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Sample records for reduces spike timing

  1. Spike-timing-dependent BDNF secretion and synaptic plasticity.

    PubMed

    Lu, Hui; Park, Hyungju; Poo, Mu-Ming

    2014-01-01

    In acute hippocampal slices, we found that the presence of extracellular brain-derived neurotrophic factor (BDNF) is essential for the induction of spike-timing-dependent long-term potentiation (tLTP). To determine whether BDNF could be secreted from postsynaptic dendrites in a spike-timing-dependent manner, we used a reduced system of dissociated hippocampal neurons in culture. Repetitive pairing of iontophoretically applied glutamate pulses at the dendrite with neuronal spikes could induce persistent alterations of glutamate-induced responses at the same dendritic site in a manner that mimics spike-timing-dependent plasticity (STDP)-the glutamate-induced responses were potentiated and depressed when the glutamate pulses were applied 20 ms before and after neuronal spiking, respectively. By monitoring changes in the green fluorescent protein (GFP) fluorescence at the dendrite of hippocampal neurons expressing GFP-tagged BDNF, we found that pairing of iontophoretic glutamate pulses with neuronal spiking resulted in BDNF secretion from the dendrite at the iontophoretic site only when the glutamate pulses were applied within a time window of approximately 40 ms prior to neuronal spiking, consistent with the timing requirement of synaptic potentiation via STDP. Thus, BDNF is required for tLTP and BDNF secretion could be triggered in a spike-timing-dependent manner from the postsynaptic dendrite. PMID:24298135

  2. Spike-Timing Theory of Working Memory

    PubMed Central

    Szatmáry, Botond; Izhikevich, Eugene M.

    2010-01-01

    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. PMID:20808877

  3. Time-free spiking neural P systems.

    PubMed

    Pan, Linqiang; Zeng, Xiangxiang; Zhang, Xingyi

    2011-05-01

    Different biological processes take different times to be completed, which can also be influenced by many environmental factors. In this work, a realistic definition of nonsynchronized spiking neural P systems (SN P systems, for short) is considered: during the work of an SN P system, the execution times of spiking rules cannot be known exactly (i.e., they are arbitrary). In order to establish robust systems against the environmental factors, a special class of SN P systems, called time-free SN P systems, is introduced, which always produce the same computation result independent of the execution times of the rules. The universality of time-free SN P systems is investigated. It is proved that these P systems with extended rules (several spikes can be produced by a rule) are equivalent to register machines. However, if the number of spikes present in the system is bounded, then the power of time-free SN P systems falls, and in this case, a characterization of semilinear sets of natural numbers is obtained. PMID:21299423

  4. Spiking neuron computation with the time machine.

    PubMed

    Garg, Vaibhav; Shekhar, Ravi; Harris, John G

    2012-04-01

    The Time Machine (TM) is a spike-based computation architecture that represents synaptic weights in time. This choice of weight representation allows the use of virtual synapses, providing an excellent tradeoff in terms of flexibility, arbitrary weight connections and hardware usage compared to dedicated synapse architectures. The TM supports an arbitrary number of synapses and is limited only by the number of simultaneously active synapses to each neuron. SpikeSim, a behavioral hardware simulator for the architecture, is described along with example algorithms for edge detection and objection recognition. The TM can implement traditional spike-based processing as well as recently developed time mode operations where step functions serve as the input and output of each neuron block. A custom hybrid digital/analog implementation and a fully digital realization of the TM are discussed. An analog chip with 32 neurons, 1024 synapses and an address event representation (AER) block has been fabricated in 0.5 μm technology. A fully digital field-programmable gate array (FPGA)-based implementation of the architecture has 6,144 neurons and 100,352 simultaneously active synapses. Both implementations utilize a digital controller for routing spikes that can process up to 34 million synapses per second. PMID:23852979

  5. Millisecond Precision Spike Timing Shapes Tactile Perception

    PubMed Central

    Mackevicius, Emily L.; Best, Matthew D.; Saal, Hannes P.

    2012-01-01

    In primates, the sense of touch has traditionally been considered to be a spatial modality, drawing an analogy to the visual system. In this view, stimuli are encoded in spatial patterns of activity over the sheet of receptors embedded in the skin. We propose that the spatial processing mode is complemented by a temporal one. Indeed, the transduction and processing of complex, high-frequency skin vibrations have been shown to play an important role in tactile texture perception, and the frequency composition of vibrations shapes the evoked percept. Mechanoreceptive afferents innervating the glabrous skin exhibit temporal patterning in their responses, but the importance and behavioral relevance of spike timing, particularly for naturalistic stimuli, remains to be elucidated. Based on neurophysiological recordings from Rhesus macaques, we show that spike timing conveys information about the frequency composition of skin vibrations, both for individual afferents and for afferent populations, and that the temporal fidelity varies across afferent class. Furthermore, the perception of skin vibrations, measured in human subjects, is better predicted when spike timing is taken into account, and the resolution that predicts perception best matches the optimal resolution of the respective afferent classes. In light of these results, the peripheral representation of complex skin vibrations draws a powerful analogy with the auditory and vibrissal systems. PMID:23115169

  6. Spike timing precision changes with spike rate adaptation in the owl's auditory space map.

    PubMed

    Keller, Clifford H; Takahashi, Terry T

    2015-10-01

    Spike rate adaptation (SRA) is a continuing change of responsiveness to ongoing stimuli, which is ubiquitous across species and levels of sensory systems. Under SRA, auditory responses to constant stimuli change over time, relaxing toward a long-term rate often over multiple timescales. With more variable stimuli, SRA causes the dependence of spike rate on sound pressure level to shift toward the mean level of recent stimulus history. A model based on subtractive adaptation (Benda J, Hennig RM. J Comput Neurosci 24: 113-136, 2008) shows that changes in spike rate and level dependence are mechanistically linked. Space-specific neurons in the barn owl's midbrain, when recorded under ketamine-diazepam anesthesia, showed these classical characteristics of SRA, while at the same time exhibiting changes in spike timing precision. Abrupt level increases of sinusoidally amplitude-modulated (SAM) noise initially led to spiking at higher rates with lower temporal precision. Spike rate and precision relaxed toward their long-term values with a time course similar to SRA, results that were also replicated by the subtractive model. Stimuli whose amplitude modulations (AMs) were not synchronous across carrier frequency evoked spikes in response to stimulus envelopes of a particular shape, characterized by the spectrotemporal receptive field (STRF). Again, abrupt stimulus level changes initially disrupted the temporal precision of spiking, which then relaxed along with SRA. We suggest that shifts in latency associated with stimulus level changes may differ between carrier frequency bands and underlie decreased spike precision. Thus SRA is manifest not simply as a change in spike rate but also as a change in the temporal precision of spiking. PMID:26269555

  7. Enhancement of Spike-Timing-Dependent Plasticity in Spiking Neural Systems with Noise.

    PubMed

    Nobukawa, Sou; Nishimura, Haruhiko

    2016-08-01

    Synaptic plasticity is widely recognized to support adaptable information processing in the brain. Spike-timing-dependent plasticity, one subtype of plasticity, can lead to synchronous spike propagation with temporal spiking coding information. Recently, it was reported that in a noisy environment, like the actual brain, the spike-timing-dependent plasticity may be made efficient by the effect of stochastic resonance. In the stochastic resonance, the presence of noise helps a nonlinear system in amplifying a weak (under barrier) signal. However, previous studies have ignored the full variety of spiking patterns and many relevant factors in neural dynamics. Thus, in order to prove the physiological possibility for the enhancement of spike-timing-dependent plasticity by stochastic resonance, it is necessary to demonstrate that this stochastic resonance arises in realistic cortical neural systems. In this study, we evaluate this stochastic resonance phenomenon in the realistic cortical neural system described by the Izhikevich neuron model and compare the characteristics of typical spiking patterns of regular spiking, intrinsically bursting and chattering experimentally observed in the cortex. PMID:26678248

  8. Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity.

    PubMed

    Babadi, Baktash; Abbott, L F

    2016-03-01

    Spike-timing dependent plasticity (STDP) is a widespread plasticity mechanism in the nervous system. The simplest description of STDP only takes into account pairs of pre- and postsynaptic spikes, with potentiation of the synapse when a presynaptic spike precedes a postsynaptic spike and depression otherwise. In light of experiments that explored a variety of spike patterns, the pair-based STDP model has been augmented to account for multiple pre- and postsynaptic spike interactions. As a result, a number of different "multi-spike" STDP models have been proposed based on different experimental observations. The behavior of these models at the population level is crucial for understanding mechanisms of learning and memory. The challenging balance between the stability of a population of synapses and their competitive modification is well studied for pair-based models, but it has not yet been fully analyzed for multi-spike models. Here, we address this issue through numerical simulations of an integrate-and-fire model neuron with excitatory synapses subject to STDP described by three different proposed multi-spike models. We also analytically calculate average synaptic changes and fluctuations about these averages. Our results indicate that the different multi-spike models behave quite differently at the population level. Although each model can produce synaptic competition in certain parameter regions, none of them induces synaptic competition with its originally fitted parameters. The dichotomy between synaptic stability and Hebbian competition, which is well characterized for pair-based STDP models, persists in multi-spike models. However, anti-Hebbian competition can coexist with synaptic stability in some models. We propose that the collective behavior of synaptic plasticity models at the population level should be used as an additional guideline in applying phenomenological models based on observations of single synapses. PMID:26939080

  9. Spike timing control in retinal prosthetic

    NASA Astrophysics Data System (ADS)

    Werblin, Frank

    2005-03-01

    To restore meaningful vision to blind patients requires a retinal prosthetic device that can generate precise spiking patterns in retinal ganglion cells. We sought to develop a stimulus protocol that could reliably elicit one ganglion cell spike for every stimulation pulse over a broad frequency range. Small tipped platinum-iridium epiretinal electrodes were used to deliver biphasic cathodal electrical stimulus pulses at frequencies ranging from 10 to 125 Hz. We measured spiking responses with on-cell patch clamp from ganglion cells in the flat mount rabbit retina, identified by light response and morphology. Single electrical 30 pA cathodal pulses of 1 msec duration elicited both by direct electrical activation of ganglion cells and synaptic excitation and inhibition. Direct activation elicited a single spike that followed the onset of the cathodic pulse by about 100 μsec; presynaptic activation typically elicited multiple spikes which began after 10 msec and could persist for more than 50 ms depending on pulse amplitude levels. Limiting the pulse duration to 100 μsec eliminated all presynaptic activity: only ganglion cells were driven. Each pulse elicited a single pike for stimulation frequencies tested from 10 to125 Hz. Our ability to elicit one spike per pulse provides many important advantages: This protocol can be used to generate temporal patterns of activity in ganglion cells with precision. We can now mimic normal light evoked responses for either transient or sustained cells, and we can modulate spike frequency to simulate changes in intensity, contrast, motion and other essential cues in the visual environment.

  10. Reduction of the fast electrons preheating by changing the spike launch time in shock ignition approach

    NASA Astrophysics Data System (ADS)

    Jafar Jafari, Mohammad; Farahbod, Amir Hossein; Rezaei, Somayeh

    2016-01-01

    Target characteristic parameters in shock ignition approach before launching the spike pulse are studied using a 1-D hydrodynamic simulation code. By delaying the spike launch time, the shell areal density, ρR, is increased. The enhanced shell areal density prevents the hot electrons preheating of main fuel which in turn is generated from the intense laser plasma interaction with corona. To consider the effect of the spike launch time on the target performance, the target gain for a wide range of spike powers and launch times are computed. It is noticed that for HiPER reference target, few tenth nanoseconds displacement of spike launch time increases the areal density, ρR, value up to 30-70 percent. Furthermore, by choosing an appropriate spike energy and peak power, the optimum target gain is achieved in which the total driver energy is reduced.

  11. Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity

    PubMed Central

    Babadi, Baktash; Abbott, L. F.

    2016-01-01

    Spike-timing dependent plasticity (STDP) is a widespread plasticity mechanism in the nervous system. The simplest description of STDP only takes into account pairs of pre- and postsynaptic spikes, with potentiation of the synapse when a presynaptic spike precedes a postsynaptic spike and depression otherwise. In light of experiments that explored a variety of spike patterns, the pair-based STDP model has been augmented to account for multiple pre- and postsynaptic spike interactions. As a result, a number of different “multi-spike” STDP models have been proposed based on different experimental observations. The behavior of these models at the population level is crucial for understanding mechanisms of learning and memory. The challenging balance between the stability of a population of synapses and their competitive modification is well studied for pair-based models, but it has not yet been fully analyzed for multi-spike models. Here, we address this issue through numerical simulations of an integrate-and-fire model neuron with excitatory synapses subject to STDP described by three different proposed multi-spike models. We also analytically calculate average synaptic changes and fluctuations about these averages. Our results indicate that the different multi-spike models behave quite differently at the population level. Although each model can produce synaptic competition in certain parameter regions, none of them induces synaptic competition with its originally fitted parameters. The dichotomy between synaptic stability and Hebbian competition, which is well characterized for pair-based STDP models, persists in multi-spike models. However, anti-Hebbian competition can coexist with synaptic stability in some models. We propose that the collective behavior of synaptic plasticity models at the population level should be used as an additional guideline in applying phenomenological models based on observations of single synapses. PMID:26939080

  12. Ribbon Reduces Spiking in Electron-Beam Welding

    NASA Technical Reports Server (NTRS)

    Olson, R. E.

    1984-01-01

    Spiking in electron-beam welding reduced by placing high-vapor-pressure substance along path of electron beam. Strip of metal having vapor pressure higher than base metal at same temperature placed in slot machined along weld line. Strip vaporizes as beam strikes it, and vapor pressure keeps surface tension from closing off top of channel. Technique used successfully on nickel alloys and aluminum alloys and effective on steel and titanium.

  13. Adaptive time-frequency parametrization of epileptic spikes

    NASA Astrophysics Data System (ADS)

    Durka, Piotr J.

    2004-05-01

    Adaptive time-frequency approximations of signals have proven to be a valuable tool in electroencephalogram (EEG) analysis and research, where it is believed that oscillatory phenomena play a crucial role in the brain’s information processing. This paper extends this paradigm to the nonoscillating structures such as the epileptic EEG spikes, and presents the advantages of their parametrization in general terms such as amplitude and half-width. A simple detector of epileptic spikes in the space of these parameters, tested on a limited data set, gives very promising results. It also provides a direct distinction between randomly occurring spikes or spike/wave complexes and rhythmic discharges.

  14. Computing Complex Visual Features with Retinal Spike Times

    PubMed Central

    Sompolinsky, Haim; Meister, Markus

    2013-01-01

    Neurons in sensory systems can represent information not only by their firing rate, but also by the precise timing of individual spikes. For example, certain retinal ganglion cells, first identified in the salamander, encode the spatial structure of a new image by their first-spike latencies. Here we explore how this temporal code can be used by downstream neural circuits for computing complex features of the image that are not available from the signals of individual ganglion cells. To this end, we feed the experimentally observed spike trains from a population of retinal ganglion cells to an integrate-and-fire model of post-synaptic integration. The synaptic weights of this integration are tuned according to the recently introduced tempotron learning rule. We find that this model neuron can perform complex visual detection tasks in a single synaptic stage that would require multiple stages for neurons operating instead on neural spike counts. Furthermore, the model computes rapidly, using only a single spike per afferent, and can signal its decision in turn by just a single spike. Extending these analyses to large ensembles of simulated retinal signals, we show that the model can detect the orientation of a visual pattern independent of its phase, an operation thought to be one of the primitives in early visual processing. We analyze how these computations work and compare the performance of this model to other schemes for reading out spike-timing information. These results demonstrate that the retina formats spatial information into temporal spike sequences in a way that favors computation in the time domain. Moreover, complex image analysis can be achieved already by a simple integrate-and-fire model neuron, emphasizing the power and plausibility of rapid neural computing with spike times. PMID:23301021

  15. A comparison of learning abilities of spiking networks with different spike timing-dependent plasticity forms

    NASA Astrophysics Data System (ADS)

    Sboev, Alexander; Vlasov, Danila; Serenko, Alexey; Rybka, Roman; Moloshnikov, Ivan

    2016-02-01

    A study of possibility to model the learning process on base of different forms of timing-dependent plasticity (STDP) was performed. It is shown that the learning ability depends on the choice of spike pairing scheme and the type of input signal used for learning. The comparison of performance of several STDP rules along with several neuron models (leaky integrate-and-fire, static, Izhikevich and Hodgkin-Huxley) was carried out using the NEST simulator. The combinations of input signal and STDP spike pairing scheme, which demonstrate the best learning abilities, were extracted.

  16. Spike-timing-dependent plasticity in spiking neuron networks for robot navigation control

    NASA Astrophysics Data System (ADS)

    Arena, Paolo; Danieli, Fabio; Fortuna, Luigi; Frasca, Mattia; Patane, Luca

    2005-06-01

    In this paper a biologically-inspired network of spiking neurons is used for robot navigation control. The implemented scheme is able to process information coming from the robot contact sensors in order to avoid obstacles and on the basis of these actions to learn how to respond to stimuli coming from range finder sensors. The implemented network is therefore able of reinforcement learning through a mechanism based on operant conditioning. This learning takes place according to a plasticity law in the synapses, based on spike timing. Simulation results discussed in the paper show the suitability of the approach and interesting adaptive properties of the network.

  17. Spike-timing error backpropagation in theta neuron networks.

    PubMed

    McKennoch, Sam; Voegtlin, Thomas; Bushnell, Linda

    2009-01-01

    The main contribution of this letter is the derivation of a steepest gradient descent learning rule for a multilayer network of theta neurons, a one-dimensional nonlinear neuron model. Central to our model is the assumption that the intrinsic neuron dynamics are sufficient to achieve consistent time coding, with no need to involve the precise shape of postsynaptic currents; this assumption departs from other related models such as SpikeProp and Tempotron learning. Our results clearly show that it is possible to perform complex computations by applying supervised learning techniques to the spike times and time response properties of nonlinear integrate and fire neurons. Networks trained with our multilayer training rule are shown to have similar generalization abilities for spike latency pattern classification as Tempotron learning. The rule is also able to train networks to perform complex regression tasks that neither SpikeProp or Tempotron learning appears to be capable of. PMID:19431278

  18. An implantable VLSI architecture for real time spike sorting in cortically controlled Brain Machine Interfaces.

    PubMed

    Aghagolzadeh, Mehdi; Zhang, Fei; Oweiss, Karim

    2010-01-01

    Brain Machine Interface (BMI) systems demand real-time spike sorting to instantaneously decode the spike trains of simultaneously recorded cortical neurons. Real-time spike sorting, however, requires extensive computational power that is not feasible to implement in implantable BMI architectures, thereby requiring transmission of high-bandwidth raw neural data to an external computer. In this work, we describe a miniaturized, low power, programmable hardware module capable of performing this task within the resource constraints of an implantable chip. The module computes a sparse representation of the spike waveforms followed by "smart" thresholding. This cascade restricts the sparse representation to a subset of projections that preserve the discriminative features of neuron-specific spike waveforms. In addition, it further reduces telemetry bandwidth making it feasible to wirelessly transmit only the important biological information to the outside world, thereby improving the efficiency, practicality and viability of BMI systems in clinical applications. PMID:21096383

  19. Multiple Spike Time Patterns Occur at Bifurcation Points of Membrane Potential Dynamics

    PubMed Central

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

    2012-01-01

    The response of a neuron to repeated somatic fluctuating current injections in vitro can elicit a reliable and precisely timed sequence of action potentials. The set of responses obtained across trials can also be interpreted as the response of an ensemble of similar neurons receiving the same input, with the precise spike times representing synchronous volleys that would be effective in driving postsynaptic neurons. To study the reproducibility of the output spike times for different conditions that might occur in vivo, we somatically injected aperiodic current waveforms into cortical neurons in vitro and systematically varied the amplitude and DC offset of the fluctuations. As the amplitude of the fluctuations was increased, reliability increased and the spike times remained stable over a wide range of values. However, at specific values called bifurcation points, large shifts in the spike times were obtained in response to small changes in the stimulus, resulting in multiple spike patterns that were revealed using an unsupervised classification method. Increasing the DC offset, which mimicked an overall increase in network background activity, also revealed bifurcation points and increased the reliability. Furthermore, the spike times shifted earlier with increasing offset. Although the reliability was reduced at bifurcation points, a theoretical analysis showed that the information about the stimulus time course was increased because each of the spike time patterns contained different information about the input. PMID:23093916

  20. Designing optimal stimuli to control neuronal spike timing.

    PubMed

    Ahmadian, Yashar; Packer, Adam M; Yuste, Rafael; Paninski, Liam

    2011-08-01

    Recent advances in experimental stimulation methods have raised the following important computational question: how can we choose a stimulus that will drive a neuron to output a target spike train with optimal precision, given physiological constraints? Here we adopt an approach based on models that describe how a stimulating agent (such as an injected electrical current or a laser light interacting with caged neurotransmitters or photosensitive ion channels) affects the spiking activity of neurons. Based on these models, we solve the reverse problem of finding the best time-dependent modulation of the input, subject to hardware limitations as well as physiologically inspired safety measures, that causes the neuron to emit a spike train that with highest probability will be close to a target spike train. We adopt fast convex constrained optimization methods to solve this problem. Our methods can potentially be implemented in real time and may also be generalized to the case of many cells, suitable for neural prosthesis applications. With the use of biologically sensible parameters and constraints, our method finds stimulation patterns that generate very precise spike trains in simulated experiments. We also tested the intracellular current injection method on pyramidal cells in mouse cortical slices, quantifying the dependence of spiking reliability and timing precision on constraints imposed on the applied currents. PMID:21511704

  1. Spike count, spike timing and temporal information in the cortex of awake, freely moving rats

    NASA Astrophysics Data System (ADS)

    Scaglione, Alessandro; Foffani, Guglielmo; Moxon, Karen A.

    2014-08-01

    Objective. Sensory processing of peripheral information is not stationary but is, in general, a dynamic process related to the behavioral state of the animal. Yet the link between the state of the behavior and the encoding properties of neurons is unclear. This report investigates the impact of the behavioral state on the encoding mechanisms used by cortical neurons for both detection and discrimination of somatosensory stimuli in awake, freely moving, rats. Approach. Neuronal activity was recorded from the primary somatosensory cortex of five rats under two different behavioral states (quiet versus whisking) while electrical stimulation of increasing stimulus strength was delivered to the mystacial pad. Information theoretical measures were then used to measure the contribution of different encoding mechanisms to the information carried by neurons in response to the whisker stimulation. Main results. We found that the behavioral state of the animal modulated the total amount of information conveyed by neurons and that the timing of individual spikes increased the information compared to the total count of spikes alone. However, the temporal information, i.e. information exclusively related to when the spikes occur, was not modulated by behavioral state. Significance. We conclude that information about somatosensory stimuli is modulated by the behavior of the animal and this modulation is mainly expressed in the spike count while the temporal information is more robust to changes in behavioral state.

