 0.3, respectively. The energy ratios of IMF3 in the δ band to its referred total energy for the EEG signal without spike waves, and of the Spike I, II, and III waves were 4.72, 6.75, 5.41, and 5.55%, respectively. The weighted average frequency of the IMF1, IMF2, and IMF3 of the EEG signal without spike waves was lower than that of the IMF1, IMF2, and IMF3 of the spike waves, respectively. The weighted average magnitude of the IMF3, IMF4, and IMF5 of the EEG signal without spike waves was lower than that of the IMF1, IMF2, and IMF3 of spike waves, respectively.

  1. Protein-Based Three-Dimensional Memories and Associative Processors

    Science.gov (United States)

    Birge, Robert

    2008-03-01

    The field of bioelectronics has benefited from the fact that nature has often solved problems of a similar nature to those which must be solved to create molecular electronic or photonic devices that operate with efficiency and reliability. Retinal proteins show great promise in bioelectronic devices because they operate with high efficiency (˜0.65%), high cyclicity (>10^7), operate over an extended wavelength range (360 -- 630 nm) and can convert light into changes in voltage, pH, absorption or refractive index. This talk will focus on a retinal protein called bacteriorhodopsin, the proton pump of the organism Halobacterium salinarum. Two memories based on this protein will be described. The first is an optical three-dimensional memory. This memory stores information using volume elements (voxels), and provides as much as a thousand-fold improvement in effective capacity over current technology. A unique branching reaction of a variant of bacteriorhodopsin is used to turn each protein into an optically addressed latched AND gate. Although three working prototypes have been developed, a number of cost/performance and architectural issues must be resolved prior to commercialization. The major issue is that the native protein provides a very inefficient branching reaction. Genetic engineering has improved performance by nearly 500-fold, but a further order of magnitude improvement is needed. Protein-based holographic associative memories will also be discussed. The human brain stores and retrieves information via association, and human intelligence is intimately connected to the nature and enormous capacity of this associative search and retrieval process. To a first order approximation, creativity can be viewed as the association of two seemingly disparate concepts to form a totally new construct. Thus, artificial intelligence requires large scale associative memories. Current computer hardware does not provide an optimal environment for creating artificial

  2. Experimental and modelling investigation of surface EMG spike analysis.

    Science.gov (United States)

    Gabriel, David A; Christie, Anita; Inglis, J Greig; Kamen, Gary

    2011-05-01

    A pattern classification method based on five measures extracted from the surface electromyographic (sEMG) signal is used to provide a unique characterization of the interference pattern for different motor unit behaviours. This study investigated the sensitivity of the five sEMG measures during the force gradation process. Tissue and electrode filtering effects were further evaluated using a sEMG model. Subjects (N=8) performed isometric elbow flexion contractions from 0 to 100% MVC. The sEMG signals from the biceps brachii were recorded simultaneously with force. The basic building block of the sEMG model was the detection of single fibre action potentials (SFAPs) through a homogeneous, equivalent isotropic, infinite volume conduction medium. The SFAPs were summed to generate single motor unit action potentials. The physiologic properties from a well-known muscle model and motor unit recruitment and firing rate schemes were combined to generate synthetic sEMG signals. The following pattern classification measures were calculated: mean spike amplitude, mean spike frequency, mean spike slope, mean spike duration, and the mean number of peaks per spike. Root-mean-square amplitude and mean power frequency were also calculated. Taken together, the experimental data and modelling analysis showed that below 50% MVC, the pattern classification measures were more sensitive to changes in force than traditional time and frequency measures. However, there are additional limitations associated with electrode distance from the source that must be explored further. Future experimental work should ensure that the inter-electrode distance is no greater than 1cm to mitigate the effects of tissue filtering.

  3. Estimating extracellular spike waveforms from CA1 pyramidal cells with multichannel electrodes.

    Science.gov (United States)

    Molden, Sturla; Moldestad, Olve; Storm, Johan F

    2013-01-01

    Extracellular (EC) recordings of action potentials from the intact brain are embedded in background voltage fluctuations known as the "local field potential" (LFP). In order to use EC spike recordings for studying biophysical properties of neurons, the spike waveforms must be separated from the LFP. Linear low-pass and high-pass filters are usually insufficient to separate spike waveforms from LFP, because they have overlapping frequency bands. Broad-band recordings of LFP and spikes were obtained with a 16-channel laminar electrode array (silicone probe). We developed an algorithm whereby local LFP signals from spike-containing channel were modeled using locally weighted polynomial regression analysis of adjoining channels without spikes. The modeled LFP signal was subtracted from the recording to estimate the embedded spike waveforms. We tested the method both on defined spike waveforms added to LFP recordings, and on in vivo-recorded extracellular spikes from hippocampal CA1 pyramidal cells in anaesthetized mice. We show that the algorithm can correctly extract the spike waveforms embedded in the LFP. In contrast, traditional high-pass filters failed to recover correct spike shapes, albeit produceing smaller standard errors. We found that high-pass RC or 2-pole Butterworth filters with cut-off frequencies below 12.5 Hz, are required to retrieve waveforms comparable to our method. The method was also compared to spike-triggered averages of the broad-band signal, and yielded waveforms with smaller standard errors and less distortion before and after the spike.

  4. Spike-Interval Triggered Averaging Reveals a Quasi-Periodic Spiking Alternative for Stochastic Resonance in Catfish Electroreceptors

    NARCIS (Netherlands)

    Lankheet, M.J.M.; Klink, P.C.; Borghuis, B.G.; Noest, A.J.

    2012-01-01

    Catfish detect and identify invisible prey by sensing their ultra-weak electric fields with electroreceptors. Any neuron that deals with small-amplitude input has to overcome sensitivity limitations arising from inherent threshold non-linearities in spike-generation mechanisms. Many sensory cells so

  5. EPILEPTIC ENCEPHALOPATHY WITH CONTINUOUS SPIKES-WAVES ACTIVITY DURING SLEEP

    Directory of Open Access Journals (Sweden)

    E. D. Belousova

    2012-01-01

    Full Text Available The author represents the review and discussion of current scientific literature devoted to epileptic encephalopathy with continuous spikes-waves activity during sleep — the special form of partly reversible age-dependent epileptic encephalopathy, characterized by triad of symptoms: continuous prolonged epileptiform (spike-wave activity on EEG in sleep, epileptic seizures and cognitive disorders. The author describes the aspects of classification, pathogenesis and etiology, prevalence, clinical picture and diagnostics of this disorder, including the peculiar anomalies on EEG. The especial attention is given to approaches to the treatment of epileptic encephalopathy with continuous spikeswaves activity during sleep. Efficacy of valproates, corticosteroid hormones and antiepileptic drugs of other groups is considered. The author represents own experience of treatment this disorder with corticosteroids, scheme of therapy and assessment of efficacy.

  6. Method for spiking soil samples with organic compounds

    DEFF Research Database (Denmark)

    Brinch, Ulla C; Ekelund, Flemming; Jacobsen, Carsten S

    2002-01-01

    We examined the harmful side effects on indigenous soil microorganisms of two organic solvents, acetone and dichloromethane, that are normally used for spiking of soil with polycyclic aromatic hydrocarbons for experimental purposes. The solvents were applied in two contamination protocols to either...... higher than in control soil, probably due mainly to release of predation from indigenous protozoa. In order to minimize solvent effects on indigenous soil microorganisms when spiking native soil samples with compounds having a low water solubility, we propose a common protocol in which the contaminant...... the whole soil sample or 25% of the soil volume, which was subsequently mixed with 75% untreated soil. For dichloromethane, we included a third protocol, which involved application to 80% of the soil volume with or without phenanthrene and introduction of Pseudomonas fluorescens VKI171 SJ132 genetically...

  7. Note on the coefficient of variations of neuronal spike trains.

    Science.gov (United States)

    Lengler, Johannes; Steger, Angelika

    2017-08-01

    It is known that many neurons in the brain show spike trains with a coefficient of variation (CV) of the interspike times of approximately 1, thus resembling the properties of Poisson spike trains. Computational studies have been able to reproduce this phenomenon. However, the underlying models were too complex to be examined analytically. In this paper, we offer a simple model that shows the same effect but is accessible to an analytic treatment. The model is a random walk model with a reflecting barrier; we give explicit formulas for the CV in the regime of excess inhibition. We also analyze the effect of probabilistic synapses in our model and show that it resembles previous findings that were obtained by simulation.

  8. Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware

    Directory of Open Access Journals (Sweden)

    Andreas Stöckel

    2017-08-01

    Full Text Available Large-scale neuromorphic hardware platforms, specialized computer systems for energy efficient simulation of spiking neural networks, are being developed around the world, for example as part of the European Human Brain Project (HBP. Due to conceptual differences, a universal performance analysis of these systems in terms of runtime, accuracy and energy efficiency is non-trivial, yet indispensable for further hard- and software development. In this paper we describe a scalable benchmark based on a spiking neural network implementation of the binary neural associative memory. We treat neuromorphic hardware and software simulators as black-boxes and execute exactly the same network description across all devices. Experiments on the HBP platforms under varying configurations of the associative memory show that the presented method allows to test the quality of the neuron model implementation, and to explain significant deviations from the expected reference output.

  9. Spin-orbit torque induced spike-timing dependent plasticity

    Energy Technology Data Exchange (ETDEWEB)

    Sengupta, Abhronil, E-mail: asengup@purdue.edu; Al Azim, Zubair; Fong, Xuanyao; Roy, Kaushik [School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907 (United States)

    2015-03-02

    Nanoelectronic devices that mimic the functionality of synapses are a crucial requirement for performing cortical simulations of the brain. In this work, we propose a ferromagnet-heavy metal heterostructure that employs spin-orbit torque to implement spike-timing dependent plasticity. The proposed device offers the advantage of decoupled spike transmission and programming current paths, thereby leading to reliable operation during online learning. Possible arrangement of such devices in a crosspoint architecture can pave the way for ultra-dense neural networks. Simulation studies indicate that the device has the potential of achieving pico-Joule level energy consumption (maximum 2 pJ per synaptic event) which is comparable to the energy consumption for synaptic events in biological synapses.

  10. The Ripple Pond: Enabling Spiking Networks to See

    Directory of Open Access Journals (Sweden)

    Saeed eAfshar

    2013-11-01

    Full Text Available We present the biologically inspired Ripple Pond Network (RPN, a simply connected spiking neural network which performs a transformation converting two dimensional images to one dimensional temporal patterns suitable for recognition by temporal coding learning and memory networks. The RPN has been developed as a hardware solution linking previously implemented neuromorphic vision and memory structures such as frameless vision sensors and neuromorphic temporal coding spiking neural networks. Working together such systems are potentially capable of delivering end-to-end high-speed, low-power and low-resolution recognition for mobile and autonomous applications where slow, highly sophisticated and power hungry signal processing solutions are ineffective. Key aspects in the proposed approach include utilising the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional collapse of imagery information into amenable temporal patterns and the use of asynchronous frames for information binding.

  11. Origin of the spike-timing-dependent plasticity rule

    Science.gov (United States)

    Cho, Myoung Won; Choi, M. Y.

    2016-08-01

    A biological synapse changes its efficacy depending on the difference between pre- and post-synaptic spike timings. Formulating spike-timing-dependent interactions in terms of the path integral, we establish a neural-network model, which makes it possible to predict relevant quantities rigorously by means of standard methods in statistical mechanics and field theory. In particular, the biological synaptic plasticity rule is shown to emerge as the optimal form for minimizing the free energy. It is further revealed that maximization of the entropy of neural activities gives rise to the competitive behavior of biological learning. This demonstrates that statistical mechanics helps to understand rigorously key characteristic behaviors of a neural network, thus providing the possibility of physics serving as a useful and relevant framework for probing life.

  12. Prolonging the postcomplex spike pause speeds eyeblink conditioning.

    Science.gov (United States)

    Maiz, Jaione; Karakossian, Movses H; Pakaprot, Narawut; Robleto, Karla; Thompson, Richard F; Otis, Thomas S

    2012-10-09

    Climbing fiber input to the cerebellum is believed to serve as a teaching signal during associative, cerebellum-dependent forms of motor learning. However, it is not understood how this neural pathway coordinates changes in cerebellar circuitry during learning. Here, we use pharmacological manipulations to prolong the postcomplex spike pause, a component of the climbing fiber signal in Purkinje neurons, and show that these manipulations enhance the rate of learning in classical eyelid conditioning. Our findings elucidate an unappreciated aspect of the climbing fiber teaching signal, and are consistent with a model in which convergent postcomplex spike pauses drive learning-related plasticity in the deep cerebellar nucleus. They also suggest a physiological mechanism that could modulate motor learning rates.

  13. Spiking Neural P Systems with Neuron Division and Dissolution

    Science.gov (United States)

    Liu, Xiyu; Wang, Wenping

    2016-01-01

    Spiking neural P systems are a new candidate in spiking neural network models. By using neuron division and budding, such systems can generate/produce exponential working space in linear computational steps, thus provide a way to solve computational hard problems in feasible (linear or polynomial) time with a “time-space trade-off” strategy. In this work, a new mechanism called neuron dissolution is introduced, by which redundant neurons produced during the computation can be removed. As applications, uniform solutions to two NP-hard problems: SAT problem and Subset Sum problem are constructed in linear time, working in a deterministic way. The neuron dissolution strategy is used to eliminate invalid solutions, and all answers to these two problems are encoded as indices of output neurons. Our results improve the one obtained in Science China Information Sciences, 2011, 1596-1607 by Pan et al. PMID:27627104

  14. Simulation of collision cascades and thermal spikes in ceramics

    Energy Technology Data Exchange (ETDEWEB)

    Devanathan, Ramaswami; Weber, William J.

    2010-10-01

    Classical molecular dynamics simulations have been employed to examine defect production by energetic recoils in UO2, Gd2Ti2O7, Gd2Zr2O7 and ZrSiO4. These atomistic simulations provide details of the nature and size distribution of defect clusters produced in collision cascades. The accommodation of recoil damage by lower energy cation exchange and greater occupation of anion structural vacancies is a contributing factor for the greater radiation tolerance of Gd2Zr2O7 relative to Gd2Ti2O7. In addition, electronic energy loss processes in UO2 has been modeled in the form of a thermal spike to study the details of track formation and track structure. For thermal spikes with energy deposition of 4 keV/nm in UO2, a track was not formed and mainly isolated Frenkel pairs are produced.

  15. Control of a brain-computer interface without spike sorting

    Science.gov (United States)

    Fraser, George W.; Chase, Steven M.; Whitford, Andrew; Schwartz, Andrew B.

    2009-10-01

    Two rhesus monkeys were trained to move a cursor using neural activity recorded with silicon arrays of 96 microelectrodes implanted in the primary motor cortex. We have developed a method to extract movement information from the recorded single and multi-unit activity in the absence of spike sorting. By setting a single threshold across all channels and fitting the resultant events with a spline tuning function, a control signal was extracted from this population using a Bayesian particle-filter extraction algorithm. The animals achieved high-quality control comparable to the performance of decoding schemes based on sorted spikes. Our results suggest that even the simplest signal processing is sufficient for high-quality neuroprosthetic control.

  16. Nonlinear electronic circuit with neuron like bursting and spiking dynamics.

    Science.gov (United States)

    Savino, Guillermo V; Formigli, Carlos M

    2009-07-01

    It is difficult to design electronic nonlinear devices capable of reproducing complex oscillations because of the lack of general constructive rules, and because of stability problems related to the dynamical robustness of the circuits. This is particularly true for current analog electronic circuits that implement mathematical models of bursting and spiking neurons. Here we describe a novel, four-dimensional and dynamically robust nonlinear analog electronic circuit that is intrinsic excitable, and that displays frequency adaptation bursting and spiking oscillations. Despite differences from the classical Hodgkin-Huxley (HH) neuron model, its bifurcation sequences and dynamical properties are preserved, validating the circuit as a neuron model. The circuit's performance is based on a nonlinear interaction of fast-slow circuit blocks that can be clearly dissected, elucidating burst's starting, sustaining and stopping mechanisms, which may also operate in real neurons. Our analog circuit unit is easily linked and may be useful in building networks that perform in real-time.

  17. Negotiating Multicollinearity with Spike-and-Slab Priors.

    Science.gov (United States)

    Ročková, Veronika; George, Edward I

    2014-08-01

    In multiple regression under the normal linear model, the presence of multicollinearity is well known to lead to unreliable and unstable maximum likelihood estimates. This can be particularly troublesome for the problem of variable selection where it becomes more difficult to distinguish between subset models. Here we show how adding a spike-and-slab prior mitigates this difficulty by filtering the likelihood surface into a posterior distribution that allocates the relevant likelihood information to each of the subset model modes. For identification of promising high posterior models in this setting, we consider three EM algorithms, the fast closed form EMVS version of Rockova and George (2014) and two new versions designed for variants of the spike-and-slab formulation. For a multimodal posterior under multicollinearity, we compare the regions of convergence of these three algorithms. Deterministic annealing versions of the EMVS algorithm are seen to substantially mitigate this multimodality. A single simple running example is used for illustration throughout.

  18. Evolving Spiking Neural Networks for Control of Artificial Creatures

    Directory of Open Access Journals (Sweden)

    Arash Ahmadi

    2013-10-01

    Full Text Available To understand and analysis behavior of complicated and intelligent organisms, scientists apply bio-inspired concepts including evolution and learning to mathematical models and analyses. Researchers utilize these perceptions in different applications, searching for improved methods andapproaches for modern computational systems. This paper presents a genetic algorithm based evolution framework in which Spiking Neural Network (SNN of artificial creatures are evolved for higher chance of survival in a virtual environment. The artificial creatures are composed ofrandomly connected Izhikevich spiking reservoir neural networks using population activity rate coding. Inspired by biological neurons, the neuronal connections are considered with different axonal conduction delays. Simulations results prove that the evolutionary algorithm has thecapability to find or synthesis artificial creatures which can survive in the environment successfully.

  19. Proficiency test on incurred and spiked pesticide residues in cereals

    DEFF Research Database (Denmark)

    Poulsen, Mette Erecius; Christensen, Hanne Bjerre; Herrmann, Susan Strange

    2009-01-01

    -methyl, difenconazole, epoxiconazole, glyphosate, iprodione, malathion, pirimicarb, prochloraz, spiroxamin and trifloxystrobin. After harvest, the test material was additionally spiked in the laboratory with three pesticides, that where the residues were too low, and axozystrobin. In total, 72 laboratories submitted...... results and z-scores were calculated for all laboratories and pesticides, except for glyphosate where only five laboratories submitted results and summed weighted z-scores were calculated for the laboratories with a sufficient scope. For several pesticides, the submitted results were strongly depending......A proficiency test on incurred and spiked pesticide residues in wheat was organised in 2008. The test material was grown in 2007 and treated in the field with 14 pesticides formulations containing the active substances, alpha-cypermethrin, bifentrin, carbendazim, chlormequat, chlorpyrifos...

  20. Interaction of inhibition and triplets of excitatory spikes modulates the NMDA-R-mediated synaptic plasticity in a computational model of spike timing-dependent plasticity.

    Science.gov (United States)

    Cutsuridis, Vassilis

    2013-01-01

    Spike timing-dependent plasticity (STDP) experiments have shown that a synapse is strengthened when a presynaptic spike precedes a postsynaptic one and depressed vice versa. The canonical form of STDP has been shown to have an asymmetric shape with the peak long-term potentiation at +6 ms and the peak long-term depression at -5 ms. Experiments in hippocampal cultures with more complex stimuli such as triplets (one presynaptic spike combined with two postsynaptic spikes or one postsynaptic spike with two presynaptic spikes) have shown that pre-post-pre spike triplets result in no change in synaptic strength, whereas post-pre-post spike triplets lead to significant potentiation. The sign and magnitude of STDP have also been experimentally hypothesized to be modulated by inhibition. Recently, a computational study showed that the asymmetrical form of STDP in the CA1 pyramidal cell dendrite when two spikes interact switches to a symmetrical one in the presence of inhibition under certain conditions. In the present study, I investigate computationally how inhibition modulates STDP in the CA1 pyramidal neuron dendrite when it is driven by triplets. The model uses calcium as the postsynaptic signaling agent for STDP and is shown to be consistent with the experimental triplet observations in the absence of inhibition: simulated pre-post-pre spike triplets result in no change in synaptic strength, whereas simulated post-pre-post spike triplets lead to significant potentiation. When inhibition is bounded by the onset and offset of the triplet stimulation, then the strength of the synapse is decreased as the strength of inhibition increases. When inhibition arrives either few milliseconds before or at the onset of the last spike in the pre-post-pre triplet stimulation, then the synapse is potentiated. Variability in the frequency of inhibition (50 vs. 100 Hz) produces no change in synaptic strength. Finally, a 5% variation in model's calcium parameters (calcium thresholds

  1. Spiking and LFP activity in PRR during symbolically instructed reaches.

    Science.gov (United States)

    Hwang, Eun Jung; Andersen, Richard A

    2012-02-01

    The spiking activity in the parietal reach region (PRR) represents the spatial goal of an impending reach when the reach is directed toward or away from a visual object. The local field potentials (LFPs) in this region also represent the reach goal when the reach is directed to a visual object. Thus PRR is a candidate area for reading out a patient's intended reach goals for neural prosthetic applications. For natural behaviors, reach goals are not always based on the location of a visual object, e.g., playing the piano following sheet music or moving following verbal directions. So far it has not been directly tested whether and how PRR represents reach goals in such cognitive, nonlocational conditions, and knowing the encoding properties in various task conditions would help in designing a reach goal decoder for prosthetic applications. To address this issue, we examined the macaque PRR under two reach conditions: reach goal determined by the stimulus location (direct) or shape (symbolic). For the same goal, the spiking activity near reach onset was indistinguishable between the two tasks, and thus a reach goal decoder trained with spiking activity in one task performed perfectly in the other. In contrast, the LFP activity at 20-40 Hz showed small but significantly enhanced reach goal tuning in the symbolic task, but its spatial preference remained the same. Consequently, a decoder trained with LFP activity performed worse in the other task than in the same task. These results suggest that LFP decoders in PRR should take into account the task context (e.g., locational vs. nonlocational) to be accurate, while spike decoders can robustly provide reach goal information regardless of the task context in various prosthetic applications.

  2. Spiking Neural P Systems with Neuron Division and Budding

    OpenAIRE

    Pan, Linqiang; Paun, Gheorghe; Pérez Jiménez, Mario de Jesús

    2009-01-01

    In order to enhance the e±ciency of spiking neural P systems, we introduce the features of neuron division and neuron budding, which are processes inspired by neural stem cell division. As expected (as it is the case for P systems with active membranes), in this way we get the possibility to solve computationally hard problems in polynomial time. We illustrate this possibility with SAT problem.

  3. Isolated neuron amplitude spike decrease under static magnetic fields

    Science.gov (United States)

    Azanza, María J.; del Moral, A.

    1996-05-01

    Isolated Helix aspersa neurons under strong enough static magnetic fields B (0.07-0.7 T) show a decrease of the spike depolarization voltage of the form ∼exp(αB2), with α dependent on neuron parameters. A tentative model is proposed which explains such behaviour through a deactivation of Na+K+-ATP-ase pumps due to protein superdiamagnetic rotation. Values for the cluster and protein in cluster numbers are estimated.

  4. Chaotically spiking attractors in suspended mirror optical cavities

    CERN Document Server

    Marino, Francesco

    2010-01-01

    A high-finesse suspended mirror Fabry-Perot cavity is experimentally studied in a regime where radiation pressure and photothermal effect are both relevant. The competition between these phenomena, operating at different time scales, produces unobserved dynamical scenarios where an initial Hopf instability is followed by the birth of small-amplitude chaotic attractors which erratically but deterministically trigger optical spikes. The observed dynamical regimes are well reproduced by a detailed physical model of the system.

  5. The Stereo Electron Spikes and the Interplanetary Magnetic Field

    Science.gov (United States)

    Jokipii, J. R.; Sheeley, N. R., Jr.; Wang, Y. M.; Giacalone, J.

    2016-12-01

    A recent paper (Klassen etal, 2015) discussed observations of a spike event of 55-65 keV electrons which occurred very nearly simultaneously at STEREO A and STEREO B, which at the time were separated in longitude by 38 degrees. The authors associated the spikes with a flare at the Sun near the footpoint of the nominal Archimedean spiral magnetic field line passing through STEREO A. The spike at STEREO A was delayed by 2.2 minutes from that at STEREOB. We discuss the observations in terms of a model in which the electrons, accelerated at the flare, propagate without significant scattering along magnetic field lines which separate or diverge as a function of radial distance from the Sun. The near simultaneity of the spikes at the two spacecraft is a natural consequence of this model. We interpret the divergence of the magnetic field lines as a consequence of field-line random walk and flux-tube expansion. We show that the field-line random walk in the absence of flux-tube expansion produces an rms spread of field lines significantly less than that which is required to produce to observed divergence. We find that observations of the solar wind and its source region at the time of the event can account for the observations in terms of propagation along interplanetary magnetic field-lines. Klassen, A., Dresing, N., Gomez-Herrero, R, and Heber, B., A&A 580, A115 (2015) Financial support for NS and YMW was provided by NASA and CNR.

  6. Evolution of Plastic Learning in Spiking Networks via Memristive Connections

    OpenAIRE

    Howard, Gerard; Gale, Ella; Bull, Larry; Costello, Ben de Lacy; Adamatzky, Andy

    2012-01-01

    This article presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e. whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable topologies, allowing the number of neurons, connection weights, and inter-neural connectivity pattern to emerge. By comparing two phenomenological real-world memristor implementations with networks comprised of (i) linear resistors (ii) constant-valued connections...

  7. Stochastic models for spike trains of single neurons

    CERN Document Server

    Sampath, G

    1977-01-01

    1 Some basic neurophysiology 4 The neuron 1. 1 4 1. 1. 1 The axon 7 1. 1. 2 The synapse 9 12 1. 1. 3 The soma 1. 1. 4 The dendrites 13 13 1. 2 Types of neurons 2 Signals in the nervous system 14 2. 1 Action potentials as point events - point processes in the nervous system 15 18 2. 2 Spontaneous activi~ in neurons 3 Stochastic modelling of single neuron spike trains 19 3. 1 Characteristics of a neuron spike train 19 3. 2 The mathematical neuron 23 4 Superposition models 26 4. 1 superposition of renewal processes 26 4. 2 Superposition of stationary point processe- limiting behaviour 34 4. 2. 1 Palm functions 35 4. 2. 2 Asymptotic behaviour of n stationary point processes superposed 36 4. 3 Superposition models of neuron spike trains 37 4. 3. 1 Model 4. 1 39 4. 3. 2 Model 4. 2 - A superposition model with 40 two input channels 40 4. 3. 3 Model 4. 3 4. 4 Discussion 41 43 5 Deletion models 5. 1 Deletion models with 1nd~endent interaction of excitatory and inhibitory sequences 44 VI 5. 1. 1 Model 5. 1 The basic de...

  8. From the entropy to the statistical structure of spike trains

    CERN Document Server

    Gao, Yun; Bienenstock, Elie

    2007-01-01

    We use statistical estimates of the entropy rate of spike train data in order to make inferences about the underlying structure of the spike train itself. We first examine a number of different parametric and nonparametric estimators (some known and some new), including the ``plug-in'' method, several versions of Lempel-Ziv-based compression algorithms, a maximum likelihood estimator tailored to renewal processes, and the natural estimator derived from the Context-Tree Weighting method (CTW). The theoretical properties of these estimators are examined, several new theoretical results are developed, and all estimators are systematically applied to various types of synthetic data and under different conditions. Our main focus is on the performance of these entropy estimators on the (binary) spike trains of 28 neurons recorded simultaneously for a one-hour period from the primary motor and dorsal premotor cortices of a monkey. We show how the entropy estimates can be used to test for the existence of long-term s...

  9. Dissection of SARS Coronavirus Spike Protein into Discrete Folded Fragments

    Institute of Scientific and Technical Information of China (English)

    LI Shuang; CAI Zhen; CHEN Yong; LIN Zhanglin

    2006-01-01

    The spike protein of the severe acute respiratory syndrome coronavirus (SARS-CoV) mediates cell fusion by binding to target cell surface receptors. This paper reports a simple method for dissecting the viral protein and for searching for foldable fragments in a random but systematic manner. The method involves digestion by DNase I to generate a pool of short DNA segments, followed by an additional step of reassembly of these segments to produce a library of DNA fragments with random ends but controllable lengths. To rapidly screen for discrete folded polypeptide fragments, the reassembled gene fragments were further cloned into a vector as N-terminal fusions to a folding reporter gene which was a variant of green fluorescent protein. Two foldable fragments were identified for the SARS-CoV spike protein, which coincide with various anti-SARS peptides derived from the hepated repeat (HR) region 2 of the spike protein. The method should be applicable to other viral proteins to isolate antigen or vaccine candidates, thus providing an alternative to the full-length proteins (subunits) or linear short peptides.

  10. A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

    Directory of Open Access Journals (Sweden)

    Karim El-Laithy

    2011-01-01

    Full Text Available An integration of both the Hebbian-based and reinforcement learning (RL rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

  11. On the Mathematical Consequences of Binning Spike Trains.

    Science.gov (United States)

    Cessac, Bruno; Le Ny, Arnaud; Löcherbach, Eva

    2017-01-01

    We initiate a mathematical analysis of hidden effects induced by binning spike trains of neurons. Assuming that the original spike train has been generated by a discrete Markov process, we show that binning generates a stochastic process that is no longer Markov but is instead a variable-length Markov chain (VLMC) with unbounded memory. We also show that the law of the binned raster is a Gibbs measure in the DLR (Dobrushin-Lanford-Ruelle) sense coined in mathematical statistical mechanics. This allows the derivation of several important consequences on statistical properties of binned spike trains. In particular, we introduce the DLR framework as a natural setting to mathematically formalize anticipation, that is, to tell "how good" our nervous system is at making predictions. In a probabilistic sense, this corresponds to condition a process by its future, and we discuss how binning may affect our conclusions on this ability. We finally comment on the possible consequences of binning in the detection of spurious phase transitions or in the detection of incorrect evidence of criticality.

