AMORE Mo-99 Spike Test Results

Energy Technology Data Exchange (ETDEWEB)

Youker, Amanda J. [Argonne National Lab. (ANL), Argonne, IL (United States); Krebs, John F. [Argonne National Lab. (ANL), Argonne, IL (United States); Quigley, Kevin J. [Argonne National Lab. (ANL), Argonne, IL (United States); Byrnes, James P. [Argonne National Lab. (ANL), Argonne, IL (United States); Rotsch, David A [Argonne National Lab. (ANL), Argonne, IL (United States); Brossard, Thomas [Argonne National Lab. (ANL), Argonne, IL (United States); Wesolowski, Kenneth [Argonne National Lab. (ANL), Argonne, IL (United States); Alford, Kurt [Argonne National Lab. (ANL), Argonne, IL (United States); Chemerisov, Sergey [Argonne National Lab. (ANL), Argonne, IL (United States); Vandegrift, George F. [Argonne National Lab. (ANL), Argonne, IL (United States)

2017-09-27

With funding from the National Nuclear Security Administrations Material Management and Minimization Office, Argonne National Laboratory (Argonne) is providing technical assistance to help accelerate the U.S. production of Mo-99 using a non-highly enriched uranium (non-HEU) source. A potential Mo-99 production pathway is by accelerator-initiated fissioning in a subcritical uranyl sulfate solution containing low enriched uranium (LEU). As part of the Argonne development effort, we are undertaking the AMORE (Argonne Molybdenum Research Experiment) project, which is essentially a pilot facility for all phases of Mo-99 production, recovery, and purification. Production of Mo-99 and other fission products in the subcritical target solution is initiated by putting an electron beam on a depleted uranium (DU) target; the fast neutrons produced in the DU target are thermalized and lead to fissioning of U-235. At the end of irradiation, Mo is recovered from the target solution and separated from uranium and most of the fission products by using a titania column. The Mo is stripped from the column with an alkaline solution. After acidification of the Mo product solution from the recovery column, the Mo is concentrated (and further purified) in a second titania column. The strip solution from the concentration column is then purified with the LEU Modified Cintichem process. A full description of the process can be found elsewhere [1–3]. The initial commissioning steps for the AMORE project include performing a Mo-99 spike test with pH 1 sulfuric acid in the target vessel without a beam on the target to demonstrate the initial Mo separation-and-recovery process, followed by the concentration column process. All glovebox operations were tested with cold solutions prior to performing the Mo-99 spike tests. Two Mo-99 spike tests with pH 1 sulfuric acid have been performed to date. Figure 1 shows the flow diagram for the remotely operated Mo-recovery system for the AMORE project

The selection of a matrix for the recovery of uranium by wet high-intensity magnetic separation

International Nuclear Information System (INIS)

Svoboda, J.

1985-01-01

The proper choice of a suitable matrix for high-intensity magnetic separation is of the utmost importance, since the geometry and size of the matrix play decisive roles in the achievement of optimum separation conditions. In relatively simple filtration applications, the matrix must offer a high efficiency of collision with suspended particles, a high probability of retention of intercepted particles, and high loading capacity. Also, it must be easily cleaned. The results obtained by the use of theoretical models of magnetic separation fail to agree with the experimental results for basic parameters like the ratio of particle size to matrix size, the length of the matrix, and the magnetic properties of the matrix material. Preconceived ideas about the matrix often lead to the erroneous choice of a matrix, and hence to its unsatisfactory performance during magnetic separation. The potential value of high-intensity magnetic separation as applied to the recovery of uranium and gold from leach residues and in association with the development of a large-scale magnetic separator to be used for the same purpose led to the present investigation in which a wide spectrum of matrix shapes and sizes were tested. It was found that the optimum recovery and selectivity of separation are obtained at a ratio of particle size to matrix-element size ranging from 200 to 300. The use of these matrices also results in a low degree of mechanical entrapment, particularly of coarser particles, for which straining plays a significant role for fine matrices. It was also found that the magnetization of a matrix plays a minor role, contrary to the theoretical predictions. Furthermore, the effects of matrix height, matrix loading, and scalping of the pulp by paramagnetic matrices were evaluated for various types of matrices

3D shape recovery from image focus using gray level co-occurrence matrix

Science.gov (United States)

Mahmood, Fahad; Munir, Umair; Mehmood, Fahad; Iqbal, Javaid

2018-04-01

Recovering a precise and accurate 3-D shape of the target object utilizing robust 3-D shape recovery algorithm is an ultimate objective of computer vision community. Focus measure algorithm plays an important role in this architecture which convert the color values of each pixel of the acquired 2-D image dataset into corresponding focus values. After convolving the focus measure filter with the input 2-D image dataset, a 3-D shape recovery approach is applied which will recover the depth map. In this document, we are concerned with proposing Gray Level Co-occurrence Matrix along with its statistical features for computing the focus information of the image dataset. The Gray Level Co-occurrence Matrix quantifies the texture present in the image using statistical features and then applies joint probability distributive function of the gray level pairs of the input image. Finally, we quantify the focus value of the input image using Gaussian Mixture Model. Due to its little computational complexity, sharp focus measure curve, robust to random noise sources and accuracy, it is considered as superior alternative to most of recently proposed 3-D shape recovery approaches. This algorithm is deeply investigated on real image sequences and synthetic image dataset. The efficiency of the proposed scheme is also compared with the state of art 3-D shape recovery approaches. Finally, by means of two global statistical measures, root mean square error and correlation, we claim that this approach -in spite of simplicity generates accurate results.

Optimization of the BLASTN substitution matrix for prediction of non-specific DNA microarray hybridization

DEFF Research Database (Denmark)

Eklund, Aron Charles; Friis, Pia; Wernersson, Rasmus

2010-01-01

BLASTN accuracy by modifying the substitution matrix and gap penalties. We generated gene expression microarray data for samples in which 1 or 10% of the target mass was an exogenous spike of known sequence. We found that the 10% spike induced 2-fold intensity changes in 3% of the probes, two......-third of which were decreases in intensity likely caused by bulk-hybridization. These changes were correlated with similarity between the spike and probe sequences. Interestingly, even very weak similarities tended to induce a change in probe intensity with the 10% spike. Using this data, we optimized the BLASTN...... substitution matrix to more accurately identify probes susceptible to non-specific hybridization with the spike. Relative to the default substitution matrix, the optimized matrix features a decreased score for A–T base pairs relative to G–C base pairs, resulting in a 5–15% increase in area under the ROC curve...

Feasibility of the recovery of uranium from alkaline waste by amidoximated grafted polypropylene polymer matrix

International Nuclear Information System (INIS)

Misra, S.K.; Gandhi, P.M.; Bhardwaj, Y.K.

2013-01-01

The amidoximated grafted polypropylene polymer matrix was prepared by post irradiation grafting of acrylonitrile (AN) onto thermally bonded non-woven matrix of poly(propylene) sheet using electron beams. This precursor polymer was reacted with hydroxylamine to convert AN to poly(acrylamidoxime) (AO) groups, and conditioned by treating them with 2.5 % KOH at 80 deg C for 1 h. The polymer matrix was having the degree of AN grafting ∼106 wt% and its subsequent conversion to AO groups ∼70 %. The water uptake capacity of AO polymer matrix were found to be 100 ± 5 % (w/w). Quantitative recovery of uranium from alkaline waste (ammonium diuranate supernatant) solution was achieved by this polymer matrix. The other radionuclides present in the waste solution were not extracted by the polymer matrix. For all other radionuclides, the uptake was found to be <6 %. (author)

Linear stability analysis of retrieval state in associative memory neural networks of spiking neurons

International Nuclear Information System (INIS)

Yoshioka, Masahiko

2002-01-01

We study associative memory neural networks of the Hodgkin-Huxley type of spiking neurons in which multiple periodic spatiotemporal patterns of spike timing are memorized as limit-cycle-type attractors. In encoding the spatiotemporal patterns, we assume the spike-timing-dependent synaptic plasticity with the asymmetric time window. Analysis for periodic solution of retrieval state reveals that if the area of the negative part of the time window is equivalent to the positive part, then crosstalk among encoded patterns vanishes. Phase transition due to the loss of the stability of periodic solution is observed when we assume fast α function for direct interaction among neurons. In order to evaluate the critical point of this phase transition, we employ Floquet theory in which the stability problem of the infinite number of spiking neurons interacting with α function is reduced to the eigenvalue problem with the finite size of matrix. Numerical integration of the single-body dynamics yields the explicit value of the matrix, which enables us to determine the critical point of the phase transition with a high degree of precision

Platinum stable isotope ratio measurements by double-spike multiple collector ICPMS

DEFF Research Database (Denmark)

Creech, John; Baker, Joel; Handler, Monica

2013-01-01

We present a new technique for the precise determination of platinum (Pt) stable isotope ratios by multiple-collector inductively coupled plasma mass spectrometry (MC-ICPMS) using two different Pt double-spikes ( Pt-Pt and Pt-Pt). Results are expressed relative to the IRMM-010 Pt isotope standard......) can be obtained on Pt stable isotope ratios with either double-spike. Elemental doping tests reveal that double-spike corrected Pt stable isotope ratios are insensitive to the presence of relatively high (up to 10%) levels of matrix elements, although the Pt-Pt double-spike is affected by an isobaric...... = 7.308%) results in a redefined Pt atomic weight of 195.08395 ± 0.00068. Using our technique we have measured small, reproducible and statistically significant offsets in Pt stable isotope ratios between different Pt element standards and the IRMM-010 standard, which potentially indicates...

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

Science.gov (United States)

Zenke, Friedemann; Ganguli, Surya

2018-04-13

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

Application of Box-Behnken design to optimize multi-sorbent solid phase extraction for trace neonicotinoids in water containing high level of matrix substances.

Science.gov (United States)

Zhang, Junjie; Wei, Yanli; Li, Huizhen; Zeng, Eddy Y; You, Jing

2017-08-01

Extensive use of neonicotinoid insecticides has raised great concerns about their ecological risk. A reliable method to measure trace neonicotinoids in complicated aquatic environment is a premise for assessing their aquatic risk. To effectively remove matrix interfering substances from field water samples before instrumental analysis with HPLC/MS/MS, a multi-sorbent solid phase extraction method was developed using Box-Behnken design. The optimized method employed 200mg HLB/GCB (w/w, 8/2) as the sorbents and 6mL of 20% acetone in acetonitrile as the elution solution. The method was applied for measuring neonicotinoids in water at a wide range of concentrations (0.03-100μg/L) containing various amounts of matrix components. The recoveries of acetamiprid, imidacloprid, thiacloprid and thiamethoxam from the spiked samples ranged from 76.3% to 107% while clothianidin and dinotefuran had relatively lower recoveries. The recoveries of neonicotinoids in water with various amounts of matrix interfering substances were comparable and the matrix removal rates were approximately 50%. The method was sensitive with method detection limits in the range of 1.8-6.8ng/L for all target neonicotinoids. Finally, the developed method was validated by measurement of trace neonicotinoids in natural water. Copyright © 2017 Elsevier B.V. All rights reserved.

Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI.

Science.gov (United States)

Yu, T; Sejnowski, T J; Cauwenberghs, G

2011-10-01

We study a range of neural dynamics under variations in biophysical parameters underlying extended Morris-Lecar and Hodgkin-Huxley models in three gating variables. The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane dynamics. The dynamics exhibit a wide range of time scales extending beyond 100 ms neglected in typical silicon models of tonic spiking neurons. Circuit simulations and measurements show transition from tonic spiking to tonic bursting dynamics through variation of a single conductance parameter governing calcium recovery. We similarly demonstrate transition from graded to all-or-none neural excitability in the onset of spiking dynamics through the variation of channel kinetic parameters governing the speed of potassium activation. Other combinations of variations in conductance and channel kinetic parameters give rise to phasic spiking and spike frequency adaptation dynamics. The NeuroDyn chip consumes 1.29 mW and occupies 3 mm × 3 mm in 0.5 μm CMOS, supporting emerging developments in neuromorphic silicon-neuron interfaces.

Spiked sample standards; their uses and disadvantages in analytical quality control

International Nuclear Information System (INIS)

Bowen, V.T.; Volchok, H.L

1980-01-01

The advantages and disadvantages of spiked standards are discussed and contrasted with those of natural matrix standards. The preparation of the former class of standards and the evidence supporting recommendation of caution in their use are considered. (author)

Spiking neural network for recognizing spatiotemporal sequences of spikes

International Nuclear Information System (INIS)

Jin, Dezhe Z.

2004-01-01

Sensory neurons in many brain areas spike with precise timing to stimuli with temporal structures, and encode temporally complex stimuli into spatiotemporal spikes. How the downstream neurons read out such neural code is an important unsolved problem. In this paper, we describe a decoding scheme using a spiking recurrent neural network. The network consists of excitatory neurons that form a synfire chain, and two globally inhibitory interneurons of different types that provide delayed feedforward and fast feedback inhibition, respectively. The network signals recognition of a specific spatiotemporal sequence when the last excitatory neuron down the synfire chain spikes, which happens if and only if that sequence was present in the input spike stream. The recognition scheme is invariant to variations in the intervals between input spikes within some range. The computation of the network can be mapped into that of a finite state machine. Our network provides a simple way to decode spatiotemporal spikes with diverse types of neurons

Optimisation of decontamination method and influence of culture media on the recovery of Mycobacterium avium subspecies paratuberculosis from spiked water sediments.

Science.gov (United States)

Aboagye, G; Rowe, M T

2018-07-01

The recovery of Mycobacterium avium subspecies paratuberculosis (Map) from the environment can be a laborious process - owing to Map being fastidious, its low number, and also high numbers of other microbial populations in such settings. Protocols i.e. filtration, decontamination and modified elution were devised to recover Map from spiked water sediments. Three culture media: Herrold's Egg Yolk Media (HEYM), Middlebrook 7H10 (M-7H10) and Bactec 12B were then employed to grow the organism following its elution. In the sterile sediment samples the recovery of Map was significant between the time of exposure for each of HEYM and M-7H10, and insignificant between both media (P < 0.05). However, in the non-sterile sediment samples, the HEYM grew other background microflora including moulds at all the times of exposure whilst 4 h followed by M-7H10 culture yielded Map colonies without any background microflora. Using sterile samples only for the Bactec 12B, the recovery of Map decreased as time of exposure increased. Based on these findings, M-7H10 should be considered for the recovery of Map from the natural environment including water sediments where the recovery of diverse microbial species remains a challenge. Map is a robust pathogen that abides in the environment. In water treatment operations, Map associates with floccules and other particulate matter including sediments. It is also a fastidious organism, and its detection and recovery from the water environment is a laborious process and can be misleading within the abundance of other mycobacterial species owing to their close resemblance in phylogenetic traits. In the absence of a reliable recovery method, Map continues to pose public health risks through biofilm in household water tanks, hence the need for the development of a reliable recovery protocol to monitor the presence of Map in water systems in order to curtail its public health risks. Copyright © 2018 Elsevier B.V. All rights reserved.

Routes to Chaos Induced by a Discontinuous Resetting Process in a Hybrid Spiking Neuron Model.

Science.gov (United States)

Nobukawa, Sou; Nishimura, Haruhiko; Yamanishi, Teruya

2018-01-10

Several hybrid spiking neuron models combining continuous spike generation mechanisms and discontinuous resetting processes following spiking have been proposed. The Izhikevich neuron model, for example, can reproduce many spiking patterns. This model clearly possesses various types of bifurcations and routes to chaos under the effect of a state-dependent jump in the resetting process. In this study, we focus further on the relation between chaotic behaviour and the state-dependent jump, approaching the subject by comparing spiking neuron model versions with and without the resetting process. We first adopt a continuous two-dimensional spiking neuron model in which the orbit in the spiking state does not exhibit divergent behaviour. We then insert the resetting process into the model. An evaluation using the Lyapunov exponent with a saltation matrix and a characteristic multiplier of the Poincar'e map reveals that two types of chaotic behaviour (i.e. bursting chaotic spikes and near-period-two chaotic spikes) are induced by the resetting process. In addition, we confirm that this chaotic bursting state is generated from the periodic spiking state because of the slow- and fast-scale dynamics that arise when jumping to the hyperpolarization and depolarization regions, respectively.

Analysis and solution of current spike occurred in dynamic compensation of self-powered neutron detectors

International Nuclear Information System (INIS)

Peng, Xingjie; Li, Qing; Wang, Kan

2017-01-01

Highlights: • The current spike problem is observed in the dynamic compensation process of SPNDs. • The current spike is caused by unphysical current change due to range switching. • Modification on the compensation algorithm is introduced to deal with current spike. - Abstract: Dynamic compensation methods are required to improve the response speed of the Self-Powered Neutron Detectors (SPNDs) and make it possible to apply the SPNDs for core monitoring and surveillance. During the experimental test of the compensation method based on linear matrix inequality (LMI), spikes are observed in the compensated SPND current. After analyzing the measurement data, the cause is fixed on the unphysical change of the uncompensated SPND current due to range switching. Then some modifications on the dynamic compensation algorithms are proposed to solve the current spike problem.

Gene transfer device utilizing micron-spiked electrodes produced by the self-organization phenomenon of Fe-alloy.

Science.gov (United States)

Miyano, Naoki; Inoue, Yuuki; Teramura, Yuji; Fujii, Keisuke; Tsumori, Fujio; Iwata, Hiroo; Kotera, Hidetoshi

2008-07-01

In the diffusional phase transformation of two-phase alloys, the new phase precipitates form the matrix phase at specific temperatures, followed by the formation of a mixed microstructure comprising the precipitate and the matrix. It has been found that by specific chemical-etching treatment, the precipitate in Fe-25Cr-6Ni alloy projects substantially and clusters at the surface. The configuration of the precipitate has an extremely high aspect ratio: it is several microns in width and several tens of microns in length (known as micron-spiked). This study targets the development of a gene transfer device with a micro-spike produced based on the self-organization phenomenon of the Fe-25Cr-6Ni alloy. With this spike-projected device, we tried to efficiently transfer plasmid DNA into adherent cells by electric pulse-triggered gene transfer using a plasmid-loaded electrode (electroporation-based reverse transfection). The spiked structure was applied to a substrate of the device to allow efficient gene transfer into adherent cells, although the general substrate was flat and had a smooth surface. The results suggest that this unique spike-projected device has potential applications in gene transfer devices for the analysis of the human genome in the post-genome period.

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.

iSpike: a spiking neural interface for the iCub robot

International Nuclear Information System (INIS)

Gamez, D; Fidjeland, A K; Lazdins, E

2012-01-01

This paper presents iSpike: a C++ library that interfaces between spiking neural network simulators and the iCub humanoid robot. It uses a biologically inspired approach to convert the robot’s sensory information into spikes that are passed to the neural network simulator, and it decodes output spikes from the network into motor signals that are sent to control the robot. Applications of iSpike range from embodied models of the brain to the development of intelligent robots using biologically inspired spiking neural networks. iSpike is an open source library that is available for free download under the terms of the GPL. (paper)

A titanium surface with nano-ordered spikes and pores enhances human dermal fibroblastic extracellular matrix production and integration of collagen fibers

International Nuclear Information System (INIS)

Yamada, Masahiro; Kato, Eiji; Sakurai, Kaoru; Yamamoto, Akiko

2016-01-01

The acquisition of substantial dermal sealing determines the prognosis of percutaneous titanium-based medical devices or prostheses. A nano-topographic titanium surface with ordered nano-spikes and pores has been shown to induce periodontal-like connective tissue attachment and activate gingival fibroblastic functions. This in vitro study aimed to determine whether an alkali-heat (AH) treatment-created nano-topographic titanium surface could enhance human dermal fibroblastic functions and binding strength to the deposited collagen on the titanium surface. The surface topographies of commercially pure titanium machined discs exposed to two different AH treatments were evaluated. Human dermal fibroblastic cultures grown on the discs were evaluated in terms of cellular morphology, proliferation, extracellular matrix (ECM) and proinflammatory cytokine synthesis, and physicochemical binding strength of surface-deposited collagen. An isotropically-patterned, shaggy nano-topography with a sponge-like inner network and numerous well-organized, anisotropically-patterned fine nano-spikes and pores were observed on each nano-topographic surface type via scanning electron microscopy. In contrast to the typical spindle-shaped cells on the machined surfaces, the isotropically- and anisotropically-patterned nano-topographic titanium surfaces had small circular/angular cells containing contractile ring-like structures and elongated, multi-shaped cells with a developed cytoskeletal network and multiple filopodia and lamellipodia, respectively. These nano-topographic surfaces enhanced dermal-related ECM synthesis at both the protein and gene levels, without proinflammatory cytokine synthesis or reduced proliferative activity. Deposited collagen fibers were included in these surfaces and sufficiently bound to the nano-topographies to resist the physical, enzymatic and chemical detachment treatments, in contrast to machined surfaces. Well-organized, isotropically

Ionization Suppression and Recovery in Direct Biofluid Analysis Using Paper Spray Mass Spectrometry

Science.gov (United States)

Vega, Carolina; Spence, Corina; Zhang, Chengsen; Bills, Brandon J.; Manicke, Nicholas E.

2016-04-01

Paper spray mass spectrometry is a method for the direct analysis of biofluid samples in which extraction of analytes from dried biofluid spots and electrospray ionization occur from the paper on which the dried sample is stored. We examined matrix effects in the analysis of small molecule drugs from urine, plasma, and whole blood. The general method was to spike stable isotope labeled analogs of each analyte into the spray solvent, while the analyte itself was in the dried biofluid. Intensity of the labeled analog is proportional to ionization efficiency, whereas the ratio of the analyte intensity to the labeled analog in the spray solvent is proportional to recovery. Ion suppression and recovery were found to be compound- and matrix-dependent. Highest levels of ion suppression were obtained for poor ionizers (e.g., analytes lacking basic aliphatic amine groups) in urine and approached -90%. Ion suppression was much lower or even absent for good ionizers (analytes with aliphatic amines) in dried blood spots. Recovery was generally highest in urine and lowest in blood. We also examined the effect of two experimental parameters on ion suppression and recovery: the spray solvent and the sample position (how far away from the paper tip the dried sample was spotted). Finally, the change in ion suppression and analyte elution as a function of time was examined by carrying out a paper spray analysis of dried plasma spots for 5 min by continually replenishing the spray solvent.

Feasibility of the development of reference materials for the detection of Ag nanoparticles in food: neat dispersions and spiked chicken meat

NARCIS (Netherlands)

Grombe, R.; Allmaier, G.; Charoud-Got, J.; Dudkiewicz, A.; Emteborg, H.; Hofmann, T.; Huusfeldt-Larsen, E.; Lehner, A.; Llinas, M.; Loeschner, K.; Molhave, K.; Peters, R.J.B.; Seghers, J.; Solans, C.; Kammer, van den F.; Wagner, S.; Weigel, S.; Linsinger, T.P.J.

2015-01-01

The feasibility of producing colloidal silver nanoparticle reference materials and silver nanoparticle spiked reference matrix materials was investigated. Two concentrations of PVP-coated silver nanoparticle dispersions were evaluated and used to spike chicken meat, with the aim of producing a set

Training spiking neural networks to associate spatio-temporal input-output spike patterns

OpenAIRE

Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N

2013-01-01

In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input–output spike trains) [1] we have proposed a supervised learning algorithm based on temporal coding to train a spiking neuron to associate input spatiotemporal spike patterns to desired output spike patterns. The algorithm is based on the conversion of spike trains into analogue signals and the application of the Widrow–Hoff learning rule. In this paper we present a mathematical formulation of the prop...

Spike persistence and normalization in benign epilepsy with centrotemporal spikes - Implications for management.

Science.gov (United States)

Kim, Hunmin; Kim, Soo Yeon; Lim, Byung Chan; Hwang, Hee; Chae, Jong-Hee; Choi, Jieun; Kim, Ki Joong; Dlugos, Dennis J

2018-05-10

This study was performed 1) to determine the timing of spike normalization in patients with benign epilepsy with centrotemporal spikes (BECTS); 2) to identify relationships between age of seizure onset, age of spike normalization, years of spike persistence and treatment; and 3) to assess final outcomes between groups of patients with or without spikes at the time of medication tapering. Retrospective analysis of BECTS patients confirmed by clinical data, including age of onset, seizure semiology and serial electroencephalography (EEG) from diagnosis to remission. Age at spike normalization, years of spike persistence, and time of treatment onset to spike normalization were assessed. Final seizure and EEG outcome were compared between the groups with or without spikes at the time of AED tapering. One hundred and thirty-four patients were included. Mean age at seizure onset was 7.52 ± 2.11 years. Mean age at spike normalization was 11.89 ± 2.11 (range: 6.3-16.8) years. Mean time of treatment onset to spike normalization was 4.11 ± 2.13 (range: 0.24-10.08) years. Younger age of seizure onset was correlated with longer duration of spike persistence (r = -0.41, p < 0.001). In treated patients, spikes persisted for 4.1 ± 1.95 years, compared with 2.9 ± 1.97 years in untreated patients. No patients had recurrent seizures after AED was discontinued, regardless of the presence/absence of spikes at time of AED tapering. Years of spike persistence was longer in early onset BECTS patients. Treatment with AEDs did not shorten years of spike persistence. Persistence of spikes at time of treatment withdrawal was not associated with seizure recurrence. Copyright © 2018 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Reconstruction of sparse connectivity in neural networks from spike train covariances

International Nuclear Information System (INIS)

Pernice, Volker; Rotter, Stefan

2013-01-01

The inference of causation from correlation is in general highly problematic. Correspondingly, it is difficult to infer the existence of physical synaptic connections between neurons from correlations in their activity. Covariances in neural spike trains and their relation to network structure have been the subject of intense research, both experimentally and theoretically. The influence of recurrent connections on covariances can be characterized directly in linear models, where connectivity in the network is described by a matrix of linear coupling kernels. However, as indirect connections also give rise to covariances, the inverse problem of inferring network structure from covariances can generally not be solved unambiguously. Here we study to what degree this ambiguity can be resolved if the sparseness of neural networks is taken into account. To reconstruct a sparse network, we determine the minimal set of linear couplings consistent with the measured covariances by minimizing the L 1 norm of the coupling matrix under appropriate constraints. Contrary to intuition, after stochastic optimization of the coupling matrix, the resulting estimate of the underlying network is directed, despite the fact that a symmetric matrix of count covariances is used for inference. The performance of the new method is best if connections are neither exceedingly sparse, nor too dense, and it is easily applicable for networks of a few hundred nodes. Full coupling kernels can be obtained from the matrix of full covariance functions. We apply our method to networks of leaky integrate-and-fire neurons in an asynchronous–irregular state, where spike train covariances are well described by a linear model. (paper)

The Second Spiking Threshold: Dynamics of Laminar Network Spiking in the Visual Cortex

DEFF Research Database (Denmark)

Forsberg, Lars E.; Bonde, Lars H.; Harvey, Michael A.

2016-01-01

and moving visual stimuli from the spontaneous ongoing spiking state, in all layers and zones of areas 17 and 18 indicating that the second threshold is a property of the network. Spontaneous and evoked spiking, thus can easily be distinguished. In addition, the trajectories of spontaneous ongoing states......Most neurons have a threshold separating the silent non-spiking state and the state of producing temporal sequences of spikes. But neurons in vivo also have a second threshold, found recently in granular layer neurons of the primary visual cortex, separating spontaneous ongoing spiking from...... visually evoked spiking driven by sharp transients. Here we examine whether this second threshold exists outside the granular layer and examine details of transitions between spiking states in ferrets exposed to moving objects. We found the second threshold, separating spiking states evoked by stationary...

Span: spike pattern association neuron for learning spatio-temporal spike patterns.

Science.gov (United States)

Mohemmed, Ammar; Schliebs, Stefan; Matsuda, Satoshi; Kasabov, Nikola

2012-08-01

Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN - a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the precise timing of spikes. The idea of the proposed algorithm is to transform spike trains during the learning phase into analog signals so that common mathematical operations can be performed on them. Using this conversion, it is possible to apply the well-known Widrow-Hoff rule directly to the transformed spike trains in order to adjust the synaptic weights and to achieve a desired input/output spike behavior of the neuron. In the presented experimental analysis, the proposed learning algorithm is evaluated regarding its learning capabilities, its memory capacity, its robustness to noisy stimuli and its classification performance. Differences and similarities of SPAN regarding two related algorithms, ReSuMe and Chronotron, are discussed.

Deep Spiking Networks

NARCIS (Netherlands)

O'Connor, P.; Welling, M.

2016-01-01

We introduce an algorithm to do backpropagation on a spiking network. Our network is "spiking" in the sense that our neurons accumulate their activation into a potential over time, and only send out a signal (a "spike") when this potential crosses a threshold and the neuron is reset. Neurons only

Efficient Architecture for Spike Sorting in Reconfigurable Hardware

Science.gov (United States)

Hwang, Wen-Jyi; Lee, Wei-Hao; Lin, Shiow-Jyu; Lai, Sheng-Ying

2013-01-01

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. PMID:24189331

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.

Decoding spikes in a spiking neuronal network

Energy Technology Data Exchange (ETDEWEB)

Feng Jianfeng [Department of Informatics, University of Sussex, Brighton BN1 9QH (United Kingdom); Ding, Mingzhou [Department of Mathematics, Florida Atlantic University, Boca Raton, FL 33431 (United States)

2004-06-04

We investigate how to reliably decode the input information from the output of a spiking neuronal network. A maximum likelihood estimator of the input signal, together with its Fisher information, is rigorously calculated. The advantage of the maximum likelihood estimation over the 'brute-force rate coding' estimate is clearly demonstrated. It is pointed out that the ergodic assumption in neuroscience, i.e. a temporal average is equivalent to an ensemble average, is in general not true. Averaging over an ensemble of neurons usually gives a biased estimate of the input information. A method on how to compensate for the bias is proposed. Reconstruction of dynamical input signals with a group of spiking neurons is extensively studied and our results show that less than a spike is sufficient to accurately decode dynamical inputs.

Decoding spikes in a spiking neuronal network

International Nuclear Information System (INIS)

Feng Jianfeng; Ding, Mingzhou

2004-01-01

We investigate how to reliably decode the input information from the output of a spiking neuronal network. A maximum likelihood estimator of the input signal, together with its Fisher information, is rigorously calculated. The advantage of the maximum likelihood estimation over the 'brute-force rate coding' estimate is clearly demonstrated. It is pointed out that the ergodic assumption in neuroscience, i.e. a temporal average is equivalent to an ensemble average, is in general not true. Averaging over an ensemble of neurons usually gives a biased estimate of the input information. A method on how to compensate for the bias is proposed. Reconstruction of dynamical input signals with a group of spiking neurons is extensively studied and our results show that less than a spike is sufficient to accurately decode dynamical inputs

The dynamic relationship between cerebellar Purkinje cell simple spikes and the spikelet number of complex spikes.

Science.gov (United States)

Burroughs, Amelia; Wise, Andrew K; Xiao, Jianqiang; Houghton, Conor; Tang, Tianyu; Suh, Colleen Y; Lang, Eric J; Apps, Richard; Cerminara, Nadia L

2017-01-01

Purkinje cells are the sole output of the cerebellar cortex and fire two distinct types of action potential: simple spikes and complex spikes. Previous studies have mainly considered complex spikes as unitary events, even though the waveform is composed of varying numbers of spikelets. The extent to which differences in spikelet number affect simple spike activity (and vice versa) remains unclear. We found that complex spikes with greater numbers of spikelets are preceded by higher simple spike firing rates but, following the complex spike, simple spikes are reduced in a manner that is graded with spikelet number. This dynamic interaction has important implications for cerebellar information processing, and suggests that complex spike spikelet number may maintain Purkinje cells within their operational range. Purkinje cells are central to cerebellar function because they form the sole output of the cerebellar cortex. They exhibit two distinct types of action potential: simple spikes and complex spikes. It is widely accepted that interaction between these two types of impulse is central to cerebellar cortical information processing. Previous investigations of the interactions between simple spikes and complex spikes have mainly considered complex spikes as unitary events. However, complex spikes are composed of an initial large spike followed by a number of secondary components, termed spikelets. The number of spikelets within individual complex spikes is highly variable and the extent to which differences in complex spike spikelet number affects simple spike activity (and vice versa) remains poorly understood. In anaesthetized adult rats, we have found that Purkinje cells recorded from the posterior lobe vermis and hemisphere have high simple spike firing frequencies that precede complex spikes with greater numbers of spikelets. This finding was also evident in a small sample of Purkinje cells recorded from the posterior lobe hemisphere in awake cats. In addition

The Second Spiking Threshold: Dynamics of Laminar Network Spiking in the Visual Cortex

Science.gov (United States)

Forsberg, Lars E.; Bonde, Lars H.; Harvey, Michael A.; Roland, Per E.

2016-01-01

Most neurons have a threshold separating the silent non-spiking state and the state of producing temporal sequences of spikes. But neurons in vivo also have a second threshold, found recently in granular layer neurons of the primary visual cortex, separating spontaneous ongoing spiking from visually evoked spiking driven by sharp transients. Here we examine whether this second threshold exists outside the granular layer and examine details of transitions between spiking states in ferrets exposed to moving objects. We found the second threshold, separating spiking states evoked by stationary and moving visual stimuli from the spontaneous ongoing spiking state, in all layers and zones of areas 17 and 18 indicating that the second threshold is a property of the network. Spontaneous and evoked spiking, thus can easily be distinguished. In addition, the trajectories of spontaneous ongoing states were slow, frequently changing direction. In single trials, sharp as well as smooth and slow transients transform the trajectories to be outward directed, fast and crossing the threshold to become evoked. Although the speeds of the evolution of the evoked states differ, the same domain of the state space is explored indicating uniformity of the evoked states. All evoked states return to the spontaneous evoked spiking state as in a typical mono-stable dynamical system. In single trials, neither the original spiking rates, nor the temporal evolution in state space could distinguish simple visual scenes. PMID:27582693

Automatic EEG spike detection.

Science.gov (United States)

Harner, Richard

2009-10-01

Since the 1970s advances in science and technology during each succeeding decade have renewed the expectation of efficient, reliable automatic epileptiform spike detection (AESD). But even when reinforced with better, faster tools, clinically reliable unsupervised spike detection remains beyond our reach. Expert-selected spike parameters were the first and still most widely used for AESD. Thresholds for amplitude, duration, sharpness, rise-time, fall-time, after-coming slow waves, background frequency, and more have been used. It is still unclear which of these wave parameters are essential, beyond peak-peak amplitude and duration. Wavelet parameters are very appropriate to AESD but need to be combined with other parameters to achieve desired levels of spike detection efficiency. Artificial Neural Network (ANN) and expert-system methods may have reached peak efficiency. Support Vector Machine (SVM) technology focuses on outliers rather than centroids of spike and nonspike data clusters and should improve AESD efficiency. An exemplary spike/nonspike database is suggested as a tool for assessing parameters and methods for AESD and is available in CSV or Matlab formats from the author at brainvue@gmail.com. Exploratory Data Analysis (EDA) is presented as a graphic method for finding better spike parameters and for the step-wise evaluation of the spike detection process.

SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

Science.gov (United States)

Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

2017-01-01

This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

Computing Generalized Matrix Inverse on Spiking Neural Substrate

Directory of Open Access Journals (Sweden)

Rohit Shukla

2018-03-01

Full Text Available Emerging neural hardware substrates, such as IBM's TrueNorth Neurosynaptic System, can provide an appealing platform for deploying numerical algorithms. For example, a recurrent Hopfield neural network can be used to find the Moore-Penrose generalized inverse of a matrix, thus enabling a broad class of linear optimizations to be solved efficiently, at low energy cost. However, deploying numerical algorithms on hardware platforms that severely limit the range and precision of representation for numeric quantities can be quite challenging. This paper discusses these challenges and proposes a rigorous mathematical framework for reasoning about range and precision on such substrates. The paper derives techniques for normalizing inputs and properly quantizing synaptic weights originating from arbitrary systems of linear equations, so that solvers for those systems can be implemented in a provably correct manner on hardware-constrained neural substrates. The analytical model is empirically validated on the IBM TrueNorth platform, and results show that the guarantees provided by the framework for range and precision hold under experimental conditions. Experiments with optical flow demonstrate the energy benefits of deploying a reduced-precision and energy-efficient generalized matrix inverse engine on the IBM TrueNorth platform, reflecting 10× to 100× improvement over FPGA and ARM core baselines.

Computing Generalized Matrix Inverse on Spiking Neural Substrate

Science.gov (United States)

Shukla, Rohit; Khoram, Soroosh; Jorgensen, Erik; Li, Jing; Lipasti, Mikko; Wright, Stephen

2018-01-01

Emerging neural hardware substrates, such as IBM's TrueNorth Neurosynaptic System, can provide an appealing platform for deploying numerical algorithms. For example, a recurrent Hopfield neural network can be used to find the Moore-Penrose generalized inverse of a matrix, thus enabling a broad class of linear optimizations to be solved efficiently, at low energy cost. However, deploying numerical algorithms on hardware platforms that severely limit the range and precision of representation for numeric quantities can be quite challenging. This paper discusses these challenges and proposes a rigorous mathematical framework for reasoning about range and precision on such substrates. The paper derives techniques for normalizing inputs and properly quantizing synaptic weights originating from arbitrary systems of linear equations, so that solvers for those systems can be implemented in a provably correct manner on hardware-constrained neural substrates. The analytical model is empirically validated on the IBM TrueNorth platform, and results show that the guarantees provided by the framework for range and precision hold under experimental conditions. Experiments with optical flow demonstrate the energy benefits of deploying a reduced-precision and energy-efficient generalized matrix inverse engine on the IBM TrueNorth platform, reflecting 10× to 100× improvement over FPGA and ARM core baselines. PMID:29593483

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 (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 (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.

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.

Reliability of MEG source imaging of anterior temporal spikes: analysis of an intracranially characterized spike focus.

Science.gov (United States)

Wennberg, Richard; Cheyne, Douglas

2014-05-01

To assess the reliability of MEG source imaging (MSI) of anterior temporal spikes through detailed analysis of the localization and orientation of source solutions obtained for a large number of spikes that were separately confirmed by intracranial EEG to be focally generated within a single, well-characterized spike focus. MSI was performed on 64 identical right anterior temporal spikes from an anterolateral temporal neocortical spike focus. The effects of different volume conductors (sphere and realistic head model), removal of noise with low frequency filters (LFFs) and averaging multiple spikes were assessed in terms of the reliability of the source solutions. MSI of single spikes resulted in scattered dipole source solutions that showed reasonable reliability for localization at the lobar level, but only for solutions with a goodness-of-fit exceeding 80% using a LFF of 3 Hz. Reliability at a finer level of intralobar localization was limited. Spike averaging significantly improved the reliability of source solutions and averaging 8 or more spikes reduced dependency on goodness-of-fit and data filtering. MSI performed on topographically identical individual spikes from an intracranially defined classical anterior temporal lobe spike focus was limited by low reliability (i.e., scattered source solutions) in terms of fine, sublobar localization within the ipsilateral temporal lobe. Spike averaging significantly improved reliability. MSI performed on individual anterior temporal spikes is limited by low reliability. Reduction of background noise through spike averaging significantly improves the reliability of MSI solutions. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Decoding spatiotemporal spike sequences via the finite state automata dynamics of spiking neural networks

International Nuclear Information System (INIS)

Jin, Dezhe Z

2008-01-01

Temporally complex stimuli are encoded into spatiotemporal spike sequences of neurons in many sensory areas. Here, we describe how downstream neurons with dendritic bistable plateau potentials can be connected to decode such spike sequences. Driven by feedforward inputs from the sensory neurons and controlled by feedforward inhibition and lateral excitation, the neurons transit between UP and DOWN states of the membrane potentials. The neurons spike only in the UP states. A decoding neuron spikes at the end of an input to signal the recognition of specific spike sequences. The transition dynamics is equivalent to that of a finite state automaton. A connection rule for the networks guarantees that any finite state automaton can be mapped into the transition dynamics, demonstrating the equivalence in computational power between the networks and finite state automata. The decoding mechanism is capable of recognizing an arbitrary number of spatiotemporal spike sequences, and is insensitive to the variations of the spike timings in the sequences

Multi-class multi-residue analysis of veterinary drugs in meat using enhanced matrix removal lipid cleanup and liquid chromatography-tandem mass spectrometry.

Science.gov (United States)

Zhao, Limian; Lucas, Derick; Long, David; Richter, Bruce; Stevens, Joan

2018-05-11

This study presents the development and validation of a quantitation method for the analysis of multi-class, multi-residue veterinary drugs using lipid removal cleanup cartridges, enhanced matrix removal lipid (EMR-Lipid), for different meat matrices by liquid chromatography tandem mass spectrometry detection. Meat samples were extracted using a two-step solid-liquid extraction followed by pass-through sample cleanup. The method was optimized based on the buffer and solvent composition, solvent additive additions, and EMR-Lipid cartridge cleanup. The developed method was then validated in five meat matrices, porcine muscle, bovine muscle, bovine liver, bovine kidney and chicken liver to evaluate the method performance characteristics, such as absolute recoveries and precision at three spiking levels, calibration curve linearity, limit of quantitation (LOQ) and matrix effect. The results showed that >90% of veterinary drug analytes achieved satisfactory recovery results of 60-120%. Over 97% analytes achieved excellent reproducibility results (relative standard deviation (RSD) meat matrices. The matrix co-extractive removal efficiency by weight provided by EMR-lipid cartridge cleanup was 42-58% in samples. The post column infusion study showed that the matrix ion suppression was reduced for samples with the EMR-Lipid cartridge cleanup. The reduced matrix ion suppression effect was also confirmed with 30%) for all tested veterinary drugs in all of meat matrices. The results showed that the two-step solid-liquid extraction provides efficient extraction for the entire spectrum of veterinary drugs, including the difficult classes such as tetracyclines, beta-lactams etc. EMR-Lipid cartridges after extraction provided efficient sample cleanup with easy streamlined protocol and minimal impacts on analytes recovery, improving method reliability and consistency. Copyright © 2018 Elsevier B.V. All rights reserved.