  2. Spatio-temporal pattern recognizers using spiking neurons and spike-timing-dependent plasticity.

    PubMed

    Humble, James; Denham, Susan; Wennekers, Thomas

    2012-01-01

    It has previously been shown that by using spike-timing-dependent plasticity (STDP), neurons can adapt to the beginning of a repeating spatio-temporal firing pattern in their input. In the present work, we demonstrate that this mechanism can be extended to train recognizers for longer spatio-temporal input signals. Using a number of neurons that are mutually connected by plastic synapses and subject to a global winner-takes-all mechanism, chains of neurons can form where each neuron is selective to a different segment of a repeating input pattern, and the neurons are feed-forwardly connected in such a way that both the correct input segment and the firing of the previous neurons are required in order to activate the next neuron in the chain. This is akin to a simple class of finite state automata. We show that nearest-neighbor STDP (where only the pre-synaptic spike most recent to a post-synaptic one is considered) leads to "nearest-neighbor" chains where connections only form between subsequent states in a chain (similar to classic "synfire chains"). In contrast, "all-to-all spike-timing-dependent plasticity" (where all pre- and post-synaptic spike pairs matter) leads to multiple connections that can span several temporal stages in the chain; these connections respect the temporal order of the neurons. It is also demonstrated that previously learnt individual chains can be "stitched together" by repeatedly presenting them in a fixed order. This way longer sequence recognizers can be formed, and potentially also nested structures. Robustness of recognition with respect to speed variations in the input patterns is shown to depend on rise-times of post-synaptic potentials and the membrane noise. It is argued that the memory capacity of the model is high, but could theoretically be increased using sparse codes. PMID:23087641

  3. Spike timing and visual processing in the retinogeniculocortical pathway.

    PubMed Central

    Usrey, W Martin

    2002-01-01

    Although the visual response properties of neurons along the retinogeniculocortical pathway have been studied for decades, relatively few studies have examined how individual neurons along the pathway communicate with each other. Recent studies in the cat (Felis domestica) now show that the strength of these connections is very dynamic and spike timing plays an important part in determining whether action potentials will be transferred from pre- to postsynaptic cells. This review explores recent progress in our understanding of what role spike timing has in establishing different patterns of geniculate activity and how these patterns ultimately drive the cortex. PMID:12626007

  4. Financial Time Series Prediction Using Spiking Neural Networks

    PubMed Central

    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. PMID:25170618

  5. Financial time series prediction using spiking neural networks.

    PubMed

    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. PMID:25170618

  6. State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data

    PubMed Central

    Shimazaki, Hideaki; Amari, Shun-ichi; Brown, Emery N.; Grün, Sonja

    2012-01-01

    Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods

  7. State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data.

    PubMed

    Shimazaki, Hideaki; Amari, Shun-Ichi; Brown, Emery N; Grün, Sonja

    2012-01-01

    Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods

  8. Spike-Timing-Based Computation in Sound Localization

    PubMed Central

    Goodman, Dan F. M.; Brette, Romain

    2010-01-01

    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. PMID:21085681

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

    PubMed

    Giglio, Anna M; Storm, Johan F

    2014-01-01

    Spike timing and network synchronization are important for plasticity, development and maturation of brain circuits. Spike delays and timing can be strongly modulated by a low-threshold, slowly inactivating, voltage-gated potassium current called D-current (ID ). ID can delay the onset of spiking, cause temporal integration of multiple inputs, and regulate spike threshold and network synchrony. Recent data indicate that ID can also undergo activity-dependent, homeostatic regulation. Therefore, we have studied the postnatal development of ID -dependent mechanisms in CA1 pyramidal cells in hippocampal slices from young rats (P7-27), using somatic whole-cell recordings. At P21-27, these neurons showed long spike delays and pronounced temporal integration in response to a series of brief depolarizing current pulses or a single long pulse, whereas younger cells (P7-20) showed shorter discharge delays and weak temporal integration, although the spike threshold became increasingly negative with maturation. Application of α-dendrotoxin (α-DTX), which blocks ID , reduced the spiking latency and temporal integration most strongly in mature cells, while shifting the spike threshold most strongly in a depolarizing direction in these cells. Voltage-clamp analysis revealed an α-DTX-sensitive outward current (ID ) that increased in amplitude during development. In contrast to P21-23, ID in the youngest group (P7-9) showed smaller peri-threshold amplitude. This may explain why long discharge delays and robust temporal integration only appear later, 3 weeks postnatally. We conclude that ID properties and ID -dependent functions develop postnatally in rat CA1 pyramidal cells, and ID may modulate network activity and plasticity through its effects on synaptic integration, spike threshold, timing and synchrony. PMID:24148023

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

    NASA Astrophysics Data System (ADS)

    Sengupta, Abhronil; Al Azim, Zubair; Fong, Xuanyao; Roy, Kaushik

    2015-03-01

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

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

    SciTech Connect

    Sengupta, Abhronil Al Azim, Zubair; Fong, Xuanyao; Roy, Kaushik

    2015-03-02

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

  12. Network Structures Arising from Spike-Timing Dependent Plasticity

    NASA Astrophysics Data System (ADS)

    Babadi, Baktash

    Spike-timing dependent plasticity (STDP), a widespread synaptic modification mechanism, is sensitive to correlations between presynaptic spike trains, and organizes neural circuits in functionally useful ways. In this dissertation, I study the structures arising from STDP in a population of synapses with an emphasis on the interplay between synaptic stability and Hebbian competition, explained in Chapter 1. Starting from the simplest description of STDP which relates synaptic modification to the intervals between pairs of pre- and postsynaptic spikes, I show in Chapter 2 that stability and Hebbian competition are incompatible in this class of "pair-based" STDP models, either when hard bounds or soft bounds are imposed to the synapses. In chapter 3, I propose an alternative biophysically inspired method for imposing bounds to synapses, i.e. introducing a small temporal shift in the STDP window. Shifted STDP overcomes the incompatibility of synaptic stability and competition and can implement both Hebbian and anti-Hebbian forms of competitive plasticity. In light of experiments the explored a variety of spike patterns, STDP models have been augmented to account for interactions between multiple pre- and postsynaptic action potentials. In chapter 4, I study the stability/competition interplay in three different proposed multi-spike models of STDP. I show that the "triplet model" leads to a partially steady-state distribution of synaptic weights and induces Hebbian competition. The "suppression model" develops a stable distribution of weights when the average weight is high and shows predominantly anti-Hebbian competition. The "NMDAR-based" model can lead to either stable or partially stable synaptic weight distribution and exhibits both Hebbian and anti-Hebbian competition, depending on the parameters. I conclude that multi-spike STDP models can produce radically different effects at the population level depending on how they implement multi-spike interactions

  13. Spike-timing dependent plasticity in the striatum.

    PubMed

    Fino, Elodie; Venance, Laurent

    2010-01-01

    The striatum is the major input nucleus of basal ganglia, an ensemble of interconnected sub-cortical nuclei associated with fundamental processes of action-selection and procedural learning and memory. The striatum receives afferents from the cerebral cortex and the thalamus. In turn, it relays the integrated information towards the basal ganglia output nuclei through which it operates a selected activation of behavioral effectors. The striatal output neurons, the GABAergic medium-sized spiny neurons (MSNs), are in charge of the detection and integration of behaviorally relevant information. This property confers to the striatum the ability to extract relevant information from the background noise and select cognitive-motor sequences adapted to environmental stimuli. As long-term synaptic efficacy changes are believed to underlie learning and memory, the corticostriatal long-term plasticity provides a fundamental mechanism for the function of the basal ganglia in procedural learning. Here, we reviewed the different forms of spike-timing dependent plasticity (STDP) occurring at corticostriatal synapses. Most of the studies have focused on MSNs and their ability to develop long-term plasticity. Nevertheless, the striatal interneurons (the fast-spiking GABAergic, NO-synthase and cholinergic interneurons) also receive monosynaptic afferents from the cortex and tightly regulated corticostriatal information processing. Therefore, it is important to take into account the variety of striatal neurons to fully understand the ability of striatum to develop long-term plasticity. Corticostriatal STDP with various spike-timing dependence have been observed depending on the neuronal sub-populations and experimental conditions. This complexity highlights the extraordinary potentiality in term of plasticity of the corticostriatal pathway. PMID:21423492

  14. Equation-free analysis of spike-timing-dependent plasticity.

    PubMed

    Laing, Carlo R; Kevrekidis, Ioannis G

    2015-12-01

    Spike-timing-dependent plasticity is the process by which the strengths of connections between neurons are modified as a result of the precise timing of the action potentials fired by the neurons. We consider a model consisting of one integrate-and-fire neuron receiving excitatory inputs from a large number-here, 1000-of Poisson neurons whose synapses are plastic. When correlations are introduced between the firing times of these input neurons, the distribution of synaptic strengths shows interesting, and apparently low-dimensional, dynamical behaviour. This behaviour is analysed in two different parameter regimes using equation-free techniques, which bypass the explicit derivation of the relevant low-dimensional dynamical system. We demonstrate both coarse projective integration (which speeds up the time integration of a dynamical system) and the use of recently developed data mining techniques to identify the appropriate low-dimensional description of the complex dynamical systems in our model. PMID:26577337

  15. Memory Retention and Spike-Timing-Dependent Plasticity

    PubMed Central

    Billings, Guy; van Rossum, Mark C. W.

    2009-01-01

    Memory systems should be plastic to allow for learning; however, they should also retain earlier memories. Here we explore how synaptic weights and memories are retained in models of single neurons and networks equipped with spike-timing-dependent plasticity. We show that for single neuron models, the precise learning rule has a strong effect on the memory retention time. In particular, a soft-bound, weight-dependent learning rule has a very short retention time as compared with a learning rule that is independent of the synaptic weights. Next, we explore how the retention time is reflected in receptive field stability in networks. As in the single neuron case, the weight-dependent learning rule yields less stable receptive fields than a weight-independent rule. However, receptive fields stabilize in the presence of sufficient lateral inhibition, demonstrating that plasticity in networks can be regulated by inhibition and suggesting a novel role for inhibition in neural circuits. PMID:19297513

  16. Neonatal NMDA Receptor Blockade Disrupts Spike Timing and Glutamatergic Synapses in Fast Spiking Interneurons in a NMDA Receptor Hypofunction Model of Schizophrenia

    PubMed Central

    Jones, Kevin S.; Corbin, Joshua G.; Huntsman, Molly M.

    2014-01-01

    The dysfunction of parvalbumin-positive, fast-spiking interneurons (FSI) is considered a primary contributor to the pathophysiology of schizophrenia (SZ), but deficits in FSI physiology have not been explicitly characterized. We show for the first time, that a widely-employed model of schizophrenia minimizes first spike latency and increases GluN2B-mediated current in neocortical FSIs. The reduction in FSI first-spike latency coincides with reduced expression of the Kv1.1 potassium channel subunit which provides a biophysical explanation for the abnormal spiking behavior. Similarly, the increase in NMDA current coincides with enhanced expression of the GluN2B NMDA receptor subunit, specifically in FSIs. In this study mice were treated with the NMDA receptor antagonist, MK-801, during the first week of life. During adolescence, we detected reduced spike latency and increased GluN2B-mediated NMDA current in FSIs, which suggests transient disruption of NMDA signaling during neonatal development exerts lasting changes in the cellular and synaptic physiology of neocortical FSIs. Overall, we propose these physiological disturbances represent a general impairment to the physiological maturation of FSIs which may contribute to schizophrenia-like behaviors produced by this model. PMID:25290690

  17. Spike timing analysis in neural networks with unsupervised synaptic plasticity

    NASA Astrophysics Data System (ADS)

    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.

  18. Endocannabinoids mediate bidirectional striatal spike-timing-dependent plasticity

    PubMed Central

    Cui, Yihui; Paillé, Vincent; Xu, Hao; Genet, Stéphane; Delord, Bruno; Fino, Elodie; Berry, Hugues; Venance, Laurent

    2015-01-01

    Key points Although learning can arise from few or even a single trial, synaptic plasticity is commonly assessed under prolonged activation. Here, we explored the existence of rapid responsiveness of synaptic plasticity at corticostriatal synapses in a major synaptic learning rule, spike-timing-dependent plasticity (STDP). We found that spike-timing-dependent depression (tLTD) progressively disappears when the number of paired stimulations (below 50 pairings) is decreased whereas spike-timing-dependent potentiation (tLTP) displays a biphasic profile: tLTP is observed for 75–100 pairings, is absent for 25–50 pairings and re-emerges for 5–10 pairings. This tLTP induced by low numbers of pairings (5–10) depends on activation of the endocannabinoid system, type-1 cannabinoid receptor and the transient receptor potential vanilloid type-1. Endocannabinoid-tLTP may represent a physiological mechanism operating during the rapid learning of new associative memories and behavioural rules characterizing the flexible behaviour of mammals or during the initial stages of habit learning. Abstract Synaptic plasticity, a main substrate for learning and memory, is commonly assessed with prolonged stimulations. Since learning can arise from few or even a single trial, synaptic strength is expected to adapt rapidly. However, whether synaptic plasticity occurs in response to limited event occurrences remains elusive. To answer this question, we investigated whether a low number of paired stimulations can induce plasticity in a major synaptic learning rule, spike-timing-dependent plasticity (STDP). It is known that 100 pairings induce bidirectional STDP, i.e. spike-timing-dependent potentiation (tLTP) and depression (tLTD) at most central synapses. In rodent striatum, we found that tLTD progressively disappears when the number of paired stimulations is decreased (below 50 pairings) whereas tLTP displays a biphasic profile: tLTP is observed for 75–100 pairings, absent for 25

  19. Shifting Spike Times or Adding and Deleting Spikes-How Different Types of Noise Shape Signal Transmission in Neural Populations.

    PubMed

    Voronenko, Sergej O; Stannat, Wilhelm; Lindner, Benjamin

    2015-12-01

    We study a population of spiking neurons which are subject to independent noise processes and a strong common time-dependent input. We show that the response of output spikes to independent noise shapes information transmission of such populations even when information transmission properties of single neurons are left unchanged. In particular, we consider two Poisson models in which independent noise either (i) adds and deletes spikes (AD model) or (ii) shifts spike times (STS model). We show that in both models suprathreshold stochastic resonance (SSR) can be observed, where the information transmitted by a neural population is increased with addition of independent noise. In the AD model, the presence of the SSR effect is robust and independent of the population size or the noise spectral statistics. In the STS model, the information transmission properties of the population are determined by the spectral statistics of the noise, leading to a strongly increased effect of SSR in some regimes, or an absence of SSR in others. Furthermore, we observe a high-pass filtering of information in the STS model that is absent in the AD model. We quantify information transmission by means of the lower bound on the mutual information rate and the spectral coherence function. To this end, we derive the signal-output cross-spectrum, the output power spectrum, and the cross-spectrum of two spike trains for both models analytically. PMID:26458900

  20. Spike-time reliability of layered neural oscillator networks

    NASA Astrophysics Data System (ADS)

    Lin, K. K.; Shea-Brown, E.; Young, L.-S.

    2013-01-01

    If a network of neurons is repeatedly driven by the same fluctuating signal, will it give the same response each time? If so, the network is said to be reliable. Reliability is of interest in computational neuroscience because the degree to which a network is reliable constrains its ability to encode information in precise temporal patterns of spikes. This note outlines how the question of reliability may be fruitfully formulated and studied within the framework of random dynamical systems theory. A specific network architecture, that of a single-layer network, is examined. For the type of single-neuron dynamics and coupling considered here, single-layer networks are found to be very reliable. A qualitative explanation is proposed for this phenomenon.

  1. Spike-timing control by dendritic plateau potentials in the presence of synaptic barrages

    PubMed Central

    Shai, Adam S.; Koch, Christof; Anastassiou, Costas A.

    2014-01-01

    Apical and tuft dendrites of pyramidal neurons support regenerative electrical potentials, giving rise to long-lasting (approximately hundreds of milliseconds) and strong (~50 mV from rest) depolarizations. Such plateau events rely on clustered glutamatergic input, can be mediated by calcium or by NMDA currents, and often generate somatic depolarizations that last for the time course of the dendritic plateau event. We address the computational significance of such single-neuron processing via reduced but biophysically realistic modeling. We introduce a model based on two discrete integration zones, a somatic and a dendritic one, that communicate from the dendritic to the somatic compartment via a long plateau-conductance. We show principled differences in the way dendritic vs. somatic inhibition controls spike timing, and demonstrate how this could implement spike time control in the face of barrages of synaptic inputs. PMID:25177288

  2. A Computational Study of Spike Time Reliability in Two Types of Threshold Dynamics

    PubMed Central

    2013-01-01

    Spike time reliability (STR) refers to the phenomenon in which repetitive applications of a frozen copy of one stochastic signal to a neuron trigger spikes with reliable timing while a constant signal fails to do so. Observed and explored in numerous experimental and theoretical studies, STR is a complex dynamic phenomenon depending on the nature of external inputs as well as intrinsic properties of a neuron. The neuron under consideration could be either quiescent or spontaneously spiking in the absence of the external stimulus. Focusing on the situation in which the unstimulated neuron is quiescent but close to a switching point to oscillations, we numerically analyze STR treating each spike occurrence as a time localized event in a model neuron. We study both the averaged properties as well as individual features of spike-evoking epochs (SEEs). The effects of interactions between spikes is minimized by selecting signals that generate spikes with relatively long interspike intervals (ISIs). Under these conditions, the frequency content of the input signal has little impact on STR. We study two distinct cases, Type I in which the f–I relation (f for frequency, I for applied current) is continuous and Type II where the f–I relation exhibits a jump. STR in the two types shows a number of similar features and differ in some others. SEEs that are capable of triggering spikes show great variety in amplitude and time profile. On average, reliable spike timing is associated with an accelerated increase in the “action” of the signal as a threshold for spike generation is approached. Here, “action” is defined as the average amount of current delivered during a fixed time interval. When individual SEEs are studied, however, their time profiles are found important for triggering more precisely timed spikes. The SEEs that have a more favorable time profile are capable of triggering spikes with higher precision even at lower action levels. PMID:23945258

  3. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

    PubMed Central

    Pecevski, Dejan

    2016-01-01

    Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p* that generates the examples it receives. This holds even if p* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. PMID:27419214

  4. Endocannabinoid dynamics gate spike-timing dependent depression and potentiation

    PubMed Central

    Cui, Yihui; Prokin, Ilya; Xu, Hao; Delord, Bruno; Genet, Stephane; Venance, Laurent; Berry, Hugues

    2016-01-01

    Synaptic plasticity is a cardinal cellular mechanism for learning and memory. The endocannabinoid (eCB) system has emerged as a pivotal pathway for synaptic plasticity because of its widely characterized ability to depress synaptic transmission on short- and long-term scales. Recent reports indicate that eCBs also mediate potentiation of the synapse. However, it is not known how eCB signaling may support bidirectionality. Here, we combined electrophysiology experiments with mathematical modeling to question the mechanisms of eCB bidirectionality in spike-timing dependent plasticity (STDP) at corticostriatal synapses. We demonstrate that STDP outcome is controlled by eCB levels and dynamics: prolonged and moderate levels of eCB lead to eCB-mediated long-term depression (eCB-tLTD) while short and large eCB transients produce eCB-mediated long-term potentiation (eCB-tLTP). Moreover, we show that eCB-tLTD requires active calcineurin whereas eCB-tLTP necessitates the activity of presynaptic PKA. Therefore, just like glutamate or GABA, eCB form a bidirectional system to encode learning and memory. DOI: http://dx.doi.org/10.7554/eLife.13185.001 PMID:26920222

  5. A General and Efficient Method for Incorporating Precise Spike Times in Globally Time-Driven Simulations

    PubMed Central

    Hanuschkin, Alexander; Kunkel, Susanne; Helias, Moritz; Morrison, Abigail; Diesmann, Markus

    2010-01-01

    Traditionally, event-driven simulations have been limited to the very restricted class of neuronal models for which the timing of future spikes can be expressed in closed form. Recently, the class of models that is amenable to event-driven simulation has been extended by the development of techniques to accurately calculate firing times for some integrate-and-fire neuron models that do not enable the prediction of future spikes in closed form. The motivation of this development is the general perception that time-driven simulations are imprecise. Here, we demonstrate that a globally time-driven scheme can calculate firing times that cannot be discriminated from those calculated by an event-driven implementation of the same model; moreover, the time-driven scheme incurs lower computational costs. The key insight is that time-driven methods are based on identifying a threshold crossing in the recent past, which can be implemented by a much simpler algorithm than the techniques for predicting future threshold crossings that are necessary for event-driven approaches. As run time is dominated by the cost of the operations performed at each incoming spike, which includes spike prediction in the case of event-driven simulation and retrospective detection in the case of time-driven simulation, the simple time-driven algorithm outperforms the event-driven approaches. Additionally, our method is generally applicable to all commonly used integrate-and-fire neuronal models; we show that a non-linear model employing a standard adaptive solver can reproduce a reference spike train with a high degree of precision. PMID:21031031

  6. A History of Spike-Timing-Dependent Plasticity

    PubMed Central

    Markram, Henry; Gerstner, Wulfram; Sjöström, Per Jesper

    2011-01-01

    How learning and memory is achieved in the brain is a central question in neuroscience. Key to today’s research into information storage in the brain is the concept of synaptic plasticity, a notion that has been heavily influenced by Hebb's (1949) postulate. Hebb conjectured that repeatedly and persistently co-active cells should increase connective strength among populations of interconnected neurons as a means of storing a memory trace, also known as an engram. Hebb certainly was not the first to make such a conjecture, as we show in this history. Nevertheless, literally thousands of studies into the classical frequency-dependent paradigm of cellular learning rules were directly inspired by the Hebbian postulate. But in more recent years, a novel concept in cellular learning has emerged, where temporal order instead of frequency is emphasized. This new learning paradigm – known as spike-timing-dependent plasticity (STDP) – has rapidly gained tremendous interest, perhaps because of its combination of elegant simplicity, biological plausibility, and computational power. But what are the roots of today’s STDP concept? Here, we discuss several centuries of diverse thinking, beginning with philosophers such as Aristotle, Locke, and Ribot, traversing, e.g., Lugaro’s plasticità and Rosenblatt’s perceptron, and culminating with the discovery of STDP. We highlight interactions between theoretical and experimental fields, showing how discoveries sometimes occurred in parallel, seemingly without much knowledge of the other field, and sometimes via concrete back-and-forth communication. We point out where the future directions may lie, which includes interneuron STDP, the functional impact of STDP, its mechanisms and its neuromodulatory regulation, and the linking of STDP to the developmental formation and continuous plasticity of neuronal networks. PMID:22007168

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

    PubMed Central

    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. PMID:26900845

  8. Distinct roles for IT and IH in controlling the frequency and timing of rebound spike responses

    PubMed Central

    Engbers, Jordan D T; Anderson, Dustin; Tadayonnejad, Reza; Mehaffey, W Hamish; Molineux, Michael L; Turner, Ray W

    2011-01-01

    Abstract The ability for neurons to generate rebound bursts following inhibitory synaptic input relies on ion channels that respond in a unique fashion to hyperpolarization. Inward currents provided by T-type calcium channels (IT) and hyperpolarization-activated HCN channels (IH) increase in availability upon hyperpolarization, allowing for a rebound depolarization after a period of inhibition. Although rebound responses have long been recognized in deep cerebellar nuclear (DCN) neurons, the actual extent to which IT and IH contribute to rebound spike output following physiological levels of membrane hyperpolarization has not been clearly established. The current study used recordings and simulations of large diameter cells of the in vitro rat DCN slice preparation to define the roles for IT and IH in a rebound response. We find that physiological levels of hyperpolarization make only small proportions of the total IT and IH available, but that these are sufficient to make substantial contributions to a rebound response. At least 50% of the early phase of the rebound spike frequency increase is generated by an IT-mediated depolarization. An additional frequency increase is provided by IH in reducing the time constant and thus the extent of IT inactivation as the membrane returns from a hyperpolarized state to the resting level. An IH-mediated depolarization creates an inverse voltage-first spike latency relationship and produces a 35% increase in the precision of the first spike latency of a rebound. IT and IH can thus be activated by physiologically relevant stimuli and have distinct roles in the frequency, timing and precision of rebound responses. PMID:21969455

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

    PubMed

    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. PMID:25035965

  10. Learning Spike Time Codes Through Morphological Learning With Binary Synapses.

    PubMed

    Roy, Subhrajit; San, Phyo Phyo; Hussain, Shaista; Wei, Lee Wang; Basu, Arindam

    2016-07-01

    In this brief, a neuron with nonlinear dendrites (NNLDs) and binary synapses that is able to learn temporal features of spike input patterns is considered. Since binary synapses are considered, learning happens through formation and elimination of connections between the inputs and the dendritic branches to modify the structure or morphology of the NNLD. A morphological learning algorithm inspired by the tempotron, i.e., a recently proposed temporal learning algorithm is presented in this brief. Unlike tempotron, the proposed learning rule uses a technique to automatically adapt the NNLD threshold during training. Experimental results indicate that our NNLD with 1-bit synapses can obtain accuracy similar to that of a traditional tempotron with 4-bit synapses in classifying single spike random latency and pairwise synchrony patterns. Hence, the proposed method is better suited for robust hardware implementation in the presence of statistical variations. We also present results of applying this rule to real-life spike classification problems from the field of tactile sensing. PMID:26173221

  11. Associative memory based on synchronized firing of spiking neurons with time-delayed interactions

    NASA Astrophysics Data System (ADS)

    Yoshioka, Masahiko; Shiino, Masatoshi

    1998-09-01

    We study associative memory of a neural network of spiking neurons with time-delayed synaptic interactions incorporating the time taken by an action potential to propagate along the axon. Individual spiking neurons are described by a set of nonlinear differential equations capable of exhibiting excitability such as that of Hodgkin-Huxley and FitzHugh neurons. When a simple learning rule of the autocorrelation type based on random patterns is assumed, memory retrieval is shown to be accompanied by synchronized firing of neurons. The reduced dynamics with a few degrees of freedom of the network with a finite number of stored patterns is analytically derived in the limit of infinitely many neurons. The dependence of the appearance of retrieval states on the distribution of time delay and on the size of refractory period given implicitly in the model is obtained, showing good agreement between the result of numerical simulations and that obtained from the reduced dynamics. The behavior of the network with an extensive number of patterns is also investigated and an approximate analysis is presented to discuss the storage capacity.