  12. Spike library based simulator for extracellular single unit neuronal signals.

    Science.gov (United States)

    Thorbergsson, P T; Jorntell, H; Bengtsson, F; Garwicz, M; Schouenborg, J; Johansson, A

    2009-01-01

    A well defined set of design criteria is of great importance in the process of designing brain machine interfaces (BMI) based on extracellular recordings with chronically implanted micro-electrode arrays in the central nervous system (CNS). In order to compare algorithms and evaluate their performance under various circumstances, ground truth about their input needs to be present. Obtaining ground truth from real data would require optimal algorithms to be used, given that those exist. This is not possible since it relies on the very algorithms that are to be evaluated. Using realistic models of the recording situation facilitates the simulation of extracellular recordings. The simulation gives access to a priori known signal characteristics such as spike times and identities. In this paper, we describe a simulator based on a library of spikes obtained from recordings in the cat cerebellum and observed statistics of neuronal behavior during spontaneous activity. The simulator has proved to be useful in the task of generating extracellular recordings with realistic background noise and known ground truth to use in the evaluation of algorithms for spike detection and sorting.

  13. Analyzing large-scale spiking neural data with HRLAnalysis

    Directory of Open Access Journals (Sweden)

    Corey Michael Thibeault

    2014-03-01

    Full Text Available The additional capabilities provided by high-performance neural simulation environments and modern computing hardware has allowed for the modeling of increasingly larger spiking neural networks. This is important for exploring more anatomically detailed networks but the corresponding accumulation in data can make analyzing the results of these simulations difficult. This is further compounded by the fact that many existing analysis packages were not developed with large spiking data sets in mind. Presented here is a software suite developed to not only process the increased amount of spike-train data in a reasonable amount of time, but also provide a user friendly Python interface. We describe the design considerations, implementation and features of the HRLAnalysis suite. In addition, performance benchmarks demonstrating the speedup of this design compared to a published Python implementation are also presented. The result is a high-performance analysis toolkit that is not only usable and readily extensible, but also straightforward to interface with existing Python modules.

  14. Repertoires of spike avalanches are modulated by behavior and novelty

    Directory of Open Access Journals (Sweden)

    Tiago Lins Ribeiro

    2016-03-01

    Full Text Available Neuronal avalanches measured as consecutive bouts of thresholded field potentials represent a statistical signature that the brain operates near a critical point. In theory, criticality optimizes stimulus sensitivity, information transmission, computational capability and mnemonic repertoires size. Field potential avalanches recorded via multielectrode arrays from cortical slice cultures are repeatable spatiotemporal activity patterns. It remains unclear whether avalanches of action potentials observed in forebrain regions of freely-behaving rats also form recursive repertoires, and whether these have any behavioral relevance. Here we show that spike avalanches, recorded from hippocampus and sensory neocortex of freely-behaving rats, constitute distinct families of recursive spatiotemporal patterns. A significant number of those patterns were specific to a behavioral state. Although avalanches produced during sleep were mostly similar to others that occurred during waking, the repertoire of patterns recruited during sleep differed significantly from that of waking. More importantly, exposure to novel objects increased the rate at which new patterns arose, also leading to changes in post-exposure repertoires, which were significantly different from those before the exposure. A significant number of families occurred exclusively during periods of whisker contact with objects, but few were associated with specific objects. Altogether, the results provide original evidence linking behavior and criticality at the spike level: spike avalanches form repertoires that emerge in waking, recur during sleep, are diversified by novelty and contribute to object representation.

  15. Repertoires of Spike Avalanches Are Modulated by Behavior and Novelty.

    Science.gov (United States)

    Ribeiro, Tiago L; Ribeiro, Sidarta; Copelli, Mauro

    2016-01-01

    Neuronal avalanches measured as consecutive bouts of thresholded field potentials represent a statistical signature that the brain operates near a critical point. In theory, criticality optimizes stimulus sensitivity, information transmission, computational capability and mnemonic repertoires size. Field potential avalanches recorded via multielectrode arrays from cortical slice cultures are repeatable spatiotemporal activity patterns. It remains unclear whether avalanches of action potentials observed in forebrain regions of freely-behaving rats also form recursive repertoires, and whether these have any behavioral relevance. Here, we show that spike avalanches, recorded from hippocampus (HP) and sensory neocortex of freely-behaving rats, constitute distinct families of recursive spatiotemporal patterns. A significant number of those patterns were specific to a behavioral state. Although avalanches produced during sleep were mostly similar to others that occurred during waking, the repertoire of patterns recruited during sleep differed significantly from that of waking. More importantly, exposure to novel objects increased the rate at which new patterns arose, also leading to changes in post-exposure repertoires, which were significantly different from those before the exposure. A significant number of families occurred exclusively during periods of whisker contact with objects, but few were associated with specific objects. Altogether, the results provide original evidence linking behavior and criticality at the spike level: spike avalanches form repertoires that emerge in waking, recur during sleep, are diversified by novelty and contribute to object representation.

  16. Emergent properties of interacting populations of spiking neurons.

    Science.gov (United States)

    Cardanobile, Stefano; Rotter, Stefan

    2011-01-01

    Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations.

  17. Emergent properties of interacting populations of spiking neurons

    Directory of Open Access Journals (Sweden)

    Stefano eCardanobile

    2011-12-01

    Full Text Available Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system.Here, we discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks on the population level is faithfully reflected by a set of non-linear rate equations, describing all interactions on this level. These equations, in turn, are similar in structure to the Lotka-Volterra equations, well known by their use in modeling predator-prey relationships in population biology, but abundant applications to economic theory have also been described.We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of neural populations.

  18. Spike latency and response properties of an excitable micropillar laser

    Science.gov (United States)

    Selmi, F.; Braive, R.; Beaudoin, G.; Sagnes, I.; Kuszelewicz, R.; Erneux, T.; Barbay, S.

    2016-10-01

    We present experimental measurements concerning the response of an excitable micropillar laser with saturable absorber to incoherent as well as coherent perturbations. The excitable response is similar to the behavior of spiking neurons but with much faster time scales. It is accompanied by a subnanosecond nonlinear delay that is measured for different bias pump values. This mechanism provides a natural scheme for encoding the strength of an ultrafast stimulus in the response delay of excitable spikes (temporal coding). Moreover, we demonstrate coherent and incoherent perturbations techniques applied to the micropillar with perturbation thresholds in the range of a few femtojoules. Responses to coherent perturbations assess the cascadability of the system. We discuss the physical origin of the responses to single and double perturbations with the help of numerical simulations of the Yamada model and, in particular, unveil possibilities to control the relative refractory period that we recently evidenced in this system. Experimental measurements are compared to both numerical simulations of the Yamada model and analytic expressions obtained in the framework of singular perturbation techniques. This system is thus a good candidate to perform photonic spike processing tasks in the framework of novel neuroinspired computing systems.

  19. Spike Penetration in Blast-Wave-Driven Instabilities

    Science.gov (United States)

    Drake, R. Paul

    2010-05-01

    Recent experiments by C. Kuranz and collaborators, motivated by structure in supernovae, have studied systems in which planar blast waves encounter interfaces where the density decreases. During the Rayleigh-Taylor (RT) phase of such experiments, they observed greater penetration of the RT spikes than tends to be seen in simulations. Here we seek to employ semi-analytic theory to understand the general nature and regimes of spike penetration for blast-wave-driven instabilities. This problem is not trivial as one must account for the initial vorticity deposition at the interface, for its time-dependent deceleration, for the expansion of the shocked material in time and space, and for the drag on the broadened tips of the spikes. We offer here an improved evaluation of the material expansion in comparison to past work. The goal is to use such models to increase our ability to interpret the behavior of simulations of such systems, in both the laboratory and astrophysics. Supported by the US DOE NNSA under the Predictive Sci. Academic Alliance Program by grant DE-FC52-08NA28616, the Stewardship Sci. Academic Alliances program by grant DE-FG52-04NA00064, and the Nat. Laser User Facility by grant DE-FG03-00SF22021.

  20. Efficient Architecture for Spike Sorting in Reconfigurable Hardware

    Directory of Open Access Journals (Sweden)

    Sheng-Ying Lai

    2013-11-01

    Full Text Available This paper presents a novel hardware architecture for fast spike sorting. The architecture is able to perform both the feature extraction and clustering in hardware. The generalized Hebbian algorithm (GHA and fuzzy C-means (FCM algorithm are used for feature extraction and clustering, respectively. The employment of GHA allows efficient computation of principal components for subsequent clustering operations. The FCM is able to achieve near optimal clustering for spike sorting. Its performance is insensitive to the selection of initial cluster centers. The hardware implementations of GHA and FCM feature low area costs and high throughput. In the GHA architecture, the computation of different weight vectors share the same circuit for lowering the area costs. Moreover, in the FCM hardware implementation, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. To show the effectiveness of the circuit, the proposed architecture is physically implemented by field programmable gate array (FPGA. It is embedded in a System-on-Chip (SOC platform for performance measurement. Experimental results show that the proposed architecture is an efficient spike sorting design for attaining high classification correct rate and high speed computation.

  1. Iterative learning control algorithm for spiking behavior of neuron model

    Science.gov (United States)

    Li, Shunan; Li, Donghui; Wang, Jiang; Yu, Haitao

    2016-11-01

    Controlling neurons to generate a desired or normal spiking behavior is the fundamental building block of the treatment of many neurologic diseases. The objective of this work is to develop a novel control method-closed-loop proportional integral (PI)-type iterative learning control (ILC) algorithm to control the spiking behavior in model neurons. In order to verify the feasibility and effectiveness of the proposed method, two single-compartment standard models of different neuronal excitability are specifically considered: Hodgkin-Huxley (HH) model for class 1 neural excitability and Morris-Lecar (ML) model for class 2 neural excitability. ILC has remarkable advantages for the repetitive processes in nature. To further highlight the superiority of the proposed method, the performances of the iterative learning controller are compared to those of classical PI controller. Either in the classical PI control or in the PI control combined with ILC, appropriate background noises are added in neuron models to approach the problem under more realistic biophysical conditions. Simulation results show that the controller performances are more favorable when ILC is considered, no matter which neuronal excitability the neuron belongs to and no matter what kind of firing pattern the desired trajectory belongs to. The error between real and desired output is much smaller under ILC control signal, which suggests ILC of neuron’s spiking behavior is more accurate.

  2. Emergent Properties of Interacting Populations of Spiking Neurons

    Science.gov (United States)

    Cardanobile, Stefano; Rotter, Stefan

    2011-01-01

    Dynamic neuronal networks are a key paradigm of increasing importance in brain research, concerned with the functional analysis of biological neuronal networks and, at the same time, with the synthesis of artificial brain-like systems. In this context, neuronal network models serve as mathematical tools to understand the function of brains, but they might as well develop into future tools for enhancing certain functions of our nervous system. Here, we present and discuss our recent achievements in developing multiplicative point processes into a viable mathematical framework for spiking network modeling. The perspective is that the dynamic behavior of these neuronal networks is faithfully reflected by a set of non-linear rate equations, describing all interactions on the population level. These equations are similar in structure to Lotka-Volterra equations, well known by their use in modeling predator-prey relations in population biology, but abundant applications to economic theory have also been described. We present a number of biologically relevant examples for spiking network function, which can be studied with the help of the aforementioned correspondence between spike trains and specific systems of non-linear coupled ordinary differential equations. We claim that, enabled by the use of multiplicative point processes, we can make essential contributions to a more thorough understanding of the dynamical properties of interacting neuronal populations. PMID:22207844

  3. Learning and Prospective Recall of Noisy Spike Pattern Episodes

    Directory of Open Access Journals (Sweden)

    Karl eDockendorf

    2013-06-01

    Full Text Available Spike patterns in vivo are often incomplete or corrupted with noise that makes inputs to neuronal networks appear to vary although they may, in fact, be samples of a single underlying pattern or repeated presentation. Here we present a recurrent spiking neural network (SNN model that learns noisy pattern sequences through the use of homeostasis and spike-timing dependent plasticity (STDP. We find that the changes in the synaptic weight vector during learning of patterns of random ensembles are approximately orthogonal in a reduced dimension space when the patterns are constructed to minimize overlap in representations. Using this model, representations of sparse patterns maybe associated through co-activated firing and integrated into ensemble representations. While the model is tolerant to noise, prospective activity and pattern completion differ in their ability to adapt in the presence of noise. One version of the model is able to demonstrate the recently discovered phenomena of preplay and replay reminiscent of hippocampal like behaviors.

  4. Electrophoretic fingerprinting of benzodiazepine tablets in spike drinks.

    Science.gov (United States)

    Sáiz, Jorge; Ortega-Ojeda, Fernando; López-Melero, Lucía; Montalvo, Gema; García-Ruiz, Carmen

    2014-11-01

    Over the last few years, there has been an increase in the reports of drug-facilitated crimes. The list of drugs associated with these crimes is extensive and benzodiazepines constitute one of the groups of substances more commonly used. The sedative properties, which characterize benzodiazepines, are enhanced when such drugs are combined with alcohol, being more attractive for committing these types of crimes. In this work, a capillary electrophoresis method was applied to the analysis of 63 different samples of club drinks spiked with benzodiazepine tablets. The resulting electropherograms were processed and analyzed with the chemometric multivariate techniques: principal component analysis (PCA) and soft independent modeling of class analogies (SIMCA) classification. The PCA results allowed a clear differentiation of each drug class in a 3D plot. In addition, the SIMCA classification model (5% significance level) showed that eight out of nine test samples were automatically assigned by software to their proper sample class. The conflicting sample was correctly classified in the Coomans' plot (95% confidence). This novel approach based on the comparison of electrophoretic profiles of spiked drinks by chemometric tools allows determining the benzodiazepine used for drink spiking without the use of drug standards. Moreover, it provides an opportunity for the forensic laboratories to incorporate the identification capability provided by the electrophoretic fingerprinting of benzodiazepine solutions in existing or new databases. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. How adaptation shapes spike rate oscillations in recurrent neuronal networks

    Directory of Open Access Journals (Sweden)

    Moritz eAugustin

    2013-02-01

    Full Text Available Neural mass signals from in-vivo recordings often show oscillations with frequencies ranging from <1 Hz to 100 Hz. Fast rhythmic activity in the beta and gamma range can be generated by network based mechanisms such as recurrent synaptic excitation-inhibition loops. Slower oscillations might instead depend on neuronal adaptation currents whose timescales range from tens of milliseconds to seconds. Here we investigate how the dynamics of such adaptation currents contribute to spike rate oscillations and resonance properties in recurrent networks of excitatory and inhibitory neurons. Based on a network of sparsely coupled spiking model neurons with two types of adaptation current and conductance based synapses with heterogeneous strengths and delays we use a mean-field approach to analyze oscillatory network activity. For constant external input, we find that spike-triggered adaptation currents provide a mechanism to generate slow oscillations over a wide range of adaptation timescales as long as recurrent synaptic excitation is sufficiently strong. Faster rhythms occur when recurrent inhibition is slower than excitation and oscillation frequency increases with the strength of inhibition. Adaptation facilitates such network based oscillations for fast synaptic inhibition and leads to decreased frequencies. For oscillatory external input, adaptation currents amplify a narrow band of frequencies and cause phase advances for low frequencies in addition to phase delays at higher frequencies. Our results therefore identify the different key roles of neuronal adaptation dynamics for rhythmogenesis and selective signal propagation in recurrent networks.

  6. On the mathematical consequences of binning spike trains

    CERN Document Server

    Cessac, B; Loecherbach, E

    2016-01-01

    We initiate a mathematical analysis of hidden effects induced by binning spike trains of neurons. Assuming that the original spike train has been generated by a discrete Markov process, we show that binning generates a stochastic process which is not Markov any more, but is instead a Variable Length Markov Chain (VLMC) with unbounded memory. We also show that the law of the binned raster is a Gibbs measure in the DLR (Dobrushin-Lanford-Ruelle) sense coined in mathematical statistical mechanics. This allows the derivation of several important consequences on statistical properties of binned spike trains. In particular, we introduce the DLR framework as a natural setting to mathematically formalize anticipation, i.e. to tell "how good" our nervous system is at making predictions. In a probabilistic sense, this corresponds to condition a process by its future and we discuss how binning may affect our conclusions on this ability. We finally comment what could be the consequences of binning in the detection of spu...

  7. Spike timing and synaptic dynamics at the awake thalamocortical synapse.

    Science.gov (United States)

    Swadlow, Harvey A; Bezdudnaya, Tatiana; Gusev, Alexander G

    2005-01-01

    Thalamocortical (TC) neurons form only a small percentage of the synapses onto neurons of cortical layer 4, but the response properties of these cortical neurons are arguably dominated by thalamic input. This discrepancy is explained, in part, by studies showing that TC synapses are of high efficacy. However, TC synapses display activity-dependent depression. Because of this, in vitro measures of synaptic efficacy will not reflect the situation in vivo, where different neuronal populations have widely varying levels of "spontaneous" activity. Indeed, TC neurons of awake subjects generate high rates of spontaneous activity that would be expected, in a depressing synapse, to result in a chronic state of synaptic depression. Here, we review recent work in the somatosensory thalamocortical system of awake rabbits in which the relationship between TC spike timing and TC synaptic efficacy was examined during both thalamic "relay mode" (alert state) and "burst mode" (drowsy state). Two largely independent methodological approaches were used. First, we employed cross-correlation methods to examine the synaptic impact of single TC "barreloid" neurons on a single neuronal subtype in the topographically aligned layer 4 "barrel" - putative fast-spike inhibitory interneurons. We found that the initial spike of a TC burst, as well as isolated TC spikes with long preceding interspike intervals (ISIs) elicited postsynaptic action potentials far more effectively than did TC impulses with short ISIs. Our second approach took a broader view of the postsynaptic impact of TC impulses. In these experiments we examined spike-triggered extracellular field potentials and synaptic currents (using current source-density analysis) generated through the depths of a cortical barrel column by the impulses of single topographically aligned TC neurons. We found that (a) closely neighboring TC neurons may elicit very different patterns of monosynaptic activation within layers 4 and 6 of the aligned

  8. The spike timing precision of FitzHugh-Nagumo neuron network coupled by gap junctions

    Institute of Scientific and Technical Information of China (English)

    Zhang Su-Hua; Zhan Yong; Yu Hui; An Hai-Long; Zhao Tong-Jun

    2006-01-01

    It has been proved recently that the spike timing can play an important role in information transmission, so in this paper we develop a network with N-unit FitzHugh-Nagumo neurons coupled by gap junctions and discuss the dependence of the spike timing precision on synaptic coupling strength, the noise intensity and the size of the neuron ensemble. The calculated results show that the spike timing precision decreases as the noise intensity increases; and the ensemble spike timing precision increases with coupling strength increasing. The electric synapse coupling has a more important effect on the spike timing precision than the chemical synapse coupling.

  9. Correlations Decrease with Propagation of Spiking Activity in the Mouse Barrel Cortex

    Science.gov (United States)

    Ranganathan, Gayathri Nattar; Koester, Helmut Joachim

    2011-01-01

    Propagation of suprathreshold spiking activity through neuronal populations is important for the function of the central nervous system. Neural correlations have an impact on cortical function particularly on the signaling of information and propagation of spiking activity. Therefore we measured the change in correlations as suprathreshold spiking activity propagated between recurrent neuronal networks of the mammalian cerebral cortex. Using optical methods we recorded spiking activity from large samples of neurons from two neural populations simultaneously. The results indicate that correlations decreased as spiking activity propagated from layer 4 to layer 2/3 in the rodent barrel cortex. PMID:21629764

  10. Controllable spiking patterns in long-wavelength vertical cavity surface emitting lasers for neuromorphic photonics systems

    Energy Technology Data Exchange (ETDEWEB)

    Hurtado, Antonio, E-mail: antonio.hurtado@strath.ac.uk [Institute of Photonics, SUPA Department of Physics, University of Strathclyde, TIC Centre, 99 George Street, Glasgow G1 1RD (United Kingdom); Javaloyes, Julien [Departament de Fisica, Universitat de les Illes Balears, c/Valldemossa km 7.5, 07122 Mallorca (Spain)

    2015-12-14

    Multiple controllable spiking patterns are achieved in a 1310 nm Vertical-Cavity Surface Emitting Laser (VCSEL) in response to induced perturbations and for two different cases of polarized optical injection, namely, parallel and orthogonal. Furthermore, reproducible spiking responses are demonstrated experimentally at sub-nanosecond speed resolution and with a controlled number of spikes fired. This work opens therefore exciting research avenues for the use of VCSELs in ultrafast neuromorphic photonic systems for non-traditional computing applications, such as all-optical binary-to-spiking format conversion and spiking information encoding.

  11. PySpike—A Python library for analyzing spike train synchrony

    Directory of Open Access Journals (Sweden)

    Mario Mulansky

    2016-01-01

    Full Text Available Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains plays a crucial role in addressing this problem. Here, the PySpike library is introduced, a Python package for spike train analysis providing parameter-free and time-scale independent measures of spike train synchrony. It allows to compute similarity and dissimilarity profiles, averaged values and distance matrices. Although mainly focusing on neuroscience, PySpike can also be applied in other contexts like climate research or social sciences. The package is available as Open Source on Github and PyPI.

  12. Correlations decrease with propagation of spiking activity in the mouse barrel cortex

    Directory of Open Access Journals (Sweden)

    Gayathri Nattar Ranganathan

    2011-05-01

    Full Text Available Propagation of suprathreshold spiking activity through neuronal populations is important for the function of the central nervous system. Neural correlations have an impact on cortical function particularly on the signaling of information and propagation of spiking activity. Therefore we measured the change in correlations as suprathreshold spiking activity propagated between recurrent neuronal networks of the mammalian cerebral cortex. Using optical methods we recorded spiking activity from large samples of neurons from two neural populations simultaneously. The results indicate that correlations decreased as spiking activity propagated from layer 4 to layer 2/3 in the rodent barrel cortex.

  13. Dejittered spike-conditioned stimulus waveforms yield improved estimates of neuronal feature selectivity and spike-timing precision of sensory interneurons.

    Science.gov (United States)

    Aldworth, Zane N; Miller, John P; Gedeon, Tomás; Cummins, Graham I; Dimitrov, Alexander G

    2005-06-01

    What is the meaning associated with a single action potential in a neural spike train? The answer depends on the way the question is formulated. One general approach toward formulating this question involves estimating the average stimulus waveform preceding spikes in a spike train. Many different algorithms have been used to obtain such estimates, ranging from spike-triggered averaging of stimuli to correlation-based extraction of "stimulus-reconstruction" kernels or spatiotemporal receptive fields. We demonstrate that all of these approaches miscalculate the stimulus feature selectivity of a neuron. Their errors arise from the manner in which the stimulus waveforms are aligned to one another during the calculations. Specifically, the waveform segments are locked to the precise time of spike occurrence, ignoring the intrinsic "jitter" in the stimulus-to-spike latency. We present an algorithm that takes this jitter into account. "Dejittered" estimates of the feature selectivity of a neuron are more accurate (i.e., provide a better estimate of the mean waveform eliciting a spike) and more precise (i.e., have smaller variance around that waveform) than estimates obtained using standard techniques. Moreover, this approach yields an explicit measure of spike-timing precision. We applied this technique to study feature selectivity and spike-timing precision in two types of sensory interneurons in the cricket cercal system. The dejittered estimates of the mean stimulus waveforms preceding spikes were up to three times larger than estimates based on the standard techniques used in previous studies and had power that extended into higher-frequency ranges. Spike timing precision was approximately 5 ms.

  14. Modulating protein interaction on a molecular and Microstructural level for texture control in protein based gels

    NARCIS (Netherlands)

    Martin, A.H.; Baigts Allende, D.; Munialo, C.D.; Urbonaite, V.; Pouvreau, L.A.M.; Jongh, de H.H.J.

    2014-01-01

    The exploration of microstructures and textures of protein based systems is essential to understand (oral) breakdown properties and thereby textural aspects, or macroscopic functionalities such as water holding. Upon structure breakdown, the applied energy (W) is primarily directed towards fracture

  15. Technology and mechanism of a new protein-based core sand for aluminum casting

    Institute of Scientific and Technical Information of China (English)

    石晶玉; 黄天佑; 石红玉; 何镇明

    2001-01-01

    The protein-based binding material is from natural products, which is nontoxic and recyclable. This kind of green binder is earnestly needed by aluminum casting products. The new protein-based core possesses higher strength and easier shakeout. Its tensile strength is close to that of common resin sands. The micro-mechanism of protein binder was investigated by using infrared spectrum, chemical element analysis, SEM and thermal lost-mass analysis.

  16. A stimulus-dependent spike threshold is an optimal neural coder

    Directory of Open Access Journals (Sweden)

    Douglas L Jones

    2015-06-01

    Full Text Available A neural code based on sequences of spikes can consume a significant portion of the brain’s energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding and fidelity (decoding. The threshold mimics a post-synaptic membrane (a low-pass filter and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint. The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code.

  17. A stimulus-dependent spike threshold is an optimal neural coder.

    Science.gov (United States)

    Jones, Douglas L; Johnson, Erik C; Ratnam, Rama

    2015-01-01

    A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code.

  18. "I Told You I Was Ill" (Spike's Preferred Epitaph): In Honour and in Memory of (Terence Alan) Spike Milligan, 1918-2002.

    Science.gov (United States)

    De Rijke, Victoria

    2002-01-01

    Presents a personal tribute in memory of the work of eccentric writer and performer Spike Milligan. Celebrates and examines Spike's absurdist poetry and sketches for children in the context of both British nonsense traditions and the poetry of American writer Ogden Nash and Dr. Seuss. (SG)

  19. Systematic regional variations in Purkinje cell spiking patterns.

    Directory of Open Access Journals (Sweden)

    Jianqiang Xiao

    Full Text Available In contrast to the uniform anatomy of the cerebellar cortex, molecular and physiological studies indicate that significant differences exist between cortical regions, suggesting that the spiking activity of Purkinje cells (PCs in different regions could also show distinct characteristics. To investigate this possibility we obtained extracellular recordings from PCs in different zebrin bands in crus IIa and vermis lobules VIII and IX in anesthetized rats in order to compare PC firing characteristics between zebrin positive (Z+ and negative (Z- bands. In addition, we analyzed recordings from PCs in the A2 and C1 zones of several lobules in the posterior lobe, which largely contain Z+ and Z- PCs, respectively. In both datasets significant differences in simple spike (SS activity were observed between cortical regions. Specifically, Z- and C1 PCs had higher SS firing rates than Z+ and A2 PCs, respectively. The irregularity of SS firing (as assessed by measures of interspike interval distribution was greater in Z+ bands in both absolute and relative terms. The results regarding systematic variations in complex spike (CS activity were less consistent, suggesting that while real differences can exist, they may be sensitive to other factors than the cortical location of the PC. However, differences in the interactions between SSs and CSs, including the post-CS pause in SSs and post-pause modulation of SSs, were also consistently observed between bands. Similar, though less strong trends were observed in the zonal recordings. These systematic variations in spontaneous firing characteristics of PCs between zebrin bands in vivo, raises the possibility that fundamental differences in information encoding exist between cerebellar cortical regions.

  20. Spike-timing dependent plasticity in the striatum

    Directory of Open Access Journals (Sweden)

    Elodie Fino

    2010-06-01

    Full Text Available The striatum is the major input nucleus of basal ganglia, an ensemble of interconnected sub-cortical nuclei associated with fundamental processes of action-selection and procedural learning and memory. The striatum receives afferents from the cerebral cortex and the thalamus. In turn, it relays the integrated information towards the basal ganglia output nuclei through which it operates a selected activation of behavioral effectors. The striatal output neurons, the GABAergic medium-sized spiny neurons (MSNs, are in charge of the detection and integration of behaviorally relevant information. This property confers to the striatum the ability to extract relevant information from the background noise and select cognitive-motor sequences adapted to environmental stimuli. As long-term synaptic efficacy changes are believed to underlie learning and memory, the corticostriatal long-term plasticity provides a fundamental mechanism for the function of the basal ganglia in procedural learning. Here, we reviewed the different forms of spike-timing dependent plasticity (STDP occurring at corticostriatal synapses. Most of the studies have focused on MSNs and their ability to develop long-term plasticity. Nevertheless, the striatal interneurons (the fast-spiking GABAergic, the NO synthase and cholinergic interneurons also receive monosynaptic afferents from the cortex and tightly regulated corticostriatal information processing. Therefore, it is important to take into account the variety of striatal neurons to fully understand the ability of striatum to develop long-term plasticity. Corticostriatal STDP with various spike-timing dependence have been observed depending on the neuronal sub-populations and experimental conditions. This complexity highlights the extraordinary potentiality in term of plasticity of the corticostriatal pathway.

  1. From spiking neuron models to linear-nonlinear models.

    Directory of Open Access Journals (Sweden)

    Srdjan Ostojic

    Full Text Available Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF, exponential integrate-and-fire (EIF and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

  2. Considerations regarding the optimisation of the spike in modern volleyball

    Directory of Open Access Journals (Sweden)

    Hans-Eric REITMAYER

    2017-03-01

    Full Text Available The aim of this paper is to analyse in detail the aspects regarding increased efficiency of the spike in the game of volleyball, given the requirements of a flawless technique that will maximize the potential of the players. Our intention was to verify whether the steps of approach are unfolding in a uniformly accelerated motion and are leading to a vertical leap in the desired parameters at the moment of take off. We also wanted to check if the range of upper body motion is a determinant in the efficiency of the spike consisting in a high striking point and a remakable hitting force. Material and methods: The video footage was obtained from 6 players of “U” Timisoara’s volleyball team and 6 international players. For the analysis and processing of the footage the program Kinovea was used and the following parameters were assessed: striking speed(m/s, range of motion (cm and °, flexion between arm and forearm, angle of arm with the vertical at contact, movement speed for the first, second and third approach steps. Results: The international subjects had a 12 cm longer path of the hand in striking motion. Given the shorter segments of “U” Timisoara’s players, they compensate by having a 2° larger range of motion, above the elite players. We recorded diferent angles of the spiking arm with the vertical for the 2 groups, namely a mean of 24.5° for the elite players and just 15° for the players of “U” Timisoara. These aspects lead to a striking speed 3m/s higher for the international spikers compared to ”U” Timisoara sportsmen. Having measured a uniformly accelerated approach for the international subjects, “U” Timisoara’s players don’t respond to the same requirement with close values of the second and third step of approach.