Application of cross-correlated delay shift rule in spiking neural networks for interictal spike detection.

Science.gov (United States)

Lilin Guo; Zhenzhong Wang; Cabrerizo, Mercedes; Adjouadi, Malek

2016-08-01

This study proposes a Cross-Correlated Delay Shift (CCDS) supervised learning rule to train neurons with associated spatiotemporal patterns to classify spike patterns. The objective of this study was to evaluate the feasibility of using the CCDS rule to automate the detection of interictal spikes in electroencephalogram (EEG) data on patients with epilepsy. Encoding is the initial yet essential step for spiking neurons to process EEG patterns. A new encoding method is utilized to convert the EEG signal into spike patterns. The simulation results show that the proposed algorithm identified 69 spikes out of 82 spikes, or 84% detection rate, which is quite high considering the subtleties of interictal spikes and the tediousness of monitoring long EEG records. This CCDS rule is also benchmarked by ReSuMe on the same task.

Trace analysis of antidepressant pharmaceuticals and their select degradates in aquatic matrixes by LC/ESI/MS/MS

Science.gov (United States)

Schultz, M.M.; Furlong, E.T.

2008-01-01

Treated wastewater effluent is a potential environmental point source for antidepressant pharmaceuticals. A quantitative method was developed for the determination of trace levels of antidepressants in environmental aquatic matrixes using solid-phase extraction coupled with liquid chromatography- electrospray ionization tandem mass spectrometry. Recoveries of parent antidepressants from matrix spiking experiments for the individual antidepressants ranged from 72 to 118% at low concentrations (0.5 ng/L) and 70 to 118% at high concentrations (100 ng/L) for the solid-phase extraction method. Method detection limits for the individual antidepressant compounds ranged from 0.19 to 0.45 ng/L. The method was applied to wastewater effluent and samples collected from a wastewater-dominated stream. Venlafaxine was the predominant antidepressant observed in wastewater and river water samples. Individual antidepressant concentrations found in the wastewater effluent ranged from 3 (duloxetine) to 2190 ng/L (venlafaxine), whereas individual concentrations in the waste-dominated stream ranged from 0.72 (norfluoxetine) to 1310 ng/L (venlafaxine). ?? 2008 American Chemical Society.

Spike rate and spike timing contributions to coding taste quality information in rat periphery

Directory of Open Access Journals (Sweden)

Vernon eLawhern

2011-05-01

Full Text Available There is emerging evidence that individual sensory neurons in the rodent brain rely on temporal features of the discharge pattern to code differences in taste quality information. In contrast, in-vestigations of individual sensory neurons in the periphery have focused on analysis of spike rate and mostly disregarded spike timing as a taste quality coding mechanism. The purpose of this work was to determine the contribution of spike timing to taste quality coding by rat geniculate ganglion neurons using computational methods that have been applied successfully in other sys-tems. We recorded the discharge patterns of narrowly-tuned and broadly-tuned neurons in the rat geniculate ganglion to representatives of the five basic taste qualities. We used mutual in-formation to determine significant responses and the van Rossum metric to characterize their temporal features. While our findings show that spike timing contributes a significant part of the message, spike rate contributes the largest portion of the message relayed by afferent neurons from rat fungiform taste buds to the brain. Thus, spike rate and spike timing together are more effective than spike rate alone in coding stimulus quality information to a single basic taste in the periphery for both narrowly-tuned specialist and broadly-tuned generalist neurons.

Analysis of pharmaceutical and other organic wastewater compounds in filtered and unfiltered water samples by gas chromatography/mass spectrometry

Science.gov (United States)

Zaugg, Steven D.; Phillips, Patrick J.; Smith, Steven G.

2014-01-01

the compounds included in the unfiltered method, 17 had mean spike recoveries ranging from 74 to 129 percent and RSDs ranging from 5 to 25 percent for low-level and high-level surface water spikes. The remaining compounds had poor mean recoveries (130 percent), or high RSDs (>29 percent) for these spikes. For wastewater, 14 of the compounds included in the unfiltered method had mean recoveries ranging from 62 to 127 percent and RSDs 130 percent), or low mean recoveries (33 percent) for the low-level wastewater spikes. Of the compounds found in wastewater, 24 had mean set spike recoveries ranging from 64 to 104 percent and RSDs Separate method detection limits (MDLs) were computed for surface water and wastewater for both the filtered and unfiltered methods. Filtered method MDLs ranged from 0.007 to 0.14 microgram per liter (μg/L) for the surface water matrix and from 0.004 to 0.62 μg/L for the wastewater matrix. Unfiltered method MDLs ranged from 0.014 to 0.33 μg/L for the surface water matrix and from 0.008 to 0.36 μg/L for the wastewater matrix.

Impact of spike train autostructure on probability distribution of joint spike events.

Science.gov (United States)

Pipa, Gordon; Grün, Sonja; van Vreeswijk, Carl

2013-05-01

The discussion whether temporally coordinated spiking activity really exists and whether it is relevant has been heated over the past few years. To investigate this issue, several approaches have been taken to determine whether synchronized events occur significantly above chance, that is, whether they occur more often than expected if the neurons fire independently. Most investigations ignore or destroy the autostructure of the spiking activity of individual cells or assume Poissonian spiking as a model. Such methods that ignore the autostructure can significantly bias the coincidence statistics. Here, we study the influence of the autostructure on the probability distribution of coincident spiking events between tuples of mutually independent non-Poisson renewal processes. In particular, we consider two types of renewal processes that were suggested as appropriate models of experimental spike trains: a gamma and a log-normal process. For a gamma process, we characterize the shape of the distribution analytically with the Fano factor (FFc). In addition, we perform Monte Carlo estimations to derive the full shape of the distribution and the probability for false positives if a different process type is assumed as was actually present. We also determine how manipulations of such spike trains, here dithering, used for the generation of surrogate data change the distribution of coincident events and influence the significance estimation. We find, first, that the width of the coincidence count distribution and its FFc depend critically and in a nontrivial way on the detailed properties of the structure of the spike trains as characterized by the coefficient of variation CV. Second, the dependence of the FFc on the CV is complex and mostly nonmonotonic. Third, spike dithering, even if as small as a fraction of the interspike interval, can falsify the inference on coordinated firing.

Nicotine-Mediated ADP to Spike Transition: Double Spiking in Septal Neurons.

Science.gov (United States)

Kodirov, Sodikdjon A; Wehrmeister, Michael; Colom, Luis

2016-04-01

The majority of neurons in lateral septum (LS) are electrically silent at resting membrane potential. Nicotine transiently excites a subset of neurons and occasionally leads to long lasting bursting activity upon longer applications. We have observed simultaneous changes in frequencies and amplitudes of spontaneous action potentials (AP) in the presence of nicotine. During the prolonged exposure, nicotine increased numbers of spikes within a burst. One of the hallmarks of nicotine effects was the occurrences of double spikes (known also as bursting). Alignment of 51 spontaneous spikes, triggered upon continuous application of nicotine, revealed that the slope of after-depolarizing potential gradually increased (1.4 vs. 3 mV/ms) and neuron fired the second AP, termed as double spiking. A transition from a single AP to double spikes increased the amplitude of after-hyperpolarizing potential. The amplitude of the second (premature) AP was smaller compared to the first one, and this correlation persisted in regard to their duration (half-width). A similar bursting activity in the presence of nicotine, to our knowledge, has not been reported previously in the septal structure in general and in LS in particular.

Investigation of the Results of Combined Reclamation on the Particular Stages of Grain Matrix Recovery

Directory of Open Access Journals (Sweden)

Łucarz M.

2016-12-01

Full Text Available In this article, there were presented the results of research on combined mechanical and thermal regeneration of waste moulding sand with furfuryl resin originated from one of national foundries manufacturing aluminium alloys castings. Attempts of mechanical reclamation were led on the REGMAS reclaimer enabling to realize preliminary and primary reclamation with use of two modes of mechanical interactions on waste moulding sand. In the first attempt the reclaimer worked without any additional regenerating elements, and as the second solution, the reclaimer operated with additional crushing and abrasive elements to increase the result of primary reclamation. Thermal reclamation was led in the prototypic thermal reclaimer, enabling to fully control the process of grain matrix recovery. As a result of completed investigations the small efficiency of mechanical reclamation was determined. However, use of combined regeneration allowed for obtaining grain matrix of high purity. Thermal regeneration was conducted in prototypic thermal reclaimer. Evaluation of reclaim (reclaimed material quality was carried out in the way of iginition losses and grain-size analysis, surface morphology and also by executing of strength testing of moulding sand prepared on obtained grain matrix for the particular reclamation operations.

Effect of different spiking procedures on the distribution and toxicity of ZnO nanoparticles in soil.

Science.gov (United States)

Waalewijn-Kool, Pauline L; Diez Ortiz, Maria; van Gestel, Cornelis A M

2012-10-01

Due to the difficulty in dispersing some engineered nanomaterials in exposure media, realizing homogeneous distributions of nanoparticles (NP) in soil may pose major challenges. The present study investigated the distribution of zinc oxide (ZnO) NP (30 nm) and non-nano ZnO (200 nm) in natural soil using two different spiking procedures, i.e. as dry powder and as suspension in soil extract. Both spiking procedures showed a good recovery (>85 %) of zinc and based on total zinc concentrations no difference was found between the two spiking methods. Both spiking procedures resulted in a fairly homogeneous distribution of the ZnO particles in soil, as evidenced by the low variation in total zinc concentration between replicate samples (soil spiked at concentrations up to 6,400 mg Zn kg(-1) d.w. was not affected for both compounds. Reproduction was reduced in a concentration-dependent manner with EC50 values of 3,159 and 2,914 mg Zn kg(-1) d.w. for 30 and 200 nm ZnO spiked as dry powder and 3,593 and 5,633 mg Zn kg(-1) d.w. introduced as suspension. Toxicity of ZnO at 30 and 200 nm did not differ. We conclude that the ZnO particle toxicity is not size related and that the spiking of the soil with ZnO as dry powder or as a suspension in soil extract does not affect its toxicity to F. candida.

Spike timing rigidity is maintained in bursting neurons under pentobarbital-induced anesthetic conditions

Directory of Open Access Journals (Sweden)

Risako Kato

2016-11-01

Full Text Available Pentobarbital potentiates γ-aminobutyric acid (GABA-mediated inhibitory synaptic transmission by prolonging the open time of GABAA receptors. However, it is unknown how pentobarbital regulates cortical neuronal activities via local circuits in vivo. To examine this question, we performed extracellular unit recording in rat insular cortex under awake and anesthetic conditions. Not a few studies apply time-rescaling theorem to detect the features of repetitive spike firing. Similar to these methods, we define an average spike interval locally in time using random matrix theory (RMT, which enables us to compare different activity states on a universal scale. Neurons with high spontaneous firing frequency (> 5 Hz and bursting were classified as HFB neurons (n = 10, and those with low spontaneous firing frequency (< 10 Hz and without bursting were classified as non-HFB neurons (n = 48. Pentobarbital injection (30 mg/kg reduced firing frequency in all HFB neurons and in 78% of non-HFB neurons. RMT analysis demonstrated that pentobarbital increased in the number of neurons with repulsion in both HFB and non-HFB neurons, suggesting that there is a correlation between spikes within a short interspike interval. Under awake conditions, in 50% of HFB and 40% of non-HFB neurons, the decay phase of normalized histograms of spontaneous firing were fitted to an exponential function, which indicated that the first spike had no correlation with subsequent spikes. In contrast, under pentobarbital-induced anesthesia conditions, the number of non-HFB neurons that were fitted to an exponential function increased to 80%, but almost no change in HFB neurons was observed. These results suggest that under both awake and pentobarbital-induced anesthetized conditions, spike firing in HFB neurons is more robustly regulated by preceding spikes than by non-HFB neurons, which may reflect the GABAA receptor-mediated regulation of cortical activities. Whole-cell patch

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.

Information transmission with spiking Bayesian neurons

International Nuclear Information System (INIS)

Lochmann, Timm; Deneve, Sophie

2008-01-01

Spike trains of cortical neurons resulting from repeatedpresentations of a stimulus are variable and exhibit Poisson-like statistics. Many models of neural coding therefore assumed that sensory information is contained in instantaneous firing rates, not spike times. Here, we ask how much information about time-varying stimuli can be transmitted by spiking neurons with such input and output variability. In particular, does this variability imply spike generation to be intrinsically stochastic? We consider a model neuron that estimates optimally the current state of a time-varying binary variable (e.g. presence of a stimulus) by integrating incoming spikes. The unit signals its current estimate to other units with spikes whenever the estimate increased by a fixed amount. As shown previously, this computation results in integrate and fire dynamics with Poisson-like output spike trains. This output variability is entirely due to the stochastic input rather than noisy spike generation. As a result such a deterministic neuron can transmit most of the information about the time varying stimulus. This contrasts with a standard model of sensory neurons, the linear-nonlinear Poisson (LNP) model which assumes that most variability in output spike trains is due to stochastic spike generation. Although it yields the same firing statistics, we found that such noisy firing results in the loss of most information. Finally, we use this framework to compare potential effects of top-down attention versus bottom-up saliency on information transfer with spiking neurons

Newborn screening by matrix-assisted laser desorption/ionization mass spectrometry based on parylene-matrix chip.

Science.gov (United States)

Kim, Jo-Il; Noh, Joo-Yoon; Kim, Mira; Park, Jong-Min; Song, Hyun-Woo; Kang, Min-Jung; Pyun, Jae-Chul

2017-08-01

Newborn screening for diagnosis of phenylketonuria, homocystinuria, and maple syrup urine disease have been conducted by analyzing the concentration of target amino acids using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) based on parylene-matrix chip. Parylene-matrix chip was applied to MALDI-ToF MS analysis reducing the matrix peaks significantly at low mass-to-charge ratio range (m/z  0.98) and the LODs were ranging from 9.0 to 22.9 μg/mL. Effect of proteins in serum was estimated by comparing MALDI-ToF mass spectra of amino acids-spiked serum before and after the methanol extraction. Interference of other amino acids on analysis of target analyte was determined to be insignificant. From these results, MALDI-ToF MS based on parylene-matrix chip could be applicable to medical diagnosis of neonatal metabolic disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

Barbed micro-spikes for micro-scale biopsy

Science.gov (United States)

Byun, Sangwon; Lim, Jung-Min; Paik, Seung-Joon; Lee, Ahra; Koo, Kyo-in; Park, Sunkil; Park, Jaehong; Choi, Byoung-Doo; Seo, Jong Mo; Kim, Kyung-ah; Chung, Hum; Song, Si Young; Jeon, Doyoung; Cho, Dongil

2005-06-01

Single-crystal silicon planar micro-spikes with protruding barbs are developed for micro-scale biopsy and the feasibility of using the micro-spike as a micro-scale biopsy tool is evaluated for the first time. The fabrication process utilizes a deep silicon etch to define the micro-spike outline, resulting in protruding barbs of various shapes. Shanks of the fabricated micro-spikes are 3 mm long, 100 µm thick and 250 µm wide. Barbs protruding from micro-spike shanks facilitate the biopsy procedure by tearing off and retaining samples from target tissues. Micro-spikes with barbs successfully extracted tissue samples from the small intestines of the anesthetized pig, whereas micro-spikes without barbs failed to obtain a biopsy sample. Parylene coating can be applied to improve the biocompatibility of the micro-spike without deteriorating the biopsy function of the micro-spike. In addition, to show that the biopsy with the micro-spike can be applied to tissue analysis, samples obtained by micro-spikes were examined using immunofluorescent staining. Nuclei and F-actin of cells which are extracted by the micro-spike from a transwell were clearly visualized by immunofluorescent staining.

Bayesian population decoding of spiking neurons.

Science.gov (United States)

Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias

2009-01-01

The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a 'spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

Bayesian population decoding of spiking neurons

Directory of Open Access Journals (Sweden)

Sebastian Gerwinn

2009-10-01

Full Text Available The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a `spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

Identification and quantification of metallothionein isoforms and superoxide dismutase in spiked liver extracts using HPLC-ESI-MS offline coupling and HPLC-ICP-MS online coupling.

Science.gov (United States)

Nischwitz, V; Michalke, B; Kettrup, A

2003-01-01

A two-dimensional chromatographic method for the characterization of metallothionein isoforms (MT) and superoxide dismutase (SOD) in spiked liver extracts was developed for the optimization of extraction procedures from liver samples. Element-specific detection (ICP-MS) and molecule-specific detection (ESI-MS) were applied for maximum species information. A special focus was laid on the quantitative data evaluation (species stoichiometry, calibration with and without matrix, recovery), which is neglected in most MT/SOD publications with hyphenated techniques. Linearity, precision (residual standard deviation of calibration curves <10%), and detection limits (<0.6 mg L(-1) for MT isoforms and 13 mg L(-1) for SOD) prove the suitability of the method for quantification. An alternative quantification is proposed for the extension towards other lesser or even unknown trace element species, especially the native porcine MT and SOD.

Heterogeneity of Purkinje cell simple spike-complex spike interactions: zebrin- and non-zebrin-related variations.

Science.gov (United States)

Tang, Tianyu; Xiao, Jianqiang; Suh, Colleen Y; Burroughs, Amelia; Cerminara, Nadia L; Jia, Linjia; Marshall, Sarah P; Wise, Andrew K; Apps, Richard; Sugihara, Izumi; Lang, Eric J

2017-08-01

Cerebellar Purkinje cells (PCs) generate two types of action potentials, simple and complex spikes. Although they are generated by distinct mechanisms, interactions between the two spike types exist. Zebrin staining produces alternating positive and negative stripes of PCs across most of the cerebellar cortex. Thus, here we compared simple spike-complex spike interactions both within and across zebrin populations. Simple spike activity undergoes a complex modulation preceding and following a complex spike. The amplitudes of the pre- and post-complex spike modulation phases were correlated across PCs. On average, the modulation was larger for PCs in zebrin positive regions. Correlations between aspects of the complex spike waveform and simple spike activity were found, some of which varied between zebrin positive and negative PCs. The implications of the results are discussed with regard to hypotheses that complex spikes are triggered by rises in simple spike activity for either motor learning or homeostatic functions. Purkinje cells (PCs) generate two types of action potentials, called simple and complex spikes (SSs and CSs). We first investigated the CS-associated modulation of SS activity and its relationship to the zebrin status of the PC. The modulation pattern consisted of a pre-CS rise in SS activity, and then, following the CS, a pause, a rebound, and finally a late inhibition of SS activity for both zebrin positive (Z+) and negative (Z-) cells, though the amplitudes of the phases were larger in Z+ cells. Moreover, the amplitudes of the pre-CS rise with the late inhibitory phase of the modulation were correlated across PCs. In contrast, correlations between modulation phases across CSs of individual PCs were generally weak. Next, the relationship between CS spikelets and SS activity was investigated. The number of spikelets/CS correlated with the average SS firing rate only for Z+ cells. In contrast, correlations across CSs between spikelet numbers and the

Calcium Isotope Analysis with "Peak Cut" Method on Column Chemistry

Science.gov (United States)

Zhu, H.; Zhang, Z.; Liu, F.; Li, X.

2017-12-01

To eliminate isobaric interferences from elemental and molecular isobars (e.g., 40K+, 48Ti+, 88Sr2+, 24Mg16O+, 27Al16O+) on Ca isotopes during mass determination, samples should be purified through ion-exchange column chemistry before analysis. However, large Ca isotopic fractionation has been observed during column chemistry (Russell and Papanastassiou, 1978; Zhu et al., 2016). Therefore, full recovery during column chemistry is greatly needed, otherwise uncertainties would be caused by poor recovery (Zhu et al., 2016). Generally, matrix effects could be enhanced by full recovery, as other elements might overlap with Ca cut during column chemistry. Matrix effects and full recovery are difficult to balance and both need to be considered for high-precision analysis of stable Ca isotopes. Here, we investigate the influence of poor recovery on δ44/40Ca using TIMS with the double spike technique. The δ44/40Ca values of IAPSO seawater, ML3B-G and BHVO-2 in different Ca subcats (e.g., 0-20, 20-40, 40-60, 60-80, 80-100%) with 20% Ca recovery on column chemistry display limited variation after correction by the 42Ca-43Ca double spike technique with the exponential law. Notably, δ44/40Ca of each Ca subcut is quite consistent with δ44/40Ca of Ca cut with full recovery within error. Our results indicate that the 42Ca-43Ca double spike technique can simultaneously correct both of the Ca isotopic fractionation that occurred during column chemistry and thermal ionization mass spectrometry (TIMS) determination properly, because both of the isotopic fractionation occurred during analysis follow the exponential law well. Therefore, we propose the "peak cut" method on Ca column chemistry for samples with complex matrix effects. Briefly, for samples with low Ca contents, we can add the double spike before column chemistry, and only collect the middle of the Ca eluate and abandon the both sides of Ca eluate that might overlap with other elements (e.g., K, Sr). This method would

Recovery evaluation of organophosphorus pesticides from bee pollen by matrix solid-phase dispersion extraction using sorbents based on silica and titania

International Nuclear Information System (INIS)

Torres-Perea, C; Muñoz-Rodríguez, D; Carrera-Figueiras, C; Medina-Peralta, S; Moguel-Ordóñez, Y B

2013-01-01

This work focused on the evaluation of the recovery of organophosphorus pesticides from bee pollen after matrix solid phase-dispersion extraction (MSPD). Materials based on silica, titania and titania modified with polivylnylimidazole or polyestirene were used as adsorbents for the extraction of pesticides. Small amounts of fortified pollen (0.1 g, at 1 micro-g/g of pesticides), adsorbent (0.4 g) and solvent elution (1 mL de acetonitrile – ACN) were used in the extractions. For recovery evaluation, pollen extracts were analyzed by gas chromatography coupled with mass spectrometry.

Trehalose significantly enhances the recovery of serum and serum exosomal miRNA from a paper-based matrix

OpenAIRE

Neo, Shu Hui; Chung, Ka Yan; Quek, Jia Min; Too, Heng-Phon

2017-01-01

The preservation of nucleic acids from clinical samples is critical to facilitate accurate molecular diagnosis. The use of a paper matrix, Flinders Technology Associates (FTA) Elute cards, to archive DNA and viral RNA is well-documented. However, the feasibility of FTA Elute cards for archiving serum and serum exosomal microRNAs (miRNAs) remains unclear. Here, we performed a comprehensive evaluation of FTA Elute cards for miRNA storage and recovery in different pre-analytical conditions. The ...

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.

Optimized Projection Matrix for Compressive Sensing

Directory of Open Access Journals (Sweden)

Jianping Xu

2010-01-01

Full Text Available Compressive sensing (CS is mainly concerned with low-coherence pairs, since the number of samples needed to recover the signal is proportional to the mutual coherence between projection matrix and sparsifying matrix. Until now, papers on CS always assume the projection matrix to be a random matrix. In this paper, aiming at minimizing the mutual coherence, a method is proposed to optimize the projection matrix. This method is based on equiangular tight frame (ETF design because an ETF has minimum coherence. It is impossible to solve the problem exactly because of the complexity. Therefore, an alternating minimization type method is used to find a feasible solution. The optimally designed projection matrix can further reduce the necessary number of samples for recovery or improve the recovery accuracy. The proposed method demonstrates better performance than conventional optimization methods, which brings benefits to both basis pursuit and orthogonal matching pursuit.

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

Directory of Open Access Journals (Sweden)

Qiang Yu

Full Text Available A new learning rule (Precise-Spike-Driven (PSD Synaptic Plasticity is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns.

Science.gov (United States)

Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Li, Haizhou

2013-01-01

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.

Development and evaluation of thin-layer chromatography-digital image-based analysis for the quantitation of the botanical pesticide azadirachtin in agricultural matrixes and commercial formulations: comparison with ELISA.

Science.gov (United States)

Tanuja, Penmatsa; Venugopal, Namburi; Sashidhar, Rao Beedu

2007-01-01

A simple thin-layer chromatography-digital image-based analytical method has been developed for the quantitation of the botanical pesticide, azadirachtin. The method was validated by analyzing azadirachtin in the spiked food matrixes and processed commercial pesticide formulations, using acidified vanillin reagent as a postchromatographic derivatizing agent. The separated azadirachtin was clearly identified as a green spot. The Rf value was found to be 0.55, which was similar to that of a reference standard. A standard calibration plot was established using a reference standard, based on the linear regression analysis [r2 = 0.996; y = 371.43 + (634.82)x]. The sensitivity of the method was found to be 0.875 microg azadirachtin. Spiking studies conducted at the 1 ppm (microg/g) level in various agricultural matrixes, such as brinjal, tomato, coffee, and cotton seeds, revealed the recoveries of azadirachtin in the range of 67-92%. Azadirachtin content of commercial neem formulations analyzed by the method was in the range of 190-1825 ppm (microg/mL). Further, the present method was compared with an immunoanalytical method enzyme-linked immonosorbent assay developed earlier in our laboratory. Statistical comparison of the 2 methods, using Fischer's F-test, indicated no significant difference in variance, suggesting that both methods are comparable.

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.

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

Science.gov (United States)

de Santos-Sierra, Daniel; Sanchez-Jimenez, Abel; Garcia-Vellisca, Mariano A; Navas, Adrian; Villacorta-Atienza, Jose A

2015-01-01

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 (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior 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 with 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.

Extraction of acetanilides in rice using ionic liquid-based matrix solid phase dispersion-solvent flotation.

Science.gov (United States)

Zhang, Liyuan; Wang, Changyuan; Li, Zuotong; Zhao, Changjiang; Zhang, Hanqi; Zhang, Dongjie

2018-04-15

Ionic liquid-based matrix solid phase dispersion-solvent flotation coupled with high performance liquid chromatography was developed for the determination of the acetanilide herbicides, including metazachlor, propanil, alachlor, propisochlor, pretilachlor, and butachlor in rice samples. Some experimental parameters, including the type of dispersant, the mass ratio of dispersant to sample, pH of sample solution, the type of extraction solvent, the type of ionic liquid, flotation time, and flow rate of N 2 were optimized. The average recoveries of the acetanilide herbicides at spiked concentrations of 50, 125, and 250 µg/kg ranged from 89.4% to 108.7%, and relative standard deviations were equal to or lower than 7.1%, the limits of quantification were in the range of 38.0 to 84.7 µg/kg. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Spike timing analysis in neural networks with unsupervised synaptic plasticity

Science.gov (United States)

Mizusaki, B. E. P.; Agnes, E. J.; Brunnet, L. G.; Erichsen, R., Jr.

2013-01-01

The synaptic plasticity rules that sculpt a neural network architecture are key elements to understand cortical processing, as they may explain the emergence of stable, functional activity, while avoiding runaway excitation. For an associative memory framework, they should be built in a way as to enable the network to reproduce a robust spatio-temporal trajectory in response to an external stimulus. Still, how these rules may be implemented in recurrent networks and the way they relate to their capacity of pattern recognition remains unclear. We studied the effects of three phenomenological unsupervised rules in sparsely connected recurrent networks for associative memory: spike-timing-dependent-plasticity, short-term-plasticity and an homeostatic scaling. The system stability is monitored during the learning process of the network, as the mean firing rate converges to a value determined by the homeostatic scaling. Afterwards, it is possible to measure the recovery efficiency of the activity following each initial stimulus. This is evaluated by a measure of the correlation between spike fire timings, and we analysed the full memory separation capacity and limitations of this system.

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.

Consensus-Based Sorting of Neuronal Spike Waveforms.

Science.gov (United States)

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

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.

Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

Science.gov (United States)

Huys, Raoul; Jirsa, Viktor K.; Darokhan, Ziauddin; Valentiniene, Sonata; Roland, Per E.

2016-01-01

Neurons in the primary visual cortex spontaneously spike even when there are no visual stimuli. It is unknown whether the spiking evoked by visual stimuli is just a modification of the spontaneous ongoing cortical spiking dynamics or whether the spontaneous spiking state disappears and is replaced by evoked spiking. This study of laminar recordings of spontaneous spiking and visually evoked spiking of neurons in the ferret primary visual cortex shows that the spiking dynamics does not change: the spontaneous spiking as well as evoked spiking is controlled by a stable and persisting fixed point attractor. Its existence guarantees that evoked spiking return to the spontaneous state. However, the spontaneous ongoing spiking state and the visual evoked spiking states are qualitatively different and are separated by a threshold (separatrix). The functional advantage of this organization is that it avoids the need for a system reorganization following visual stimulation, and impedes the transition of spontaneous spiking to evoked spiking and the propagation of spontaneous spiking from layer 4 to layers 2–3. PMID:26778982

Misuse of Checklist Assessments in Endangered Species Recovery Efforts

Directory of Open Access Journals (Sweden)

Thomas P. Good

2003-12-01

Full Text Available Natural resource agencies worldwide must develop species recovery plans that specify threats, propose targets required for recovery, and evaluate the extent to which habitat alteration and restoration may influence species decline and recovery. To evaluate the impacts of proposed habitat alterations on species of conservation concern, standardized protocols may be adopted even when supporting data are scarce. For example, a habitat matrix was developed by the National Marine Fisheries Service (NMFS to guide consultations under the Endangered Species Act for actions that may affect the functioning of the freshwater habitat used by several federally listed salmonid species. The habitat matrix has also been advocated as a tool for recovery planning by agencies apart from the NMFS, who could use it to define the habitat conditions assumed to be necessary for salmonid population viability and hence recovery. This use of the habitat matrix in a recovery context has not been evaluated, and, despite its widespread use as a regulatory tool, the empirical relationships between many of the habitat matrix variables and salmonid populations remain unexplored. By amassing data on habitat assessments and trends in fish abundance, we empirically evaluate the relationship between habitat matrix scores and salmonid population metrics. We found that abundance trends for populations of three species of threatened and endangered salmonids (chinook, coho, and steelhead were unrelated to these habitat matrix assessments. This study reveals the danger of assuming quantitative relationships between habitat and organism and cautions against co-opting protocols from the regulatory realm for recovery planning for endangered species.

Spike voltage topography in temporal lobe epilepsy.

Science.gov (United States)

Asadi-Pooya, Ali A; Asadollahi, Marjan; Shimamoto, Shoichi; Lorenzo, Matthew; Sperling, Michael R

2016-07-15

We investigated the voltage topography of interictal spikes in patients with temporal lobe epilepsy (TLE) to see whether topography was related to etiology for TLE. Adults with TLE, who had epilepsy surgery for drug-resistant seizures from 2011 until 2014 at Jefferson Comprehensive Epilepsy Center were selected. Two groups of patients were studied: patients with mesial temporal sclerosis (MTS) on MRI and those with other MRI findings. The voltage topography maps of the interictal spikes at the peak were created using BESA software. We classified the interictal spikes as polar, basal, lateral, or others. Thirty-four patients were studied, from which the characteristics of 340 spikes were investigated. The most common type of spike orientation was others (186 spikes; 54.7%), followed by lateral (146; 42.9%), polar (5; 1.5%), and basal (3; 0.9%). Characteristics of the voltage topography maps of the spikes between the two groups of patients were somewhat different. Five spikes in patients with MTS had polar orientation, but none of the spikes in patients with other MRI findings had polar orientation (odds ratio=6.98, 95% confidence interval=0.38 to 127.38; p=0.07). Scalp topographic mapping of interictal spikes has the potential to offer different information than visual inspection alone. The present results do not allow an immediate clinical application of our findings; however, detecting a polar spike in a patient with TLE may increase the possibility of mesial temporal sclerosis as the underlying etiology. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Spiking Neural P Systems with Communication on Request.

Science.gov (United States)

Pan, Linqiang; Păun, Gheorghe; Zhang, Gexiang; Neri, Ferrante

2017-12-01

Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

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.

Cognitive performance and aphasia recovery.

Science.gov (United States)

Fonseca, José; Raposo, Ana; Martins, Isabel Pavão

2018-03-01

Objectives This study assessed cognitive performance of subjects with aphasia during the acute stage of stroke and evaluated how such performance relates to recovery at 3 months. Materials & methods Patients with aphasia following a left hemisphere stroke were evaluated during the first (baseline) and the fourth-month post onset. Assessment comprised non-verbal tests of attention/processing speed (Symbol Search, Cancelation Task), executive functioning (Matrix Reasoning, Tower of Hanoi, Clock Drawing, Motor Initiative), semantic (Camel and Cactus Test), episodic and immediate memory (Memory for Faces Test, 5 Objects Memory Test, and Spatial Span. Recovery was measured by the Token Test score at 3 months. The impact of baseline performance on recovery was evaluated by logistic regression adjusting for age, education, severity of aphasia and the Alberta Stroke Program Early CT (ASPECT) score. Results Thirty-nine subjects (with a mean of 66.5 ± 10.6 years of age, 17 men) were included. Average baseline cognitive performance was within normal range in all tests except in memory tests (semantic, episodic and immediate memory) for which scores were ≤-1.5sd. Subjects with poor aphasia recovery (N = 27) were older and had fewer years of formal education but had identical ASPECT score compared to those with favorable recovery. Considering each test individually, the score obtained on the Matrix Reasoning test was the only one to predict aphasia recovery (Exp(B)=24.085 p = 0.038). Conclusions The Matrix Reasoning Test may contribute to predict aphasia recovery. Cognitive performance is a measure of network disruption but may also indicate the availability of recovery strategies.

Non-orthogonally transitive G2 spike solution

International Nuclear Information System (INIS)

Lim, Woei Chet

2015-01-01

We generalize the orthogonally transitive (OT) G 2 spike solution to the non-OT G 2 case. This is achieved by applying Geroch’s transformation on a Kasner seed. The new solution contains two more parameters than the OT G 2 spike solution. Unlike the OT G 2 spike solution, the new solution always resolves its spike. (fast track communication)

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.

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.

Motor control by precisely timed spike patterns

DEFF Research Database (Denmark)

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

2017-01-01

whether the information in spike timing actually plays a role in brain function. By examining the activity of individual motor units (the muscle fibers innervated by a single motor neuron) and manipulating patterns of activation of these neurons, we provide both correlative and causal evidence......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...... interval (spike rate), recent studies have shown that additional information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear...

Effects of Mesenchymal Stem Cell Treatment on the Expression of Matrix Metalloproteinases and Angiogenesis during Ischemic Stroke Recovery.

Directory of Open Access Journals (Sweden)

Hyo Suk Nam

Full Text Available The efficacy of mesenchymal stem cell (MSC transplantation in ischemic stroke might depend on the timing of administration. We investigated the optimal time point of MSC transplantation. After MSC treatment, we also investigated the expression of matrix metalloproteinases (MMPs, which play a role in vascular and tissue remodeling.Human bone marrow-derived MSCs (2 × 10(6, passage 5 were administrated intravenously after permanent middle cerebral artery occlusion (MCAO was induced in male Sprague-Dawley rats. First, we determined the time point of MSC transplantation that led to maximal neurological recovery at 1 h, 1 day, and 3 days after MCAO. Next, we measured activity of MMP-2 and MMP-9, neurological recovery, infarction volume, and vascular density after transplanting MSCs at the time that led to maximal neurological recovery.Among the MSC-transplanted rats, those of the MSC 1-hour group showed maximal recovery in the rotarod test (P = 0.023 and the Longa score (P = 0.018. MMP-2 activity at 1 day after MCAO in the MSC 1-hour group was significantly higher than that in the control group (P = 0.002, but MMP-9 activity was not distinct. The MSC 1-hour group also showed smaller infarction volume and higher vascular density than did the control group.In a permanent model of rodent MCAO, very early transplantation of human MSCs (1 h after MCAO produced greater neurological recovery and decreased infraction volume. The elevation of MMP-2 activity and the increase in vascular density after MSC treatment suggest that MSCs might help promote angiogenesis and lead to neurological improvement during the recovery phase after ischemic stroke.

A low-rank matrix recovery approach for energy efficient EEG acquisition for a wireless body area network.

Science.gov (United States)

Majumdar, Angshul; Gogna, Anupriya; Ward, Rabab

2014-08-25

We address the problem of acquiring and transmitting EEG signals in Wireless Body Area Networks (WBAN) in an energy efficient fashion. In WBANs, the energy is consumed by three operations: sensing (sampling), processing and transmission. Previous studies only addressed the problem of reducing the transmission energy. For the first time, in this work, we propose a technique to reduce sensing and processing energy as well: this is achieved by randomly under-sampling the EEG signal. We depart from previous Compressed Sensing based approaches and formulate signal recovery (from under-sampled measurements) as a matrix completion problem. A new algorithm to solve the matrix completion problem is derived here. We test our proposed method and find that the reconstruction accuracy of our method is significantly better than state-of-the-art techniques; and we achieve this while saving sensing, processing and transmission energy. Simple power analysis shows that our proposed methodology consumes considerably less power compared to previous CS based techniques.

Generalized activity equations for spiking neural network dynamics

Directory of Open Access Journals (Sweden)

Michael A Buice

2013-11-01

Full Text Available Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales - the spike duration time is much shorter than the inter-spike time, which is much shorter than any learning time scale. In numerical analysis, this is a classic stiff problem. Spiking neurons are also much more difficult to study analytically. One possible approach to making spiking networks more tractable is to augment mean field activity models with some information about spiking correlations. For example, such a generalized activity model could carry information about spiking rates and correlations between spikes self-consistently. Here, we will show how this can be accomplished by constructing a complete formal probabilistic description of the network and then expanding around a small parameter such as the inverse of the number of neurons in the network. The mean field theory of the system gives a rate-like description. The first order terms in the perturbation expansion keep track of covariances.

Trehalose significantly enhances the recovery of serum and serum exosomal miRNA from a paper-based matrix.

Science.gov (United States)

Neo, Shu Hui; Chung, Ka Yan; Quek, Jia Min; Too, Heng-Phon

2017-11-30

The preservation of nucleic acids from clinical samples is critical to facilitate accurate molecular diagnosis. The use of a paper matrix, Flinders Technology Associates (FTA) Elute cards, to archive DNA and viral RNA is well-documented. However, the feasibility of FTA Elute cards for archiving serum and serum exosomal microRNAs (miRNAs) remains unclear. Here, we performed a comprehensive evaluation of FTA Elute cards for miRNA storage and recovery in different pre-analytical conditions. The recovery of serum miRNA dry-spotted on FTA Elute cards by direct elution with water at high temperature was poor. However, serum miRNAs dry-spotted on the cards were isolated with about 40% yield when using QIAzol lysis reagent and recovery was improved remarkably (>80%) upon extraction from cards pre-treated with trehalose. miRNAs stored on the cards remained stable at room temperature and can be kept for prolonged periods. Furthermore, miRNAs could be similarly recovered from serum exosomes dry-spotted on the cards. Importantly, when using sera from gastric cancer (GC) patients, the miRNAs were efficiently recovered from trehalose pre-treated cards without affecting their representation. Collectively, we have demonstrated the potential of FTA Elute cards to archive serum and serum exosomal miRNAs, making it useful for biomarker discovery and diagnostics.

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...

Separating DDTs in edible animal fats using matrix solid-phase dispersion extraction with activated carbon filter, Toyobo-KF.

Science.gov (United States)

Furusawa, Naoto

2006-09-01

A technique is presented for the economical, routine, and quantitative analysis of contamination by dichloro-diphenyl-trichloroethanes (DDTs) [pp'-DDT, pp'-dichlorodiphenyl dichloroethylene, and pp'-dichlorodiphenyl dichloreothane in beef tallow and chicken fat samples, based on their separation using matrix solid-phase dispersion (MSPD) extraction with Toyobo-KF, an activated carbon fiber. Toyobo-KF is a newly applied MSPD sorbent, and it is followed by reversed-phase high-performance liquid chromatography (HPLC) with a photodiode array detector. The resulting analytical performance parameters [recoveries of spiked DDTs (0.1, 0.2, and 0.4 microg/g) > or = 81%, with relative standard deviations of < or = 8% (n = 5), and quantitation limits < or = 0.03 microg/g], with minimal handling and cost-efficiency, indicate that the present MSPD-HPLC method may be a useful tool for routine monitoring of DDT contamination in meat.

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.

Preparation of mixed matrix adsorber membranes for protein recovery

NARCIS (Netherlands)

Avramescu, M.E.; Girones nogue, Miriam; Borneman, Zandrie; Wessling, Matthias

2003-01-01

This paper presents a generic technology allowing the incorporation of functional entities into a porous substrate. Various ion exchange particles were incorporated into an ethylene vinyl alcohol (EVAL) copolymer porous matrix by an immersion phase separation process and a heterogeneous matrix,

Learning Universal Computations with Spikes

Science.gov (United States)

Thalmeier, Dominik; Uhlmann, Marvin; Kappen, Hilbert J.; Memmesheimer, Raoul-Martin

2016-01-01

Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them. PMID:27309381

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

The Omega-Infinity Limit of Single Spikes

CERN Document Server

Axenides, Minos; Linardopoulos, Georgios

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.

Stress-Induced Impairment of a Working Memory Task: Role of Spiking Rate and Spiking History Predicted Discharge

Science.gov (United States)

Devilbiss, David M.; Jenison, Rick L.; Berridge, Craig W.