  12. What can neuromorphic event-driven precise timing add to spike-based pattern recognition?

    PubMed

    Akolkar, Himanshu; Meyer, Cedric; Clady, Zavier; Marre, Olivier; Bartolozzi, Chiara; Panzeri, Stefano; Benosman, Ryad

    2015-03-01

    This letter introduces a study to precisely measure what an increase in spike timing precision can add to spike-driven pattern recognition algorithms. The concept of generating spikes from images by converting gray levels into spike timings is currently at the basis of almost every spike-based modeling of biological visual systems. The use of images naturally leads to generating incorrect artificial and redundant spike timings and, more important, also contradicts biological findings indicating that visual processing is massively parallel, asynchronous with high temporal resolution. A new concept for acquiring visual information through pixel-individual asynchronous level-crossing sampling has been proposed in a recent generation of asynchronous neuromorphic visual sensors. Unlike conventional cameras, these sensors acquire data not at fixed points in time for the entire array but at fixed amplitude changes of their input, resulting optimally sparse in space and time-pixel individually and precisely timed only if new, (previously unknown) information is available (event based). This letter uses the high temporal resolution spiking output of neuromorphic event-based visual sensors to show that lowering time precision degrades performance on several recognition tasks specifically when reaching the conventional range of machine vision acquisition frequencies (30-60 Hz). The use of information theory to characterize separability between classes for each temporal resolution shows that high temporal acquisition provides up to 70% more information that conventional spikes generated from frame-based acquisition as used in standard artificial vision, thus drastically increasing the separability between classes of objects. Experiments on real data show that the amount of information loss is correlated with temporal precision. Our information-theoretic study highlights the potentials of neuromorphic asynchronous visual sensors for both practical applications and theoretical

  13. A Spiking Neural Network Model of the Medial Superior Olive Using Spike Timing Dependent Plasticity for Sound Localization

    PubMed Central

    Glackin, Brendan; Wall, Julie A.; McGinnity, Thomas M.; Maguire, Liam P.; McDaid, Liam J.

    2010-01-01

    Sound localization can be defined as the ability to identify the position of an input sound source and is considered a powerful aspect of mammalian perception. For low frequency sounds, i.e., in the range 270 Hz–1.5 KHz, the mammalian auditory pathway achieves this by extracting the Interaural Time Difference between sound signals being received by the left and right ear. This processing is performed in a region of the brain known as the Medial Superior Olive (MSO). This paper presents a Spiking Neural Network (SNN) based model of the MSO. The network model is trained using the Spike Timing Dependent Plasticity learning rule using experimentally observed Head Related Transfer Function data in an adult domestic cat. The results presented demonstrate how the proposed SNN model is able to perform sound localization with an accuracy of 91.82% when an error tolerance of ±10° is used. For angular resolutions down to 2.5°, it will be demonstrated how software based simulations of the model incur significant computation times. The paper thus also addresses preliminary implementation on a Field Programmable Gate Array based hardware platform to accelerate system performance. PMID:20802855

  14. Theta-Frequency Resonance at the Cerebellum Input Stage Improves Spike Timing on the Millisecond Time-Scale

    PubMed Central

    Gandolfi, Daniela; Lombardo, Paola; Mapelli, Jonathan; Solinas, Sergio; D’Angelo, Egidio

    2013-01-01

    The neuronal circuits of the brain are thought to use resonance and oscillations to improve communication over specific frequency bands (Llinas, 1988; Buzsaki, 2006). However, the properties and mechanism of these phenomena in brain circuits remain largely unknown. Here we show that, at the cerebellum input stage, the granular layer (GRL) generates its maximum response at 5–7 Hz both in vivo following tactile sensory stimulation of the whisker pad and in acute slices following mossy fiber bundle stimulation. The spatial analysis of GRL activity performed using voltage-sensitive dye (VSD) imaging revealed 5–7 Hz resonance covering large GRL areas. In single granule cells, resonance appeared as a reorganization of output spike bursts on the millisecond time-scale, such that the first spike occurred earlier and with higher temporal precision and the probability of spike generation increased. Resonance was independent from circuit inhibition, as it persisted with little variation in the presence of the GABAA receptor blocker, gabazine. However, circuit inhibition reduced the resonance area more markedly at 7 Hz. Simulations with detailed computational models suggested that resonance depended on intrinsic granule cells ionic mechanisms: specifically, Kslow (M-like) and KA currents acted as resonators and the persistent Na current and NMDA current acted as amplifiers. This form of resonance may play an important role for enhancing coherent spike emission from the GRL when theta-frequency bursts are transmitted by the cerebral cortex and peripheral sensory structures during sensory-motor processing, cognition, and learning. PMID:23596398

  15. Seasonal plasticity of precise spike timing in the avian auditory system.

    PubMed

    Caras, Melissa L; Sen, Kamal; Rubel, Edwin W; Brenowitz, Eliot A

    2015-02-25

    Vertebrate audition is a dynamic process, capable of exhibiting both short- and long-term adaptations to varying listening conditions. Precise spike timing has long been known to play an important role in auditory encoding, but its role in sensory plasticity remains largely unexplored. We addressed this issue in Gambel's white-crowned sparrow (Zonotrichia leucophrys gambelii), a songbird that shows pronounced seasonal fluctuations in circulating levels of sex-steroid hormones, which are known to be potent neuromodulators of auditory function. We recorded extracellular single-unit activity in the auditory forebrain of males and females under different breeding conditions and used a computational approach to explore two potential strategies for the neural discrimination of sound level: one based on spike counts and one based on spike timing reliability. We report that breeding condition has robust sex-specific effects on spike timing. Specifically, in females, breeding condition increases the proportion of cells that rely solely on spike timing information and increases the temporal resolution required for optimal intensity encoding. Furthermore, in a functionally distinct subset of cells that are particularly well suited for amplitude encoding, female breeding condition enhances spike timing-based discrimination accuracy. No effects of breeding condition were observed in males. Our results suggest that high-resolution temporal discharge patterns may provide a plastic neural substrate for sensory coding. PMID:25716843

  16. Seasonal Plasticity of Precise Spike Timing in the Avian Auditory System

    PubMed Central

    Sen, Kamal; Rubel, Edwin W; Brenowitz, Eliot A.

    2015-01-01

    Vertebrate audition is a dynamic process, capable of exhibiting both short- and long-term adaptations to varying listening conditions. Precise spike timing has long been known to play an important role in auditory encoding, but its role in sensory plasticity remains largely unexplored. We addressed this issue in Gambel's white-crowned sparrow (Zonotrichia leucophrys gambelii), a songbird that shows pronounced seasonal fluctuations in circulating levels of sex-steroid hormones, which are known to be potent neuromodulators of auditory function. We recorded extracellular single-unit activity in the auditory forebrain of males and females under different breeding conditions and used a computational approach to explore two potential strategies for the neural discrimination of sound level: one based on spike counts and one based on spike timing reliability. We report that breeding condition has robust sex-specific effects on spike timing. Specifically, in females, breeding condition increases the proportion of cells that rely solely on spike timing information and increases the temporal resolution required for optimal intensity encoding. Furthermore, in a functionally distinct subset of cells that are particularly well suited for amplitude encoding, female breeding condition enhances spike timing-based discrimination accuracy. No effects of breeding condition were observed in males. Our results suggest that high-resolution temporal discharge patterns may provide a plastic neural substrate for sensory coding. PMID:25716843

  17. Real-Time Classification of Complex Patterns Using Spike-Based Learning in Neuromorphic VLSI.

    PubMed

    Mitra, S; Fusi, S; Indiveri, G

    2009-02-01

    Real-time classification of patterns of spike trains is a difficult computational problem that both natural and artificial networks of spiking neurons are confronted with. The solution to this problem not only could contribute to understanding the fundamental mechanisms of computation used in the biological brain, but could also lead to efficient hardware implementations of a wide range of applications ranging from autonomous sensory-motor systems to brain-machine interfaces. Here we demonstrate real-time classification of complex patterns of mean firing rates, using a VLSI network of spiking neurons and dynamic synapses which implement a robust spike-driven plasticity mechanism. The learning rule implemented is a supervised one: a teacher signal provides the output neuron with an extra input spike-train during training, in parallel to the spike-trains that represent the input pattern. The teacher signal simply indicates if the neuron should respond to the input pattern with a high rate or with a low one. The learning mechanism modifies the synaptic weights only as long as the current generated by all the stimulated plastic synapses does not match the output desired by the teacher, as in the perceptron learning rule. We describe the implementation of this learning mechanism and present experimental data that demonstrate how the VLSI neural network can learn to classify patterns of neural activities, also in the case in which they are highly correlated. PMID:23853161

  18. Influence of developmental nicotine exposure on spike-timing precision and reliability in hypoglossal motoneurons.

    PubMed

    Powell, Gregory L; Levine, Richard B; Frazier, Amanda M; Fregosi, Ralph F

    2015-03-15

    Smoothly graded muscle contractions depend in part on the precision and reliability of motoneuron action potential generation. Whether or not a motoneuron generates spikes precisely and reliably depends on both its intrinsic membrane properties and the nature of the synaptic input that it receives. Factors that perturb neuronal intrinsic properties and/or synaptic drive may compromise the temporal precision and the reliability of action potential generation. We have previously shown that developmental nicotine exposure (DNE) alters intrinsic properties and synaptic transmission in hypoglossal motoneurons (XIIMNs). Here we show that the effects of DNE also include alterations in spike-timing precision and reliability, and spike-frequency adaptation, in response to sinusoidal current injection. Current-clamp experiments in brainstem slices from neonatal rats show that DNE lowers the threshold for spike generation but increases the variability of spike-timing mechanisms. DNE is also associated with an increase in spike-frequency adaptation and reductions in both peak and steady-state firing rate in response to brief, square wave current injections. Taken together, our data indicate that DNE causes significant alterations in the input-output efficiency of XIIMNs. These alterations may play a role in the increased frequency of obstructive apneas and altered suckling strength and coordination observed in nicotine-exposed neonatal humans. PMID:25552642

  19. Neuronal Spike Timing Adaptation Described with a Fractional Leaky Integrate-and-Fire Model

    PubMed Central

    Teka, Wondimu; Marinov, Toma M.; Santamaria, Fidel

    2014-01-01

    The voltage trace of neuronal activities can follow multiple timescale dynamics that arise from correlated membrane conductances. Such processes can result in power-law behavior in which the membrane voltage cannot be characterized with a single time constant. The emergent effect of these membrane correlations is a non-Markovian process that can be modeled with a fractional derivative. A fractional derivative is a non-local process in which the value of the variable is determined by integrating a temporal weighted voltage trace, also called the memory trace. Here we developed and analyzed a fractional leaky integrate-and-fire model in which the exponent of the fractional derivative can vary from 0 to 1, with 1 representing the normal derivative. As the exponent of the fractional derivative decreases, the weights of the voltage trace increase. Thus, the value of the voltage is increasingly correlated with the trajectory of the voltage in the past. By varying only the fractional exponent, our model can reproduce upward and downward spike adaptations found experimentally in neocortical pyramidal cells and tectal neurons in vitro. The model also produces spikes with longer first-spike latency and high inter-spike variability with power-law distribution. We further analyze spike adaptation and the responses to noisy and oscillatory input. The fractional model generates reliable spike patterns in response to noisy input. Overall, the spiking activity of the fractional leaky integrate-and-fire model deviates from the spiking activity of the Markovian model and reflects the temporal accumulated intrinsic membrane dynamics that affect the response of the neuron to external stimulation. PMID:24675903

  20. Vibrational resonance in adaptive small-world neuronal networks with spike-timing-dependent plasticity

    NASA Astrophysics Data System (ADS)

    Yu, Haitao; Guo, Xinmeng; Wang, Jiang; Deng, Bin; Wei, Xile

    2015-10-01

    The phenomenon of vibrational resonance is investigated in adaptive Newman-Watts small-world neuronal networks, where the strength of synaptic connections between neurons is modulated based on spike-timing-dependent plasticity. Numerical results demonstrate that there exists appropriate amplitude of high-frequency driving which is able to optimize the neural ensemble response to the weak low-frequency periodic signal. The effect of networked vibrational resonance can be significantly affected by spike-timing-dependent plasticity. It is shown that spike-timing-dependent plasticity with dominant depression can always improve the efficiency of vibrational resonance, and a small adjusting rate can promote the transmission of weak external signal in small-world neuronal networks. In addition, the network topology plays an important role in the vibrational resonance in spike-timing-dependent plasticity-induced neural systems, where the system response to the subthreshold signal is maximized by an optimal network structure. Furthermore, it is demonstrated that the introduction of inhibitory synapses can considerably weaken the phenomenon of vibrational resonance in the hybrid small-world neuronal networks with spike-timing-dependent plasticity.

  1. Cell Type-Specific Control of Spike Timing by Gamma-Band Oscillatory Inhibition.

    PubMed

    Hasenstaub, Andrea; Otte, Stephani; Callaway, Edward

    2016-02-01

    Many lines of theoretical and experimental investigation have suggested that gamma oscillations provide a temporal framework for cortical information processing, acting to either synchronize neuronal firing, restrict neuron's relative spike times, and/or provide a global reference signal to which neurons encode input strength. Each theory has been disputed and some believe that gamma is an epiphenomenon. We investigated the biophysical plausibility of these theories by performing in vitro whole-cell recordings from 6 cortical neuron subtypes and examining how gamma-band and slow fluctuations in injected input affect precision and phase of spike timing. We find that gamma is at least partially able to restrict the spike timing in all subtypes tested, but to varying degrees. Gamma exerts more precise control of spike timing in pyramidal neurons involved in cortico-cortical versus cortico-subcortical communication and in inhibitory neurons that target somatic versus dendritic compartments. We also find that relatively few subtypes are capable of phase-based information coding. Using simple neuron models and dynamic clamp, we determine which intrinsic differences lead to these variations in responsiveness and discuss both the flexibility and confounds of gamma-based spike-timing systems. PMID:25778344

  2. Single pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25 ms to 125 μs

    PubMed Central

    Du, Nan; Kiani, Mahdi; Mayr, Christian G.; You, Tiangui; Bürger, Danilo; Skorupa, Ilona; Schmidt, Oliver G.; Schmidt, Heidemarie

    2015-01-01

    Memristive devices are popular among neuromorphic engineers for their ability to emulate forms of spike-driven synaptic plasticity by applying specific voltage and current waveforms at their two terminals. In this paper, we investigate spike-timing dependent plasticity (STDP) with a single pairing of one presynaptic voltage spike and one post-synaptic voltage spike in a BiFeO3 memristive device. In most memristive materials the learning window is primarily a function of the material characteristics and not of the applied waveform. In contrast, we show that the analog resistive switching of the developed artificial synapses allows to adjust the learning time constant of the STDP function from 25 ms to 125 μs via the duration of applied voltage spikes. Also, as the induced weight change may degrade, we investigate the remanence of the resistance change for several hours after analog resistive switching, thus emulating the processes expected in biological synapses. As the power consumption is a major constraint in neuromorphic circuits, we show methods to reduce the consumed energy per setting pulse to only 4.5 pJ in the developed artificial synapses. PMID:26175666

  3. Single pairing spike-timing dependent plasticity in BiFeO3 memristors with a time window of 25 ms to 125 μs.

    PubMed

    Du, Nan; Kiani, Mahdi; Mayr, Christian G; You, Tiangui; Bürger, Danilo; Skorupa, Ilona; Schmidt, Oliver G; Schmidt, Heidemarie

    2015-01-01

    Memristive devices are popular among neuromorphic engineers for their ability to emulate forms of spike-driven synaptic plasticity by applying specific voltage and current waveforms at their two terminals. In this paper, we investigate spike-timing dependent plasticity (STDP) with a single pairing of one presynaptic voltage spike and one post-synaptic voltage spike in a BiFeO3 memristive device. In most memristive materials the learning window is primarily a function of the material characteristics and not of the applied waveform. In contrast, we show that the analog resistive switching of the developed artificial synapses allows to adjust the learning time constant of the STDP function from 25 ms to 125 μs via the duration of applied voltage spikes. Also, as the induced weight change may degrade, we investigate the remanence of the resistance change for several hours after analog resistive switching, thus emulating the processes expected in biological synapses. As the power consumption is a major constraint in neuromorphic circuits, we show methods to reduce the consumed energy per setting pulse to only 4.5 pJ in the developed artificial synapses. PMID:26175666

  4. Frequency and time properties of decimeter narrowband spikes in solar flares

    NASA Astrophysics Data System (ADS)

    Wang, Shujuan

    2013-07-01

    In this paper, we focus to study the frequency and time properties of a group of spikes recorded by the 1.08-2.04 GHz spectrometer of NAOC on 27 October 2003. At the first we calculate the mean and minimum bandwidth of the spikes. We apply two different methods based on the wavelet analysis according to Messmer & Benz (2000). The first method determines the dominant spike bandwidth scale based on their scalegram, and the second method is a feature detection algorithm in the time-frequency plane. Secondly the time profile of each single spike was fitted and analyzed. In particular, we determined the e-folding rise and decay times corresponding to the ascending and decaying parts of the time profile, respectively. Several important correlations were studied and compared with the results in earlier literature, i.e. those between duration and frequency, e-folding rise time and decay time, e-folding decay time and duration, and e-folding decay time and peak flux. Finally some parameters of source region were estimated and the possible decaying mechanism was discussed.

  5. Design of an electronic synapse with spike time dependent plasticity based on resistive memory device

    NASA Astrophysics Data System (ADS)

    Hu, S. G.; Wu, H. T.; Liu, Y.; Chen, T. P.; Liu, Z.; Yu, Q.; Yin, Y.; Hosaka, Sumio

    2013-03-01

    This paper presents a design of electronic synapse with Spike Time Dependent Plasticity (STDP) based on resistive memory device. With the resistive memory device whose resistance can be purposely changed, the weight of the synaptic connection between two neurons can be modified. The synapse can work according to the STDP rule, ensuring that the timing between pre and post-spikes leads to either the long term potentiation or long term depression. By using the synapse, a neural network with three neurons has been constructed to realize the STDP learning.

  6. Delay selection by spike-timing-dependent plasticity in recurrent networks of spiking neurons receiving oscillatory inputs.

    PubMed

    Kerr, Robert R; Burkitt, Anthony N; Thomas, Doreen A; Gilson, Matthieu; Grayden, David B

    2013-01-01

    Learning rules, such as spike-timing-dependent plasticity (STDP), change the structure of networks of neurons based on the firing activity. A network level understanding of these mechanisms can help infer how the brain learns patterns and processes information. Previous studies have shown that STDP selectively potentiates feed-forward connections that have specific axonal delays, and that this underlies behavioral functions such as sound localization in the auditory brainstem of the barn owl. In this study, we investigate how STDP leads to the selective potentiation of recurrent connections with different axonal and dendritic delays during oscillatory activity. We develop analytical models of learning with additive STDP in recurrent networks driven by oscillatory inputs, and support the results using simulations with leaky integrate-and-fire neurons. Our results show selective potentiation of connections with specific axonal delays, which depended on the input frequency. In addition, we demonstrate how this can lead to a network becoming selective in the amplitude of its oscillatory response to this frequency. We extend this model of axonal delay selection within a single recurrent network in two ways. First, we show the selective potentiation of connections with a range of both axonal and dendritic delays. Second, we show axonal delay selection between multiple groups receiving out-of-phase, oscillatory inputs. We discuss the application of these models to the formation and activation of neuronal ensembles or cell assemblies in the cortex, and also to missing fundamental pitch perception in the auditory brainstem. PMID:23408878

  7. Time resolution dependence of information measures for spiking neurons: scaling and universality

    PubMed Central

    Marzen, Sarah E.; DeWeese, Michael R.; Crutchfield, James P.

    2015-01-01

    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 toward that larger goal is to develop information measures for individual output processes, including information generation (entropy rate), stored information (statistical complexity), 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., τ-entropy rates that diverge less quickly than the firing rate indicated by 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. PMID:26379538

  8. Time resolution dependence of information measures for spiking neurons: scaling and universality.

    PubMed

    Marzen, Sarah E; DeWeese, Michael R; Crutchfield, James P

    2015-01-01

    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 toward that larger goal is to develop information measures for individual output processes, including information generation (entropy rate), stored information (statistical complexity), 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., τ-entropy rates that diverge less quickly than the firing rate indicated by 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. PMID:26379538

  9. Reading Between the Spikes: Real-Time Signal Processing in Neural Systems

    NASA Astrophysics Data System (ADS)

    Warland, David Karsten

    This thesis discusses biological strategies for real-time signal processing in neural systems. Nearly all creatures encode information about the world as patterns of identically shaped action potentials, or "spikes". As a result, all the animal's knowledge of the world is contained in the occurrence times of these discrete events. Traditional approaches to the study of neural coding emphasize the encoding process, resulting in predictions of average neural responses to a limited class of stimuli. However, these studies fail to address the relevant biological question: What can the organism "learn" about the outside world from real-time observations of its own spike trains? Therefore, this thesis approaches neural coding from the point of view of the organism itself: We learn to decode neural spike trains to obtain real-time estimates of sensory stimuli. In particular, this ability to extract continuous signals from spiking cells, together with the definition of an equivalent spectral noise level for a spiking neuron allows characterization of the information contained in patterns of neural response as well as forming the basis for the prediction of optimal neural computation strategies with spike trains. These methods are applied to the design and analysis of experiments on a single wide field, movement -sensitive neuron (H1) in the visual system of the blowfly Calliphora erythrocephela and to the filiform hair receptors of the wind-sensing system of the cricket Acheta domestica. This thesis also discusses the generalization of these strategies to collections of neurons and the applications to future work in the context of neural computation in the retina.

  10. Potassium conductance dynamics confer robust spike-time precision in a neuromorphic model of the auditory brain stem

    PubMed Central

    Boahen, Kwabena

    2013-01-01

    A fundamental question in neuroscience is how neurons perform precise operations despite inherent variability. This question also applies to neuromorphic engineering, where low-power microchips emulate the brain using large populations of diverse silicon neurons. Biological neurons in the auditory pathway display precise spike timing, critical for sound localization and interpretation of complex waveforms such as speech, even though they are a heterogeneous population. Silicon neurons are also heterogeneous, due to a key design constraint in neuromorphic engineering: smaller transistors offer lower power consumption and more neurons per unit area of silicon, but also more variability between transistors and thus between silicon neurons. Utilizing this variability in a neuromorphic model of the auditory brain stem with 1,080 silicon neurons, we found that a low-voltage-activated potassium conductance (gKL) enables precise spike timing via two mechanisms: statically reducing the resting membrane time constant and dynamically suppressing late synaptic inputs. The relative contribution of these two mechanisms is unknown because blocking gKL in vitro eliminates dynamic adaptation but also lengthens the membrane time constant. We replaced gKL with a static leak in silico to recover the short membrane time constant and found that silicon neurons could mimic the spike-time precision of their biological counterparts, but only over a narrow range of stimulus intensities and biophysical parameters. The dynamics of gKL were required for precise spike timing robust to stimulus variation across a heterogeneous population of silicon neurons, thus explaining how neural and neuromorphic systems may perform precise operations despite inherent variability. PMID:23554436

  11. A generative spike train model with time-structured higher order correlations

    PubMed Central

    Trousdale, James; Hu, Yu; Shea-Brown, Eric; Josić, Krešimir

    2013-01-01

    Emerging technologies are revealing the spiking activity in ever larger neural ensembles. Frequently, this spiking is far from independent, with correlations in the spike times of different cells. Understanding how such correlations impact the dynamics and function of neural ensembles remains an important open problem. Here we describe a new, generative model for correlated spike trains that can exhibit many of the features observed in data. Extending prior work in mathematical finance, this generalized thinning and shift (GTaS) model creates marginally Poisson spike trains with diverse temporal correlation structures. We give several examples which highlight the model's flexibility and utility. For instance, we use it to examine how a neural network responds to highly structured patterns of inputs. We then show that the GTaS model is analytically tractable, and derive cumulant densities of all orders in terms of model parameters. The GTaS framework can therefore be an important tool in the experimental and theoretical exploration of neural dynamics. PMID:23908626

  12. Hippocampal Theta Modulation of Neocortical Spike Times and Gamma Rhythm: A Biophysical Model Study

    PubMed Central

    Spaak, Eelke; Zeitler, Magteld; Gielen, Stan

    2012-01-01

    The hippocampal theta and neocortical gamma rhythms are two prominent examples of oscillatory neuronal activity. The hippocampus has often been hypothesized to influence neocortical networks by its theta rhythm, and, recently, evidence for such a direct influence has been found. We examined a possible mechanism for this influence by means of a biophysical model study using conductance-based model neurons. We found, in agreement with previous studies, that networks of fast-spiking GABA -ergic interneurons, coupled with shunting inhibition, synchronize their spike activity at a gamma frequency and are able to impose this rhythm on a network of pyramidal cells to which they are coupled. When our model was supplied with hippocampal theta-modulated input fibres, the theta rhythm biased the spike timings of both the fast-spiking and pyramidal cells. Furthermore, both the amplitude and frequency of local field potential gamma oscillations were influenced by the phase of the theta rhythm. We show that the fast-spiking cells, not pyramidal cells, are essential for this latter phenomenon, thus highlighting their crucial role in the interplay between hippocampus and neocortex. PMID:23056213

  13. Simulations of drastically reduced SBS with laser pulses composed of a Spike Train of Uneven Duration and Delay (STUD pulses)

    NASA Astrophysics Data System (ADS)

    Hüller, Stefan; Afeyan, Bedros

    2013-11-01

    By comparing the impact of established laser smoothing techniques like Random Phase Plates (RPP) and Smoothing by Spectral Dispersion (SSD) to the concept of "Spike Trains of Uneven Duration and Delay" (STUD pulses) on the amplification of parametric instabilities in laser-produced plasmas, we show with the help of numerical simulations, that STUD pulses can drastically reduce instability growth by orders of magnitude. The simulation results, obtained with the code Harmony in a nonuniformly flowing mm-size plasma for the Stimulated Brillouin Scattering (SBS) instability, show that the efficiency of the STUD pulse technique is due to the fact that successive re-amplification in space and time of parametrically excited plasma waves inside laser hot spots is minimized. An overall mean fluctuation level of ion acoustic waves at low amplitude is established because of the frequent change of the speckle pattern in successive spikes. This level stays orders of magnitude below the levels of ion acoustic waves excited in hot spots of RPP and SSD laser beams.