  3. Topological analysis of chaos in neural spike train bursts

    Energy Technology Data Exchange (ETDEWEB)

    Gilmore, R. (Department of Physics and Atmospheric Science, Drexel University, Philadelphia, Pennsylvania 19104 (United States)); Pei, X.; Moss, F. (Center for Neurodynamics, University of Missouri, St. Louis, Missouri 63121 (United States))

    1999-09-01

    We show how a topological model which describes the stretching and squeezing mechanisms responsible for creating chaotic behavior can be extracted from the neural spike train data. The mechanism we have identified is the same one ([open quotes]gateau roul[acute e],[close quotes] or jelly-roll) which has previously been identified in the Duffing oscillator [Gilmore and McCallum, Phys. Rev. E [bold 51], 935 (1995)] and in a YAG laser [Boulant [ital et al.], Phys. Rev. E [bold 55], 5082 (1997)]. [copyright] [ital 1999 American Institute of Physics.

  4. Method for spiking soil samples with organic compounds

    DEFF Research Database (Denmark)

    Brinch, Ulla C; Ekelund, Flemming; Jacobsen, Carsten S

    2002-01-01

    We examined the harmful side effects on indigenous soil microorganisms of two organic solvents, acetone and dichloromethane, that are normally used for spiking of soil with polycyclic aromatic hydrocarbons for experimental purposes. The solvents were applied in two contamination protocols to either...... the whole soil sample or 25% of the soil volume, which was subsequently mixed with 75% untreated soil. For dichloromethane, we included a third protocol, which involved application to 80% of the soil volume with or without phenanthrene and introduction of Pseudomonas fluorescens VKI171 SJ132 genetically...

  5. Single spike operation in SPARC SASE-FEL

    Energy Technology Data Exchange (ETDEWEB)

    Boscolo, M. [INFN/LNF, Via Enrico Fermi 40, 00044 Frascati, Roma (Italy)], E-mail: Manuela.Boscolo@lnf.infn.it; Ferrario, M. [INFN/LNF, Via Enrico Fermi 40, 00044 Frascati, Roma (Italy); Boscolo, I.; Castelli, F.; Cialdi, S.; Petrillo, V. [University of Milano, Via Celoria 16, 20133 Milano (Italy); Bonifacio, R. [CBPF, Rio de Janeiro (Brazil); Palumbo, L. [La Sapienza, Roma (Italy); Serafini, L. [INFN/MI, Via Celoria 16, 20133 Milano (Italy)

    2008-08-01

    We describe in this paper a possible experiment with the existing SPARC photoinjector to test the generation of sub-picosecond high brightness electron bunches able to produce single spike radiation pulses at 500 nm in the SPARC self-amplified spontaneous emission free-electron laser (SASE-FEL). The main purpose of the experiment will be the production of short electron bunches as long as few SASE cooperation lengths and to validate scaling laws to foresee operation at shorter wavelength in the future operation with SPARX. The basic physics, the experimental parameters and 3D simulations are discussed.

  6. Spiking and LFP activity in PRR during symbolically instructed reaches

    OpenAIRE

    2011-01-01

    The spiking activity in the parietal reach region (PRR) represents the spatial goal of an impending reach when the reach is directed toward or away from a visual object. The local field potentials (LFPs) in this region also represent the reach goal when the reach is directed to a visual object. Thus PRR is a candidate area for reading out a patient's intended reach goals for neural prosthetic applications. For natural behaviors, reach goals are not always based on the location of a visual obj...

  7. Minimizing RF Performance Spikes in a Cryogenic Orthomode Transducer (OMT)

    Science.gov (United States)

    Henke, Doug; Claude, Stephane

    2014-04-01

    The turnstile junction exhibits very low cross-polarization leakage and is suitable for low-noise millimeter-wave receivers. For use in a cryogenic receiver, it is best if the orthomode transducer (OMT) is implemented in waveguide, contains no additional assembly features, and may be directly machined. However, machined OMTs are prone to sharp signal drop-outs that are costly to overall performance since they show up directly as spikes in receiver noise. We explore the various factors contributing to this degradation and discuss how the current design mitigates each cause. Final performance is demonstrated at cryogenic temperatures.

  8. Distribution of extant populations of Quadrula mitchelli (false spike)

    Science.gov (United States)

    Randklev, Charles R.; Tsakiris, Eric; Howells, Robert G.; Groce, Julie; Johnson, Matthew S.; Bergmann, Joseph; Robertson, Clint; Blair, Andy; Littrell, Brad; Johnson, Nathan

    2013-01-01

    The False Spike, Quadrula mitchelli (Simpson 1896), is a rare species of mussel endemic to Central Texas and the Rio Grande drainage (Howells 2010). This species was thought to have been extinct until the discovery of several live individuals in the Guadalupe River and a fresh dead individual in the San Saba River in 2011 (Randklev et al. 2012; Randklev et al. in press). Since then, this species has been reported at several other locations within its historic range (Sowards et al. in press; Tsakiris and Randklev 2013; Mabe and Kennedy 2013). Here, we report on the current known distribution of this species.

  9. Estimating the correlation between bursty spike trains and local field potentials.

    Science.gov (United States)

    Li, Zhaohui; Ouyang, Gaoxiang; Yao, Li; Li, Xiaoli

    2014-09-01

    To further understand rhythmic neuronal synchronization, an increasingly useful method is to determine the relationship between the spiking activity of individual neurons and the local field potentials (LFPs) of neural ensembles. Spike field coherence (SFC) is a widely used method for measuring the synchronization between spike trains and LFPs. However, due to the strong dependency of SFC on the burst index, it is not suitable for analyzing the relationship between bursty spike trains and LFPs, particularly in high frequency bands. To address this issue, we developed a method called weighted spike field correlation (WSFC), which uses the first spike in each burst multiple times to estimate the relationship. In the calculation, the number of times that the first spike is used is equal to the spike count per burst. The performance of this method was demonstrated using simulated bursty spike trains and LFPs, which comprised sinusoids with different frequencies, amplitudes, and phases. This method was also used to estimate the correlation between pyramidal cells in the hippocampus and gamma oscillations in rats performing behaviors. Analyses using simulated and real data demonstrated that the WSFC method is a promising measure for estimating the correlation between bursty spike trains and high frequency LFPs.

  10. Estimating extracellular spike waveforms from CA1 pyramidal cells with multichannel electrodes.

    Directory of Open Access Journals (Sweden)

    Sturla Molden

    Full Text Available Extracellular (EC recordings of action potentials from the intact brain are embedded in background voltage fluctuations known as the "local field potential" (LFP. In order to use EC spike recordings for studying biophysical properties of neurons, the spike waveforms must be separated from the LFP. Linear low-pass and high-pass filters are usually insufficient to separate spike waveforms from LFP, because they have overlapping frequency bands. Broad-band recordings of LFP and spikes were obtained with a 16-channel laminar electrode array (silicone probe. We developed an algorithm whereby local LFP signals from spike-containing channel were modeled using locally weighted polynomial regression analysis of adjoining channels without spikes. The modeled LFP signal was subtracted from the recording to estimate the embedded spike waveforms. We tested the method both on defined spike waveforms added to LFP recordings, and on in vivo-recorded extracellular spikes from hippocampal CA1 pyramidal cells in anaesthetized mice. We show that the algorithm can correctly extract the spike waveforms embedded in the LFP. In contrast, traditional high-pass filters failed to recover correct spike shapes, albeit produceing smaller standard errors. We found that high-pass RC or 2-pole Butterworth filters with cut-off frequencies below 12.5 Hz, are required to retrieve waveforms comparable to our method. The method was also compared to spike-triggered averages of the broad-band signal, and yielded waveforms with smaller standard errors and less distortion before and after the spike.

  11. Developing Novel Protein-based Materials using Ultrabithorax: Production, Characterization, and Functionalization

    Science.gov (United States)

    Huang, Zhao

    2011-12-01

    Compared to 'conventional' materials made from metal, glass, or ceramics, protein-based materials have unique mechanical properties. Furthermore, the morphology, mechanical properties, and functionality of protein-based materials may be optimized via sequence engineering for use in a variety of applications, including textile materials, biosensors, and tissue engineering scaffolds. The development of recombinant DNA technology has enabled the production and engineering of protein-based materials ex vivo. However, harsh production conditions can compromise the mechanical properties of protein-based materials and diminish their ability to incorporate functional proteins. Developing a new generation of protein-based materials is crucial to (i) improve materials assembly conditions, (ii) create novel mechanical properties, and (iii) expand the capacity to carry functional protein/peptide sequences. This thesis describes development of novel protein-based materials using Ultrabithorax, a member of the Hox family of proteins that regulate developmental pathways in Drosophila melanogaster. The experiments presented (i) establish the conditions required for the assembly of Ubx-based materials, (ii) generate a wide range of Ubx morphologies, (iii) examine the mechanical properties of Ubx fibers, (iv) incorporate protein functions to Ubx-based materials via gene fusion, (v) pattern protein functions within the Ubx materials, and (vi) examine the biocompatibility of Ubx materials in vitro. Ubx-based materials assemble at mild conditions compatible with protein folding and activity, which enables Ubx chimeric materials to retain the function of appended proteins in spatial patterns determined by materials assembly. Ubx-based materials also display mechanical properties comparable to existing protein-based materials and demonstrate good biocompatibility with living cells in vitro. Taken together, this research demonstrates the unique features and future potential of novel Ubx

  12. Effect of heavy metals on pH buffering capacity and solubility of Ca, Mg, K, and P in non-spiked and heavy metal-spiked soils.

    Science.gov (United States)

    Najafi, Sarvenaz; Jalali, Mohsen

    2016-06-01

    In many parts of the world, soil acidification and heavy metal contamination has become a serious concern due to the adverse effects on chemical properties of soil and crop yield. The aim of this study was to investigate the effect of pH (in the range of 1 to 3 units above and below the native pH of soils) on calcium (Ca), magnesium (Mg), potassium (K), and phosphorus (P) solubility in non-spiked and heavy metal-spiked soil samples. Spiked samples were prepared by cadmium (Cd), copper (Cu), nickel (Ni), and zinc (Zn) as chloride salts and incubating soils for 40 days. The pH buffering capacity (pHBC) of each sample was determined by plotting the amount of H(+) or OH(-) added (mmol kg(-1)) versus the related pH value. The pHBC of soils ranged from 47.1 to 1302.5 mmol kg(-1) for non-spiked samples and from 45.0 to 1187.4 mmol kg(-1) for spiked soil samples. The pHBC values were higher in soil 2 (non-spiked and spiked) which had higher calcium carbonate content. The results indicated the presence of heavy metals in soils generally decreased the solution pH and pHBC values in spiked samples. In general, solubility of Ca, Mg, and K decreased with increasing equilibrium pH of non-spiked and spiked soil samples. In the case of P, increasing the pH to about 7, decreased the solubility in all soils but further increase of pH from 7, enhanced P solubility. The solubility trends and values for Ca, Mg, and K did not differed significantly in non-spiked and spiked samples. But in the case of P, a reduction in solubility was observed in heavy metal-spiked soils. The information obtained in this study can be useful to make better estimation of the effects of soil pollutants on anion and cation solubility from agricultural and environmental viewpoints.

  13. SPIKE: a database of highly curated human signaling pathways.

    Science.gov (United States)

    Paz, Arnon; Brownstein, Zippora; Ber, Yaara; Bialik, Shani; David, Eyal; Sagir, Dorit; Ulitsky, Igor; Elkon, Ran; Kimchi, Adi; Avraham, Karen B; Shiloh, Yosef; Shamir, Ron

    2011-01-01

    The rapid accumulation of knowledge on biological signaling pathways and their regulatory mechanisms has highlighted the need for specific repositories that can store, organize and allow retrieval of pathway information in a way that will be useful for the research community. SPIKE (Signaling Pathways Integrated Knowledge Engine; http://www.cs.tau.ac.il/&~spike/) is a database for achieving this goal, containing highly curated interactions for particular human pathways, along with literature-referenced information on the nature of each interaction. To make database population and pathway comprehension straightforward, a simple yet informative data model is used, and pathways are laid out as maps that reflect the curator’s understanding and make the utilization of the pathways easy. The database currently focuses primarily on pathways describing DNA damage response, cell cycle, programmed cell death and hearing related pathways. Pathways are regularly updated, and additional pathways are gradually added. The complete database and the individual maps are freely exportable in several formats. The database is accompanied by a stand-alone software tool for analysis and dynamic visualization of pathways.

  14. Spike-Based Bayesian-Hebbian Learning of Temporal Sequences.

    Directory of Open Access Journals (Sweden)

    Philip J Tully

    2016-05-01

    Full Text Available Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx. We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

  15. Advances in spike localization with EEG dipole modeling.

    Science.gov (United States)

    Rose, Sandra; Ebersole, John S

    2009-10-01

    EEG interpretation by visual inspection of waveforms, using the assumption that activity at a given electrode is a representation of only the activity of the cortex immediately beneath it, has been the traditional form of EEG analysis since its inception. The relatively recent advent of digital EEG has allowed more advanced analysis of EEG data and has shown that the simple visual inspection described above is a simplistic form of analysis. This is especially true when one is attempting to localize an epileptogenic focus using EEG spikes or seizure onset data. Spatiotemporal analysis of scalp voltage fields has allowed for improved localization of likely cerebral origins of such waveforms. Equivalent dipole source modeling is one such technique and, although not perfect, provides improved characterization of spike and seizure sources as compared to previous methods when properly interpreted. The use of other modern techniques, such as 3D MRI reconstructions and realistic head models, can further improve accuracy of dipole localization and allow for the synthesis of EEG and imaging data, which may be invaluable, especially in cases of pre-surgical epilepsy evaluation.

  16. Conditional Probability Analyses of the Spike Activity of Single Neurons

    Science.gov (United States)

    Gray, Peter R.

    1967-01-01

    With the objective of separating stimulus-related effects from refractory effects in neuronal spike data, various conditional probability analyses have been developed. These analyses are introduced and illustrated with examples based on electrophysiological data from auditory nerve fibers. The conditional probability analyses considered here involve the estimation of the conditional probability of a firing in a specified time interval (defined relative to the time of the stimulus presentation), given that the last firing occurred during an earlier specified time interval. This calculation enables study of the stimulus-related effects in the spike data with the time-since-the-last-firing as a controlled variable. These calculations indicate that auditory nerve fibers “recover” from the refractory effects that follow a firing in the following sense: after a “recovery time” of approximately 20 msec, the firing probabilities no longer depend on the time-since-the-last-firing. Probabilities conditional on this minimum time since the last firing are called “recovered probabilities.” The recovered probabilities presented in this paper are contrasted with the corresponding poststimulus time histograms, and the differences are related to the refractory properties of the nerve fibers. Imagesp[762]-a PMID:19210997

  17. Efficient Discovery of Large Synchronous Events in Neural Spike Streams

    CERN Document Server

    Viswanathan, Raajay; Unnikrishnan, K P

    2010-01-01

    We address the problem of finding patterns from multi-neuronal spike trains that give us insights into the multi-neuronal codes used in the brain and help us design better brain computer interfaces. We focus on the synchronous firings of groups of neurons as these have been shown to play a major role in coding and communication. With large electrode arrays, it is now possible to simultaneously record the spiking activity of hundreds of neurons over large periods of time. Recently, techniques have been developed to efficiently count the frequency of synchronous firing patterns. However, when the number of neurons being observed grows they suffer from the combinatorial explosion in the number of possible patterns and do not scale well. In this paper, we present a temporal data mining scheme that overcomes many of these problems. It generates a set of candidate patterns from frequent patterns of smaller size; all possible patterns are not counted. Also we count only a certain well defined subset of occurrences a...

  18. Spike-Based Bayesian-Hebbian Learning of Temporal Sequences.

    Science.gov (United States)

    Tully, Philip J; Lindén, Henrik; Hennig, Matthias H; Lansner, Anders

    2016-05-01

    Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

  19. Transfer of Timing Information from RGC to LGN Spike Trains

    Science.gov (United States)

    Teich, Malvin C.; Lowen, Steven B.; Saleh, Bahaa E. A.; Kaplan, Ehud

    1998-03-01

    We have studied the firing patterns of retinal ganglion cells (RGCs) and their target lateral geniculate nucleus (LGN) cells. We find that clusters of spikes in the RGC neural firing pattern appear at the LGN output essentially unchanged, while isolated RGC firing events are more likely to be eliminated; thus the LGN action-potential sequence is therefore not merely a randomly deleted version of the RGC spike train. Employing information-theoretic techniques we developed for point processes,(B. E. A. Saleh and M. C. Teich, Phys. Rev. Lett.) 58, 2656--2659 (1987). we are able to estimate the information efficiency of the LGN neuronal output --- the proportion of the variation in the LGN firing pattern that carries information about its associated RGC input. A suitably modified integrate-and-fire neural model reproduces both the enhanced clustering in the LGN data (which accounts for the increased coefficient of variation) and the measured value of information efficiency, as well as mimicking the results of other observed statistical measures. Reliable information transmission therefore coexists with fractal fluctuations, which appear in RGC and LGN firing patterns.(M. C. Teich, C. Heneghan, S. B. Lowen, T. Ozaki, and E. Kaplan, J. Opt. Soc. Am. A) 14, 529--546 (1997).

  20. Spike-and-Slab Sparse Coding for Unsupervised Feature Discovery

    CERN Document Server

    Goodfellow, Ian J; Bengio, Yoshua

    2012-01-01

    We consider the problem of using a factor model we call {\\em spike-and-slab sparse coding} (S3C) to learn features for a classification task. The S3C model resembles both the spike-and-slab RBM and sparse coding. Since exact inference in this model is intractable, we derive a structured variational inference procedure and employ a variational EM training algorithm. Prior work on approximate inference for this model has not prioritized the ability to exploit parallel architectures and scale to enormous problem sizes. We present an inference procedure appropriate for use with GPUs which allows us to dramatically increase both the training set size and the amount of latent factors. We demonstrate that this approach improves upon the supervised learning capabilities of both sparse coding and the ssRBM on the CIFAR-10 dataset. We evaluate our approach's potential for semi-supervised learning on subsets of CIFAR-10. We demonstrate state-of-the art self-taught learning performance on the STL-10 dataset and use our m...

  1. Spike neural models (part I: The Hodgkin-Huxley model

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2017-05-01

    Full Text Available Artificial neural networks, or ANNs, have grown a lot since their inception back in the 1940s. But no matter the changes, one of the most important components of neural networks is still the node, which represents the neuron. Within spiking neural networks, the node is especially important because it contains the functions and properties of neurons that are necessary for their network. One important aspect of neurons is the ionic flow which produces action potentials, or spikes. Forces of diffusion and electrostatic pressure work together with the physical properties of the cell to move ions around changing the cell membrane potential which ultimately produces the action potential. This tutorial reviews the Hodkgin-Huxley model and shows how it simulates the ionic flow of the giant squid axon via four differential equations. The model is implemented in Matlab using Euler's Method to approximate the differential equations. By using Euler's method, an extra parameter is created, the time step. This new parameter needs to be carefully considered or the results of the node may be impaired.

  2. ROBUST RVM BASED ON SPIKE-SLAB PRIOR

    Institute of Scientific and Technical Information of China (English)

    Ding Xinghao; Mi Zengyuan; Huang Yue; Jin Wenbo

    2012-01-01

    Although Relevance Vector Machine (RVM) is the most popular algorithms in machine learning and computer vision,outliers in the training data make the estimation unreliable.In the paper,a robust RVM model under non-parametric Bayesian framework is proposed.We decompose the noise term in the RVM model into two components,a Gaussian noise term and a spiky noise term.Therefore the observed data is assumed represented as:y=Dw+s+e,where Dw is the relevance vector component,of which D is the kernel function matrix and w is the weight matrix,s is the spiky term and e is the Gaussian noise term.A spike-slab sparse prior is imposed on the weight vector w,which gives a more intuitive constraint on the sparsity than the Student's t-distribution described in the traditional RVM.For the spiky component s,a spike-slab sparse prior is also introduced to recognize outliers in the training data effectively.Several experiments demonstrate the better performance over the RVM regression.

  3. Bistability induces episodic spike communication by inhibitory neurons in neuronal networks

    Science.gov (United States)

    Kazantsev, V. B.; Asatryan, S. Yu.

    2011-09-01

    Bistability is one of the important features of nonlinear dynamical systems. In neurodynamics, bistability has been found in basic Hodgkin-Huxley equations describing the cell membrane dynamics. When the neuron is clamped near its threshold, the stable rest potential may coexist with the stable limit cycle describing periodic spiking. However, this effect is often neglected in network computations where the neurons are typically reduced to threshold firing units (e.g., integrate-and-fire models). We found that the bistability may induce spike communication by inhibitory coupled neurons in the spiking network. The communication is realized in the form of episodic discharges with synchronous (correlated) spikes during the episodes. A spiking phase map is constructed to describe the synchronization and to estimate basic spike phase locking modes.

  4. The power ratio and the interval map spiking models and extracellular data

    CERN Document Server

    Reich, D S; Knight, B W; Reich, Daniel S.; Victor, Jonathan D.; Knight, Bruce W.

    1998-01-01

    We describe a new, computationally simple method for analyzing the dynamics of neuronal spike trains driven by external stimuli. The goal of our method is to test the predictions of simple spike-generating models against extracellularly recorded neuronal responses. Through a new statistic called the power ratio, we distinguish between two broad classes of responses: (1) responses that can be completely characterized by a variable firing rate, (for example, modulated Poisson and gamma spike trains); and (2) responses for which firing rate variations alone are not sufficient to characterize response dynamics (for example, leaky integrate-and-fire spike trains as well as Poisson spike trains with long absolute refractory periods). We show that the responses of many visual neurons in the cat retinal ganglion, cat lateral geniculate nucleus, and macaque primary visual cortex fall into the second class, which implies that the pattern of spike times can carry significant information about visual stimuli. Our results...

  5. Upregulation of transmitter release probability improves a conversion of synaptic analogue signals into neuronal digital spikes

    Science.gov (United States)

    2012-01-01

    Action potentials at the neurons and graded signals at the synapses are primary codes in the brain. In terms of their functional interaction, the studies were focused on the influence of presynaptic spike patterns on synaptic activities. How the synapse dynamics quantitatively regulates the encoding of postsynaptic digital spikes remains unclear. We investigated this question at unitary glutamatergic synapses on cortical GABAergic neurons, especially the quantitative influences of release probability on synapse dynamics and neuronal encoding. Glutamate release probability and synaptic strength are proportionally upregulated by presynaptic sequential spikes. The upregulation of release probability and the efficiency of probability-driven synaptic facilitation are strengthened by elevating presynaptic spike frequency and Ca2+. The upregulation of release probability improves spike capacity and timing precision at postsynaptic neuron. These results suggest that the upregulation of presynaptic glutamate release facilitates a conversion of synaptic analogue signals into digital spikes in postsynaptic neurons, i.e., a functional compatibility between presynaptic and postsynaptic partners. PMID:22852823

  6. The Mechanisms of Repetitive Spike Generation in an Axonless Retinal Interneuron

    Directory of Open Access Journals (Sweden)

    Mark S. Cembrowski

    2012-02-01

    Full Text Available Several types of retinal interneurons exhibit spikes but lack axons. One such neuron is the AII amacrine cell, in which spikes recorded at the soma exhibit small amplitudes (5 ms. Here, we used electrophysiological recordings and computational analysis to examine the mechanisms underlying this atypical spiking. We found that somatic spikes likely represent large, brief action potential-like events initiated in a single, electrotonically distal dendritic compartment. In this same compartment, spiking undergoes slow modulation, likely by an M-type K conductance. The structural correlate of this compartment is a thin neurite that extends from the primary dendritic tree: local application of TTX to this neurite, or excision of it, eliminates spiking. Thus, the physiology of the axonless AII is much more complex than would be anticipated from morphological descriptions and somatic recordings; in particular, the AII possesses a single dendritic structure that controls its firing pattern.

  7. Phase-locking of epileptic spikes to ongoing delta oscillations in non-convulsive status epilepticus.

    Science.gov (United States)

    Hindriks, Rikkert; Meijer, Hil G E; van Gils, Stephan A; van Putten, Michel J A M

    2013-01-01

    The EEG of patients in non-convulsive status epilepticus (NCSE) often displays delta oscillations or generalized spike-wave discharges. In some patients, these delta oscillations coexist with intermittent epileptic spikes. In this study we verify the prediction of a computational model of the thalamo-cortical system that these spikes are phase-locked to the delta oscillations. We subsequently describe the physiological mechanism underlying this observation as suggested by the model. It is suggested that the spikes reflect inhibitory stochastic fluctuations in the input to thalamo-cortical relay neurons and phase-locking is a consequence of differential excitability of relay neurons over the delta cycle. Further analysis shows that the observed phase-locking can be regarded as a stochastic precursor of generalized spike-wave discharges. This study thus provides an explanation of intermittent spikes during delta oscillations in NCSE and might be generalized to other encephathologies in which delta activity can be observed.

  8. Time-resolved and time-scale adaptive measures of spike train synchrony

    CERN Document Server

    Kreuz, Thomas; Greschner, Martin; Andrzejak, Ralph G

    2010-01-01

    A wide variety of approaches to estimate the degree of synchrony between two or more spike trains have been proposed. One of the most recent methods is the ISI-distance which extracts information from the interspike intervals (ISIs) by evaluating the ratio of the instantaneous firing rates. In contrast to most previously proposed measures it is parameter free and time-scale independent. However, it is not well suited to track changes in synchrony that are based on spike coincidences. Here we propose the SPIKE-distance, a complementary measure which is sensitive to spike coincidences but still shares the fundamental advantages of the ISI-distance. In particular, it is easy to visualize in a time-resolved manner and can be extended to a method that is also applicable to larger sets of spike trains. We show the merit of the SPIKE-distance using both simulated and real data.

  9. Time-resolved and time-scale adaptive measures of spike train synchrony.

    Science.gov (United States)

    Kreuz, Thomas; Chicharro, Daniel; Greschner, Martin; Andrzejak, Ralph G

    2011-01-30

    A wide variety of approaches to estimate the degree of synchrony between two or more spike trains have been proposed. One of the most recent methods is the ISI-distance which extracts information from the interspike intervals (ISIs) by evaluating the ratio of the instantaneous firing rates. In contrast to most previously proposed measures it is parameter free and time-scale independent. However, it is not well suited to track changes in synchrony that are based on spike coincidences. Here we propose the SPIKE-distance, a complementary measure which is sensitive to spike coincidences but still shares the fundamental advantages of the ISI-distance. In particular, it is easy to visualize in a time-resolved manner and can be extended to a method that is also applicable to larger sets of spike trains. We show the merit of the SPIKE-distance using both simulated and real data. Copyright © 2010 Elsevier B.V. All rights reserved.

  10. The flexibility of a generic LC-MS/MS method for the quantitative analysis of therapeutic proteins based on human immunoglobulin G and related constructs in animal studies.

    Science.gov (United States)

    Lanshoeft, Christian; Wolf, Thierry; Walles, Markus; Barteau, Samuel; Picard, Franck; Kretz, Olivier; Cianférani, Sarah; Heudi, Olivier

    2016-11-30

    An increasing demand of new analytical methods is associated with the growing number of biotherapeutic programs being prosecuted in the pharmaceutical industry. Whilst immunoassay has been the standard method for decades, a great interest in assays based on liquid chromatography tandem mass spectrometry (LC-MS/MS) is evolving. In this present work, the development of a generic method for the quantitative analysis of therapeutic proteins based on human immunoglobulin G (hIgG) in rat serum is reported. The method is based on four generic peptides GPSVFPLAPSSK (GPS), TTPPVLDSDGSFFLYSK (TTP), VVSVLTVLHQDWLNGK (VVS) and FNWYVDGVEVHNAK (FNW) originating from different parts of the fraction crystallizable (Fc) region of a reference hIgG1 (hIgG1A). A tryptic pellet digestion of rat serum spiked with hIgG1A and a stable isotope labeled protein (hIgG1B) used as internal standard (ISTD) was applied prior LC-MS/MS analysis. The upper limit of quantification was at 1000μg/mL. The lower limit of quantitation was for GPS, TTP and VVS at 1.00μg/mL whereas for FNW at 5.00μg/mL. Accuracy and precision data met acceptance over three days. The presented method was further successfully applied to the quantitative analysis of other hIgG1s (hIgG1C and hIgG1D) and hIgG4-based therapeutic proteins on spiked quality control (QC) samples in monkey and rat serum using calibration standards (Cs) prepared with hIgG1A in rat serum. In order to extend the applicability of our generic approach, a bispecific-bivalent hIgG1 (bb-hIgG1) and two lysine conjugated antibody-drug conjugates (ADC1 and ADC2) were incorporated as well. The observed values on spiked QC samples in monkey serum were satisfactory with GPS for the determination of bb-hIgG1 whereas the FNW and TTP peptides were suitable for the ADCs. Moreover, comparable mean concentration-time profiles were obtained from monkeys previously dosed intravenously with ADC2 measured against Cs samples prepared either with hIgG1A in rat serum

  11. Postnatal development of temporal integration, spike timing and spike threshold regulation by a dendrotoxin-sensitive K⁺ current in rat CA1 hippocampal cells.