2012-01-01

Stress, pervasive in society, contributes to over half of all work place accidents a year and over time can contribute to a variety of psychiatric disorders including depression, schizophrenia, and post-traumatic stress disorder. Stress impairs higher cognitive processes, dependent on the prefrontal cortex (PFC) and that involve maintenance and integration of information over extended periods, including working memory and attention. Substantial evidence has demonstrated a relationship between patterns of PFC neuron spiking activity (action-potential discharge) and components of delayed-response tasks used to probe PFC-dependent cognitive function in rats and monkeys. During delay periods of these tasks, persistent spiking activity is posited to be essential for the maintenance of information for working memory and attention. However, the degree to which stress-induced impairment in PFC-dependent cognition involves changes in task-related spiking rates or the ability for PFC neurons to retain information over time remains unknown. In the current study, spiking activity was recorded from the medial PFC of rats performing a delayed-response task of working memory during acute noise stress (93 db). Spike history-predicted discharge (SHPD) for PFC neurons was quantified as a measure of the degree to which ongoing neuronal discharge can be predicted by past spiking activity and reflects the degree to which past information is retained by these neurons over time. We found that PFC neuron discharge is predicted by their past spiking patterns for nearly one second. Acute stress impaired SHPD, selectively during delay intervals of the task, and simultaneously impaired task performance. Despite the reduction in delay-related SHPD, stress increased delay-related spiking rates. These findings suggest that neural codes utilizing SHPD within PFC networks likely reflects an additional important neurophysiological mechanism for maintenance of past information over time. Stress

Spikes and matter inhomogeneities in massless scalar field models

International Nuclear Information System (INIS)

Coley, A A; Lim, W C

2016-01-01

We shall discuss the general relativistic generation of spikes in a massless scalar field or stiff perfect fluid model. We first investigate orthogonally transitive (OT) G 2 stiff fluid spike models both heuristically and numerically, and give a new exact OT G 2 stiff fluid spike solution. We then present a new two-parameter family of non-OT G 2 stiff fluid spike solutions, obtained by the generalization of non-OT G 2 vacuum spike solutions to the stiff fluid case by applying Geroch’s transformation on a Jacobs seed. The dynamics of these new stiff fluid spike solutions is qualitatively different from that of the vacuum spike solutions in that the matter (stiff fluid) feels the spike directly and the stiff fluid spike solution can end up with a permanent spike. We then derive the evolution equations of non-OT G 2 stiff fluid models, including a second perfect fluid, in full generality, and briefly discuss some of their qualitative properties and their potential numerical analysis. Finally, we discuss how a fluid, and especially a stiff fluid or massless scalar field, affects the physics of the generation of spikes. (paper)

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.

Geomagnetic spikes on the core-mantle boundary

Science.gov (United States)

Davies, C. J.; Constable, C.

2017-12-01

Extreme variations of Earth's magnetic field occurred in the Levantine region around 1000 BC, where the field intensity rose and fell by a factor of 2-3 over a short time and confined spatial region. There is presently no coherent link between this intensity spike and the generating processes in Earth's liquid core. Here we test the attribution of a surface spike to a flux patch visible on the core-mantle boundary (CMB), calculating geometric and energetic bounds on resulting surface geomagnetic features. We show that the Levantine intensity high must span at least 60 degrees in longitude. Models providing the best trade-off between matching surface spike intensity, minimizing L1 and L2 misfit to the available data and satisfying core energy constraints produce CMB spikes 8-22 degrees wide with peak values of O(100) mT. We propose that the Levantine spike grew in place before migrating northward and westward, contributing to the growth of the axial dipole field seen in Holocene field models. Estimates of Ohmic dissipation suggest that diffusive processes, which are often neglected, likely govern the ultimate decay of geomagnetic spikes. Using these results, we search for the presence of spike-like features in geodynamo simulations.

Bursts generate a non-reducible spike-pattern code

Directory of Open Access Journals (Sweden)

Hugo G Eyherabide

2009-05-01

Full Text Available On the single-neuron level, precisely timed spikes can either constitute firing-rate codes or spike-pattern codes that utilize the relative timing between consecutive spikes. There has been little experimental support for the hypothesis that such temporal patterns contribute substantially to information transmission. Using grasshopper auditory receptors as a model system, we show that correlations between spikes can be used to represent behaviorally relevant stimuli. The correlations reflect the inner structure of the spike train: a succession of burst-like patterns. We demonstrate that bursts with different spike counts encode different stimulus features, such that about 20% of the transmitted information corresponds to discriminating between different features, and the remaining 80% is used to allocate these features in time. In this spike-pattern code, the "what" and the "when" of the stimuli are encoded in the duration of each burst and the time of burst onset, respectively. Given the ubiquity of burst firing, we expect similar findings also for other neural systems.

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.

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.

Effect of fungal mycelia on the HPLC-UV and UV-vis spectrophotometric assessment of mycelium-bound epoxide hydrolase using glycidyl phenyl ether.

Science.gov (United States)

Dolcet, Marta M; Torres, Mercè; Canela, Ramon

2016-06-25

The use of mycelia as biocatalysts has technical and economic advantages. However, there are several difficulties in obtaining accurate results in mycelium-catalysed reactions. Firstly, sample extraction, indispensable because of the presence of mycelia, can bring into the extract components with a similar structure to that of the analyte of interest; secondly, mycelia can influence the recovery of the analyte. We prepared calibration standards of 3-phenoxy-1,2-propanediol (PPD) in the pure solvent and in the presence of mycelia (spiked before or after extraction) from five fungi (Aspergillus niger, Aspergillus tubingensis, Penicillium aurantiogriseum, Penicillium sp. and Aspergillus terreus). The quantification of PPD was carried out by HPLC-UV and UV-vis spectrophotometry. The manuscript shows that the last method is as accurate as the HPLC method. However, the colorimetric method led to a higher data throughput, which allowed the study of more samples in a shorter time. Matrix effects were evaluated visually from the plotted calibration data and statistically by simultaneously comparing the intercept and slope of calibration curves performed with solvent, post-extraction spiked standards and pre-extraction spiked standards. Significant differences were found between the post- and pre-extraction spiked matrix-matched functions. Pre-extraction spiked matrix-matched functions based on A. tubingensis mycelia, selected as the reference, were validated and used to compensate for low recoveries. These validated functions were successfully applied to the quantification of PPD achieved during the hydrolysis of glycidyl phenyl ether by mycelium-bound epoxide hydrolases and equivalent hydrolysis yields were determined by HPLC-UV and UV-vis spectrophotometry. This study may serve as starting point to implement matrix effects evaluation when mycelium-bound epoxide hydrolases are studied. Copyright © 2016 Elsevier B.V. All rights reserved.

Inferring oscillatory modulation in neural spike trains.

Science.gov (United States)

Arai, Kensuke; Kass, Robert E

2017-10-01

Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak.

Nanobody-based enzyme immunoassay for ochratoxin A in cereal with high resistance to matrix interference.

Science.gov (United States)

Liu, Xing; Tang, Zongwen; Duan, Zhenhua; He, Zhenyun; Shu, Mei; Wang, Xianxian; Gee, Shirley J; Hammock, Bruce D; Xu, Yang

2017-03-01

A sensitive indirect competitive nanobody-based enzyme linked immunosorbent assay (Nb-ELISA) for ochratoxin A (OTA) with high resistance to cereal matrix interference was developed. Nanobodies against OTA (Nb15, Nb28, Nb32, Nb36) were expressed in E. coli cells and their thermal stabilities were compared with that of an OTA-specific monoclonal antibody 6H8. All nanobodies could still retain their antigen-binding activity after exposure to temperature 95°C for 5min or to 90°C for 75min. Nb28 that exhibited the highest sensitivity in ELISA was selected for further research. An indirect competitive ELISA based on Nb28 was developed for OTA, with an IC 50 of 0.64ng/mL and a linear range (IC 20 -IC 80 ) of 0.27-1.47ng/mL. Cereal samples were analyzed following a 2.5 fold dilution of sample extracts, showing the good resistance to matrix interference of the Nb-ELSIA. The recovery of spiked cereal samples (rice, oats, barley) ranged from 80% to 105% and the Nb-ELISA results of OTA content in naturally contamined samples were in good agreement with those determined by a commercial ELISA kit. The results indicated the reliablity of nanobody as a promising immunoassay reagent for detection of mycotoxins in food matrix and its potential in biosensor development. Copyright © 2016 Elsevier B.V. All rights reserved.

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...

Hg stable isotope analysis by the double-spike method.

Science.gov (United States)

Mead, Chris; Johnson, Thomas M

2010-06-01

Recent publications suggest great potential for analysis of Hg stable isotope abundances to elucidate sources and/or chemical processes that control the environmental impact of mercury. We have developed a new MC-ICP-MS method for analysis of mercury isotope ratios using the double-spike approach, in which a solution containing enriched (196)Hg and (204)Hg is mixed with samples and provides a means to correct for instrumental mass bias and most isotopic fractionation that may occur during sample preparation and introduction into the instrument. Large amounts of isotopic fractionation induced by sample preparation and introduction into the instrument (e.g., by batch reactors) are corrected for. This may greatly enhance various Hg pre-concentration methods by correcting for minor fractionation that may occur during preparation and removing the need to demonstrate 100% recovery. Current precision, when ratios are normalized to the daily average, is 0.06 per thousand, 0.06 per thousand, 0.05 per thousand, and 0.05 per thousand (2sigma) for (202)Hg/(198)Hg, (201)Hg/(198)Hg, (200)Hg/(198)Hg, and (199)Hg/(198)Hg, respectively. This is slightly better than previously published methods. Additionally, this precision was attained despite the presence of large amounts of other Hg isotopes (e.g., 5.0% atom percent (198)Hg) in the spike solution; substantially better precision could be achieved if purer (196)Hg were used.

Supervised Learning Based on Temporal Coding in Spiking Neural Networks.

Science.gov (United States)

Mostafa, Hesham

2017-08-01

Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piecewise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior cannot be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.

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

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.

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.

Surfing a spike wave down the ventral stream.

Science.gov (United States)

VanRullen, Rufin; Thorpe, Simon J

2002-10-01

Numerous theories of neural processing, often motivated by experimental observations, have explored the computational properties of neural codes based on the absolute or relative timing of spikes in spike trains. Spiking neuron models and theories however, as well as their experimental counterparts, have generally been limited to the simulation or observation of isolated neurons, isolated spike trains, or reduced neural populations. Such theories would therefore seem inappropriate to capture the properties of a neural code relying on temporal spike patterns distributed across large neuronal populations. Here we report a range of computer simulations and theoretical considerations that were designed to explore the possibilities of one such code and its relevance for visual processing. In a unified framework where the relation between stimulus saliency and spike relative timing plays the central role, we describe how the ventral stream of the visual system could process natural input scenes and extract meaningful information, both rapidly and reliably. The first wave of spikes generated in the retina in response to a visual stimulation carries information explicitly in its spatio-temporal structure: the most salient information is represented by the first spikes over the population. This spike wave, propagating through a hierarchy of visual areas, is regenerated at each processing stage, where its temporal structure can be modified by (i). the selectivity of the cortical neurons, (ii). lateral interactions and (iii). top-down attentional influences from higher order cortical areas. The resulting model could account for the remarkable efficiency and rapidity of processing observed in the primate visual system.

Spike timing precision of neuronal circuits.

Science.gov (United States)

Kilinc, Deniz; Demir, Alper

2018-04-17

Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits. These techniques, which are orders of magnitude faster than traditional Monte Carlo type simulations, can be used to directly compute the spike timing jitter variance, power spectral densities, correlation functions, and other stochastic characterizations of neuronal circuit operation. We consider three distinct neuronal circuit motifs: Feedback inhibition, synaptic integration, and synaptic coupling. First, we show that both the spike timing precision and the energy efficiency of a spiking neuron are improved with feedback inhibition. We unveil the underlying mechanism through which this is achieved. Then, we demonstrate that a neuron can improve on the timing precision of its synaptic inputs, coming from multiple sources, via synaptic integration: The phase of the output spikes of the integrator neuron has the same variance as that of the sample average of the phases of its inputs. Finally, we reveal that weak synaptic coupling among neurons, in a fully connected network, enables them to behave like a single neuron with a larger membrane area, resulting in an improvement in the timing precision through cooperation.

Causal Inference and Explaining Away in a Spiking Network

Science.gov (United States)

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-01-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification. PMID:26621426

Neuro-Inspired Spike-Based Motion: From Dynamic Vision Sensor to Robot Motor Open-Loop Control through Spike-VITE

Directory of Open Access Journals (Sweden)

Fernando Perez-Peña

2013-11-01

Full Text Available In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM. All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6 and power requirements (3.4 W to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6. It also evidences the suitable use of AER as a communication protocol between processing and actuation.

Neuro-Inspired Spike-Based Motion: From Dynamic Vision Sensor to Robot Motor Open-Loop Control through Spike-VITE

Science.gov (United States)

Perez-Peña, Fernando; Morgado-Estevez, Arturo; Linares-Barranco, Alejandro; Jimenez-Fernandez, Angel; Gomez-Rodriguez, Francisco; Jimenez-Moreno, Gabriel; Lopez-Coronado, Juan

2013-01-01

In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina) to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE) that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM). All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6) and power requirements (3.4 W) to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6). It also evidences the suitable use of AER as a communication protocol between processing and actuation. PMID:24264330

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

Directory of Open Access Journals (Sweden)

Zedong Bi

2016-08-01

Full Text Available 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.

Joint Probability-Based Neuronal Spike Train Classification

Directory of Open Access Journals (Sweden)

Yan Chen

2009-01-01

Full Text Available Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-based methods (EDBMs have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs. The activity of individual SARs was recorded in anaesthetized, paralysed adult male rabbits, which were artificially-ventilated at constant rate and one of three different volumes. Two-thirds of the responses to the 600 stimuli presented at each volume were used to construct three response models (one for each stimulus volume consisting of a series of time bins, each with spike probabilities. The remaining one-third of the responses where used as test responses to be classified into one of the three model responses. This was done by computing the joint probability of observing the same series of events (spikes or no spikes, dictated by the test response in a given model and determining which probability of the three was highest. The JPBM generally produced better classification accuracy than the EDBM, and both performed well above chance. Both methods were similarly affected by variations in discretization parameters, response epoch duration, and two different response alignment strategies. Increasing bin widths increased classification accuracy, which also improved with increased observation time, but primarily during periods of increasing lung inflation. Thus, the JPBM is a simple and effective method performing spike train classification.

Fitting neuron models to spike trains

Directory of Open Access Journals (Sweden)

Cyrille eRossant

2011-02-01

Full Text Available Computational modeling is increasingly used to understand the function of neural circuitsin systems neuroscience.These studies require models of individual neurons with realisticinput-output properties.Recently, it was found that spiking models can accurately predict theprecisely timed spike trains produced by cortical neurons in response tosomatically injected currents,if properly fitted. This requires fitting techniques that are efficientand 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 allowsthe user to define and fit arbitrary neuron models to electrophysiological recordings.It relies on vectorization and parallel computing techniques toachieve efficiency.We demonstrate its use on neural recordings in the barrel cortex andin the auditory brainstem, and confirm that simple adaptive spiking modelscan accurately predict the response of cortical neurons. Finally, we show how a complexmulticompartmental model can be reduced to a simple effective spiking model.

Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE

Directory of Open Access Journals (Sweden)

Pietro Quaglio

2017-05-01

Full Text Available Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs. STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons. In this work, we introduce a novel method capable of overcoming the computational and statistical limits of existing analysis techniques in detecting repeating STPs within massively parallel spike trains (MPST. We employ advanced data mining techniques to efficiently extract repeating sequences of spikes from the data. Then, we introduce and compare two alternative approaches to distinguish statistically significant patterns from chance sequences. The first approach uses a measure known as conceptual stability, of which we investigate a computationally cheap approximation for applications to such large data sets. The second approach is based on the evaluation of pattern statistical significance. In particular, we provide an extension to STPs of a method we recently introduced for the evaluation of statistical significance of synchronous spike patterns. The performance of the two approaches is evaluated in terms of computational load and statistical power on a variety of artificial data sets that replicate specific features of experimental data. Both methods provide an effective and robust procedure for detection of STPs in MPST data. The method based on significance evaluation shows the best overall performance, although at a higher computational cost. We name the novel procedure the spatio-temporal Spike PAttern Detection and Evaluation (SPADE analysis.

Off-line supercritical fluid extraction-capillary GC applications in environmental analysis

NARCIS (Netherlands)

David, F.; Verschuere, M.; Sandra, P.J.F.

1992-01-01

The successful application of supercrit. fluid extn. for environmental samples requires that the extn. for environmental samples requires that the extn. conditions detd. for spiked samples must be optimized in order to overcome the solute-matrix interactions that are responsible for lower recoveries

ZnO core spike particles and nano-networks and their wide range of applications

Science.gov (United States)

Wille, S.; Mishra, Y. K.; Gedamu, D.; Kaps, S.; Jin, X.; Koschine, T.; Bathnagar, A.; Adelung, R.

2011-05-01

In our approach we are producing a polymer composite material with ZnO core spike particles as concave fillers. The core spike particles are synthesized by a high throughput method. Using PDMS (Polydimethylsiloxane) as a matrix material the core spike particles achieve not only a high mechanical reinforcement but also influence other material properties in a very interesting way, making such a composite very interesting for a wide range of applications. In a very similar synthesis route a nanoscopic ZnO-network is produced. As a ceramic this network can withstand high temperatures like 1300 K. In addition this material is quite elastic. To find a material with these two properties is a really difficult task, as polymers tend to decompose already at lower temperatures and metals melt. Especially under ambient conditions, often oxygen creates a problem for metals at these temperatures. If this material is at the same time a semiconductor, it has a high potential as a multifunctional material. Ceramic or classical semiconductors like III-V or IIVI type are high temperature stable, but typically brittle. This is different on the nanoscale. Even semiconductor wires like silicon with a very small diameter do not easily built up enough stress that leads to a failure while being bent, because in a first order approximation the maximum stress of a fiber scales with its diameter.

Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

Science.gov (United States)

Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

2015-07-15

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks

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.

Recent progress in multi-electrode spike sorting methods.

Science.gov (United States)

Lefebvre, Baptiste; Yger, Pierre; Marre, Olivier

2016-11-01

In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multineuron spike train analysis with R-convolution linear combination kernel.

Science.gov (United States)

Tezuka, Taro

2018-06-01

A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

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...

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/.

ViSAPy: a Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms.

Science.gov (United States)

Hagen, Espen; Ness, Torbjørn V; Khosrowshahi, Amir; Sørensen, Christina; Fyhn, Marianne; Hafting, Torkel; Franke, Felix; Einevoll, Gaute T

2015-04-30

New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times. We here present a general simulation tool for computing benchmarking data for evaluation of spike-sorting algorithms entitled ViSAPy (Virtual Spiking Activity in Python). The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy. ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval. ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers. ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Recovery of Dynamics and Function in Spiking Neural Networks with Closed-Loop Control.

Science.gov (United States)

Vlachos, Ioannis; Deniz, Taşkin; Aertsen, Ad; Kumar, Arvind

2016-02-01

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system.

Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

DEFF Research Database (Denmark)

Huys, Raoul; Jirsa, Viktor K; Darokhan, Ziauddin

2016-01-01

attractor. Its existence guarantees that evoked spiking return to the spontaneous state. However, the spontaneous ongoing spiking state and the visual evoked spiking states are qualitatively different and are separated by a threshold (separatrix). The functional advantage of this organization...

Multichannel interictal spike activity detection using time-frequency entropy measure.

Science.gov (United States)

Thanaraj, Palani; Parvathavarthini, B

2017-06-01

Localization of interictal spikes is an important clinical step in the pre-surgical assessment of pharmacoresistant epileptic patients. The manual selection of interictal spike periods is cumbersome and involves a considerable amount of analysis workload for the physician. The primary focus of this paper is to automate the detection of interictal spikes for clinical applications in epilepsy localization. The epilepsy localization procedure involves detection of spikes in a multichannel EEG epoch. Therefore, a multichannel Time-Frequency (T-F) entropy measure is proposed to extract features related to the interictal spike activity. Least squares support vector machine is used to train the proposed feature to classify the EEG epochs as either normal or interictal spike period. The proposed T-F entropy measure, when validated with epilepsy dataset of 15 patients, shows an interictal spike classification accuracy of 91.20%, sensitivity of 100% and specificity of 84.23%. Moreover, the area under the curve of Receiver Operating Characteristics plot of 0.9339 shows the superior classification performance of the proposed T-F entropy measure. The results of this paper show a good spike detection accuracy without any prior information about the spike morphology.

Structured Matrix Completion with Applications to Genomic Data Integration.

Science.gov (United States)

Cai, Tianxi; Cai, T Tony; Zhang, Anru

2016-01-01

Matrix completion has attracted significant recent attention in many fields including statistics, applied mathematics and electrical engineering. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. We provide theoretical justification for the proposed SMC method and derive lower bound for the estimation errors, which together establish the optimal rate of recovery over certain classes of approximately low-rank matrices. Simulation studies show that the method performs well in finite sample under a variety of configurations. The method is applied to integrate several ovarian cancer genomic studies with different extent of genomic measurements, which enables us to construct more accurate prediction rules for ovarian cancer survival.

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.

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.

Grain price spikes and beggar-thy-neighbor policy responses

DEFF Research Database (Denmark)

Jensen, Hans Grinsted; Anderson, Kym

2017-01-01

When prices spike in international grain markets, national governments often reduce the extent to which that spike affects their domestic food markets. Those actions exacerbate the price spike and international welfare transfer associated with that terms of trade change. Several recent analyses...

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.

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.

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.

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

2014-02-05

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. Copyright © 2014 Elsevier Inc. All rights reserved.

Error-backpropagation in temporally encoded networks of spiking neurons

NARCIS (Netherlands)

S.M. Bohte (Sander); J.A. La Poutré (Han); J.N. Kok (Joost)

2000-01-01

textabstractFor a network of spiking neurons that encodes information in the timing of individual spike-times, we derive a supervised learning rule, emph{SpikeProp, akin to traditional error-backpropagation and show how to overcome the discontinuities introduced by thresholding. With this algorithm,

Spike Pattern Structure Influences Synaptic Efficacy Variability Under STDP and Synaptic Homeostasis. I: Spike Generating Models on Converging Motifs

Directory of Open Access Journals (Sweden)

Zedong eBi

2016-02-01

Full Text Available In neural systems, synaptic plasticity is usually driven by spike trains. Due to the inherent noises of neurons and synapses as well as the randomness of connection details, spike trains typically exhibit variability such as spatial randomness and temporal stochasticity, resulting in variability of synaptic changes under plasticity, which we call efficacy variability. How the variability of spike trains influences the efficacy variability of synapses remains unclear. In this paper, we try to understand this influence under pair-wise additive spike-timing dependent plasticity (STDP when the mean strength of plastic synapses into a neuron is bounded (synaptic homeostasis. Specifically, we systematically study, analytically and numerically, how four aspects of statistical features, i.e. synchronous firing, burstiness/regularity, heterogeneity of rates and heterogeneity of cross-correlations, as well as their interactions influence the efficacy variability in converging motifs (simple networks in which one neuron receives from many other neurons. Neurons (including the post-synaptic neuron in a converging motif generate spikes according to statistical models with tunable parameters. In this way, we can explicitly control the statistics of the spike patterns, and investigate their influence onto the efficacy variability, without worrying about the feedback from synaptic changes onto the dynamics of the post-synaptic neuron. We separate efficacy variability into two parts: the drift part (DriftV induced by the heterogeneity of change rates of different synapses, and the diffusion part (DiffV induced by weight diffusion caused by stochasticity of spike trains. Our main findings are: (1 synchronous firing and burstiness tend to increase DiffV, (2 heterogeneity of rates induces DriftV when potentiation and depression in STDP are not balanced, and (3 heterogeneity of cross-correlations induces DriftV together with heterogeneity of rates. We anticipate our

Mimickers of generalized spike and wave discharges.

Science.gov (United States)

Azzam, Raed; Bhatt, Amar B

2014-06-01

Overinterpretation of benign EEG variants is a common problem that can lead to the misdiagnosis of epilepsy. We review four normal patterns that mimic generalized spike and wave discharges: phantom spike-and-wave, hyperventilation hypersynchrony, hypnagogic/ hypnopompic hypersynchrony, and mitten patterns.

A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.

Science.gov (United States)

Guo, Lilin; Wang, Zhenzhong; Cabrerizo, Mercedes; Adjouadi, Malek

2017-05-01

This study introduces a novel learning algorithm for spiking neurons, called CCDS, which is able to learn and reproduce arbitrary spike patterns in a supervised fashion allowing the processing of spatiotemporal information encoded in the precise timing of spikes. Unlike the Remote Supervised Method (ReSuMe), synapse delays and axonal delays in CCDS are variants which are modulated together with weights during learning. The CCDS rule is both biologically plausible and computationally efficient. The properties of this learning rule are investigated extensively through experimental evaluations in terms of reliability, adaptive learning performance, generality to different neuron models, learning in the presence of noise, effects of its learning parameters and classification performance. Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule. The merit of CCDS rule is further validated on a practical example involving the automated detection of interictal spikes in EEG records of patients with epilepsy. Results again show that with proper encoding, the CCDS rule achieves good recognition performance.

Platinum stable isotope analysis of geological standard reference materials by double-spike MC-ICPMS

DEFF Research Database (Denmark)

Creech, John Benjamin; Baker, J. A.; Handler, M. R.

2014-01-01

. Double-spiking of samples was carried out prior to digestion and chemical separation to correct for any mass-dependent fractionation that may occur due to incomplete recovery of Pt. Samples were digested using a NiS fire assay method, which pre-concentrates Pt into a metallic bead that is readily...... metal standard solution doped with a range of synthetic matrices and results in Pt yields of ≥90% with purity of ≥95%. Using this chemical separation technique, we have separated Pt from 11 international geological standard reference materials comprising of PGE ores, mantle rocks, igneous rocks and one...

Platinum stable isotope analysis of geological standard reference materials by double-spike MC-ICPMS.

Science.gov (United States)

Creech, J B; Baker, J A; Handler, M R; Bizzarro, M

2014-01-10

We report a method for the chemical purification of Pt from geological materials by ion-exchange chromatography for subsequent Pt stable isotope analysis by multiple-collector inductively coupled plasma mass spectrometry (MC-ICPMS) using a 196 Pt- 198 Pt double-spike to correct for instrumental mass bias. Double-spiking of samples was carried out prior to digestion and chemical separation to correct for any mass-dependent fractionation that may occur due to incomplete recovery of Pt. Samples were digested using a NiS fire assay method, which pre-concentrates Pt into a metallic bead that is readily dissolved in acid in preparation for anion-exchange chemistry. Pt was recovered from anion-exchange resin in concentrated HNO 3 acid after elution of matrix elements, including the other platinum group elements (PGE), in dilute HCl and HNO 3 acids. The separation method has been calibrated using a precious metal standard solution doped with a range of synthetic matrices and results in Pt yields of ≥90% with purity of ≥95%. Using this chemical separation technique, we have separated Pt from 11 international geological standard reference materials comprising of PGE ores, mantle rocks, igneous rocks and one sample from the Cretaceous-Paleogene boundary layer. Pt concentrations in these samples range from ca. 5 ng g -1 to 4 μg g -1 . This analytical method has been shown to have an external reproducibility on δ 198 Pt (permil difference in the 198 Pt/ 194 Pt ratio from the IRMM-010 standard) of ±0.040 (2 sd) on Pt solution standards (Creech et al., 2013, J. Anal. At. Spectrom. 28, 853-865). The reproducibility in natural samples is evaluated by processing multiple replicates of four standard reference materials, and is conservatively taken to be ca. ±0.088 (2 sd). Pt stable isotope data for the full set of reference materials have a range of δ 198 Pt values with offsets of up to 0.4‰ from the IRMM-010 standard, which are readily resolved with this technique. These

Unsupervised spike sorting based on discriminative subspace learning.

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

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 separability in the learned subspace than in the subspace obtained by principal component analysis or wavelet transform.

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.

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

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.

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...

Evaluation of matrix effect on the determination of rare earth elements and As, Bi, Cd, Pb, Se and In in honey and pollen of native Brazilian bees (Tetragonisca angustula - Jataí) by Q-ICP-MS.

Science.gov (United States)

de Oliveira, Fernanda Ataide; de Abreu, Adriana Trópia; de Oliveira Nascimento, Nathália; Froes-Silva, Roberta Eliane Santos; Antonini, Yasmine; Nalini, Hermínio Arias; de Lena, Jorge Carvalho

2017-01-01

Bees are considered the main pollinators in natural and agricultural environments. Chemical elements from honey and pollen have been used for monitoring the environment, the health of bees and the quality of their products. Nevertheless, there are not many studies on honey and pollen of native Brazilian bees. The goal of this work was to determine important chemical elements (Sc, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Ho, Er, Tm, Lu and Yb) along with As, Bi, Cd, Pb, Se and In, in honey and pollen of native Brazilian bees, assessing analytical interferences from the matrix. A proposed analytical method was developed for these elements by quadrupole ICP-MS. Matrix effect was verified in honey matrix in the quantification of As, Bi and Dy; and in pollen matrix for Bi, Cd, Ce, Gd, La, Pb and Sc. The quality of the method was considered satisfactory taking into consideration the recovery rate of each element in the spiked solutions: honey matrix (91.6-103.9%) and pollen matrix (94.1-115.6%). The quantification limits of the method ranged between 0.00041 and 10.3μgL -1 for honey and 0.00041-0.095μgL -1 for pollen. The results demonstrate that the method is accurate, precise and suitable. Copyright © 2016 Elsevier B.V. All rights reserved.

Orthobunyavirus ultrastructure and the curious tripodal glycoprotein spike.

Directory of Open Access Journals (Sweden)

Thomas A Bowden

Full Text Available The genus Orthobunyavirus within the family Bunyaviridae constitutes an expanding group of emerging viruses, which threaten human and animal health. Despite the medical importance, little is known about orthobunyavirus structure, a prerequisite for understanding virus assembly and entry. Here, using electron cryo-tomography, we report the ultrastructure of Bunyamwera virus, the prototypic member of this genus. Whilst Bunyamwera virions are pleomorphic in shape, they display a locally ordered lattice of glycoprotein spikes. Each spike protrudes 18 nm from the viral membrane and becomes disordered upon introduction to an acidic environment. Using sub-tomogram averaging, we derived a three-dimensional model of the trimeric pre-fusion glycoprotein spike to 3-nm resolution. The glycoprotein spike consists mainly of the putative class-II fusion glycoprotein and exhibits a unique tripod-like arrangement. Protein-protein contacts between neighbouring spikes occur at membrane-proximal regions and intra-spike contacts at membrane-distal regions. This trimeric assembly deviates from previously observed fusion glycoprotein arrangements, suggesting a greater than anticipated repertoire of viral fusion glycoprotein oligomerization. Our study provides evidence of a pH-dependent conformational change that occurs during orthobunyaviral entry into host cells and a blueprint for the structure of this group of emerging pathogens.

Independent component analysis separates spikes of different origin in the EEG.

Science.gov (United States)

Urrestarazu, Elena; Iriarte, Jorge; Artieda, Julio; Alegre, Manuel; Valencia, Miguel; Viteri, César

2006-02-01

Independent component analysis (ICA) is a novel system that finds independent sources in recorded signals. Its usefulness in separating epileptiform activity of different origin has not been determined. The goal of this study was to demonstrate that ICA is useful for separating different spikes using samples of EEG of patients with focal epilepsy. Digital EEG samples from four patients with focal epilepsy were included. The patients had temporal (n = 2), centrotemporal (n = 1) or frontal spikes (n = 1). Twenty-six samples with two (or more) spikes from two different patients were created. The selection of the two spikes for each mixed EEG was performed randomly, trying to have all the different combinations and rejecting the mixture of two spikes from the same patient. Two different examiners studied the EEGs using ICA with JADE paradigm in Matlab platform, trying to separate and to identify the spikes. They agreed in the correct separation of the spikes in 24 of the 26 samples, classifying the spikes as frontal, temporal or centrotemporal, left or right sided. The demonstration of the possibility of detecting different artificially mixed spikes confirms that ICA may be useful in separating spikes or other elements in real EEGs.

Spiking and bursting patterns of fractional-order Izhikevich model

Science.gov (United States)

Teka, Wondimu W.; Upadhyay, Ranjit Kumar; Mondal, Argha

2018-03-01

Bursting and spiking oscillations play major roles in processing and transmitting information in the brain through cortical neurons that respond differently to the same signal. These oscillations display complex dynamics that might be produced by using neuronal models and varying many model parameters. Recent studies have shown that models with fractional order can produce several types of history-dependent neuronal activities without the adjustment of several parameters. We studied the fractional-order Izhikevich model and analyzed different kinds of oscillations that emerge from the fractional dynamics. The model produces a wide range of neuronal spike responses, including regular spiking, fast spiking, intrinsic bursting, mixed mode oscillations, regular bursting and chattering, by adjusting only the fractional order. Both the active and silent phase of the burst increase when the fractional-order model further deviates from the classical model. For smaller fractional order, the model produces memory dependent spiking activity after the pulse signal turned off. This special spiking activity and other properties of the fractional-order model are caused by the memory trace that emerges from the fractional-order dynamics and integrates all the past activities of the neuron. On the network level, the response of the neuronal network shifts from random to scale-free spiking. Our results suggest that the complex dynamics of spiking and bursting can be the result of the long-term dependence and interaction of intracellular and extracellular ionic currents.

Spike Bursts from an Excitable Optical System

Science.gov (United States)

Rios Leite, Jose R.; Rosero, Edison J.; Barbosa, Wendson A. S.; Tredicce, Jorge R.

Diode Lasers with double optical feedback are shown to present power drop spikes with statistical distribution controllable by the ratio of the two feedback times. The average time between spikes and the variance within long time series are studied. The system is shown to be excitable and present bursting of spikes created with specific feedback time ratios and strength. A rate equation model, extending the Lang-Kobayashi single feedback for semiconductor lasers proves to match the experimental observations. Potential applications to construct network to mimic neural systems having controlled bursting properties in each unit will be discussed. Brazilian Agency CNPQ.

Spike Code Flow in Cultured Neuronal Networks

Directory of Open Access Journals (Sweden)

Shinichi Tamura

2016-01-01

Full Text Available 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.

Recovery of soil nitrification after long-term zinc exposure and its co-tolerance to Cu in different soils.

Science.gov (United States)

Liu, Aiju; Fang, Dianmei; Wang, Chao; Li, Menghong; Young, Robert B

2015-01-01

Soils sampled from different locations of China were used to manipulate soil microbial diversity and to assess the effect of the diversity of the soil nitrifying community on the recovery of the soil nitrification to metal stress (zinc). Ten treatments were either or not amended with ZnCl2. Subsequently, a spike-on-spike assay was set up to test for the tolerance of soil nitrification to zinc (Zn) and copper (Cu). Initially, Zn amendment completely inhibited nitrification. After a year of Zn exposure, recovery of the potential nitrification rate in Zn-amended soils ranged from 28 to 126% of the potential nitrification rate in the corresponding Zn-nonamended soils. This recovery was strongly related to the potential nitrification rate before Zn amendment and soil pH. Increased Zn tolerance of the soil nitrification was consistently observed in response to corresponding soil contamination. Co-tolerance to Cu was obtained in all 1,000-mg kg(-1) Zn-amended soils. This tolerance was also strongly related to the potential nitrification rate before Zn amendment and soil pH. Our data indicate that inherently microbial activity can be a significant factor for the recovery of soil functioning derived from metal contamination.

Compensation of matrix effects in gas chromatography-mass spectrometry analysis of pesticides using a combination of matrix matching and multiple isotopically labeled internal standards.

Science.gov (United States)

Tsuchiyama, Tomoyuki; Katsuhara, Miki; Nakajima, Masahiro

2017-11-17

In the multi-residue analysis of pesticides using GC-MS, the quantitative results are adversely affected by a phenomenon known as the matrix effect. Although the use of matrix-matched standards is considered to be one of the most practical solutions to this problem, complete removal of the matrix effect is difficult in complex food matrices owing to their inconsistency. As a result, residual matrix effects can introduce analytical errors. To compensate for residual matrix effects, we have developed a novel method that employs multiple isotopically labeled internal standards (ILIS). The matrix effects of ILIS and pesticides were evaluated in spiked matrix extracts of various agricultural commodities, and the obtained data were subjected to simple statistical analysis. Based on the similarities between the patterns of variation in the analytical response, a total of 32 isotopically labeled compounds were assigned to 338 pesticides as internal standards. It was found that by utilizing multiple ILIS, residual matrix effects could be effectively compensated. The developed method exhibited superior quantitative performance compared with the common single-internal-standard method. The proposed method is more feasible for regulatory purposes than that using only predetermined correction factors and is considered to be promising for practical applications. Copyright © 2017 Elsevier B.V. All rights reserved.

Spectrofluorimetric determination of aliskiren in tablets and spiked human plasma through derivatization with dansyl chloride.

Science.gov (United States)

Aydoğmuş, Zeynep; Sarı, Ferhat; Ulu, Sevgi Tatar

2012-03-01

A simple and sensitive method has been developed and validated for the determination of aliskiren (ALS) in its dosage forms and spiked plasma. The method was based on the reaction of the drug with dansyl chloride in the presence of bicarbonate solution of pH 10.5 to give a highly fluorescent derivative which was measured at 501 nm with excitition at 378 nm in dichloromethane. Different experimental parameters affecting the development of the method and stability were carefully studied and optimized. The calibration curves were linear over the concentration ranges of 100-700 and 50-150 ng/mL for standard solution and plasma, respectively. The limits of detection were 27.52 ng/mL in standard solution, 4.91 ng/mL in plasma. The developed method was successfully applied to the analysis the drug in the commercial tablets and spiked plasma samples. The mean recovery of ALS from tablets and plasma was 100.10 and 97.81%, respectively. A proposal of the reaction pathway was presented.

Determination of trace amounts of lead, arsenic, nickel and cobalt in high-purity iron oxide pigment by inductively coupled plasma atomic emission spectrometry after iron matrix removal with extractant-contained resin

International Nuclear Information System (INIS)

Xu Yuyu; Zhou Jianfeng; Wang Guoxin; Zhou Jinfan; Tao Guanhong

2007-01-01

Inductively coupled plasma atomic emission spectrometry (ICP-AES) was applied to the determination of lead, arsenic, nickel and cobalt in high-purity iron oxide pigment. Samples were dissolved with hydrochloric acid and hydrogen peroxide. The digest was passed through a column, which was packed with a polymer resin containing a neutral organophosphorus extractant, tri-n-butylphosphate. Iron was sorbed selectively on the resin and the analytes of interest passed through the column, allowing the effective separation of them from the iron matrix. Conditions of separation were optimized. The detection limits (3σ) in solution were 10, 40, 7 and 5 μg L -1 , and in pigment were 0.2, 0.8, 0.14 and 0.1 mg kg -1 for lead, arsenic, cobalt and nickel, respectively. The recoveries ranged from 95% to 107% when sample digests were spiked with 5 μg of the analytes of interest, and relative standard deviations (n = 6) were 1.5-17.6% for the determination of the spiked samples. The method was successfully applied to the determination of trace amounts of these elements in high-purity iron oxide pigment samples

Comparison of Classifier Architectures for Online Neural Spike Sorting.

Science.gov (United States)

Saeed, Maryam; Khan, Amir Ali; Kamboh, Awais Mehmood

2017-04-01

High-density, intracranial recordings from micro-electrode arrays need to undergo Spike Sorting in order to associate the recorded neuronal spikes to particular neurons. This involves spike detection, feature extraction, and classification. To reduce the data transmission and power requirements, on-chip real-time processing is becoming very popular. However, high computational resources are required for classifiers in on-chip spike-sorters, making scalability a great challenge. In this review paper, we analyze several popular classifiers to propose five new hardware architectures using the off-chip training with on-chip classification approach. These include support vector classification, fuzzy C-means classification, self-organizing maps classification, moving-centroid K-means classification, and Cosine distance classification. The performance of these architectures is analyzed in terms of accuracy and resource requirement. We establish that the neural networks based Self-Organizing Maps classifier offers the most viable solution. A spike sorter based on the Self-Organizing Maps classifier, requires only 7.83% of computational resources of the best-reported spike sorter, hierarchical adaptive means, while offering a 3% better accuracy at 7 dB SNR.

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.

Flow-through immunomagnetic separation system for waterborne pathogen isolation and detection: Application to Giardia and Cryptosporidium cell isolation

Energy Technology Data Exchange (ETDEWEB)

Ramadan, Qasem, E-mail: qasem.alramadan@epfl.ch [Bioelectronics Program, Institute of Microelectronics, 11 Science Park Road, Singapore 117685 (Singapore); Christophe, Lay; Teo, William; ShuJun, Li; Hua, Feng Han [Bioelectronics Program, Institute of Microelectronics, 11 Science Park Road, Singapore 117685 (Singapore)

2010-07-12

Simultaneous sample washing and concentration of two waterborne pathogen samples were demonstrated using a rotational magnetic system under continuous flow conditions. The rotation of periodically arranged small permanent magnets close to a fluidic channel carrying magnetic particle suspension allows the trapping and release of particles along the fluidic channel in a periodic manner. Each trapping and release event resembles one washing cycle. The performance of the magnetic separation system (MSS) was evaluated in order to test its functionality to isolate magnetic-labelled protozoan cells from filtered, concentrated tap water, secondary effluent water, and purified water. Experimental protocols described in US Environmental Protection Agency method 1623 which rely on the use of a magnetic particle concentrator, were applied to test and compare our continuous flow cell separation system to the standard magnetic bead-based isolation instruments. The recovery efficiencies for Giardia cysts using the magnetic tube holder and our magnetic separation system were 90.5% and 90.1%, respectively, from a tap water matrix and about 31% and 18.5%, respectively, from a spiked secondary effluent matrix. The recovery efficiencies for Cryptosporidium cells using the magnetic tube holder and our magnetic separation system were 90% and 83.3%, respectively, from a tap water matrix and about 38% and 36%, respectively, from a spiked secondary effluent matrix. Recoveries from all matrices with the continuous flow system were typically higher in glass tubing conduits than in molded plastic conduits.