  14. Retroactive modulation of spike timing-dependent plasticity by dopamine

    PubMed Central

    Brzosko, Zuzanna; Schultz, Wolfram; Paulsen, Ole

    2015-01-01

    Most reinforcement learning models assume that the reward signal arrives after the activity that led to the reward, placing constraints on the possible underlying cellular mechanisms. Here we show that dopamine, a positive reinforcement signal, can retroactively convert hippocampal timing-dependent synaptic depression into potentiation. This effect requires functional NMDA receptors and is mediated in part through the activation of the cAMP/PKA cascade. Collectively, our results support the idea that reward-related signaling can act on a pre-established synaptic eligibility trace, thereby associating specific experiences with behaviorally distant, rewarding outcomes. This finding identifies a biologically plausible mechanism for solving the ‘distal reward problem’. DOI: http://dx.doi.org/10.7554/eLife.09685.001 PMID:26516682

  15. Temporal Characteristics of the Predictive Synchronous Firing Modeled by Spike-Timing-Dependent Plasticity

    ERIC Educational Resources Information Center

    Kitano, Katsunori; Fukai, Tomoki

    2004-01-01

    When a sensory cue was repeatedly followed by a behavioral event with fixed delays, pairs of premotor and primary motor neurons showed significant increases of coincident spikes at times a monkey was expecting the event. These results provided evidence that neuronal firing synchrony has predictive power. To elucidate the underlying mechanism, here…

  16. Reduced Spiking in Entorhinal Cortex during the Delay Period of a Cued Spatial Response Task

    ERIC Educational Resources Information Center

    Gupta, Kishan; Keller, Lauren A.; Hasselmo, Michael E.

    2012-01-01

    Intrinsic persistent spiking mechanisms in medial entorhinal cortex (mEC) neurons may play a role in active maintenance of working memory. However, electrophysiological studies of rat mEC units have primarily focused on spatial modulation. We sought evidence of differential spike rates in the mEC in rats trained on a T-maze, cued spatial delayed…

  17. Synchronization of time-delayed chemically coupled burst-spiking neurons with correlated noises.

    PubMed

    Zhang, X; Yang, J; Wu, F P; Wu, W J; Jiang, M; Chen, L; Wang, H J; Qi, G X; Huang, H B

    2014-06-01

    Synchronization of two time-delayed chemically coupled neurons with burst-spiking states is studied. Different from the previous study by N. Buric et al. (Phys. Rev. E 78, 036211 (2008)), it is found that exactly synchronous burst-spiking dynamics can occur for small coupling strengths and time delays. The results are confirmed by common time delays and non-equal time delays. When common noise is added to the two neurons, synchronization is enhanced as noise strength is increased. But the results are different for larger time delay and smaller time delay. When noises are correlated, it is found that only strong noises with large correlation coefficient can induce exact synchronization. Even one percent of independent noises can influence synchronization much. PMID:24965152

  18. Adaptive and phase selective spike timing dependent plasticity in synaptically coupled neuronal oscillators.

    PubMed

    Kazantsev, Victor; Tyukin, Ivan

    2012-01-01

    We consider and analyze the influence of spike-timing dependent plasticity (STDP) on homeostatic states in synaptically coupled neuronal oscillators. In contrast to conventional models of STDP in which spike-timing affects weights of synaptic connections, we consider a model of STDP in which the time lags between pre- and/or post-synaptic spikes change internal state of pre- and/or post-synaptic neurons respectively. The analysis reveals that STDP processes of this type, modeled by a single ordinary differential equation, may ensure efficient, yet coarse, phase-locking of spikes in the system to a given reference phase. Precision of the phase locking, i.e. the amplitude of relative phase deviations from the reference, depends on the values of natural frequencies of oscillators and, additionally, on parameters of the STDP law. These deviations can be optimized by appropriate tuning of gains (i.e. sensitivity to spike-timing mismatches) of the STDP mechanism. However, as we demonstrate, such deviations can not be made arbitrarily small neither by mere tuning of STDP gains nor by adjusting synaptic weights. Thus if accurate phase-locking in the system is required then an additional tuning mechanism is generally needed. We found that adding a very simple adaptation dynamics in the form of slow fluctuations of the base line in the STDP mechanism enables accurate phase tuning in the system with arbitrary high precision. Adaptation operating at a slow time scale may be associated with extracellular matter such as matrix and glia. Thus the findings may suggest a possible role of the latter in regulating synaptic transmission in neuronal circuits. PMID:22412830

  19. Adaptive and Phase Selective Spike Timing Dependent Plasticity in Synaptically Coupled Neuronal Oscillators

    PubMed Central

    Kazantsev, Victor; Tyukin, Ivan

    2012-01-01

    We consider and analyze the influence of spike-timing dependent plasticity (STDP) on homeostatic states in synaptically coupled neuronal oscillators. In contrast to conventional models of STDP in which spike-timing affects weights of synaptic connections, we consider a model of STDP in which the time lags between pre- and/or post-synaptic spikes change internal state of pre- and/or post-synaptic neurons respectively. The analysis reveals that STDP processes of this type, modeled by a single ordinary differential equation, may ensure efficient, yet coarse, phase-locking of spikes in the system to a given reference phase. Precision of the phase locking, i.e. the amplitude of relative phase deviations from the reference, depends on the values of natural frequencies of oscillators and, additionally, on parameters of the STDP law. These deviations can be optimized by appropriate tuning of gains (i.e. sensitivity to spike-timing mismatches) of the STDP mechanism. However, as we demonstrate, such deviations can not be made arbitrarily small neither by mere tuning of STDP gains nor by adjusting synaptic weights. Thus if accurate phase-locking in the system is required then an additional tuning mechanism is generally needed. We found that adding a very simple adaptation dynamics in the form of slow fluctuations of the base line in the STDP mechanism enables accurate phase tuning in the system with arbitrary high precision. Adaptation operating at a slow time scale may be associated with extracellular matter such as matrix and glia. Thus the findings may suggest a possible role of the latter in regulating synaptic transmission in neuronal circuits. PMID:22412830

  20. Spike-timing computation properties of a feed-forward neural network model

    PubMed Central

    Sinha, Drew B.; Ledbetter, Noah M.; Barbour, Dennis L.

    2014-01-01

    Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g., serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape these transformations, we modeled feed-forward networks of 7–22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity (STDP) rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS) in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses. PMID:24478688

  1. Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors.

    PubMed

    Prezioso, M; Merrikh Bayat, F; Hoskins, B; Likharev, K; Strukov, D

    2016-01-01

    Metal-oxide memristors have emerged as promising candidates for hardware implementation of artificial synapses - the key components of high-performance, analog neuromorphic networks - due to their excellent scaling prospects. Since some advanced cognitive tasks require spiking neuromorphic networks, which explicitly model individual neural pulses ("spikes") in biological neural systems, it is crucial for memristive synapses to support the spike-time-dependent plasticity (STDP). A major challenge for the STDP implementation is that, in contrast to some simplistic models of the plasticity, the elementary change of a synaptic weight in an artificial hardware synapse depends not only on the pre-synaptic and post-synaptic signals, but also on the initial weight (memristor's conductance) value. Here we experimentally demonstrate, for the first time, an STDP behavior that ensures self-adaptation of the average memristor conductance, making the plasticity stable, i.e. insensitive to the initial state of the devices. The experiments have been carried out with 200-nm Al2O3/TiO2-x memristors integrated into 12 × 12 crossbars. The experimentally observed self-adaptive STDP behavior has been complemented with numerical modeling of weight dynamics in a simple system with a leaky-integrate-and-fire neuron with a random spike-train input, using a compact model of memristor plasticity, fitted for quantitatively correct description of our memristors. PMID:26893175

  2. Analysis and modelling of variability and covariability of population spike trains across multiple time scales.

    PubMed

    Lyamzin, Dmitry R; Garcia-Lazaro, Jose A; Lesica, Nicholas A

    2012-01-01

    As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modelling such data must also be developed. We present a model of responses to repeated trials of a sensory stimulus based on thresholded Gaussian processes that allows for analysis and modelling of variability and covariability of population spike trains across multiple time scales. The model framework can be used to specify the values of many different variability measures including spike timing precision across trials, coefficient of variation of the interspike interval distribution, and Fano factor of spike counts for individual neurons, as well as signal and noise correlations and correlations of spike counts across multiple neurons. Using both simulated data and data from different stages of the mammalian auditory pathway, we demonstrate the range of possible independent manipulations of different variability measures, and explore how this range depends on the sensory stimulus. The model provides a powerful framework for the study of experimental and surrogate data and for analyzing dependencies between different statistical properties of neuronal populations. PMID:22578115

  3. Predicting SA-I mechanoreceptor spike times with a skin-neuron model

    PubMed Central

    Lesniak, Daine R.; Gerling, Gregory J.

    2009-01-01

    Slowly adapting type I (SA-I) mechanoreceptors encode the edges and curvature of touched objects by generating neural spikes in response to indentation of the skin. Beneath this general input-output relationship, models are of great utility for understanding the sub-processes, as SA-I transduction sites are inaccessible to whole-cell recording. This work develops and validates a SA-I skin-receptor model that combines a finite element model of skin mechanics, a sigmoidal function of transduction, and a leaky integrate-and-fire model of neural dynamics. The model produced a R2=0.80 goodness of fit between predicted and observed firing rates for 3 mm and 5 mm grating stimuli. In addition, modulation indices of predicted firing rates for 3 mm and 5 mm gratings are 0.46 and 0.59 respectively, compared to the 0.71 and 0.72 found in vivo. An analysis of predicted first spikes indicates their latency may also be enhanced by edges, as edge proximity shortened first spike latencies by 26.2 and 41.8 ms for the 3 mm and 5 mm gratings, respectively. The model described here bridges the gap between those models that transform sustained indentation to firing rates and those that transform vibration to spike times. PMID:19362097

  4. Spike Timing Dependent Plasticity: A Consequence of More Fundamental Learning Rules

    PubMed Central

    Shouval, Harel Z.; Wang, Samuel S.-H.; Wittenberg, Gayle M.

    2010-01-01

    Spike timing dependent plasticity (STDP) is a phenomenon in which the precise timing of spikes affects the sign and magnitude of changes in synaptic strength. STDP is often interpreted as the comprehensive learning rule for a synapse – the “first law” of synaptic plasticity. This interpretation is made explicit in theoretical models in which the total plasticity produced by complex spike patterns results from a superposition of the effects of all spike pairs. Although such models are appealing for their simplicity, they can fail dramatically. For example, the measured single-spike learning rule between hippocampal CA3 and CA1 pyramidal neurons does not predict the existence of long-term potentiation one of the best-known forms of synaptic plasticity. Layers of complexity have been added to the basic STDP model to repair predictive failures, but they have been outstripped by experimental data. We propose an alternate first law: neural activity triggers changes in key biochemical intermediates, which act as a more direct trigger of plasticity mechanisms. One particularly successful model uses intracellular calcium as the intermediate and can account for many observed properties of bidirectional plasticity. In this formulation, STDP is not itself the basis for explaining other forms of plasticity, but is instead a consequence of changes in the biochemical intermediate, calcium. Eventually a mechanism-based framework for learning rules should include other messengers, discrete change at individual synapses, spread of plasticity among neighboring synapses, and priming of hidden processes that change a synapse's susceptibility to future change. Mechanism-based models provide a rich framework for the computational representation of synaptic plasticity. PMID:20725599

  5. Proportional spike-timing precision and firing reliability underlie efficient temporal processing of periodicity and envelope shape cues

    PubMed Central

    Zheng, Y.

    2013-01-01

    Temporal sound cues are essential for sound recognition, pitch, rhythm, and timbre perception, yet how auditory neurons encode such cues is subject of ongoing debate. Rate coding theories propose that temporal sound features are represented by rate tuned modulation filters. However, overwhelming evidence also suggests that precise spike timing is an essential attribute of the neural code. Here we demonstrate that single neurons in the auditory midbrain employ a proportional code in which spike-timing precision and firing reliability covary with the sound envelope cues to provide an efficient representation of the stimulus. Spike-timing precision varied systematically with the timescale and shape of the sound envelope and yet was largely independent of the sound modulation frequency, a prominent cue for pitch. In contrast, spike-count reliability was strongly affected by the modulation frequency. Spike-timing precision extends from sub-millisecond for brief transient sounds up to tens of milliseconds for sounds with slow-varying envelope. Information theoretic analysis further confirms that spike-timing precision depends strongly on the sound envelope shape, while firing reliability was strongly affected by the sound modulation frequency. Both the information efficiency and total information were limited by the firing reliability and spike-timing precision in a manner that reflected the sound structure. This result supports a temporal coding strategy in the auditory midbrain where proportional changes in spike-timing precision and firing reliability can efficiently signal shape and periodicity temporal cues. PMID:23636724

  6. On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex

    PubMed Central

    Zamarreño-Ramos, Carlos; Camuñas-Mesa, Luis A.; Pérez-Carrasco, Jose A.; Masquelier, Timothée; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

    2011-01-01

    In this paper we present a very exciting overlap between emergent nanotechnology and neuroscience, which has been discovered by neuromorphic engineers. Specifically, we are linking one type of memristor nanotechnology devices to the biological synaptic update rule known as spike-time-dependent-plasticity (STDP) found in real biological synapses. Understanding this link allows neuromorphic engineers to develop circuit architectures that use this type of memristors to artificially emulate parts of the visual cortex. We focus on the type of memristors referred to as voltage or flux driven memristors and focus our discussions on a behavioral macro-model for such devices. The implementations result in fully asynchronous architectures with neurons sending their action potentials not only forward but also backward. One critical aspect is to use neurons that generate spikes of specific shapes. We will see how by changing the shapes of the neuron action potential spikes we can tune and manipulate the STDP learning rules for both excitatory and inhibitory synapses. We will see how neurons and memristors can be interconnected to achieve large scale spiking learning systems, that follow a type of multiplicative STDP learning rule. We will briefly extend the architectures to use three-terminal transistors with similar memristive behavior. We will illustrate how a V1 visual cortex layer can assembled and how it is capable of learning to extract orientations from visual data coming from a real artificial CMOS spiking retina observing real life scenes. Finally, we will discuss limitations of currently available memristors. The results presented are based on behavioral simulations and do not take into account non-idealities of devices and interconnects. The aim of this paper is to present, in a tutorial manner, an initial framework for the possible development of fully asynchronous STDP learning neuromorphic architectures exploiting two or three-terminal memristive type devices

  7. Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

    PubMed

    Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

    2013-04-01

    Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity. PMID:23592970

  8. Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

    PubMed Central

    Frémaux, Nicolas; Sprekeler, Henning; Gerstner, Wulfram

    2013-01-01

    Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity. PMID:23592970

  9. Evolving spike-timing-dependent plasticity for single-trial learning in robots.

    PubMed

    Di Paolo, Ezequiel A

    2003-10-15

    Single-trial learning is studied in an evolved robot model of synaptic spike-timing-dependent plasticity (STDP). Robots must perform positive phototaxis but must learn to perform negative phototaxis in the presence of a short-lived aversive sound stimulus. STDP acts at the millisecond range and depends asymmetrically on the relative timing of pre- and post-synaptic spikes. Although it has been involved in learning models of input prediction, these models require the iterated presentation of the input pattern, and it is hard to see how this mechanism could sustain single-trial learning over a time-scale of tens of seconds. An incremental evolutionary approach is used to answer this question. The evolved robots succeed in learning the appropriate behaviour, but learning does not depend on achieving the right synaptic configuration but rather the right pattern of neural activity. Robot performance during positive phototaxis is quite robust to loss of spike-timing information, but in contrast, this loss is catastrophic for learning negative phototaxis where entrained firing is common. Tests show that the final weight configuration carries no information about whether a robot is performing one behaviour or the other. Fixing weights, however, has the effect of degrading performance, thus demonstrating that plasticity is used to sustain the neural activity corresponding both to the normal phototaxis condition and to the learned behaviour. The implications and limitations of this result are discussed. PMID:14599321

  10. Intraglomerular lateral inhibition promotes spike timing variability in principal neurons of the olfactory bulb.

    PubMed

    Najac, Marion; Sanz Diez, Alvaro; Kumar, Arvind; Benito, Nuria; Charpak, Serge; De Saint Jan, Didier

    2015-03-11

    The activity of mitral and tufted cells, the principal neurons of the olfactory bulb, is modulated by several classes of interneurons. Among them, diverse periglomerular (PG) cell types interact with the apical dendrites of mitral and tufted cells inside glomeruli at the first stage of olfactory processing. We used paired recording in olfactory bulb slices and two-photon targeted patch-clamp recording in vivo to characterize the properties and connections of a genetically identified population of PG cells expressing enhanced yellow fluorescent protein (EYFP) under the control of the Kv3.1 potassium channel promoter. Kv3.1-EYFP(+) PG cells are axonless and monoglomerular neurons that constitute ∼30% of all PG cells and include calbindin-expressing neurons. They respond to an olfactory nerve stimulation with a short barrage of excitatory inputs mediated by mitral, tufted, and external tufted cells, and, in turn, they indiscriminately release GABA onto principal neurons. They are activated by even the weakest olfactory nerve input or by the discharge of a single principal neuron in slices and at each respiration cycle in anesthetized mice. They participate in a fast-onset intraglomerular lateral inhibition between principal neurons from the same glomerulus, a circuit that reduces the firing rate and promotes spike timing variability in mitral cells. Recordings in other PG cell subtypes suggest that this pathway predominates in generating glomerular inhibition. Intraglomerular lateral inhibition may play a key role in olfactory processing by reducing the similarity of principal cells discharge in response to the same incoming input. PMID:25762678

  11. A model of grid cell development through spatial exploration and spike time-dependent plasticity.

    PubMed

    Widloski, John; Fiete, Ila R

    2014-07-16

    Grid cell responses develop gradually after eye opening, but little is known about the rules that govern this process. We present a biologically plausible model for the formation of a grid cell network. An asymmetric spike time-dependent plasticity rule acts upon an initially unstructured network of spiking neurons that receive inputs encoding animal velocity and location. Neurons develop an organized recurrent architecture based on the similarity of their inputs, interacting through inhibitory interneurons. The mature network can convert velocity inputs into estimates of animal location, showing that spatially periodic responses and the capacity of path integration can arise through synaptic plasticity, acting on inputs that display neither. The model provides numerous predictions about the necessity of spatial exploration for grid cell development, network topography, the maturation of velocity tuning and neural correlations, the abrupt transition to stable patterned responses, and possible mechanisms to set grid period across grid modules. PMID:25033187

  12. Mechanisms of Induction and Maintenance of Spike-Timing Dependent Plasticity in Biophysical Synapse Models

    PubMed Central

    Graupner, Michael; Brunel, Nicolas

    2010-01-01

    We review biophysical models of synaptic plasticity, with a focus on spike-timing dependent plasticity (STDP). The common property of the discussed models is that synaptic changes depend on the dynamics of the intracellular calcium concentration, which itself depends on pre- and postsynaptic activity. We start by discussing simple models in which plasticity changes are based directly on calcium amplitude and dynamics. We then consider models in which dynamic intracellular signaling cascades form the link between the calcium dynamics and the plasticity changes. Both mechanisms of induction of STDP (through the ability of pre/postsynaptic spikes to evoke changes in the state of the synapse) and of maintenance of the evoked changes (through bistability) are discussed. PMID:20948584

  13. Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors

    PubMed Central

    Prezioso, M.; Merrikh Bayat, F.; Hoskins, B.; Likharev, K.; Strukov, D.

    2016-01-01

    Metal-oxide memristors have emerged as promising candidates for hardware implementation of artificial synapses – the key components of high-performance, analog neuromorphic networks - due to their excellent scaling prospects. Since some advanced cognitive tasks require spiking neuromorphic networks, which explicitly model individual neural pulses (“spikes”) in biological neural systems, it is crucial for memristive synapses to support the spike-time-dependent plasticity (STDP). A major challenge for the STDP implementation is that, in contrast to some simplistic models of the plasticity, the elementary change of a synaptic weight in an artificial hardware synapse depends not only on the pre-synaptic and post-synaptic signals, but also on the initial weight (memristor’s conductance) value. Here we experimentally demonstrate, for the first time, an STDP behavior that ensures self-adaptation of the average memristor conductance, making the plasticity stable, i.e. insensitive to the initial state of the devices. The experiments have been carried out with 200-nm Al2O3/TiO2−x memristors integrated into 12 × 12 crossbars. The experimentally observed self-adaptive STDP behavior has been complemented with numerical modeling of weight dynamics in a simple system with a leaky-integrate-and-fire neuron with a random spike-train input, using a compact model of memristor plasticity, fitted for quantitatively correct description of our memristors. PMID:26893175

  14. Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors

    NASA Astrophysics Data System (ADS)

    Prezioso, M.; Merrikh Bayat, F.; Hoskins, B.; Likharev, K.; Strukov, D.

    2016-02-01

    Metal-oxide memristors have emerged as promising candidates for hardware implementation of artificial synapses - the key components of high-performance, analog neuromorphic networks - due to their excellent scaling prospects. Since some advanced cognitive tasks require spiking neuromorphic networks, which explicitly model individual neural pulses (“spikes”) in biological neural systems, it is crucial for memristive synapses to support the spike-time-dependent plasticity (STDP). A major challenge for the STDP implementation is that, in contrast to some simplistic models of the plasticity, the elementary change of a synaptic weight in an artificial hardware synapse depends not only on the pre-synaptic and post-synaptic signals, but also on the initial weight (memristor’s conductance) value. Here we experimentally demonstrate, for the first time, an STDP behavior that ensures self-adaptation of the average memristor conductance, making the plasticity stable, i.e. insensitive to the initial state of the devices. The experiments have been carried out with 200-nm Al2O3/TiO2-x memristors integrated into 12 × 12 crossbars. The experimentally observed self-adaptive STDP behavior has been complemented with numerical modeling of weight dynamics in a simple system with a leaky-integrate-and-fire neuron with a random spike-train input, using a compact model of memristor plasticity, fitted for quantitatively correct description of our memristors.

  15. Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

    PubMed

    Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B; Liu, Shih-Chii

    2015-01-01

    Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time. PMID:26217169

  16. Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

    PubMed Central

    Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B.; Liu, Shih-Chii

    2015-01-01

    Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time. PMID:26217169

  17. Spike timing-dependent serotonergic neuromodulation of synaptic strength intrinsic to a central pattern generator circuit.