    Science.gov (United States)

    Giglio, Anna M; Storm, Johan F

    2014-01-01

    Spike timing and network synchronization are important for plasticity, development and maturation of brain circuits. Spike delays and timing can be strongly modulated by a low-threshold, slowly inactivating, voltage-gated potassium current called D-current (ID ). ID can delay the onset of spiking, cause temporal integration of multiple inputs, and regulate spike threshold and network synchrony. Recent data indicate that ID can also undergo activity-dependent, homeostatic regulation. Therefore, we have studied the postnatal development of ID -dependent mechanisms in CA1 pyramidal cells in hippocampal slices from young rats (P7-27), using somatic whole-cell recordings. At P21-27, these neurons showed long spike delays and pronounced temporal integration in response to a series of brief depolarizing current pulses or a single long pulse, whereas younger cells (P7-20) showed shorter discharge delays and weak temporal integration, although the spike threshold became increasingly negative with maturation. Application of α-dendrotoxin (α-DTX), which blocks ID , reduced the spiking latency and temporal integration most strongly in mature cells, while shifting the spike threshold most strongly in a depolarizing direction in these cells. Voltage-clamp analysis revealed an α-DTX-sensitive outward current (ID ) that increased in amplitude during development. In contrast to P21-23, ID in the youngest group (P7-9) showed smaller peri-threshold amplitude. This may explain why long discharge delays and robust temporal integration only appear later, 3 weeks postnatally. We conclude that ID properties and ID -dependent functions develop postnatally in rat CA1 pyramidal cells, and ID may modulate network activity and plasticity through its effects on synaptic integration, spike threshold, timing and synchrony.

  12. Strategies for high-performance resource-efficient compression of neural spike recordings.

    Science.gov (United States)

    Thorbergsson, Palmi Thor; Garwicz, Martin; Schouenborg, Jens; Johansson, Anders J

    2014-01-01

    Brain-machine interfaces (BMIs) based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve spike detection, spike alignment and spike compression. Performance is analyzed in terms of spike reconstruction and spike sorting performance after wireless transmission of the compressed spike waveforms. Compression is performed with transform coding, using five different compression bases, one of which we pay special attention to. That basis is a fixed basis derived, by singular value decomposition, from a large assembly of experimentally obtained spike waveforms, and therefore represents a generic basis specially suitable for compressing spike waveforms. Our results show that a compression factor of 99.8%, compared to transmitting the raw acquired data, can be achieved using the fixed generic compression basis without compromising performance in spike reconstruction and spike sorting. Besides illustrating the relative performances of various system architectures and compression bases, our findings show that compression of spikes with a fixed generic compression basis derived from spike data provides better performance than compression with downsampling or the Haar basis, given that no optimization procedures are implemented for compression coefficients, and the performance is similar to that obtained when the optimal SVD based

  13. Strategies for high-performance resource-efficient compression of neural spike recordings.

    Directory of Open Access Journals (Sweden)

    Palmi Thor Thorbergsson

    Full Text Available Brain-machine interfaces (BMIs based on extracellular recordings with microelectrodes provide means of observing the activities of neurons that orchestrate fundamental brain function, and are therefore powerful tools for exploring the function of the brain. Due to physical restrictions and risks for post-surgical complications, wired BMIs are not suitable for long-term studies in freely behaving animals. Wireless BMIs ideally solve these problems, but they call for low-complexity techniques for data compression that ensure maximum utilization of the wireless link and energy resources, as well as minimum heat dissipation in the surrounding tissues. In this paper, we analyze the performances of various system architectures that involve spike detection, spike alignment and spike compression. Performance is analyzed in terms of spike reconstruction and spike sorting performance after wireless transmission of the compressed spike waveforms. Compression is performed with transform coding, using five different compression bases, one of which we pay special attention to. That basis is a fixed basis derived, by singular value decomposition, from a large assembly of experimentally obtained spike waveforms, and therefore represents a generic basis specially suitable for compressing spike waveforms. Our results show that a compression factor of 99.8%, compared to transmitting the raw acquired data, can be achieved using the fixed generic compression basis without compromising performance in spike reconstruction and spike sorting. Besides illustrating the relative performances of various system architectures and compression bases, our findings show that compression of spikes with a fixed generic compression basis derived from spike data provides better performance than compression with downsampling or the Haar basis, given that no optimization procedures are implemented for compression coefficients, and the performance is similar to that obtained when the

  14. On the first G 1 stiff fluid spike solution in General Relativity

    Science.gov (United States)

    Coley, A. A.; Gregoris, D.; Lim, W. C.

    2016-11-01

    Using the Geroch transformation we obtain the first example of an exact stiff fluid spike solution to the Einstein field equations in a closed form exhibiting a spacelike G 1 group of symmetries (i.e., with a single isometry). This new solution is of Petrov type I and exhibits a spike crossing which persists to the past, which allows us to better understand spike crossings in the context of structure formation.

  15. Focal cortical lesions induce bidirectional changes in the excitability of fast spiking and non fast spiking cortical interneurons.

    Science.gov (United States)

    Imbrosci, Barbara; Neitz, Angela; Mittmann, Thomas

    2014-01-01

    A physiological brain function requires neuronal networks to operate within a well-defined range of activity. Indeed, alterations in neuronal excitability have been associated with several pathological conditions, ranging from epilepsy to neuropsychiatric disorders. Changes in inhibitory transmission are known to play a key role in the development of hyperexcitability. However it is largely unknown whether specific interneuronal subpopulations contribute differentially to such pathological condition. In the present study we investigated functional alterations of inhibitory interneurons embedded in a hyperexcitable cortical circuit at the border of chronically induced focal lesions in mouse visual cortex. Interestingly, we found opposite alterations in the excitability of non fast-spiking (Non Fs) and fast-spiking (Fs) interneurons in acute cortical slices from injured animals. Non Fs interneurons displayed a depolarized membrane potential and a higher frequency of spontaneous excitatory postsynaptic currents (sEPSCs). In contrast, Fs interneurons showed a reduced sEPSCs amplitude. The observed downscaling of excitatory synapses targeting Fs interneurons may prevent the recruitment of this specific population of interneurons to the hyperexcitable network. This mechanism is likely to seriously affect neuronal network function and to exacerbate hyperexcitability but it may be important to protect this particular vulnerable population of GABAegic neurons from excitotoxicity.

  16. Exact computation of the maximum-entropy potential of spiking neural-network models.

    Science.gov (United States)

    Cofré, R; Cessac, B

    2014-05-01

    Understanding how stimuli and synaptic connectivity influence the statistics of spike patterns in neural networks is a central question in computational neuroscience. The maximum-entropy approach has been successfully used to characterize the statistical response of simultaneously recorded spiking neurons responding to stimuli. However, in spite of good performance in terms of prediction, the fitting parameters do not explain the underlying mechanistic causes of the observed correlations. On the other hand, mathematical models of spiking neurons (neuromimetic models) provide a probabilistic mapping between the stimulus, network architecture, and spike patterns in terms of conditional probabilities. In this paper we build an exact analytical mapping between neuromimetic and maximum-entropy models.

  17. Ion channels generating complex spikes in cartwheel cells of the dorsal cochlear nucleus.

    Science.gov (United States)

    Kim, Yuil; Trussell, Laurence O

    2007-02-01

    Cartwheel cells are glycinergic interneurons that modify somatosensory input to the dorsal cochlear nucleus. They are characterized by firing of mixtures of both simple and complex action potentials. To understand what ion channels determine the generation of these two types of spike waveforms, we recorded from cartwheel cells using the gramicidin perforated-patch technique in brain slices of mouse dorsal cochlear nucleus and applied channel-selective blockers. Complex spikes were distinguished by whether they arose directly from a negative membrane potential or later during a long depolarization. Ca(2+) channels and Ca(2+)-dependent K(+) channels were major determinants of complex spikes. Onset complex spikes required T-type and possibly R-type Ca(2+) channels and were shaped by BK and SK K(+) channels. Complex spikes arising later in a depolarization were dependent on P/Q- and L-type Ca(2+) channels as well as BK and SK channels. BK channels also contributed to fast repolarization of simple spikes. Simple spikes featured an afterdepolarization that is probably the trigger for complex spiking and is shaped by T/R-type Ca(2+) and SK channels. Fast spikes were dependent on Na(+) channels; a large persistent Na(+) current may provide a depolarizing drive for spontaneous activity in cartwheel cells. Thus the diverse electrical behavior of cartwheel cells is determined by the interaction of a wide variety of ion channels with a prominent role played by Ca(2+).

  18. Experimental observation of transition from chaotic bursting to chaotic spiking in a neural pacemaker.

    Science.gov (United States)

    Gu, Huaguang

    2013-06-01

    The transition from chaotic bursting to chaotic spiking has been simulated and analyzed in theoretical neuronal models. In the present study, we report experimental observations in a neural pacemaker of a transition from chaotic bursting to chaotic spiking within a bifurcation scenario from period-1 bursting to period-1 spiking. This was induced by adjusting extracellular calcium or potassium concentrations. The bifurcation scenario began from period-doubling bifurcations or period-adding sequences of bursting pattern. This chaotic bursting is characterized by alternations between multiple continuous spikes and a long duration of quiescence, whereas chaotic spiking is comprised of fast, continuous spikes without periods of quiescence. Chaotic bursting changed to chaotic spiking as long interspike intervals (ISIs) of quiescence disappeared within bursting patterns, drastically decreasing both ISIs and the magnitude of the chaotic attractors. Deterministic structures of the chaotic bursting and spiking patterns are also identified by a short-term prediction. The experimental observations, which agree with published findings in theoretical neuronal models, demonstrate the existence and reveal the dynamics of a neuronal transition from chaotic bursting to chaotic spiking in the nervous system.

  19. Thermal impact on spiking properties in Hodgkin-Huxley neuron with synaptic stimulus

    Indian Academy of Sciences (India)

    Shenbing Kuang; Jiafu Wang; Ting Zeng; Aiyin Cao

    2008-01-01

    The effect of environmental temperature on neuronal spiking behaviors is investigated by numerically simulating the temperature dependence of spiking threshold of the Hodgkin-Huxley neuron subject to synaptic stimulus. We find that the spiking threshold exhibits a global minimum in a specific temperature range where spike initiation needs weakest synaptic strength, which form the engineering perspective indicates the occurrence of optimal use of synaptic transmission in the nervous system. We further explore the biophysical origin of this phenomenon associated with ion channel gating kinetics and also discuss its possible biological relevance in information processing in neuronal systems.

  20. Strange nonchaotic spiking in the quasiperiodically-forced Hodgkin-Huxley neuron

    Energy Technology Data Exchange (ETDEWEB)

    Lim, Woochang; Kim, Sangyoon [Kangwon National University, Chunchon (Korea, Republic of)

    2010-06-15

    We study the transition from a silent state to a spiking state by varying the dc stimulus in the quasiperiodically-forced Hodgkin-Huxley neuron. For this quasiperiodically-forced case, a new type of strange nonchaotic (SN) spiking state is found to appear between the silent state and the chaotic spiking state as intermediate one. Using a rational approximation to the quasiperiodic forcing, we investigate the mechanism for the appearance of such an SN spiking state. We thus find that a smooth torus (corresponding to the silent state) is transformed into an SN spiking attractor via a phase-dependent saddle-node bifurcation. This is in contrast to the periodically-forced case where the silent state transforms directly to a chaotic spiking state. SN spiking states are characterized in terms of the interspike interval, so they are found to be aperiodic complex ones, as in the case of chaotic spiking states. Hence, aperiodic complex spikings may result from two dynamically different states with strange geometry (one is chaotic and the other one is nonchaotic).

  1. Self-organization of repetitive spike patterns in developing neuronal networks in vitro.

    Science.gov (United States)

    Sun, Jyh-Jang; Kilb, Werner; Luhmann, Heiko J

    2010-10-01

    The appearance of spontaneous correlated activity is a fundamental feature of developing neuronal networks in vivo and in vitro. To elucidate whether the ontogeny of correlated activity is paralleled by the appearance of specific spike patterns we used a template-matching algorithm to detect repetitive spike patterns in multi-electrode array recordings from cultures of dissociated mouse neocortical neurons between 6 and 15 days in vitro (div). These experiments demonstrated that the number of spiking neurons increased significantly between 6 and 15 div, while a significantly synchronized network activity appeared at 9 div and became the main discharge pattern in the subsequent div. Repetitive spike patterns with a low complexity were first observed at 8 div. The number of repetitive spike patterns in each dataset as well as their complexity and recurrence increased during development in vitro. The number of links between neurons implicated in repetitive spike patterns, as well as their strength, showed a gradual increase during development. About 8% of the spike sequences contributed to more than one repetitive spike patterns and were classified as core patterns. These results demonstrate for the first time that defined neuronal assemblies, as represented by repetitive spike patterns, appear quite early during development in vitro, around the time synchronized network burst become the dominant network pattern. In summary, these findings suggest that dissociated neurons can self-organize into complex neuronal networks that allow reliable flow and processing of neuronal information already during early phases of development.

  2. Production in Pichia pastoris of protein-based polymers with small heterodimer-forming blocks.

    Science.gov (United States)

    Domeradzka, Natalia E; Werten, Marc W T; de Vries, Renko; de Wolf, Frits A

    2016-05-01

    Some combinations of leucine zipper peptides are capable of forming α-helical heterodimeric coiled coils with very high affinity. These can be used as physical cross-linkers in the design of protein-based polymers that form supramolecular structures, for example hydrogels, upon mixing solutions containing the complementary blocks. Such two-component physical networks are of interest for many applications in biomedicine, pharmaceutics, and diagnostics. This article describes the efficient secretory production of A and B type leucine zipper peptides fused to protein-based polymers in Pichia pastoris. By adjusting the fermentation conditions, we were able to significantly reduce undesirable proteolytic degradation. The formation of A-B heterodimers in mixtures of the purified products was confirmed by size exclusion chromatography. Our results demonstrate that protein-based polymers incorporating functional heterodimer-forming blocks can be produced with P. pastoris in sufficient quantities for use in future supramolecular self-assembly studies and in various applications.

  3. Recent progress in design of protein-based fluorescent biosensors and their cellular applications.

    Science.gov (United States)

    Tamura, Tomonori; Hamachi, Itaru

    2014-12-19

    Protein-based fluorescent biosensors have emerged as key bioanalytical tools to visualize and quantify a wide range of biological substances and events in vitro, in cells, and even in vivo. On the basis of the construction method, the protein-based fluorescent biosensors can be principally classified into two classes: (1) genetically encoded fluorescent biosensors harnessing fluorescent proteins (FPs) and (2) semisynthetic biosensors comprised of protein scaffolds and synthetic fluorophores. Recent advances in protein engineering and chemical biology not only allowed the further optimization of conventional biosensors but also facilitated the creation of novel biosensors based on unique strategies. In this review, we survey the recent studies in the development and improvement of protein-based fluorescent biosensors and highlight the successful applications to live cell and in vivo imaging. Furthermore, we provide perspectives on possible future directions of the technique.

  4. Quiet Spike Build-Up Ground Vibration Testing Approach

    Science.gov (United States)

    Spivey, Natalie D.; Herrera, Claudia Y.; Truax, Roger; Pak, Chan-gi; Freund, Donald

    2007-01-01

    NASA's Dryden Flight Research Center uses a modified F-15B (836) aircraft as a testbed for a variety of flight research:experiments mounted underneath the aircraft fuselage. The F-15B was selected to fly Gulfstream Aerospace Corporation's (GAC)QuietSpike(TM)(QS) project; however, this experiment is very unique and unlike any of the previous testbed experiments flown on the F-15B. It involves the addition of a relatively long quiet spike boom attached to the radar bulkhead of the aircraft. This QS experiment is a stepping stone to airframe structural morphing technologies designed to mitigate sonic born strength of business jets over land. The QS boom is a concept in Which an aircraft's front-end would be extended prior to supersonic acceleration. This morphing would effectively lengthen the aircraft, reducing peak sonic boom amplitude, but is also expected to partition the otherwise strong bow shock into a series of reduced-strength, non-coalescing shocklets. Prior to flying the Quietspike(TM) experiment on the F-15B aircraft several ground vibration tests (GVT) were required in order to understand the QS modal characteristics and coupling effects with the F-15B. However, due to the project's late hardware delivery of the QS and the intense schedule, a "traditional" GVT of the mated F-1513 Quietspike(tm) ready-for-flight configuration would not have left sufficient time available for the finite element model update and flutter analyses before flight testing. Therefore, a "nontraditional" ground vibration testing approach was taken. The objective of the QuietSpike (TM) build-up ground testing approach was to ultimately obtain confidence in the F-15B Quietspike(TM) finite element model (FEM) to be used for the flutter analysis. In order to obtain the F15B QS FEM with reliable foundation stiffness between the QS and the F-15B radar bulkhead as well as QS modal characteristics, several different GVT configurations were performed. EAch of the four GVT's performed had a

  5. Identification of a novel conserved HLA-A*0201-restricted epitope from the spike protein of SARS-CoV

    Directory of Open Access Journals (Sweden)

    Ni Bing

    2009-12-01

    Full Text Available Abstract Background The spike (S protein is a major structural glycoprotein of coronavirus (CoV, the causal agent of severe acute respiratory syndrome (SARS. The S protein is a potent target for SARS-specific cell-mediated immune responses. However, the mechanism CoV pathogenesis in SARS and the role of special CTLs in virus clearance are still largely uncharacterized. Here, we describe a study that leads to the identification of a novel HLA-A*0201-restricted epitope from conserved regions of S protein. Results First, different SARS-CoV sequences were analyzed to predict eight candidate peptides from conserved regions of the S protein based upon HLA-A*0201 binding and proteosomal cleavage. Four of eight candidate peptides were tested by HLA-A*0201 binding assays. Among the four candidate peptides, Sp8 (S958-966, VLNDILSRL induced specific CTLs both ex vivo in PBLs of healthy HLA-A2+ donors and in HLA-A2.1/Kb transgenic mice immunized with a plasmid encoding full-length S protein. The immunized mice released IFN-γ and lysed target cells upon stimulation with Sp8 peptide-pulsed autologous dendritic cells in comparison to other candidates. Conclusion These results suggest that Sp8 is a naturally processed epitope. We propose that Sp8 epitope should help in the characterization of mechanisms of virus control and immunopathology in SARS-CoV infection.

  6. Protein-based biomemory device consisting of the cysteine-modified azurin

    Science.gov (United States)

    Choi, Jeong-Woo; Oh, Byung-Keun; Kim, Young Jun; Min, Junhong

    2007-12-01

    We demonstrated a protein-based memory device using recombinant Pseudomonas aeruginosa azurin (azurin), a metalloprotein with a redox property. Azurin was recombined with a cysteine residue to enhance the stability of the self-assembled protein on the gold surface. The memory device characteristics, including the "read," "write," and "erase" functions of the self-assembled azurin layer, were well demonstrated with three distinct electrical states of azurin layers by cyclic voltammetry. The robustness of the protein-based biomemory device was validated by the repeated electrochemical performance of 500000cycles.

  7. Simulating Spiking Neural P systems without delays using GPUs

    CERN Document Server

    Cabarle, Francis; Martinez-del-Amor, Miguel A

    2011-01-01

    We present in this paper our work regarding simulating a type of P system known as a spiking neural P system (SNP system) using graphics processing units (GPUs). GPUs, because of their architectural optimization for parallel computations, are well-suited for highly parallelizable problems. Due to the advent of general purpose GPU computing in recent years, GPUs are not limited to graphics and video processing alone, but include computationally intensive scientific and mathematical applications as well. Moreover P systems, including SNP systems, are inherently and maximally parallel computing models whose inspirations are taken from the functioning and dynamics of a living cell. In particular, SNP systems try to give a modest but formal representation of a special type of cell known as the neuron and their interactions with one another. The nature of SNP systems allowed their representation as matrices, which is a crucial step in simulating them on highly parallel devices such as GPUs. The highly parallel natu...

  8. Firing regulation of fast-spiking interneurons by autaptic inhibition

    Science.gov (United States)

    Guo, Daqing; Chen, Mingming; Perc, Matjaž; Wu, Shengdun; Xia, Chuan; Zhang, Yangsong; Xu, Peng; Xia, Yang; Yao, Dezhong

    2016-05-01

    Fast-spiking (FS) interneurons in the brain are self-innervated by powerful inhibitory GABAergic autaptic connections. By computational modelling, we investigate how autaptic inhibition regulates the firing response of such interneurons. Our results indicate that autaptic inhibition both boosts the current threshold for action potential generation and modulates the input-output gain of FS interneurons. The autaptic transmission delay is identified as a key parameter that controls the firing patterns and determines multistability regions of FS interneurons. Furthermore, we observe that neuronal noise influences the firing regulation of FS interneurons by autaptic inhibition and extends their dynamic range for encoding inputs. Importantly, autaptic inhibition modulates noise-induced irregular firing of FS interneurons, such that coherent firing appears at an optimal autaptic inhibition level. Our results reveal the functional roles of autaptic inhibition in taming the firing dynamics of FS interneurons.

  9. From wheels to wings with evolutionary spiking circuits.

    Science.gov (United States)

    Floreano, Dario; Zufferey, Jean-Christophe; Nicoud, Jean-Daniel

    2005-01-01

    We give an overview of the EPFL indoor flying project, whose goal is to evolve neural controllers for autonomous, adaptive, indoor micro-flyers. Indoor flight is still a challenge because it requires miniaturization, energy efficiency, and control of nonlinear flight dynamics. This ongoing project consists of developing a flying, vision-based micro-robot, a bio-inspired controller composed of adaptive spiking neurons directly mapped into digital microcontrollers, and a method to evolve such a neural controller without human intervention. This article describes the motivation and methodology used to reach our goal as well as the results of a number of preliminary experiments on vision-based wheeled and flying robots.

  10. Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

    DEFF Research Database (Denmark)

    Tully, Philip J; Lindén, Henrik; Hennig, Matthias H

    2016-01-01

    of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning......Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed...... in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods...

  11. SIMPEL: Circuit model for photonic spike processing laser neurons

    CERN Document Server

    Shastri, Bhavin J; Tait, Alexander N; Wu, Ben; Prucnal, Paul R

    2014-01-01

    We propose an equivalent circuit model for photonic spike processing laser neurons with an embedded saturable absorber---a simulation model for photonic excitable lasers (SIMPEL). We show that by mapping the laser neuron rate equations into a circuit model, SPICE analysis can be used as an efficient and accurate engine for numerical calculations, capable of generalization to a variety of different laser neuron types found in literature. The development of this model parallels the Hodgkin--Huxley model of neuron biophysics, a circuit framework which brought efficiency, modularity, and generalizability to the study of neural dynamics. We employ the model to study various signal-processing effects such as excitability with excitatory and inhibitory pulses, binary all-or-nothing response, and bistable dynamics.

  12. Spontaneous spiking in an autaptic Hodgkin-Huxley set up

    CERN Document Server

    Li, Yunyun; Hanggi, Peter; Schimansky-Geier, Lutz

    2010-01-01

    The effect of intrinsic channel noise is investigated for the dynamic response of a neuronal cell with a delayed feedback loop. The loop is based on the so-called autapse phenomenon in which dendrites establish not only connections to neighboring cells but as well to its own axon. The biophysical modeling is achieved in terms of a stochastic Hodgkin-Huxley model containing such a built in delayed feedback. The fluctuations stem from intrinsic channel noise, being caused by the stochastic nature of the gating dynamics of ion channels. The influence of the delayed stimulus is systematically analyzed with respect to the coupling parameter and the delay time in terms of the interspike interval histograms and the average interspike interval. The delayed feedback manifests itself in the occurrence of bursting and a rich multimodal interspike interval distribution, exhibiting a delay-induced reduction of the spontaneous spiking activity at characteristic frequencies. Moreover, a specific frequency-locking mechanism ...

  13. Interpretation of "fungal spikes" in Permian-Triassic boundary sections

    Science.gov (United States)

    Hochuli, Peter A.

    2016-09-01

    Abundant occurrences of the palynomorph Reduviasporonites have been described as ;fungal spike; from several Permian/Triassic boundary sections and related to the supposed destruction of woody vegetation by fungal pathogens during the Permian/Triassic extinction event. The biological affinity of this taxa considered by some authors of fungal origin is still controversially discussed since there is geochemical evidence that it is most probably related to algae. The abundance peak of this species is used by some authors as a stratigraphic marker, notably in terrestrial Permian/Triassic boundary sections from South China. Illustrations of the reported fungal remains however show potentially erroneous taxonomic identification of Reduviasporonites, and, based on differences in thermal maturation, they may represent recent contamination. Here Reduviasporonites chalastus of Early Triassic age is illustrated together with recent fungal remains originating from a strongly weathered and otherwise barren sample from a Middle Triassic section.

  14. SPIKE a Processing Software dedicated to Fourier Spectroscopies

    CERN Document Server

    Chiron, Lionel; Starck, Jean-Philippe; Rolando, Christian; Delsuc, Marc-André

    2016-01-01

    We present SPIKE (Spectrometry Processing Innovative KErnel), an open-source Python package dedicated to Fourier spectroscopies. It provides basic functionalities such as apodisation, a complete set of Fourier transforms, phasing for NMR), peak-picking, baseline correction and also tools such as Linear Prediction. Beside its versatility, the most prominent novelty of this package is to incorporate new tools for Big Data processing. This is exemplified by its ability to handle the processing and visualization of very large data-sets, with multiprocessor capabilities and a low memory footprint. The software contains also all the tools necessary for the specific fast processing and visualization of 2D-FTICR-MS data-sets.

  15. Incorporation of Spike and Membrane Glycoproteins into Coronavirus Virions

    Directory of Open Access Journals (Sweden)

    Makoto Ujike

    2015-04-01

    Full Text Available The envelopes of coronaviruses (CoVs contain primarily three proteins; the two major glycoproteins spike (S and membrane (M, and envelope (E, a non-glycosylated protein. Unlike other enveloped viruses, CoVs bud and assemble at the endoplasmic reticulum (ER-Golgi intermediate compartment (ERGIC. For efficient virion assembly, these proteins must be targeted to the budding site and to interact with each other or the ribonucleoprotein. Thus, the efficient incorporation of viral envelope proteins into CoV virions depends on protein trafficking and protein–protein interactions near the ERGIC. The goal of this review is to summarize recent findings on the mechanism of incorporation of the M and S glycoproteins into the CoV virion, focusing on protein trafficking and protein–protein interactions.

  16. Screening fluorescent voltage indicators with spontaneously spiking HEK cells.

    Directory of Open Access Journals (Sweden)

    Jeehae Park

    Full Text Available Development of improved fluorescent voltage indicators is a key challenge in neuroscience, but progress has been hampered by the low throughput of patch-clamp characterization. We introduce a line of non-fluorescent HEK cells that stably express NaV 1.3 and KIR 2.1 and generate spontaneous electrical action potentials. These cells enable rapid, electrode-free screening of speed and sensitivity of voltage sensitive dyes or fluorescent proteins on a standard fluorescence microscope. We screened a small library of mutants of archaerhodopsin 3 (Arch in spiking HEK cells and identified two mutants with greater voltage-sensitivity than found in previously published Arch voltage indicators.

  17. Balanced Networks of Spiking Neurons with Spatially Dependent Recurrent Connections

    Science.gov (United States)

    Rosenbaum, Robert; Doiron, Brent

    2014-04-01

    Networks of model neurons with balanced recurrent excitation and inhibition capture the irregular and asynchronous spiking activity reported in cortex. While mean-field theories of spatially homogeneous balanced networks are well understood, a mean-field analysis of spatially heterogeneous balanced networks has not been fully developed. We extend the analysis of balanced networks to include a connection probability that depends on the spatial separation between neurons. In the continuum limit, we derive that stable, balanced firing rate solutions require that the spatial spread of external inputs be broader than that of recurrent excitation, which in turn must be broader than or equal to that of recurrent inhibition. Notably, this implies that network models with broad recurrent inhibition are inconsistent with the balanced state. For finite size networks, we investigate the pattern-forming dynamics arising when balanced conditions are not satisfied. Our study highlights the new challenges that balanced networks pose for the spatiotemporal dynamics of complex systems.

  18. X-ray harmonic comb from relativistic electron spikes

    CERN Document Server

    Pirozhkov, Alexander S; Esirkepov, Timur Zh; Ragozin, Eugene N; Faenov, Anatoly Ya; Pikuz, Tatiana A; Kawachi, Tetsuya; Sagisaka, Akito; Mori, Michiaki; Kawase, Keigo; Koga, James K; Kameshima, Takashi; Fukuda, Yuji; Chen, Liming; Daito, Izuru; Ogura, Koichi; Hayashi, Yukio; Kotaki, Hideyuki; Kiriyama, Hiromitsu; Okada, Hajime; Nishimori, Nobuyuki; Kondo, Kiminori; Kimura, Toyoaki; Tajima, Toshiki; Daido, Hiroyuki; Kato, Yoshiaki; Bulanov, Sergei V

    2010-01-01

    X-ray devices are far superior to optical ones for providing nanometre spatial and attosecond temporal resolutions. Such resolution is indispensable in biology, medicine, physics, material sciences, and their applications. A bright ultrafast coherent X-ray source is highly desirable, for example, for the diffractive imaging of individual large molecules, viruses, or cells. Here we demonstrate experimentally a new compact X-ray source involving high-order harmonics produced by a relativistic-irradiance femtosecond laser in a gas target. In our first implementation using a 9 Terawatt laser, coherent soft X-rays are emitted with a comb-like spectrum reaching the 'water window' range. The generation mechanism is robust being based on phenomena inherent in relativistic laser plasmas: self-focusing, nonlinear wave generation accompanied by electron density singularities, and collective radiation by a compact electric charge. The formation of singularities (electron density spikes) is described by the elegant mathem...

  19. Brian: a simulator for spiking neural networks in Python

    Directory of Open Access Journals (Sweden)

    Dan F M Goodman

    2008-11-01

    Full Text Available Brian is a new simulator for spiking neural networks, written in Python (http://brian.di.ens.fr. It is an intuitive and highly flexible tool for rapidly developing new models, especially networks of single-compartment neurons. In addition to using standard types of neuron models, users can define models by writing arbitrary differential equations in ordinary mathematical notation. Python scientific libraries can also be used for defining models and analysing data. Vectorisation techniques allow efficient simulations despite the overheads of an interpreted language. Brian will be especially valuable for working on non-standard neuron models not easily covered by existing software, and as an alternative to using Matlab or C for simulations. With its easy and intuitive syntax, Brian is also very well suited for teaching computational neuroscience.