Scaling of spiking and humping in keyhole welding

Energy Technology Data Exchange (ETDEWEB)

Wei, P S; Chuang, K C [Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan (China); DebRoy, T [Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA 16802 (United States); Ku, J S, E-mail: pswei@mail.nsysu.edu.tw, E-mail: cielo.zhuang@gmail.com, E-mail: rtd1@psu.edu, E-mail: jsku@mail.nsysu.edu.tw [Institute of Materials Science and Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan (China)

2011-06-22

Spiking, rippling and humping seriously reduce the strength of welds. The effects of beam focusing, volatile alloying element concentration and welding velocity on spiking, coarse rippling and humping in keyhole mode electron-beam welding are examined through scale analysis. Although these defects have been studied in the past, the mechanisms for their formation are not fully understood. This work relates the average amplitudes of spikes to fusion zone depth for the welding of Al 6061, SS 304 and carbon steel, and Al 5083. The scale analysis introduces welding and melting efficiencies and an appropriate power distribution to account for the focusing effects, and the energy which is reflected and escapes through the keyhole opening to the surroundings. The frequency of humping and spiking can also be predicted from the scale analysis. The analysis also reveals the interrelation between coarse rippling and humping. The data and the mechanistic findings reported in this study are useful for understanding and preventing spiking and humping during keyhole mode electron and laser beam welding.

A metric space approach to the information capacity of spike trains

OpenAIRE

HOUGHTON, CONOR JAMES; GILLESPIE, JAMES

2010-01-01

PUBLISHED Classical information theory can be either discrete or continuous, corresponding to discrete or continuous random variables. However, although spike times in a spike train are described by continuous variables, the information content is usually calculated using discrete information theory. This is because the number of spikes, and hence, the number of variables, varies from spike train to spike train, making the continuous theory difficult to apply.It is possible to avoid ...

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.

Voltage-spike analysis for a free-running parallel inverter

Science.gov (United States)

Lee, F. C. Y.; Wilson, T. G.

1974-01-01

Unwanted and sometimes damaging high-amplitude voltage spikes occur during each half cycle in many transistor saturable-core inverters at the moment when the core saturates and the transistors switch. The analysis shows that spikes are an intrinsic characteristic of certain types of inverters even with negligible leakage inductance and purely resistive load. The small but unavoidable after-saturation inductance of the saturable-core transformer plays an essential role in creating these undesired thigh-voltage spikes. State-plane analysis provides insight into the complex interaction between core and transistors, and shows the circuit parameters upon which the magnitude of these spikes depends.

In-reactor creep of zirconium alloys by thermal spikes

International Nuclear Information System (INIS)

Ibrahim, E.F.

1975-01-01

The size and duration of thermal spikes from fast neutrons have been calculated for zirconium alloys, showing that spikes up to 1.8 nm radius may exist for 2 x 10 -11 s at greater than melting point, at 570K ambient temperature. Creep rates have been calculated assuming that the elastic strain from the applied stress relaxes in the volume of the spikes (by preferential loop alignment or modification of an existing dislocation network). The calculated rates are consistent with strain rates observed in long term tests-in-reactor, if spike lifetimes are 2 to 2.5 x 10 -11 s. (Auth.)

Automated spike preparation system for Isotope Dilution Mass Spectrometry (IDMS)

International Nuclear Information System (INIS)

Maxwell, S.L. III; Clark, J.P.

1990-01-01

Isotope Dilution Mass Spectrometry (IDMS) is a method frequently employed to measure dissolved, irradiated nuclear materials. A known quantity of a unique isotope of the element to be measured (referred to as the ''spike'') is added to the solution containing the analyte. The resulting solution is chemically purified then analyzed by mass spectrometry. By measuring the magnitude of the response for each isotope and the response for the ''unique spike'' then relating this to the known quantity of the ''spike'', the quantity of the nuclear material can be determined. An automated spike preparation system was developed at the Savannah River Site (SRS) to dispense spikes for use in IDMS analytical methods. Prior to this development, technicians weighed each individual spike manually to achieve the accuracy required. This procedure was time-consuming and subjected the master stock solution to evaporation. The new system employs a high precision SMI Model 300 Unipump dispenser interfaced with an electronic balance and a portable Epson HX-20 notebook computer to automate spike preparation

Spike-threshold adaptation predicted by membrane potential dynamics in vivo.

Directory of Open Access Journals (Sweden)

Bertrand Fontaine

2014-04-01

Full Text Available Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo.

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.

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.

The role of Matrix Gla Protein in ossification and recovery of the avian growth plate

Directory of Open Access Journals (Sweden)

Harel eDan

2012-07-01

Full Text Available ECM mineralization is an essential physiologic process in bone, teeth, and hypertrophic cartilage. Matrix Gla Protein (MGP, an inhibitor of mineralization, is expressed by chondrocytes and vascular smooth muscle cells to inhibit calcification of those soft tissues.Tibial Dyschondroplasia (TD, a skeletal abnormality apparent as a plug of non-vascularized, non-mineralized, white opaque cartilage in the tibial growth plate of avian species can serve as a good model for studying process and genes involved in matrix mineralization and calcification. In this work, we studied the involvement of MGP in the development of TD, as well as in the processes of spontaneous and induced recovery from this syndrome. First, we found that during normal bone development, MGP is expressed in specific time and locations, starting from wide spread expression in the yet un-ossified diaphysis during embryonic development, to specific expression in hypertrophic chondrocytes adjacent to the chondro-osseous junction and the secondary ossification center just prior to calcification. In addition, we show that MGP is not expressed in the impaired TD lesion, however when the lesion begins to heal, it strongly express MGP prior to its calcification. Moreover, we show that when calcification is inhibited, a gap is formed between the expression zones of MGP and BMP2 and that this gap is closed during the healing process. To conclude, we suggest that MGP, directly or through interaction with BMP2, plays a role as ossification regulator, rather then simple inhibitor that acts prior to ossification.

Emergent dynamics of spiking neurons with fluctuating threshold

Science.gov (United States)

Bhattacharjee, Anindita; Das, M. K.

2017-05-01

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

Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study

International Nuclear Information System (INIS)

Shimazaki, Hideaki

2013-01-01

Neurons in cortical circuits exhibit coordinated spiking activity, and can produce correlated synchronous spikes during behavior and cognition. We recently developed a method for estimating the dynamics of correlated ensemble activity by combining a model of simultaneous neuronal interactions (e.g., a spin-glass model) with a state-space method (Shimazaki et al. 2012 PLoS Comput Biol 8 e1002385). This method allows us to estimate stimulus-evoked dynamics of neuronal interactions which is reproducible in repeated trials under identical experimental conditions. However, the method may not be suitable for detecting stimulus responses if the neuronal dynamics exhibits significant variability across trials. In addition, the previous model does not include effects of past spiking activity of the neurons on the current state of ensemble activity. In this study, we develop a parametric method for simultaneously estimating the stimulus and spike-history effects on the ensemble activity from single-trial data even if the neurons exhibit dynamics that is largely unrelated to these effects. For this goal, we model ensemble neuronal activity as a latent process and include the stimulus and spike-history effects as exogenous inputs to the latent process. We develop an expectation-maximization algorithm that simultaneously achieves estimation of the latent process, stimulus responses, and spike-history effects. The proposed method is useful to analyze an interaction of internal cortical states and sensory evoked activity

The electric potential of tripolar spikes

Energy Technology Data Exchange (ETDEWEB)

Nocera, L. [Theoretical Plasma Physics, IPCF-CNR, Via Moruzzi 1, I-56124 Pisa (Italy)

2010-02-22

We present an analytical formula for the waveform of the electric potential associated with a tripolar spike in a plasma. This formula is based on the construction and on the subsequent solution of a differential equation for the waveform. We work out this equation as a direct consequence of the morphological and functional properties of the observed waveform, without making any reference to the velocity distributions of the electrons and of the ions which sustain the spike. In the approximation of small potential amplitudes, we solve this equation by quadrature. In particular, in the second order approximation, the solution of this equation is given in terms of elementary functions. This analytical solution is able to reproduce the potential waveforms associated with electron holes, ion holes, monotonic and nonmonotonic double layers and tripolar spikes, in excellent agreement with observations.

The electric potential of tripolar spikes

International Nuclear Information System (INIS)

Nocera, L.

2010-01-01

We present an analytical formula for the waveform of the electric potential associated with a tripolar spike in a plasma. This formula is based on the construction and on the subsequent solution of a differential equation for the waveform. We work out this equation as a direct consequence of the morphological and functional properties of the observed waveform, without making any reference to the velocity distributions of the electrons and of the ions which sustain the spike. In the approximation of small potential amplitudes, we solve this equation by quadrature. In particular, in the second order approximation, the solution of this equation is given in terms of elementary functions. This analytical solution is able to reproduce the potential waveforms associated with electron holes, ion holes, monotonic and nonmonotonic double layers and tripolar spikes, in excellent agreement with observations.

Potential effects of elevated base flow and midsummer spike flow experiments on riparian vegetation along the Green River

Science.gov (United States)

Friedman, Jonathan M.

2018-01-01

The Upper Colorado River Endangered Fish Recovery Program has requested experimental flow releases from Flaming Gorge Dam for (1) elevated summer base flows to promote larval endangered Colorado pikeminnow, and (2) midsummer spike flows to disadvantage spawning invasive smallmouth bass. This white paper explores the effects of these proposed flow modifications on riparian vegetation and sediment deposition downstream along the Green River. Although modest in magnitude, the elevated base flows and possible associated reductions in magnitude or duration of peak flows would exacerbate a long-term trend of flow stabilization on the Green River that is already leading to proliferation of vegetation including invasive tamarisk along the channel and associated sediment deposition, channel narrowing and channel simplification. Midsummer spike flows could promote establishment of late-flowering plants like tamarisk. Because channel narrowing and simplification threaten persistence and quality of backwater and side channel features needed by endangered fish, the proposed flow modifications could lead to degradation of fish habitat. Channel narrowing and vegetation encroachment could be countered by increases in peak flows or reductions in base flows in some years and by prescription of rapid flow declines following midsummer spike flows. These strategies for reducing vegetation encroachment would need to be balanced with flow

Noisy Spiking in Visual Area V2 of Amblyopic Monkeys.

Science.gov (United States)

Wang, Ye; Zhang, Bin; Tao, Xiaofeng; Wensveen, Janice M; Smith, Earl L; Chino, Yuzo M

2017-01-25

Interocular decorrelation of input signals in developing visual cortex can cause impaired binocular vision and amblyopia. Although increased intrinsic noise is thought to be responsible for a range of perceptual deficits in amblyopic humans, the neural basis for the elevated perceptual noise in amblyopic primates is not known. Here, we tested the idea that perceptual noise is linked to the neuronal spiking noise (variability) resulting from developmental alterations in cortical circuitry. To assess spiking noise, we analyzed the contrast-dependent dynamics of spike counts and spiking irregularity by calculating the square of the coefficient of variation in interspike intervals (CV 2 ) and the trial-to-trial fluctuations in spiking, or mean matched Fano factor (m-FF) in visual area V2 of monkeys reared with chronic monocular defocus. In amblyopic neurons, the contrast versus response functions and the spike count dynamics exhibited significant deviations from comparable data for normal monkeys. The CV 2 was pronounced in amblyopic neurons for high-contrast stimuli and the m-FF was abnormally high in amblyopic neurons for low-contrast gratings. The spike count, CV 2 , and m-FF of spontaneous activity were also elevated in amblyopic neurons. These contrast-dependent spiking irregularities were correlated with the level of binocular suppression in these V2 neurons and with the severity of perceptual loss for individual monkeys. Our results suggest that the developmental alterations in normalization mechanisms resulting from early binocular suppression can explain much of these contrast-dependent spiking abnormalities in V2 neurons and the perceptual performance of our amblyopic monkeys. Amblyopia is a common developmental vision disorder in humans. Despite the extensive animal studies on how amblyopia emerges, we know surprisingly little about the neural basis of amblyopia in humans and nonhuman primates. Although the vision of amblyopic humans is often described as

Synchronous spikes are necessary but not sufficient for a synchrony code in populations of spiking neurons.

Science.gov (United States)

Grewe, Jan; Kruscha, Alexandra; Lindner, Benjamin; Benda, Jan

2017-03-07

Synchronous activity in populations of neurons potentially encodes special stimulus features. Selective readout of either synchronous or asynchronous activity allows formation of two streams of information processing. Theoretical work predicts that such a synchrony code is a fundamental feature of populations of spiking neurons if they operate in specific noise and stimulus regimes. Here we experimentally test the theoretical predictions by quantifying and comparing neuronal response properties in tuberous and ampullary electroreceptor afferents of the weakly electric fish Apteronotus leptorhynchus These related systems show similar levels of synchronous activity, but only in the more irregularly firing tuberous afferents a synchrony code is established, whereas in the more regularly firing ampullary afferents it is not. The mere existence of synchronous activity is thus not sufficient for a synchrony code. Single-cell features such as the irregularity of spiking and the frequency dependence of the neuron's transfer function determine whether synchronous spikes possess a distinct meaning for the encoding of time-dependent signals.

Voltage spikes in Nb3Sn and NbTi strands

International Nuclear Information System (INIS)

Bordini, B.; Ambrosio, G.; Barzi, E.; Carcagno, R.; Feher, S.; Kashikhin, V.V.; Lamm, M.J.; Orris, D.; Tartaglia, M.; Tompkins, J.C.; Turrioni, D.; Yamada, R.; Zlobin, A.V.; Fermilab

2005-01-01

As part of the High Field Magnet program at Fermilab several NbTi and Nb 3 Sn strands were tested with particular emphasis on the study of voltage spikes and their relationship to superconductor instabilities. The voltage spikes were detected under various experimental conditions using voltage-current (V-I) and voltage-field (V-H) methods. Two types of spikes, designated ''magnetization'' and ''transport current'' spikes, have been identified. Their origin is most likely related to magnetization flux jump and transport current redistribution, respectively. Many of the signals observed appear to be a combination of these two types of spikes; the combination of these two instability mechanisms should play a dominant role in determining the minimum quench current

A supervised learning rule for classification of spatiotemporal spike patterns.

Science.gov (United States)

Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

2016-08-01

This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'.

Science.gov (United States)

Swindale, Nicholas V; Mitelut, Catalin; Murphy, Timothy H; Spacek, Martin A

2017-02-10

Few stand-alone software applications are available for sorting spikes from recordings made with multi-electrode arrays. Ideally, an application should be user friendly with a graphical user interface, able to read data files in a variety of formats, and provide users with a flexible set of tools giving them the ability to detect and sort extracellular voltage waveforms from different units with some degree of reliability. Previously published spike sorting methods are now available in a software program, SpikeSorter, intended to provide electrophysiologists with a complete set of tools for sorting, starting from raw recorded data file and ending with the export of sorted spikes times. Procedures are automated to the extent this is currently possible. The article explains and illustrates the use of the program. A representative data file is opened, extracellular traces are filtered, events are detected and then clustered. A number of problems that commonly occur during sorting are illustrated, including the artefactual over-splitting of units due to the tendency of some units to fire spikes in pairs where the second spike is significantly smaller than the first, and over-splitting caused by slow variation in spike height over time encountered in some units. The accuracy of SpikeSorter's performance has been tested with surrogate ground truth data and found to be comparable to that of other algorithms in current development.

A Markovian event-based framework for stochastic spiking neural networks.

Science.gov (United States)

Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

Fast convergence of spike sequences to periodic patterns in recurrent networks

International Nuclear Information System (INIS)

Jin, Dezhe Z.

2002-01-01

The dynamical attractors are thought to underlie many biological functions of recurrent neural networks. Here we show that stable periodic spike sequences with precise timings are the attractors of the spiking dynamics of recurrent neural networks with global inhibition. Almost all spike sequences converge within a finite number of transient spikes to these attractors. The convergence is fast, especially when the global inhibition is strong. These results support the possibility that precise spatiotemporal sequences of spikes are useful for information encoding and processing in biological neural networks

Improvement of characteristics of diffraction gratings in Dot-matrix holograms

International Nuclear Information System (INIS)

ZHUMALIEV, K.M.; ISMAILOV, D.A.; ZHEENBEKOV, A.A.; SARYBAEVA, A.A.; KAZAKBAEVA, Z.M.

2014-01-01

This paper describes the results of research of the formation and recording of matrix hologram by Dot-matrix (dot-matrix hologram) technology on the photosensitive material of the photoresist. The principle of creating and modifying the characteristics of diffraction gratings of each pixel based on the diffraction efficiency, and recovery of colors and dynamic visual effects in dot-matrix holograms are discussed. An optical schematic diagram of the device and the process of recording dot-matrix holograms are presented. (authors)

Noise-enhanced coding in phasic neuron spike trains.

Science.gov (United States)

Ly, Cheng; Doiron, Brent

2017-01-01

The stochastic nature of neuronal response has lead to conjectures about the impact of input fluctuations on the neural coding. For the most part, low pass membrane integration and spike threshold dynamics have been the primary features assumed in the transfer from synaptic input to output spiking. Phasic neurons are a common, but understudied, neuron class that are characterized by a subthreshold negative feedback that suppresses spike train responses to low frequency signals. Past work has shown that when a low frequency signal is accompanied by moderate intensity broadband noise, phasic neurons spike trains are well locked to the signal. We extend these results with a simple, reduced model of phasic activity that demonstrates that a non-Markovian spike train structure caused by the negative feedback produces a noise-enhanced coding. Further, this enhancement is sensitive to the timescales, as opposed to the intensity, of a driving signal. Reduced hazard function models show that noise-enhanced phasic codes are both novel and separate from classical stochastic resonance reported in non-phasic neurons. The general features of our theory suggest that noise-enhanced codes in excitable systems with subthreshold negative feedback are a particularly rich framework to study.

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.

Genes with a spike expression are clustered in chromosome (sub)bands and spike (sub)bands have a powerful prognostic value in patients with multiple myeloma

Science.gov (United States)

Kassambara, Alboukadel; Hose, Dirk; Moreaux, Jérôme; Walker, Brian A.; Protopopov, Alexei; Reme, Thierry; Pellestor, Franck; Pantesco, Véronique; Jauch, Anna; Morgan, Gareth; Goldschmidt, Hartmut; Klein, Bernard

2012-01-01

Background Genetic abnormalities are common in patients with multiple myeloma, and may deregulate gene products involved in tumor survival, proliferation, metabolism and drug resistance. In particular, translocations may result in a high expression of targeted genes (termed spike expression) in tumor cells. We identified spike genes in multiple myeloma cells of patients with newly-diagnosed myeloma and investigated their prognostic value. Design and Methods Genes with a spike expression in multiple myeloma cells were picked up using box plot probe set signal distribution and two selection filters. Results In a cohort of 206 newly diagnosed patients with multiple myeloma, 2587 genes/expressed sequence tags with a spike expression were identified. Some spike genes were associated with some transcription factors such as MAF or MMSET and with known recurrent translocations as expected. Spike genes were not associated with increased DNA copy number and for a majority of them, involved unknown mechanisms. Of spiked genes, 36.7% clustered significantly in 149 out of 862 documented chromosome (sub)bands, of which 53 had prognostic value (35 bad, 18 good). Their prognostic value was summarized with a spike band score that delineated 23.8% of patients with a poor median overall survival (27.4 months versus not reached, Pband score was independent of other gene expression profiling-based risk scores, t(4;14), or del17p in an independent validation cohort of 345 patients. Conclusions We present a new approach to identify spike genes and their relationship to patients’ survival. PMID:22102711

Predictive coding of dynamical variables in balanced spiking networks.

Science.gov (United States)

Boerlin, Martin; Machens, Christian K; Denève, Sophie

2013-01-01

Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated.

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.

A spiking neuron circuit based on a carbon nanotube transistor

International Nuclear Information System (INIS)

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

2012-01-01

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. (paper)

An online supervised learning method based on gradient descent for spiking neurons.

Science.gov (United States)

Xu, Yan; Yang, Jing; Zhong, Shuiming

2017-09-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Voltage spikes in Nb3Sn and NbTi strands

Energy Technology Data Exchange (ETDEWEB)

Bordini, B.; Ambrosio, G.; Barzi, E.; Carcagno, R.; Feher, S.; Kashikhin, V.V.; Lamm, M.J.; Orris, D.; Tartaglia, M.; Tompkins, J.C.; Turrioni, D.; Yamada, R.; Zlobin,; /Fermilab

2005-09-01

As part of the High Field Magnet program at Fermilab several NbTi and Nb{sub 3}Sn strands were tested with particular emphasis on the study of voltage spikes and their relationship to superconductor instabilities. The voltage spikes were detected under various experimental conditions using voltage-current (V-I) and voltage-field (V-H) methods. Two types of spikes, designated ''magnetization'' and ''transport current'' spikes, have been identified. Their origin is most likely related to magnetization flux jump and transport current redistribution, respectively. Many of the signals observed appear to be a combination of these two types of spikes; the combination of these two instability mechanisms should play a dominant role in determining the minimum quench current.

Pressurized water reactor iodine spiking behavior under power transient conditions

International Nuclear Information System (INIS)

Ho, J.C.

1992-01-01

The most accepted theory explaining the cause of pressurized water reactor iodine spiking is steam formation and condensation in damaged fuel rods. The phase transformation of the primary coolant from water to steam and back again is believed to cause the iodine spiking phenomenon. But due to the complex nature of the phenomenon, a comprehensive model of the behavior has not yet been successfully developed. This paper presents a new model based on an empirical approach, which gives a first-order estimation of the peak iodine spiking magnitude. Based on the proposed iodine spiking model, it is apparent that it is feasible to derive a correlation using the plant operating data base to monitor and control the peak iodine spiking magnitude

Exploring multicollinearity using a random matrix theory approach.

Science.gov (United States)

Feher, Kristen; Whelan, James; Müller, Samuel

2012-01-01

Clustering of gene expression data is often done with the latent aim of dimension reduction, by finding groups of genes that have a common response to potentially unknown stimuli. However, what is poorly understood to date is the behaviour of a low dimensional signal embedded in high dimensions. This paper introduces a multicollinear model which is based on random matrix theory results, and shows potential for the characterisation of a gene cluster's correlation matrix. This model projects a one dimensional signal into many dimensions and is based on the spiked covariance model, but rather characterises the behaviour of the corresponding correlation matrix. The eigenspectrum of the correlation matrix is empirically examined by simulation, under the addition of noise to the original signal. The simulation results are then used to propose a dimension estimation procedure of clusters from data. Moreover, the simulation results warn against considering pairwise correlations in isolation, as the model provides a mechanism whereby a pair of genes with `low' correlation may simply be due to the interaction of high dimension and noise. Instead, collective information about all the variables is given by the eigenspectrum.

Contamination spike simulation and measurement in a clean metal vapor laser

International Nuclear Information System (INIS)

Lin, C.E.; Yang, C.Y.

1990-01-01

This paper describes a new method for the generation of contamination-induced voltage spikes in a clean metal vapor laser. The method facilitates the study of the characteristics of this troublesome phenomenon in laser systems. Analysis of these artificially generated dirt spikes shows that the breakdown time of the laser tube is increased when these spike appear. The concept of a Townsend discharge is used to identify the parameter which changes the breakdown time of the discharges. The residual ionization control method is proposed to generate dirt spikes in a clean laser. Experimental results show that a wide range of dirt spike magnitudes can be obtained by using the proposed method. The method provides easy and accurate control of the magnitude of the dirt spike, and the laser tube does not become polluted. Results based on the measurements can be used in actual laser systems to monitor the appearance of dirt spikes and thus avoid the danger of thyratron failure

Development of the automated circulating tumor cell recovery system with microcavity array.

Science.gov (United States)

Negishi, Ryo; Hosokawa, Masahito; Nakamura, Seita; Kanbara, Hisashige; Kanetomo, Masafumi; Kikuhara, Yoshihito; Tanaka, Tsuyoshi; Matsunaga, Tadashi; Yoshino, Tomoko

2015-05-15

Circulating tumor cells (CTCs) are well recognized as useful biomarker for cancer diagnosis and potential target of drug discovery for metastatic cancer. Efficient and precise recovery of extremely low concentrations of CTCs from blood has been required to increase the detection sensitivity. Here, an automated system equipped with a microcavity array (MCA) was demonstrated for highly efficient and reproducible CTC recovery. The use of MCA allows selective recovery of cancer cells from whole blood on the basis of differences in size between tumor and blood cells. Intra- and inter-assays revealed that the automated system achieved high efficiency and reproducibility equal to the assay manually performed by well-trained operator. Under optimized assay workflow, the automated system allows efficient and precise cell recovery for non-small cell lung cancer cells spiked in whole blood. The automated CTC recovery system will contribute to high-throughput analysis in the further clinical studies on large cohort of cancer patients. Copyright © 2014 Elsevier B.V. All rights reserved.

Spike and burst coding in thalamocortical relay cells.

Directory of Open Access Journals (Sweden)

Fleur Zeldenrust

2018-02-01

Full Text Available Mammalian thalamocortical relay (TCR neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. Identical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices as well as in a validated three-compartment TCR model cell. The resulting membrane voltage traces and spike trains were analyzed by calculating the coherence and impedance. Reverse correlation techniques gave the Event-Triggered Average (ETA and the Event-Triggered Covariance (ETC. This demonstrated that the feature selectivity started relatively long before the events (up to 300 ms and showed a clear distinction between spikes (selective for fluctuations and bursts (selective for integration. The model cell was fine-tuned to mimic the frozen noise initiated spike and burst responses to within experimental accuracy, especially for the mixed mode regimes. The information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. The model was then used to elucidate properties that could not be assessed experimentally, in particular the role of two important subthreshold voltage-dependent currents: the low threshold activated calcium current (IT and the cyclic nucleotide modulated h current (Ih. The ETAs of those currents and their underlying activation/inactivation states not only explained the state dependence of the firing regime but also the long-lasting concerted dynamic action of the two

Determination of trace levels of iodine in table salt by ICP-TOF MS

International Nuclear Information System (INIS)

Waqar, F.; Muhammad, B.; Hakim, M.; Jan, S.

2012-01-01

An ion exchange method was established for the effective removal of sodium matrix for iodine determination by ICP-TOF-MS technique. Since the direct determination of trace level analytes in the presence of heavy matrix is not recommended by this technique. Therefore, the removal of matrix is essentially required to achieve better detection limits and to avoid memory effects. The extraction system was designed for the removal of matrix prior to the analysis by ICP-MS, various parameters were optimized to achieve efficient removal of matrix. The accuracy of the method was evaluated by spiking salt samples with known amount of iodine (50 mu g/g) and % recoveries were calculated. The recoveries obtained were > 98% with relative standard deviation (RSD) < 5%. The established method was applied for the analysis of commercially available iodized table salt samples. The results and % recoveries are given. The most commonly used iodo metric titration method is not satisfactory as it has 5-10 % quantitative error. Our method is reliable and could be conveniently applied for the determination of iodine in table salt samples. (Orig./A.B.)

Stochastic optimal control of single neuron spike trains

DEFF Research Database (Denmark)

Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë

2014-01-01

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...... origin. In particular, we allow for the noise to be of arbitrary intensity. The optimal control problem is solved using dynamic programming when the controller has access to the voltage (closed-loop control), and using a maximum principle for the transition density when the controller only has access...... 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...

Anticipating Activity in Social Media Spikes

OpenAIRE

Higham, Desmond J.; Grindrod, Peter; Mantzaris, Alexander V.; Otley, Amanda; Laflin, Peter

2014-01-01

We propose a novel mathematical model for the activity of microbloggers during an external, event-driven spike. The model leads to a testable prediction of who would become most active if a spike were to take place. This type of information is of great interest to commercial organisations, governments and charities, as it identifies key players who can be targeted with information in real time when the network is most receptive. The model takes account of the fact that dynamic interactions ev...

Multimodal imaging of spike propagation: a technical case report.

Science.gov (United States)

Tanaka, N; Grant, P E; Suzuki, N; Madsen, J R; Bergin, A M; Hämäläinen, M S; Stufflebeam, S M

2012-06-01

We report an 11-year-old boy with intractable epilepsy, who had cortical dysplasia in the right superior frontal gyrus. Spatiotemporal source analysis of MEG and EEG spikes demonstrated a similar time course of spike propagation from the superior to inferior frontal gyri, as observed on intracranial EEG. The tractography reconstructed from DTI showed a fiber connection between these areas. Our multimodal approach demonstrates spike propagation and a white matter tract guiding the propagation.

The Analysis and Suppression of the spike noise in vibrator record

Science.gov (United States)

Jia, H.; Jiang, T.; Xu, X.; Ge, L.; Lin, J.; Yang, Z.

2013-12-01

During the seismic exploration with vibrator, seismic recording systems have often been affected by random spike noise in the background, which leads to strong data distortions as a result of the cross-correlation processing of the vibrator method. Partial or total loss of the desired seismic information is possible if no automatic spike reduction is available in the field prior to correlation of the field record. Generally speaking, original record of vibrator is uncorrelated data, in which the signal is non-wavelet form. In order to obtain the seismic record similar to explosive source, the signal of uncorrelated data needs to use the correlation algorithm to compress into wavelet form. The correlation process results in that the interference of spike in correlated data is not only being suppressed, but also being expanded. So the spike noise suppression of vibrator is indispensable. According to numerical simulation results, the effect of spike in the vibrator record is mainly affected by the amplitude and proportional points in the uncorrelated record. When the spike noise ratio in uncorrelated record reaches 1.5% and the average amplitude exceeds 200, it will make the SNR(signal-to-noise ratio) of the correlated record lower than 0dB, so that it is difficult to separate the signal. While the amplitude and ratio is determined by the intensity of background noise. Therefore, when the noise level is strong, in order to improve SNR of the seismic data, the uncorrelated record of vibrator need to take necessary steps to suppress spike noise. For the sake of reducing the influence of the spike noise, we need to make the detection and suppression of spike noise process for the uncorrelated record. Because vibrator works by inputting sweep signal into the underground long time, ideally, the peak and valley values of each trace have little change. On the basis of the peak and valley values, we can get a reference amplitude value. Then the spike can be detected and

Stealthy false data injection attacks using matrix recovery and independent component analysis in smart grid

Science.gov (United States)

JiWei, Tian; BuHong, Wang; FuTe, Shang; Shuaiqi, Liu

2017-05-01

Exact state estimation is vital important to maintain common operations of smart grids. Existing researches demonstrate that state estimation output could be compromised by malicious attacks. However, to construct the attack vectors, a usual presumption in most works is that the attacker has perfect information regarding the topology and so on even such information is difficult to acquire in practice. Recent research shows that Independent Component Analysis (ICA) can be used for inferring topology information which can be used to originate undetectable attacks and even to alter the price of electricity for the profits of attackers. However, we found that the above ICA-based blind attack tactics is merely feasible in the environment with Gaussian noises. If there are outliers (device malfunction and communication errors), the Bad Data Detector will easily detect the attack. Hence, we propose a robust ICA based blind attack strategy that one can use matrix recovery to circumvent the outlier problem and construct stealthy attack vectors. The proposed attack strategies are tested with IEEE representative 14-bus system. Simulations verify the feasibility of the proposed method.

Potential for uranium recovery at Nolans

International Nuclear Information System (INIS)

Soldenhoff, K.; Ho, E.

2007-01-01

The concentration of uranium in Nolans is higher than is typical of phosphate rock deposits worldwide. This requires appropriate management of the radioactivity during ore processing, but also provides an opportunity for recovery of uranium as a by-product. The recovery must be integrated into the rare earth process, which is the primary focus of the project. Furthermore, the separation of rare earths from the phosphate matrix and the recovery of phosphoric acid or other fertiliser products is also an important consideration. This paper discusses the various process options that are being considered for the development of a process for Nolans that integrates the recovery of phosphate values and uranium as by-products or rare earth processing

Cytoplasmic tail of coronavirus spike protein has intracellular

Indian Academy of Sciences (India)

https://www.ias.ac.in/article/fulltext/jbsc/042/02/0231-0244. Keywords. Coronavirus spike protein trafficking; cytoplasmic tail signal; endoplasmic reticulum–Golgi intermediate complex; lysosome. Abstract. Intracellular trafficking and localization studies of spike protein from SARS and OC43 showed that SARS spikeprotein is ...

A novel automated spike sorting algorithm with adaptable feature extraction.

Science.gov (United States)

Bestel, Robert; Daus, Andreas W; Thielemann, Christiane

2012-10-15

To study the electrophysiological properties of neuronal networks, in vitro studies based on microelectrode arrays have become a viable tool for analysis. Although in constant progress, a challenging task still remains in this area: the development of an efficient spike sorting algorithm that allows an accurate signal analysis at the single-cell level. Most sorting algorithms currently available only extract a specific feature type, such as the principal components or Wavelet coefficients of the measured spike signals in order to separate different spike shapes generated by different neurons. However, due to the great variety in the obtained spike shapes, the derivation of an optimal feature set is still a very complex issue that current algorithms struggle with. To address this problem, we propose a novel algorithm that (i) extracts a variety of geometric, Wavelet and principal component-based features and (ii) automatically derives a feature subset, most suitable for sorting an individual set of spike signals. Thus, there is a new approach that evaluates the probability distribution of the obtained spike features and consequently determines the candidates most suitable for the actual spike sorting. These candidates can be formed into an individually adjusted set of spike features, allowing a separation of the various shapes present in the obtained neuronal signal by a subsequent expectation maximisation clustering algorithm. Test results with simulated data files and data obtained from chick embryonic neurons cultured on microelectrode arrays showed an excellent classification result, indicating the superior performance of the described algorithm approach. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

Femtosecond laser fabricated spike structures for selective control of cellular behavior.

Science.gov (United States)

Schlie, Sabrina; Fadeeva, Elena; Koch, Jürgen; Ngezahayo, Anaclet; Chichkov, Boris N

2010-09-01

In this study we investigate the potential of femtosecond laser generated micrometer sized spike structures as functional surfaces for selective cell controlling. The spike dimensions as well as the average spike to spike distance can be easily tuned by varying the process parameters. Moreover, negative replications in soft materials such as silicone elastomer can be produced. This allows tailoring of wetting properties of the spike structures and their negative replicas representing a reduced surface contact area. Furthermore, we investigated material effects on cellular behavior. By comparing human fibroblasts and SH-SY5Y neuroblastoma cells we found that the influence of the material was cell specific. The cells not only changed their morphology, but also the cell growth was affected. Whereas, neuroblastoma cells proliferated at the same rate on the spike structures as on the control surfaces, the proliferation of fibroblasts was reduced by the spike structures. These effects can result from the cell specific adhesion patterns as shown in this work. These findings show a possibility to design defined surface microstructures, which could control cellular behavior in a cell specific manner.

Google Searches for "Cheap Cigarettes" Spike at Tax Increases: Evidence from an Algorithm to Detect Spikes in Time Series Data.

Science.gov (United States)

Caputi, Theodore L

2018-05-03

Online cigarette dealers have lower prices than brick-and-mortar retailers and advertise tax-free status.1-8 Previous studies show smokers search out these online alternatives at the time of a cigarette tax increase.9,10 However, these studies rely upon researchers' decision to consider a specific date and preclude the possibility that researchers focus on the wrong date. The purpose of this study is to introduce an unbiased methodology to the field of observing search patterns and to use this methodology to determine whether smokers search Google for "cheap cigarettes" at cigarette tax increases and, if so, whether the increased level of searches persists. Publicly available data from Google Trends is used to observe standardized search volumes for the term, "cheap cigarettes". Seasonal Hybrid Extreme Studentized Deviate and E-Divisive with Means tests were performed to observe spikes and mean level shifts in search volume. Of the twelve cigarette tax increases studied, ten showed spikes in searches for "cheap cigarettes" within two weeks of the tax increase. However, the mean level shifts did not occur for any cigarette tax increase. Searches for "cheap cigarettes" spike around the time of a cigarette tax increase, but the mean level of searches does not shift in response to a tax increase. The SHESD and EDM tests are unbiased methodologies that can be used to identify spikes and mean level shifts in time series data without an a priori date to be studied. SHESD and EDM affirm spikes in interest are related to tax increases. • Applies improved statistical techniques (SHESD and EDM) to Google search data related to cigarettes, reducing bias and increasing power • Contributes to the body of evidence that state and federal tax increases are associated with spikes in searches for cheap cigarettes and may be good dates for increased online health messaging related to tobacco.

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.

Study on distributions and recoveries of tetrachlorodibenzo-p-dioxin and octachlorodibenzo-p-dioxin in a mm5 sampling train

International Nuclear Information System (INIS)

Finkel, J.M.; James, R.H.; Baughman, K.W.

1990-12-01

14 C-dioxin tracers were used to evaluate whole MM5 sampling train recoveries of dioxin and to determine the distribution of dioxins spiked into a sampling train that was concurrently sampling emissions from a burn of either natural gas ('clean' burn) or kerosene ('dirty' burn). The spike tests were made with a pilot-scale furnace constructed and operated in the laboratory. Recovery of 14 C-dioxin from the MM5 sampling train was determined by scintillation spectrometry. The experimental results indicate that the amount of spiked TCDD- 14 C recovered was approximately 85% during a natural gas test and 83% during a kerosene test. The amount of spiked OCDD- 14 C recovered was approximately 88% during a kerosene test. Also, the data indicate that during the kerosene tests OCDD- 14 C is collected primarily in the front half of the sampling train but TCDD- 14 C is often found in the XAD and the rear filter bell, riser and condenser of the sampling train. During the natural gas tests, TCDD- 14 C was primarily in the XAD. The distribution of the TCDD- 14 C in the kerosene tests was dependent on the rigid operation of the sampling train. The information from the study will be used to determine procedural areas that need improvements or modifications to allow the efficient collection and accurate determination of trace levels of dioxins and furans using the MM5 Method

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.

Measures of spike train synchrony for data with multiple time scales

NARCIS (Netherlands)

Satuvuori, Eero; Mulansky, Mario; Bozanic, Nebojsa; Malvestio, Irene; Zeldenrust, Fleur; Lenk, Kerstin; Kreuz, Thomas

2017-01-01

Background Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time scale parametric measures, since by

Recovery of the Dirac system from the rectangular Weyl matrix function

International Nuclear Information System (INIS)

Fritzsche, B; Kirstein, B; Roitberg, I Ya; Sakhnovich, A L

2012-01-01

Weyl theory for Dirac systems with rectangular matrix potentials is non-classical. The corresponding Weyl functions are rectangular matrix functions. Furthermore, they are non-expansive in the upper semi-plane. Inverse problems are studied for such Weyl functions, and some results are new even for the square Weyl functions. High-energy asymptotics of Weyl functions and Borg–Marchenko-type uniqueness results are derived too. (paper)

A novel unsupervised spike sorting algorithm for intracranial EEG.

Science.gov (United States)

Yadav, R; Shah, A K; Loeb, J A; Swamy, M N S; Agarwal, R

2011-01-01

This paper presents a novel, unsupervised spike classification algorithm for intracranial EEG. The method combines template matching and principal component analysis (PCA) for building a dynamic patient-specific codebook without a priori knowledge of the spike waveforms. The problem of misclassification due to overlapping classes is resolved by identifying similar classes in the codebook using hierarchical clustering. Cluster quality is visually assessed by projecting inter- and intra-clusters onto a 3D plot. Intracranial EEG from 5 patients was utilized to optimize the algorithm. The resulting codebook retains 82.1% of the detected spikes in non-overlapping and disjoint clusters. Initial results suggest a definite role of this method for both rapid review and quantitation of interictal spikes that could enhance both clinical treatment and research studies on epileptic patients.

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

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.

Building functional networks of spiking model neurons.

Science.gov (United States)

Abbott, L F; DePasquale, Brian; Memmesheimer, Raoul-Martin

2016-03-01

Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.

Spike morphology in blast-wave-driven instability experiments

International Nuclear Information System (INIS)

Kuranz, C. C.; Drake, R. P.; Grosskopf, M. J.; Fryxell, B.; Budde, A.; Hansen, J. F.; Miles, A. R.; Plewa, T.; Hearn, N.; Knauer, J.

2010-01-01

The laboratory experiments described in the present paper observe the blast-wave-driven Rayleigh-Taylor instability with three-dimensional (3D) initial conditions. About 5 kJ of energy from the Omega laser creates conditions similar to those of the He-H interface during the explosion phase of a supernova. The experimental target is a 150 μm thick plastic disk followed by a low-density foam. The plastic piece has an embedded, 3D perturbation. The basic structure of the pattern is two orthogonal sine waves where each sine wave has an amplitude of 2.5 μm and a wavelength of 71 μm. In some experiments, an additional wavelength is added to explore the interaction of modes. In experiments with 3D initial conditions the spike morphology differs from what has been observed in other Rayleigh-Taylor experiments and simulations. Under certain conditions, experimental radiographs show some mass extending from the interface to the shock front. Current simulations show neither the spike morphology nor the spike penetration observed in the experiments. The amount of mass reaching the shock front is analyzed and potential causes for the spike morphology and the spikes reaching the shock are discussed. One such hypothesis is that these phenomena may be caused by magnetic pressure, generated by an azimuthal magnetic field produced by the plasma dynamics.

Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

NARCIS (Netherlands)

D. Zambrano (Davide); S.M. Bohte (Sander)

2016-01-01

textabstractBiological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on

Characterizing neural activities evoked by manual acupuncture through spiking irregularity measures

International Nuclear Information System (INIS)

Xue Ming; Wang Jiang; Deng Bin; Wei Xi-Le; Yu Hai-Tao; Chen Ying-Yuan

2013-01-01

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. (interdisciplinary physics and related areas of science and technology)

Coincidence Detection Using Spiking Neurons with Application to Face Recognition

Directory of Open Access Journals (Sweden)

Fadhlan Kamaruzaman

2015-01-01

Full Text Available We elucidate the practical implementation of Spiking Neural Network (SNN as local ensembles of classifiers. Synaptic time constant τs is used as learning parameter in representing the variations learned from a set of training data at classifier level. This classifier uses coincidence detection (CD strategy trained in supervised manner using a novel supervised learning method called τs Prediction which adjusts the precise timing of output spikes towards the desired spike timing through iterative adaptation of τs. This paper also discusses the approximation of spike timing in Spike Response Model (SRM for the purpose of coincidence detection. This process significantly speeds up the whole process of learning and classification. Performance evaluations with face datasets such as AR, FERET, JAFFE, and CK+ datasets show that the proposed method delivers better face classification performance than the network trained with Supervised Synaptic-Time Dependent Plasticity (STDP. We also found that the proposed method delivers better classification accuracy than k nearest neighbor, ensembles of kNN, and Support Vector Machines. Evaluation on several types of spike codings also reveals that latency coding delivers the best result for face classification as well as for classification of other multivariate datasets.

Adaptive coupling optimized spiking coherence and synchronization in Newman-Watts neuronal networks.

Science.gov (United States)

Gong, Yubing; Xu, Bo; Wu, Ya'nan

2013-09-01

In this paper, we have numerically studied the effect of adaptive coupling on the temporal coherence and synchronization of spiking activity in Newman-Watts Hodgkin-Huxley neuronal networks. It is found that random shortcuts can enhance the spiking synchronization more rapidly when the increment speed of adaptive coupling is increased and can optimize the temporal coherence of spikes only when the increment speed of adaptive coupling is appropriate. It is also found that adaptive coupling strength can enhance the synchronization of spikes and can optimize the temporal coherence of spikes when random shortcuts are appropriate. These results show that adaptive coupling has a big influence on random shortcuts related spiking activity and can enhance and optimize the temporal coherence and synchronization of spiking activity of the network. These findings can help better understand the roles of adaptive coupling for improving the information processing and transmission in neural systems.

A matched-filter algorithm to detect amperometric spikes resulting from quantal secretion.

Science.gov (United States)

Balaji Ramachandran, Supriya; Gillis, Kevin D

2018-01-01

Electrochemical microelectrodes located immediately adjacent to the cell surface can detect spikes of amperometric current during exocytosis as the transmitter released from a single vesicle is oxidized on the electrode surface. Automated techniques to detect spikes are needed in order to quantify the spike rate as a measure of the rate of exocytosis. We have developed a Matched Filter (MF) detection algorithm that scans the data set with a library of prototype spike templates while performing a least-squares fit to determine the amplitude and standard error. The ratio of the fit amplitude to the standard error constitutes a criterion score that is assigned for each time point and for each template. A spike is detected when the criterion score exceeds a threshold and the highest-scoring template and the time of peak score is identified. The search for the next spike commences only after the score falls below a second, lower threshold to reduce false positives. The approach was extended to detect spikes with double-exponential decays with the sum of two templates. Receiver Operating Characteristic plots (ROCs) demonstrate that the algorithm detects >95% of manually identified spikes with a false-positive rate of ∼2%. ROCs demonstrate that the MF algorithm performs better than algorithms that detect spikes based on a derivative-threshold approach. The MF approach performs well and leads into approaches to identify spike parameters. Copyright © 2017 Elsevier B.V. All rights reserved.

Spike sorting based upon machine learning algorithms (SOMA).

Science.gov (United States)

Horton, P M; Nicol, A U; Kendrick, K M; Feng, J F

2007-02-15

We have developed a spike sorting method, using a combination of various machine learning algorithms, to analyse electrophysiological data and automatically determine the number of sampled neurons from an individual electrode, and discriminate their activities. We discuss extensions to a standard unsupervised learning algorithm (Kohonen), as using a simple application of this technique would only identify a known number of clusters. Our extra techniques automatically identify the number of clusters within the dataset, and their sizes, thereby reducing the chance of misclassification. We also discuss a new pre-processing technique, which transforms the data into a higher dimensional feature space revealing separable clusters. Using principal component analysis (PCA) alone may not achieve this. Our new approach appends the features acquired using PCA with features describing the geometric shapes that constitute a spike waveform. To validate our new spike sorting approach, we have applied it to multi-electrode array datasets acquired from the rat olfactory bulb, and from the sheep infero-temporal cortex, and using simulated data. The SOMA sofware is available at http://www.sussex.ac.uk/Users/pmh20/spikes.

Thermal spike analysis of highly charged ion tracks

International Nuclear Information System (INIS)

Karlušić, M.; Jakšić, M.

2012-01-01

The irradiation of material using swift heavy ion or highly charged ion causes excitation of the electron subsystem at nanometer scale along the ion trajectory. According to the thermal spike model, energy deposited into the electron subsystem leads to temperature increase due to electron–phonon coupling. If ion-induced excitation is sufficiently intensive, then melting of the material can occur, and permanent damage (i.e., ion track) can be formed upon rapid cooling. We present an extension of the analytical thermal spike model of Szenes for the analysis of surface ion track produced after the impact of highly charged ion. By applying the model to existing experimental data, more than 60% of the potential energy of the highly charged ion was shown to be retained in the material during the impact and transformed into the energy of the thermal spike. This value is much higher than 20–40% of the transferred energy into the thermal spike by swift heavy ion. Thresholds for formation of highly charged ion track in different materials show uniform behavior depending only on few material parameters.

A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.

Science.gov (United States)

Pillow, Jonathan W; Shlens, Jonathon; Chichilnisky, E J; Simoncelli, Eero P

2013-01-01

We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call "binary pursuit". The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.

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.

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.

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

Science.gov (United States)

Albers, Christian; Westkott, Maren; Pawelzik, Klaus

2016-01-01

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.

Method for the removal and recovery of mercury

Science.gov (United States)

Easterly, Clay E.; Vass, Arpad A.; Tyndall, Richard L.

1997-01-01

The present invention is an enhanced method for the removal and recovery of mercury from mercury-contaminated matrices. The method involves contacting a mercury-contaminated matrix with an aqueous dispersant solution derived from specific intra-amoebic isolates to release the mercury from the mercury-contaminated matrix and emulsify the mercury; then, contacting the matrix with an amalgamating metal from a metal source to amalgamate the mercury to the amalgamating metal; removing the metallic source from the mercury-contaminated matrix; and heating the metallic source to vaporize the mercury in a closed system to capture the mercury vapors.

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.

Absolute Ca Isotopic Measurement Using an Improved Double Spike Technique

Directory of Open Access Journals (Sweden)

Jason Jiun-San Shen

2009-01-01

Full Text Available A new vector analytical method has been developed in order to obtain the true isotopic composition of the 42Ca-48Ca double spike. This is achieved by using two different sample-spike mixtures combined with the double spike and natural Ca data. Be cause the natural sample (two mixtures and the spike should all lie on a single mixing line, we are able to con strain the true isotopic composition of our double spike using this new approach. Once the isotopic composition of the Ca double spike is established, we are able to obtain the true Ca isotopic composition of the NIST Ca standard SRM915a, 40Ca/44Ca = 46.537 ± 2 (2sm, n = 55, 42Ca/44Ca = 0.31031 ± 1, 43Ca/44Ca = 0.06474 ± 1, and 48Ca/44Ca = 0.08956 ± 1. De spite an off set of 1.3% in 40Ca/44Ca between our result and the previously re ported value (Russell et al. 1978, our data indicate an off set of 1.89__in 40Ca/44Ca between SRM915a and seawater, entirely consistent with the published results.

The Nature of Power Spikes: a regime-switch approach

NARCIS (Netherlands)

C.M. de Jong (Cyriel)

2005-01-01

textabstractDue to its non-storable nature, electricity is a commodity with probably the most volatile spot prices, exemplified by occasional spikes. Appropriate pricing, portfolio, and risk management models have to incorporate these characteristics, and the spikes in particular. We investigate the

Transcriptome Analysis for Abnormal Spike Development of the Wheat Mutant dms.

Science.gov (United States)

Zhu, Xin-Xin; Li, Qiao-Yun; Shen, Chun-Cai; Duan, Zong-Biao; Yu, Dong-Yan; Niu, Ji-Shan; Ni, Yong-Jing; Jiang, Yu-Mei

2016-01-01

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.

Axonal propagation of simple and complex spikes in cerebellar Purkinje neurons.

Science.gov (United States)

Khaliq, Zayd M; Raman, Indira M

2005-01-12

In cerebellar Purkinje neurons, the reliability of propagation of high-frequency simple spikes and spikelets of complex spikes is likely to regulate inhibition of Purkinje target neurons. To test the extent to which a one-to-one correspondence exists between somatic and axonal spikes, we made dual somatic and axonal recordings from Purkinje neurons in mouse cerebellar slices. Somatic action potentials were recorded with a whole-cell pipette, and the corresponding axonal signals were recorded extracellularly with a loose-patch pipette. Propagation of spontaneous and evoked simple spikes was highly reliable. At somatic firing rates of approximately 200 spikes/sec, 375 Hz during somatic hyperpolarizations that silenced spontaneous firing to approximately 150 Hz during spontaneous activity. The probability of propagation of individual spikelets could be described quantitatively as a saturating function of spikelet amplitude, rate of rise, or preceding interspike interval. The results suggest that ion channels of Purkinje axons are adapted to produce extremely short refractory periods and that brief bursts of forward-propagating action potentials generated by complex spikes may contribute transiently to inhibition of postsynaptic neurons.

Fluid-thermal analysis of aerodynamic heating over spiked blunt body configurations

Science.gov (United States)

Qin, Qihao; Xu, Jinglei; Guo, Shuai

2017-03-01

When flying at hypersonic speeds, the spiked blunt body is constantly subjected to severe aerodynamic heating. To illustrate the thermal response of different configurations and the relevant flow field variation, a loosely-coupled fluid-thermal analysis is performed in this paper. The Mesh-based parallel Code Coupling Interface (MpCCI) is adopted to implement the data exchange between the fluid solver and the thermal solver. The results indicate that increases in spike diameter and length will result in a sharp decline of the wall temperature along the spike, and the overall heat flux is remarkably reduced to less than 300 W/cm2 with the aerodome mounted at the spike tip. Moreover, the presence and evolution of small vortices within the recirculation zone are observed and proved to be induced by the stagnation effect of reattachment points on the spike. In addition, the drag coefficient of the configuration with a doubled spike length presents a maximum drop of 4.59% due to the elevated wall temperature. And the growing difference of the drag coefficient is further increased during the accelerating process.

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.

Accelerated spike resampling for accurate multiple testing controls.

Science.gov (United States)

Harrison, Matthew T

2013-02-01

Controlling for multiple hypothesis tests using standard spike resampling techniques often requires prohibitive amounts of computation. Importance sampling techniques can be used to accelerate the computation. The general theory is presented, along with specific examples for testing differences across conditions using permutation tests and for testing pairwise synchrony and precise lagged-correlation between many simultaneously recorded spike trains using interval jitter.

Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity.

Science.gov (United States)

D'Souza, Prashanth; Liu, Shih-Chii; Hahnloser, Richard H R

2010-03-09

It is widely believed that sensory and motor processing in the brain is based on simple computational primitives rooted in cellular and synaptic physiology. However, many gaps remain in our understanding of the connections between neural computations and biophysical properties of neurons. Here, we show that synaptic spike-time-dependent plasticity (STDP) combined with spike-frequency adaptation (SFA) in a single neuron together approximate the well-known perceptron learning rule. Our calculations and integrate-and-fire simulations reveal that delayed inputs to a neuron endowed with STDP and SFA precisely instruct neural responses to earlier arriving inputs. We demonstrate this mechanism on a developmental example of auditory map formation guided by visual inputs, as observed in the external nucleus of the inferior colliculus (ICX) of barn owls. The interplay of SFA and STDP in model ICX neurons precisely transfers the tuning curve from the visual modality onto the auditory modality, demonstrating a useful computation for multimodal and sensory-guided processing.

Uranium recovery from AVLIS slag

International Nuclear Information System (INIS)

D'Agostino, A.E.; Mycroft, J.R.; Oliver, A.J.; Schneider, P.G.; Richardson, K.L.

2000-01-01

Uranium metal for the Atomic Vapor Laser Isotope Separation (AVLIS) project was to have been produced by the magnesiothermic reduction of uranium tetrafluoride. The other product from this reaction is a magnesium fluoride slag, which contains fine and entrained natural uranium as metal and oxide. Recovery of the uranium through conventional mill leaching would not give a magnesium residue free of uranium but to achieve more complete uranium recovery requires the destruction of the magnesium fluoride matrix and liberation of the entrapped uranium. Alternate methods of carrying out such treatments and the potential for recovery of other valuable byproducts were examined. Based on the process flowsheets, a number of economic assessments were performed, conclusions were drawn and the preferred processing alternatives were identified. (author)

Real-time computing platform for spiking neurons (RT-spike).

Science.gov (United States)

Ros, Eduardo; Ortigosa, Eva M; Agís, Rodrigo; Carrillo, Richard; Arnold, Michael

2006-07-01

A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as input-driven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing software-based event-driven approaches. As an alternative we have designed an efficient time-based computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important.

Magnesium isotope compositions of Solar System materials determined by double spiking

Science.gov (United States)

Hin, R.; Lai, Y. J.; Coath, C.; Elliott, T.

2015-12-01

As a major element, magnesium is of interest for investigating large scale processes governing the formation and evolution of rocky planetary bodies. Determining the Mg isotope composition of the Earth and other planetary bodies has hence been a topic of interest ever since mass-dependent fractionation of 'non-traditional' stable isotopes has been used to study high-temperature processes. Published results, however, suffer from disagreement on the Mg isotope compositions of the Earth and chondrites [1-5], which is attributed to residual matrix effects. Nonetheless, most recent studied have converged towards a homogeneous (chondritic) Mg isotope composition in the Solar System [2-5]. However, in several of the recent studies there is a hint of a systematic difference of about 0.02-0.06‰ in the 26Mg/24Mg isotope compositions of chondrites and Earth. Such difference, however, is only resolvable by taking standard errors, which assumes robust data for homogenous sample sets. The discrepancies between various studies unfortunately undermine the confidence in such robustness and homogeneity. The issues with matrix effects during isotopic analyses can be overcome by using a double spike approach. Such methodology generally requires three isotope ratios to solve for three unknowns, a requirement that cannot be met for Mg. However, using a newly developed approach, we present Mg isotope compositions obtained by critical mixture double spiking. This new approach should allow greater confidence in the robustness of the data and hence enable improvement of. Preliminary data indicate that chondrites have a resolvable ~0.04‰ lighter 26Mg/24Mg than (ultra)mafic rocks from Earth, Mars and the eucrite parent body, which appear indistinguishable from each other. It seems implausible that this difference is caused by magmatic process such as partial melting or crystallisation. More likely, Mg isotopes are fractionated by a non-magmatic process during the formation of planets, e

Quantifying intracellular metabolites in yeast using a matrix with minimal interference from naturally occurring analytes

DEFF Research Database (Denmark)

Magdenoska, Olivera; Knudsen, Peter Boldsen; Svenssen, Daniel Killerup

2015-01-01

in [13C6]glucose/nonlabeled glucose (50:50, w/w) growth medium. The areas of both 12C6 and 13C6 fractions of ATP in the matrix were measured to be 2% of the sum of the areas of all ATP isotopes detected. The matrix allowed for spiking of both the nonlabeled and SIL-ISs and more straightforward validation...... of the redox compounds was challenging due to the oxidation of NADH and NADPH, when dissolved in water or tributylamine. The oxidation was reduced by dissolving them in ammonium acetate solution (pH 8.0)....

SPAN: spike pattern association neuron for learning spatio-temporal sequences

OpenAIRE

Mohemmed, A; Schliebs, S; Matsuda, S; Kasabov, N

2012-01-01

Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN — a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the prec...

Impact of substance P on the correlation of spike train evoked by electro acupuncture

International Nuclear Information System (INIS)

Jin, Chen; Zhang, Xuan; Wang, Jiang; Guo, Yi; Zhao, Xue; Guo, Yong-Ming

2016-01-01

Highlights: • We analyze spike trains induced by EA before and after inhibiting SP in PC6 area. • Inhibiting SP leads to an increase of spiking rate of median nerve. • SP may modulate membrane potential to affect the spiking rate. • SP has an influence on long-range correlation of spike train evoked by EA. • SP play an important role in EA-induced neural spiking and encoding. - Abstract: Substance P (SP) participates in the neural signal transmission evoked by electro-acupuncture (EA). This paper investigates the impact of SP on the correlation of spike train in the median nerve evoked by EA at 'Neiguan' acupoint (PC6). It shows that the spiking rate and interspike interval (ISI) distribution change obviously after inhibiting SP. This variation of spiking activity indicates that SP affects the temporal structure of spike train through modulating the action potential on median nerve filaments. Furtherly, the correlation coefficient and scaling exponent are considered to measure the correlation of spike train. Scaled Windowed Variance (SWV) method is applied to calculate scaling exponent which quantifies the long-range correlation of the neural electrical signals. It is found that the correlation coefficients of ISI increase after inhibiting SP released. In addition, the scaling exponents of neuronal spike train have significant differences between before and after inhibiting SP. These findings demonstrate that SP has an influence on the long-range correlation of spike train. Our results indicate that SP may play an important role in EA-induced neural spiking and encoding.

Robust spike sorting of retinal ganglion cells tuned to spot stimuli.

Science.gov (United States)

Ghahari, Alireza; Badea, Tudor C

2016-08-01

We propose an automatic spike sorting approach for the data recorded from a microelectrode array during visual stimulation of wild type retinas with tiled spot stimuli. The approach first detects individual spikes per electrode by their signature local minima. With the mixture probability distribution of the local minima estimated afterwards, it applies a minimum-squared-error clustering algorithm to sort the spikes into different clusters. A template waveform for each cluster per electrode is defined, and a number of reliability tests are performed on it and its corresponding spikes. Finally, a divisive hierarchical clustering algorithm is used to deal with the correlated templates per cluster type across all the electrodes. According to the measures of performance of the spike sorting approach, it is robust even in the cases of recordings with low signal-to-noise ratio.

The transfer function of neuron spike.

Science.gov (United States)

Palmieri, Igor; Monteiro, Luiz H A; Miranda, Maria D

2015-08-01

The mathematical modeling of neuronal signals is a relevant problem in neuroscience. The complexity of the neuron behavior, however, makes this problem a particularly difficult task. Here, we propose a discrete-time linear time-invariant (LTI) model with a rational function in order to represent the neuronal spike detected by an electrode located in the surroundings of the nerve cell. The model is presented as a cascade association of two subsystems: one that generates an action potential from an input stimulus, and one that represents the medium between the cell and the electrode. The suggested approach employs system identification and signal processing concepts, and is dissociated from any considerations about the biophysical processes of the neuronal cell, providing a low-complexity alternative to model the neuronal spike. The model is validated by using in vivo experimental readings of intracellular and extracellular signals. A computational simulation of the model is presented in order to assess its proximity to the neuronal signal and to observe the variability of the estimated parameters. The implications of the results are discussed in the context of spike sorting. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cochlear spike synchronization and neuron coincidence detection model

Science.gov (United States)

Bader, Rolf

2018-02-01

Coincidence detection of a spike pattern fed from the cochlea into a single neuron is investigated using a physical Finite-Difference model of the cochlea and a physiologically motivated neuron model. Previous studies have shown experimental evidence of increased spike synchronization in the nucleus cochlearis and the trapezoid body [Joris et al., J. Neurophysiol. 71(3), 1022-1036 and 1037-1051 (1994)] and models show tone partial phase synchronization at the transition from mechanical waves on the basilar membrane into spike patterns [Ch. F. Babbs, J. Biophys. 2011, 435135]. Still the traveling speed of waves on the basilar membrane cause a frequency-dependent time delay of simultaneously incoming sound wavefronts up to 10 ms. The present model shows nearly perfect synchronization of multiple spike inputs as neuron outputs with interspike intervals (ISI) at the periodicity of the incoming sound for frequencies from about 30 to 300 Hz for two different amounts of afferent nerve fiber neuron inputs. Coincidence detection serves here as a fusion of multiple inputs into one single event enhancing pitch periodicity detection for low frequencies, impulse detection, or increased sound or speech intelligibility due to dereverberation.

The variational spiked oscillator

International Nuclear Information System (INIS)

Aguilera-Navarro, V.C.; Ullah, N.

1992-08-01

A variational analysis of the spiked harmonic oscillator Hamiltonian -d 2 / d x 2 + x 2 + δ/ x 5/2 , δ > 0, is reported in this work. A trial function satisfying Dirichlet boundary conditions is suggested. The results are excellent for a large range of values of the coupling parameter. (author)

Trace element ink spiking for signature authentication

International Nuclear Information System (INIS)

Hatzistavros, V.S.; Kallithrakas-Kontos, N.G.

2008-01-01

Signature authentication is a critical question in forensic document examination. Last years the evolution of personal computers made signature copying a quite easy task, so the development of new ways for signature authentication is crucial. In the present work a commercial ink was spiked with many trace elements in various concentrations. Inorganic and organometallic ink soluble compounds were used as spiking agents, whilst ink retained its initial properties. The spiked inks were used for paper writing and the documents were analyzed by a non destructive method, the energy dispersive X-ray fluorescence. The thin target model was proved right for quantitative analysis and a very good linear relationship of the intensity (X-ray signal) against concentration was estimated for all used elements. Intensity ratios between different elements in the same ink gave very stable results, independent on the writing alterations. The impact of time both to written document and prepared inks was also investigated. (author)

A Novel and Simple Spike Sorting Implementation.

Science.gov (United States)

Petrantonakis, Panagiotis C; Poirazi, Panayiota

2017-04-01

Monitoring the activity of multiple, individual neurons that fire spikes in the vicinity of an electrode, namely perform a Spike Sorting (SS) procedure, comprises one of the most important tools for contemporary neuroscience in order to reverse-engineer the brain. As recording electrodes' technology rabidly evolves by integrating thousands of electrodes in a confined spatial setting, the algorithms that are used to monitor individual neurons from recorded signals have to become even more reliable and computationally efficient. In this work, we propose a novel framework of the SS approach in which a single-step processing of the raw (unfiltered) extracellular signal is sufficient for both the detection and sorting of the activity of individual neurons. Despite its simplicity, the proposed approach exhibits comparable performance with state-of-the-art approaches, especially for spike detection in noisy signals, and paves the way for a new family of SS algorithms with the potential for multi-recording, fast, on-chip implementations.

A Fully Automated Approach to Spike Sorting.

Science.gov (United States)

Chung, Jason E; Magland, Jeremy F; Barnett, Alex H; Tolosa, Vanessa M; Tooker, Angela C; Lee, Kye Y; Shah, Kedar G; Felix, Sarah H; Frank, Loren M; Greengard, Leslie F

2017-09-13

Understanding the detailed dynamics of neuronal networks will require the simultaneous measurement of spike trains from hundreds of neurons (or more). Currently, approaches to extracting spike times and labels from raw data are time consuming, lack standardization, and involve manual intervention, making it difficult to maintain data provenance and assess the quality of scientific results. Here, we describe an automated clustering approach and associated software package that addresses these problems and provides novel cluster quality metrics. We show that our approach has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques with desktop central processing unit (CPU) runtimes faster than acquisition time for up to hundreds of electrodes. Moreover, a single choice of parameters in the algorithm is effective for a variety of electrode geometries and across multiple brain regions. This algorithm has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible. Copyright © 2017 Elsevier Inc. All rights reserved.

Citizenship and recovery: two intertwined concepts for civic-recovery.

Science.gov (United States)

Pelletier, Jean-François; Corbière, Marc; Lecomte, Tania; Briand, Catherine; Corrigan, Patrick; Davidson, Larry; Rowe, Michael

2015-03-04

Validation of the psychometric properties of a new measure of citizenship was required for a research project in the province of Quebec, Canada. This study was meant to study the interplay between recovery- and citizenship-oriented supportive employment. As recovery and citizenship were expected to be two related concepts, convergent validity between the Citizenship Measure (CM) and the Recovery Assessment Scale (RAS) was tested. Study objectives were to: 1) conduct exploratory factor analyses on the CM and confirmatory factor analysis on the RAS tools (construct validity), 2) calculate Cronbach's alphas for each dimension emerging from objective 1 (reliability), and 3) calculate correlations between all dimensions from both tools (convergent validity). Data were collected from 174 individuals with serious mental illness, working in social firms. Serious mental illnesses include major depression, schizophrenia, bipolar disorder, obsessive compulsive disorder, panic disorder, post traumatic stress disorder and borderline personality disorder. Five factors emerged from the exploratory factor analysis of the CM, with good reliability. Confirmatory factor analyses showed that the short and the long versions of the RAS present satisfactory results. Finally, the correlation matrix indicated that all dimensions from both tools are significantly correlated, thus confirming their convergent validity. This study confirms the validity and reliability of two tools, CM and RAS. These tools can be used in combination to assess citizenship and recovery, both of which may be combined in the new concept of civic-recovery.

A case-control study of wicket spikes using video-EEG monitoring.

Science.gov (United States)

Vallabhaneni, Maya; Baldassari, Laura E; Scribner, James T; Cho, Yong Won; Motamedi, Gholam K

2013-01-01

To investigate clinical characteristics associated with wicket spikes in patients undergoing long-term video-EEG monitoring. A case-control study was performed in 479 patients undergoing video-EEG monitoring, with 3 age- (±3 years) and gender-matched controls per patient with wicket spikes. Logistic regression was utilized to investigate the association between wicket spikes and other factors, including conditions that have been previously associated with wicket spikes. Wicket spikes were recorded in 48 patients. There was a significantly higher prevalence of dizziness/vertigo (p=0.002), headaches (p=0.005), migraine (p=0.015), and seizures (p=0.016) in patients with wickets. The majority of patients with wicket spikes did not exhibit epileptiform activity on EEG; however, patients with history of seizures were more likely to have wickets (p=0.017). There was no significant difference in the prevalence of psychogenic non-epileptic seizures between the groups. Wickets were more common on the left, during sleep, and more likely to be first recorded on day 1-2 of monitoring. Patients with wicket spikes are more likely to have dizziness/vertigo, headaches, migraine, and seizures. Patients with history of seizures are more likely to have wickets. The prevalence of psychogenic non-epileptic seizures is not significantly higher in patients with wickets. Copyright © 2012 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

On the Spike Train Variability Characterized by Variance-to-Mean Power Relationship.

Science.gov (United States)

Koyama, Shinsuke

2015-07-01

We propose a statistical method for modeling the non-Poisson variability of spike trains observed in a wide range of brain regions. Central to our approach is the assumption that the variance and the mean of interspike intervals are related by a power function characterized by two parameters: the scale factor and exponent. It is shown that this single assumption allows the variability of spike trains to have an arbitrary scale and various dependencies on the firing rate in the spike count statistics, as well as in the interval statistics, depending on the two parameters of the power function. We also propose a statistical model for spike trains that exhibits the variance-to-mean power relationship. Based on this, a maximum likelihood method is developed for inferring the parameters from rate-modulated spike trains. The proposed method is illustrated on simulated and experimental spike trains.

Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex

Science.gov (United States)

Storchi, Riccardo; Zippo, Antonio G.; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E. M.

2012-01-01

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 neronal 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. PMID:22586452

An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.

Science.gov (United States)

Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

2016-01-01

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.

Isotope and Patient Age Predict for PSA Spikes After Permanent Prostate Brachytherapy

International Nuclear Information System (INIS)

Bostancic, Chelsea; Merrick, Gregory S.; Butler, Wayne M.; Wallner, Kent E.; Allen, Zachariah; Galbreath, Robert; Lief, Jonathan; Gutman, Sarah E.

2007-01-01

Purpose: To evaluate prostate-specific antigen (PSA) spikes after permanent prostate brachytherapy in low-risk patients. Methods and Materials: The study population consisted of 164 prostate cancer patients who were part of a prospective randomized trial comparing 103 Pd and 125 I for low-risk disease. Of the 164 patients, 61 (37.2%) received short-course androgen deprivation therapy. The median follow-up was 5.4 years. On average, 11.1 post-treatment PSA measurements were obtained per patient. Biochemical disease-free survival was defined as a PSA level of ≤0.40 ng/mL after nadir. A PSA spike was defined as an increase of ≥0.2 ng/mL, followed by a durable decline to prespike levels. Multiple parameters were evaluated as predictors for a PSA spike. Results: Of the 164 patients, 44 (26.9%) developed a PSA spike. Of the 46 hormone-naive 125 I patients and 57 hormone-naive 103 Pd patients, 21 (45.7%) and 8 (14.0%) developed a PSA spike. In the hormone-naive patients, the mean time between implantation and the spike was 22.6 months and 18.7 months for 125 I and 103 Pd, respectively. In patients receiving neoadjuvant androgen deprivation therapy, the incidence of spikes was comparable between isotopes ( 125 I 28.1% and 103 Pd 20.7%). The incidence of spikes was substantially different in patients 125 I and/or <65 years of age. Differences in isotope-related spikes are most pronounced in hormone-naive patients

Boobs, Boxing, and Bombs: Problematizing the Entertainment of Spike TV

OpenAIRE

Walton, Gerald; Potvin, L.

2009-01-01

Spike is the only television network in North America “for men.” Its motto, “Get more action,” is suggestive of pursuits of various forms of violence. We conceptualize Spike not as trivial entertainment, but rather as a form of pop culture that erodes the gains of feminists who have challenged the prevalence of normalized hegemonic masculinity (HM). Our paper highlights themes of Spike content, and connects those themes to the literature on HM. Moreover, we validate the identities and lives ...

Matrix removal in state of the art sample preparation methods for serum by charged aerosol detection and metabolomics-based LC-MS.

Science.gov (United States)

Schimek, Denise; Francesconi, Kevin A; Mautner, Anton; Libiseller, Gunnar; Raml, Reingard; Magnes, Christoph

2016-04-07

Investigations into sample preparation procedures usually focus on analyte recovery with no information provided about the fate of other components of the sample (matrix). For many analyses, however, and particularly those using liquid chromatography-mass spectrometry (LC-MS), quantitative measurements are greatly influenced by sample matrix. Using the example of the drug amitriptyline and three of its metabolites in serum, we performed a comprehensive investigation of nine commonly used sample clean-up procedures in terms of their suitability for preparing serum samples. We were monitoring the undesired matrix compounds using a combination of charged aerosol detection (CAD), LC-CAD, and a metabolomics-based LC-MS/MS approach. In this way, we compared analyte recovery of protein precipitation-, liquid-liquid-, solid-phase- and hybrid solid-phase extraction methods. Although all methods provided acceptable recoveries, the highest recovery was obtained by protein precipitation with acetonitrile/formic acid (amitriptyline 113%, nortriptyline 92%, 10-hydroxyamitriptyline 89%, and amitriptyline N-oxide 96%). The quantification of matrix removal by LC-CAD showed that the solid phase extraction method (SPE) provided the lowest remaining matrix load (48-123 μg mL(-1)), which is a 10-40 fold better matrix clean-up than the precipitation- or hybrid solid phase extraction methods. The metabolomics profiles of eleven compound classes, comprising 70 matrix compounds showed the trends of compound class removal for each sample preparation strategy. The collective data set of analyte recovery, matrix removal and matrix compound profile was used to assess the effectiveness of each sample preparation method. The best performance in matrix clean-up and practical handling of small sample volumes was showed by the SPE techniques, particularly HLB SPE. CAD proved to be an effective tool for revealing the considerable differences between the sample preparation methods. This detector can

Numerical simulation on current spike behaviour of JT-60U disruptive plasmas

International Nuclear Information System (INIS)

Takei, N; Nakamura, Y; Tsutsui, H; Yoshino, R; Kawano, Y; Ozeki, T; Tobita, K; Tsuji-Iio, S; Shimada, R; Jardin, S C

2004-01-01

Characteristics and underlying mechanisms for plasma current spikes, which have been frequently observed during the thermal quench of JT-60U disruptions, were investigated through tokamak simulation code simulations including the passive shell effects of the vacuum vessel. Positive shear and reversed shear (PS and RS) plasmas were shown to have various current spike features in the experiments, e.g. an impulsive increase in the plasma current (positive spike) in the majority of thermal quenches, and a sudden decrease (negative spike), that has been excluded from past consideration, as an exception. It was first clarified that the shell effects, which become significant especially at a strong pressure drop due to the thermal quench of high β p plasmas, play an important role in the current spike in accordance with the initial relation of the radial location between the plasma equilibria and the vacuum vessel. As a consequence, a negative current spike may appear at thermal quench when the plasma is positioned further out from the geometric centre of the vacuum vessel. It was also pointed out that a further lowering in the internal inductance, in contradiction to previous interpretation in the past, is a plausible candidate for the mechanism for positive current spikes observed even in RS plasmas. The new interpretation enables us to reason out the whole character of current spikes of JT-60U disruptions

Statistical characteristics of climbing fiber spikes necessary for efficient cerebellar learning.

Science.gov (United States)

Kuroda, S; Yamamoto, K; Miyamoto, H; Doya, K; Kawat, M

2001-03-01

Mean firing rates (MFRs), with analogue values, have thus far been used as information carriers of neurons in most brain theories of learning. However, the neurons transmit the signal by spikes, which are discrete events. The climbing fibers (CFs), which are known to be essential for cerebellar motor learning, fire at the ultra-low firing rates (around 1 Hz), and it is not yet understood theoretically how high-frequency information can be conveyed and how learning of smooth and fast movements can be achieved. Here we address whether cerebellar learning can be achieved by CF spikes instead of conventional MFR in an eye movement task, such as the ocular following response (OFR), and an arm movement task. There are two major afferents into cerebellar Purkinje cells: parallel fiber (PF) and CF, and the synaptic weights between PFs and Purkinje cells have been shown to be modulated by the stimulation of both types of fiber. The modulation of the synaptic weights is regulated by the cerebellar synaptic plasticity. In this study we simulated cerebellar learning using CF signals as spikes instead of conventional MFR. To generate the spikes we used the following four spike generation models: (1) a Poisson model in which the spike interval probability follows a Poisson distribution, (2) a gamma model in which the spike interval probability follows the gamma distribution, (3) a max model in which a spike is generated when a synaptic input reaches maximum, and (4) a threshold model in which a spike is generated when the input crosses a certain small threshold. We found that, in an OFR task with a constant visual velocity, learning was successful with stochastic models, such as Poisson and gamma models, but not in the deterministic models, such as max and threshold models. In an OFR with a stepwise velocity change and an arm movement task, learning could be achieved only in the Poisson model. In addition, for efficient cerebellar learning, the distribution of CF spike

Computational investigations of blunt body drag-reduction spikes in hypersonic flows

International Nuclear Information System (INIS)

Kamran, N.; Zahir, S.; Khan, M.A.

2003-01-01

Drag is an important parameter in the designing of high-speed vehicles. Such vehicles include hypervelocity projectiles, reentry modules, and hypersonic aircrafts. Therefore, there exists an active or passive technique to reduce drag due to the high pressures at nosetip region of the vehicle. Drag can be reduced by attaching a forward facing spike on the nose of the vehicle. The present study reviews and deals with the CFD analysis made on a standard blunt body to reduce aerodynamic drag due to the attachment of forward facing spikes for High-Speed vehicles. Different spike lengths have been examined to study the forebody flowfield. The investigation concludes that spikes are an effective way to reduce the aerodynamic drag due to reduced dynamic pressure on the nose caused by the separated flow on the spikes. With the accomplishment of confidence on computational data, study was extended in hypersonic Mach range with a drag prediction accuracy of ± 10%. In the present work, viscous fluid dynamics studies were performed for a complete freestream Mach number range of 5.0, 6.0, 7.0 and 8.0 for different spike lengths and zero degree angle of attack. (author)

Transient reduction in theta power caused by interictal spikes in human temporal lobe epilepsy.

Science.gov (United States)

Manling Ge; Jundan Guo; Yangyang Xing; Zhiguo Feng; Weide Lu; Xinxin Ma; Yuehua Geng; Xin Zhang

2017-07-01

The inhibitory impacts of spikes on LFP theta rhythms(4-8Hz) are investigated around sporadic spikes(SSs) based on intracerebral EEG of 4 REM sleep patients with temporal lobe epilepsy(TLE) under the pre-surgical monitoring. Sequential interictal spikes in both genesis area and extended propagation pathway are collected, that, SSs genesis only in anterior hippocampus(aH)(possible propagation pathway in Entorhinal cortex(EC)), only in EC(possible propagation pathway in aH), and in both aH and EC synchronously. Instantaneous theta power was estimated by using Gabor wavelet transform, and theta power level was estimated by averaged over time and frequency before SSs(350ms pre-spike) and after SSs(350ms post-spike). The inhibitory effect around spikes was evaluated by the ratio of theta power level difference between pre-spike and post-spike to pre-spike theta power level. The findings were that theta power level was reduced across SSs, and the effects were more sever in the case of SSs in both aH and EC synchronously than either SSs only in EC or SSs only in aH. It is concluded that interictal spikes impair LFP theta rhythms transiently and directly. The work suggests that the reduction of theta power after the interictal spike might be an evaluation indicator of damage of epilepsy to human cognitive rhythms.

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...

Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks

NARCIS (Netherlands)

S.M. Bohte (Sander); J.A. La Poutré (Han); J.N. Kok (Joost)

2000-01-01

textabstractWe demonstrate that spiking neural networks encoding information in spike times are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on

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.

Neural Spike Train Synchronisation Indices: Definitions, Interpretations and Applications.

Science.gov (United States)

Halliday, D M; Rosenberg, J R

2017-04-24

A comparison of previously defined spike train syncrhonization indices is undertaken within a stochastic point process framework. The second order cumulant density (covariance density) is shown to be common to all the indices. Simulation studies were used to investigate the sampling variability of a single index based on the second order cumulant. The simulations used a paired motoneurone model and a paired regular spiking cortical neurone model. The sampling variability of spike trains generated under identical conditions from the paired motoneurone model varied from 50% { 160% of the estimated value. On theoretical grounds, and on the basis of simulated data a rate dependence is present in all synchronization indices. The application of coherence and pooled coherence estimates to the issue of synchronization indices is considered. This alternative frequency domain approach allows an arbitrary number of spike train pairs to be evaluated for statistically significant differences, and combined into a single population measure. The pooled coherence framework allows pooled time domain measures to be derived, application of this to the simulated data is illustrated. Data from the cortical neurone model is generated over a wide range of firing rates (1 - 250 spikes/sec). The pooled coherence framework correctly characterizes the sampling variability as not significant over this wide operating range. The broader applicability of this approach to multi electrode array data is briefly discussed.

Visualizing spikes in source-space

DEFF Research Database (Denmark)

Beniczky, Sándor; Duez, Lene; Scherg, Michael

2016-01-01

OBJECTIVE: Reviewing magnetoencephalography (MEG) recordings is time-consuming: signals from the 306 MEG-sensors are typically reviewed divided into six arrays of 51 sensors each, thus browsing each recording six times in order to evaluate all signals. A novel method of reconstructing the MEG...... signals in source-space was developed using a source-montage of 29 brain-regions and two spatial components to remove magnetocardiographic (MKG) artefacts. Our objective was to evaluate the accuracy of reviewing MEG in source-space. METHODS: In 60 consecutive patients with epilepsy, we prospectively...... evaluated the accuracy of reviewing the MEG signals in source-space as compared to the classical method of reviewing them in sensor-space. RESULTS: All 46 spike-clusters identified in sensor-space were also identified in source-space. Two additional spike-clusters were identified in source-space. As 29...

Temporal Correlations and Neural Spike Train Entropy

International Nuclear Information System (INIS)

Schultz, Simon R.; Panzeri, Stefano

2001-01-01

Sampling considerations limit the experimental conditions under which information theoretic analyses of neurophysiological data yield reliable results. We develop a procedure for computing the full temporal entropy and information of ensembles of neural spike trains, which performs reliably for limited samples of data. This approach also yields insight to the role of correlations between spikes in temporal coding mechanisms. The method, when applied to recordings from complex cells of the monkey primary visual cortex, results in lower rms error information estimates in comparison to a 'brute force' approach

Matrix-Matching as an Improvement Strategy for the Detection of Pesticide Residues.

Science.gov (United States)

Giacinti, Géraldine; Raynaud, Christine; Capblancq, Sophie; Simon, Valérie

2016-05-01

More than 90% of the pesticides residues in apples are located in the peel. We developed a gas chromatography/ion trap tandem mass spectrometry method for investigating all detectable residues in the peel of 3 apple varieties. Sample preparation is based on the use of the Quick Easy Cheap Effective Rugged and Safe method on the whole fruit, the flesh, and the peel. Pesticide residues were quantified with solvent-matched and matrix-matched standards, by spiking apple sample extracts. Matrix effects dependent on the type of extract (fruit, flesh, or peel) and the apple variety were detected. The best data processing methods involved normalizing matrix effect rates by matrix-matched internal/external calibration. Boscalid, captan, chlorpyrifos, fludioxonil, and pyraclostrobin were the most frequently detected pesticides. However, their concentrations in the whole fruit were below European maximum residue levels. Despite negative matrix effects, the residues in peel were detected at concentrations up to 10 times higher than those in whole fruits. Consequently, other pesticide residues present at concentrations below the limit of quantification in the whole fruit were detected in the peel. © 2016 Institute of Food Technologists®

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.

Transformation-invariant visual representations in self-organizing spiking neural networks.