    PubMed

    Sakurai, Akira; Katz, Paul S

    2003-11-26

    Neuromodulation is often thought to have a static, gain-setting function in neural circuits. Here we report a counter example: the neuromodulatory effect of a serotonergic neuron is dependent on the interval between its spikes and those of the neuron being modulated. The serotonergic dorsal swim interneurons (DSIs) are members of the escape swim central pattern generator (CPG) in the mollusk Tritonia diomedea. DSI spike trains heterosynaptically enhanced synaptic potentials evoked by another CPG neuron, ventral swim interneuron B (VSI-B), when VSI-B action potentials occurred within 10 sec of a DSI spike train; however, if VSI-B was stimulated 20-120 sec after DSI, then the amplitude of VSI-B synaptic potentials decreased. Consistent with this, VSI-B-evoked synaptic currents exhibited a temporally biphasic and bidirectional change in amplitude after DSI stimulation. Both the DSI-evoked enhancement and decrement were occluded by serotonin and blocked by the serotonin receptor antagonist methysergide, suggesting that both phases are mediated by serotonin. In most preparations, however, bath-applied serotonin caused only a sustained enhancement of VSI-B synaptic strength. The heterosynaptic modulation interacted with short-term homosynaptic plasticity: DSI-evoked depression was offset by VSI-B homosynaptic facilitation. This caused a complicated temporal pattern of neuromodulation when DSI and VSI-B were stimulated to fire in alternating bursts to mimic the natural motor pattern: DSI strongly enhanced summated VSI-B synaptic potentials and suppressed single synaptic potentials after the cessation of the artificial motor pattern. Thus, spike timing-dependent serotonergic neuromodulatory actions can impart temporal information that may be relevant to the operation of the CPG. PMID:14645466

  18. Neuron-specific stimulus masking reveals interference in spike timing at the cortical level.

    PubMed

    Larson, Eric; Maddox, Ross K; Perrone, Ben P; Sen, Kamal; Billimoria, Cyrus P

    2012-02-01

    The auditory system is capable of robust recognition of sounds in the presence of competing maskers (e.g., other voices or background music). This capability arises despite the fact that masking stimuli can disrupt neural responses at the cortical level. Since the origins of such interference effects remain unknown, in this study, we work to identify and quantify neural interference effects that originate due to masking occurring within and outside receptive fields of neurons. We record from single and multi-unit auditory sites from field L, the auditory cortex homologue in zebra finches. We use a novel method called spike timing-based stimulus filtering that uses the measured response of each neuron to create an individualized stimulus set. In contrast to previous adaptive experimental approaches, which have typically focused on the average firing rate, this method uses the complete pattern of neural responses, including spike timing information, in the calculation of the receptive field. When we generate and present novel stimuli for each neuron that mask the regions within the receptive field, we find that the time-varying information in the neural responses is disrupted, degrading neural discrimination performance and decreasing spike timing reliability and sparseness. We also find that, while removing stimulus energy from frequency regions outside the receptive field does not significantly affect neural responses for many sites, adding a masker in these frequency regions can nonetheless have a significant impact on neural responses and discriminability without a significant change in the average firing rate. These findings suggest that maskers can interfere with neural responses by disrupting stimulus timing information with power either within or outside the receptive fields of neurons. PMID:21964794

  19. Decoding of the spike timing of primary afferents during voluntary arm movements in monkeys

    PubMed Central

    Umeda, Tatsuya; Watanabe, Hidenori; Sato, Masa-aki; Kawato, Mitsuo; Isa, Tadashi; Nishimura, Yukio

    2014-01-01

    Understanding the mechanisms of encoding forelimb kinematics in the activity of peripheral afferents is essential for developing a somatosensory neuroprosthesis. To investigate whether the spike timing of dorsal root ganglion (DRG) neurons could be estimated from the forelimb kinematics of behaving monkeys, we implanted two multi-electrode arrays chronically in the DRGs at the level of the cervical segments in two monkeys. Neuronal activity during voluntary reach-to-grasp movements were recorded simultaneously with the trajectories of hand/arm movements, which were tracked in three-dimensional space using a motion capture system. Sixteen and 13 neurons, including muscle spindles, skin receptors, and tendon organ afferents, were recorded in the two monkeys, respectively. We were able to reconstruct forelimb joint kinematics from the temporal firing pattern of a subset of DRG neurons using sparse linear regression (SLiR) analysis, suggesting that DRG neuronal ensembles encoded information about joint kinematics. Furthermore, we estimated the spike timing of the DRG neuronal ensembles from joint kinematics using an integrate-and-fire model (IF) incorporating the SLiR algorithm. The temporal change of firing frequency of a subpopulation of neurons was reconstructed precisely from forelimb kinematics using the SLiR. The estimated firing pattern of the DRG neuronal ensembles encoded forelimb joint angles and velocities as precisely as the originally recorded neuronal activity. These results suggest that a simple model can be used to generate an accurate estimate of the spike timing of DRG neuronal ensembles from forelimb joint kinematics, and is useful for designing a proprioceptive decoder in a brain machine interface. PMID:24860416

  20. Unsupervised learning of digit recognition using spike-timing-dependent plasticity

    PubMed Central

    Diehl, Peter U.; Cook, Matthew

    2015-01-01

    In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN) can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns), since most such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e., conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks. PMID:26941637

  1. Spiking Neurons Learning Phase Delays: How Mammals May Develop Auditory Time-Difference Sensitivity

    NASA Astrophysics Data System (ADS)

    Leibold, Christian; van Hemmen, J. Leo

    2005-04-01

    Time differences between the two ears are an important cue for animals to azimuthally locate a sound source. The first binaural brainstem nucleus, in mammals the medial superior olive, is generally believed to perform the necessary computations. Its cells are sensitive to variations of interaural time differences of about 10 μs. The classical explanation of such a neuronal time-difference tuning is based on the physical concept of delay lines. Recent data, however, are inconsistent with a temporal delay and rather favor a phase delay. By means of a biophysical model we show how spike-timing-dependent synaptic learning explains precise interplay of excitation and inhibition and, hence, accounts for a physical realization of a phase delay.

  2. Spike train auto-structure impacts post-synaptic firing and timing-based plasticity.

    PubMed

    Scheller, Bertram; Castellano, Marta; Vicente, Raul; Pipa, Gordon

    2011-01-01

    Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step, we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e., hard bound spike-timing-dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes are modulated by the temporal structure of the input. Finally, we highlight that the sensitivity of both the post-synaptic firing as well as the spike-timing-dependent plasticity on the auto-structure of the input of a neuron could be used to modulate the learning rate of synaptic modification. PMID:22203800

  3. A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity.

    PubMed

    Rachmuth, Guy; Shouval, Harel Z; Bear, Mark F; Poon, Chi-Sang

    2011-12-01

    Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and power-efficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems. PMID:22089232

  4. Hopf bifurcation in the evolution of networks driven by spike-timing-dependent plasticity

    NASA Astrophysics Data System (ADS)

    Ren, Quansheng; Kolwankar, Kiran M.; Samal, Areejit; Jost, Jürgen

    2012-11-01

    We study the interplay of topology and dynamics in a neural network connected with spike-timing-dependent plasticity (STDP) synapses. Stimulated with periodic spike trains, the STDP-driven network undergoes a synaptic pruning process and evolves to a residual network. We examine the variation of topological and dynamical properties of the residual network by varying two key parameters of STDP: synaptic delay and the ratio between potentiation and depression. Our extensive numerical simulations of the leaky integrate-and-fire model show that there exists two regions in the parameter space. The first corresponds to fixed-point configurations, where the distribution of peak synaptic conductances and the firing rate of neurons remain constant over time. The second corresponds to oscillating configurations, where both topological and dynamical properties vary periodically, which is a result of a fixed point becoming a limit cycle via a Hopf bifurcation. This leads to interesting questions regarding the implications of these rhythms in the topology and dynamics of the network for learning and cognitive processing.

  5. Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity

    PubMed Central

    Scheller, Bertram; Castellano, Marta; Vicente, Raul; Pipa, Gordon

    2011-01-01

    Cortical neurons are typically driven by several thousand synapses. The precise spatiotemporal pattern formed by these inputs can modulate the response of a post-synaptic cell. In this work, we explore how the temporal structure of pre-synaptic inhibitory and excitatory inputs impact the post-synaptic firing of a conductance-based integrate and fire neuron. Both the excitatory and inhibitory input was modeled by renewal gamma processes with varying shape factors for modeling regular and temporally random Poisson activity. We demonstrate that the temporal structure of mutually independent inputs affects the post-synaptic firing, while the strength of the effect depends on the firing rates of both the excitatory and inhibitory inputs. In a second step, we explore the effect of temporal structure of mutually independent inputs on a simple version of Hebbian learning, i.e., hard bound spike-timing-dependent plasticity. We explore both the equilibrium weight distribution and the speed of the transient weight dynamics for different mutually independent gamma processes. We find that both the equilibrium distribution of the synaptic weights and the speed of synaptic changes are modulated by the temporal structure of the input. Finally, we highlight that the sensitivity of both the post-synaptic firing as well as the spike-timing-dependent plasticity on the auto-structure of the input of a neuron could be used to modulate the learning rate of synaptic modification. PMID:22203800

  6. Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.

    PubMed

    Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito

    2015-12-01

    We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks. PMID:26595417

  7. Recovery cycle times of inferior colliculus neurons in the awake bat measured with spike counts and latencies

    PubMed Central

    Sayegh, Riziq; Aubie, Brandon; Fazel-Pour, Siavosh; Faure, Paul A.

    2012-01-01

    Neural responses in the mammalian auditory midbrain (inferior colliculus; IC) arise from complex interactions of synaptic excitation, inhibition, and intrinsic properties of the cell. Temporally selective duration-tuned neurons (DTNs) in the IC are hypothesized to arise through the convergence of excitatory and inhibitory synaptic inputs offset in time. Synaptic inhibition can be inferred from extracellular recordings by presenting pairs of pulses (paired tone stimulation) and comparing the evoked responses of the cell to each pulse. We obtained single unit recordings from the IC of the awake big brown bat (Eptesicus fuscus) and used paired tone stimulation to measure the recovery cycle times of DTNs and non-temporally selective auditory neurons. By systematically varying the interpulse interval (IPI) of the paired tone stimulus, we determined the minimum IPI required for a neuron's spike count or its spike latency (first- or last-spike latency) in response to the second tone to recover to within ≥50% of the cell's baseline count or to within 1 SD of it's baseline latency in response to the first tone. Recovery times of shortpass DTNs were significantly shorter than those of bandpass DTNs, and recovery times of bandpass DTNs were longer than allpass neurons not selective for stimulus duration. Recovery times measured with spike counts were positively correlated with those measured with spike latencies. Recovery times were also correlated with first-spike latency (FSL). These findings, combined with previous studies on duration tuning in the IC, suggest that persistent inhibition is a defining characteristic of DTNs. Herein, we discuss measuring recovery times of neurons with spike counts and latencies. We also highlight how persistent inhibition could determine neural recovery times and serve as a potential mechanism underlying the precedence effect in humans. Finally, we explore implications of recovery times for DTNs in the context of bat hearing and

  8. Is a 4-Bit Synaptic Weight Resolution Enough? – Constraints on Enabling Spike-Timing Dependent Plasticity in Neuromorphic Hardware

    PubMed Central

    Pfeil, Thomas; Potjans, Tobias C.; Schrader, Sven; Potjans, Wiebke; Schemmel, Johannes; Diesmann, Markus; Meier, Karlheinz

    2012-01-01

    Large-scale neuromorphic hardware systems typically bear the trade-off between detail level and required chip resources. Especially when implementing spike-timing dependent plasticity, reduction in resources leads to limitations as compared to floating point precision. By design, a natural modification that saves resources would be reducing synaptic weight resolution. In this study, we give an estimate for the impact of synaptic weight discretization on different levels, ranging from random walks of individual weights to computer simulations of spiking neural networks. The FACETS wafer-scale hardware system offers a 4-bit resolution of synaptic weights, which is shown to be sufficient within the scope of our network benchmark. Our findings indicate that increasing the resolution may not even be useful in light of further restrictions of customized mixed-signal synapses. In addition, variations due to production imperfections are investigated and shown to be uncritical in the context of the presented study. Our results represent a general framework for setting up and configuring hardware-constrained synapses. We suggest how weight discretization could be considered for other backends dedicated to large-scale simulations. Thus, our proposition of a good hardware verification practice may rise synergy effects between hardware developers and neuroscientists. PMID:22822388

  9. Distributed Cerebellar Motor Learning: A Spike-Timing-Dependent Plasticity Model

    PubMed Central

    Luque, Niceto R.; Garrido, Jesús A.; Naveros, Francisco; Carrillo, Richard R.; D'Angelo, Egidio; Ros, Eduardo

    2016-01-01

    Deep cerebellar nuclei neurons receive both inhibitory (GABAergic) synaptic currents from Purkinje cells (within the cerebellar cortex) and excitatory (glutamatergic) synaptic currents from mossy fibers. Those two deep cerebellar nucleus inputs are thought to be also adaptive, embedding interesting properties in the framework of accurate movements. We show that distributed spike-timing-dependent plasticity mechanisms (STDP) located at different cerebellar sites (parallel fibers to Purkinje cells, mossy fibers to deep cerebellar nucleus cells, and Purkinje cells to deep cerebellar nucleus cells) in close-loop simulations provide an explanation for the complex learning properties of the cerebellum in motor learning. Concretely, we propose a new mechanistic cerebellar spiking model. In this new model, deep cerebellar nuclei embed a dual functionality: deep cerebellar nuclei acting as a gain adaptation mechanism and as a facilitator for the slow memory consolidation at mossy fibers to deep cerebellar nucleus synapses. Equipping the cerebellum with excitatory (e-STDP) and inhibitory (i-STDP) mechanisms at deep cerebellar nuclei afferents allows the accommodation of synaptic memories that were formed at parallel fibers to Purkinje cells synapses and then transferred to mossy fibers to deep cerebellar nucleus synapses. These adaptive mechanisms also contribute to modulate the deep-cerebellar-nucleus-output firing rate (output gain modulation toward optimizing its working range). PMID:26973504

  10. Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity.

    PubMed

    Nessler, Bernhard; Pfeiffer, Michael; Buesing, Lars; Maass, Wolfgang

    2013-04-01

    The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex. PMID:23633941

  11. A model of spike-timing dependent plasticity: one or two coincidence detectors?

    PubMed

    Karmarkar, Uma R; Buonomano, Dean V

    2002-07-01

    In spike-timing dependent plasticity (STDP), synapses exhibit LTD or LTP depending on the order of activity in the presynaptic and postsynaptic cells. LTP occurs when a single presynaptic spike precedes a postsynaptic one (a positive interspike interval, or ISI), while the reverse order of activity (a negative ISI) produces LTD. A fundamental question is whether the "standard model" of plasticity in which moderate increases in Ca(2+) influx through the N-methyl-D-aspartate (NMDA) channels induce LTD and large increases induce LTP, can account for the order and interval sensitivity of STDP. To examine this issue we developed a model that captures postsynaptic Ca(2+) influx dynamics and the associativity of the NMDA receptors. While this model can generate both LTD and LTP, it predicts that LTD will be observed at both negative and positive ISIs. This is because longer and longer positive ISIs induce monotonically decreasing levels of Ca(2+), which eventually fall into the same range that produced LTD at negative ISIs. A second model that incorporated a second coincidence detector in addition to the NMDA receptor generated LTP at positive intervals and LTD only at negative ones. Our findings suggest that a single coincidence detector model based on the standard model of plasticity cannot account for order-specific STDP, and we predict that STDP requires two coincidence detectors. PMID:12091572

  12. Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules

    PubMed Central

    Frémaux, Nicolas; Gerstner, Wulfram

    2016-01-01

    Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulators on synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide “when” to create new memories in response to a flow of sensory stimuli. In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discuss some experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity. We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators. PMID:26834568

  13. Presynaptic Spike Timing-Dependent Long-Term Depression in the Mouse Hippocampus

    PubMed Central

    Andrade-Talavera, Yuniesky; Duque-Feria, Paloma; Paulsen, Ole; Rodríguez-Moreno, Antonio

    2016-01-01

    Spike timing-dependent plasticity (STDP) is a Hebbian learning rule important for synaptic refinement during development and for learning and memory in the adult. Given the importance of the hippocampus in memory, surprisingly little is known about the mechanisms and functions of hippocampal STDP. In the present work, we investigated the requirements for induction of hippocampal spike timing-dependent long-term potentiation (t-LTP) and spike timing-dependent long-term depression (t-LTD) and the mechanisms of these 2 forms of plasticity at CA3-CA1 synapses in young (P12–P18) mouse hippocampus. We found that both t-LTP and t-LTD can be induced at hippocampal CA3-CA1 synapses by pairing presynaptic activity with single postsynaptic action potentials at low stimulation frequency (0.2 Hz). Both t-LTP and t-LTD require NMDA-type glutamate receptors for their induction, but the location and properties of these receptors are different: While t-LTP requires postsynaptic ionotropic NMDA receptor function, t-LTD does not, and whereas t-LTP is blocked by antagonists at GluN2A and GluN2B subunit-containing NMDA receptors, t-LTD is blocked by GluN2C or GluN2D subunit-preferring NMDA receptor antagonists. Both t-LTP and t-LTD require postsynaptic Ca2+ for their induction. Induction of t-LTD also requires metabotropic glutamate receptor activation, phospholipase C activation, postsynaptic IP3 receptor-mediated Ca2+ release from internal stores, postsynaptic endocannabinoid (eCB) synthesis, activation of CB1 receptors and astrocytic signaling, possibly via release of the gliotransmitter d-serine. We furthermore found that presynaptic calcineurin is required for t-LTD induction. t-LTD is expressed presynaptically as indicated by fluctuation analysis, paired-pulse ratio, and rate of use-dependent depression of postsynaptic NMDA receptor currents by MK801. The results show that CA3-CA1 synapses display both NMDA receptor-dependent t-LTP and t-LTD during development and identify a

  14. Presynaptic Spike Timing-Dependent Long-Term Depression in the Mouse Hippocampus.

    PubMed

    Andrade-Talavera, Yuniesky; Duque-Feria, Paloma; Paulsen, Ole; Rodríguez-Moreno, Antonio

    2016-08-01

    Spike timing-dependent plasticity (STDP) is a Hebbian learning rule important for synaptic refinement during development and for learning and memory in the adult. Given the importance of the hippocampus in memory, surprisingly little is known about the mechanisms and functions of hippocampal STDP. In the present work, we investigated the requirements for induction of hippocampal spike timing-dependent long-term potentiation (t-LTP) and spike timing-dependent long-term depression (t-LTD) and the mechanisms of these 2 forms of plasticity at CA3-CA1 synapses in young (P12-P18) mouse hippocampus. We found that both t-LTP and t-LTD can be induced at hippocampal CA3-CA1 synapses by pairing presynaptic activity with single postsynaptic action potentials at low stimulation frequency (0.2 Hz). Both t-LTP and t-LTD require NMDA-type glutamate receptors for their induction, but the location and properties of these receptors are different: While t-LTP requires postsynaptic ionotropic NMDA receptor function, t-LTD does not, and whereas t-LTP is blocked by antagonists at GluN2A and GluN2B subunit-containing NMDA receptors, t-LTD is blocked by GluN2C or GluN2D subunit-preferring NMDA receptor antagonists. Both t-LTP and t-LTD require postsynaptic Ca(2+) for their induction. Induction of t-LTD also requires metabotropic glutamate receptor activation, phospholipase C activation, postsynaptic IP3 receptor-mediated Ca(2+) release from internal stores, postsynaptic endocannabinoid (eCB) synthesis, activation of CB1 receptors and astrocytic signaling, possibly via release of the gliotransmitter d-serine. We furthermore found that presynaptic calcineurin is required for t-LTD induction. t-LTD is expressed presynaptically as indicated by fluctuation analysis, paired-pulse ratio, and rate of use-dependent depression of postsynaptic NMDA receptor currents by MK801. The results show that CA3-CA1 synapses display both NMDA receptor-dependent t-LTP and t-LTD during development and identify a

  15. Effect of spike-timing-dependent plasticity on coherence resonance and synchronization transitions by time delay in adaptive neuronal networks

    NASA Astrophysics Data System (ADS)

    Xie, Huijuan; Gong, Yubing; Wang, Qi

    2016-06-01

    In this paper, we numerically study how time delay induces multiple coherence resonance (MCR) and synchronization transitions (ST) in adaptive Hodgkin-Huxley neuronal networks with spike-timing dependent plasticity (STDP). It is found that MCR induced by time delay STDP can be either enhanced or suppressed as the adjusting rate Ap of STDP changes, and ST by time delay varies with the increase of Ap, and there is optimal Ap by which the ST becomes strongest. It is also found that there are optimal network randomness and network size by which ST by time delay becomes strongest, and when Ap increases, the optimal network randomness and optimal network size increase and related ST is enhanced. These results show that STDP can either enhance or suppress MCR and optimal STDP can enhance ST induced by time delay in the adaptive neuronal networks. These findings provide a new insight into STDP's role for the information processing and transmission in neural systems.

  16. An Unsorted Spike-Based Pattern Recognition Method for Real-Time Continuous Sensory Event Detection from Dorsal Root Ganglion Recording.

    PubMed

    Han, Sungmin; Chu, Jun-Uk; Kim, Hyungmin; Choi, Kuiwon; Park, Jong Woong; Youn, Inchan

    2016-06-01

    In functional neuromuscular stimulation systems, sensory information-based closed-loop control can be useful for restoring lost function in patients with hemiplegia or quadriplegia. The goal of this study was to detect sensory events from tactile afferent signals continuously in real time using a novel unsorted spike-based pattern recognition method. The tactile afferent signals were recorded with a 16-channel microelectrode in the dorsal root ganglion, and unsorted spike-based feature vectors were extracted as a novel combination of the time and time-frequency domain features. Principal component analysis was used to reduce the dimensionality of the feature vectors, and a multilayer perceptron classifier was used to detect sensory events. The proposed method showed good performance for classification accuracy, and the processing time delay of sensory event detection was less than 200 ms. These results indicated that the proposed method could be applicable for sensory feedback in closed-loop control systems. PMID:26672029

  17. Spike-timing dependent plasticity in a transistor-selected resistive switching memory

    NASA Astrophysics Data System (ADS)

    Ambrogio, S.; Balatti, S.; Nardi, F.; Facchinetti, S.; Ielmini, D.

    2013-09-01

    In a neural network, neuron computation is achieved through the summation of input signals fed by synaptic connections. The synaptic activity (weight) is dictated by the synchronous firing of neurons, inducing potentiation/depression of the synaptic connection. This learning function can be supported by the resistive switching memory (RRAM), which changes its resistance depending on the amplitude, the pulse width and the bias polarity of the applied signal. This work shows a new synapse circuit comprising a MOS transistor as a selector and a RRAM as a variable resistance, displaying spike-timing dependent plasticity (STDP) similar to the one originally experienced in biological neural networks. We demonstrate long-term potentiation and long-term depression by simulations with an analytical model of resistive switching. Finally, the experimental demonstration of the new STDP scheme is presented.

  18. Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses

    PubMed Central

    Ambrogio, Stefano; Ciocchini, Nicola; Laudato, Mario; Milo, Valerio; Pirovano, Agostino; Fantini, Paolo; Ielmini, Daniele

    2016-01-01

    We present a novel one-transistor/one-resistor (1T1R) synapse for neuromorphic networks, based on phase change memory (PCM) technology. The synapse is capable of spike-timing dependent plasticity (STDP), where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors. PMID:27013934

  19. Spike-timing dependent plasticity in a transistor-selected resistive switching memory.

    PubMed

    Ambrogio, S; Balatti, S; Nardi, F; Facchinetti, S; Ielmini, D

    2013-09-27

    In a neural network, neuron computation is achieved through the summation of input signals fed by synaptic connections. The synaptic activity (weight) is dictated by the synchronous firing of neurons, inducing potentiation/depression of the synaptic connection. This learning function can be supported by the resistive switching memory (RRAM), which changes its resistance depending on the amplitude, the pulse width and the bias polarity of the applied signal. This work shows a new synapse circuit comprising a MOS transistor as a selector and a RRAM as a variable resistance, displaying spike-timing dependent plasticity (STDP) similar to the one originally experienced in biological neural networks. We demonstrate long-term potentiation and long-term depression by simulations with an analytical model of resistive switching. Finally, the experimental demonstration of the new STDP scheme is presented. PMID:23999495

  20. Self-organized noise resistance of oscillatory neural networks with spike timing-dependent plasticity

    PubMed Central

    Popovych, Oleksandr V.; Yanchuk, Serhiy; Tass, Peter A.

    2013-01-01

    Intuitively one might expect independent noise to be a powerful tool for desynchronizing a population of synchronized neurons. We here show that, intriguingly, for oscillatory neural populations with adaptive synaptic weights governed by spike timing-dependent plasticity (STDP) the opposite is true. We found that the mean synaptic coupling in such systems increases dynamically in response to the increase of the noise intensity, and there is an optimal noise level, where the amount of synaptic coupling gets maximal in a resonance-like manner as found for the stochastic or coherence resonances, although the mechanism in our case is different. This constitutes a noise-induced self-organization of the synaptic connectivity, which effectively counteracts the desynchronizing impact of independent noise over a wide range of the noise intensity. Given the attempts to counteract neural synchrony underlying tinnitus with noisers and maskers, our results may be of clinical relevance. PMID:24113385

  1. Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses.