  20. Spike-timing dependent plasticity and the cognitive map

    Directory of Open Access Journals (Sweden)

    Daniel eBush

    2010-10-01

    Full Text Available Since the discovery of place cells – single pyramidal neurons that encode spatial location – it has been hypothesised that the hippocampus may act as a cognitive map of known environments. This putative function has been extensively modelled using auto-associative networks, which utilise rate-coded synaptic plasticity rules in order to generate strong bi-directional connections between concurrently active place cells that encode for neighbouring place fields. However, empirical studies using hippocampal cultures have demonstrated that the magnitude and direction of changes in synaptic strength can also be dictated by the relative timing of pre- and post- synaptic firing according to a spike-timing dependent plasticity (STDP rule. Furthermore, electrophysiology studies have identified persistent ‘theta-coded’ temporal correlations in place cell activity in vivo, characterised by phase precession of firing as the corresponding place field is traversed. It is not yet clear if STDP and theta-coded neural dynamics are compatible with cognitive map theory and previous rate-coded models of spatial learning in the hippocampus. Here, we demonstrate that an STDP rule based on empirical data obtained from the hippocampus can mediate rate-coded Hebbian learning when pre- and post- synaptic activity is stochastic and has no persistent sequence bias. We subsequently demonstrate that a spiking recurrent neural network that utilises this STDP rule, alongside theta-coded neural activity, allows the rapid development of a cognitive map during directed or random exploration of an environment of overlapping place fields. Hence, we establish that STDP and phase precession are compatible with rate-coded models of cognitive map development.

  1. A systematic method for configuring VLSI networks of spiking neurons.

    Science.gov (United States)

    Neftci, Emre; Chicca, Elisabetta; Indiveri, Giacomo; Douglas, Rodney

    2011-10-01

    An increasing number of research groups are developing custom hybrid analog/digital very large scale integration (VLSI) chips and systems that implement hundreds to thousands of spiking neurons with biophysically realistic dynamics, with the intention of emulating brainlike real-world behavior in hardware and robotic systems rather than simply simulating their performance on general-purpose digital computers. Although the electronic engineering aspects of these emulation systems is proceeding well, progress toward the actual emulation of brainlike tasks is restricted by the lack of suitable high-level configuration methods of the kind that have already been developed over many decades for simulations on general-purpose computers. The key difficulty is that the dynamics of the CMOS electronic analogs are determined by transistor biases that do not map simply to the parameter types and values used in typical abstract mathematical models of neurons and their networks. Here we provide a general method for resolving this difficulty. We describe a parameter mapping technique that permits an automatic configuration of VLSI neural networks so that their electronic emulation conforms to a higher-level neuronal simulation. We show that the neurons configured by our method exhibit spike timing statistics and temporal dynamics that are the same as those observed in the software simulated neurons and, in particular, that the key parameters of recurrent VLSI neural networks (e.g., implementing soft winner-take-all) can be precisely tuned. The proposed method permits a seamless integration between software simulations with hardware emulations and intertranslatability between the parameters of abstract neuronal models and their emulation counterparts. Most important, our method offers a route toward a high-level task configuration language for neuromorphic VLSI systems.

  2. Analog memristive synapse in spiking networks implementing unsupervised learning

    Directory of Open Access Journals (Sweden)

    Erika Covi

    2016-10-01

    Full Text Available Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e. the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity. This implies a device able to change its resistance (synaptic strength, or weight upon proper electrical stimuli (synaptic activity and showing several stable resistive states throughout its dynamic range (analog behavior. Moreover, it should be able to perform spike timing dependent plasticity (STDP, an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy characters are displayed and it is robust to a device-to-device variability of up to +/-30%.

  3. Adaptive robotic control driven by a versatile spiking cerebellar network.

    Science.gov (United States)

    Casellato, Claudia; Antonietti, Alberto; Garrido, Jesus A; Carrillo, Richard R; Luque, Niceto R; Ros, Eduardo; Pedrocchi, Alessandra; D'Angelo, Egidio

    2014-01-01

    The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

  4. Adaptive robotic control driven by a versatile spiking cerebellar network.

    Directory of Open Access Journals (Sweden)

    Claudia Casellato

    Full Text Available The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning, a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

  5. Empirical Bayesian significance measure of neuronal spike response.

    Science.gov (United States)

    Oba, Shigeyuki; Nakae, Ken; Ikegaya, Yuji; Aki, Shunsuke; Yoshimoto, Junichiro; Ishii, Shin

    2016-05-21

    Functional connectivity analyses of multiple neurons provide a powerful bottom-up approach to reveal functions of local neuronal circuits by using simultaneous recording of neuronal activity. A statistical methodology, generalized linear modeling (GLM) of the spike response function, is one of the most promising methodologies to reduce false link discoveries arising from pseudo-correlation based on common inputs. Although recent advancement of fluorescent imaging techniques has increased the number of simultaneously recoded neurons up to the hundreds or thousands, the amount of information per pair of neurons has not correspondingly increased, partly because of the instruments' limitations, and partly because the number of neuron pairs increase in a quadratic manner. Consequently, the estimation of GLM suffers from large statistical uncertainty caused by the shortage in effective information. In this study, we propose a new combination of GLM and empirical Bayesian testing for the estimation of spike response functions that enables both conservative false discovery control and powerful functional connectivity detection. We compared our proposed method's performance with those of sparse estimation of GLM and classical Granger causality testing. Our method achieved high detection performance of functional connectivity with conservative estimation of false discovery rate and q values in case of information shortage due to short observation time. We also showed that empirical Bayesian testing on arbitrary statistics in place of likelihood-ratio statistics reduce the computational cost without decreasing the detection performance. When our proposed method was applied to a functional multi-neuron calcium imaging dataset from the rat hippocampal region, we found significant functional connections that are possibly mediated by AMPA and NMDA receptors. The proposed empirical Bayesian testing framework with GLM is promising especially when the amount of information per a

  6. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Directory of Open Access Journals (Sweden)

    Christian Albers

    Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  7. Channel noise effects on first spike latency of a stochastic Hodgkin-Huxley neuron

    Science.gov (United States)

    Maisel, Brenton; Lindenberg, Katja

    2017-02-01

    While it is widely accepted that information is encoded in neurons via action potentials or spikes, it is far less understood what specific features of spiking contain encoded information. Experimental evidence has suggested that the timing of the first spike may be an energy-efficient coding mechanism that contains more neural information than subsequent spikes. Therefore, the biophysical features of neurons that underlie response latency are of considerable interest. Here we examine the effects of channel noise on the first spike latency of a Hodgkin-Huxley neuron receiving random input from many other neurons. Because the principal feature of a Hodgkin-Huxley neuron is the stochastic opening and closing of channels, the fluctuations in the number of open channels lead to fluctuations in the membrane voltage and modify the timing of the first spike. Our results show that when a neuron has a larger number of channels, (i) the occurrence of the first spike is delayed and (ii) the variation in the first spike timing is greater. We also show that the mean, median, and interquartile range of first spike latency can be accurately predicted from a simple linear regression by knowing only the number of channels in the neuron and the rate at which presynaptic neurons fire, but the standard deviation (i.e., neuronal jitter) cannot be predicted using only this information. We then compare our results to another commonly used stochastic Hodgkin-Huxley model and show that the more commonly used model overstates the first spike latency but can predict the standard deviation of first spike latencies accurately. We end by suggesting a more suitable definition for the neuronal jitter based upon our simulations and comparison of the two models.

  8. Extracting information in spike time patterns with wavelets and information theory.

    Science.gov (United States)

    Lopes-dos-Santos, Vítor; Panzeri, Stefano; Kayser, Christoph; Diamond, Mathew E; Quian Quiroga, Rodrigo

    2015-02-01

    We present a new method to assess the information carried by temporal patterns in spike trains. The method first performs a wavelet decomposition of the spike trains, then uses Shannon information to select a subset of coefficients carrying information, and finally assesses timing information in terms of decoding performance: the ability to identify the presented stimuli from spike train patterns. We show that the method allows: 1) a robust assessment of the information carried by spike time patterns even when this is distributed across multiple time scales and time points; 2) an effective denoising of the raster plots that improves the estimate of stimulus tuning of spike trains; and 3) an assessment of the information carried by temporally coordinated spikes across neurons. Using simulated data, we demonstrate that the Wavelet-Information (WI) method performs better and is more robust to spike time-jitter, background noise, and sample size than well-established approaches, such as principal component analysis, direct estimates of information from digitized spike trains, or a metric-based method. Furthermore, when applied to real spike trains from monkey auditory cortex and from rat barrel cortex, the WI method allows extracting larger amounts of spike timing information. Importantly, the fact that the WI method incorporates multiple time scales makes it robust to the choice of partly arbitrary parameters such as temporal resolution, response window length, number of response features considered, and the number of available trials. These results highlight the potential of the proposed method for accurate and objective assessments of how spike timing encodes information. Copyright © 2015 the American Physiological Society.

  9. Do spike insoles enhance postural stability and plantar-surface cutaneous sensitivity in the elderly?

    Science.gov (United States)

    Palluel, Estelle; Nougier, Vincent; Olivier, Isabelle

    2008-03-01

    Balance problems are often related to a loss of plantar-sensitivity in elderly people. The purpose of this study was to explore the contribution of plantar cutaneous inputs induced by a spike support surface to the control of stance. Nineteen elderly (mean age 69.0 years, range 62-80) and 19 young adults (mean age 25.9 years, range 21-32) were instructed to stand (standing session) or to walk (walking session) for 5 min with sandals equipped with spike insoles (spike condition). Both sessions also involved a no spike condition in which participants stood or walked for 5 min without these insoles (no spike condition). In all conditions, postural responses were assessed during unperturbed stance and were performed (1) immediately after putting the spike or the no spike insoles, and (2) 5 min after standing or walking with them. Sway parameters, such as centre of foot pressure mean location, surface area, mean speed, root mean square and median frequency on the antero-posterior and medio-lateral axes, were calculated. As postural performances are often related to plantar-surface sensitivity, cutaneous sensitivity threshold was also evaluated with Semmes-Weinstein monofilaments. Although no immediate effect of the spike insoles was found, results indicated that standing or walking for 5 min with sandals equipped with spike insoles led to a significant improvement of quiet standing in the elderly. Balance improvement was also observed in young adults. The results provided evidence that wearing sandals with spike insoles can contribute, at least temporarily, to the improvement of unperturbed stance in elderly people with relatively intact plantar cutaneous sensation. Further research is needed to assess the effects of longer and discontinuous stimulations with spike insoles on postural control.

  10. Taurine supplemented plant protein based diets with alternative lipid sources for juvenile sea bream, sparus aurata

    Science.gov (United States)

    Two lipid sources were evaluated as fish oil replacements in fishmeal free, plant protein based diets for juvenile gilthead sea bream, Sparus aurata. A twelve week feeding study was undertaken to examine the performance of fish fed the diets with different sources of essential fatty acids (canola o...

  11. Mutations in GRIN2A> cause idiopathic focal epilepsy with rolandic spikes

    DEFF Research Database (Denmark)

    Lemke, Johannes R; Lal, Dennis; Reinthaler, Eva M

    2013-01-01

    Idiopathic focal epilepsy (IFE) with rolandic spikes is the most common childhood epilepsy, comprising a phenotypic spectrum from rolandic epilepsy (also benign epilepsy with centrotemporal spikes, BECTS) to atypical benign partial epilepsy (ABPE), Landau-Kleffner syndrome (LKS) and epileptic enc...

  12. Membrane properties and synaptic connectivity of fast-spiking interneurons in rat ventral striatum

    NARCIS (Netherlands)

    Taverna, S.; Canciani, B.; Pennartz, C.M.A.

    2007-01-01

    In vitro patch-clamp recordings were made to study the membrane properties and synaptic connectivity of fast-spiking interneurons in rat ventral striatum. Using a whole-cell configuration in acutely prepared slices, fast-spiking interneurons were recognized based on their firing properties and their

  13. Spike, a novel BH3-only protein, regulates apoptosis at the endoplasmic reticulum.

    Science.gov (United States)

    Mund, Thomas; Gewies, Andreas; Schoenfeld, Nicole; Bauer, Manuel K A; Grimm, Stefan

    2003-04-01

    We have isolated Spike, a novel and evolutionary conserved BH3-only protein. BH3-only proteins constitute a family of apoptosis inducers that mediate proapoptotic signals. In contrast to most proteins of this family, Spike was not found to be associated with mitochondria. Furthermore, unlike the known BH3-only proteins, Spike could not interact with all tested Bcl-2 family members, despite its BH3 domain being necessary for cell killing. Our findings indicate that Spike is localized to the endoplasmic reticulum. The endoplasmic reticulum is an organelle that has only recently been implicated in regulation of apoptosis. At this locale, Spike interacts with Bap31, an adaptor protein for pro-caspase-8 and Bcl-XL. In doing so, Spike is able to inhibit the formation of a complex between Bap31 and the antiapoptotic Bcl-XL protein. Furthermore, Spike transmits the signal of specific death receptors. Its down-regulation in certain tumors suggests that Spike may also play a role in tumorigenesis. Our findings add new insight for how BH3-only and antiapoptotic Bcl-2 proteins regulate cell death.

  14. A case for spiking neural network simulation based on configurable multiple-FPGA systems.

    Science.gov (United States)

    Yang, Shufan; Wu, Qiang; Li, Renfa

    2011-09-01

    Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

  15. Why is There a Spike in the Job Finding Rate at Benefit Exhaustion?

    NARCIS (Netherlands)

    Boone, J.; van Ours, J.C.

    2009-01-01

    Putting a limit on the duration of unemployment benefits tends to introduce a \\spike" in the job finding rate shortly before benefits are exhausted. Current theories explain this spike from workers' behavior. We present a theoretical model in which also the nature of the job matters. End-of-benefit

  16. Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex.

    Directory of Open Access Journals (Sweden)

    Laureline Logiaco

    2015-08-01

    Full Text Available The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70-200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys' behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.

  17. Remifentanil-induced spike activity as a diagnostic tool in epilepsy surgery

    DEFF Research Database (Denmark)

    Grønlykke, L; Knudsen, M L; Høgenhaven, H

    2008-01-01

    To assess the value of remifentanil in intraoperative evaluation of spike activity in patients undergoing surgery for mesial temporal lobe epilepsy (MTLE).......To assess the value of remifentanil in intraoperative evaluation of spike activity in patients undergoing surgery for mesial temporal lobe epilepsy (MTLE)....

  18. The effect of lamotrigine, a novel anticonvulsant, on interictal spikes in patients with epilepsy.

    OpenAIRE

    Jawad, S; Oxley, J; Yuen, W. C.; Richens, A

    1986-01-01

    Lamotrigine, a novel anticonvulsant, has been evaluated in patients with epilepsy using the method of interictal EEG spike counting. In a randomised double-blind trial it was found that oral lamotrigine (240 mg) was more effective than placebo, but less effective than diazepam (20 mg) in suppressing interictal spikes. Further clinical evaluation of lamotrigine is recommended in patients with epilepsy.

  19. Phase-locking of epileptic spikes to ongoing delta oscillations in non-convulsive status epilepticus

    NARCIS (Netherlands)

    Hindriks, Rikkert; Meijer, Hil G.E.; Gils, van Stephan A.; Putten, van Michel J.A.M

    2013-01-01

    The EEG of patients in non-convulsive status epilepticus (NCSE) often displays delta oscillations or generalized spike-wave discharges. In some patients, these delta oscillations coexist with intermittent epileptic spikes. In this study we verify the prediction of a computational model of the thalam

  20. Usefulness of MEG magnetometer for spike detection in patients with mesial temporal epileptic focus.

    Science.gov (United States)

    Enatsu, R; Mikuni, N; Usui, K; Matsubayashi, J; Taki, J; Begum, T; Matsumoto, R; Ikeda, A; Nagamine, T; Fukuyama, H; Hashimoto, N

    2008-07-15

    The present study investigated the sensitivity of magnetoencephalography (MEG) for spikes depending on sensor type in patients with mesial temporal epileptic focus. We recorded MEG in 6 patients with mesial temporal epileptic focus using two sensor types (magnetometer and gradiometer) simultaneously. The number of spikes detected and the corresponding equivalent current dipole (ECD) parameters (distance from the coordinated head center (radius), and dipole moment) were evaluated with respect to sensor type. Among 426 MEG 'consensus spikes' determined by 3 reviewers, 378 spikes satisfied the predetermined criteria for source localization. Comparing ECD parameters, spikes detected by magnetometer alone displayed a smaller radius and larger dipole moment than those detected by gradiometer alone. Spikes estimated in the mesial temporal area were more frequently detected by magnetometer alone (38.5%) than by gradiometer alone (11.5%), whereas spikes in the lateral temporal area were detected less by magnetometer alone (3.7%) than by gradiometer alone (53.9%). The present results suggest that a magnetometer is advantageous for spike detection in patients with mesial temporal epileptic focus. This also implies the higher sensitivity of magnetometer for deep sources.

  1. The Influence of Temperature on Spike Probability in Day-Ahead Power Prices

    NARCIS (Netherlands)

    R. Huisman (Ronald)

    2007-01-01

    textabstractIt is well known that day-ahead prices in power markets exhibit spikes. These spikes are sudden increases in the day-ahead price that occur because power production is not flexible enough to respond to demand and/or supply shocks in the short term. This paper focuses on how temperature i

  2. Increase of spike-LFP coordination in rat prefrontal cortex during working memory.

    Science.gov (United States)

    Li, Shuangyan; Ouyang, Mei; Liu, Tiaotiao; Bai, Wenwen; Yi, Hu; Tian, Xin

    2014-03-15

    Working memory (WM) refers to the short-term maintenance of information with higher cognitive functions. Recent researches show that local field potentials (LFPs) and spikes, as different modes of neural signals, encode WM, respectively. There is a growing interest in how these two signals encode WM in coordination. The aim of this study is to investigate spike-LFP coupling coding of WM via the joint entropy analysis. The experimental data were multi-channel spikes and LFPs obtained from SD rat prefrontal cortex through the implanted microelectrode array during the WM tasks in Y-maze. The short-time Fourier transform (STFT) was applied to analyze the power changes of WM related frequency bands in the LFPs. The joint entropy indexes (JEIs) between spikes and the principle components of LFPs were calculated for each pair of the spike and the LFP series during WM. The results showed that the power of theta (4-12 Hz), low gamma (LG, 30-60 Hz) and high gamma band (HG, 60-100 Hz) in LFPs increased during the WM tasks. In addition, the JEIs between spikes and LFPs components (theta, LG and HG) significantly increased in the correct trials. Besides, the coupling levels were low when the rats waiting in the starting area. These results suggest that the JEIs between spikes and LFPs components (theta, LG and HG) encode WM effectively. These findings could lead to improved understanding of the WM mechanism from the view of spike-LFP joint encoding.

  3. Spike sorting of heterogeneous neuron types by multimodality-weighted PCA and explicit robust variational Bayes

    Directory of Open Access Journals (Sweden)

    Takashi eTakekawa

    2012-03-01

    Full Text Available This study introduces a new spike sorting method that classifies spike waveforms from multiunit recordings into spike trains of individual neurons. In particular, we develop a method to sort a spike mixture generated by a heterogeneous neural population. Such a spike sorting has a significant practical value, but was previously difficult. The method combines a feature extraction method, which we may term multimodality-weighted principal component analysis (mPCA, and a clustering method by variational Bayes for Student’s t mixture model (SVB. The performance of the proposed method was compared with that of other conventional methods for simulated and experimental data sets. We found that the mPCA efficiently extracts highly informative features as clusters clearly separable in a relatively low-dimensional feature space. The SVB was implemented explicitly without relying on Maximum-A-Posterior (MAP inference for the degree of freedom parameters. The explicit SVB is faster than the conventional SVB derived with MAP inference and works more reliably over various data sets that include spiking patterns difficult to sort. For instance, spikes of a single bursting neuron may be separated incorrectly into multiple clusters, whereas those of a sparsely firing neuron tend to be merged into clusters for other neurons. Our method showed significantly improved performance in spike sorting of these difficult neurons. A parallelized implementation of the proposed algorithm (EToS version 3 is available as open-source code at http://etos.sourceforge.net/.

  4. Characterizing neural activities evoked by manual acupuncture through spiking irregularity measures

    Science.gov (United States)

    Xue, Ming; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Yu, Hai-Tao; Chen, Ying-Yuan

    2013-09-01

    The neural system characterizes information in external stimulations by different spiking patterns. In order to examine how neural spiking patterns are related to acupuncture manipulations, experiments are designed in such a way that different types of manual acupuncture (MA) manipulations are taken at the ‘Zusanli’ point of experimental rats, and the induced electrical signals in the spinal dorsal root ganglion are detected and recorded. The interspike interval (ISI) statistical histogram is fitted by the gamma distribution, which has two parameters: one is the time-dependent firing rate and the other is a shape parameter characterizing the spiking irregularities. The shape parameter is the measure of spiking irregularities and can be used to identify the type of MA manipulations. The coefficient of variation is mostly used to measure the spike time irregularity, but it overestimates the irregularity in the case of pronounced firing rate changes. However, experiments show that each acupuncture manipulation will lead to changes in the firing rate. So we combine four relatively rate-independent measures to study the irregularity of spike trains evoked by different types of MA manipulations. Results suggest that the MA manipulations possess unique spiking statistics and characteristics and can be distinguished according to the spiking irregularity measures. These studies have offered new insights into the coding processes and information transfer of acupuncture.

  5. Fast Na+ spike generation in dendrites of guinea-pig substantia nigra pars compacta neurons

    DEFF Research Database (Denmark)

    Nedergaard, S; Hounsgaard, Jørn Dybkjær

    1996-01-01

    were not inhibited in the presence of glutamate receptor antagonists or during Ca2+ channel blockade. Blockers of gap junctional conductance (sodium propionate, octanol and halothane) did not affect the field-induced spikes. The spike generation was highly sensitive to changes in membrane conductance...

  6. Immunogenicity of recombinant feline infectious peritonitis virus spike protein in mice and kittens

    NARCIS (Netherlands)

    Horzinek, M.C.; Vennema, H.; Groot, R. de; Harbour, D.A.; Dalderup, M.; Gruffydd-Jones, T.; Spaan, W.J.M.

    1990-01-01

    The gene encoding the fusogenic spike protein of the coronavirus causing feline infectious peritonitis (FIVP) was recombined into the genome of vaccinia virus, strain WR. The recombinant induced spike protein specific, in vitro neutralizing antibodies in mkice. When kittens were immunized with the r

  7. Neuronal Networks in Children with Continuous Spikes and Waves during Slow Sleep

    Science.gov (United States)

    Siniatchkin, Michael; Groening, Kristina; Moehring, Jan; Moeller, Friederike; Boor, Rainer; Brodbeck, Verena; Michel, Christoph M.; Rodionov, Roman; Lemieux, Louis; Stephani, Ulrich

    2010-01-01

    Epileptic encephalopathy with continuous spikes and waves during slow sleep is an age-related disorder characterized by the presence of interictal epileptiform discharges during at least greater than 85% of sleep and cognitive deficits associated with this electroencephalography pattern. The pathophysiological mechanisms of continuous spikes and…

  8. An implantable VLSI architecture for real time spike sorting in cortically controlled Brain Machine Interfaces.

    Science.gov (United States)

    Aghagolzadeh, Mehdi; Zhang, Fei; Oweiss, Karim

    2010-01-01

    Brain Machine Interface (BMI) systems demand real-time spike sorting to instantaneously decode the spike trains of simultaneously recorded cortical neurons. Real-time spike sorting, however, requires extensive computational power that is not feasible to implement in implantable BMI architectures, thereby requiring transmission of high-bandwidth raw neural data to an external computer. In this work, we describe a miniaturized, low power, programmable hardware module capable of performing this task within the resource constraints of an implantable chip. The module computes a sparse representation of the spike waveforms followed by "smart" thresholding. This cascade restricts the sparse representation to a subset of projections that preserve the discriminative features of neuron-specific spike waveforms. In addition, it further reduces telemetry bandwidth making it feasible to wirelessly transmit only the important biological information to the outside world, thereby improving the efficiency, practicality and viability of BMI systems in clinical applications.

  9. An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm.

    Science.gov (United States)

    Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

    2015-08-13

    A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction.

  10. Regulation of granule cell excitability by a low-threshold calcium spike in turtle olfactory bulb

    DEFF Research Database (Denmark)

    Pinato, Giulietta; Midtgaard, Jens

    2003-01-01

    Granule cells excitability in the turtle olfactory bulb was analyzed using whole cell recordings in current- and voltage-clamp mode. Low-threshold spikes (LTSs) were evoked at potentials that are subthreshold for Na spikes in normal medium. The LTSs were evoked from rest, but hyperpolarization...... of the cell usually increased their amplitude so that they more easily boosted Na spike initiation. The LTS persisted in the presence of TTX but was antagonized by blockers of T-type calcium channels. The voltage dependence, kinetics, and inactivation properties of the LTS were characteristic of a low......-threshold calcium spike. The threshold of the LTS was slightly above the resting potential but well below the Na spike threshold, and the LTS was often evoked in isolation in normal medium. Tetraethylammonium (TEA) and 4-aminopyridine (4-AP) had only minimal effects on the LTS but revealed the presence of a high...

  11. Sparse generalized volterra model of human hippocampal spike train transformation for memory prostheses.

    Science.gov (United States)

    Song, Dong; Robinson, Brian S; Hampson, Robert E; Marmarelis, Vasilis Z; Deadwyler, Sam A; Berger, Theodore W

    2015-01-01

    In order to build hippocampal prostheses for restoring memory functions, we build multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from the hippocampal CA3 and CA1 regions of epileptic patients performing a memory-dependent delayed match-to-sample task. Using CA3 and CA1 spike trains as inputs and outputs respectively, second-order sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains and thus will serve as the computational basis of the hippocampal memory prosthesis.

  12. Knowledge extraction from evolving spiking neural networks with rank order population coding.

    Science.gov (United States)

    Soltic, Snjezana; Kasabov, Nikola

    2010-12-01

    This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.

  13. Developing a supervised training algorithm for limited precision feed-forward spiking neural networks

    CERN Document Server

    Stromatias, Evangelos

    2011-01-01

    Spiking neural networks have been referred to as the third generation of artificial neural networks where the information is coded as time of the spikes. There are a number of different spiking neuron models available and they are categorized based on their level of abstraction. In addition, there are two known learning methods, unsupervised and supervised learning. This thesis focuses on supervised learning where a new algorithm is proposed, based on genetic algorithms. The proposed algorithm is able to train both synaptic weights and delays and also allow each neuron to emit multiple spikes thus taking full advantage of the spatial-temporal coding power of the spiking neurons. In addition, limited synaptic precision is applied; only six bits are used to describe and train a synapse, three bits for the weights and three bits for the delays. Two limited precision schemes are investigated. The proposed algorithm is tested on the XOR classification problem where it produces better results for even smaller netwo...

  14. Comparison of two thermal spike models for ion-solid interaction

    Energy Technology Data Exchange (ETDEWEB)

    Szenes, G., E-mail: szenes@ludens.elte.h [Department of Materials Physics, Eoetvoes University, P.O. Box 32, H-1518 Budapest (Hungary)

    2011-01-15

    A comparative review of the inelastic thermal spike model (ITSM, Meftah et al., 1994) and the analytical thermal spike model (ATSM Szenes, 1995) is given. The ITSM follows the formation of the ion-induced thermal spike based on the Fourier equation while the ATSM skips this stage and a final Gaussian temperature distribution is assumed. Each of the two models doubts the basic assumptions of the other. The ITSM rejects the Gaussian temperature distribution while according to ATSM several thermophysical parameters used by the ITSM are irrelevant to the formation of the thermal spike and the equilibrium values are not valid under spike conditions. The essentially different conclusions of the models are discussed in connection with experiments performed in BaFe{sub 12}O{sub 19}, Al{sub 2}O{sub 3}, silica and high-T{sub c} superconductors.

  15. Comparison of two thermal spike models for ion-solid interaction

    Science.gov (United States)

    Szenes, G.

    2011-01-01

    A comparative review of the inelastic thermal spike model (ITSM, Meftah et al., 1994) and the analytical thermal spike model (ATSM Szenes, 1995) is given. The ITSM follows the formation of the ion-induced thermal spike based on the Fourier equation while the ATSM skips this stage and a final Gaussian temperature distribution is assumed. Each of the two models doubts the basic assumptions of the other. The ITSM rejects the Gaussian temperature distribution while according to ATSM several thermophysical parameters used by the ITSM are irrelevant to the formation of the thermal spike and the equilibrium values are not valid under spike conditions. The essentially different conclusions of the models are discussed in connection with experiments performed in BaFe 12O 19, Al 2O 3, silica and high- T c superconductors.

  16. Challenges in the quantification and interpretation of spike-LFP relationships.

    Science.gov (United States)

    Ray, Supratim

    2015-04-01

    Brain signals often show fluctuations in particular frequency bands, which are highly conserved across species and are associated with specific behavioural states. Such rhythmic patterns can be captured in the local field potential (LFP), which is obtained by low-pass filtering the extracellular signal recorded from microelectrodes. However, LFP also captures other neural processes that are associated with spikes, such as synaptic events preceding a spike, low-frequency component of the action potential ("spike bleed-through") and spike afterhyperpolarization, which pose difficulties in the estimation of the amplitude and phase of the rhythm with respect to spikes. Here we discuss these issues and different techniques that have been used to dissociate the rhythm from other neural events in the LFP.

  17. Environmental Impacts on Spiking Properties in Hodgkin-Huxley Neuron with Direct Current Stimulus

    Institute of Scientific and Technical Information of China (English)

    YUAN Chang-Qing; ZHAO Tong-Jun; ZHAN Yong; ZHANG Su-Hua; LIU Hui; ZHANG Yu-Hong

    2009-01-01

    Based on the well accepted Hodgkin-Huxley neuron model, the neuronal intrinsic excitability is studied when the neuron is subject to varying environmental temperatures, the typical impact for its regulating ways. With computer simulation, it is found that altering environmental temperature can improve or inhibit the neuronal intrinsic excitability so as to influence the neuronal spiking properties. The impacts from environmental factors can be understood that ,the neuronal spiking threshold is essentially influenced by the fluctuations in the environ-ment. With the environmental temperature varying, burst spiking is realized for the neuronal membrane voltage because of the environment-dependent spiking threshold. This burst induced by changes in spiking threshold is different from that excited by input currents or other stimulus.