Science.gov (United States)

Evans, Benjamin D; Stringer, Simon M

2012-01-01

The ventral visual pathway achieves object and face recognition by building transformation-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transformation-invariant representations has been demonstrated using either of two biologically plausible learning mechanisms, Trace learning and Continuous Transformation (CT) learning. However, it has not previously been investigated how transformation-invariant representations may be learned in a more biologically accurate spiking neural network. A key issue is how the synaptic connection strengths in such a spiking network might self-organize through Spike-Time Dependent Plasticity (STDP) where the change in synaptic strength is dependent on the relative times of the spikes emitted by the presynaptic and postsynaptic neurons rather than simply correlated activity driving changes in synaptic efficacy. Here we present simulations with conductance-based integrate-and-fire (IF) neurons using a STDP learning rule to address these gaps in our understanding. It is demonstrated that with the appropriate selection of model parameters and training regime, the spiking network model can utilize either Trace-like or CT-like learning mechanisms to achieve transform-invariant representations.

Transform-invariant visual representations in self-organizing spiking neural networks

Directory of Open Access Journals (Sweden)

Benjamin eEvans

2012-07-01

Full Text Available The ventral visual pathway achieves object and face recognition by building transform-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transform invariant representations has been demonstrated using either of two biologically plausible learning mechanisms, Trace learning and Continuous Transformation (CT learning. However, it has not previously been investigated how transform invariant representations may be learned in a more biologically accurate spiking neural network. A key issue is how the synaptic connection strengths in such a spiking network might self-organize through Spike-Time Dependent Plasticity (STDP where the change in synaptic strength is dependent on the relative times of the spikes emitted by the pre- and postsynaptic neurons rather than simply correlated activity driving changes in synaptic efficacy. Here we present simulations with conductance-based integrate-and-fire (IF neurons using a STDP learning rule to address these gaps in our understanding. It is demonstrated that with the appropriate selection of model pa- rameters and training regime, the spiking network model can utilize either Trace-like or CT-like learning mechanisms to achieve transform-invariant representations.

Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns.

Science.gov (United States)

Matsubara, Takashi

2017-01-01

Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning.

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.

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

Science.gov (United States)

Logiaco, Laureline; Quilodran, René; Procyk, Emmanuel; Arleo, Angelo

2015-08-01

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.

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.

Spike Neural Models Part II: Abstract Neural Models

Directory of Open Access Journals (Sweden)

Johnson, Melissa G.

2018-02-01

Full Text Available Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF model which is not biologically realistic but does quickly and easily integrate input to produce spikes. Izhikevich's model is based on Hodgkin-Huxley's model but simplified such that it uses only two differentiation equations and four parameters to produce various realistic spike patterns. LIF is based on a standard electrical circuit and contains one equation. Either of these two models, or any of the many other models in literature can be used in a SNN. Choosing a neural model is an important task that depends on the goal of the research and the resources available. Once a model is chosen, network decisions such as connectivity, delay, and sparseness, need to be made. Understanding neural models and how they are incorporated into the network is the first step in creating a SNN.

Interspike Interval Based Filtering of Directional Selective Retinal Ganglion Cells Spike Trains

Directory of Open Access Journals (Sweden)

Aurel Vasile Martiniuc

2012-01-01

Full Text Available The information regarding visual stimulus is encoded in spike trains at the output of retina by retinal ganglion cells (RGCs. Among these, the directional selective cells (DSRGC are signaling the direction of stimulus motion. DSRGCs' spike trains show accentuated periods of short interspike intervals (ISIs framed by periods of isolated spikes. Here we use two types of visual stimulus, white noise and drifting bars, and show that short ISI spikes of DSRGCs spike trains are more often correlated to their preferred stimulus feature (that is, the direction of stimulus motion and carry more information than longer ISI spikes. Firstly, our results show that correlation between stimulus and recorded neuronal response is best at short ISI spiking activity and decrease as ISI becomes larger. We then used grating bars stimulus and found that as ISI becomes shorter the directional selectivity is better and information rates are higher. Interestingly, for the less encountered type of DSRGC, known as ON-DSRGC, short ISI distribution and information rates revealed consistent differences when compared with the other directional selective cell type, the ON-OFF DSRGC. However, these findings suggest that ISI-based temporal filtering integrates a mechanism for visual information processing at the output of retina toward higher stages within early visual system.

Structured chaos shapes spike-response noise entropy in balanced neural networks

Directory of Open Access Journals (Sweden)

Guillaume eLajoie

2014-10-01

Full Text Available Large networks of sparsely coupled, excitatory and inhibitory cells occur throughout the brain. For many models of these networks, a striking feature is that their dynamics are chaotic and thus, are sensitive to small perturbations. How does this chaos manifest in the neural code? Specifically, how variable are the spike patterns that such a network produces in response to an input signal? To answer this, we derive a bound for a general measure of variability -- spike-train entropy. This leads to important insights on the variability of multi-cell spike pattern distributions in large recurrent networks of spiking neurons responding to fluctuating inputs. The analysis is based on results from random dynamical systems theory and is complemented by detailed numerical simulations. We find that the spike pattern entropy is an order of magnitude lower than what would be extrapolated from single cells. This holds despite the fact that network coupling becomes vanishingly sparse as network size grows -- a phenomenon that depends on ``extensive chaos, as previously discovered for balanced networks without stimulus drive. Moreover, we show how spike pattern entropy is controlled by temporal features of the inputs. Our findings provide insight into how neural networks may encode stimuli in the presence of inherently chaotic dynamics.

Rapid and selective screening of melamine in bovine milk using molecularly imprinted matrix solid-phase dispersion coupled with liquid chromatography-ultraviolet detection.

Science.gov (United States)

Yan, Hongyuan; Cheng, Xiaoling; Sun, Ning; Cai, Tianyu; Wu, Ruijun; Han, Kun

2012-11-01

A simple, convenient and high selective molecularly imprinted matrix solid-phase dispersion (MI-MSPD) using water-compatible cyromazine-imprinted polymer as adsorbent was proposed for the rapid screening of melamine from bovine milk coupled with liquid chromatography-ultraviolet detection. The molecularly imprinted polymers (MIPs) synthesized by cyromazine as dummy template and reformative methanol-water system as reaction medium showed higher affinity and selectivity to melamine, and so they were applied as the specific dispersant of MSPD to extraction of melamine and simultaneously eliminate the effect of template leakage on quantitative analysis. Under the optimized conditions, good linearity was obtained in a range of 0.24-60.0μgg(-1) with the correlation coefficient of 0.9994. The recoveries of melamine at three spiked levels were ranged from 86.0 to 96.2% with the relative standard deviation (RSD)≤4.0%. This proposed MI-MSPD method combined the advantages of MSPD and MIPs, and could be used as an alternative tool for analyzing the residues of melamine in complex milk samples. Copyright © 2012 Elsevier B.V. All rights reserved.

Magnetic matrix solid phase dispersion assisted dispersive liquid liquid microextraction of ultra trace polychlorinated biphenyls in water prior to GC-ECD determination

International Nuclear Information System (INIS)

Diao, Chunpeng; Li, Cong; Yang, Xiao; Sun, Ailing; Liu, Renmin

2016-01-01

Magnetic matrix solid phase dispersion (MMSPD) assisted dispersive liquid liquid microextraction (DLLME) was applied to extract ultra traces of polychlorinated biphenyls (PCBs) from water samples prior to gas chromatography with electron capture detection. PCBs in water were adsorbed by micro particles of magnetic bamboo charcoal and then transferred into the elution solvent. PCBs in the elution solvent of the MMSPD were further concentrated into trace volume extraction solvent of the DLLME procedure. Under optimized conditions, good linearity in the range of 0.2–100 ng L"−"1 was obtained with regression coefficients (r) higher than 0.9987. Based on a signal-noise ratio of 3, the limits of detection (LODs) range from 0.05–0.1 ng L"−"1. These LODs are much lower than those of MMSPD or DLLME alone. Relative standard deviations are between 4.9–8.2 %. The method was successfully applied to the determination of PCBs in lake and river water. Relative recoveries were 85.5–117.4 % for the spiked environmental water samples. (author)

Synaptic convergence regulates synchronization-dependent spike transfer in feedforward neural networks.

Science.gov (United States)

Sailamul, Pachaya; Jang, Jaeson; Paik, Se-Bum

2017-12-01

Correlated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that, the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.

Simple and suitable immunosensor for β-lactam antibiotics analysis in real matrixes: milk, serum, urine.

Science.gov (United States)

Merola, Giovanni; Martini, Elisabetta; Tomassetti, Mauro; Campanella, Luigi

2015-03-15

The anti-penicillin G was conjugated to avidin-peroxidase and biotin to obtain immunogen and competitor which were then used to develop a competitive immunosensor assay for the detection of penicillin G and other β-lactam antibiotics, with Kaff values of the order of 10(8) M(-1). The new immunosensor appears to afford a number of advantages in terms of sensitivity, possibility of "in situ" analysis, but especially of simplicity and lower costs, compared with other existing devices, or different chemical instrumental methods reported in the literature and used for the analysis of β-lactam compounds. Satisfactory results were found in the analysis of real matrixes and good recoveries were obtained by applying the standard addition method to spiked milk, urine, serum and drug samples. The new device uses an amperometric electrode for hydrogen peroxide as transducer, the BSA-penicillin G immobilized on polymeric membrane overlapping the amperometric transducer and the peroxidase enzyme as marker. It proved to be highly sensitive, inexpensive and easily reproducible; LOD was of the order of 10(-11)M. Lastly, the new immunosensor displayed low selectivity versus the entire class of β-lactam antibiotics and higher selectivity toward other classes of non-β-lactam antibiotics. Copyright © 2014 Elsevier B.V. All rights reserved.

Potential sources of analytical bias and error in selected trace element data-quality analyses

Science.gov (United States)

Paul, Angela P.; Garbarino, John R.; Olsen, Lisa D.; Rosen, Michael R.; Mebane, Christopher A.; Struzeski, Tedmund M.

2016-09-28

Potential sources of analytical bias and error associated with laboratory analyses for selected trace elements where concentrations were greater in filtered samples than in paired unfiltered samples were evaluated by U.S. Geological Survey (USGS) Water Quality Specialists in collaboration with the USGS National Water Quality Laboratory (NWQL) and the Branch of Quality Systems (BQS).Causes for trace-element concentrations in filtered samples to exceed those in associated unfiltered samples have been attributed to variability in analytical measurements, analytical bias, sample contamination either in the field or laboratory, and (or) sample-matrix chemistry. These issues have not only been attributed to data generated by the USGS NWQL but have been observed in data generated by other laboratories. This study continues the evaluation of potential analytical bias and error resulting from matrix chemistry and instrument variability by evaluating the performance of seven selected trace elements in paired filtered and unfiltered surface-water and groundwater samples collected from 23 sampling sites of varying chemistries from six States, matrix spike recoveries, and standard reference materials.Filtered and unfiltered samples have been routinely analyzed on separate inductively coupled plasma-mass spectrometry instruments. Unfiltered samples are treated with hydrochloric acid (HCl) during an in-bottle digestion procedure; filtered samples are not routinely treated with HCl as part of the laboratory analytical procedure. To evaluate the influence of HCl on different sample matrices, an aliquot of the filtered samples was treated with HCl. The addition of HCl did little to differentiate the analytical results between filtered samples treated with HCl from those samples left untreated; however, there was a small, but noticeable, decrease in the number of instances where a particular trace-element concentration was greater in a filtered sample than in the associated

Aspects of Quantitation in Mass Spectrometry Imaging Investigated on Cryo-Sections of Spiked Tissue Homogenates.

Science.gov (United States)

Hansen, Heidi Toft; Janfelt, Christian

2016-12-06

Internal standards have been introduced in quantitative mass spectrometry imaging in order to compensate for differences in intensities throughout an image caused by, for example, difference in ion suppression or analyte extraction efficiency. To test how well the internal standards compensate for differences in tissue types in, for example, whole-body imaging, a set of tissue homogenates of different tissue types (lung, liver, kidney, heart, and brain) from rabbit was spiked to the same concentration with the drug amitriptyline and imaged in the same experiment using isotope labeled amitriptyline as internal standard. The results showed, even after correction with internal standard, significantly lower intensities from brain and to some extent also lung tissue, differences which may be ascribed to binding of the drug to proteins or lipids as known from traditional bioanalysis. The differences, which for these results range approximately within a factor of 3 (but for other compounds in other tissues could be higher), underscore the importance of preparing the standard curve in the same matrix as the unknown sample whenever possible. In, for example, whole-body imaging where a diversity of tissue types are present, this variation across tissue types will therefore add to the overall uncertainty in quantitation. The tissue homogenates were also used in a characterization of various phenomena in quantitative MSI, such as to study how the signal depends of the thickness of the cryo-section, and to assess the accuracy of calibration by droplet deposition. For experiments on liver tissue, calibration by spiked tissue homogenates and droplet deposition was found to provide highly similar results and in both cases linearity with R 2 values of 0.99. In the process, a new method was developed for preparation of standard curves of spiked tissue homogenates, based on the drilling of holes in a block of frozen liver homogenate, providing easy cryo-slicing and good quantitative

Should spikes on post-resection ECoG guide pediatric epilepsy surgery?

Science.gov (United States)

Greiner, Hansel M; Horn, Paul S; Tenney, Jeffrey R; Arya, Ravindra; Jain, Sejal V; Holland, Katherine D; Leach, James L; Miles, Lili; Rose, Douglas F; Fujiwara, Hisako; Mangano, Francesco T

2016-05-01

There is wide variation in clinical practice regarding the role of electrocorticography immediately after resection (post-resection ECoG) for pediatric epilepsy surgery. Results can guide further resection of potentially epileptogenic tissue. We hypothesized that post-resection ECoG spiking represents a biomarker of the epileptogenic zone and predicts seizure outcome in children undergoing epilepsy surgery. We retrospectively identified 124 children with post-resection ECoG performed on the margins of resection. ECoG records were scored in a blinded fashion based on presence of frequent spiking. For patients identified as having additional resection based on clinical post-resection ECoG interpretation, these "second-look" ECoG results were re-reviewed for ongoing discharges or completeness of resection. Frequent spike populations were grouped using a standard scoring system into three ranges: 0.1-0.5Hz, 0.5-1Hz, >1Hz. Seizure outcomes were determined at minimum 12-month followup. Of 124 patients who met inclusion criteria, 60 (48%) had an identified spike population on post-resection ECoG. Thirty (50%) of these had further resection based on clinical interpretation. Overall, good outcome (ILAE 1) was seen in 56/124 (45%). Completeness of resection of spiking (absence of spiking on initial post-resection ECoG or resolution of spiking after further resection) showed a trend toward good outcome (OR 2.03, p=0.099). Patients with completeness of resection had good outcome in 41/80 (51%) of cases; patients with continued spikes had good outcome in 15/44 (35%) of cases. Post-resection ECoG identifies residual epileptogenic tissue in a significant number of children. Lower frequency or absence of discharges on initial recording showed a trend toward good outcome. Completeness of resection demonstrated on final ECoG recording did not show a significant difference in outcome. This suggests that post-resection discharges represent a prognostic marker rather than a remediable

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.

Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights

Directory of Open Access Journals (Sweden)

Wilten eNicola

2016-02-01

Full Text Available A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF. The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks

Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights.

Science.gov (United States)

Nicola, Wilten; Tripp, Bryan; Scott, Matthew

2016-01-01

A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF). The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks.

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

Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering.

Science.gov (United States)

Oliynyk, Andriy; Bonifazzi, Claudio; Montani, Fernando; Fadiga, Luciano

2012-08-08

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. 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 competitive with respect to other robust spike

The influence of single bursts vs. single spikes at excitatory dendrodendritic synapses

Science.gov (United States)

Masurkar, Arjun V.; Chen, Wei R.

2015-01-01

The synchronization of neuronal activity is thought to enhance information processing. There is much evidence supporting rhythmically bursting external tufted cells (ETCs) of the rodent olfactory bulb glomeruli coordinating the activation of glomerular interneurons and mitral cells via dendrodendritic excitation. However, as bursting has variable significance at axodendritic cortical synapses, it is not clear if ETC bursting imparts a specific functional advantage over the preliminary spike in dendrodendritic synaptic networks. To answer this question, we investigated the influence of single ETC bursts and spikes with the in-vitro rat olfactory bulb preparation at different levels of processing, via calcium imaging of presynaptic ETC dendrites, dual electrical recording of ETC–interneuron synaptic pairs, and multicellular calcium imaging of ETC-induced population activity. Our findings supported single ETC bursts, vs. single spikes, driving robust presynaptic calcium signaling, which in turn was associated with profound extension of the initial monosynaptic spike-driven dendrodendritic excitatory postsynaptic potential. This extension could be driven by either the spike-dependent or spike-independent components of the burst. At the population level, burst-induced excitation was more widespread and reliable compared with single spikes. This further supports the ETC network, in part due to a functional advantage of bursting at excitatory dendrodendritic synapses, coordinating synchronous activity at behaviorally relevant frequencies related to odor processing in vivo. PMID:22277089

Nonlinear evolution of single spike in Richtmyer-Meshkov instability

International Nuclear Information System (INIS)

Fukuda, Y.; Nishihara, K.; Wouchuk, J.G.

2000-01-01

Nonlinear evolution of single spike structure and vortex in the Richtmyer-Meshkov instability is investigated with the use of a two-dimensional hydrodynamic code. It is shown that singularity appears in the vorticity left by transmitted and reflected shocks at a corrugated interface. This singularity results in opposite sign of vorticity along the interface that causes double spiral structure of the spike. (authors)

Comparison of spike-sorting algorithms for future hardware implementation.

Science.gov (United States)

Gibson, Sarah; Judy, Jack W; Markovic, Dejan

2008-01-01

Applications such as brain-machine interfaces require hardware spike sorting in order to (1) obtain single-unit activity and (2) perform data reduction for wireless transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.

Memristors Empower Spiking Neurons With Stochasticity

KAUST Repository

Al-Shedivat, Maruan; Naous, Rawan; Cauwenberghs, Gert; Salama, Khaled N.

2015-01-01

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

Development of on-off spiking in superior paraolivary nucleus neurons of the mouse

Science.gov (United States)

Felix, Richard A.; Vonderschen, Katrin; Berrebi, Albert S.

2013-01-01

The superior paraolivary nucleus (SPON) is a prominent cell group in the auditory brain stem that has been increasingly implicated in representing temporal sound structure. Although SPON neurons selectively respond to acoustic signals important for sound periodicity, the underlying physiological specializations enabling these responses are poorly understood. We used in vitro and in vivo recordings to investigate how SPON neurons develop intrinsic cellular properties that make them well suited for encoding temporal sound features. In addition to their hallmark rebound spiking at the stimulus offset, SPON neurons were characterized by spiking patterns termed onset, adapting, and burst in response to depolarizing stimuli in vitro. Cells with burst spiking had some morphological differences compared with other SPON neurons and were localized to the dorsolateral region of the nucleus. Both membrane and spiking properties underwent strong developmental regulation, becoming more temporally precise with age for both onset and offset spiking. Single-unit recordings obtained in young mice demonstrated that SPON neurons respond with temporally precise onset spiking upon tone stimulation in vivo, in addition to the typical offset spiking. Taken together, the results of the present study demonstrate that SPON neurons develop sharp on-off spiking, which may confer sensitivity to sound amplitude modulations or abrupt sound transients. These findings are consistent with the proposed involvement of the SPON in the processing of temporal sound structure, relevant for encoding communication cues. PMID:23515791

Spikes and memory in (Nord Pool) electricity price spot prices

DEFF Research Database (Denmark)

Proietti, Tomasso; Haldrup, Niels; Knapik, Oskar

Electricity spot prices are subject to transitory sharp movements commonly referred to as spikes. The paper aims at assessing their effects on model based inferences and predictions, with reference to the Nord Pool power exchange. We identify a spike as a price value which deviates substantially...

Neural spike sorting using iterative ICA and a deflation-based approach.

Science.gov (United States)

Tiganj, Z; Mboup, M

2012-12-01

We propose a spike sorting method for multi-channel recordings. When applied in neural recordings, the performance of the independent component analysis (ICA) algorithm is known to be limited, since the number of recording sites is much lower than the number of neurons. The proposed method uses an iterative application of ICA and a deflation technique in two nested loops. In each iteration of the external loop, the spiking activity of one neuron is singled out and then deflated from the recordings. The internal loop implements a sequence of ICA and sorting for removing the noise and all the spikes that are not fired by the targeted neuron. Then a final step is appended to the two nested loops in order to separate simultaneously fired spikes. We solve this problem by taking all possible pairs of the sorted neurons and apply ICA only on the segments of the signal during which at least one of the neurons in a given pair was active. We validate the performance of the proposed method on simulated recordings, but also on a specific type of real recordings: simultaneous extracellular-intracellular. We quantify the sorting results on the extracellular recordings for the spikes that come from the neurons recorded intracellularly. The results suggest that the proposed solution significantly improves the performance of ICA in spike sorting.

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator

Directory of Open Access Journals (Sweden)

Jan Hahne

2017-05-01

Full Text Available Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.

Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.

Science.gov (United States)

Hahne, Jan; Dahmen, David; Schuecker, Jannis; Frommer, Andreas; Bolten, Matthias; Helias, Moritz; Diesmann, Markus

2017-01-01

Contemporary modeling approaches to the dynamics of neural networks include two important classes of models: biologically grounded spiking neuron models and functionally inspired rate-based units. We present a unified simulation framework that supports the combination of the two for multi-scale modeling, enables the quantitative validation of mean-field approaches by spiking network simulations, and provides an increase in reliability by usage of the same simulation code and the same network model specifications for both model classes. While most spiking simulations rely on the communication of discrete events, rate models require time-continuous interactions between neurons. Exploiting the conceptual similarity to the inclusion of gap junctions in spiking network simulations, we arrive at a reference implementation of instantaneous and delayed interactions between rate-based models in a spiking network simulator. The separation of rate dynamics from the general connection and communication infrastructure ensures flexibility of the framework. In addition to the standard implementation we present an iterative approach based on waveform-relaxation techniques to reduce communication and increase performance for large-scale simulations of rate-based models with instantaneous interactions. Finally we demonstrate the broad applicability of the framework by considering various examples from the literature, ranging from random networks to neural-field models. The study provides the prerequisite for interactions between rate-based and spiking models in a joint simulation.

Superficial dorsal horn neurons with double spike activity in the rat.

Science.gov (United States)

Rojas-Piloni, Gerardo; Dickenson, Anthony H; Condés-Lara, Miguel

2007-05-29

Superficial dorsal horn neurons promote the transfer of nociceptive information from the periphery to supraspinal structures. The membrane and discharge properties of spinal cord neurons can alter the reliability of peripheral signals. In this paper, we analyze the location and response properties of a particular class of dorsal horn neurons that exhibits double spike discharge with a very short interspike interval (2.01+/-0.11 ms). These neurons receive nociceptive C-fiber input and are located in laminae I-II. Double spikes are generated spontaneously or by depolarizing current injection (interval of 2.37+/-0.22). Cells presenting double spike (interval 2.28+/-0.11) increased the firing rate by electrical noxious stimulation, as well as, in the first minutes after carrageenan injection into their receptive field. Carrageenan is a polysaccharide soluble in water and it is used for producing an experimental model of semi-chronic pain. In the present study carrageenan also produces an increase in the interval between double spikes and then, reduced their occurrence after 5-10 min. The results suggest that double spikes are due to intrinsic membrane properties and that their frequency is related to C-fiber nociceptive activity. The present work shows evidence that double spikes in superficial spinal cord neurones are related to the nociceptive stimulation, and they are possibly part of an acute pain-control mechanism.

Activity-Dependent Plasticity of Spike Pauses in Cerebellar Purkinje Cells

Directory of Open Access Journals (Sweden)

Giorgio Grasselli

2016-03-01

Full Text Available The plasticity of intrinsic excitability has been described in several types of neurons, but the significance of non-synaptic mechanisms in brain plasticity and learning remains elusive. Cerebellar Purkinje cells are inhibitory neurons that spontaneously fire action potentials at high frequencies and regulate activity in their target cells in the cerebellar nuclei by generating a characteristic spike burst-pause sequence upon synaptic activation. Using patch-clamp recordings from mouse Purkinje cells, we find that depolarization-triggered intrinsic plasticity enhances spike firing and shortens the duration of spike pauses. Pause plasticity is absent from mice lacking SK2-type potassium channels (SK2−/− mice and in occlusion experiments using the SK channel blocker apamin, while apamin wash-in mimics pause reduction. Our findings demonstrate that spike pauses can be regulated through an activity-dependent, exclusively non-synaptic, SK2 channel-dependent mechanism and suggest that pause plasticity—by altering the Purkinje cell output—may be crucial to cerebellar information storage and learning.

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.

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.

Spike-adding in parabolic bursters: The role of folded-saddle canards

Science.gov (United States)

Desroches, Mathieu; Krupa, Martin; Rodrigues, Serafim

2016-09-01

The present work develops a new approach to studying parabolic bursting, and also proposes a novel four-dimensional canonical and polynomial-based parabolic burster. In addition to this new polynomial system, we also consider the conductance-based model of the Aplysia R15 neuron known as the Plant model, and a reduction of this prototypical biophysical parabolic burster to three variables, including one phase variable, namely the Baer-Rinzel-Carillo (BRC) phase model. Revisiting these models from the perspective of slow-fast dynamics reveals that the number of spikes per burst may vary upon parameter changes, however the spike-adding process occurs in an explosive fashion that involves special solutions called canards. This spike-adding canard explosion phenomenon is analysed by using tools from geometric singular perturbation theory in tandem with numerical bifurcation techniques. We find that the bifurcation structure persists across all considered systems, that is, spikes within the burst are incremented via the crossing of an excitability threshold given by a particular type of canard orbit, namely the true canard of a folded-saddle singularity. However there can be a difference in the spike-adding transitions in parameter space from one case to another, according to whether the process is continuous or discontinuous, which depends upon the geometry of the folded-saddle canard. Using these findings, we construct a new polynomial approximation of the Plant model, which retains all the key elements for parabolic bursting, including the spike-adding transitions mediated by folded-saddle canards. Finally, we briefly investigate the presence of spike-adding via canards in planar phase models of parabolic bursting, namely the theta model by Ermentrout and Kopell.

Fast computation with spikes in a recurrent neural network

International Nuclear Information System (INIS)

Jin, Dezhe Z.; Seung, H. Sebastian

2002-01-01

Neural networks with recurrent connections are sometimes regarded as too slow at computation to serve as models of the brain. Here we analytically study a counterexample, a network consisting of N integrate-and-fire neurons with self excitation, all-to-all inhibition, instantaneous synaptic coupling, and constant external driving inputs. When the inhibition and/or excitation are large enough, the network performs a winner-take-all computation for all possible external inputs and initial states of the network. The computation is done very quickly: As soon as the winner spikes once, the computation is completed since no other neurons will spike. For some initial states, the winner is the first neuron to spike, and the computation is done at the first spike of the network. In general, there are M potential winners, corresponding to the top M external inputs. When the external inputs are close in magnitude, M tends to be larger. If M>1, the selection of the actual winner is strongly influenced by the initial states. If a special relation between the excitation and inhibition is satisfied, the network always selects the neuron with the maximum external input as the winner

Computing with Spiking Neuron Networks

NARCIS (Netherlands)

H. Paugam-Moisy; S.M. Bohte (Sander); G. Rozenberg; T.H.W. Baeck (Thomas); J.N. Kok (Joost)

2012-01-01

htmlabstractAbstract 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

Code-specific learning rules improve action selection by populations of spiking neurons.

Science.gov (United States)

Friedrich, Johannes; Urbanczik, Robert; Senn, Walter

2014-08-01

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

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.

Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons.

Science.gov (United States)

Xiao, Dongsheng; Vanni, Matthieu P; Mitelut, Catalin C; Chan, Allen W; LeDue, Jeffrey M; Xie, Yicheng; Chen, Andrew Cn; Swindale, Nicholas V; Murphy, Timothy H

2017-02-04

Understanding the basis of brain function requires knowledge of cortical operations over wide-spatial scales, but also within the context of single neurons. In vivo, wide-field GCaMP imaging and sub-cortical/cortical cellular electrophysiology were used in mice to investigate relationships between spontaneous single neuron spiking and mesoscopic cortical activity. We make use of a rich set of cortical activity motifs that are present in spontaneous activity in anesthetized and awake animals. A mesoscale spike-triggered averaging procedure allowed the identification of motifs that are preferentially linked to individual spiking neurons by employing genetically targeted indicators of neuronal activity. Thalamic neurons predicted and reported specific cycles of wide-scale cortical inhibition/excitation. In contrast, spike-triggered maps derived from single cortical neurons yielded spatio-temporal maps expected for regional cortical consensus function. This approach can define network relationships between any point source of neuronal spiking and mesoscale cortical maps.

Iron oxide nanomatrix facilitating metal ionization in matrix-assisted laser desorption/ionization mass spectrometry.

Science.gov (United States)

Obena, Rofeamor P; Lin, Po-Chiao; Lu, Ying-Wei; Li, I-Che; del Mundo, Florian; Arco, Susan dR; Nuesca, Guillermo M; Lin, Chung-Chen; Chen, Yu-Ju

2011-12-15

The significance and epidemiological effects of metals to life necessitate the development of direct, efficient, and rapid method of analysis. Taking advantage of its simple, fast, and high-throughput features, we present a novel approach to metal ion detection by matrix-functionalized magnetic nanoparticle (matrix@MNP)-assisted MALDI-MS. Utilizing 21 biologically and environmentally relevant metal ion solutions, the performance of core and matrix@MNP against conventional matrixes in MALDI-MS and laser desorption ionization (LDI) MS were systemically tested to evaluate the versatility of matrix@MNP as ionization element. The matrix@MNPs provided 20- to >100-fold enhancement on detection sensitivity of metal ions and unambiguous identification through characteristic isotope patterns and accurate mass (<5 ppm), which may be attributed to its multifunctional role as metal chelator, preconcentrator, absorber, and reservoir of energy. Together with the comparison on the ionization behaviors of various metals having different ionization potentials (IP), we formulated a metal ionization mechanism model, alluding to the role of exciton pooling in matrix@MNP-assisted MALDI-MS. Moreover, the detection of Cu in spiked tap water demonstrated the practicability of this new approach as an efficient and direct alternative tool for fast, sensitive, and accurate determination of trace metal ions in real samples.

A unified approach to linking experimental, statistical and computational analysis of spike train data.

Directory of Open Access Journals (Sweden)

Liang Meng

Full Text Available A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data, but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach--linking statistical, computational, and experimental neuroscience--provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.

Relationship between focal penicillin spikes and cortical spindles in the cerveau isolé cat.

Science.gov (United States)

McLachlan, R S; Kaibara, M; Girvin, J P

1983-01-01

Using the unanesthetized, cerveau isolé preparation in the cat, the association between artificially induced penicillin (PCN) spikes and spontaneously occurring electrocorticographic (ECoG) spindles was investigated. Spikes were elicited by surface application of small pledgets of PCN. After the application of PCN, there was a decrease in spindle amplitude but no change in frequency, duration, or spindle wave frequency in the area of the focus. Examination of the times of occurrence of the spikes and spindles disclosed that in the majority of cases, within a few minutes of the initiation of the foci, there was very high simultaneity, usually 100% between the occurrences of these two events. Examination of the times of occurrence of the spikes within the ECoG spindles failed to disclose any compelling evidence which would favor either the hypothesis that spikes "trigger" spindles or the hypothesis that spindles predispose to focal spikes. Thus, whether spikes trigger spindles, or spikes simply occur in a nonspecific manner during the occurrence of the spindle, or whether it is a combination of both these explanations, must remain an open question on the basis of the data available.

Method for the determination of catechin and epicatechin enantiomers in cocoa-based ingredients and products by high-performance liquid chromatography: single-laboratory validation.

Science.gov (United States)

Machonis, Philip R; Jones, Matthew A; Schaneberg, Brian T; Kwik-Uribe, Catherine L

2012-01-01

A single-laboratory validation study was performed for an HPLC method to identify and quantify the flavanol enantiomers (+)- and (-)-epicatechin and (+)- and (-)-catechin in cocoa-based ingredients and products. These compounds were eluted isocratically with an ammonium acetate-methanol mobile phase applied to a modified beta-cyclodextrin chiral stationary phase and detected using fluorescence. Spike recovery experiments using appropriate matrix blanks, along with cocoa extract, cocoa powder, and dark chocolate, were used to evaluate accuracy, repeatability, specificity, LOD, LOQ, and linearity of the method as performed by a single analyst on multiple days. In all samples analyzed, (-)-epicatechin was the predominant flavanol and represented 68-91% of the total monomeric flavanols detected. For the cocoa-based products, within-day (intraday) precision for (-)-epicatechin was between 1.46-3.22%, for (+)-catechin between 3.66-6.90%, and for (-)-catechin between 1.69-6.89%; (+)-epicatechin was not detected in these samples. Recoveries for the three sample types investigated ranged from 82.2 to 102.1% at the 50% spiking level, 83.7 to 102.0% at the 100% spiking level, and 80.4 to 101.1% at the 200% spiking level. Based on performance results, this method may be suitable for routine laboratory use in analysis of cocoa-based ingredients and products.

Poisson-Like Spiking in Circuits with Probabilistic Synapses

Science.gov (United States)

Moreno-Bote, Rubén

2014-01-01

Neuronal activity in cortex is variable both spontaneously and during stimulation, and it has the remarkable property that it is Poisson-like over broad ranges of firing rates covering from virtually zero to hundreds of spikes per second. The mechanisms underlying cortical-like spiking variability over such a broad continuum of rates are currently unknown. We show that neuronal networks endowed with probabilistic synaptic transmission, a well-documented source of variability in cortex, robustly generate Poisson-like variability over several orders of magnitude in their firing rate without fine-tuning of the network parameters. Other sources of variability, such as random synaptic delays or spike generation jittering, do not lead to Poisson-like variability at high rates because they cannot be sufficiently amplified by recurrent neuronal networks. We also show that probabilistic synapses predict Fano factor constancy of synaptic conductances. Our results suggest that synaptic noise is a robust and sufficient mechanism for the type of variability found in cortex. PMID:25032705

On the Universality and Non-Universality of Spiking Neural P Systems With Rules on Synapses.

Science.gov (United States)

Song, Tao; Xu, Jinbang; Pan, Linqiang

2015-12-01

Spiking neural P systems with rules on synapses are a new variant of spiking neural P systems. In the systems, the neuron contains only spikes, while the spiking/forgetting rules are moved on the synapses. It was obtained that such system with 30 neurons (using extended spiking rules) or with 39 neurons (using standard spiking rules) is Turing universal. In this work, this number is improved to 6. Specifically, we construct a Turing universal spiking neural P system with rules on synapses having 6 neurons, which can generate any set of Turing computable natural numbers. As well, it is obtained that spiking neural P system with rules on synapses having less than two neurons are not Turing universal: i) such systems having one neuron can characterize the family of finite sets of natural numbers; ii) the family of sets of numbers generated by the systems having two neurons is included in the family of semi-linear sets of natural numbers.

Which spike train distance is most suitable for distinguishing rate and temporal coding?

Science.gov (United States)

Satuvuori, Eero; Kreuz, Thomas

2018-04-01

It is commonly assumed in neuronal coding that repeated presentations of a stimulus to a coding neuron elicit similar responses. One common way to assess similarity are spike train distances. These can be divided into spike-resolved, such as the Victor-Purpura and the van Rossum distance, and time-resolved, e.g. the ISI-, the SPIKE- and the RI-SPIKE-distance. We use independent steady-rate Poisson processes as surrogates for spike trains with fixed rate and no timing information to address two basic questions: How does the sensitivity of the different spike train distances to temporal coding depend on the rates of the two processes and how do the distances deal with very low rates? Spike-resolved distances always contain rate information even for parameters indicating time coding. This is an issue for reasonably high rates but beneficial for very low rates. In contrast, the operational range for detecting time coding of time-resolved distances is superior at normal rates, but these measures produce artefacts at very low rates. The RI-SPIKE-distance is the only measure that is sensitive to timing information only. While our results on rate-dependent expectation values for the spike-resolved distances agree with Chicharro et al. (2011), we here go one step further and specifically investigate applicability for very low rates. The most appropriate measure depends on the rates of the data being analysed. Accordingly, we summarize our results in one table that allows an easy selection of the preferred measure for any kind of data. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Colloidal Gold Nanoclusters Spiked Silica Fillers in Mixed Matrix Coatings: Simultaneous Detection and Inhibition of Healthcare-Associated Infections.

Science.gov (United States)

Alsaiari, Shahad K; Hammami, Mohammed A; Croissant, Jonas G; Omar, Haneen W; Neelakanda, Pradeep; Yapici, Tahir; Peinemann, Klaus-Viktor; Khashab, Niveen M

2017-03-01

Healthcare-associated infections (HAIs) are the infections that patients get while receiving medical treatment in a medical facility with bacterial HAIs being the most common. Silver and gold nanoparticles (NPs) have been successfully employed as antibacterial motifs; however, NPs leaching in addition to poor dispersion and overall reproducibility are major hurdles to further product development. In this study, the authors design and fabricate a smart antibacterial mixed-matrix membrane coating comprising colloidal lysozyme-templated gold nanoclusters as nanofillers in poly(ethylene oxide)/poly(butylene terephthalate) amphiphilic polymer matrix. Mesoporous silica nanoparticles-lysozyme functionalized gold nanoclusters disperse homogenously within the polymer matrix with no phase separation and zero NPs leaching. This mixed-matrix coating can successfully sense and inhibit bacterial contamination via a controlled release mechanism that is only triggered by bacteria. The system is coated on a common radiographic dental imaging device (photostimulable phosphor plate) that is prone to oral bacteria contamination. Variation and eventually disappearance of the red fluorescence surface under UV light signals bacterial infection. Kanamycin, an antimicrobial agent, is controllably released to instantly inhibit bacterial growth. Interestingly, the quality of the images obtained with these coated surfaces is the same as uncoated surfaces and thus the safe application of such smart coatings can be expanded to include other medical devices without compromising their utility. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Colloidal Gold Nanoclusters Spiked Silica Fillers in Mixed Matrix Coatings: Simultaneous Detection and Inhibition of Healthcare-Associated Infections

KAUST Repository

Alsaiari, Shahad K.

2017-01-25

Healthcare-associated infections (HAIs) are the infections that patients get while receiving medical treatment in a medical facility with bacterial HAIs being the most common. Silver and gold nanoparticles (NPs) have been successfully employed as antibacterial motifs; however, NPs leaching in addition to poor dispersion and overall reproducibility are major hurdles to further product development. In this study, the authors design and fabricate a smart antibacterial mixed-matrix membrane coating comprising colloidal lysozyme-templated gold nanoclusters as nanofillers in poly(ethylene oxide)/poly(butylene terephthalate) amphiphilic polymer matrix. Mesoporous silica nanoparticles-lysozyme functionalized gold nanoclusters disperse homogenously within the polymer matrix with no phase separation and zero NPs leaching. This mixed-matrix coating can successfully sense and inhibit bacterial contamination via a controlled release mechanism that is only triggered by bacteria. The system is coated on a common radiographic dental imaging device (photostimulable phosphor plate) that is prone to oral bacteria contamination. Variation and eventually disappearance of the red fluorescence surface under UV light signals bacterial infection. Kanamycin, an antimicrobial agent, is controllably released to instantly inhibit bacterial growth. Interestingly, the quality of the images obtained with these coated surfaces is the same as uncoated surfaces and thus the safe application of such smart coatings can be expanded to include other medical devices without compromising their utility.

Colloidal Gold Nanoclusters Spiked Silica Fillers in Mixed Matrix Coatings: Simultaneous Detection and Inhibition of Healthcare-Associated Infections

KAUST Repository

Alsaiari, Shahad K.; Hammami, Mohamed Amen; Croissant, Jonas G.; Omar, Haneen; Neelakanda, Pradeep; Yapici, Tahir; Peinemann, Klaus-Viktor; Khashab, Niveen M.

2017-01-01

Healthcare-associated infections (HAIs) are the infections that patients get while receiving medical treatment in a medical facility with bacterial HAIs being the most common. Silver and gold nanoparticles (NPs) have been successfully employed as antibacterial motifs; however, NPs leaching in addition to poor dispersion and overall reproducibility are major hurdles to further product development. In this study, the authors design and fabricate a smart antibacterial mixed-matrix membrane coating comprising colloidal lysozyme-templated gold nanoclusters as nanofillers in poly(ethylene oxide)/poly(butylene terephthalate) amphiphilic polymer matrix. Mesoporous silica nanoparticles-lysozyme functionalized gold nanoclusters disperse homogenously within the polymer matrix with no phase separation and zero NPs leaching. This mixed-matrix coating can successfully sense and inhibit bacterial contamination via a controlled release mechanism that is only triggered by bacteria. The system is coated on a common radiographic dental imaging device (photostimulable phosphor plate) that is prone to oral bacteria contamination. Variation and eventually disappearance of the red fluorescence surface under UV light signals bacterial infection. Kanamycin, an antimicrobial agent, is controllably released to instantly inhibit bacterial growth. Interestingly, the quality of the images obtained with these coated surfaces is the same as uncoated surfaces and thus the safe application of such smart coatings can be expanded to include other medical devices without compromising their utility.