    PubMed

    Ambrogio, Stefano; Ciocchini, Nicola; Laudato, Mario; Milo, Valerio; Pirovano, Agostino; Fantini, Paolo; Ielmini, Daniele

    2016-01-01

    We present a novel one-transistor/one-resistor (1T1R) synapse for neuromorphic networks, based on phase change memory (PCM) technology. The synapse is capable of spike-timing dependent plasticity (STDP), where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors. PMID:27013934

  2. Seven neurons memorizing sequences of alphabetical images via spike-timing dependent plasticity

    PubMed Central

    Osogami, Takayuki; Otsuka, Makoto

    2015-01-01

    An artificial neural network, such as a Boltzmann machine, can be trained with the Hebb rule so that it stores static patterns and retrieves a particular pattern when an associated cue is presented to it. Such a network, however, cannot effectively deal with dynamic patterns in the manner of living creatures. Here, we design a dynamic Boltzmann machine (DyBM) and a learning rule that has some of the properties of spike-timing dependent plasticity (STDP), which has been postulated for biological neural networks. We train a DyBM consisting of only seven neurons in a way that it memorizes the sequence of the bitmap patterns in an alphabetical image “SCIENCE” and its reverse sequence and retrieves either sequence when a partial sequence is presented as a cue. The DyBM is to STDP as the Boltzmann machine is to the Hebb rule. PMID:26374672

  3. Self-organized noise resistance of oscillatory neural networks with spike timing-dependent plasticity

    NASA Astrophysics Data System (ADS)

    Popovych, Oleksandr V.; Yanchuk, Serhiy; Tass, Peter A.

    2013-10-01

    Intuitively one might expect independent noise to be a powerful tool for desynchronizing a population of synchronized neurons. We here show that, intriguingly, for oscillatory neural populations with adaptive synaptic weights governed by spike timing-dependent plasticity (STDP) the opposite is true. We found that the mean synaptic coupling in such systems increases dynamically in response to the increase of the noise intensity, and there is an optimal noise level, where the amount of synaptic coupling gets maximal in a resonance-like manner as found for the stochastic or coherence resonances, although the mechanism in our case is different. This constitutes a noise-induced self-organization of the synaptic connectivity, which effectively counteracts the desynchronizing impact of independent noise over a wide range of the noise intensity. Given the attempts to counteract neural synchrony underlying tinnitus with noisers and maskers, our results may be of clinical relevance.

  4. Closed-loop optical stimulation and recording system with GPU-based real-time spike sorting

    NASA Astrophysics Data System (ADS)

    Wang, Ling; Nguyen, Thoa; Cabral, Henrique; Gysbrechts, Barbara; Battaglia, Francesco; Bartic, Carmen

    2014-05-01

    Closed-loop brain computer interfaces are rapidly progressing due to their applications in fundamental neuroscience and prosthetics. For optogenetic experiments, the integration of optical stimulation and electrophysiological recordings is emerging as an imperative engineering research topic. Optical stimulation does not only bring the advantage of cell-type selectivity, but also provides an alternative solution to the electrical stimulation-induced artifacts, a challenge in closedloop architectures. A closed-loop system must identify the neuronal signals in real-time such that a strategy is selected immediately (within a few milliseconds) for delivering stimulation patterns. Real-time spike sorting poses important challenges especially when a large number of recording channels are involved. Here we present a prototype allowing simultaneous optical stimulation and electro-physiological recordings in a closed-loop manner. The prototype was implemented with online spike detection and classification capabilities for selective cell stimulation. Real-time spike sorting was achieved by computations with a high speed, low cost graphic processing unit (GPU). We have successfully demonstrated the closed-loop operation, i.e. optical stimulation in vivo based on spike detection from 8 tetrodes (32 channels). The performance of GPU computation in spike sorting for different channel numbers and signal lengths was also investigated.

  5. Spike Timing-Dependent Plasticity as the Origin of the Formation of Clustered Synaptic Efficacy Engrams

    PubMed Central

    Iannella, Nicolangelo Libero; Launey, Thomas; Tanaka, Shigeru

    2010-01-01

    Synapse location, dendritic active properties and synaptic plasticity are all known to play some role in shaping the different input streams impinging onto a neuron. It remains unclear however, how the magnitude and spatial distribution of synaptic efficacies emerge from this interplay. Here, we investigate this interplay using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and spike timing-dependent plasticity (STDP). Specifically, we focus on the issue of how the efficacy of synapses contributed by different input streams are spatially represented in dendrites after STDP learning. We construct a simple feed forward network where a detailed model neuron receives synaptic inputs independently from multiple yet equally sized groups of afferent fibers with correlated activity, mimicking the spike activity from different neuronal populations encoding, for example, different sensory modalities. Interestingly, ensuing STDP learning, we observe that for all afferent groups, STDP leads to synaptic efficacies arranged into spatially segregated clusters effectively partitioning the dendritic tree. These segregated clusters possess a characteristic global organization in space, where they form a tessellation in which each group dominates mutually exclusive regions of the dendrite. Put simply, the dendritic imprint from different input streams left after STDP learning effectively forms what we term a “dendritic efficacy mosaic.” Furthermore, we show how variations of the inputs and STDP rule affect such an organization. Our model suggests that STDP may be an important mechanism for creating a clustered plasticity engram, which shapes how different input streams are spatially represented in dendrite. PMID:20725522

  6. Comparison of Genomic Selection Models to Predict Flowering Time and Spike Grain Number in Two Hexaploid Wheat Doubled Haploid Populations.

    PubMed

    Thavamanikumar, Saravanan; Dolferus, Rudy; Thumma, Bala R

    2015-10-01

    Genomic selection (GS) is becoming an important selection tool in crop breeding. In this study, we compared the ability of different GS models to predict time to young microspore (TYM), a flowering time-related trait, spike grain number under control conditions (SGNC) and spike grain number under osmotic stress conditions (SGNO) in two wheat biparental doubled haploid populations with unrelated parents. Prediction accuracies were compared using BayesB, Bayesian least absolute shrinkage and selection operator (Bayesian LASSO / BL), ridge regression best linear unbiased prediction (RR-BLUP), partial least square regression (PLS), and sparse partial least square regression (SPLS) models. Prediction accuracy was tested with 10-fold cross-validation within a population and with independent validation in which marker effects from one population were used to predict traits in the other population. High prediction accuracies were obtained for TYM (0.51-0.84), whereas moderate to low accuracies were observed for SGNC (0.10-0.42) and SGNO (0.27-0.46) using cross-validation. Prediction accuracies based on independent validation are generally lower than those based on cross-validation. BayesB and SPLS outperformed all other models in predicting TYM with both cross-validation and independent validation. Although the accuracies of all models are similar in predicting SGNC and SGNO with cross-validation, BayesB and SPLS had the highest accuracy in predicting SGNC with independent validation. In independent validation, accuracies of all the models increased by using only the QTL-linked markers. Results from this study indicate that BayesB and SPLS capture the linkage disequilibrium between markers and traits effectively leading to higher accuracies. Excluding markers from QTL studies reduces prediction accuracies. PMID:26206349

  7. Precise manipulation on spike train of uneven duration or delay pulses with a time grating system.

    PubMed

    Li, Yue; Wang, Shiwei; Xu, Jianqiu; Tang, Yulong

    2015-11-16

    In this paper, we proposed a time grating system to achieve spike train of uneven duration or delay (STUD) pulses, and theoretically study their features under various modulation conditions. This time grating scheme, which is a temporal analogy of spatial grating, introduces great degree of freedom for controlling the output pulse characteristics (pulse width, repetition rate, pulse shape, etc.) through simply tuning the electronics elements and the programmable phase modulation function. The narrowest pulse width is highly determined by the modulation parameters and the branch number N, and the numerically obtained value is around tens of femtoseconds in the current case. When super-Gaussian pulses are modulated with an optimized and modified trapezoidal function, the pulse rising/falling edge can be greatly compressed to form a clean nearly-square wave (with edges less than 10 fs). STUD pulses generated with this time grating system have high-degree controllability and are very beneficial for suppressing parametric instabilities in laser driven inertial confinement fusion. PMID:26698432

  8. Hearing aid gain prescriptions balance restoration of auditory nerve mean-rate and spike-timing representations of speech.

    PubMed

    Dinath, Faheem; Bruce, Ian C

    2008-01-01

    Linear and nonlinear amplification schemes for hearing aids have thus far been developed and evaluated based on perceptual criteria such as speech intelligibility, sound comfort, and loudness equalization. Finding amplification schemes that optimize all of these perceptual metrics has proven difficult. Using a physiological model, Bruce et al. [1] investigated the effects of single-band gain adjustments to linear amplification prescriptions. Optimal gain adjustments for model auditory-nerve fiber responses to speech sentences from the TIMIT database were dependent on whether the error metric included the spike timing information (i.e., a time-resolution of several microseconds) or the mean firing rates (i.e., a time-resolution of several milliseconds). Results showed that positive gain adjustments are required to optimize the mean firing rate responses, whereas negative gain adjustments tend to optimize spike timing information responses. In this paper we examine the results in more depth using a similar optimization scheme applied to a synthetic vowel /E/. It is found that negative gain adjustments (i.e., below the linear gain prescriptions) minimize the spread of synchrony and deviation of the phase response to vowel formants in responses containing spike-timing information. In contrast, positive gain adjustments (i.e., above the linear gain prescriptions) normalize the distribution of mean discharge rates in the auditory nerve responses. Thus, linear amplification prescriptions appear to find a balance between restoring the spike-timing and mean-rate information in auditory-nerve responses. PMID:19163029

  9. Triphasic spike-timing-dependent plasticity organizes networks to produce robust sequences of neural activity

    PubMed Central

    Waddington, Amelia; Appleby, Peter A.; De Kamps, Marc; Cohen, Netta

    2012-01-01

    Synfire chains have long been proposed to generate precisely timed sequences of neural activity. Such activity has been linked to numerous neural functions including sensory encoding, cognitive and motor responses. In particular, it has been argued that synfire chains underlie the precise spatiotemporal firing patterns that control song production in a variety of songbirds. Previous studies have suggested that the development of synfire chains requires either initial sparse connectivity or strong topological constraints, in addition to any synaptic learning rules. Here, we show that this necessity can be removed by using a previously reported but hitherto unconsidered spike-timing-dependent plasticity (STDP) rule and activity-dependent excitability. Under this rule the network develops stable synfire chains that possess a non-trivial, scalable multi-layer structure, in which relative layer sizes appear to follow a universal function. Using computational modeling and a coarse grained random walk model, we demonstrate the role of the STDP rule in growing, molding and stabilizing the chain, and link model parameters to the resulting structure. PMID:23162457

  10. Using Multiple Whole-Cell Recordings to Study Spike-Timing-Dependent Plasticity in Acute Neocortical Slices.

    PubMed

    Lalanne, Txomin; Abrahamsson, Therese; Sjöström, P Jesper

    2016-01-01

    This protocol provides a method for quadruple whole-cell recording to study synaptic plasticity of neocortical connections, with a special focus on spike-timing-dependent plasticity (STDP). It also describes how to morphologically identify recorded cells from two-photon laser-scanning microscopy (2PLSM) stacks. PMID:27250948

  11. A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks

    PubMed Central

    Wang, Runchun M.; Hamilton, Tara J.; Tapson, Jonathan C.; van Schaik, André

    2015-01-01

    We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP) and Spike Timing Dependent Delay Plasticity (STDDP). We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 226 (64M) synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted or delayed pre-synaptic spike to the post-synaptic neuron in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 236 (64G) synaptic adaptors on a current high-end FPGA platform. PMID:26041985

  12. Linking Neuromodulated Spike-Timing Dependent Plasticity with the Free-Energy Principle.

    PubMed

    Isomura, Takuya; Sakai, Koji; Kotani, Kiyoshi; Jimbo, Yasuhiko

    2016-09-01

    The free-energy principle is a candidate unified theory for learning and memory in the brain that predicts that neurons, synapses, and neuromodulators work in a manner that minimizes free energy. However, electrophysiological data elucidating the neural and synaptic bases for this theory are lacking. Here, we propose a novel theory bridging the information-theoretical principle with the biological phenomenon of spike-timing dependent plasticity (STDP) regulated by neuromodulators, which we term mSTDP. We propose that by integrating an mSTDP equation, we can obtain a form of Friston's free energy (an information-theoretical function). Then we analytically and numerically show that dopamine (DA) and noradrenaline (NA) influence the accuracy of a principal component analysis (PCA) performed using the mSTDP algorithm. From the perspective of free-energy minimization, these neuromodulatory changes alter the relative weighting or precision of accuracy and prior terms, which induces a switch from pattern completion to separation. These results are consistent with electrophysiological findings and validate the free-energy principle and mSTDP. Moreover, our scheme can potentially be applied in computational psychiatry to build models of the faulty neural networks that underlie the positive symptoms of schizophrenia, which involve abnormal DA levels, as well as models of the NA contribution to memory triage and posttraumatic stress disorder. PMID:27391680

  13. Spike-timing-dependent synaptic plasticity and synaptic democracy in dendrites.

    PubMed

    Gidon, Albert; Segev, Idan

    2009-06-01

    We explored in a computational study the effect of dendrites on excitatory synapses undergoing spike-timing-dependent plasticity (STDP), using both cylindrical dendritic models and reconstructed dendritic trees. We show that even if the initial strength, g(peak), of distal synapses is augmented in a location independent manner, the efficacy of distal synapses diminishes following STDP and proximal synapses would eventually dominate. Indeed, proximal synapses always win over distal synapses following linear STDP rule, independent of the initial synaptic strength distribution in the dendritic tree. This effect is more pronounced as the dendritic cable length increases but it does not depend on the dendritic branching structure. Adding a small multiplicative component to the linear STDP rule, whereby already strong synapses tend to be less potentiated than depressed (and vice versa for weak synapses) did partially "save" distal synapses from "dying out." Another successful strategy for balancing the efficacy of distal and proximal synapses following STDP is to increase the upper bound for the synaptic conductance (g(max)) with distance from the soma. We conclude by discussing an experiment for assessing which of these possible strategies might actually operate in dendrites. PMID:19357339

  14. Recognition of disturbances with specified morphology in time series: Part 2. Spikes on 1-s magnetograms

    NASA Astrophysics Data System (ADS)

    Soloviev, A. A.; Agayan, S. M.; Gvishiani, A. D.; Bogoutdinov, Sh. R.; Chulliat, A.

    2012-05-01

    Preliminary magnetograms contain different types of temporal anthropogenic disturbances: spikes, baseline jumps, drifts, etc. These disturbances should be identified and filtered out during the preprocessing of the preliminary records for the definitive data. As of now, at the geomagnetic observatories, such filtering is carried out manually. Most of the disturbances in the records sampled every second are spikes, which are much more abundant than those on the magnetograms sampled every minute. Another important feature of the 1-s magnetograms is the presence of a plenty of specific disturbances caused by short-period geomagnetic pulsations, which must be retained in the definitive records. Thus, creating an instrument for formalized and unified recognition of spikes on the preliminary 1-s magnetograms would largely solve the problem of labor-consuming manual preprocessing of the magnetic records. In the context of this idea, in the present paper, we focus on recognition of the spikes on the 1-s magnetograms as a key point of the problem. We describe here the new algorithm of pattern recognition, SPs, which is capable of automatically identifying the spikes on the 1-s magnetograms with a low probability of missed events and false alarms. The algorithm was verified on the real magnetic data recorded at the French observatory located on Easter Island in the Pacific.

  15. Uniform olivocerebellar conduction time underlies Purkinje cell complex spike synchronicity in the rat cerebellum.

    PubMed Central

    Sugihara, I; Lang, E J; Llinás, R

    1993-01-01

    1. The issue of isochronicity of olivocerebellar fibre conduction time as a basis for synchronizing complex spike activity in cerebellar Purkinje cells has been addressed by latency measurement, multiple-electrode recording and Phaseolus vulgaris leucoagglutinin (PHA-L) tracing of climbing fibres in the adult rat. 2. The conduction time of the olivocerebellar fibres was measured by recording Purkinje cell complex spike (CS) responses from various areas of the cerebellum. The CSs were evoked by stimulating the olivocerebellar fibres near the inferior olive. In spite of a difference in length, as determined directly by light microscopy, the conduction times of different climbing fibres were quite uniform, 3.98 +/- 0.36 ms (mean +/- S.D., n = 660). 3. Multiple-electrode recording of spontaneous Purkinje cell CS activity was employed to study the spatial extent of CS synchronicity in the cerebellar cortex. Recordings of CS were obtained from Purkinje cells located on the surface and along the walls of lobule crus 2a. The rostrocaudal band-like distribution of simultaneous (within 1 ms) CS activity in Purkinje cells extended down the sides of the cerebellar folia to the deepest areas recorded (1.6-2.6 mm deep). As shown in previous experiments, the distribution of simultaneous CS activity did not extend significantly (500 microns) in the mediolateral axis of the cerebellar cortex. 4. In two animals a detailed determination of the length of the olivocerebellar fibre bundles was performed by staining the fibres with PHA-L injected into the contralateral inferior olive. This measurement included fibre bundles terminating in twenty-six different areas, ranging from the tops of the various folia to the bottoms of the fissures in both the hemisphere and the vermis. There was a 47.5% difference between the length of the longest measured fibre bundle (15.8 mm, terminating in lobule 6b, zone A) and the length of the shortest measured fibre bundle (8.3 mm, terminating in the

  16. Depression biased non-Hebbian spike-timing-dependent synaptic plasticity in the rat subiculum.

    PubMed

    Pandey, Anurag; Sikdar, Sujit Kumar

    2014-08-15

    The subiculum is a structure that forms a bridge between the hippocampus and the entorhinal cortex (EC), and plays a major role in the memory consolidation process. Here, we demonstrate spike-timing-dependent plasticity (STDP) at the proximal excitatory inputs on the subicular pyramidal neurons of juvenile rat. Causal (positive) pairing of a single EPSP with a single back-propagating action potential (bAP) after a time interval of 10 ms (+10 ms) failed to induce plasticity. However, increasing the number of bAPs in a burst to three, at two different frequencies of 50 Hz (bAP burst) and 150 Hz, induced long-term depression (LTD) after a time interval of +10 ms in both the regular-firing (RF), and the weak burst firing (WBF) neurons. The LTD amplitude decreased with increasing time interval between the EPSP and the bAP burst. Reversing the order of the pairing of the EPSP and the bAP burst induced LTP at a time interval of -10 ms. This finding is in contrast with reports at other synapses, wherein pre- before postsynaptic (causal) pairing induced LTP and vice versa. Our results reaffirm the earlier observations that the relative timing of the pre- and postsynaptic activities can lead to multiple types of plasticity profiles. The induction of timing-dependent LTD (t-LTD) was dependent on postsynaptic calcium change via NMDA receptors in the WBF neurons, while it was independent of postsynaptic calcium change, but required active L-type calcium channels in the RF neurons. Thus the mechanism of synaptic plasticity may vary within a hippocampal subfield depending on the postsynaptic neuron involved. This study also reports a novel mechanism of LTD induction, where L-type calcium channels are involved in a presynaptically induced synaptic plasticity. The findings may have strong implications in the memory consolidation process owing to the central role of the subiculum and LTD in this process. PMID:24907304

  17. Depression biased non-Hebbian spike-timing-dependent synaptic plasticity in the rat subiculum

    PubMed Central

    Pandey, Anurag; Sikdar, Sujit Kumar

    2014-01-01

    The subiculum is a structure that forms a bridge between the hippocampus and the entorhinal cortex (EC), and plays a major role in the memory consolidation process. Here, we demonstrate spike-timing-dependent plasticity (STDP) at the proximal excitatory inputs on the subicular pyramidal neurons of juvenile rat. Causal (positive) pairing of a single EPSP with a single back-propagating action potential (bAP) after a time interval of 10 ms (+10 ms) failed to induce plasticity. However, increasing the number of bAPs in a burst to three, at two different frequencies of 50 Hz (bAP burst) and 150 Hz, induced long-term depression (LTD) after a time interval of +10 ms in both the regular-firing (RF), and the weak burst firing (WBF) neurons. The LTD amplitude decreased with increasing time interval between the EPSP and the bAP burst. Reversing the order of the pairing of the EPSP and the bAP burst induced LTP at a time interval of −10 ms. This finding is in contrast with reports at other synapses, wherein pre- before postsynaptic (causal) pairing induced LTP and vice versa. Our results reaffirm the earlier observations that the relative timing of the pre- and postsynaptic activities can lead to multiple types of plasticity profiles. The induction of timing-dependent LTD (t-LTD) was dependent on postsynaptic calcium change via NMDA receptors in the WBF neurons, while it was independent of postsynaptic calcium change, but required active L-type calcium channels in the RF neurons. Thus the mechanism of synaptic plasticity may vary within a hippocampal subfield depending on the postsynaptic neuron involved. This study also reports a novel mechanism of LTD induction, where L-type calcium channels are involved in a presynaptically induced synaptic plasticity. The findings may have strong implications in the memory consolidation process owing to the central role of the subiculum and LTD in this process. PMID:24907304

  18. Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model

    PubMed Central

    Luz, Yotam; Shamir, Maoz

    2016-01-01

    Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes including the encoding of external stimuli, attention, and learning. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. This raises the question of the origin of these oscillations: are the mechanisms for generating these oscillations genetically hard-wired or can they be acquired via a learning process? Here, we study the conditions under which oscillatory activity emerges through a process of spike timing dependent plasticity (STDP) in a feed-forward architecture. First, we analyze the effect of oscillations on STDP-driven synaptic dynamics of a single synapse, and study how the parameters that characterize the STDP rule and the oscillations affect the resultant synaptic weight. Next, we analyze STDP-driven synaptic dynamics of a pre-synaptic population of neurons onto a single post-synaptic cell. The pre-synaptic neural population is assumed to be oscillating at the same frequency, albeit with different phases, such that the net activity of the pre-synaptic population is constant in time. Thus, in the homogeneous case in which all synapses are equal, the post-synaptic neuron receives constant input and hence does not oscillate. To investigate the transition to oscillatory activity, we develop a mean-field Fokker-Planck approximation of the synaptic dynamics. We analyze the conditions causing the homogeneous solution to lose its stability. The findings show that oscillatory activity appears through a mechanism of spontaneous symmetry breaking. However, in the general case the homogeneous solution is unstable, and the synaptic dynamics does not converge to a different fixed point, but rather to a limit cycle. We show how the temporal structure of the STDP rule determines the stability of the homogeneous solution and the drift velocity of the limit cycle. PMID

  19. Distinct coincidence detectors govern the corticostriatal spike timing-dependent plasticity.

    PubMed

    Fino, Elodie; Paille, Vincent; Cui, Yihui; Morera-Herreras, Teresa; Deniau, Jean-Michel; Venance, Laurent

    2010-08-15

    Corticostriatal projections constitute the main input to the basal ganglia, an ensemble of interconnected subcortical nuclei involved in procedural learning. Thus, long-term plasticity at corticostriatal synapses would provide a basic mechanism for the function of basal ganglia in learning and memory. We had previously reported the existence of a corticostriatal anti-Hebbian spike timing-dependent plasticity (STDP) at synapses onto striatal output neurons, the medium-sized spiny neurons. Here, we show that the blockade of GABAergic transmission reversed the time dependence of corticostriatal STDP. We explored the receptors and signalling mechanisms involved in the corticostriatal STDP. Although classical models for STDP propose NMDA receptors as the unique coincidence detector, the involvement of multiple coincidence detectors has also been demonstrated. Here, we show that corticostriatal STDP depends on distinct coincidence detectors. Specifically, long-term potentiation is dependent on NMDA receptor activation, while long-term depression requires distinct coincidence detectors: the phospholipase Cbeta (PLCbeta) and the inositol-trisphosphate receptor (IP3R)-gated calcium stores. Furthermore, we found that PLCbeta activation is controlled by group-I metabotropic glutamate receptors, type-1 muscarinic receptors and voltage-sensitive calcium channel activities. Activation of PLCbeta and IP3Rs leads to robust retrograde endocannabinoid signalling mediated by 2-arachidonoyl-glycerol and cannabinoid CB1 receptors. Interestingly, the same coincidence detectors govern the corticostriatal anti-Hebbian STDP and the Hebbian STDP reported at cortical synapses. Therefore, LTP and LTD induced by STDP at corticostriatal synapses are mediated by independent signalling mechanisms, each one being controlled by distinct coincidence detectors. PMID:20603333

  20. Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model.