  18. Impacts of clustering on noise-induced spiking regularity in the excitatory neuronal networks of subnetworks.

    Science.gov (United States)

    Li, Huiyan; Sun, Xiaojuan; Xiao, Jinghua

    2015-01-01

    In this paper, we investigate how clustering factors influent spiking regularity of the neuronal network of subnetworks. In order to do so, we fix the averaged coupling probability and the averaged coupling strength, and take the cluster number M, the ratio of intra-connection probability and inter-connection probability R, the ratio of intra-coupling strength and inter-coupling strength S as controlled parameters. With the obtained simulation results, we find that spiking regularity of the neuronal networks has little variations with changing of R and S when M is fixed. However, cluster number M could reduce the spiking regularity to low level when the uniform neuronal network's spiking regularity is at high level. Combined the obtained results, we can see that clustering factors have little influences on the spiking regularity when the entire energy is fixed, which could be controlled by the averaged coupling strength and the averaged connection probability.

  19. Consequences and mechanisms of spike broadening of R20 cells in Aplysia californica.

    Science.gov (United States)

    Ma, M; Koester, J

    1995-10-01

    We studied frequency-dependent spike broadening in the two electrically coupled R20 neurons in the abdominal ganglion of Aplysia. The peptidergic R20 cells excite the R25/L25 interneurons (which trigger respiratory pumping) and inhibit the RB cells. When fired at 1-10 Hz, the duration of the falling phase of the action potential in R20 neurons increases 2-10 fold during a spike train. Spike broadening recorded from the somata of the R20 cells affected synaptic transmission to nearby follower cells. Chemically mediated synaptic output was reduced by approximately 50% when recorded trains of nonbroadened action potentials were used as command signals for a voltage-clamped R20 cell. Electrotonic EPSPs between the R20 cells, which normally facilitated by two- to fourfold during a high frequency spike train, showed no facilitation when spike broadening was prevented under voltage-clamp control. To examine the mechanism of frequency-dependent spike broadening, we applied two-electrode voltage-clamp and pharmacological techniques to the somata of R20 cells. Several voltage-gated ionic currents were isolated, including INa, a multicomponent ICa, and three K+ currents--a high threshold, fast transient A-type K+ current (IAdepol), a delayed rectifier K+ current (IK-V), and a Ca(2+)-sensitive K+ current (IK-Ca), made up of two components. The influences of different currents on spike broadening were determined by using the recorded train of gradually broadening action potentials as the command for the voltage clamp. We found the following. (1) IAdepol is the major outward current that contributes to repolarization of nonbroadened spikes. It undergoes pronounced cumulative inactivation that is a critical determinant of spike broadening. (2) Activity-dependent changes in IK-V, IK-Ca, and ICa have complex effects on the kinetics and extent of broadening. (3) The time integral of ICa during individual action potentials increases approximately threefold during spike broadening.

  20. Intracellular calcium spikes in rat suprachiasmatic nucleus neurons induced by BAPTA-based calcium dyes.

    Science.gov (United States)

    Hong, Jin Hee; Min, Cheol Hong; Jeong, Byeongha; Kojiya, Tomoyoshi; Morioka, Eri; Nagai, Takeharu; Ikeda, Masayuki; Lee, Kyoung J

    2010-03-10

    Circadian rhythms in spontaneous action potential (AP) firing frequencies and in cytosolic free calcium concentrations have been reported for mammalian circadian pacemaker neurons located within the hypothalamic suprachiasmatic nucleus (SCN). Also reported is the existence of "Ca(2+) spikes" (i.e., [Ca(2+)](c) transients having a bandwidth of 10 approximately 100 seconds) in SCN neurons, but it is unclear if these SCN Ca(2+) spikes are related to the slow circadian rhythms. We addressed this issue based on a Ca(2+) indicator dye (fluo-4) and a protein Ca(2+) sensor (yellow cameleon). Using fluo-4 AM dye, we found spontaneous Ca(2+) spikes in 18% of rat SCN cells in acute brain slices, but the Ca(2+) spiking frequencies showed no day/night variation. We repeated the same experiments with rat (and mouse) SCN slice cultures that expressed yellow cameleon genes for a number of different circadian phases and, surprisingly, spontaneous Ca(2+) spike was barely observed (fluo-4 AM or BAPTA-AM was loaded in addition to the cameleon-expressing SCN cultures, however, the number of cells exhibiting Ca(2+) spikes was increased to 13 approximately 14%. Despite our extensive set of experiments, no evidence of a circadian rhythm was found in the spontaneous Ca(2+) spiking activity of SCN. Furthermore, our study strongly suggests that the spontaneous Ca(2+) spiking activity is caused by the Ca(2+) chelating effect of the BAPTA-based fluo-4 dye. Therefore, this induced activity seems irrelevant to the intrinsic circadian rhythm of [Ca(2+)](c) in SCN neurons. The problems with BAPTA based dyes are widely known and our study provides a clear case for concern, in particular, for SCN Ca(2+) spikes. On the other hand, our study neither invalidates the use of these dyes as a whole, nor undermines the potential role of SCN Ca(2+) spikes in the function of SCN.

  1. Wheat floret survival as related to pre-anthesis spike growth.

    Science.gov (United States)

    González, Fernanda G; Miralles, Daniel J; Slafer, Gustavo A

    2011-10-01

    Further improvements to wheat yield potential will be essential to meet future food demand. As yield is related to the number of fertile florets and grains, an understanding of the basis of their generation is instrumental to raising yield. Based on (i) a strong positive association between the number of fertile florets or grains and spike dry weight at anthesis; and (ii) the finding that floret death occurs when spikes grow at maximum rate, it was always assumed that floret survival depends on the growth of the spike. However, this assumption was recently questioned, suggesting that assimilates diverted to the spike do not determine the number of florets and grains and that the onset of floret death may instead be a developmental process that is not associated with spike growth. In this study, the relationships between the fate of floret primordia and spike growth from six independent experiments that included different growing conditions (greenhouse/field experiments, growing seasons, photoperiod/shading treatments during the floret primordia phase) and diverse cultivar types (winter/spring, semi-dwarf/standard-height, photoperiod sensitive/insensitive) were re-analysed together. Onset of floret death was associated with the beginning of spike growth at the maximum rate in c. 80% of the cases analysed; and the rate of floret death (the main determinant of floret survival) showed a negative quantitative relationship with spike weight at anthesis. As floret death and survival were shown to be linked to pre-anthesis spike growth, the strategy of focusing on traits associated with pre-anthesis spike growth when breeding to increase wheat yield potential further is valuable.

  2. iRaster: a novel information visualization tool to explore spatiotemporal patterns in multiple spike trains.

    Science.gov (United States)

    Somerville, J; Stuart, L; Sernagor, E; Borisyuk, R

    2010-12-15

    Over the last few years, simultaneous recordings of multiple spike trains have become widely used by neuroscientists. Therefore, it is important to develop new tools for analysing multiple spike trains in order to gain new insight into the function of neural systems. This paper describes how techniques from the field of visual analytics can be used to reveal specific patterns of neural activity. An interactive raster plot called iRaster has been developed. This software incorporates a selection of statistical procedures for visualization and flexible manipulations with multiple spike trains. For example, there are several procedures for the re-ordering of spike trains which can be used to unmask activity propagation, spiking synchronization, and many other important features of multiple spike train activity. Additionally, iRaster includes a rate representation of neural activity, a combined representation of rate and spikes, spike train removal and time interval removal. Furthermore, it provides multiple coordinated views, time and spike train zooming windows, a fisheye lens distortion, and dissemination facilities. iRaster is a user friendly, interactive, flexible tool which supports a broad range of visual representations. This tool has been successfully used to analyse both synthetic and experimentally recorded datasets. In this paper, the main features of iRaster are described and its performance and effectiveness are demonstrated using various types of data including experimental multi-electrode array recordings from the ganglion cell layer in mouse retina. iRaster is part of an ongoing research project called VISA (Visualization of Inter-Spike Associations) at the Visualization Lab in the University of Plymouth. The overall aim of the VISA project is to provide neuroscientists with the ability to freely explore and analyse their data. The software is freely available from the Visualization Lab website (see www.plymouth.ac.uk/infovis).

  3. Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

    Science.gov (United States)

    Onken, Arno; Liu, Jian K; Karunasekara, P P Chamanthi R; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

    2016-11-01

    Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image

  4. Comparison of degradation between indigenous and spiked bisphenol A and triclosan in a biosolids amended soil

    Energy Technology Data Exchange (ETDEWEB)

    Langdon, Kate A., E-mail: Kate.Langdon@csiro.au [School of Agriculture, Food and Wine and Waite Research Institute, University of Adelaide, South Australia, 5005, Adelaide (Australia); Water for a Healthy Country Research Flagship, Commonwealth Scientific and Industrial Research Organisation (CSIRO), PMB 2, Glen Osmond, South Australia, 5064, Adelaide (Australia); Warne, Michael StJ. [Water for a Healthy Country Research Flagship, Commonwealth Scientific and Industrial Research Organisation (CSIRO), PMB 2, Glen Osmond, South Australia, 5064, Adelaide (Australia); Smernik, Ronald J. [School of Agriculture, Food and Wine and Waite Research Institute, University of Adelaide, South Australia, 5005, Adelaide (Australia); Shareef, Ali; Kookana, Rai S. [Water for a Healthy Country Research Flagship, Commonwealth Scientific and Industrial Research Organisation (CSIRO), PMB 2, Glen Osmond, South Australia, 5064, Adelaide (Australia)

    2013-03-01

    This study compared the degradation of indigenous bisphenol A (BPA) and triclosan (TCS) in a biosolids-amended soil, to the degradation of spiked labelled surrogates of the same compounds (BPA-d{sub 16} and TCS-{sup 13}C{sub 12}). The aim was to determine if spiking experiments accurately predict the degradation of compounds in biosolids-amended soils using two different types of biosolids, a centrifuge dried biosolids (CDB) and a lagoon dried biosolids (LDB). The rate of degradation of the compounds was examined and the results indicated that there were considerable differences between the indigenous and spiked compounds. These differences were more marked for BPA, for which the indigenous compound was detectable throughout the study, whereas the spiked compound decreased to below the detection limit prior to the study completion. The rate of degradation for the indigenous BPA was approximately 5-times slower than that of the spiked BPA-d{sub 16}. The indigenous and spiked TCS were both detectable throughout the study, however, the shape of the degradation curves varied considerably, particularly in the CDB treatment. These findings show that spiking experiments may not be suitable to predict the degradation and persistence of organic compounds following land application of biosolids. - Highlights: ► Degradation of indigenous and spiked compounds from biosolids were compared. ► Differences were observed for both the rate and pattern of degradation. ► Spiked bisphenol A entirely degraded however the indigenous compound remained. ► TCS was detectable during the experiment however the degradation patterns varied. ► Spiking experiments may not be suitable to predict degradation of organic compounds.

  5. The effects of sevoflurane and hyperventilation on electrocorticogram spike activity in patients with refractory epilepsy.

    Science.gov (United States)

    Kurita, Naoko; Kawaguchi, Masahiko; Hoshida, Tohru; Nakase, Hiroyuki; Sakaki, Toshisuke; Furuya, Hitoshi

    2005-08-01

    We investigated the effects of sevoflurane and hyperventilation on intraoperative electrocorticogram (ECoG) spike activity in 13 patients with intractable epilepsy. Grid electrodes were placed on the brain surface and ECoG was recorded under the following conditions: 1) 0.5 minimal alveolar anesthetic concentration (MAC) sevoflurane, 2) 1.5 MAC sevoflurane, and 3) 1.5 MAC sevoflurane with hyperventilation. The number of spikes per 5 min and the percentage of leads with spikes were assessed in each condition. In 4 patients with chronically implanted-subdural electrodes, the leads with seizure onset and with spikes during the interictal periods in the awake state were compared with those during sevoflurane anesthesia at 0.5 MAC and 1.5 MAC. The number of spikes and the percentage of leads with spikes were significantly more under 1.5 MAC sevoflurane anesthesia compared with those under 0.5 MAC sevoflurane (P MAC sevoflurane, the leads with spikes were similar to those at seizure onset in the awake state, whereas with 1.5 MAC sevoflurane, spikes were similar to those occurring during interictal periods in the awake state. These results indicate that sevoflurane and hyperventilation can affect the frequency and extent of ECoG spike activity in patients with intractable epilepsy. Careful attention should be paid to the concentration of sevoflurane used and ventilatory status when intraoperative EcoG is used to localize epileptic lesions. Electrocorticogram can be used to define the location and extent of epileptic foci during epilepsy surgery. However, electrocorticogram can be affected by anesthetic technique. The present study found that sevoflurane concentration and hyperventilation affected the frequency and the extent of electrocorticogram spike activity in epileptic patients.

  6. The chronotron: a neuron that learns to fire temporally precise spike patterns.

    Directory of Open Access Journals (Sweden)

    Răzvan V Florian

    Full Text Available In many cases, neurons process information carried by the precise timings of spikes. Here we show how neurons can learn to generate specific temporally precise output spikes in response to input patterns of spikes having precise timings, thus processing and memorizing information that is entirely temporally coded, both as input and as output. We introduce two new supervised learning rules for spiking neurons with temporal coding of information (chronotrons, one that provides high memory capacity (E-learning, and one that has a higher biological plausibility (I-learning. With I-learning, the neuron learns to fire the target spike trains through synaptic changes that are proportional to the synaptic currents at the timings of real and target output spikes. We study these learning rules in computer simulations where we train integrate-and-fire neurons. Both learning rules allow neurons to fire at the desired timings, with sub-millisecond precision. We show how chronotrons can learn to classify their inputs, by firing identical, temporally precise spike trains for different inputs belonging to the same class. When the input is noisy, the classification also leads to noise reduction. We compute lower bounds for the memory capacity of chronotrons and explore the influence of various parameters on chronotrons' performance. The chronotrons can model neurons that encode information in the time of the first spike relative to the onset of salient stimuli or neurons in oscillatory networks that encode information in the phases of spikes relative to the background oscillation. Our results show that firing one spike per cycle optimizes memory capacity in neurons encoding information in the phase of firing relative to a background rhythm.

  7. Predicting spike occurrence and neuronal responsiveness from LFPs in primary somatosensory cortex.

    Directory of Open Access Journals (Sweden)

    Riccardo Storchi

    Full Text Available Local Field Potentials (LFPs integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neuronal coding and in the clinics (e.g. for improving invasive Brain-Machine Interface devices. However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role.

  8. Input-output relation and energy efficiency in the neuron with different spike threshold dynamics.

    Science.gov (United States)

    Yi, Guo-Sheng; Wang, Jiang; Tsang, Kai-Ming; Wei, Xi-Le; Deng, Bin

    2015-01-01

    Neuron encodes and transmits information through generating sequences of output spikes, which is a high energy-consuming process. The spike is initiated when membrane depolarization reaches a threshold voltage. In many neurons, threshold is dynamic and depends on the rate of membrane depolarization (dV/dt) preceding a spike. Identifying the metabolic energy involved in neural coding and their relationship to threshold dynamic is critical to understanding neuronal function and evolution. Here, we use a modified Morris-Lecar model to investigate neuronal input-output property and energy efficiency associated with different spike threshold dynamics. We find that the neurons with dynamic threshold sensitive to dV/dt generate discontinuous frequency-current curve and type II phase response curve (PRC) through Hopf bifurcation, and weak noise could prohibit spiking when bifurcation just occurs. The threshold that is insensitive to dV/dt, instead, results in a continuous frequency-current curve, a type I PRC and a saddle-node on invariant circle bifurcation, and simultaneously weak noise cannot inhibit spiking. It is also shown that the bifurcation, frequency-current curve and PRC type associated with different threshold dynamics arise from the distinct subthreshold interactions of membrane currents. Further, we observe that the energy consumption of the neuron is related to its firing characteristics. The depolarization of spike threshold improves neuronal energy efficiency by reducing the overlap of Na(+) and K(+) currents during an action potential. The high energy efficiency is achieved at more depolarized spike threshold and high stimulus current. These results provide a fundamental biophysical connection that links spike threshold dynamics, input-output relation, energetics and spike initiation, which could contribute to uncover neural encoding mechanism.

  9. Intracellular calcium spikes in rat suprachiasmatic nucleus neurons induced by BAPTA-based calcium dyes.

    Directory of Open Access Journals (Sweden)

    Jin Hee Hong

    Full Text Available BACKGROUND: Circadian rhythms in spontaneous action potential (AP firing frequencies and in cytosolic free calcium concentrations have been reported for mammalian circadian pacemaker neurons located within the hypothalamic suprachiasmatic nucleus (SCN. Also reported is the existence of "Ca(2+ spikes" (i.e., [Ca(2+](c transients having a bandwidth of 10 approximately 100 seconds in SCN neurons, but it is unclear if these SCN Ca(2+ spikes are related to the slow circadian rhythms. METHODOLOGY/PRINCIPAL FINDINGS: We addressed this issue based on a Ca(2+ indicator dye (fluo-4 and a protein Ca(2+ sensor (yellow cameleon. Using fluo-4 AM dye, we found spontaneous Ca(2+ spikes in 18% of rat SCN cells in acute brain slices, but the Ca(2+ spiking frequencies showed no day/night variation. We repeated the same experiments with rat (and mouse SCN slice cultures that expressed yellow cameleon genes for a number of different circadian phases and, surprisingly, spontaneous Ca(2+ spike was barely observed (<3%. When fluo-4 AM or BAPTA-AM was loaded in addition to the cameleon-expressing SCN cultures, however, the number of cells exhibiting Ca(2+ spikes was increased to 13 approximately 14%. CONCLUSIONS/SIGNIFICANCE: Despite our extensive set of experiments, no evidence of a circadian rhythm was found in the spontaneous Ca(2+ spiking activity of SCN. Furthermore, our study strongly suggests that the spontaneous Ca(2+ spiking activity is caused by the Ca(2+ chelating effect of the BAPTA-based fluo-4 dye. Therefore, this induced activity seems irrelevant to the intrinsic circadian rhythm of [Ca(2+](c in SCN neurons. The problems with BAPTA based dyes are widely known and our study provides a clear case for concern, in particular, for SCN Ca(2+ spikes. On the other hand, our study neither invalidates the use of these dyes as a whole, nor undermines the potential role of SCN Ca(2+ spikes in the function of SCN.

  10. Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

    Directory of Open Access Journals (Sweden)

    Arno Onken

    2016-11-01

    Full Text Available Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations, in their temporal dimension (temporal neural response variations, or in their combination (temporally coordinated neural population firing. Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together, temporal firing patterns (temporal activation of these groups of neurons and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial. We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine

  11. A-current modifies the spike of C-type neurones in the rabbit nodose ganglion.

    Science.gov (United States)

    Ducreux, C; Puizillout, J J

    1995-07-15

    1. In the rabbit nodose ganglion, C-type fibre neurones (C neurones) can be divided into two subtypes according to their after-hyperpolarizing potential (AHP) i.e. those with a fast AHP only and those with a fast AHP and a subsequent slow AHP produced by a slow calcium-dependent potassium current. In addition we have shown that some C neurones can be divided into two groups according to the effect of membrane hyperpolarization on their spikes i.e. type 1 in which duration and amplitude do not change and type 2 in which duration and amplitude decrease significantly. 2. In the present report we studied the effect of A-current (IA) on spike duration, amplitude and slow AHP using intracellular recording techniques. 3. To detect the presence of IA, we first applied a series of increasing rectangular hyperpolarizing pulses to remove IA inactivation and then a short depolarizing pulse to trigger a spike. In type 1 C neurones the lag time of the spike in relation to hyperpolarization remains constant whereas in type 2 C neurones the spike only appears after IA inactivation and lag time in relation to hyperpolarization is lengthened. Thus, type 2 C neurones have an IA while type 1 C neurones do not. The fact that addition of cadmium did not change the lag time in type 2 C neurones shows that the IA is not calcium dependent. 4. Nodose neurones can be orthodromically activated by stimulation of the vagal peripheral process. In this way, after a hyperpolarizing pulse, IA can be fully activated by the orthodromic spike itself. Under these conditions it is possible to analyse the effects of IA on the spike. This was done by increasing either the hyperpolarizing potential, pulse duration, or the delay of the spike after the end of the pulse. We observed that maximum IA inactivation removal was always associated with the lowest duration and amplitude of the spike. 5. When IA inhibitors, 4-aminopyridine (4-AP) or catechol, were applied to type 2 C neurones, the delay of the spike

  12. Broad-band solar bursts of the spike type

    Energy Technology Data Exchange (ETDEWEB)

    Bakunin, L.M.; Chernov, G.P.

    1985-10-01

    Observations of high-resolution dynamic spectra of solar broad-band spike bursts (BSB), consisting of the instantaneous brightening of the continuum in the entire spectrograph range of 175-235 MHz, are reported. In noise storms events of the BSB type are rarely encountered and have the form of individual bursts with an average duration of 0.1-0.2 sec or groups of such bursts connected with type III bursts at lower frequencies. Series of aperiodic BSB structures unconnected with type III bursts and observed together with one-second pulsations and fiber bursts, predominate in type IV bursts. In noise storms BSB bursts can be emitted by electrons accelerated instantaneously as a result of the reconnection of magnetic fields, both in a small source (about 10 to the 8th cm) with a broad velocity dispersion and in an extended source (about 5 x 10 to the 9th cm). Structures of the BSB type in type IV bursts may be the result of the scattering of Langmuir waves on whistlers in a large height range. 30 references.

  13. A Low Noise Amplifier for Neural Spike Recording Interfaces

    Directory of Open Access Journals (Sweden)

    Jesus Ruiz-Amaya

    2015-09-01

    Full Text Available This paper presents a Low Noise Amplifier (LNA for neural spike recording applications. The proposed topology, based on a capacitive feedback network using a two-stage OTA, efficiently solves the triple trade-off between power, area and noise. Additionally, this work introduces a novel transistor-level synthesis methodology for LNAs tailored for the minimization of their noise efficiency factor under area and noise constraints. The proposed LNA has been implemented in a 130 nm CMOS technology and occupies 0.053 mm-sq. Experimental results show that the LNA offers a noise efficiency factor of 2.16 and an input referred noise of 3.8 μVrms for 1.2 V power supply. It provides a gain of 46 dB over a nominal bandwidth of 192 Hz–7.4 kHz and consumes 1.92 μW. The performance of the proposed LNA has been validated through in vivo experiments with animal models.

  14. A Low Noise Amplifier for Neural Spike Recording Interfaces

    Science.gov (United States)

    Ruiz-Amaya, Jesus; Rodriguez-Perez, Alberto; Delgado-Restituto, Manuel

    2015-01-01

    This paper presents a Low Noise Amplifier (LNA) for neural spike recording applications. The proposed topology, based on a capacitive feedback network using a two-stage OTA, efficiently solves the triple trade-off between power, area and noise. Additionally, this work introduces a novel transistor-level synthesis methodology for LNAs tailored for the minimization of their noise efficiency factor under area and noise constraints. The proposed LNA has been implemented in a 130 nm CMOS technology and occupies 0.053 mm-sq. Experimental results show that the LNA offers a noise efficiency factor of 2.16 and an input referred noise of 3.8 μVrms for 1.2 V power supply. It provides a gain of 46 dB over a nominal bandwidth of 192 Hz–7.4 kHz and consumes 1.92 μW. The performance of the proposed LNA has been validated through in vivo experiments with animal models. PMID:26437411

  15. Recurrent coupling improves discrimination of temporal spike patterns

    Directory of Open Access Journals (Sweden)

    Chun-Wei eYuan

    2012-05-01

    Full Text Available Despite the ubiquitous presence of recurrent synaptic connections insensory neuronal systems, their general functional purpose is not wellunderstood. A recent conceptual advance has been achieved by theoriesof reservoir computing in which recurrent networks have been proposedto generate short-term memory as well as to improve neuronalrepresentation of the sensory input for subsequent computations.Here, we present a numerical study on the distinct effects ofinhibitory and excitatory recurrence in a canonical linearclassification task. It is found that both types of coupling improvethe ability to discriminate temporal spike patterns as compared to apurely feed-forward system, although in different ways. For a largeclass of inhibitory networks, the network's performance is optimal aslong as a fraction of roughly 50% of neurons per stimulus is activein the resulting population code. Thereby the contribution of inactiveneurons to the neural code is found to be even more informative thanthat of the active neurons, generating an inherent robustness ofclassification performance against temporal jitter of the inputspikes. Excitatory couplings are found to not only produce ashort-term memory buffer but also to improve linear separability ofthe population patterns by evoking more irregular firing as comparedto the purely inhibitory case. As the excitatory connectivity becomesmore sparse, firing becomes more variable and pattern separabilityimproves. We argue that the proposed paradigm is particularlywell-suited as a conceptual framework for processing of sensoryinformation in the auditory pathway.

  16. Spike phase synchronization in multiplex cortical neural networks

    Science.gov (United States)

    Jalili, Mahdi

    2017-01-01

    In this paper we study synchronizability of two multiplex cortical networks: whole-cortex of hermaphrodite C. elegans and posterior cortex in male C. elegans. These networks are composed of two connection layers: network of chemical synapses and the one formed by gap junctions. This work studies the contribution of each layer on the phase synchronization of non-identical spiking Hindmarsh-Rose neurons. The network of male C. elegans shows higher phase synchronization than its randomized version, while it is not the case for hermaphrodite type. The random networks in each layer are constructed such that the nodes have the same degree as the original network, thus providing an unbiased comparison. In male C. elegans, although the gap junction network is sparser than the chemical network, it shows higher contribution in the synchronization phenomenon. This is not the case in hermaphrodite type, which is mainly due to significant less density of gap junction layer (0.013) as compared to chemical layer (0.028). Also, the gap junction network in this type has stronger community structure than the chemical network, and this is another driving factor for its weaker synchronizability.

  17. Spontaneous spiking in an autaptic Hodgkin-Huxley setup

    Science.gov (United States)

    Li, Yunyun; Schmid, Gerhard; Hänggi, Peter; Schimansky-Geier, Lutz

    2010-12-01

    The effect of intrinsic channel noise is investigated for the dynamic response of a neuronal cell with a delayed feedback loop. The loop is based on the so-called autapse phenomenon in which dendrites establish connections not only to neighboring cells but also to its own axon. The biophysical modeling is achieved in terms of a stochastic Hodgkin-Huxley model containing such a built in delayed feedback. The fluctuations stem from intrinsic channel noise, being caused by the stochastic nature of the gating dynamics of ion channels. The influence of the delayed stimulus is systematically analyzed with respect to the coupling parameter and the delay time in terms of the interspike interval histograms and the average interspike interval. The delayed feedback manifests itself in the occurrence of bursting and a rich multimodal interspike interval distribution, exhibiting a delay-induced reduction in the spontaneous spiking activity at characteristic frequencies. Moreover, a specific frequency-locking mechanism is detected for the mean interspike interval.

  18. Relaxation Oscillation with Picosecond Spikes in a Conjugated Polymer Laser

    Directory of Open Access Journals (Sweden)

    Wafa Musa Mujamammi

    2016-10-01

    Full Text Available Optically pumped conjugated polymer lasers are good competitors for dye lasers, often complementing and occasionally replacing them. This new type of laser material has broad bandwidths and high optical gains comparable to conventional laser dyes. Since the Stokes’ shift is unusually large, the conjugated polymer has a potential for high power laser action, facilitated by high concentration. This paper reports the results of a new conjugated polymer, the poly[(9,9-dioctyl-2,7-divinylenefluorenylene-alt-co-{2-methoxy-5-(2-ethylhexyloxy-1,4-phenylene}](PFO-co-MEH-PPV material, working in the green region. Also discussed are the spectral and temporal features of the amplified spontaneous emissions (ASE from the conjugated polymer PFO-co-MEH-PPV in a few solvents. When pumped by the third harmonic of the Nd:YAG laser of 10 ns pulse width, the time-resolved spectra of the ASE show relaxation oscillations and spikes of 600 ps pulses. To the best of our knowledge, this is the first report on relaxation oscillations in conjugated-polymer lasers.

  19. Spike Triggered Covariance in Strongly Correlated Gaussian Stimuli

    Science.gov (United States)

    Aljadeff, Johnatan; Segev, Ronen; Berry, Michael J.; Sharpee, Tatyana O.

    2013-01-01

    Many biological systems perform computations on inputs that have very large dimensionality. Determining the relevant input combinations for a particular computation is often key to understanding its function. A common way to find the relevant input dimensions is to examine the difference in variance between the input distribution and the distribution of inputs associated with certain outputs. In systems neuroscience, the corresponding method is known as spike-triggered covariance (STC). This method has been highly successful in characterizing relevant input dimensions for neurons in a variety of sensory systems. So far, most studies used the STC method with weakly correlated Gaussian inputs. However, it is also important to use this method with inputs that have long range correlations typical of the natural sensory environment. In such cases, the stimulus covariance matrix has one (or more) outstanding eigenvalues that cannot be easily equalized because of sampling variability. Such outstanding modes interfere with analyses of statistical significance of candidate input dimensions that modulate neuronal outputs. In many cases, these modes obscure the significant dimensions. We show that the sensitivity of the STC method in the regime of strongly correlated inputs can be improved by an order of magnitude or more. This can be done by evaluating the significance of dimensions in the subspace orthogonal to the outstanding mode(s). Analyzing the responses of retinal ganglion cells probed with Gaussian noise, we find that taking into account outstanding modes is crucial for recovering relevant input dimensions for these neurons. PMID:24039563

  20. Spike triggered covariance in strongly correlated gaussian stimuli.

    Directory of Open Access Journals (Sweden)

    Johnatan Aljadeff

    Full Text Available Many biological systems perform computations on inputs that have very large dimensionality. Determining the relevant input combinations for a particular computation is often key to understanding its function. A common way to find the relevant input dimensions is to examine the difference in variance between the input distribution and the distribution of inputs associated with certain outputs. In systems neuroscience, the corresponding method is known as spike-triggered covariance (STC. This method has been highly successful in characterizing relevant input dimensions for neurons in a variety of sensory systems. So far, most studies used the STC method with weakly correlated Gaussian inputs. However, it is also important to use this method with inputs that have long range correlations typical of the natural sensory environment. In such cases, the stimulus covariance matrix has one (or more outstanding eigenvalues that cannot be easily equalized because of sampling variability. Such outstanding modes interfere with analyses of statistical significance of candidate input dimensions that modulate neuronal outputs. In many cases, these modes obscure the significant dimensions. We show that the sensitivity of the STC method in the regime of strongly correlated inputs can be improved by an order of magnitude or more. This can be done by evaluating the significance of dimensions in the subspace orthogonal to the outstanding mode(s. Analyzing the responses of retinal ganglion cells probed with [Formula: see text] Gaussian noise, we find that taking into account outstanding modes is crucial for recovering relevant input dimensions for these neurons.