MoS_2/Ag nanohybrid: A novel matrix with synergistic effect for small molecule drugs analysis by negative-ion matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

International Nuclear Information System (INIS)

Zhao, Yaju; Deng, Guoqing; Liu, Xiaohui; Sun, Liang; Li, Hui; Cheng, Quan; Xi, Kai; Xu, Danke

2016-01-01

This paper reports a facile synthesis of molybdenum disulfide nanosheets/silver nanoparticles (MoS_2/Ag) hybrid and its use as an effective matrix in negative ion matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The nanohybrid exerts a strong synergistic effect, leading to high performance detection of small molecule analytes including amino acids, peptides, fatty acids and drugs. The enhancement of laser desorption/ionization (LDI) efficiency is largely attributed to the high surface roughness and large surface area for analyte adsorption, better dispersibility, increased thermal conductivity and enhanced UV energy absorption as compared to pure MoS_2. Moreover, both Ag nanoparticles and the edge of the MoS_2 layers function as deprotonation sites for proton capture, facilitating the charging process in negative ion mode and promoting formation of negative ions. As a result, the MoS_2/Ag nanohybrid proves to be a highly attractive matrix in MALDI-TOF MS, with desired features such as high desorption/ionization efficiency, low fragmentation interference, high salt tolerance, and no sweet-spots for mass signal. These characteristic properties allowed for simultaneous analysis of eight different drugs and quantification of acetylsalicylic acid in the spiked human serum. This work demonstrates for the first time the fabrication and application of a novel MoS_2/Ag hybrid, and provides a new platform for use in the rapid and high throughput analysis of small molecules by mass spectrometry. - Highlights: • MoS_2/Ag nanohybrid was applied as a novel matrix in negative-ion MALDI-TOF MS. • The MoS_2/Ag nanohybrid exerted synergistic effect on the detection of small molecules. • The MoS_2/Ag nanohybrid showed good signal reproducibility and low background interferences comparing to organic matrices. • MoS_2/Ag allows simultaneous analysis of multiple drugs and quantification of acetylsalicylic acid in spiked serum samples.

Basalt FRP Spike Repairing of Wood Beams

Directory of Open Access Journals (Sweden)

Luca Righetti

2015-08-01

Full Text Available This article describes aspects within an experimental program aimed at improving the structural performance of cracked solid fir-wood beams repaired with Basalt Fiber Reinforced Polymer (BFRP spikes. Fir wood is characterized by its low density, low compression strength, and high level of defects, and it is likely to distort when dried and tends to fail under tension due to the presence of cracks, knots, or grain deviation. The proposed repair technique consists of the insertion of BFRP spikes into timber beams to restore the continuity of cracked sections. The experimental efforts deal with the evaluation of the bending strength and deformation properties of 24 timber beams. An artificially simulated cracking was produced by cutting the wood beams in half or notching. The obtained results for the repaired beams were compared with those of solid undamaged and damaged beams, and increases of beam capacity, bending strength and of modulus of elasticity, and analysis of failure modes was discussed. For notched beams, the application of the BFRP spikes was able to restore the original bending capacity of undamaged beams, while only a small part of the original capacity was recovered for beams that were cut in half.

Diallel analysis to study the genetic makeup of spike and yield ...

African Journals Online (AJOL)

African Journal of Biotechnology ... Five wheat genotypes were crossed in complete diallel fashion for gene ... by pursuing pedigree method while heterosis can be exploited for spike length, grain weight per spike and grain yield per plant.

The influence of single bursts versus single spikes at excitatory dendrodendritic synapses.

Science.gov (United States)

Masurkar, Arjun V; Chen, Wei R

2012-02-01

The synchronization of neuronal activity is thought to enhance information processing. There is much evidence supporting rhythmically bursting external tufted cells (ETCs) of the rodent olfactory bulb glomeruli coordinating the activation of glomerular interneurons and mitral cells via dendrodendritic excitation. However, as bursting has variable significance at axodendritic cortical synapses, it is not clear if ETC bursting imparts a specific functional advantage over the preliminary spike in dendrodendritic synaptic networks. To answer this question, we investigated the influence of single ETC bursts and spikes with the in vitro rat olfactory bulb preparation at different levels of processing, via calcium imaging of presynaptic ETC dendrites, dual electrical recording of ETC -interneuron synaptic pairs, and multicellular calcium imaging of ETC-induced population activity. Our findings supported single ETC bursts, versus single spikes, driving robust presynaptic calcium signaling, which in turn was associated with profound extension of the initial monosynaptic spike-driven dendrodendritic excitatory postsynaptic potential. This extension could be driven by either the spike-dependent or spike-independent components of the burst. At the population level, burst-induced excitation was more widespread and reliable compared with single spikes. This further supports the ETC network, in part due to a functional advantage of bursting at excitatory dendrodendritic synapses, coordinating synchronous activity at behaviorally relevant frequencies related to odor processing in vivo. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Coherent and intermittent ensemble oscillations emerge from networks of irregular spiking neurons.

Science.gov (United States)

Hoseini, Mahmood S; Wessel, Ralf

2016-01-01

Local field potential (LFP) recordings from spatially distant cortical circuits reveal episodes of coherent gamma oscillations that are intermittent, and of variable peak frequency and duration. Concurrently, single neuron spiking remains largely irregular and of low rate. The underlying potential mechanisms of this emergent network activity have long been debated. Here we reproduce such intermittent ensemble oscillations in a model network, consisting of excitatory and inhibitory model neurons with the characteristics of regular-spiking (RS) pyramidal neurons, and fast-spiking (FS) and low-threshold spiking (LTS) interneurons. We find that fluctuations in the external inputs trigger reciprocally connected and irregularly spiking RS and FS neurons in episodes of ensemble oscillations, which are terminated by the recruitment of the LTS population with concurrent accumulation of inhibitory conductance in both RS and FS neurons. The model qualitatively reproduces experimentally observed phase drift, oscillation episode duration distributions, variation in the peak frequency, and the concurrent irregular single-neuron spiking at low rate. Furthermore, consistent with previous experimental studies using optogenetic manipulation, periodic activation of FS, but not RS, model neurons causes enhancement of gamma oscillations. In addition, increasing the coupling between two model networks from low to high reveals a transition from independent intermittent oscillations to coherent intermittent oscillations. In conclusion, the model network suggests biologically plausible mechanisms for the generation of episodes of coherent intermittent ensemble oscillations with irregular spiking neurons in cortical circuits. Copyright © 2016 the American Physiological Society.

Delphine Letort, The Spike Lee Brand: A Study of Documentary Filmmaking

OpenAIRE

Lipson, David

2017-01-01

Spike Lee is known the world over for films like She’s Gotta Have It (1986), School Daze (1988), Do the Right Thing (1989), etc. This association with fiction films is so strong that one could mistakenly think that Delphine Letort’s book The Spike Lee Brand: A Study of Documentary Filmmaking would explore the connection between these fiction films and the documentary genre. However, the first pages of the book clearly indicate that it will focus on Spike Lee the documentary filmmaker. Making ...

Character recognition from trajectory by recurrent spiking neural networks.

Science.gov (United States)

Jiangrong Shen; Kang Lin; Yueming Wang; Gang Pan

2017-07-01

Spiking neural networks are biologically plausible and power-efficient on neuromorphic hardware, while recurrent neural networks have been proven to be efficient on time series data. However, how to use the recurrent property to improve the performance of spiking neural networks is still a problem. This paper proposes a recurrent spiking neural network for character recognition using trajectories. In the network, a new encoding method is designed, in which varying time ranges of input streams are used in different recurrent layers. This is able to improve the generalization ability of our model compared with general encoding methods. The experiments are conducted on four groups of the character data set from University of Edinburgh. The results show that our method can achieve a higher average recognition accuracy than existing methods.

Space-filling, multifractal, localized thermal spikes in Si, Ge and ZnO

Science.gov (United States)

Ahmad, Shoaib; Abbas, Muhammad Sabtain; Yousuf, Muhammad; Javeed, Sumera; Zeeshan, Sumaira; Yaqub, Kashif

2018-04-01

The mechanism responsible for the emission of clusters from heavy ion irradiated solids is proposed to be thermal spikes. Collision cascade-based theories describe atomic sputtering but cannot explain the consistently observed experimental evidence for significant cluster emission. Statistical thermodynamic arguments for thermal spikes are employed here for qualitative and quantitative estimation of the thermal spike-induced cluster emission from Si, Ge and ZnO. The evolving cascades and spikes in elemental and molecular semiconducting solids are shown to have fractal characteristics. Power law potential is used to calculate the fractal dimension. With the loss of recoiling particles' energy the successive branching ratios get smaller. The fractal dimension is shown to be dependent upon the exponent of the power law interatomic potential D = 1/2m. Each irradiating ion has the probability of initiating a space-filling, multifractal thermal spike that may sublime a localized region near the surface by emitting clusters in relative ratios that depend upon the energies of formation of respective surface vacancies.

Modulation of the spike activity of neocortex neurons during a conditioned reflex.

Science.gov (United States)

Storozhuk, V M; Sanzharovskii, A V; Sachenko, V V; Busel, B I

2000-01-01

Experiments were conducted on cats to study the effects of iontophoretic application of glutamate and a number of modulators on the spike activity of neurons in the sensorimotor cortex during a conditioned reflex. These studies showed that glutamate, as well as exerting a direct influence on neuron spike activity, also had a delayed facilitatory action lasting 10-20 min after iontophoresis was finished. Adrenomimetics were found to have a double modulatory effect on intracortical glutamate connections: inhibitory and facilitatory effects were mediated by beta1 and beta2 adrenoceptors respectively. Although dopamine, like glutamate, facilitated neuron spike activity during the period of application, the simultaneous facilitatory actions of glutamate and L-DOPA were accompanied by occlusion of spike activity, and simultaneous application of glutamate and haloperidol suppressed spike activity associated with the conditioned reflex response. Facilitation thus appears to show a significant level of dependence on metabotropic glutamate receptors which, like dopamine receptors, are linked to the intracellular medium via Gi proteins.

Mixed signal learning by spike correlation propagation in feedback inhibitory circuits.

Directory of Open Access Journals (Sweden)

Naoki Hiratani

2015-04-01

Full Text Available The brain can learn and detect mixed input signals masked by various types of noise, and spike-timing-dependent plasticity (STDP is the candidate synaptic level mechanism. Because sensory inputs typically have spike correlation, and local circuits have dense feedback connections, input spikes cause the propagation of spike correlation in lateral circuits; however, it is largely unknown how this secondary correlation generated by lateral circuits influences learning processes through STDP, or whether it is beneficial to achieve efficient spike-based learning from uncertain stimuli. To explore the answers to these questions, we construct models of feedforward networks with lateral inhibitory circuits and study how propagated correlation influences STDP learning, and what kind of learning algorithm such circuits achieve. We derive analytical conditions at which neurons detect minor signals with STDP, and show that depending on the origin of the noise, different correlation timescales are useful for learning. In particular, we show that non-precise spike correlation is beneficial for learning in the presence of cross-talk noise. We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. In addition, we demonstrate that the model performs blind source separation in a manner similar to the sequential sampling approximation of the Bayesian independent component analysis algorithm. Our results provide a basic understanding of STDP learning in feedback circuits by integrating analyses from both dynamical systems and information theory.

Optimization of ciguatoxin extraction method from blood for Pacific ciguatoxin (P-CTX-1).

Science.gov (United States)

Bottein Dechraoui, Marie-Yasmine; Wang, Zhihong; Ramsdell, John S

2007-01-01

Ciguatera diagnosis relies on clinical observations associated with a recent consumption of fish. Although needed, direct confirmation of exposure in subjects showing ciguatera disease symptoms is currently unavailable. We previously reported that ciguatoxins were measurable in the blood of mice exposed to extracts of Pacific ciguatoxins isolated from Gambierdiscus polynesiensis, and of Indian Ocean or Caribbean Sea ciguatoxins, isolated from fish. Although highly efficient for extracting spiked purified Caribbean-CTX-1, the methanolic extraction method previously described is found here to yield only 6% recovery of spiked Pacific-CTX-1 (P-CTX-1). We report in this short communication a substantially modified method for ciguatoxin extraction from both dried and fresh blood. With this method, toxin measurement is directly accomplished in acetonitrile deproteinated whole fresh blood or phosphate buffer solution (PBS) eluted dried blood using the N2A cell-based assay. Spike studies using increasing concentrations of purified ciguatoxins reveal linear (r2 above 0.87 for all toxins) and overall efficient toxin recoveries (62%, 96%, and 96% from fresh blood and 75%, 90%, and 74% from dried blood, for C-CTX-1, P-CTX-3C, and P-CTX-1, respectively). Comparative blood matrix analysis for P-CTX-1 recovery shows increased recovery of ciguatoxin activity from whole fresh blood than from dried blood, greater by 20% in P-CTX-1 spiked mice blood and by over 85% in P-CTX-1 exposed mouse blood. In conclusion, both Caribbean and Pacific ciguatoxins can be readily extracted from blood using this modified method; however, in the case of P-CTX-1 we find that fresh blood is optimal.

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.

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.

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.

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.

A 16-Channel Nonparametric Spike Detection ASIC Based on EC-PC Decomposition.

Science.gov (United States)

Wu, Tong; Xu, Jian; Lian, Yong; Khalili, Azam; Rastegarnia, Amir; Guan, Cuntai; Yang, Zhi

2016-02-01

In extracellular neural recording experiments, detecting neural spikes is an important step for reliable information decoding. A successful implementation in integrated circuits can achieve substantial data volume reduction, potentially enabling a wireless operation and closed-loop system. In this paper, we report a 16-channel neural spike detection chip based on a customized spike detection method named as exponential component-polynomial component (EC-PC) algorithm. This algorithm features a reliable prediction of spikes by applying a probability threshold. The chip takes raw data as input and outputs three data streams simultaneously: field potentials, band-pass filtered neural data, and spiking probability maps. The algorithm parameters are on-chip configured automatically based on input data, which avoids manual parameter tuning. The chip has been tested with both in vivo experiments for functional verification and bench-top experiments for quantitative performance assessment. The system has a total power consumption of 1.36 mW and occupies an area of 6.71 mm (2) for 16 channels. When tested on synthesized datasets with spikes and noise segments extracted from in vivo preparations and scaled according to required precisions, the chip outperforms other detectors. A credit card sized prototype board is developed to provide power and data management through a USB port.

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

Science.gov (United States)

Kulkarni, Shruti R; Rajendran, Bipin

2018-07-01

We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse biological spike rates below 300Hz achieves a classification accuracy of 98.17% on the MNIST test database with four times fewer parameters compared to the state-of-the-art. We present several insights from extensive numerical experiments regarding optimization of learning parameters and network configuration to improve its accuracy. We also describe a number of strategies to optimize the SNN for implementation in memory and energy constrained hardware, including approximations in computing the neuronal dynamics and reduced precision in storing the synaptic weights. Experiments reveal that even with 3-bit synaptic weights, the classification accuracy of the designed SNN does not degrade beyond 1% as compared to the floating-point baseline. Further, the proposed SNN, which is trained based on the precise spike timing information outperforms an equivalent non-spiking artificial neural network (ANN) trained using back propagation, especially at low bit precision. Thus, our study shows the potential for realizing efficient neuromorphic systems that use spike based information encoding and learning for real-world applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Inference of neuronal network spike dynamics and topology from calcium imaging data

Directory of Open Access Journals (Sweden)

Henry eLütcke

2013-12-01

Full Text Available Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP occurrence ('spike trains' from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR and acquisition rate affect spike inference and whether additional information about network structure can be extracted. Here we present a simulation framework for quantitatively assessing how well spike dynamics and network topology can be inferred from noisy calcium imaging data. For simulated AP-evoked calcium transients in neocortical pyramidal cells, we analyzed the quality of spike inference as a function of SNR and data acquisition rate using a recently introduced peeling algorithm. Given experimentally attainable values of SNR and acquisition rate, neural spike trains could be reconstructed accurately and with up to millisecond precision. We then applied statistical neuronal network models to explore how remaining uncertainties in spike inference affect estimates of network connectivity and topological features of network organization. We define the experimental conditions suitable for inferring whether the network has a scale-free structure and determine how well hub neurons can be identified. Our findings provide a benchmark for future calcium imaging studies that aim to reliably infer neuronal network properties.

Noise influence on spike activation in a Hindmarsh–Rose small-world neural network

International Nuclear Information System (INIS)

Zhe, Sun; Micheletto, Ruggero

2016-01-01

We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh−Rose (H−R) neural networks. (paper)

Noise influence on spike activation in a Hindmarsh-Rose small-world neural network

Science.gov (United States)

Zhe, Sun; Micheletto, Ruggero

2016-07-01

We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh-Rose (H-R) neural networks.

MoS{sub 2}/Ag nanohybrid: A novel matrix with synergistic effect for small molecule drugs analysis by negative-ion matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Energy Technology Data Exchange (ETDEWEB)

Zhao, Yaju, E-mail: daisy19900911@hotmail.com [State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023 (China); Deng, Guoqing, E-mail: denggqq@sina.com [Department of Polymer Science and Engineering, Nanjing University, Nanjing, 210023 (China); Liu, Xiaohui, E-mail: lcswyh@126.com [State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023 (China); Sun, Liang, E-mail: sunliang@nju.edu.cn [State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023 (China); Li, Hui, E-mail: lihui@nju.edu.cn [State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023 (China); Cheng, Quan, E-mail: quan.cheng@ucr.edu [Department of Chemistry, University of California, Riverside, CA, 92521 (United States); Xi, Kai, E-mail: xikai@nju.edu.cn [Department of Polymer Science and Engineering, Nanjing University, Nanjing, 210023 (China); Xu, Danke, E-mail: xudanke@nju.edu.cn [State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023 (China)

2016-09-21

This paper reports a facile synthesis of molybdenum disulfide nanosheets/silver nanoparticles (MoS{sub 2}/Ag) hybrid and its use as an effective matrix in negative ion matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The nanohybrid exerts a strong synergistic effect, leading to high performance detection of small molecule analytes including amino acids, peptides, fatty acids and drugs. The enhancement of laser desorption/ionization (LDI) efficiency is largely attributed to the high surface roughness and large surface area for analyte adsorption, better dispersibility, increased thermal conductivity and enhanced UV energy absorption as compared to pure MoS{sub 2}. Moreover, both Ag nanoparticles and the edge of the MoS{sub 2} layers function as deprotonation sites for proton capture, facilitating the charging process in negative ion mode and promoting formation of negative ions. As a result, the MoS{sub 2}/Ag nanohybrid proves to be a highly attractive matrix in MALDI-TOF MS, with desired features such as high desorption/ionization efficiency, low fragmentation interference, high salt tolerance, and no sweet-spots for mass signal. These characteristic properties allowed for simultaneous analysis of eight different drugs and quantification of acetylsalicylic acid in the spiked human serum. This work demonstrates for the first time the fabrication and application of a novel MoS{sub 2}/Ag hybrid, and provides a new platform for use in the rapid and high throughput analysis of small molecules by mass spectrometry. - Highlights: • MoS{sub 2}/Ag nanohybrid was applied as a novel matrix in negative-ion MALDI-TOF MS. • The MoS{sub 2}/Ag nanohybrid exerted synergistic effect on the detection of small molecules. • The MoS{sub 2}/Ag nanohybrid showed good signal reproducibility and low background interferences comparing to organic matrices. • MoS{sub 2}/Ag allows simultaneous analysis of multiple drugs and quantification of

Simulation of petroleum recovery in naturally fractured reservoirs: physical process representation

Energy Technology Data Exchange (ETDEWEB)

Paiva, Hernani P.; Miranda Filho, Daniel N. de [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil); Schiozer, Denis J. [Universidade Estadual de Campinas (UNICAMP), SP (Brazil)

2012-07-01

The naturally fractured reservoir recovery normally involves risk especially in intermediate to oil wet systems because of the simulations poor efficiency results under waterflood displacement. Double-porosity models are generally used in fractured reservoir simulation and have been implemented in the major commercial reservoir simulators. The physical processes acting in petroleum recovery are represented in double-porosity models by matrix-fracture transfer functions, therefore commercial simulators have their own implementations, and as a result different kinetics and final recoveries are attained. In this work, a double porosity simulator was built with Kazemi et al. (1976), Sabathier et al. (1998) and Lu et al. (2008) transfer function implementations and their recovery results have been compared using waterflood displacement in oil-wet or intermediate-wet systems. The results of transfer function comparisons have showed recovery improvements in oil-wet or intermediate-wet systems under different physical processes combination, particularly in fully discontinuous porous medium when concurrent imbibition takes place, coherent with Firoozabadi (2000) experimental results. Furthermore, the implemented transfer functions, related to a double-porosity model, have been compared to double-porosity commercial simulator model, as well a discrete fracture model with refined grid, showing differences between them. Waterflood can be an effective recovery method even in fully discontinuous media for oil-wet or intermediate-wet systems where concurrent imbibition takes place with high enough pressure gradients across the matrix blocks. (author)

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.

Evaluation of accelerated solvent extraction for butyltin speciation in PACS-2 CRM using double-spike isotope dilution-GC/ICPMS.

Science.gov (United States)

Encinar, Jorge Ruiz; Rodriguez-González, Pablo; Fernandez, Julio Rodriguez; Alonso, J Ignacio Garcia; Díez, Sergi; Bayona, Josep M; Sanz-Medelt, Alfredo

2002-10-15

Pressurized liquid extraction using the accelerated solvent extractor (Dionex ASE 200) has been evaluated for the determination of mono-, di- and tributyltin (MBT, DBT, and TBT, respectively) in PACS-2 certified reference material. A double-enriched spike containing 119Sn-enriched MBT and TBT and 118Sn-enriched DBT allowed for the simultaneous determination of the three butyltin species and the factors governing species interconversion. The stability of the spike was evaluated by reverse isotope dilution experiments covering more than one year with satisfactory results. Quantitative recoveries using ASE for TBT and DBT were obtained at temperatures above 110 degrees C. The effect of the extraction time and number of static cycles was evaluated. Results suggest that extraction efficiency was quantitative with extraction times as low as 10 min for all butyltin species at 110 degrees C. Decomposition reactions were only detected at the higher temperatures assayed (140 and 175 degrees C) and that was only for the degradation of DBT to MBT (approximately 4%). The results found for MBT were approximately 25% higher than the certified value for the PACS-2 sediment reference material in agreement with previous results obtained by ultrasonic and microwave assisted extraction.

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.

Origin of frontal lobe spikes in the early onset benign occipital lobe epilepsy (Panayiotopoulos syndrome).

Science.gov (United States)

Leal, Alberto J R; Ferreira, José C; Dias, Ana I; Calado, Eulália

2008-09-01

Early onset benign occipital lobe epilepsy (Panayiotopoulos syndrome [PS]) is a common and easily recognizable epilepsy. Interictal EEG spike activity is often multifocal but most frequently localized in the occipital lobes. The origin and clinical significance of the extra-occipital spikes remain poorly understood. Three patients with the PS and interictal EEG spikes with frontal lobe topography were studied using high-resolution EEG. Independent component analysis (ICA) was used to decompose the spikes in components with distinct temporal dynamics. The components were mapped in the scalp with a spline-laplacian algorithm. The change in scalp potential topography from spike onset to peak, suggests the contribution of several intracranial generators, with different kinetics of activation and significant overlap. ICA was able to separate the major contributors to frontal spikes and consistently revealed an early activating group of components over the occipital areas in all the patients. The local origin of these early potentials was established by the spline-laplacian montage. Frontal spikes in PS are consistently associated with early and unilateral occipital lobe activation, suggesting a postero-anterior spike propagation. Frontal spikes in the PS represent a secondary activation triggered by occipital interictal discharges and do not represent an independent focus.

Analysis of a rhenium-osmium solid-solution spike by inductively coupled plasma mass spectrometry

International Nuclear Information System (INIS)

Richardson, J.M.; Dickin, A.P.; McNutt, R.H.; McAndrew, J.I.; Beneteau, S.B.

1989-01-01

The rhenium-osmium decay scheme ( 187 Re → 187 Os) offers a unique opportunity to investigate the genesis of, and directly date, ultramafic rock, sulphide and platinum ore deposits. Inductively coupled plasma mass spectrometry (ICP-MS) is a viable method for Os isotopic analysis as it provides the high temperatures necessary to ionise Os. The sample can be introduced into the ICP mass spectrometer either by conventional nebulisation or by distillation using an Os ''generator''. Generator-mode analysis is superior to nebulisation because (i) the total number of counts is two orders of magnitude higher for a given sample size and (ii) Os oxidises readily to volatile OsO 4 , which has a boiling-point lower than most Re compounds, so that Os can be efficiently separated from 187 Re. The importance of Os loss during sample preparation was minimised by combining the sample powder with a powdered Re-Os isotopic spike prior to any chemical treatment. The spike is in a nickel sulphide matrix and was made by a thioacetamide co-precipitation of Os, Re and Ni followed by a fire assay. The 190 Os/ 192 Os ratio in this preparation is 51.5 ± 0.9, the 187 Os 188 Os ratio is 0.063 ± 0.006 and the 185 Re/ 187 Re ratio is 17.04 - + 0.41. These values are within the error limits quoted by the Oak Ridge National Laboratory and Techsnabexport, the suppliers of the metallic spikes. Parameters that significantly influence the Os analyses include the oxidising agent and the run temperature. The oxidising agent H 5 IO 6 is preferred to H 2 O 2 or HNO 3 as it has the highest electrode potential, provides a prolonged, consistent reaction and is more stable when stored. Chilling the sample and the H 5 IO 6 , initially retards uncontrolled OsO 4 emission. Heat applied later in the run releases OsO 4 and helps maintain a high count rate. Thus, OsO 4 can be generated in a steady, controllable and reproducible manner. (author)

The coronavirus spike protein : mechanisms of membrane fusion and virion incorporation

NARCIS (Netherlands)

Bosch, B.J.

2004-01-01

The coronavirus spike protein is a membrane-anchored glycoprotein responsible for virus-cell attachment and membrane fusion, prerequisites for a successful virus infection. In this thesis, two aspects are described regarding the molecular biology of the coronavirus spike protein: its membrane fusion

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

DEFF Research Database (Denmark)

Gronlykke, L.; Knudsen, M.L.; Hogenhaven, H.

2008-01-01

. Electrocorticography (ECoG) recordings were performed on the intraventricular hippocampus and from the anterior inferior temporal and lateral neocortex before and after a 300 microg intravenous bolus of remifentanil. Spike activity was quantified as spike-count per minute. RESULTS: A significant increase (P

Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory; determination of dissolved arsenic, boron, lithium, selenium, strontium, thallium, and vanadium using inductively coupled plasma-mass spectrometry

Science.gov (United States)

Garbarino, John R.

1999-01-01

The inductively coupled plasma?mass spectrometric (ICP?MS) methods have been expanded to include the determination of dissolved arsenic, boron, lithium, selenium, strontium, thallium, and vanadium in filtered, acidified natural water. Method detection limits for these elements are now 10 to 200 times lower than by former U.S. Geological Survey (USGS) methods, thus providing lower variability at ambient concentrations. The bias and variability of the method was determined by using results from spike recoveries, standard reference materials, and validation samples. Spike recoveries at 5 to 10 times the method detection limit and 75 micrograms per liter in reagent-water, surface-water, and groundwater matrices averaged 93 percent for seven replicates, although selected elemental recoveries in a ground-water matrix with an extremely high iron sulfate concentration were negatively biased by 30 percent. Results for standard reference materials were within 1 standard deviation of the most probable value. Statistical analysis of the results from about 60 filtered, acidified natural-water samples indicated that there was no significant difference between ICP?MS and former USGS official methods of analysis.

Tritium uptake in rainbow trout (Oncorhynchus mykiss): HTO and OBT-spiked feed exposures simultaneously

International Nuclear Information System (INIS)

Kim, S.B.; Shultz, C.; Stuart, M.; Festarini, A.

2015-01-01

There is currently considerable interest in organically bound tritium (OBT) formation in edible fish. The major questions revolve around whether or not tritium can accumulate in fish after being released into aquatic environments. Since OBT formation rates in large, edible fish are poorly understood, rainbow trout (Oncorhynchus mykiss) studies, where fish were simultaneously exposed to tritiated water (HTO) and OBT-spiked feed over 130 days, were conducted to evaluate tritium uptake. The measured HTO activity concentrations in fish tissue confirmed that HTO in fish tissue equilibrates quickly with HTO in tank water. The data obtained also confirmed that OBT uptake is faster when fish are ingesting OBT-spiked feed compared to when fish are living in tritiated water (and consuming non-OBT-spiked feed). The difference between the two exposure types is such that the groups exposed to tritiated water and OBT-spiked feed simultaneously were showing the same uptake rates as OBT-spiked feed only exposures. Contrary to what was expected, the rate of OBT uptake (from OBT-spiked feed) seemed to be higher in slow growing fish compared to fast growing fish. Another observation from these studies was that OBT activity concentrations in all organs (viscera) had a tendency to be higher than OBT activity concentrations measured in fish flesh. - Highlights: • Edible size of rainbow trout (Oncorhynchus mykiss) were simultaneously exposed to tritiated water (HTO) and OBT-spiked feed over 130 days. • OBT uptake is faster when fish are ingesting OBT-spiked feed compared to when fish are living in tritiated water (and consuming non-OBT-spiked feed). • The rate of OBT uptake (from OBT-spiked feed) seemed to be higher in slow growing fish compared to fast growing fish

EFFECTS OF DIFFERENT GROWING CONDITIONS ON THE MORPHOLOGICAL FEATURES OF THE SPIKE OF HEXAPLOID TRITICALE

Directory of Open Access Journals (Sweden)

K. U. Kurkiev

2016-01-01

Full Text Available Aim. The aim is to study the effect of different environmental conditions on the morphological traits of the spike of hexaploid triticale varieties.Methods. We analyzed 507 samples of triticale of various eco-geographical origins, in different years of study and at different seeding times. To investigate the influence of environmental conditions on the phenotypic expression of the studied traits we held a comparative analysis of the spike of two years and, in addition, of spring triticale during winter and spring crops. Analysis on the features was carried out on the main spikes. We studied the following morphological characteristics of the spike: length, number of spikelets and density.Results and discussion. The study of differences in individual variety samples showed that more than 60% triticale samples had significant differences in the length of the spike, depending on the weather conditions of the year – with the winter crops number of spikelets per spike was significantly higher than with the spring crops. A comparative analysis of the impact of the weather conditions of the year on triticale showed that significant differences in the density of the spike were observed in less than 30%.Conclusion. Study of the influence of conditions of the year and sowing dates on the main features of the spike of triticale showed that the density of the spike is the least affected by the external environment. The length of the spikes and the number of spikelets per spike differed significantly when growing in a various conditions.

A neuro-inspired spike-based PID motor controller for multi-motor robots with low cost FPGAs.

Science.gov (United States)

Jimenez-Fernandez, Angel; Jimenez-Moreno, Gabriel; Linares-Barranco, Alejandro; Dominguez-Morales, Manuel J; Paz-Vicente, Rafael; Civit-Balcells, Anton

2012-01-01

In this paper we present a neuro-inspired spike-based close-loop controller written in VHDL and implemented for FPGAs. This controller has been focused on controlling a DC motor speed, but only using spikes for information representation, processing and DC motor driving. It could be applied to other motors with proper driver adaptation. This controller architecture represents one of the latest layers in a Spiking Neural Network (SNN), which implements a bridge between robotics actuators and spike-based processing layers and sensors. The presented control system fuses actuation and sensors information as spikes streams, processing these spikes in hard real-time, implementing a massively parallel information processing system, through specialized spike-based circuits. This spike-based close-loop controller has been implemented into an AER platform, designed in our labs, that allows direct control of DC motors: the AER-Robot. Experimental results evidence the viability of the implementation of spike-based controllers, and hardware synthesis denotes low hardware requirements that allow replicating this controller in a high number of parallel controllers working together to allow a real-time robot control.

Capturing spike variability in noisy Izhikevich neurons using point process generalized linear models

DEFF Research Database (Denmark)

Østergaard, Jacob; Kramer, Mark A.; Eden, Uri T.

2018-01-01

current. We then fit these spike train datawith a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven...... by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured....... are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input...

Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering.

Science.gov (United States)

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2014-12-30

Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. An automatic unsupervised spike sorting method is proposed in this paper. The method uses features extracted by the locality preserving projection (LPP) algorithm. These features afterwards serve as inputs for the landmark-based spectral clustering (LSC) method. Gap statistics (GS) is employed to evaluate the number of clusters before the LSC can be performed. The proposed LPP-LSC is highly accurate and computationally inexpensive spike sorting approach. LPP spike features are very discriminative; thereby boost the performance of clustering methods. Furthermore, the LSC method exhibits its efficiency when integrated with the cluster evaluator GS. The proposed method's accuracy is approximately 13% superior to that of the benchmark combination between wavelet transformation and superparamagnetic clustering (WT-SPC). Additionally, LPP-LSC computing time is six times less than that of the WT-SPC. LPP-LSC obviously demonstrates a win-win spike sorting solution meeting both accuracy and computational cost criteria. LPP and LSC are linear algorithms that help reduce computational burden and thus their combination can be applied into real-time spike analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

Linking structure and activity in nonlinear spiking networks.

Directory of Open Access Journals (Sweden)

Gabriel Koch Ocker

2017-06-01

Full Text Available Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

Linking structure and activity in nonlinear spiking networks.

Science.gov (United States)

Ocker, Gabriel Koch; Josić, Krešimir; Shea-Brown, Eric; Buice, Michael A

2017-06-01

Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

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)....

Synthesis and application of mesoporous molecular sieve for miniaturized matrix solid-phase dispersion extraction of bioactive flavonoids from toothpaste, plant, and saliva.

Science.gov (United States)

Cao, Wan; Cao, Jun; Ye, Li-Hong; Xu, Jing-Jing; Hu, Shuai-Shuai; Peng, Li-Qing

2015-12-01

This article describes the use of the mesoporous molecular sieve KIT-6 as a sorbent in miniaturized matrix solid-phase dispersion (MSPD) in combination with ultra-performance LC for the determination of bioactive flavonoids in toothpaste, Scutellariae Radix, and saliva. In this study, for the first time, KIT-6 was used as a sorbent material for this mode of extraction. Compared with common silica-based sorbents (C18 and activated silica gel), the proposed KIT-6 dispersant with a three-dimensional cubic Ia3d structure and highly ordered arrays of mesoporous channels exhibits excellent adsorption capability of the tested compounds. In addition, several experimental variables, such as the mass ratio of sample to dispersant, grinding time, and elution solvent, were optimized to maximize the extraction efficiency. The proposed analytical method is simple, fast, and entails low consumption of samples, dispersants and elution solvents, thereby meeting "green chemistry" requirements. Under the optimized conditions, the recoveries of three bioactive flavonoids obtained by analyzing the spiked samples were from 89.22 to 101.17%. Also, the LODs and LOQs for determining the analytes were in the range of 0.02-0.04 μg/mL and 0.07-0.13 μg/mL, respectively. Finally, the miniaturized matrix solid-phase dispersion method was successfully applied to the analysis of target solutes in real samples, and satisfactory results were obtained. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Reconstructing stimuli from the spike-times of leaky integrate and fire neurons

Directory of Open Access Journals (Sweden)

Sebastian eGerwinn

2011-02-01

Full Text Available Reconstructing stimuli from the spike-trains of neurons is an important approach for understanding the neural code. One of the difficulties associated with this task is that signals which are varying continuously in time are encoded into sequences of discrete events or spikes. An important problem is to determine how much information about the continuously varying stimulus can be extracted from the time-points at which spikes were observed, especially if these time-points are subject to some sort of randomness. For the special case of spike trains generated by leaky integrate and fire neurons, noise can be introduced by allowing variations in the threshold every time a spike is released. A simple decoding algorithm previously derived for the noiseless case can be extended to the stochastic case, but turns out to be biased. Here, we review a solution to this problem, by presenting a simple yet efficient algorithm which greatly reduces the bias, and therefore leads to better decoding performance in the stochastic case.

Evaluation of ammonium bifluoride fusion for rapid dissolution in post-detonation nuclear forensic analysis

International Nuclear Information System (INIS)

Hubley, Nicholas T.; Brockman, John D.; Robertson, J. David; Missouri Univ., Columbia, MO

2017-01-01

Dissolution of geological reference materials by fusion with ammonium bifluoride, NH_4HF_2 or ABF, was evaluated for its potential use in post-detonation nuclear forensics. The fusion procedure was optimized such that the total dissolution time was <3 h without compromising recovery. Geological reference materials containing various levels of silicates were dissolved and measured by ICP-MS to quantify elemental recovery. Dissolutions of NIST 278 obsidian and urban canyon matrix were performed with radiotracer spikes to measure potential loss of volatile elements during the fusion procedure via gamma-ray spectroscopy. Elemental percent recoveries obtained by ICP-MS were found to be 80-120% while recoveries of radiotracers were observed to be 90-100% with the exception of iodine.

Evaluation of ammonium bifluoride fusion for rapid dissolution in post-detonation nuclear forensic analysis

Energy Technology Data Exchange (ETDEWEB)

Hubley, Nicholas T. [Missouri Univ., Columbia, MO (United States). Dept. of Chemistry; Brockman, John D. [Missouri Univ., Columbia, MO (United States). Research Reactor Center; Robertson, J. David [Missouri Univ., Columbia, MO (United States). Research Reactor Center; Missouri Univ., Columbia, MO (United States). Dept. of Chemistry

2017-10-01

Dissolution of geological reference materials by fusion with ammonium bifluoride, NH{sub 4}HF{sub 2} or ABF, was evaluated for its potential use in post-detonation nuclear forensics. The fusion procedure was optimized such that the total dissolution time was <3 h without compromising recovery. Geological reference materials containing various levels of silicates were dissolved and measured by ICP-MS to quantify elemental recovery. Dissolutions of NIST 278 obsidian and urban canyon matrix were performed with radiotracer spikes to measure potential loss of volatile elements during the fusion procedure via gamma-ray spectroscopy. Elemental percent recoveries obtained by ICP-MS were found to be 80-120% while recoveries of radiotracers were observed to be 90-100% with the exception of iodine.

Phasic spike patterning in rat supraoptic neurones in vivo and in vitro

Science.gov (United States)

Sabatier, Nancy; Brown, Colin H; Ludwig, Mike; Leng, Gareth

2004-01-01

In vivo, most vasopressin cells of the hypothalamic supraoptic nucleus fire action potentials in a ‘phasic’ pattern when the systemic osmotic pressure is elevated, while most oxytocin cells fire continuously. The phasic firing pattern is believed to arise as a consequence of intrinsic activity-dependent changes in membrane potential, and these have been extensively studied in vitro. Here we analysed the discharge patterning of supraoptic nucleus neurones in vivo, to infer the characteristics of the post-spike sequence of hyperpolarization and depolarization from the observed spike patterning. We then compared patterning in phasic cells in vivo and in vitro, and we found systematic differences in the interspike interval distributions, and in other statistical parameters that characterized activity patterns within bursts. Analysis of hazard functions (probability of spike initiation as a function of time since the preceding spike) revealed that phasic firing in vitro appears consistent with a regenerative process arising from a relatively slow, late depolarizing afterpotential that approaches or exceeds spike threshold. By contrast, in vivo activity appears to be dominated by stochastic rather than deterministic mechanisms, and appears consistent with a relatively early and fast depolarizing afterpotential that modulates the probability that random synaptic input exceeds spike threshold. Despite superficial similarities in the phasic firing patterns observed in vivo and in vitro, there are thus fundamental differences in the underlying mechanisms. PMID:15146047

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.

Science.gov (United States)

Paraskevopoulou, Sivylla E; Wu, Di; Eftekhar, Amir; Constandinou, Timothy G

2014-09-30

This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

Absolute spike frequency as a predictor of surgical outcome in temporal lobe epilepsy.

Science.gov (United States)

Ngo, Ly; Sperling, Michael R; Skidmore, Christopher; Mintzer, Scott; Nei, Maromi

2017-04-01

Frequent interictal epileptiform abnormalities may correlate with poor prognosis after temporal lobe resection for refractory epilepsy. To date, studies have focused on limited resections such as selective amygdalohippocampectomy and apical temporal lobectomy without hippocampectomy. However, it is unclear whether the frequency of spikes predicts outcome after standard anterior temporal lobectomy. Preoperative scalp video-EEG monitoring data from patients who subsequently underwent anterior temporal lobectomy over a three year period and were followed for at least one year were reviewed for the frequency of interictal epileptiform abnormalities. Surgical outcome for those patients with frequent spikes (>60/h) was compared with those with less frequent spikes. Additionally, spike frequency was evaluated as a continuous variable and correlated with outcome to determine if increased spike frequency correlated with worse outcome, as assessed by modified Engel Class outcome. Forty-seven patients (18 men, 29 women; mean age 40 years at surgery) were included. Forty-six patients had standard anterior temporal lobectomy (24 right, 22 left) and one had a modified left temporal lobectomy. There was no significant difference in seizure outcome between those with frequent (57% Class I) vs. those with less frequent (58% Class I) spikes. Increased spike frequency did not correlate with worse outcome. Greater than 20 complex partial seizures/month and generalized tonic-clonic seizures within one year of surgery correlated with worse outcome. This study suggests that absolute spike frequency does not predict seizure outcome after anterior temporal lobectomy unlike in selective procedures, and should not be used as a prognostic factor in this population. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Grain price spikes and beggar-thy-neighbor policy responses

DEFF Research Database (Denmark)

Jensen, Hans Grinsted; Anderson, Kym

When prices spike in international grain markets, national governments often reduce the extent to which that spike affects their domestic food markets. Those actions exacerbate the price spike and international welfare transfer associated with that terms of trade change. Several recent analyses...... have assessed the extent to which those policies contributed to the 2006-08 international price rise, but only by focusing on one commodity or using a back-of-the envelope (BOTE) method. This paper provides a more-comprehensive analysis using a global economy-wide model that is able to take account...... of the interactions between markets for farm products that are closely related in production and/or consumption, and able to estimate the impacts of those insulating policies on grain prices and on the grain trade and economic welfare of the world’s various countries. Our results support the conclusion from earlier...