    PubMed

    Luz, Yotam; Shamir, Maoz

    2016-04-01

    Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes including the encoding of external stimuli, attention, and learning. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. This raises the question of the origin of these oscillations: are the mechanisms for generating these oscillations genetically hard-wired or can they be acquired via a learning process? Here, we study the conditions under which oscillatory activity emerges through a process of spike timing dependent plasticity (STDP) in a feed-forward architecture. First, we analyze the effect of oscillations on STDP-driven synaptic dynamics of a single synapse, and study how the parameters that characterize the STDP rule and the oscillations affect the resultant synaptic weight. Next, we analyze STDP-driven synaptic dynamics of a pre-synaptic population of neurons onto a single post-synaptic cell. The pre-synaptic neural population is assumed to be oscillating at the same frequency, albeit with different phases, such that the net activity of the pre-synaptic population is constant in time. Thus, in the homogeneous case in which all synapses are equal, the post-synaptic neuron receives constant input and hence does not oscillate. To investigate the transition to oscillatory activity, we develop a mean-field Fokker-Planck approximation of the synaptic dynamics. We analyze the conditions causing the homogeneous solution to lose its stability. The findings show that oscillatory activity appears through a mechanism of spontaneous symmetry breaking. However, in the general case the homogeneous solution is unstable, and the synaptic dynamics does not converge to a different fixed point, but rather to a limit cycle. We show how the temporal structure of the STDP rule determines the stability of the homogeneous solution and the drift velocity of the limit cycle. PMID

  1. Real-time classification and sensor fusion with a spiking deep belief network

    PubMed Central

    O'Connor, Peter; Neil, Daniel; Liu, Shih-Chii; Delbruck, Tobi; Pfeiffer, Michael

    2013-01-01

    Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers. This paper proposes a method based on the Siegert approximation for Integrate-and-Fire neurons to map an offline-trained DBN onto an efficient event-driven spiking neural network suitable for hardware implementation. The method is demonstrated in simulation and by a real-time implementation of a 3-layer network with 2694 neurons used for visual classification of MNIST handwritten digits with input from a 128 × 128 Dynamic Vision Sensor (DVS) silicon retina, and sensory-fusion using additional input from a 64-channel AER-EAR silicon cochlea. The system is implemented through the open-source software in the jAER project and runs in real-time on a laptop computer. It is demonstrated that the system can recognize digits in the presence of distractions, noise, scaling, translation and rotation, and that the degradation of recognition performance by using an event-based approach is less than 1%. Recognition is achieved in an average of 5.8 ms after the onset of the presentation of a digit. By cue integration from both silicon retina and cochlea outputs we show that the system can be biased to select the correct digit from otherwise ambiguous input. PMID:24115919

  2. Dynamics of stimulus-evoked spike timing correlations in the cat lateral geniculate nucleus.

    PubMed

    Ito, Hiroyuki; Maldonado, Pedro E; Gray, Charles M

    2010-12-01

    Precisely synchronized neuronal activity has been commonly observed in the mammalian visual pathway. Spike timing correlations in the lateral geniculate nucleus (LGN) often take the form of phase synchronized oscillations in the high gamma frequency range. To study the relations between oscillatory activity, synchrony, and their time-dependent properties, we recorded activity from multiple single units in the cat LGN under stimulation by stationary spots of light. Autocorrelation analysis showed that approximately one third of the cells exhibited oscillatory firing with a mean frequency ∼80 Hz. Cross-correlation analysis showed that 30% of unit pairs showed significant synchronization, and 61% of these pairs consisted of synchronous oscillations. Cross-correlation analysis assumes that synchronous firing is stationary and maintained throughout the period of stimulation. We tested this assumption by applying unitary events analysis (UEA). We found that UEA was more sensitive to weak and transient synchrony than cross-correlation analysis and detected a higher incidence (49% of cell pairs) of significant synchrony (unitary events). In many unit pairs, the unitary events were optimally characterized at a bin width of 1 ms, indicating that neural synchrony has a high degree of temporal precision. We also found that approximately one half of the unit pairs showed nonstationary changes in synchrony that could not be predicted by the modulation of firing rates. Population statistics showed that the onset of synchrony between LGN cells occurred significantly later than that observed between retinal afferents and LGN cells. The synchrony detected among unit pairs recorded on separate tetrodes tended to be more transient and have a later onset than that observed between adjacent units. These findings show that stimulus-evoked synchronous activity within the LGN is often rhythmic, highly nonstationary, and modulated by endogenous processes that are not tightly correlated

  3. Systems and methods for reducing transient voltage spikes in matrix converters

    SciTech Connect

    Kajouke, Lateef A.; Perisic, Milun; Ransom, Ray M.

    2013-06-11

    Systems and methods are provided for delivering energy using an energy conversion module that includes one or more switching elements. An exemplary electrical system comprises a DC interface, an AC interface, an isolation module, a first conversion module between the DC interface and the isolation module, and a second conversion module between the AC interface and the isolation module. A control module is configured to operate the first conversion module to provide an injection current to the second conversion module to reduce a magnitude of a current through a switching element of the second conversion module before opening the switching element.

  4. Bursts shape the NMDA-R mediated spike timing dependent plasticity curve: role of burst interspike interval and GABAergic inhibition.

    PubMed

    Cutsuridis, Vassilis

    2012-10-01

    Spike timing dependent plasticity (STDP) is a synaptic learning rule where the relative timing between the presynaptic and postsynaptic action potentials determines the sign and strength of synaptic plasticity. In its basic form STDP has an asymmetric form which incorporates both persistent increases and persistent decreases in synaptic strength. The basic form of STDP, however, is not a fixed property and depends on the dendritic location. An asymmetric curve is observed in the distal dendrites, whereas a symmetrical one is observed in the proximal ones. A recent computational study has shown that the transition from the asymmetry to symmetry is due to inhibition under certain conditions. Synapses have also been observed to be unreliable at generating plasticity when excitatory postsynaptic potentials and single spikes are paired at low frequencies. Bursts of spikes, however, are reliably signaled because transmitter release is facilitated. This article presents a two-compartment model of the CA1 pyramidal cell. The model is neurophysiologically plausible with its dynamics resulting from the interplay of many ionic and synaptic currents. Plasticity is measured by a deterministic Ca(2+) dynamics model which measures the instantaneous calcium level and its time course in the dendrite and change the strength of the synapse accordingly. The model is validated to match the asymmetrical form of STDP from the pairing of a presynaptic (dendritic) and postsynaptic (somatic) spikes as observed experimentally. With the parameter set unchanged the model investigates how pairing of bursts with single spikes and bursts in the presence or absence of inhibition shapes the STDP curve. The model predicts that inhibition strength and frequency are not the only factors of the asymmetry-to-symmetry switch of the STDP curve. Burst interspike interval is another factor. This study is an important first step towards understanding how STDP is affected under natural firing patterns in vivo

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

    NASA Astrophysics Data System (ADS)

    Shimazaki, Hideaki

    2013-12-01

    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.

  6. Monitoring spike train synchrony.

    PubMed

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

    2013-03-01

    Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings. PMID:23221419

  7. Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity.

    PubMed

    Bennett, James E M; Bair, Wyeth

    2015-08-01

    Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli. PMID:26308406

  8. Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

    PubMed Central

    Bennett, James E. M.; Bair, Wyeth

    2015-01-01

    Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli. PMID:26308406

  9. Effects of the spike timing-dependent plasticity on the synchronisation in a random Hodgkin-Huxley neuronal network

    NASA Astrophysics Data System (ADS)

    Borges, R. R.; Borges, F. S.; Lameu, E. L.; Batista, A. M.; Iarosz, K. C.; Caldas, I. L.; Viana, R. L.; Sanjuán, M. A. F.

    2016-05-01

    In this paper, we study the effects of spike timing-dependent plasticity on synchronisation in a network of Hodgkin-Huxley neurons. Neuron plasticity is a flexible property of a neuron and its network to change temporarily or permanently their biochemical, physiological, and morphological characteristics, in order to adapt to the environment. Regarding the plasticity, we consider Hebbian rules, specifically for spike timing-dependent plasticity (STDP), and with regard to network, we consider that the connections are randomly distributed. We analyse the synchronisation and desynchronisation according to an input level and probability of connections. Moreover, we verify that the transition for synchronisation depends on the neuronal network architecture, and the external perturbation level.

  10. STICK: Spike Time Interval Computational Kernel, a Framework for General Purpose Computation Using Neurons, Precise Timing, Delays, and Synchrony.

    PubMed

    Lagorce, Xavier; Benosman, Ryad

    2015-11-01

    There has been significant research over the past two decades in developing new platforms for spiking neural computation. Current neural computers are primarily developed to mimic biology. They use neural networks, which can be trained to perform specific tasks to mainly solve pattern recognition problems. These machines can do more than simulate biology; they allow us to rethink our current paradigm of computation. The ultimate goal is to develop brain-inspired general purpose computation architectures that can breach the current bottleneck introduced by the von Neumann architecture. This work proposes a new framework for such a machine. We show that the use of neuron-like units with precise timing representation, synaptic diversity, and temporal delays allows us to set a complete, scalable compact computation framework. The framework provides both linear and nonlinear operations, allowing us to represent and solve any function. We show usability in solving real use cases from simple differential equations to sets of nonlinear differential equations leading to chaotic attractors. PMID:26378879

  11. High phosphate reduces host ability to develop arbuscular mycorrhizal symbiosis without affecting root calcium spiking responses to the fungus

    PubMed Central

    Balzergue, Coline; Chabaud, Mireille; Barker, David G.; Bécard, Guillaume; Rochange, Soizic F.

    2013-01-01

    The arbuscular mycorrhizal symbiosis associates soil fungi with the roots of the majority of plants species and represents a major source of soil phosphorus acquisition. Mycorrhizal interactions begin with an exchange of molecular signals between the two partners. A root signaling pathway is recruited, for which the perception of fungal signals triggers oscillations of intracellular calcium concentration. High phosphate availability is known to inhibit the establishment and/or persistence of this symbiosis, thereby favoring the direct, non-symbiotic uptake of phosphorus by the root system. In this study, Medicago truncatula plants were used to investigate the effects of phosphate supply on the early stages of the interaction. When plants were supplied with high phosphate fungal attachment to the roots was drastically reduced. An experimental system was designed to individually study the effects of phosphate supply on the fungus, on the roots, and on root exudates. These experiments revealed that the most important effects of high phosphate supply were on the roots themselves, which became unable to host mycorrhizal fungi even when these had been appropriately stimulated. The ability of the roots to perceive their fungal partner was then investigated by monitoring nuclear calcium spiking in response to fungal signals. This response did not appear to be affected by high phosphate supply. In conclusion, high levels of phosphate predominantly impact the plant host, but apparently not in its ability to perceive the fungal partner. PMID:24194742

  12. Comparison of surgical time and IOP spikes with two ophthalmic viscosurgical devices following Visian STAAR (ICL, V4c model) insertion in the immediate postoperative period

    PubMed Central

    Ganesh, Sri; Brar, Sheetal

    2016-01-01

    Purpose To compare the effect of two ocular viscosurgical devices (OVDs) on intraocular pressure (IOP) and surgical time in immediate postoperative period after bilateral implantable collamer lens (using the V4c model) implantation. Methods A total of 20 eligible patients were randomized to receive 2% hydroxypropylmethylcellulose (HPMC) in one eye and 1% hyaluronic acid in fellow eye. Time taken for complete removal of OVD and total surgical time were recorded. At the end of surgery, IOP was adjusted between 15 and 20 mmHg in both the eyes. Results Mean time for complete OVD evacuation and total surgical time were significantly higher in the HPMC group (P=0.00). Four eyes in the HPMC group had IOP spike, requiring treatment. IOP values with noncontact tonometry at 1, 2, 4, 24, and 48 hours were not statistically significant (P>0.05) for both the groups. Conclusion The study concluded that 1% hyaluronic acid significantly reduces total surgical time, and incidence of acute spikes may be lower compared to 2% HPMC when used for implantable collamer lens (V4c model). PMID:26869754

  13. Recognition of disturbances with specified morphology in time series. Part 1: Spikes on magnetograms of the worldwide INTERMAGNET network

    NASA Astrophysics Data System (ADS)

    Bogoutdinov, Sh. R.; Gvishiani, A. D.; Agayan, S. M.; Solovyev, A. A.; Kin, E.

    2010-11-01

    The International Real-time Magnetic Observatory Network (INTERMAGNET) is the world's biggest international network of ground-based observatories, providing geomagnetic data almost in real time (within 72 hours of collection) [Kerridge, 2001]. The observation data are rapidly transferred by the observatories participating in the program to regional Geomagnetic Information Nodes (GINs), which carry out a global exchange of data and process the results. The observations of the main (core) magnetic field of the Earth and its study are one of the key problems of geophysics. The INTERMAGNET system is the basis of monitoring the state of the Earth's magnetic field; therefore, the information provided by the system is required to be very reliable. Despite the rigid high-quality standard of the recording devices, they are subject to external effects that affect the quality of the records. Therefore, an objective and formalized recognition with the subsequent remedy of the anomalies (artifacts) that occur on the records is an important task. Expanding on the ideas of Agayan [Agayan et al., 2005] and Gvishiani [Gvishiani et al., 2008a; 2008b], this paper suggests a new algorithm of automatic recognition of anomalies with specified morphology, capable of identifying both physically- and anthropogenically-derived spikes on the magnetograms. The algorithm is constructed using fuzzy logic and, as such, is highly adaptive and universal. The developed algorithmic system formalizes the work of the expert-interpreter in terms of artificial intelligence. This ensures identical processing of large data arrays, almost unattainable manually. Besides the algorithm, the paper also reports on the application of the developed algorithmic system for identifying spikes at the INTERMAGNET observatories. The main achievement of the work is the creation of an algorithm permitting the almost unmanned extraction of spike-free (definitive) magnetograms from preliminary records. This automated

  14. Nr3a-containing NMDA receptors promote neurotransmitter release and spike timing-dependent plasticity

    PubMed Central

    Larsen, Rylan S.; Corlew, Rebekah J.; Henson, Maile A.; Roberts, Adam C.; Mishina, Masayoshi; Watanabe, Masahiko; Lipton, Stuart A.; Nakanishi, Nobuki; Pérez-Otaño, Isabel; Weinberg, Richard J.; Philpot, Benjamin D.

    2012-01-01

    Recent evidence suggests that presynaptic-acting NMDA receptors (preNMDARs) are important for neocortical synaptic transmission and plasticity. We found that unique properties of the Nr3a subunit enable preNMDARs to enhance spontaneous and evoked glutamate release and that Nr3a is required for spike timing–dependent long-term depression in the juvenile mouse visual cortex. In the mature cortex, Nr2b-containing preNMDARs enhanced neurotransmission in the absence of magnesium, indicating that presynaptic NMDARs may function under depolarizing conditions throughout life. Our findings indicate that Nr3a relieves preNMDARs from the dual-activation requirement of ligand-binding and depolarization; the developmental removal of Nr3a limits preNMDAR functionality by restoring this associative property. PMID:21297630

  15. Temporal Features of Spike Trains in the Moth Antennal Lobe Revealed by a Comparative Time-Frequency Analysis

    PubMed Central

    Capurro, Alberto; Baroni, Fabiano; Kuebler, Linda S.; Kárpáti, Zsolt; Dekker, Teun; Lei, Hong; Hansson, Bill S.; Pearce, Timothy C.; Olsson, Shannon B.

    2014-01-01

    The discrimination of complex sensory stimuli in a noisy environment is an immense computational task. Sensory systems often encode stimulus features in a spatiotemporal fashion through the complex firing patterns of individual neurons. To identify these temporal features, we have developed an analysis that allows the comparison of statistically significant features of spike trains localized over multiple scales of time-frequency resolution. Our approach provides an original way to utilize the discrete wavelet transform to process instantaneous rate functions derived from spike trains, and select relevant wavelet coefficients through statistical analysis. Our method uncovered localized features within olfactory projection neuron (PN) responses in the moth antennal lobe coding for the presence of an odor mixture and the concentration of single component odorants, but not for compound identities. We found that odor mixtures evoked earlier responses in biphasic response type PNs compared to single components, which led to differences in the instantaneous firing rate functions with their signal power spread across multiple frequency bands (ranging from 0 to 45.71 Hz) during a time window immediately preceding behavioral response latencies observed in insects. Odor concentrations were coded in excited response type PNs both in low frequency band differences (2.86 to 5.71 Hz) during the stimulus and in the odor trace after stimulus offset in low (0 to 2.86 Hz) and high (22.86 to 45.71 Hz) frequency bands. These high frequency differences in both types of PNs could have particular relevance for recruiting cellular activity in higher brain centers such as mushroom body Kenyon cells. In contrast, neurons in the specialized pheromone-responsive area of the moth antennal lobe exhibited few stimulus-dependent differences in temporal response features. These results provide interesting insights on early insect olfactory processing and introduce a novel comparative approach for

  16. Comparison of DNA Extraction Kits for Detection of Burkholderia pseudomallei in Spiked Human Whole Blood Using Real-Time PCR

    PubMed Central

    Podnecky, Nicole L.; Elrod, Mindy G.; Newton, Bruce R.; Dauphin, Leslie A.; Shi, Jianrong; Chawalchitiporn, Sutthinan; Baggett, Henry C.; Hoffmaster, Alex R.; Gee, Jay E.

    2013-01-01

    Burkholderia pseudomallei, the etiologic agent of melioidosis, is endemic in northern Australia and Southeast Asia and can cause severe septicemia that may lead to death in 20% to 50% of cases. Rapid detection of B. pseudomallei infection is crucial for timely treatment of septic patients. This study evaluated seven commercially available DNA extraction kits to determine the relative recovery of B. pseudomallei DNA from spiked EDTA-containing human whole blood. The evaluation included three manual kits: the QIAamp DNA Mini kit, the QIAamp DNA Blood Mini kit, and the High Pure PCR Template Preparation kit; and four automated systems: the MagNAPure LC using the DNA Isolation Kit I, the MagNAPure Compact using the Nucleic Acid Isolation Kit I, and the QIAcube using the QIAamp DNA Mini kit and the QIAamp DNA Blood Mini kit. Detection of B. pseudomallei DNA extracted by each kit was performed using the B. pseudomallei specific type III secretion real-time PCR (TTS1) assay. Crossing threshold (CT) values were used to compare the limit of detection and reproducibility of each kit. This study also compared the DNA concentrations and DNA purity yielded for each kit. The following kits consistently yielded DNA that produced a detectable signal from blood spiked with 5.5×104 colony forming units per mL: the High Pure PCR Template Preparation, QIAamp DNA Mini, MagNA Pure Compact, and the QIAcube running the QIAamp DNA Mini and QIAamp DNA Blood Mini kits. The High Pure PCR Template Preparation kit yielded the lowest limit of detection with spiked blood, but when this kit was used with blood from patients with confirmed cases of melioidosis, the bacteria was not reliably detected indicating blood may not be an optimal specimen. PMID:23460920

  17. Event-based minimum-time control of oscillatory neuron models: phase randomization, maximal spike rate increase, and desynchronization.

    PubMed

    Danzl, Per; Hespanha, João; Moehlis, Jeff

    2009-12-01

    We present an event-based feedback control method for randomizing the asymptotic phase of oscillatory neurons. Phase randomization is achieved by driving the neuron's state to its phaseless set, a point at which its phase is undefined and is extremely sensitive to background noise. We consider the biologically relevant case of a fixed magnitude constraint on the stimulus signal, and show how the control objective can be accomplished in minimum time. The control synthesis problem is addressed using the minimum-time-optimal Hamilton-Jacobi-Bellman framework, which is quite general and can be applied to any spiking neuron model in the conductance-based Hodgkin-Huxley formalism. We also use this methodology to compute a feedback control protocol for optimal spike rate increase. This framework provides a straightforward means of visualizing isochrons, without actually calculating them in the traditional way. Finally, we present an extension of the phase randomizing control scheme that is applied at the population level, to a network of globally coupled neurons that are firing in synchrony. The applied control signal desynchronizes the population in a demand-controlled way. PMID:19911192

  18. Real-time prediction of neuronal population spiking activity using FPGA.

    PubMed

    Li, Will X Y; Cheung, Ray C C; Chan, Rosa H M; Song, Dong; Berger, Theodore W

    2013-08-01

    A field-programmable gate array (FPGA)-based hardware architecture is proposed and utilized for prediction of neuronal population firing activity. The hardware system adopts the multi-input multi-output (MIMO) generalized Laguerre-Volterra model (GLVM) structure to describe the nonlinear dynamic neural process of mammalian brain and can switch between the two important functions: estimation of GLVM coefficients and prediction of neuronal population spiking activity (model outputs). The model coefficients are first estimated using the in-sample training data; then the output is predicted using the out-of-sample testing data and the field estimated coefficients. Test results show that compared with previous software implementation of the generalized Laguerre-Volterra algorithm running on an Intel Core i7-2620M CPU, the FPGA-based hardware system can achieve up to 2.66×10(3) speedup in doing model parameters estimation and 698.84 speedup in doing model output prediction. The proposed hardware platform will facilitate research on the highly nonlinear neural process of the mammal brain, and the cognitive neural prosthesis design. PMID:23893208

  19. Spiking optical patterns and synchronization

    NASA Astrophysics Data System (ADS)

    Rosenbluh, Michael; Aviad, Yaara; Cohen, Elad; Khaykovich, Lev; Kinzel, Wolfgang; Kopelowitz, Evi; Yoskovits, Pinhas; Kanter, Ido

    2007-10-01

    We analyze the time resolved spike statistics of a solitary and two mutually interacting chaotic semiconductor lasers whose chaos is characterized by apparently random, short intensity spikes. Repulsion between two successive spikes is observed, resulting in a refractory period, which is largest at laser threshold. For time intervals between spikes greater than the refractory period, the distribution of the intervals follows a Poisson distribution. The spiking pattern is highly periodic over time windows corresponding to the optical length of the external cavity, with a slow change of the spiking pattern as time increases. When zero-lag synchronization between two lasers is established, the statistics of the nearly perfectly matched spikes are not altered. The similarity of these features to those found in complex interacting neural networks, suggests the use of laser systems as simpler physical models for neural networks.

  20. Temporal spike pattern learning

    NASA Astrophysics Data System (ADS)

    Talathi, Sachin S.; Abarbanel, Henry D. I.; Ditto, William L.

    2008-09-01

    Sensory systems pass information about an animal’s environment to higher nervous system units through sequences of action potentials. When these action potentials have essentially equivalent wave forms, all information is contained in the interspike intervals (ISIs) of the spike sequence. How do neural circuits recognize and read these ISI sequences? We address this issue of temporal sequence learning by a neuronal system utilizing spike timing dependent plasticity (STDP). We present a general architecture of neural circuitry that can perform the task of ISI recognition. The essential ingredients of this neural circuit, which we refer to as “interspike interval recognition unit” (IRU) are (i) a spike selection unit, the function of which is to selectively distribute input spikes to downstream IRU circuitry; (ii) a time-delay unit that can be tuned by STDP; and (iii) a detection unit, which is the output of the IRU and a spike from which indicates successful ISI recognition by the IRU. We present two distinct configurations for the time-delay circuit within the IRU using excitatory and inhibitory synapses, respectively, to produce a delayed output spike at time t0+τ(R) in response to the input spike received at time t0 . R is the tunable parameter of the time-delay circuit that controls the timing of the delayed output spike. We discuss the forms of STDP rules for excitatory and inhibitory synapses, respectively, that allow for modulation of R for the IRU to perform its task of ISI recognition. We then present two specific implementations for the IRU circuitry, derived from the general architecture that can both learn the ISIs of a training sequence and then recognize the same ISI sequence when it is presented on subsequent occasions.

  1. Temporal spike pattern learning.

    PubMed

    Talathi, Sachin S; Abarbanel, Henry D I; Ditto, William L

    2008-09-01

    Sensory systems pass information about an animal's environment to higher nervous system units through sequences of action potentials. When these action potentials have essentially equivalent wave forms, all information is contained in the interspike intervals (ISIs) of the spike sequence. How do neural circuits recognize and read these ISI sequences? We address this issue of temporal sequence learning by a neuronal system utilizing spike timing dependent plasticity (STDP). We present a general architecture of neural circuitry that can perform the task of ISI recognition. The essential ingredients of this neural circuit, which we refer to as "interspike interval recognition unit" (IRU) are (i) a spike selection unit, the function of which is to selectively distribute input spikes to downstream IRU circuitry; (ii) a time-delay unit that can be tuned by STDP; and (iii) a detection unit, which is the output of the IRU and a spike from which indicates successful ISI recognition by the IRU. We present two distinct configurations for the time-delay circuit within the IRU using excitatory and inhibitory synapses, respectively, to produce a delayed output spike at time t_{0}+tau(R) in response to the input spike received at time t_{0} . R is the tunable parameter of the time-delay circuit that controls the timing of the delayed output spike. We discuss the forms of STDP rules for excitatory and inhibitory synapses, respectively, that allow for modulation of R for the IRU to perform its task of ISI recognition. We then present two specific implementations for the IRU circuitry, derived from the general architecture that can both learn the ISIs of a training sequence and then recognize the same ISI sequence when it is presented on subsequent occasions. PMID:18851076

  2. Fitting Neuron Models to Spike Trains

    PubMed Central

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

    2011-01-01

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

  3. Light gas gun with reduced timing jitter

    DOEpatents

    Laabs, G.W.; Funk, D.J.; Asay, B.W.