  1. Metabolic efficiency with fast spiking in the squid axon

    Directory of Open Access Journals (Sweden)

    Abdelmalik eMoujahid

    2012-11-01

    Full Text Available Fundamentally, action potentials in the squid axon are consequence of the entrance of sodium ions during the depolarization of the rising phase of the spike mediated by the outflow of potassium ions during the hyperpolarization of the falling phase. Perfect metabolic efficiency with a minimum charge needed for the change in voltage during the action potential would confine sodium entry to the rising phase and potassium efflux to the falling phase. However, because sodium channels remain open to a significant extent during the falling phase, a certain overlap of inward and outward currents is observed. In this work we investigate the impact of ion overlap on the number of the adenosine triphosphate (ATP molecules and energy cost required per action potential as a function of the temperature in a Hodgkin-Huxley model. Based on a recent approach to computing the energy cost of neuronal AP generation not based on ion counting, we show that increased firing frequencies induced by higher temperatures imply more efficient use of sodium entry, and then a decrease in the metabolic energy cost required to restore the concentration gradients after an action potential. Also, we determine values of sodium conductance at which the hydrolysis efficiency presents a clear minimum.

  2. Population spikes in cortical networks during different functional states.

    Directory of Open Access Journals (Sweden)

    Shirley eMark

    2012-07-01

    Full Text Available Brain computational challenges vary between behavioral states. Engaged animals react according to incoming sensory information, while in relaxed and sleeping states consolidation of the learned information is believed to take place. Different states are characterized by different forms of cortical activity. We study a possible neuronal mechanism for generating these diverse dynamics and suggest their possible functional significance. Previous studies demonstrated that brief synchronized increase in a neural firing (Population Spikes can be generated in homogenous recurrent neural networks with short-term synaptic depression. Here we consider more realistic networks with clustered architecture. We show that the level of synchronization in neural activity can be controlled smoothly by network parameters. The network shifts from asynchronous activity to a regime in which clusters synchronized separately, then, the synchronization between the clusters increases gradually to fully synchronized state. We examine the effects of different synchrony levels on the transmission of information by the network. We find that the regime of intermediate synchronization is preferential for the flow of information between sparsely connected areas. Based on these results, we suggest that the regime of intermediate synchronization corresponds to engaged behavioral state of the animal, while global synchronization is exhibited during relaxed and sleeping states.

  3. Modelling Spiking Neural Network from the Architecture Evaluation Perspective

    Institute of Scientific and Technical Information of China (English)

    Yu Ji; You-Hui Zhang; Wei-Min Zheng

    2016-01-01

    The brain-inspired spiking neural network (SNN) computing paradigm offers the potential for low-power and scalable computing, suited to many intelligent tasks that conventional computational systems find difficult. On the other hand, NoC (network-on-chips) based very large scale integration (VLSI) systems have been widely used to mimic neuro-biological architectures (including SNNs). This paper proposes an evaluation methodology for SNN applications from the aspect of micro-architecture. First, we extract accurate SNN models from existing simulators of neural systems. Second, a cycle-accurate NoC simulator is implemented to execute the aforementioned SNN applications to get timing and energy-consumption information. We believe this method not only benefits the exploration of NoC design space but also bridges the gap between applications (especially those from the neuroscientists’ community) and neuromorphic hardware. Based on the method, we have evaluated some typical SNNs in terms of timing and energy. The method is valuable for the development of neuromorphic hardware and applications.

  4. U.S. Virgin Islands Petroleum Price-Spike Preparation

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, C.

    2012-06-01

    This NREL technical report details a plan for the U.S. Virgin Islands (USVI) to minimize the economic damage caused by major petroleum price increases. The assumptions for this plan are that the USVI will have very little time and money to implement it and that the population will be highly motivated to follow it because of high fuel prices. The plan's success, therefore, is highly dependent on behavior change. This plan was derived largely from a review of the actions taken and behavior changes made by companies and commuters throughout the United States in response to the oil price spike of 2008. Many of these solutions were coordinated by or reported through the 88 local representatives of the U.S. Department of Energy's Clean Cities program. The National Renewable Energy Laboratory provides technical and communications support for the Clean Cities program and therefore serves as a de facto repository of these solutions. This plan is the first publication that has tapped this repository.

  5. The detection of intestinal spike activity on surface electroenterograms

    Energy Technology Data Exchange (ETDEWEB)

    Ye-Lin, Y; Garcia-Casado, J; Martinez-de-Juan, J L; Prats-Boluda, G [Instituto interuniversitario de investigacion en bioingenierIa y tecnologIa orientada al ser humano (I3BH), Universidad Politecnica de Valencia, Camino de Vera, s/n, Ed. 8E, Acceso N, 2a, planta 46022 Valencia (Spain); Ponce, J L [Department of Surgery, Hospital Universitario La Fe de Valencia, Avenida Campanar n0. 51, 46009 Valencia (Spain)], E-mail: yiye@eln.upv.es, E-mail: jgarciac@eln.upv.es, E-mail: jlmartinez@eln.upv.es, E-mail: geprabo@eln.upv.es, E-mail: drjlponce@ono.com

    2010-02-07

    Myoelectrical recording could provide an alternative technique for assessing intestinal motility, which is a topic of great interest in gastroenterology since many gastrointestinal disorders are associated with intestinal dysmotility. The pacemaker activity (slow wave, SW) of the electroenterogram (EEnG) has been detected in abdominal surface recordings, although the activity related to bowel contractions (spike bursts, SB) has to date only been detected in experimental models with artificially favored electrical conductivity. The aim of the present work was to assess the possibility of detecting SB activity in abdominal surface recordings under physiological conditions. For this purpose, 11 recording sessions of simultaneous internal and external myolectrical signals were conducted on conscious dogs. Signal analysis was carried out in the spectral domain. The results show that in periods of intestinal contractile activity, high-frequency components of EEnG signals can be detected on the abdominal surface in addition to SW activity. The energy between 2 and 20 Hz of the surface myoelectrical recording presented good correlation with the internal intestinal motility index (0.64 {+-} 0.10 for channel 1 and 0.57 {+-} 0.11 for channel 2). This suggests that SB activity can also be detected in canine surface EEnG recording.

  6. Computational properties of networks of synchronous groups of spiking neurons.

    Science.gov (United States)

    Dayhoff, Judith E

    2007-09-01

    We demonstrate a model in which synchronously firing ensembles of neurons are networked to produce computational results. Each ensemble is a group of biological integrate-and-fire spiking neurons, with probabilistic interconnections between groups. An analogy is drawn in which each individual processing unit of an artificial neural network corresponds to a neuronal group in a biological model. The activation value of a unit in the artificial neural network corresponds to the fraction of active neurons, synchronously firing, in a biological neuronal group. Weights of the artificial neural network correspond to the product of the interconnection density between groups, the group size of the presynaptic group, and the postsynaptic potential heights in the synchronous group model. All three of these parameters can modulate connection strengths between neuronal groups in the synchronous group models. We give an example of nonlinear classification (XOR) and a function approximation example in which the capability of the artificial neural network can be captured by a neural network model with biological integrate-and-fire neurons configured as a network of synchronously firing ensembles of such neurons. We point out that the general function approximation capability proven for feedforward artificial neural networks appears to be approximated by networks of neuronal groups that fire in synchrony, where the groups comprise integrate-and-fire neurons. We discuss the advantages of this type of model for biological systems, its possible learning mechanisms, and the associated timing relationships.

  7. Cortical activity influences geniculocortical spike efficacy in the macaque monkey

    Directory of Open Access Journals (Sweden)

    Farran Briggs

    2007-11-01

    Full Text Available Thalamocortical communication is a dynamic process influenced by both presynaptic and postsynaptic mechanisms. In this study, we recorded single-unit responses from cortical neurons that received direct input from the lateral geniculate nucleus (LGN to address the question of whether prior patterns of cortical activity affect the ability of LGN inputs to drive cortical responses. By examining the ongoing activity that preceded the arrival of electrically evoked spikes from the LGN, we identified a number of activity patterns that were predictive of suprathreshold communication. Namely, cortical neurons were more likely to respond to LGN stimulation when their activity levels increased to 30-40Hz and/or their activity displayed rhythmic patterns (30 ms intervals with increased power in the gamma frequency band. Cortical neurons were also more likely to respond to LGN stimulation when their activity increased 30-40 ms prior to stimulation, suggesting that the phase of gamma activity also contributes to geniculocortical communication. Based on these results, we conclude that ongoing activity in the cortex is not random, but rather organized in a manner that can influence the dynamics of thalamocortical communication.

  8. Electrical source imaging of interictal spikes using multiple sparse volumetric priors for presurgical epileptogenic focus localization

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    Gregor Strobbe

    2016-01-01

    Full Text Available Electrical source imaging of interictal spikes observed in EEG recordings of patients with refractory epilepsy provides useful information to localize the epileptogenic focus during the presurgical evaluation. However, the selection of the time points or time epochs of the spikes in order to estimate the origin of the activity remains a challenge. In this study, we consider a Bayesian EEG source imaging technique for distributed sources, i.e. the multiple volumetric sparse priors (MSVP approach. The approach allows to estimate the time courses of the intensity of the sources corresponding with a specific time epoch of the spike. Based on presurgical averaged interictal spikes in six patients who were successfully treated with surgery, we estimated the time courses of the source intensities for three different time epochs: (i an epoch starting 50 ms before the spike peak and ending at 50% of the spike peak during the rising phase of the spike, (ii an epoch starting 50 ms before the spike peak and ending at the spike peak and (iii an epoch containing the full spike time period starting 50 ms before the spike peak and ending 230 ms after the spike peak. To identify the primary source of the spike activity, the source with the maximum energy from 50 ms before the spike peak till 50% of the spike peak was subsequently selected for each of the time windows. For comparison, the activity at the spike peaks and at 50% of the peaks was localized using the LORETA inversion technique and an ECD approach. Both patient-specific spherical forward models and patient-specific 5-layered finite difference models were considered to evaluate the influence of the forward model. Based on the resected zones in each of the patients, extracted from post-operative MR images, we compared the distances to the resection border of the estimated activity. Using the spherical models, the distances to the resection border for the MSVP approach and each of the different time

  9. Studies with spike initiators - Linearization by noise allows continuous signal modulation in neural networks

    Science.gov (United States)

    Yu, Xiaolong; Lewis, Edwin R.

    1989-01-01

    It is shown that noise can be an important element in the translation of neuronal generator potentials (summed inputs) to neuronal spike trains (outputs), creating or expanding a range of amplitudes over which the spike rate is proportional to the generator potential amplitude. Noise converts the basically nonlinear operation of a spike initiator into a nearly linear modulation process. This linearization effect of noise is examined in a simple intuitive model of a static threshold and in a more realistic computer simulation of spike initiator based on the Hodgkin-Huxley (HH) model. The results are qualitatively similar; in each case larger noise amplitude results in a larger range of nearly linear modulation. The computer simulation of the HH model with noise shows linear and nonlinear features that were earlier observed in spike data obtained from the VIIIth nerve of the bullfrog. This suggests that these features can be explained in terms of spike initiator properties, and it also suggests that the HH model may be useful for representing basic spike initiator properties in vertebrates.

  10. Energetics based spike generation of a single neuron: simulation results and analysis

    Directory of Open Access Journals (Sweden)

    Nagarajan eVenkateswaran

    2012-02-01

    Full Text Available Existing current based models that capture spike activity, though useful in studying information processing capabilities of neurons, fail to throw light on their internal functioning. It is imperative to develop a model that captures the spike train of a neuron as a function of its intra cellular parameters for non-invasive diagnosis of diseased neurons. This is the first ever article to present such an integrated model that quantifies the inter-dependency between spike activity and intra cellular energetics. The generated spike trains from our integrated model will throw greater light on the intra-cellular energetics than existing current models. Now, an abnormality in the spike of a diseased neuron can be linked and hence effectively analyzed at the energetics level. The spectral analysis of the generated spike trains in a time-frequency domain will help identify abnormalities in the internals of a neuron. As a case study, the parameters of our model are tuned for Alzheimer disease and its resultant spike trains are studied and presented.

  11. EVENT-DRIVEN SIMULATION OF INTEGRATE-AND-FIRE MODELS WITH SPIKE-FREQUENCY ADAPTATION

    Institute of Scientific and Technical Information of China (English)

    Lin Xianghong; Zhang Tianwen

    2009-01-01

    The evoked spike discharges of a neuron depend critically on the recent history of its electrical activity. A well-known example is the phenomenon of spike-frequency adaptation that is a commonly observed property of neurons. In this paper, using a leaky integrate-and-fire model that includes an adaptation current, we propose an event-driven strategy to simulate integrate-and-fire models with spike-frequency adaptation. Such approach is more precise than traditional clock-driven numerical integration approach because the timing of spikes is treated exactly. In experiments, using event-driven and clock-driven strategies we simulated the adaptation time course of single neuron and the random network with spike-timing dependent plasticity, the results indicate that (1) the temporal precision of spiking events impacts on neuronal dynamics of single as well as network in the different simulation strategies and (2) the simulation time scales linearly with the total number of spiking events in the event-driven simulation strategies.

  12. Quiet Spike(TradeMark) Build-up Ground Vibration Testing Approach

    Science.gov (United States)

    Spivey, Natalie D.; Herrera, Claudia Y.; Truax, Roger; Pak, Chan-gi; Freund, Donald

    2007-01-01

    Flight tests of the Gulfstream Aerospace Corporation s Quiet Spike(TradeMark) hardware were recently completed on the National Aeronautics and Space Administration Dryden Flight Research Center F-15B airplane. NASA Dryden uses a modified F-15B (836) airplane as a testbed aircraft to cost-effectively fly flight research experiments that are typically mounted underneath the airplane, along the fuselage centerline. For the Quiet Spike(TradeMark) experiment, instead of a centerline mounting, a forward-pointing boom was attached to the radar bulkhead of the airplane. The Quiet Spike(TradeMark) experiment is a stepping-stone to airframe structural morphing technologies designed to mitigate the sonic-boom strength of business jets flying over land. Prior to flying the Quiet Spike(TradeMark) experiment on the F-15B airplane several ground vibration tests were required to understand the Quiet Spike(TradeMark) modal characteristics and coupling effects with the F-15B airplane. Because of flight hardware availability and compressed schedule requirements, a "traditional" ground vibration test of the mated F-15B Quiet Spike(TradeMark) ready-for-flight configuration did not leave sufficient time available for the finite element model update and flutter analyses before flight-testing. Therefore, a "nontraditional" ground vibration testing approach was taken. This report provides an overview of each phase of the "nontraditional" ground vibration testing completed for the Quiet Spike(TradeMark) project.

  13. Flow field behavior with Reynolds number variance around a spiked body

    Science.gov (United States)

    Khurana, Shashank; Suzuki, Kojiro; Rathakrishnan, Ethirajan

    2016-10-01

    An experimental visualization study was performed to investigate the dependence of the pressure hill height and the influence zone expanse, for flow past a spiked body with different nose configurations, over a Reynolds number range from 2278 to 4405 to establish the vortex shedding process, and applicability in low speed flow regime for effective pressure reduction. It is found that the spike reduces the radius of curvature of the approaching streamline, leading to the deflection of the streamlines towards the shoulder of the basic body, resulting in a narrow zone of the positive pressure hill at the body nose. It is also observed that the pressure hill length and the influence zone expanse decrease with the introduction of spike over the present range of Reynolds numbers. For Reynolds numbers less than 2700, spike with conical nose is found to be more efficient than the spikes with other nose shapes of the present study in reducing the positive pressure at the nose of the blunt body. For higher Reynolds numbers, greater than 2700, the size of the vortex at the junction of the spike and basic body is the largest for the spike with hemispherical nose, and emerges as a potential candidate for application in possible wind-design resistant structures.

  14. Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems

    Science.gov (United States)

    Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

    2016-12-01

    Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.

  15. Comments on the variation of spike morphology in selected species of Elytrigia and Elymus (Triticeae

    Directory of Open Access Journals (Sweden)

    Romuald Kosina

    2014-01-01

    Full Text Available The structure of spikes of Elytrigia repens, E. intermedia and Elymus caninus was investigated. The number of spikelets per spike reveals the weakest correlations with other characters of the spike. The same concerns some character ratios. The correlations provide information about the segmented structure (metamers of the spike. There is a great difference between matrices of correlation coefficients for E. repens and E. intermedia related to the development and structure of spike. Characters important for the description of the spike were chosen - in five-character set these are among others: length of glume awn in median spikelet, length of lemma awn in the first floret of the median spikelet, number of spikelets per spike. Length of lemma awn and mean length of the rachis segment were recognized as the best discriminants for species. Ordination of forms along axes of canonical variates does not indicate the subunits within E. repens. Intermediate forms between E. repens and Elymus caninus have not been found. Between E. repens and E. intermedia there exists some proximity. Heteromorphic individuals were described by means of cluster analysis. They prove the mobility of the genome in ramets of a single genet.

  16. Canonical correlation between LFP network and spike network during working memory task in rat.

    Science.gov (United States)

    Yi, Hu; Zhang, Xiaofan; Bai, Wenwen; Liu, Tiaotiao; Tian, Xin

    2015-08-01

    Working memory refers to a system to temporary holding and manipulation of information. Previous studies suggested that local field potentials (LFPs) and spikes as well as their coordination provide potential mechanism of working memory. Popular methods for LFP-spike coordination only focus on the two modality signals, isolating each channel from multi-channel data, ignoring the entirety of the networked brain. Therefore, we investigated the coordination between the LFP network and spike network to achieve a better understanding of working memory. Multi-channel LFPs and spikes were simultaneously recorded in rat prefrontal cortex via microelectrode array during a Y-maze working memory task. Functional connectivity in the LFP network and spike network was respectively estimated by the directed transfer function (DTF) and maximum likelihood estimation (MLE). Then the coordination between the two networks was quantified via canonical correlation analysis (CCA). The results show that the canonical correlation (CC) varied during the working memory task. The CC-curve peaked before the choice point, describing the coordination between LFP network and spike network enhanced greatly. The CC value in working memory showed a significant higher level than inter-trial interval. Our results indicate that the enhanced canonical correlation between the LFP network and spike network may provide a potential network integration mechanism for working memory.

  17. Neonatal NMDA receptor blockade disrupts spike timing and glutamatergic synapses in fast spiking interneurons in a NMDA receptor hypofunction model of schizophrenia.

    Directory of Open Access Journals (Sweden)

    Kevin S Jones

    Full Text Available The dysfunction of parvalbumin-positive, fast-spiking interneurons (FSI is considered a primary contributor to the pathophysiology of schizophrenia (SZ, but deficits in FSI physiology have not been explicitly characterized. We show for the first time, that a widely-employed model of schizophrenia minimizes first spike latency and increases GluN2B-mediated current in neocortical FSIs. The reduction in FSI first-spike latency coincides with reduced expression of the Kv1.1 potassium channel subunit which provides a biophysical explanation for the abnormal spiking behavior. Similarly, the increase in NMDA current coincides with enhanced expression of the GluN2B NMDA receptor subunit, specifically in FSIs. In this study mice were treated with the NMDA receptor antagonist, MK-801, during the first week of life. During adolescence, we detected reduced spike latency and increased GluN2B-mediated NMDA current in FSIs, which suggests transient disruption of NMDA signaling during neonatal development exerts lasting changes in the cellular and synaptic physiology of neocortical FSIs. Overall, we propose these physiological disturbances represent a general impairment to the physiological maturation of FSIs which may contribute to schizophrenia-like behaviors produced by this model.

  18. A sight on protein-based nanoparticles as drug/gene delivery systems.

    Science.gov (United States)

    Salatin, Sara; Jelvehgari, Mitra; Maleki-Dizaj, Solmaz; Adibkia, Khosro

    2015-01-01

    Polymeric nanomaterials have extensively been applied for the preparation of targeted and controlled release drug/gene delivery systems. However, problems involved in the formulation of synthetic polymers such as using of the toxic solvents and surfactants have limited their desirable applications. In this regard, natural biomolecules including proteins and polysaccharide are suitable alternatives due to their safety. According to literature, protein-based nanoparticles possess many advantages for drug and gene delivery such as biocompatibility, biodegradability and ability to functionalize with targeting ligands. This review provides a general sight on the application of biodegradable protein-based nanoparticles in drug/gene delivery based on their origins. Their unique physicochemical properties that help them to be formulated as pharmaceutical carriers are also discussed.

  19. Protein-Based Graphene Biosensors: Optimizing Artificial Chemoreception in Bilayer Lipid Membranes

    Directory of Open Access Journals (Sweden)

    Christina G. Siontorou

    2016-09-01

    Full Text Available Proteinaceous moieties are critical elements in most detection systems, including biosensing platforms. Their potential is undoubtedly vast, yet many issues regarding their full exploitation remain unsolved. On the other hand, the biosensor formats with the higher marketability probabilities are enzyme in nature and electrochemical in concept. To no surprise, alternative materials for hosting catalysis within an electrode casing have received much attention lately to demonstrate a catalysis-coated device. Graphene and ZnO are presented as ideal materials to modify electrodes and biosensor platforms, especially in protein-based detection. Our group developed electrochemical sensors based on these nanomaterials for the sensitive detection of cholesterol using cholesterol oxidase incorporated in stabilized lipid films. A comparison between the two platforms is provided and discussed. In a broader sense, the not-so-remote prospect of quickly assembling a protein-based flexible biosensing detector to fulfill site-specific requirements is appealing to both university researchers and industry developers.

  20. Protein-Based Graphene Biosensors: Optimizing Artificial Chemoreception in Bilayer Lipid Membranes.

    Science.gov (United States)

    Siontorou, Christina G; Georgopoulos, Konstantinos N; Nikoleli, Georgia-Paraskevi; Nikolelis, Dimitrios P; Karapetis, Stefanos K; Bratakou, Spyridoula

    2016-09-07

    Proteinaceous moieties are critical elements in most detection systems, including biosensing platforms. Their potential is undoubtedly vast, yet many issues regarding their full exploitation remain unsolved. On the other hand, the biosensor formats with the higher marketability probabilities are enzyme in nature and electrochemical in concept. To no surprise, alternative materials for hosting catalysis within an electrode casing have received much attention lately to demonstrate a catalysis-coated device. Graphene and ZnO are presented as ideal materials to modify electrodes and biosensor platforms, especially in protein-based detection. Our group developed electrochemical sensors based on these nanomaterials for the sensitive detection of cholesterol using cholesterol oxidase incorporated in stabilized lipid films. A comparison between the two platforms is provided and discussed. In a broader sense, the not-so-remote prospect of quickly assembling a protein-based flexible biosensing detector to fulfill site-specific requirements is appealing to both university researchers and industry developers.

  1. Transcriptome Analysis for Abnormal Spike Development of the Wheat Mutant dms.

    Directory of Open Access Journals (Sweden)

    Xin-Xin Zhu

    Full Text Available Wheat (Triticum aestivum L. spike development is the foundation for grain yield. We obtained a novel wheat mutant, dms, characterized as dwarf, multi-pistil and sterility. Although the genetic changes are not clear, the heredity of traits suggests that a recessive gene locus controls the two traits of multi-pistil and sterility in self-pollinating populations of the medium plants (M, such that the dwarf genotype (D and tall genotype (T in the progeny of the mutant are ideal lines for studies regarding wheat spike development. The objective of this study was to explore the molecular basis for spike abnormalities of dwarf genotype.Four unigene libraries were assembled by sequencing the mRNAs of the super-bulked differentiating spikes and stem tips of the D and T plants. Using integrative analysis, we identified 419 genes highly expressed in spikes, including nine typical homeotic genes of the MADS-box family and the genes TaAP2, TaFL and TaDL. We also identified 143 genes that were significantly different between young spikes of T and D, and 26 genes that were putatively involved in spike differentiation. The result showed that the expression levels of TaAP1-2, TaAP2, and other genes involved in the majority of biological processes such as transcription, translation, cell division, photosynthesis, carbohydrate transport and metabolism, and energy production and conversion were significantly lower in D than in T.We identified a set of genes related to wheat floral organ differentiation, including typical homeotic genes. Our results showed that the major causal factors resulting in the spike abnormalities of dms were the lower expression homeotic genes, hormonal imbalance, repressed biological processes, and deficiency of construction materials and energy. We performed a series of studies on the homeotic genes, however the other three causal factors for spike abnormal phenotype of dms need further study.

  2. A model of reverse spike frequency adaptation and repetitive firing of subthalamic nucleus neurons.

    Science.gov (United States)

    Wilson, Charles J; Weyrick, Angela; Terman, David; Hallworth, Nicholas E; Bevan, Mark D

    2004-05-01

    Subthalamic nucleus neurons exhibit reverse spike-frequency adaptation. This occurs only at firing rates of 20-50 spikes/s and higher. Over this same frequency range, there is an increase in the steady-state frequency-intensity (F-I) curve's slope (the secondary range). Specific blockade of high-voltage activated calcium currents reduced the F-I curve slope and reverse adaptation. Blockade of calcium-dependent potassium current enhanced secondary range firing. A simple model that exhibited these properties used spike-triggered conductances similar to those in subthalamic neurons. It showed: 1) Nonaccumulating spike afterhyperpolarizations produce positively accelerating F-I curves and spike-frequency adaptation that is complete after the second spike. 2) Combinations of accumulating aftercurrents result in a linear F-I curve, whose slope depends on the relative contributions of inward and outward currents. Spike-frequency adaptation can be gradual. 3) With both accumulating and nonaccumulating aftercurrents, primary and secondary ranges will be present in the F-I curve. The slope of the primary range is determined by the nonaccumulating conductance; the accumulating conductances govern the secondary range. The transition is determined by the relative strengths of accumulating and nonaccumulating currents. 4) Spike-threshold accommodation contributes to the secondary range, reducing its slope at high firing rates. Threshold accommodation can stabilize firing when inward aftercurrents exceed outward ones. 5) Steady-state reverse adaptation results when accumulated inward aftercurrents exceed outward ones. This requires spike-threshold accommodation. Transient speedup arises when inward currents are smaller than outward ones at steady state, but accumulate more rapidly. 6) The same mechanisms alter firing in response to irregular patterns of synaptic conductances, as cell excitability fluctuates with changes in firing rate.

  3. New insights into rotavirus entry machinery: stabilization of rotavirus spike conformation is independent of trypsin cleavage.

    Directory of Open Access Journals (Sweden)

    Javier M Rodríguez

    2014-05-01

    Full Text Available The infectivity of rotavirus, the main causative agent of childhood diarrhea, is dependent on activation of the extracellular viral particles by trypsin-like proteases in the host intestinal lumen. This step entails proteolytic cleavage of the VP4 spike protein into its mature products, VP8* and VP5*. Previous cryo-electron microscopy (cryo-EM analysis of trypsin-activated particles showed well-resolved spikes, although no density was identified for the spikes in uncleaved particles; these data suggested that trypsin activation triggers important conformational changes that give rise to the rigid, entry-competent spike. The nature of these structural changes is not well understood, due to lack of data relative to the uncleaved spike structure. Here we used cryo-EM and cryo-electron tomography (cryo-ET to characterize the structure of the uncleaved virion in two model rotavirus strains. Cryo-EM three-dimensional reconstruction of uncleaved virions showed spikes with a structure compatible with the atomic model of the cleaved spike, and indistinguishable from that of digested particles. Cryo-ET and subvolume average, combined with classification methods, resolved the presence of non-icosahedral structures, providing a model for the complete structure of the uncleaved spike. Despite the similar rigid structure observed for uncleaved and cleaved particles, trypsin activation is necessary for successful infection. These observations suggest that the spike precursor protein must be proteolytically processed, not to achieve a rigid conformation, but to allow the conformational changes that drive virus entry.

  4. What can neuromorphic event-driven precise timing add to spike-based pattern recognition?

    Science.gov (United States)

    Akolkar, Himanshu; Meyer, Cedric; Clady, Zavier; Marre, Olivier; Bartolozzi, Chiara; Panzeri, Stefano; Benosman, Ryad

    2015-03-01

    This letter introduces a study to precisely measure what an increase in spike timing precision can add to spike-driven pattern recognition algorithms. The concept of generating spikes from images by converting gray levels into spike timings is currently at the basis of almost every spike-based modeling of biological visual systems. The use of images naturally leads to generating incorrect artificial and redundant spike timings and, more important, also contradicts biological findings indicating that visual processing is massively parallel, asynchronous with high temporal resolution. A new concept for acquiring visual information through pixel-individual asynchronous level-crossing sampling has been proposed in a recent generation of asynchronous neuromorphic visual sensors. Unlike conventional cameras, these sensors acquire data not at fixed points in time for the entire array but at fixed amplitude changes of their input, resulting optimally sparse in space and time-pixel individually and precisely timed only if new, (previously unknown) information is available (event based). This letter uses the high temporal resolution spiking output of neuromorphic event-based visual sensors to show that lowering time precision degrades performance on several recognition tasks specifically when reaching the conventional range of machine vision acquisition frequencies (30-60 Hz). The use of information theory to characterize separability between classes for each temporal resolution shows that high temporal acquisition provides up to 70% more information that conventional spikes generated from frame-based acquisition as used in standard artificial vision, thus drastically increasing the separability between classes of objects. Experiments on real data show that the amount of information loss is correlated with temporal precision. Our information-theoretic study highlights the potentials of neuromorphic asynchronous visual sensors for both practical applications and theoretical

  5. Accurate spike time prediction from LFP in monkey visual cortex: A non-linear system identification approach

    NARCIS (Netherlands)

    Kostoglou, K.; Hadjipapas, A.; Lowet, E.; Roberts, M.; de Weerd, P.; Mitsis, G.D.