A Hybrid Setarx Model for Spikes in Tight Electricity Markets

Directory of Open Access Journals (Sweden)

Carlo Lucheroni

2012-01-01

Full Text Available The paper discusses a simple looking but highly nonlinear regime-switching, self-excited threshold model for hourly electricity prices in continuous and discrete time. The regime structure of the model is linked to organizational features of the market. In continuous time, the model can include spikes without using jumps, by defining stochastic orbits. In passing from continuous time to discrete time, the stochastic orbits survive discretization and can be identified again as spikes. A calibration technique suitable for the discrete version of this model, which does not need deseasonalization or spike filtering, is developed, tested and applied to market data. The discussion of the properties of the model uses phase-space analysis, an approach uncommon in econometrics. (original abstract

A Neuro-Inspired Spike-Based PID Motor Controller for Multi-Motor Robots with Low Cost FPGAs

Directory of Open Access Journals (Sweden)

Anton Civit-Balcells

2012-03-01

Full Text Available In this paper we present a neuro-inspired spike-based close-loop controller written in VHDL and implemented for FPGAs. This controller has been focused on controlling a DC motor speed, but only using spikes for information representation, processing and DC motor driving. It could be applied to other motors with proper driver adaptation. This controller architecture represents one of the latest layers in a Spiking Neural Network (SNN, which implements a bridge between robotics actuators and spike-based processing layers and sensors. The presented control system fuses actuation and sensors information as spikes streams, processing these spikes in hard real-time, implementing a massively parallel information processing system, through specialized spike-based circuits. This spike-based close-loop controller has been implemented into an AER platform, designed in our labs, that allows direct control of DC motors: the AER-Robot. Experimental results evidence the viability of the implementation of spike-based controllers, and hardware synthesis denotes low hardware requirements that allow replicating this controller in a high number of parallel controllers working together to allow a real-time robot control.

A Three-Dimensional Movement Analysis of the Spike in Fistball

Directory of Open Access Journals (Sweden)

Andreas Bund

2016-12-01

Full Text Available Due to its relevancy to point scoring, the spike is considered as one of the most important skills in fistball. Biomechanical analyses of this sport are very rare. In the present study, we performed a three-dimensional kinematic analysis of the fistball spike, which helps to specify performance parameters on a descriptive level. Recorded by four synchronized cameras (120 Hz and linked to the motion capture software Simi Motion® 5.0, three female fistball players of the second German league (24–26 years, 1.63–1.69 m performed several spikes under standardized conditions. Results show that the segment velocities of the arm reached their maximum successively from proximal to distal, following the principle of temporal coordination of single impulses. The wrist shows maximum speed when the fist hits the ball. The elbow joint angle performs a rapid transition from a strong flexion to a (almost full extension; however, the extension is completed after the moment of ball impact. In contrast, the shoulder joint angle increases almost linearly until the fistball contact and decreases afterward. The findings can be used to optimize the training of the spike.

Spike-Timing Dependent Plasticity in Unipolar Silicon Oxide RRAM Devices.

Science.gov (United States)

Zarudnyi, Konstantin; Mehonic, Adnan; Montesi, Luca; Buckwell, Mark; Hudziak, Stephen; Kenyon, Anthony J

2018-01-01

Resistance switching, or Resistive RAM (RRAM) devices show considerable potential for application in hardware spiking neural networks (neuro-inspired computing) by mimicking some of the behavior of biological synapses, and hence enabling non-von Neumann computer architectures. Spike-timing dependent plasticity (STDP) is one such behavior, and one example of several classes of plasticity that are being examined with the aim of finding suitable algorithms for application in many computing tasks such as coincidence detection, classification and image recognition. In previous work we have demonstrated that the neuromorphic capabilities of silicon-rich silicon oxide (SiO x ) resistance switching devices extend beyond plasticity to include thresholding, spiking, and integration. We previously demonstrated such behaviors in devices operated in the unipolar mode, opening up the question of whether we could add plasticity to the list of features exhibited by our devices. Here we demonstrate clear STDP in unipolar devices. Significantly, we show that the response of our devices is broadly similar to that of biological synapses. This work further reinforces the potential of simple two-terminal RRAM devices to mimic neuronal functionality in hardware spiking neural networks.

Statistical evaluation of the influence of soil properties on recoveries and matrix effects during the analysis of pharmaceutical compounds and steroids by quick, easy, cheap, effective, rugged and safe extraction followed by liquid chromatography-tandem mass spectrometry.

Science.gov (United States)

Salvia, Marie-Virginie; Cren-Olivé, Cécile; Vulliet, Emmanuelle

2013-11-08

Numerous chemical products are dispersed in our environment. Many of them are recognized as harmful to humans and the ecosystem. Among these harmful substances are antibiotics and steroid hormones. Currently, very few data are available on the presence and fate of these substances in the environment, in particular for solid matrices, mainly due to a lack of analytical methodologies. Indeed, soil is a very complex matrix, and the nature and composition of the soil has a significant impact on the extraction efficiency and the sensitivity of the method. For this reason a statistical approach was performed to study the influence of soil parameters (clay, silt, sand and organic carbon percentages and cation exchange capacity (CEC)) on recoveries and matrix effects of various pharmaceuticals and steroids. Thus, an analysis of covariance (ANCOVA) was performed when several substances were analyzed simultaneously, whereas a Pearson correlation was used to study the compounds individually. To the best of our knowledge, this study is the first time such an experiment was performed. The results showed that clay and organic carbon percentages as well as the CEC have an impact on the recoveries of most of the target substances, the variables being anti-correlated. This result suggests that the compounds are trapped in soils with high levels of clay and organic carbon and a high CEC. For the matrix effects, it was shown that the organic carbon content has a significant effect on steroid hormones and penicillin G matrix effects (positive correlation). Finally, interaction effects (first order) were evaluated. This latter point corresponds to the crossed effects that occur between explanatory variables (soil parameters). Indeed, the value taken by an explanatory variable can have an influence on the effect that another explanatory variable has on a dependent variable. For instance, it was shown that some parameters (silt, sand) have an impact on the effect that clay content has on

Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory : determination of organophosphate pesticides in bottom sediment by gas chromatography with flame photometric detection

Science.gov (United States)

Jha, Virendra Kumar; Wydoski, Duane S.

2003-01-01

A method for the isolation of 20 parent organophosphate pesticides and 5 pesticide degradates from bottom-sediment samples is described. The compound O-ethyl-O-methyl-S-proplyphosphorothioate is reported as an estimated concentration because of variable performance. In this method, the sediment samples are centrifuged to remove excess waster mixed with anhydrous sodium sulfate and Soxhlet extracted overnight with dichloromethane (93 percent) and methanol (7 percent). The extract is concentrated and then filtered through a 0.2-micrometer polytetrafluoroethylene membrane syringe filter. An aliquot of the sample extract is quantitatively injected onto two polystyrene-divinylbenzene gel-permeation chromatographic columns connected in series. The compounds are eluted with dichloromethane and a fraction is collected for analysis, with some coextracted interferences, including elemental sulfur, separated and discarded. The aliquot is concentrated and solvent exchanged to ethyl acetate. The extract is analyzed by dual capillary-column gas chromatography with flame photometric detection. Single-operator method detection limits in sodium sulfate matrix samples ranged from 0.81 to 2 micrograms per kilogram. Method performance was validated by spiking all compounds into three different solid matrices (sodium sulfate, bed sediment from Clear Creek, and bed sediment from Evergreen Lake) at three different concentrations. Eight replicates were analyzed at each concentration in each matrix. Mean recoveries of method compounds spiked in Clear Creek samples ranged from 43 to 110 percent, and those in Evergreen Lake samples ranged from 62 to 118 percent for all pesticides. Mean recoveries of method compounds spiked in reagent sodium sulfate samples ranged from 41 to 101 percent for all pesticides. The only exception was O-ethyl-O-methyl-S-propylphosphorothioate, which had an average recovery of 35 percent, and, thus, sample concentration is reported as estimated ('E' remark code).

Electrical source imaging of interictal spikes using multiple sparse volumetric priors for presurgical epileptogenic focus localization

Directory of Open Access Journals (Sweden)

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

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.

Sleep deprivation and spike-wave discharges in epileptic rats

OpenAIRE

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 increase in the number of spike-wave discharges was found during the first 4 hours of the deprivation period, whereas in the following deprivation hours epileptic activity returned to baseline values. I...

Rebound spiking in layer II medial entorhinal cortex stellate cells: Possible mechanism of grid cell function

Science.gov (United States)

Shay, Christopher F.; Ferrante, Michele; Chapman, G. William; Hasselmo, Michael E.

2015-01-01

Rebound spiking properties of medial entorhinal cortex (mEC) stellate cells induced by inhibition may underlie their functional properties in awake behaving rats, including the temporal phase separation of distinct grid cells and differences in grid cell firing properties. We investigated rebound spiking properties using whole cell patch recording in entorhinal slices, holding cells near spiking threshold and delivering sinusoidal inputs, superimposed with realistic inhibitory synaptic inputs to test the capacity of cells to selectively respond to specific phases of inhibitory input. Stellate cells showed a specific phase range of hyperpolarizing inputs that elicited spiking, but non-stellate cells did not show phase specificity. In both cell types, the phase range of spiking output occurred between the peak and subsequent descending zero crossing of the sinusoid. The phases of inhibitory inputs that induced spikes shifted earlier as the baseline sinusoid frequency increased, while spiking output shifted to later phases. Increases in magnitude of the inhibitory inputs shifted the spiking output to earlier phases. Pharmacological blockade of h-current abolished the phase selectivity of hyperpolarizing inputs eliciting spikes. A network computational model using cells possessing similar rebound properties as found in vitro produces spatially periodic firing properties resembling grid cell firing when a simulated animal moves along a linear track. These results suggest that the ability of mEC stellate cells to fire rebound spikes in response to a specific range of phases of inhibition could support complex attractor dynamics that provide completion and separation to maintain spiking activity of specific grid cell populations. PMID:26385258

Validation of neural spike sorting algorithms without ground-truth information.

Science.gov (United States)

Barnett, Alex H; Magland, Jeremy F; Greengard, Leslie F

2016-05-01

The throughput of electrophysiological recording is growing rapidly, allowing thousands of simultaneous channels, and there is a growing variety of spike sorting algorithms designed to extract neural firing events from such data. This creates an urgent need for standardized, automatic evaluation of the quality of neural units output by such algorithms. We introduce a suite of validation metrics that assess the credibility of a given automatic spike sorting algorithm applied to a given dataset. By rerunning the spike sorter two or more times, the metrics measure stability under various perturbations consistent with variations in the data itself, making no assumptions about the internal workings of the algorithm, and minimal assumptions about the noise. We illustrate the new metrics on standard sorting algorithms applied to both in vivo and ex vivo recordings, including a time series with overlapping spikes. We compare the metrics to existing quality measures, and to ground-truth accuracy in simulated time series. We provide a software implementation. Metrics have until now relied on ground-truth, simulated data, internal algorithm variables (e.g. cluster separation), or refractory violations. By contrast, by standardizing the interface, our metrics assess the reliability of any automatic algorithm without reference to internal variables (e.g. feature space) or physiological criteria. Stability is a prerequisite for reproducibility of results. Such metrics could reduce the significant human labor currently spent on validation, and should form an essential part of large-scale automated spike sorting and systematic benchmarking of algorithms. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks.

Science.gov (United States)

Pyle, Ryan; Rosenbaum, Robert

2017-01-06

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.

Electricity market price spike analysis by a hybrid data model and feature selection technique

International Nuclear Information System (INIS)

Amjady, Nima; Keynia, Farshid

2010-01-01

In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets. (author)

THE POLITICAL CRITIQUE OF SPIKE Lee's Bamboozled

African Journals Online (AJOL)

Admin

CONTEMPORARY AMERICAN MEDIA: THE POLITICAL. CRITIQUE OF SPIKE ... KEYWORDS: Blackface Minstrelsy, Racist Stereotypes and American Media. INTRODUCTION ..... of a difference that is itself a process of disavowal.” In this ...

Fast EEG spike detection via eigenvalue analysis and clustering of spatial amplitude distribution

Science.gov (United States)

Fukami, Tadanori; Shimada, Takamasa; Ishikawa, Bunnoshin

2018-06-01

Objective. In the current study, we tested a proposed method for fast spike detection in electroencephalography (EEG). Approach. We performed eigenvalue analysis in two-dimensional space spanned by gradients calculated from two neighboring samples to detect high-amplitude negative peaks. We extracted the spike candidates by imposing restrictions on parameters regarding spike shape and eigenvalues reflecting detection characteristics of individual medical doctors. We subsequently performed clustering, classifying detected peaks by considering the amplitude distribution at 19 scalp electrodes. Clusters with a small number of candidates were excluded. We then defined a score for eliminating spike candidates for which the pattern of detected electrodes differed from the overall pattern in a cluster. Spikes were detected by setting the score threshold. Main results. Based on visual inspection by a psychiatrist experienced in EEG, we evaluated the proposed method using two statistical measures of precision and recall with respect to detection performance. We found that precision and recall exhibited a trade-off relationship. The average recall value was 0.708 in eight subjects with the score threshold that maximized the F-measure, with 58.6  ±  36.2 spikes per subject. Under this condition, the average precision was 0.390, corresponding to a false positive rate 2.09 times higher than the true positive rate. Analysis of the required processing time revealed that, using a general-purpose computer, our method could be used to perform spike detection in 12.1% of the recording time. The process of narrowing down spike candidates based on shape occupied most of the processing time. Significance. Although the average recall value was comparable with that of other studies, the proposed method significantly shortened the processing time.

Assembly of spikes into coronavirus particles is mediated by the carboxy-terminal domain of the spike protein

NARCIS (Netherlands)

Godeke, G J; de Haan, Cornelis A M; Rossen, J W; Vennema, H; Rottier, P J

The type I glycoprotein S of coronavirus, trimers of which constitute the typical viral spikes, is assembled into virions through noncovalent interactions with the M protein. Here we demonstrate that incorporation is mediated by the short carboxy-terminal segment comprising the transmembrane and

Methods of analysis by the U.S. Geological Survey National Water Quality Laboratory; determination of whole-water recoverable arsenic, boron, and vanadium using inductively coupled plasma-mass spectrometry

Science.gov (United States)

Garbarino, John R.

2000-01-01

Analysis of in-bottle digestate by using the inductively coupled plasma?mass spectrometric (ICP?MS) method has been expanded to include arsenic, boron, and vanadium. Whole-water samples are digested by using either the hydrochloric acid in-bottle digestion procedure or the nitric acid in-bottle digestion procedure. When the hydrochloric acid in-bottle digestion procedure is used, chloride must be removed from the digestate by subboiling evaporation before arsenic and vanadium can be accurately determined. Method detection limits for these elements are now 10 to 100 times lower than U.S. Geological Survey (USGS) methods using hydride generation? atomic absorption spectrophotometry (HG? AAS) and inductively coupled plasma? atomic emission spectrometry (ICP?AES), thus providing lower variability at ambient concentrations. The bias and variability of the methods were determined by using results from spike recoveries, standard reference materials, and validation samples. Spike recoveries in reagent-water, surface-water, ground-water, and whole-water recoverable matrices averaged 90 percent for seven replicates; spike recoveries were biased from 25 to 35 percent low for the ground-water matrix because of the abnormally high iron concentration. Results for reference material were within one standard deviation of the most probable value. There was no significant difference between the results from ICP?MS and HG?AAS or ICP?AES methods for the natural whole-water samples that were analyzed.

Salient Object Detection via Structured Matrix Decomposition.

Science.gov (United States)

Peng, Houwen; Li, Bing; Ling, Haibin; Hu, Weiming; Xiong, Weihua; Maybank, Stephen J

2016-05-04

Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank matrix representing image background and a sparse matrix identifying salient objects. Two deficiencies, however, still exist. First, previous work typically assumes the elements in the sparse matrix are mutually independent, ignoring the spatial and pattern relations of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To address these problems, we propose a novel structured matrix decomposition model with two structural regularizations: (1) a tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature space. Furthermore, high-level priors are integrated to guide the matrix decomposition and boost the detection. We evaluate our model for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics.

Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality

Directory of Open Access Journals (Sweden)

James P Crutchfield

2015-08-01

Full Text Available The mutual information between stimulus and spike-train response is commonly used to monitor neural coding efficiency, but neuronal computation broadly conceived requires more refined and targeted information measures of input-output joint processes. A first step towards that larger goal is todevelop information measures for individual output processes, including information generation (entropy rate, stored information (statisticalcomplexity, predictable information (excess entropy, and active information accumulation (bound information rate. We calculate these for spike trains generated by a variety of noise-driven integrate-and-fire neurons as a function of time resolution and for alternating renewal processes. We show that their time-resolution dependence reveals coarse-grained structural properties of interspike interval statistics; e.g., $tau$-entropy rates that diverge less quickly than the firing rate indicate interspike interval correlations. We also find evidence that the excess entropy and regularized statistical complexity of different types of integrate-and-fire neurons are universal in the continuous-time limit in the sense that they do not depend on mechanism details. This suggests a surprising simplicity in the spike trains generated by these model neurons. Interestingly, neurons with gamma-distributed ISIs and neurons whose spike trains are alternating renewal processes do not fall into the same universality class. These results lead to two conclusions. First, the dependence of information measures on time resolution reveals mechanistic details about spike train generation. Second, information measures can be used as model selection tools for analyzing spike train processes.

Noise-robust speech recognition through auditory feature detection and spike sequence decoding.

Science.gov (United States)

Schafer, Phillip B; Jin, Dezhe Z

2014-03-01

Speech recognition in noisy conditions is a major challenge for computer systems, but the human brain performs it routinely and accurately. Automatic speech recognition (ASR) systems that are inspired by neuroscience can potentially bridge the performance gap between humans and machines. We present a system for noise-robust isolated word recognition that works by decoding sequences of spikes from a population of simulated auditory feature-detecting neurons. Each neuron is trained to respond selectively to a brief spectrotemporal pattern, or feature, drawn from the simulated auditory nerve response to speech. The neural population conveys the time-dependent structure of a sound by its sequence of spikes. We compare two methods for decoding the spike sequences--one using a hidden Markov model-based recognizer, the other using a novel template-based recognition scheme. In the latter case, words are recognized by comparing their spike sequences to template sequences obtained from clean training data, using a similarity measure based on the length of the longest common sub-sequence. Using isolated spoken digits from the AURORA-2 database, we show that our combined system outperforms a state-of-the-art robust speech recognizer at low signal-to-noise ratios. Both the spike-based encoding scheme and the template-based decoding offer gains in noise robustness over traditional speech recognition methods. Our system highlights potential advantages of spike-based acoustic coding and provides a biologically motivated framework for robust ASR development.

Quantitative evaluation of the matrix effect in bioanalytical methods based on LC-MS: A comparison of two approaches.

Science.gov (United States)

Rudzki, Piotr J; Gniazdowska, Elżbieta; Buś-Kwaśnik, Katarzyna

2018-06-05

Liquid chromatography coupled to mass spectrometry (LC-MS) is a powerful tool for studying pharmacokinetics and toxicokinetics. Reliable bioanalysis requires the characterization of the matrix effect, i.e. influence of the endogenous or exogenous compounds on the analyte signal intensity. We have compared two methods for the quantitation of matrix effect. The CVs(%) of internal standard normalized matrix factors recommended by the European Medicines Agency were evaluated against internal standard normalized relative matrix effects derived from Matuszewski et al. (2003). Both methods use post-extraction spiked samples, but matrix factors require also neat solutions. We have tested both approaches using analytes of diverse chemical structures. The study did not reveal relevant differences in the results obtained with both calculation methods. After normalization with the internal standard, the CV(%) of the matrix factor was on average 0.5% higher than the corresponding relative matrix effect. The method adopted by the European Medicines Agency seems to be slightly more conservative in the analyzed datasets. Nine analytes of different structures enabled a general overview of the problem, still, further studies are encouraged to confirm our observations. Copyright © 2018 Elsevier B.V. All rights reserved.

When the Ostrich-Algorithm Fails: Blanking Method Affects Spike Train Statistics.

Science.gov (United States)

Joseph, Kevin; Mottaghi, Soheil; Christ, Olaf; Feuerstein, Thomas J; Hofmann, Ulrich G

2018-01-01

Modern electroceuticals are bound to employ the usage of electrical high frequency (130-180 Hz) stimulation carried out under closed loop control, most prominent in the case of movement disorders. However, particular challenges are faced when electrical recordings of neuronal tissue are carried out during high frequency electrical stimulation, both in-vivo and in-vitro . This stimulation produces undesired artifacts and can render the recorded signal only partially useful. The extent of these artifacts is often reduced by temporarily grounding the recording input during stimulation pulses. In the following study, we quantify the effects of this method, "blanking," on the spike count and spike train statistics. Starting from a theoretical standpoint, we calculate a loss in the absolute number of action potentials, depending on: width of the blanking window, frequency of stimulation, and intrinsic neuronal activity. These calculations were then corroborated by actual high signal to noise ratio (SNR) single cell recordings. We state that, for clinically relevant frequencies of 130 Hz (used for movement disorders) and realistic blanking windows of 2 ms, up to 27% of actual existing spikes are lost. We strongly advice cautioned use of the blanking method when spike rate quantification is attempted. Blanking (artifact removal by temporarily grounding input), depending on recording parameters, can lead to significant spike loss. Very careful use of blanking circuits is advised.

When the Ostrich-Algorithm Fails: Blanking Method Affects Spike Train Statistics

Directory of Open Access Journals (Sweden)

Kevin Joseph

2018-04-01

Full Text Available Modern electroceuticals are bound to employ the usage of electrical high frequency (130–180 Hz stimulation carried out under closed loop control, most prominent in the case of movement disorders. However, particular challenges are faced when electrical recordings of neuronal tissue are carried out during high frequency electrical stimulation, both in-vivo and in-vitro. This stimulation produces undesired artifacts and can render the recorded signal only partially useful. The extent of these artifacts is often reduced by temporarily grounding the recording input during stimulation pulses. In the following study, we quantify the effects of this method, “blanking,” on the spike count and spike train statistics. Starting from a theoretical standpoint, we calculate a loss in the absolute number of action potentials, depending on: width of the blanking window, frequency of stimulation, and intrinsic neuronal activity. These calculations were then corroborated by actual high signal to noise ratio (SNR single cell recordings. We state that, for clinically relevant frequencies of 130 Hz (used for movement disorders and realistic blanking windows of 2 ms, up to 27% of actual existing spikes are lost. We strongly advice cautioned use of the blanking method when spike rate quantification is attempted.Impact statementBlanking (artifact removal by temporarily grounding input, depending on recording parameters, can lead to significant spike loss. Very careful use of blanking circuits is advised.

Evaluation of the uranium double spike technique for environmental monitoring

International Nuclear Information System (INIS)

Hemberger, P.H.; Rokop, D.J.; Efurd, D.W.; Roensch, F.R.; Smith, D.H.; Turner, M.L.; Barshick, C.M.; Bayne, C.K.

1998-01-01

Use of a uranium double spike in analysis of environmental samples showed that a 235 U enrichment of 1% ( 235 U/ 238 U = 0.00732) can be distinguished from natural ( 235 U/ 238 U = 0.00725). Experiments performed jointly at Los Alamos National Laboratory (LANL) and Oak Ridge National Laboratory (ORNL) used a carefully calibrated double spike of 233 U and 236 U to obtain much better precision than is possible using conventional analytical techniques. A variety of different sampling media (vegetation and swipes) showed that, provided sufficient care is exercised in choice of sample type, relative standard deviations of less than ± 0.5% can be routinely obtained. This ability, unavailable without use of the double spike, has enormous potential significance in the detection of undeclared nuclear facilities

Oil recovery from naturally fractured reservoirs by steam injection methods. Final report

Energy Technology Data Exchange (ETDEWEB)

Reis, J.C.; Miller, M.A.

1995-05-01

Oil recovery by steam injection is a proven, successful technology for nonfractured reservoirs, but has received only limited study for fractured reservoirs. Preliminary studies suggest recovery efficiencies in fractured reservoirs may be increased by as much as 50% with the application of steam relative to that of low temperature processes. The key mechanisms enhancing oil production at high temperature are the differential thermal expansion between oil and the pore volume, and the generation of gases within matrix blocks. Other mechanisms may also contribute to increased production. These mechanisms are relatively independent of oil gravity, making steam injection into naturally fractured reservoirs equally attractive to light and heavy oil deposits. The objectives of this research program are to quantify the amount of oil expelled by these recovery mechanisms and to develop a numerical model for predicting oil recovery in naturally fractured reservoirs during steam injection. The experimental study consists of constructing and operating several apparatuses to isolate each of these mechanisms. The first measures thermal expansion and capillary imbibition rates at relatively low temperature, but for various lithologies and matrix block shapes. The second apparatus measures the same parameters, but at high temperatures and for only one shape. A third experimental apparatus measures the maximum gas saturations that could build up within a matrix block. A fourth apparatus measures thermal conductivity and diffusivity of porous media. The numerical study consists of developing transfer functions for oil expulsion from matrix blocks to fractures at high temperatures and incorporating them, along with the energy equation, into a dual porosity thermal reservoir simulator. This simulator can be utilized to make predictions for steam injection processes in naturally-fractured reservoirs. Analytical models for capillary imbibition have also been developed.

Simultaneous determination of vitamins D2 and D3 by electrospray ionization LC/MS/MS in infant formula and adult nutritionals: First Action 2012.11.

Science.gov (United States)

Gilliland, Donald L; Black, Charles K; Denison, James E; Seipelt, Charles T; Baugh, Steve

2013-01-01

A method was developed for the analysis of vitamins D2 and D3 in a variety of nutritional products. To extract vitamins D2 and D3 from products containing substantial amounts of fat, a saponification with alcoholic potassium hydroxide is required to release the vitamin D. Trideuterium-labeled vitamin D is added to the sample prior to saponification, and quantitation is achieved using linear regression of the ratio of peak response for 2H3-D and vitamin D. Acceptable linearity was achieved between 0.6 and 27 microg/100 g with a correlation requirement of >0.999. The method detection limit of 0.02 microg/100 g was verified by spiking placebo products carried through the saponification and extraction steps of the method. At the quantitation limit (0.12 microg/100 g), the signal was easily distinguished from the background. Vitamin D3 spike recoveries ranged from 107 to 119% at the low level and 104 to 116% at the high-level spike. Vitamin D2 recoveries were 105 to 116% and 91 to 110% for the low- and high-level spikes, respectively. SRM 1849a has a certified concentration of 11.1 +/- 1.7 microg/100 g; using this standard reference material, the range of 9.4 to 12.8 microg/100 g was met on each of the 6 days. Method repeatability, determined in 12 vitamin D3 product matrixes over 6 days, ranged from 3.9 to 48%. The adult nutrition-milk protein sample was the most notable; it failed within-day, as well as day-to-day, precision requirements. There was no attempt to optimize the sample preparation to accommodate any problem matrix.

Toward relating the subthalamic nucleus spiking activity to the local field potentials acquired intranuclearly

International Nuclear Information System (INIS)

Michmizos, K P; Nikita, K S; Sakas, D

2011-01-01

Studies on neurophysiological correlates of the functional magnetic resonance imaging (fMRI) signals reveal a strong relationship between the local field potential (LFP) acquired invasively and metabolic signal changes in fMRI experiments. Most of these studies failed to reveal an analogous relationship between metabolic signals and the spiking activity. That would allow for the prediction of the neural activity exclusively from the fMRI signals. However, the relationship between fMRI signals and spiking activity can be inferred indirectly provided that the LFPs can be used to predict the spiking activity of the area. Until now, only the LFP–spike relationship in cortical areas has been examined. Herein, we show that the spiking activity can be predicted by the LFPs acquired in a deep nucleus, namely the subthalamic nucleus (STN), using a nonlinear cascade model. The model can reproduce the spike patterns inside the motor area of the STN that represent information about the motor plans. Our findings expand the possibility of further recruiting non-invasive neuroimaging techniques to understand the activity of the STN and predict or even control movement

Spiking neuron devices consisting of single-flux-quantum circuits

International Nuclear Information System (INIS)

Hirose, Tetsuya; Asai, Tetsuya; Amemiya, Yoshihito

2006-01-01

Single-flux-quantum (SFQ) circuits can be used for making spiking neuron devices, which are useful elements for constructing intelligent, brain-like computers. The device we propose is based on the leaky integrate-and-fire neuron (IFN) model and uses a SFQ pulse as an action signal or a spike of neurons. The operation of the neuron device is confirmed by computer simulator. It can operate with a short delay of 100 ps or less and is the highest-speed neuron device ever reported

New explicit spike solutions-non-local component of the generalized Mixmaster attractor

International Nuclear Information System (INIS)

Lim, Woei Chet

2008-01-01

By applying a standard solution-generating transformation to an arbitrary vacuum Bianchi type II solution, one generates a new solution with spikes commonly observed in numerical simulations. It is conjectured that the spike solutions are part of the generalized Mixmaster attractor

Advanced correlation grid: Analysis and visualisation of functional connectivity among multiple spike trains.

Science.gov (United States)

Masud, Mohammad Shahed; Borisyuk, Roman; Stuart, Liz

2017-07-15

This study analyses multiple spike trains (MST) data, defines its functional connectivity and subsequently visualises an accurate diagram of connections. This is a challenging problem. For example, it is difficult to distinguish the common input and the direct functional connection of two spike trains. The new method presented in this paper is based on the traditional pairwise cross-correlation function (CCF) and a new combination of statistical techniques. First, the CCF is used to create the Advanced Correlation Grid (ACG) correlation where both the significant peak of the CCF and the corresponding time delay are used for detailed analysis of connectivity. Second, these two features of functional connectivity are used to classify connections. Finally, the visualization technique is used to represent the topology of functional connections. Examples are presented in the paper to demonstrate the new Advanced Correlation Grid method and to show how it enables discrimination between (i) influence from one spike train to another through an intermediate spike train and (ii) influence from one common spike train to another pair of analysed spike trains. The ACG method enables scientists to automatically distinguish between direct connections from spurious connections such as common source connection and indirect connection whereas existing methods require in-depth analysis to identify such connections. The ACG is a new and effective method for studying functional connectivity of multiple spike trains. This method can identify accurately all the direct connections and can distinguish common source and indirect connections automatically. Copyright © 2017 Elsevier B.V. All rights reserved.

On the robustness of EC-PC spike detection method for online neural recording.

Science.gov (United States)

Zhou, Yin; Wu, Tong; Rastegarnia, Amir; Guan, Cuntai; Keefer, Edward; Yang, Zhi

2014-09-30

Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

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). Copyright © 2010

Mean-field analysis of orientation selectivity in inhibition-dominated networks of spiking neurons.

Science.gov (United States)

Sadeh, Sadra; Cardanobile, Stefano; Rotter, Stefan

2014-01-01

Mechanisms underlying the emergence of orientation selectivity in the primary visual cortex are highly debated. Here we study the contribution of inhibition-dominated random recurrent networks to orientation selectivity, and more generally to sensory processing. By simulating and analyzing large-scale networks of spiking neurons, we investigate tuning amplification and contrast invariance of orientation selectivity in these networks. In particular, we show how selective attenuation of the common mode and amplification of the modulation component take place in these networks. Selective attenuation of the baseline, which is governed by the exceptional eigenvalue of the connectivity matrix, removes the unspecific, redundant signal component and ensures the invariance of selectivity across different contrasts. Selective amplification of modulation, which is governed by the operating regime of the network and depends on the strength of coupling, amplifies the informative signal component and thus increases the signal-to-noise ratio. Here, we perform a mean-field analysis which accounts for this process.

APPLICATION OF FENTON’S REAGENT ON REMEDIATION OF POLYCYCLIC AROMATIC HYDROCARBONs (PAHs IN SPIKED SOIL

Directory of Open Access Journals (Sweden)

Nursiah La Nafie

2010-06-01

Full Text Available Problem associated with Polycyclic Aromatic Hydrocarbons (PAHs contaminated site in environmental media have received increasing attention. To resolve such problems, innovative in situ methods are urgently required. This work investigated the feasibility of using Fenton's Reagent to remediate PAHs in spiked soil. PAHs were spiked into soil to simulate contaminated soil. Fenton's Reagent (H2O2 + Fe2+ and surfactant were very efficient in destruction of PAHs including naphthalene, anthracene, fluoranthene, pyrene, and benzo(apyrene from spiked soil. It was indicated by the fact that more than 96% of PAHs were degraded in the solution and the spiked soil.   Keywords: Environmental, Fenton's Reagent, Polycyclic Aromatic Hydrocarbons, and Spiked soil.

Approach for domestic preparation of standard material (LSD spike) for isotope dilution mass spectrometry

International Nuclear Information System (INIS)

Ishikawa, Fumitaka; Sumi, Mika; Chiba, Masahiko; Suzuki, Toru; Abe, Tomoyuki; Kuno, Yusuke

2008-01-01

The accountancy analysis of the nuclear fuel material at Plutonium Fuel Development Center of JAEA is performed by isotope dilution mass spectrometry (IDMS; Isotope Dilution Mass Spectrometry). IDMS requires the standard material called LSD spike (Large Size Dried spike) which is indispensable for the accountancy in the facilities where the nuclear fuel materials are handled. Although the LSD spike and Pu source material have been supplied from foreign countries, the transportation for such materials has been getting more difficult recently. This difficulty may affect the operation of nuclear facilities in the future. Therefore, research and development of the domestic LSD spike and base material has been performed at JAEA. Certification for such standard nuclear materials including spikes produced in Japan is being studied. This report presents the current status and the future plan for the technological development. (author)

Versatile Networks of Simulated Spiking Neurons Displaying Winner-Take-All Behavior

Directory of Open Access Journals (Sweden)

Yanqing eChen

2013-03-01

Full Text Available We describe simulations of large-scale networks of excitatory and inhibitory spiking neurons that can generate dynamically stable winner-take-all (WTA behavior. The network connectivity is a variant of center-surround architecture that we call center-annular-surround (CAS. In this architecture each neuron is excited by nearby neighbors and inhibited by more distant neighbors in an annular-surround region. The neural units of these networks simulate conductance-based spiking neurons that interact via mechanisms susceptible to both short-term synaptic plasticity and STDP. We show that such CAS networks display robust WTA behavior unlike the center-surround networks and other control architectures that we have studied. We find that a large-scale network of spiking neurons with separate populations of excitatory and inhibitory neurons can give rise to smooth maps of sensory input. In addition, we show that a humanoid Brain-Based-Device (BBD under the control of a spiking WTA neural network can learn to reach to target positions in its visual field, thus demonstrating the acquisition of sensorimotor coordination.

Versatile networks of simulated spiking neurons displaying winner-take-all behavior.

Science.gov (United States)

Chen, Yanqing; McKinstry, Jeffrey L; Edelman, Gerald M

2013-01-01

We describe simulations of large-scale networks of excitatory and inhibitory spiking neurons that can generate dynamically stable winner-take-all (WTA) behavior. The network connectivity is a variant of center-surround architecture that we call center-annular-surround (CAS). In this architecture each neuron is excited by nearby neighbors and inhibited by more distant neighbors in an annular-surround region. The neural units of these networks simulate conductance-based spiking neurons that interact via mechanisms susceptible to both short-term synaptic plasticity and STDP. We show that such CAS networks display robust WTA behavior unlike the center-surround networks and other control architectures that we have studied. We find that a large-scale network of spiking neurons with separate populations of excitatory and inhibitory neurons can give rise to smooth maps of sensory input. In addition, we show that a humanoid brain-based-device (BBD) under the control of a spiking WTA neural network can learn to reach to target positions in its visual field, thus demonstrating the acquisition of sensorimotor coordination.

Matrix precipitation: a general strategy to eliminate matrix interference for pharmaceutical toxic impurities analysis.

Science.gov (United States)

Yang, Xiaojing; Xiong, Xuewu; Cao, Ji; Luan, Baolei; Liu, Yongjun; Liu, Guozhu; Zhang, Lei

2015-01-30

Matrix interference, which can lead to false positive/negative results, contamination of injector or separation column, incompatibility between sample solution and the selected analytical instrument, and response inhibition or even quenching, is commonly suffered for the analysis of trace level toxic impurities in drug substance. In this study, a simple matrix precipitation strategy is proposed to eliminate or minimize the above stated matrix interference problems. Generally, a sample of active pharmaceutical ingredients (APIs) is dissolved in an appropriate solvent to achieve the desired high concentration and then an anti-solvent is added to precipitate the matrix substance. As a result, the target analyte is extracted into the mixed solution with very less residual of APIs. This strategy has the characteristics of simple manipulation, high recovery and excellent anti-interference capability. It was found that the precipitation ratio (R, representing the ability to remove matrix substance) and the proportion of solvent (the one used to dissolve APIs) in final solution (P, affecting R and also affecting the method sensitivity) are two important factors of the precipitation process. The correlation between R and P was investigated by performing precipitation with various APIs in different solvent/anti-solvent systems. After a detailed mathematical reasoning process, P=20% was proved to be an effective and robust condition to perform the precipitation strategy. The precipitation method with P=20% can be used as a general strategy for toxic impurity analysis in APIs. Finally, several typical examples are described in this article, where the challenging matrix interference issues have been resolved successfully. Copyright © 2014 Elsevier B.V. All rights reserved.

Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains

Directory of Open Access Journals (Sweden)

Rodrigo Cofré

2018-01-01

Full Text Available The spiking activity of neuronal networks follows laws that are not time-reversal symmetric; the notion of pre-synaptic and post-synaptic neurons, stimulus correlations and noise correlations have a clear time order. Therefore, a biologically realistic statistical model for the spiking activity should be able to capture some degree of time irreversibility. We use the thermodynamic formalism to build a framework in the context maximum entropy models to quantify the degree of time irreversibility, providing an explicit formula for the information entropy production of the inferred maximum entropy Markov chain. We provide examples to illustrate our results and discuss the importance of time irreversibility for modeling the spike train statistics.

Pharmacodynamics of remifentanil. Induced intracranial spike activity in mesial temporal lobe epilepsy

DEFF Research Database (Denmark)

Kjær, Troels Wesenberg; Hogenhaven, Hans; Lee, Andrea P

2017-01-01

that remifentanil potentiates spike activity is in agreement with previous findings from smaller studies. Furthermore, we were able to describe the pharmacodynamics of the remifentanil effect on spike activity. Peri-operative provocation with remifentanil may play a future role in guiding neurosurgical intervention...

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.

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.

Spiking Activity of a LIF Neuron in Distributed Delay Framework

Directory of Open Access Journals (Sweden)

Saket Kumar Choudhary

2016-06-01

Full Text Available Evolution of membrane potential and spiking activity for a single leaky integrate-and-fire (LIF neuron in distributed delay framework (DDF is investigated. DDF provides a mechanism to incorporate memory element in terms of delay (kernel function into a single neuron models. This investigation includes LIF neuron model with two different kinds of delay kernel functions, namely, gamma distributed delay kernel function and hypo-exponential distributed delay kernel function. Evolution of membrane potential for considered models is studied in terms of stationary state probability distribution (SPD. Stationary state probability distribution of membrane potential (SPDV for considered neuron models are found asymptotically similar which is Gaussian distributed. In order to investigate the effect of membrane potential delay, rate code scheme for neuronal information processing is applied. Firing rate and Fano-factor for considered neuron models are calculated and standard LIF model is used for comparative study. It is noticed that distributed delay increases the spiking activity of a neuron. Increase in spiking activity of neuron in DDF is larger for hypo-exponential distributed delay function than gamma distributed delay function. Moreover, in case of hypo-exponential delay function, a LIF neuron generates spikes with Fano-factor less than 1.

[A wavelet neural network algorithm of EEG signals data compression and spikes recognition].

Science.gov (United States)

Zhang, Y; Liu, A; Yu, K

1999-06-01

A novel method of EEG signals compression representation and epileptiform spikes recognition based on wavelet neural network and its algorithm is presented. The wavelet network not only can compress data effectively but also can recover original signal. In addition, the characters of the spikes and the spike-slow rhythm are auto-detected from the time-frequency isoline of EEG signal. This method is well worth using in the field of the electrophysiological signal processing and time-frequency analyzing.

Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation.

Science.gov (United States)

Azami, Hamed; Escudero, Javier

2016-05-01

Signal segmentation and spike detection are two important biomedical signal processing applications. Often, non-stationary signals must be segmented into piece-wise stationary epochs or spikes need to be found among a background of noise before being further analyzed. Permutation entropy (PE) has been proposed to evaluate the irregularity of a time series. PE is conceptually simple, structurally robust to artifacts, and computationally fast. It has been extensively used in many applications, but it has two key shortcomings. First, when a signal is symbolized using the Bandt-Pompe procedure, only the order of the amplitude values is considered and information regarding the amplitudes is discarded. Second, in the PE, the effect of equal amplitude values in each embedded vector is not addressed. To address these issues, we propose a new entropy measure based on PE: the amplitude-aware permutation entropy (AAPE). AAPE is sensitive to the changes in the amplitude, in addition to the frequency, of the signals thanks to it being more flexible than the classical PE in the quantification of the signal motifs. To demonstrate how the AAPE method can enhance the quality of the signal segmentation and spike detection, a set of synthetic and realistic synthetic neuronal signals, electroencephalograms and neuronal data are processed. We compare the performance of AAPE in these problems against state-of-the-art approaches and evaluate the significance of the differences with a repeated ANOVA with post hoc Tukey's test. In signal segmentation, the accuracy of AAPE-based method is higher than conventional segmentation methods. AAPE also leads to more robust results in the presence of noise. The spike detection results show that AAPE can detect spikes well, even when presented with single-sample spikes, unlike PE. For multi-sample spikes, the changes in AAPE are larger than in PE. We introduce a new entropy metric, AAPE, that enables us to consider amplitude information in the