    2014-01-01

    Aims: The relationship between collective population activity (LFP) and spikes underpins network computation, yet it remains poorly understood. Previous studies utilized pre-defined LFP features to predict spiking from simultaneously recorded LFP, and have reported good prediction of spike bursts bu

  6. On the mechanisms of inferior olivary signalling : Timing, scaled impact and plasticity mechanisms exerted by the olivary spike

    NARCIS (Netherlands)

    P.J. Warnaar (Pascal)

    2017-01-01

    textabstractThe inferior olive is regarded to be able to dictate activity in the cerebellum. Its spiking activity comprises the elusive complex spikes. In this dissertation we have looked into different putative roles of the complex spike. We have looked into a differential effect of the single

  7. Spike timing dependent plasticity (STDP) can ameliorate process variations in neuromorphic VLSI.

    Science.gov (United States)

    Cameron, Katherine; Boonsobhak, Vasin; Murray, Alan; Renshaw, David

    2005-11-01

    A transient-detecting very large scale integration (VLSI) pixel is described, suitable for use in a visual-processing, depth-recovery algorithm based upon spike timing. A small array of pixels is coupled to an adaptive system, based upon spike timing dependent plasticity (STDP), that aims to reduce the effect of VLSI process variations on the algorithm's performance. Results from 0.35 microm CMOS temporal differentiating pixels and STDP circuits show that the system is capable of adapting to substantially reduce the effects of process variations without interrupting the algorithm's natural processes. The concept is generic to all spike timing driven processing algorithms in a VLSI.

  8. Dynamic spike threshold and nonlinear dendritic computation for coincidence detection in neuromorphic circuits.

    Science.gov (United States)

    Hsu, Chih-Chieh; Parker, Alice C

    2014-01-01

    We present an electronic cortical neuron incorporating dynamic spike threshold and active dendritic properties. The circuit is simulated using a carbon nanotube field-effect transistor SPICE model. We demonstrate that our neuron has lower spike threshold for coincident synaptic inputs; however when the synaptic inputs are not in synchrony, it requires larger depolarization to evoke the neuron to fire. We also demonstrate that a dendritic spike is key to precisely-timed input-output transformation, produces reliable firing and results in more resilience to input jitter within an individual neuron.

  9. On the applicability of STDP-based learning mechanisms to spiking neuron network models

    Science.gov (United States)

    Sboev, A.; Vlasov, D.; Serenko, A.; Rybka, R.; Moloshnikov, I.

    2016-11-01

    The ways to creating practically effective method for spiking neuron networks learning, that would be appropriate for implementing in neuromorphic hardware and at the same time based on the biologically plausible plasticity rules, namely, on STDP, are discussed. The influence of the amount of correlation between input and output spike trains on the learnability by different STDP rules is evaluated. A usability of alternative combined learning schemes, involving artificial and spiking neuron models is demonstrated on the iris benchmark task and on the practical task of gender recognition.

  10. A quick overview of membrane computing with some details about spiking neural P systems

    Institute of Scientific and Technical Information of China (English)

    Gheorghe Pǎun

    2007-01-01

    We briefly present the basic elements of membrane computing,a branch of natural computing inspired by the structure and functioning of living cells,then we give some details about spiking neural P systems,a class of membrane systems recently introduced,with motivations related to the way neurons communicate by means of spikes.In both cases,of general P systems and of spiking neural P systems,we introduce the fundamental concepts,give a few examples,then recall the types of results and of applications.A series of bibliographical references are provided.

  11. The use of doping spikes in GaN Gunn diodes

    Science.gov (United States)

    Macpherson, R. F.; Dunn, G. M.

    2008-08-01

    The possibility of circumventing the difficulties of fine doping control in GaN Gunn diode devices by the substitution of a fully depleted p-type doping spike for the doping notch used to promote domain formation is explored using a Monte Carlo model. The p-type doping spike is a commonly used structure, but its potential use in GaN has not been previously evaluated. The results for a functional doping spike are compared, favorably, to those for a physically reasonable doping notch.

  12. The Site of Spontaneous Ectopic Spike Initiation Facilitates Signal Integration in a Sensory Neuron.

    Science.gov (United States)

    Städele, Carola; Stein, Wolfgang

    2016-06-22

    Essential to understanding the process of neuronal signal integration is the knowledge of where within a neuron action potentials (APs) are generated. Recent studies support the idea that the precise location where APs are initiated and the properties of spike initiation zones define the cell's information processing capabilities. Notably, the location of spike initiation can be modified homeostatically within neurons to adjust neuronal activity. Here we show that this potential mechanism for neuronal plasticity can also be exploited in a rapid and dynamic fashion. We tested whether dislocation of the spike initiation zone affects signal integration by studying ectopic spike initiation in the anterior gastric receptor neuron (AGR) of the stomatogastric nervous system of Cancer borealis Like many other vertebrate and invertebrate neurons, AGR can generate ectopic APs in regions distinct from the axon initial segment. Using voltage-sensitive dyes and electrophysiology, we determined that AGR's ectopic spike activity was consistently initiated in the neuropil region of the stomatogastric ganglion motor circuits. At least one neurite branched off the AGR axon in this area; and indeed, we found that AGR's ectopic spike activity was influenced by local motor neurons. This sensorimotor interaction was state-dependent in that focal axon modulation with the biogenic amine octopamine, abolished signal integration at the primary spike initiation zone by dislocating spike initiation to a distant region of the axon. We demonstrate that the site of ectopic spike initiation is important for signal integration and that axonal neuromodulation allows for a dynamic adjustment of signal integration. Although it is known that action potentials are initiated at specific sites in the axon, it remains to be determined how the precise location of action potential initiation affects neuronal activity and signal integration. We addressed this issue by studying ectopic spiking in the axon of

  13. Just in time connectivity for large spiking networks

    Science.gov (United States)

    Lytton, William W.; Omurtag, Ahmet; Neymotin, Samuel A; Hines, Michael L

    2008-01-01

    The scale of large neuronal network simulations is memory-limited due to the need to store connectivity information: connectivity storage grows as the square of neuron number up to anatomically-relevant limits. Using the NEURON simulator as a discrete-event simulator (no integration), we explored the consequences of avoiding the space costs of connectivity through regenerating connectivity parameters when needed – just-in-time after a presynaptic cell fires. We explored various strategies for automated generation of one or more of the basic static connectivity parameters: delays, postsynaptic cell identities and weights, as well as run-time connectivity state: the event queue. Comparison of the JitCon implementation to NEURON’s standard NetCon connectivity method showed substantial space savings, with associated run-time penalty. Although JitCon saved space by eliminating connectivity parameters, larger simulations were still memory-limited due to growth of the synaptic event queue. We therefore designed a JitEvent algorithm that only added items to the queue when required: instead of alerting multiple postsynaptic cells, a spiking presynaptic cell posted a callback event at the shortest synaptic delay time. At the time of the callback, this same presynaptic cell directly notified the first postsynaptic cell and generated another self-callback for the next delay time. The JitEvent implementation yielded substantial additional time and space savings. We conclude that just-in-time strategies are necessary for very large network simulations but that a variety of alternative strategies should be considered whose optimality will depend on the characteristics of the simulation to be run. PMID:18533821

  14. A history of spike-timing-dependent plasticity

    Directory of Open Access Journals (Sweden)

    Henry eMarkram

    2011-08-01

    Full Text Available How learning and memory is achieved in the brain is a central question in neuroscience research. Key to today’s research into information storage in the brain is the concept of synaptic plasticity, a notion that has been heavily influenced by Donald Hebb’s 1949 postulate. Hebb conjectured that repeatedly and persistently coactive cells should increase connective strength among populations of interconnected neurons as a means of storing a memory trace, also known as an engram. Hebb certainly was not the first to make such a conjecture, as we show in this history. Nevertheless, literally thousands of studies into the classical frequency-dependent paradigm of cellular learning rules were directly inspired by the Hebbian postulate. But in more recent years, a novel concept in cellular learning has emerged, where temporal order instead of frequency is emphasized. This new learning paradigm — known as Spike-Timing-Dependent Plasticity, or STDP — has rapidly gained tremendous interest, perhaps because of its combination of elegant simplicity, biological plausibility, and computational power. But what are the roots of today’s STDP concept? Here, we discuss several centuries of diverse thinking, beginning with philosophers such as Aristotle, Locke and Ribot, traversing e.g. Lugaro’s plasticità and Rosenblatt’s Perceptron, and culminating with the discovery of STDP. We highlight interactions between theoretical and experimental fields, showing how discoveries sometimes occurred in parallel, seemingly without much knowledge of the other field, and sometimes via concrete back-and-forth communication. We point out where the future directions may lie, which includes interneuron STDP, the functional impact of STDP, its mechanisms and its neuromodulatory regulation, and the linking of STDP to the developmental formation and continuous plasticity of neuronal networks.

  15. A Heparin Purification Process Removes Spiked Transmissible Spongiform Encephalopathy Agent.

    Science.gov (United States)

    Bett, Cyrus; Grgac, Ksenija; Long, Dianna; Karfunkle, Michael; Keire, David A; Asher, David M; Gregori, Luisa

    2017-01-23

    In 2000, bovine heparin was withdrawn from the US market for fear of contamination with bovine spongiform encephalopathy (BSE) agent, the cause of variant Creutzfeldt-Jakob disease in humans. Thus, US heparin is currently sourced only from pig intestines. Availability of alternative sources of crude heparin, a life-saving drug, would benefit public health. Bovine heparin is an obvious option, but BSE clearance by the bovine heparin manufacturing process should be evaluated. To this end, using hamster 263K scrapie as a surrogate for BSE agent, we applied a four-step bench-scale heparin purification protocol resembling a typical heparin manufacturing process to investigate removal of the spiked scrapie agent. We removed aliquots from each step and analyzed them for residual abnormal prion protein (PrP(TSE)) using a sensitive in vitro method, real-time quaking-induced conversion (RT-QuIC) assay, and for infectivity using animal bioassays. The purification process reduced infectivity by 3.6 log10 and removed PrP(TSE), measured as seeding activity, by 3.4 log10. NaOH treatment was the most effective removal step tested. We also investigated NaOH at different concentrations and pH: the results showed that as much as 5.2 log10 of PrP(TSE) seeding activity was removed at pH 12.5. Thus, changes to the concentration, treatment time, and temperature of alkaline extraction might further improve removal. Our results, using a basic heparin manufacturing process, inform efforts to reintroduce safe bovine heparin in the USA.

  16. Avalanches in a stochastic model of spiking neurons.

    Directory of Open Access Journals (Sweden)

    Marc Benayoun

    Full Text Available Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both in vivo and in vitro. They are characterized by irregular, isolated population bursts when many neurons fire together, where the number of spikes per burst obeys a power law distribution. We simulate, using the Gillespie algorithm, a model of neuronal avalanches based on stochastic single neurons. The network consists of excitatory and inhibitory neurons, first with all-to-all connectivity and later with random sparse connectivity. Analyzing our model using the system size expansion, we show that the model obeys the standard Wilson-Cowan equations for large network sizes ( neurons. When excitation and inhibition are closely balanced, networks of thousands of neurons exhibit irregular synchronous activity, including the characteristic power law distribution of avalanche size. We show that these avalanches are due to the balanced network having weakly stable functionally feedforward dynamics, which amplifies some small fluctuations into the large population bursts. Balanced networks are thought to underlie a variety of observed network behaviours and have useful computational properties, such as responding quickly to changes in input. Thus, the appearance of avalanches in such functionally feedforward networks indicates that avalanches may be a simple consequence of a widely present network structure, when neuron dynamics are noisy. An important implication is that a network need not be "critical" for the production of avalanches, so experimentally observed power laws in burst size may be a signature of noisy functionally feedforward structure rather than of, for example, self-organized criticality.

  17. Avalanches in a stochastic model of spiking neurons.

    Science.gov (United States)

    Benayoun, Marc; Cowan, Jack D; van Drongelen, Wim; Wallace, Edward

    2010-07-08

    Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both in vivo and in vitro. They are characterized by irregular, isolated population bursts when many neurons fire together, where the number of spikes per burst obeys a power law distribution. We simulate, using the Gillespie algorithm, a model of neuronal avalanches based on stochastic single neurons. The network consists of excitatory and inhibitory neurons, first with all-to-all connectivity and later with random sparse connectivity. Analyzing our model using the system size expansion, we show that the model obeys the standard Wilson-Cowan equations for large network sizes ( neurons). When excitation and inhibition are closely balanced, networks of thousands of neurons exhibit irregular synchronous activity, including the characteristic power law distribution of avalanche size. We show that these avalanches are due to the balanced network having weakly stable functionally feedforward dynamics, which amplifies some small fluctuations into the large population bursts. Balanced networks are thought to underlie a variety of observed network behaviours and have useful computational properties, such as responding quickly to changes in input. Thus, the appearance of avalanches in such functionally feedforward networks indicates that avalanches may be a simple consequence of a widely present network structure, when neuron dynamics are noisy. An important implication is that a network need not be "critical" for the production of avalanches, so experimentally observed power laws in burst size may be a signature of noisy functionally feedforward structure rather than of, for example, self-organized criticality.

  18. An efficient automated parameter tuning framework for spiking neural networks.

    Science.gov (United States)

    Carlson, Kristofor D; Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L

    2014-01-01

    As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier.

  19. Modeling Population Spike Trains with Specified Time-Varying Spike Rates, Trial-to-Trial Variability, and Pairwise Signal and Noise Correlations.

    Science.gov (United States)

    Lyamzin, Dmitry R; Macke, Jakob H; Lesica, Nicholas A

    2010-01-01

    As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modeling such data must also be developed. Here, we present a model for the type of data commonly recorded in early sensory pathways: responses to repeated trials of a sensory stimulus in which each neuron has it own time-varying spike rate (as described by its PSTH) and the dependencies between cells are characterized by both signal and noise correlations. This model is an extension of previous attempts to model population spike trains designed to control only the total correlation between cells. In our model, the response of each cell is represented as a binary vector given by the dichotomized sum of a deterministic "signal" that is repeated on each trial and a Gaussian random "noise" that is different on each trial. This model allows the simulation of population spike trains with PSTHs, trial-to-trial variability, and pairwise correlations that match those measured experimentally. Furthermore, the model also allows the noise correlations in the spike trains to be manipulated independently of the signal correlations and single-cell properties. To demonstrate the utility of the model, we use it to simulate and manipulate experimental responses from the mammalian auditory and visual systems. We also present a general form of the model in which both the signal and noise are Gaussian random processes, allowing the mean spike rate, trial-to-trial variability, and pairwise signal and noise correlations to be specified independently. Together, these methods for modeling spike trains comprise a potentially powerful set of tools for both theorists and experimentalists studying population responses in sensory systems.

  20. Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

    Directory of Open Access Journals (Sweden)

    Michael Krumin

    2010-01-01

    Full Text Available Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden’’ Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method.

  1. An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation.

    Science.gov (United States)

    Wang, Runchun; Cohen, Gregory; Stiefel, Klaus M; Hamilton, Tara Julia; Tapson, Jonathan; van Schaik, André

    2013-01-01

    We present an FPGA implementation of a re-configurable, polychronous spiking neural network with a large capacity for spatial-temporal patterns. The proposed neural network generates delay paths de novo, so that only connections that actually appear in the training patterns will be created. This allows the proposed network to use all the axons (variables) to store information. Spike Timing Dependent Delay Plasticity is used to fine-tune and add dynamics to the network. We use a time multiplexing approach allowing us to achieve 4096 (4k) neurons and up to 1.15 million programmable delay axons on a Virtex 6 FPGA. Test results show that the proposed neural network is capable of successfully recalling more than 95% of all spikes for 96% of the stored patterns. The tests also show that the neural network is robust to noise from random input spikes.

  2. Quantification of clustering in joint interspike interval scattergrams of spike trains.

    Science.gov (United States)

    Dodla, Ramana; Wilson, Charles J

    2010-06-02

    Joint interval scattergrams are usually employed in determining serial correlations between events of spike trains. However, any inherent structures in such scattergrams that are often seen in experimental records are not quantifiable by serial correlation coefficients. Here, we develop a method to quantify clustered structures in any two-dimensional scattergram of pairs of interspike intervals. The method gives a cluster coefficient as well as clustering density function that could be used to quantify clustering in scattergrams obtained from first- or higher-order interval return maps of single spike trains, or interspike interval pairs drawn from simultaneously recorded spike trains. The method is illustrated using numerical spike trains as well as in vitro pairwise recordings of rat striatal tonically active neurons.

  3. Noisy saltatory spike propagation: The breakdown of signal transmission due to channel noise

    CERN Document Server

    Li, Yunyun; Hanggi, Peter; 10.1140/epjst/e2010-01281-4

    2010-01-01

    Noisy saltatory spike propagation along myelinated axons is studied within a stochastic Hodgkin-Huxley model. The intrinsic noise (whose strength is inverse proportional to the nodal membrane size) arising from fluctuations of the number of open ion channels influences the dynamics of the membrane potential in a node of Ranvier where the sodium ion channels are predominantly localized. The nodes of Ranvier are linearly coupled. As the measure for the signal propagation reliability we focus on the ratio between the number of initiated spikes and the transmitted spikes. This work supplements our earlier study [A. Ochab-Marcinek, G. Schmid, I. Goychuk and P. H\\"anggi, Phys. Rev E 79, 011904 (2009)] towards stronger channel noise intensity and supra-threshold coupling. For strong supra-threshold coupling the transmission reliability decreases with increasing channel noise level until the causal relationship is completely lost and a breakdown of the spike propagation due to the intrinsic noise is observed.

  4. Neuroaminidase reduces interictal spikes in a rat temporal lobe epilepsy model.

    Science.gov (United States)

    Isaev, Dmytro; Zhao, Qian; Kleen, Jonathan K; Lenck-Santini, Pierre Pascal; Adstamongkonkul, Dusit; Isaeva, Elena; Holmes, Gregory L

    2011-03-01

    Interictal spikes have been implicated in epileptogenesis and cognitive dysfunction in epilepsy. Unfortunately, antiepileptic drugs have shown poor efficacy in suppressing interictal discharges; novel therapies are needed. Surface charge on neuronal membranes provides a novel target for abolishing interictal spikes. This property can be modulated through the use of neuraminidase, an enzyme that decreases the amount of negatively charged sialic acid. In the present report we determined whether applying neuraminidase to brains of rats with a history of status epilepticus would reduce number of interictal discharges. Following pilocarpine-induced status epilepticus, rats received intrahippocampal injections of neuraminidase, which significantly decreased the number of interictal spikes recorded in the CA1 region. This study provides evidence that sialic acid degradation can reduce the number of interictal spikes. Furthermore, the results suggest that modifying surface charge created by negatively charged sialic acid may provide new opportunities for reducing aberrant epileptiform events in epilepsy.

  5. Harmony perception and regularity of spike trains in a simple auditory model

    Science.gov (United States)

    Spagnolo, B.; Ushakov, Y. V.; Dubkov, A. A.

    2013-01-01

    A probabilistic approach for investigating the phenomena of dissonance and consonance in a simple auditory sensory model, composed by two sensory neurons and one interneuron, is presented. We calculated the interneuron's firing statistics, that is the interspike interval statistics of the spike train at the output of the interneuron, for consonant and dissonant inputs in the presence of additional "noise", representing random signals from other, nearby neurons and from the environment. We find that blurry interspike interval distributions (ISIDs) characterize dissonant accords, while quite regular ISIDs characterize consonant accords. The informational entropy of the non-Markov spike train at the output of the interneuron and its dependence on the frequency ratio of input sinusoidal signals is estimated. We introduce the regularity of spike train and suggested the high or low regularity level of the auditory system's spike trains as an indicator of feeling of harmony during sound perception or disharmony, respectively.

  6. A formula for the profile of voltammogram spikes in the quasistatic regime

    Science.gov (United States)

    Medved', I.

    2008-09-01

    A phase transition occurring at electrode-electrolyte interfaces is reflected in voltammograms (the current versus voltage plots) as a sharp spike. We derive a general formula fitting the profile of the spikes due to the first-order phase transitions that can be microscopically modeled by classical two-dimensional lattice gases. The quasistatic (near equilibrium) regime is required. The profile is especially essential when interpreting generic voltammograms, where two or more close or overlapping spikes usually appear. Simple direct links between the microscopics of a phase transition and the macroscopic properties of the associated spike are explicitly given. We demonstrate our results on the voltammograms for the underpotential deposition of copper on platinum (111) and (100) and on gold (111) and achieve very good agreement with experiment.

  7. Analog frontend for multichannel neuronal recording system with spike and LFP separation.

    Science.gov (United States)

    Perelman, Yevgeny; Ginosar, Ran

    2006-05-15

    A 0.35microm CMOS integrated circuit for multi-channel neuronal recording with twelve true-differential channels, band separation and digital offset calibration is presented. The measured signal is separated into a low-frequency local field potential and high-frequency spike data. Digitally programmable gains of up to 60 and 80 dB for the local field potential and spike bands are provided. DC offsets are compensated on both bands by means of digitally programmable DACs. Spike band is limited by a second order low-pass filter with digitally programmable cutoff frequency. The IC has been fabricated and tested. 3microV input referred noise on the spike data band was measured.

  8. Broadband shifts in LFP power spectra are correlated with single-neuron spiking in humans

    Science.gov (United States)

    Manning, Jeremy R.; Jacobs, Joshua; Fried, Itzhak; Kahana, Michael J.

    2010-01-01

    A fundamental question in neuroscience concerns the relation between the spiking of individual neurons and the aggregate electrical activity of neuronal ensembles as seen in local-field potentials (LFPs). Because LFPs reflect both spiking activity and subthreshold events, this question is not simply one of data aggregation. Recording from 20 neurosurgical patients, we directly examined the relation between LFPs and neuronal spiking. Examining 2,030 neurons in widespread brain regions, we found that firing rates were positively correlated with broadband (2 – 150 Hz) shifts in the LFP power spectrum. In contrast, narrowband oscillations correlated both positively and negatively with firing rates at different recording sites. Broadband power shifts were a more-reliable predictor of neuronal spiking than narrowband power shifts. These findings suggest that broadband LFP power provides valuable information concerning neuronal activity beyond that contained in narrowband oscillations. PMID:19864573

  9. Doping-Spike PtSi Schottky Infrared Detectors with Extended Cutoff Wavelengths

    Science.gov (United States)

    Lin, T. L.; Park, J. S.; Gunapala, S. D.; Jones, E. W.; Castillo, H. M. Del

    1994-01-01

    A technique incorporating a p+ doping spike at the silicide/Si interface to reduce the effective Schottky barrier of the silicide infrared detectors and thus extend the cutoff wavelength has been developed.

  10. Golden Spike National Historic Site Vegetation Mapping Project - 2006 True Color Orthophotography

    Data.gov (United States)

    National Park Service, Department of the Interior — This imagery dataset was used to map the vegetation at Golden Spike National Historic Site. This data set contains imagery from the National Agricultural Imagery...

  11. Non-parametric method for separating domestic hot water heating spikes and space heating

    DEFF Research Database (Denmark)

    Bacher, Peder; de Saint-Aubain, Philip Anton; Christiansen, Lasse Engbo;

    2016-01-01

    In this paper a method for separating spikes from a noisy data series, where the data change and evolve over time, is presented. The method is applied on measurements of the total heat load for a single family house. It relies on the fact that the domestic hot water heating is a process generating...... short-lived spikes in the time series, while the space heating changes in slower patterns during the day dependent on the climate and user behavior. The challenge is to separate the domestic hot water heating spikes from the space heating without affecting the natural noise in the space heating...... measurements. The assumption behind the developed method is that the space heating can be estimated by a non-parametric kernel smoother, such that every value significantly above this kernel smoother estimate is identified as a domestic hot water heating spike. First, it is showed how a basic kernel smoothing...

  12. Removing Spikes While Preserving Data and Noise using Wavelet Filter Banks

    Data.gov (United States)

    National Aeronautics and Space Administration — Many diagnostic datasets suffer from the adverse effects of spikes that are embedded in data and noise. For example, this is true for electrical power system data...

  13. Exact computation of the Maximum Entropy Potential of spiking neural networks models

    CERN Document Server

    Cofre, Rodrigo

    2014-01-01

    Understanding how stimuli and synaptic connectivity in uence the statistics of spike patterns in neural networks is a central question in computational neuroscience. Maximum Entropy approach has been successfully used to characterize the statistical response of simultaneously recorded spiking neurons responding to stimuli. But, in spite of good performance in terms of prediction, the ?tting parameters do not explain the underlying mechanistic causes of the observed correlations. On the other hand, mathematical models of spiking neurons (neuro-mimetic models) provide a probabilistic mapping between stimulus, network architecture and spike patterns in terms of conditional proba- bilities. In this paper we build an exact analytical mapping between neuro-mimetic and Maximum Entropy models.

  14. Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering

    Directory of Open Access Journals (Sweden)

    Oliynyk Andriy

    2012-08-01

    Full Text Available Abstract Background Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Results Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting, which is designed to optimize: (i fast and accurate detection, (ii offline sorting and (iii online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com using LabVIEW (National Instruments, USA. We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is

  15. Galactic center gamma-ray excess from dark matter annihilation: is there a black hole spike?

    Science.gov (United States)

    Fields, Brian D; Shapiro, Stuart L; Shelton, Jessie

    2014-10-10

    If the supermassive black hole Sgr A* at the center of the Milky Way grew adiabatically from an initial seed embedded in a Navarro-Frenk-White dark matter (DM) halo, then the DM profile near the hole has steepened into a spike. We calculate the dramatic enhancement to the gamma-ray flux from the Galactic center (GC) from such a spike if the 1-3 GeV excess observed in Fermi data is due to DM annihilations. We find that for the parameter values favored in recent fits, the point-source-like flux from the spike is 35 times greater than the flux from the inner 1° of the halo, far exceeding all Fermi point source detections near the GC. We consider the dependence of the spike signal on astrophysical and particle parameters and conclude that if the GC excess is due to DM, then a canonical adiabatic spike is disfavored by the data. We discuss alternative Galactic histories that predict different spike signals, including (i) the nonadiabatic growth of the black hole, possibly associated with halo and/or black hole mergers, (ii) gravitational interaction of DM with baryons in the dense core, such as heating by stars, or (iii) DM self-interactions. We emphasize that the spike signal is sensitive to a different combination of particle parameters than the halo signal and that the inclusion of a spike component to any DM signal in future analyses would provide novel information about both the history of the GC and the particle physics of DM annihilations.

  16. The Minimum Wage Spike in the Search Economy with Wage-Posting

    OpenAIRE

    Natalya Y. Shelkova

    2009-01-01

    Empirical wage and wage offer distributions exhibit substantial clustering in economies with a mandated minimum wage, the phenomenon knows as the minimum wage spike, as well as wage dispersion. Existing search-theoretic literature does not replicate both of the empirical phenomena simultaneously. This paper attempts to reconcile the two under assumptions of wage-posting, urn-ball matching and firm productive heterogeneity. A non-degenerate minimum wage spike and wage dispersion are obtained w...

  17. INVESTIGATION OF TRACKING OF VOLTAGE SIGNAL CONTAINING HARMONICS AND SPIKE BY USING RECURSIVE LEAST SQUARES METHOD

    Directory of Open Access Journals (Sweden)

    H. Hüseyin SAYAN

    2009-01-01

    Full Text Available In this study, recursive least squares method (RLSM that is one of the adaptable classical methods was used. Firstly forgetting factor was adapted to RLSM. Phase information of voltage signal belonging to an electric power network that contains harmonics and spike was obtained by developed approach. Then responses of the algorithm were investigated for voltage collapse, phase shift and spike. Simulation was implemented by using MATLAB® code. Results of simulation were examined and efficiency of method was presented.

  18. Extracellular stimulation of nerve cells with electric current spikes induced by voltage steps

    OpenAIRE

    2016-01-01

    A new stimulation paradigm is presented for the stimulation of nerve cells by extracellular electric currents. In the new paradigm stimulation is achieved with the current spike induced by a voltage step whenever the voltage step is applied to a live biological tissue. By experimental evidence and theoretical arguments, it is shown that this spike is well suited for the stimulation of nerve cells. Stimulation of the human tongue is used for proof of principle. Charge injection thresholds are ...

  19. Two Coincidence Detectors for Spike Timing-Dependent Plasticity in Somatosensory Cortex

    OpenAIRE

    2006-01-01

    Many cortical synapses exhibit spike timing-dependent plasticity (STDP) in which the precise timing of presynaptic and postsynaptic spikes induces synaptic strengthening [long-term potentiation (LTP)] or weakening [long-term depression (LTD)]. Standard models posit a single, postsynaptic, NMDA receptor-based coincidence detector for LTP and LTD components of STDP. We show instead that STDP at layer 4 to layer 2/3 synapses in somatosensory (S1) cortex involves separate calcium sources and coin...

  20. Transcranial direct current stimulation in refractory continuous spikes and waves during slow sleep: a controlled study

    DEFF Research Database (Denmark)

    Varga, Edina T; Terney, Daniella; Atkins, Mary D;

    2011-01-01

    Cathodal transcranial direct current stimulation (tDCS) decreases cortical excitability. The purpose of the study was to investigate whether cathodal tDCS could interrupt the continuous epileptiform activity. Five patients with focal, refractory continuous spikes and waves during slow sleep were...... recruited. Cathodal tDCS and sham stimulation were applied to the epileptic focus, before sleep (1 mA; 20 min). Cathodal tDCS did not reduce the spike-index in any of the patients....