    1998-06-09

    Gas gun with reduced timing jitter is disclosed. A gas gun having a prepressurized projectile held in place with a glass rod in compression is described. The glass rod is destroyed with an explosive at a precise time which allows a restraining pin to be moved and free the projectile. 4 figs.

  4. Light gas gun with reduced timing jitter

    DOEpatents

    Laabs, Gary W.; Funk, David J.; Asay, Blaine W.

    1998-01-01

    Gas gun with reduced timing jitter. A gas gun having a prepressurized projectile held in place with a glass rod in compression is described. The glass rod is destroyed with an explosive at a precise time which allows a restraining pin to be moved and free the projectile.

  5. Spike-train acquisition, analysis and real-time experimental control using a graphical programming language (LabView).

    PubMed

    Nordstrom, M A; Mapletoft, E A; Miles, T S

    1995-11-01

    A solution is described for the acquisition on a personal computer of standard pulses derived from neuronal discharge, measurement of neuronal discharge times, real-time control of stimulus delivery based on specified inter-pulse interval conditions in the neuronal spike train, and on-line display and analysis of the experimental data. The hardware consisted of an Apple Macintosh IIci computer and a plug-in card (National Instruments NB-MIO16) that supports A/D, D/A, digital I/O and timer functions. The software was written in the object-oriented graphical programming language LabView. Essential elements of the source code of the LabView program are presented and explained. The use of the system is demonstrated in an experiment in which the reflex responses to muscle stretch are assessed for a single motor unit in the human masseter muscle. PMID:8750090

  6. Spike sorting of synchronous spikes from local neuron ensembles.

    PubMed

    Franke, Felix; Pröpper, Robert; Alle, Henrik; Meier, Philipp; Geiger, Jörg R P; Obermayer, Klaus; Munk, Matthias H J

    2015-10-01

    Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved with a recently developed filter-based template matching procedure. Using tetrodes with a three-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of nonoverlapping spikes and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates, and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons. PMID:26289473

  7. Influence of Contact Time on the Extraction of 233Uranyl Spike and Contaminant Uranium From Hanford Sediment

    SciTech Connect

    Smith, Steven C.; Szecsody, James E.

    2011-11-01

    In this study 233Uranyl nitrate was added to uranium (U) contaminated Hanford 300 Area sediment and incubated under moist conditions for 1 year. It hypothesized that geochemical transformations and/or physical processes will result in decreased extractability of 233U as the incubation period increases, and eventually the extraction behavior of the 233U spike will be congruent to contaminant U that has been associated with sediment for decades. Following 1 week, 1 month, and 1 year incubation periods, sediment extractions were performed using either batch or dynamic (sediment column flow) chemical extraction techniques. Overall, extraction of U from sediment using batch extraction was less complicated to conduct compared to dynamic extraction, but dynamic extraction could distinguish the range of U forms associated with sediment which are eluted at different times.

  8. Analog memory and spike-timing-dependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device.

    PubMed

    Seo, Kyungah; Kim, Insung; Jung, Seungjae; Jo, Minseok; Park, Sangsu; Park, Jubong; Shin, Jungho; Biju, Kuyyadi P; Kong, Jaemin; Lee, Kwanghee; Lee, Byounghun; Hwang, Hyunsang

    2011-06-24

    We demonstrated analog memory, synaptic plasticity, and a spike-timing-dependent plasticity (STDP) function with a nanoscale titanium oxide bilayer resistive switching device with a simple fabrication process and good yield uniformity. We confirmed the multilevel conductance and analog memory characteristics as well as the uniformity and separated states for the accuracy of conductance change. Finally, STDP and a biological triple model were analyzed to demonstrate the potential of titanium oxide bilayer resistive switching device as synapses in neuromorphic devices. By developing a simple resistive switching device that can emulate a synaptic function, the unique characteristics of synapses in the brain, e.g. combined memory and computing in one synapse and adaptation to the outside environment, were successfully demonstrated in a solid state device. PMID:21572200

  9. Self-organization of feed-forward structure and entrainment in excitatory neural networks with spike-timing-dependent plasticity

    NASA Astrophysics Data System (ADS)

    Takahashi, Yuko K.; Kori, Hiroshi; Masuda, Naoki

    2009-05-01

    Spike-timing dependent plasticity (STDP) is an organizing principle of biological neural networks. While synchronous firing of neurons is considered to be an important functional block in the brain, how STDP shapes neural networks possibly toward synchrony is not entirely clear. We examine relations between STDP and synchronous firing in spontaneously firing neural populations. Using coupled heterogeneous phase oscillators placed on initial networks, we show numerically that STDP prunes some synapses and promotes formation of a feedforward network. Eventually a pacemaker, which is the neuron with the fastest inherent frequency in our numerical simulations, emerges at the root of the feedforward network. In each oscillatory cycle, a packet of neural activity is propagated from the pacemaker to downstream neurons along layers of the feedforward network. This event occurs above a clear-cut threshold value of the initial synaptic weight. Below the threshold, neurons are self-organized into separate clusters each of which is a feedforward network.

  10. Color opponent receptive fields self-organize in a biophysical model of visual cortex via spike-timing dependent plasticity

    PubMed Central

    Eguchi, Akihiro; Neymotin, Samuel A.; Stringer, Simon M.

    2014-01-01

    Although many computational models have been proposed to explain orientation maps in primary visual cortex (V1), it is not yet known how similar clusters of color-selective neurons in macaque V1/V2 are connected and develop. In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. Each color input is decomposed into a red, green, and blue representation and transmitted to the visual cortex via a simulated optic nerve in a luminance channel and red–green and blue–yellow opponent color channels. Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. Each neuron in the V1 output layer makes synaptic connections to neighboring neurons and receives the three types of signals in the different channels from the corresponding photoreceptor position. Synaptic weights are randomized and learned using spike-timing-dependent plasticity (STDP). After training with natural images, the neurons display heightened sensitivity to specific colors. Information-theoretic analysis reveals mutual information between particular stimuli and responses, and that the information reaches a maximum with fewer neurons in the higher layers, indicating that estimations of the input colors can be done using the output of fewer cells in the later stages of cortical processing. In addition, cells with similar color receptive fields form clusters. Analysis of spiking activity reveals increased firing synchrony between neurons when particular color inputs are presented or removed (ON-cell/OFF-cell). PMID:24659956

  11. Time-recovering PCI-AER interface for bio-inspired spiking systems

    NASA Astrophysics Data System (ADS)

    Paz-Vicente, R.; Linares-Barranco, A.; Cascado, D.; Vicente, S.; Jimenez, G.; Civit, A.

    2005-06-01

    Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge number neurons located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate 'events' according to their activity levels. More active neurons generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. When building multi-chip muti-layered AER systems it is absolutely necessary to have a computer interface that allows (a) to read AER interchip traffic into the computer and visualize it on screen, and (b) inject a sequence of events at some point of the AER structure. This is necessary for testing and debugging complex AER systems. This paper presents a PCI to AER interface, that dispatches a sequence of events received from the PCI bus with embedded timing information to establish when each event will be delivered. A set of specialized states machines has been introduced to recovery the possible time delays introduced by the asynchronous AER bus. On the input channel, the interface capture events assigning a timestamp and delivers them through the PCI bus to MATLAB applications. It has been implemented in real time hardware using VHDL and it has been tested in a PCI-AER board, developed by authors, that includes a Spartan II 200 FPGA. The demonstration hardware is currently capable to send and receive events at a peak rate of 8,3 Mev/sec, and a typical rate of 1 Mev/sec.

  12. Time-accurate unsteady flow simulations supporting the SRM T+68-second pressure spike anomaly investigation (STS-54B)

    NASA Astrophysics Data System (ADS)

    Dougherty, N. S.; Burnette, D. W.; Holt, J. B.; Matienzo, Jose

    1993-07-01

    Time-accurate unsteady flow simulations are being performed supporting the SRM T+68sec pressure 'spike' anomaly investigation. The anomaly occurred in the RH SRM during the STS-54 flight (STS-54B) but not in the LH SRM (STS-54A) causing a momentary thrust mismatch approaching the allowable limit at that time into the flight. Full-motor internal flow simulations using the USA-2D axisymmetric code are in progress for the nominal propellant burn-back geometry and flow conditions at T+68-sec--Pc = 630 psi, gamma = 1.1381, T(sub c) = 6200 R, perfect gas without aluminum particulate. In a cooperative effort with other investigation team members, CFD-derived pressure loading on the NBR and castable inhibitors was used iteratively to obtain nominal deformed geometry of each inhibitor, and the deformed (bent back) inhibitor geometry was entered into this model. Deformed geometry was computed using structural finite-element models. A solution for the unsteady flow has been obtained for the nominal flow conditions (existing prior to the occurrence of the anomaly) showing sustained standing pressure oscillations at nominally 14.5 Hz in the motor IL acoustic mode that flight and static test data confirm to be normally present at this time. Average mass flow discharged from the nozzle was confirmed to be the nominal expected (9550 lbm/sec). The local inlet boundary condition is being perturbed at the location of the presumed reconstructed anomaly as identified by interior ballistics performance specialist team members. A time variation in local mass flow is used to simulate sudden increase in burning area due to localized propellant grain cracks. The solution will proceed to develop a pressure rise (proportional to total mass flow rate change squared). The volume-filling time constant (equivalent to 0.5 Hz) comes into play in shaping the rise rate of the developing pressure 'spike' as it propagates at the speed of sound in both directions to the motor head end and nozzle. The

  13. Time-Accurate Unsteady Flow Simulations Supporting the SRM T+68-Second Pressure Spike Anomaly Investigation (STS-54B)

    NASA Technical Reports Server (NTRS)

    Dougherty, N. S.; Burnette, D. W.; Holt, J. B.; Matienzo, Jose

    1993-01-01

    Time-accurate unsteady flow simulations are being performed supporting the SRM T+68sec pressure 'spike' anomaly investigation. The anomaly occurred in the RH SRM during the STS-54 flight (STS-54B) but not in the LH SRM (STS-54A) causing a momentary thrust mismatch approaching the allowable limit at that time into the flight. Full-motor internal flow simulations using the USA-2D axisymmetric code are in progress for the nominal propellant burn-back geometry and flow conditions at T+68-sec--Pc = 630 psi, gamma = 1.1381, T(sub c) = 6200 R, perfect gas without aluminum particulate. In a cooperative effort with other investigation team members, CFD-derived pressure loading on the NBR and castable inhibitors was used iteratively to obtain nominal deformed geometry of each inhibitor, and the deformed (bent back) inhibitor geometry was entered into this model. Deformed geometry was computed using structural finite-element models. A solution for the unsteady flow has been obtained for the nominal flow conditions (existing prior to the occurrence of the anomaly) showing sustained standing pressure oscillations at nominally 14.5 Hz in the motor IL acoustic mode that flight and static test data confirm to be normally present at this time. Average mass flow discharged from the nozzle was confirmed to be the nominal expected (9550 lbm/sec). The local inlet boundary condition is being perturbed at the location of the presumed reconstructed anomaly as identified by interior ballistics performance specialist team members. A time variation in local mass flow is used to simulate sudden increase in burning area due to localized propellant grain cracks. The solution will proceed to develop a pressure rise (proportional to total mass flow rate change squared). The volume-filling time constant (equivalent to 0.5 Hz) comes into play in shaping the rise rate of the developing pressure 'spike' as it propagates at the speed of sound in both directions to the motor head end and nozzle. The

  14. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule.

    PubMed

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

    2015-11-01

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing. PMID:26627568

  15. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    SciTech Connect

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

    2015-11-15

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

  16. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    NASA Astrophysics Data System (ADS)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

    2015-11-01

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

  17. Efficient population coding of naturalistic whisker motion in the ventro-posterior medial thalamus based on precise spike timing

    PubMed Central

    Bale, Michael R.; Ince, Robin A. A.; Santagata, Greta; Petersen, Rasmus S.

    2015-01-01

    The rodent whisker-associated thalamic nucleus (VPM) contains a somatotopic map where whisker representation is divided into distinct neuronal sub-populations, called “barreloids”. Each barreloid projects to its associated cortical barrel column and so forms a gateway for incoming sensory stimuli to the barrel cortex. We aimed to determine how the population of neurons within one barreloid encodes naturalistic whisker motion. In rats, we recorded the extracellular activity of up to nine single neurons within a single barreloid, by implanting silicon probes parallel to the longitudinal axis of the barreloids. We found that play-back of texture-induced whisker motion evoked sparse responses, timed with millisecond precision. At the population level, there was synchronous activity: however, different subsets of neurons were synchronously active at different times. Mutual information between population responses and whisker motion increased near linearly with population size. When normalized to factor out firing rate differences, we found that texture was encoded with greater informational-efficiency than white noise. These results indicate that, within each VPM barreloid, there is a rich and efficient population code for naturalistic whisker motion based on precisely timed, population spike patterns. PMID:26441549

  18. Reducing the computational footprint for real-time BCPNN learning

    PubMed Central

    Vogginger, Bernhard; Schüffny, René; Lansner, Anders; Cederström, Love; Partzsch, Johannes; Höppner, Sebastian

    2015-01-01

    The implementation of synaptic plasticity in neural simulation or neuromorphic hardware is usually very resource-intensive, often requiring a compromise between efficiency and flexibility. A versatile, but computationally-expensive plasticity mechanism is provided by the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm. Building upon Bayesian statistics, and having clear links to biological plasticity processes, the BCPNN learning rule has been applied in many fields, ranging from data classification, associative memory, reward-based learning, probabilistic inference to cortical attractor memory networks. In the spike-based version of this learning rule the pre-, postsynaptic and coincident activity is traced in three low-pass-filtering stages, requiring a total of eight state variables, whose dynamics are typically simulated with the fixed step size Euler method. We derive analytic solutions allowing an efficient event-driven implementation of this learning rule. Further speedup is achieved by first rewriting the model which reduces the number of basic arithmetic operations per update to one half, and second by using look-up tables for the frequently calculated exponential decay. Ultimately, in a typical use case, the simulation using our approach is more than one order of magnitude faster than with the fixed step size Euler method. Aiming for a small memory footprint per BCPNN synapse, we also evaluate the use of fixed-point numbers for the state variables, and assess the number of bits required to achieve same or better accuracy than with the conventional explicit Euler method. All of this will allow a real-time simulation of a reduced cortex model based on BCPNN in high performance computing. More important, with the analytic solution at hand and due to the reduced memory bandwidth, the learning rule can be efficiently implemented in dedicated or existing digital neuromorphic hardware. PMID:25657618

  19. Reducing the computational footprint for real-time BCPNN learning.

    PubMed

    Vogginger, Bernhard; Schüffny, René; Lansner, Anders; Cederström, Love; Partzsch, Johannes; Höppner, Sebastian

    2015-01-01

    The implementation of synaptic plasticity in neural simulation or neuromorphic hardware is usually very resource-intensive, often requiring a compromise between efficiency and flexibility. A versatile, but computationally-expensive plasticity mechanism is provided by the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm. Building upon Bayesian statistics, and having clear links to biological plasticity processes, the BCPNN learning rule has been applied in many fields, ranging from data classification, associative memory, reward-based learning, probabilistic inference to cortical attractor memory networks. In the spike-based version of this learning rule the pre-, postsynaptic and coincident activity is traced in three low-pass-filtering stages, requiring a total of eight state variables, whose dynamics are typically simulated with the fixed step size Euler method. We derive analytic solutions allowing an efficient event-driven implementation of this learning rule. Further speedup is achieved by first rewriting the model which reduces the number of basic arithmetic operations per update to one half, and second by using look-up tables for the frequently calculated exponential decay. Ultimately, in a typical use case, the simulation using our approach is more than one order of magnitude faster than with the fixed step size Euler method. Aiming for a small memory footprint per BCPNN synapse, we also evaluate the use of fixed-point numbers for the state variables, and assess the number of bits required to achieve same or better accuracy than with the conventional explicit Euler method. All of this will allow a real-time simulation of a reduced cortex model based on BCPNN in high performance computing. More important, with the analytic solution at hand and due to the reduced memory bandwidth, the learning rule can be efficiently implemented in dedicated or existing digital neuromorphic hardware. PMID:25657618

  20. Self-Organized Near-Zero-Lag Synchronization Induced by Spike-Timing Dependent Plasticity in Cortical Populations

    PubMed Central

    Matias, Fernanda S.; Carelli, Pedro V.; Mirasso, Claudio R.; Copelli, Mauro

    2015-01-01

    Several cognitive tasks related to learning and memory exhibit synchronization of macroscopic cortical areas together with synaptic plasticity at neuronal level. Therefore, there is a growing effort among computational neuroscientists to understand the underlying mechanisms relating synchrony and plasticity in the brain. Here we numerically study the interplay between spike-timing dependent plasticity (STDP) and anticipated synchronization (AS). AS emerges when a dominant flux of information from one area to another is accompanied by a negative time lag (or phase). This means that the receiver region pulses before the sender does. In this paper we study the interplay between different synchronization regimes and STDP at the level of three-neuron microcircuits as well as cortical populations. We show that STDP can promote auto-organized zero-lag synchronization in unidirectionally coupled neuronal populations. We also find synchronization regimes with negative phase difference (AS) that are stable against plasticity. Finally, we show that the interplay between negative phase difference and STDP provides limited synaptic weight distribution without the need of imposing artificial boundaries. PMID:26474165

  1. Silicon nanodisk array with a fin field-effect transistor for time-domain weighted sum calculation toward massively parallel spiking neural networks

    NASA Astrophysics Data System (ADS)

    Tohara, Takashi; Liang, Haichao; Tanaka, Hirofumi; Igarashi, Makoto; Samukawa, Seiji; Endo, Kazuhiko; Takahashi, Yasuo; Morie, Takashi

    2016-03-01

    A nanodisk array connected with a fin field-effect transistor is fabricated and analyzed for spiking neural network applications. This nanodevice performs weighted sums in the time domain using rising slopes of responses triggered by input spike pulses. The nanodisk arrays, which act as a resistance of several giga-ohms, are fabricated using a self-assembly bio-nano-template technique. Weighted sums are achieved with an energy dissipation on the order of 1 fJ, where the number of inputs can be more than one hundred. This amount of energy is several orders of magnitude lower than that of conventional digital processors.

  2. Effects of spike-time-dependent plasticity on the stochastic resonance of small-world neuronal networks.

    PubMed

    Yu, Haitao; Guo, Xinmeng; Wang, Jiang; Deng, Bin; Wei, Xile

    2014-09-01

    The phenomenon of stochastic resonance in Newman-Watts small-world neuronal networks is investigated when the strength of synaptic connections between neurons is adaptively adjusted by spike-time-dependent plasticity (STDP). It is shown that irrespective of the synaptic connectivity is fixed or adaptive, the phenomenon of stochastic resonance occurs. The efficiency of network stochastic resonance can be largely enhanced by STDP in the coupling process. Particularly, the resonance for adaptive coupling can reach a much larger value than that for fixed one when the noise intensity is small or intermediate. STDP with dominant depression and small temporal window ratio is more efficient for the transmission of weak external signal in small-world neuronal networks. In addition, we demonstrate that the effect of stochastic resonance can be further improved via fine-tuning of the average coupling strength of the adaptive network. Furthermore, the small-world topology can significantly affect stochastic resonance of excitable neuronal networks. It is found that there exists an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks. PMID:25273205

  3. Effects of spike-time-dependent plasticity on the stochastic resonance of small-world neuronal networks

    SciTech Connect

    Yu, Haitao; Guo, Xinmeng; Wang, Jiang Deng, Bin; Wei, Xile

    2014-09-01

    The phenomenon of stochastic resonance in Newman-Watts small-world neuronal networks is investigated when the strength of synaptic connections between neurons is adaptively adjusted by spike-time-dependent plasticity (STDP). It is shown that irrespective of the synaptic connectivity is fixed or adaptive, the phenomenon of stochastic resonance occurs. The efficiency of network stochastic resonance can be largely enhanced by STDP in the coupling process. Particularly, the resonance for adaptive coupling can reach a much larger value than that for fixed one when the noise intensity is small or intermediate. STDP with dominant depression and small temporal window ratio is more efficient for the transmission of weak external signal in small-world neuronal networks. In addition, we demonstrate that the effect of stochastic resonance can be further improved via fine-tuning of the average coupling strength of the adaptive network. Furthermore, the small-world topology can significantly affect stochastic resonance of excitable neuronal networks. It is found that there exists an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks.

  4. Symmetric spike timing-dependent plasticity at CA3-CA3 synapses optimizes storage and recall in autoassociative networks.

    PubMed

    Mishra, Rajiv K; Kim, Sooyun; Guzman, Segundo J; Jonas, Peter

    2016-01-01

    CA3-CA3 recurrent excitatory synapses are thought to play a key role in memory storage and pattern completion. Whether the plasticity properties of these synapses are consistent with their proposed network functions remains unclear. Here, we examine the properties of spike timing-dependent plasticity (STDP) at CA3-CA3 synapses. Low-frequency pairing of excitatory postsynaptic potentials (EPSPs) and action potentials (APs) induces long-term potentiation (LTP), independent of temporal order. The STDP curve is symmetric and broad (half-width ∼150 ms). Consistent with these STDP induction properties, AP-EPSP sequences lead to supralinear summation of spine [Ca(2+)] transients. Furthermore, afterdepolarizations (ADPs) following APs efficiently propagate into dendrites of CA3 pyramidal neurons, and EPSPs summate with dendritic ADPs. In autoassociative network models, storage and recall are more robust with symmetric than with asymmetric STDP rules. Thus, a specialized STDP induction rule allows reliable storage and recall of information in the hippocampal CA3 network. PMID:27174042

  5. Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks.

    PubMed

    Garrido, Jesús A; Luque, Niceto R; Tolu, Silvia; D'Angelo, Egidio

    2016-08-01

    The majority of operations carried out by the brain require learning complex signal patterns for future recognition, retrieval and reuse. Although learning is thought to depend on multiple forms of long-term synaptic plasticity, the way this latter contributes to pattern recognition is still poorly understood. Here, we have used a simple model of afferent excitatory neurons and interneurons with lateral inhibition, reproducing a network topology found in many brain areas from the cerebellum to cortical columns. When endowed with spike-timing dependent plasticity (STDP) at the excitatory input synapses and at the inhibitory interneuron-interneuron synapses, the interneurons rapidly learned complex input patterns. Interestingly, induction of plasticity required that the network be entrained into theta-frequency band oscillations, setting the internal phase-reference required to drive STDP. Inhibitory plasticity effectively distributed multiple patterns among available interneurons, thus allowing the simultaneous detection of multiple overlapping patterns. The addition of plasticity in intrinsic excitability made the system more robust allowing self-adjustment and rescaling in response to a broad range of input patterns. The combination of plasticity in lateral inhibitory connections and homeostatic mechanisms in the inhibitory interneurons optimized mutual information (MI) transfer. The storage of multiple complex patterns in plastic interneuron networks could be critical for the generation of sparse representations of information in excitatory neuron populations falling under their control. PMID:27079422

  6. Rayleigh--Taylor spike evaporation

    SciTech Connect

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

    2001-09-01

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

  7. Persistent Nav1.6 current at axon initial segments tunes spike timing of cerebellar granule cells

    PubMed Central

    Osorio, Nancy; Cathala, Laurence; Meisler, Miriam H; Crest, Marcel; Magistretti, Jacopo; Delmas, Patrick

    2010-01-01

    Cerebellar granule (CG) cells generate high-frequency action potentials that have been proposed to depend on the unique properties of their voltage-gated ion channels. To address the in vivo function of Nav1.6 channels in developing and mature CG cells, we combined the study of the developmental expression of Nav subunits with recording of acute cerebellar slices from young and adult granule-specific Scn8a KO mice. Nav1.2 accumulated rapidly at early-formed axon initial segments (AISs). In contrast, Nav1.6 was absent at early postnatal stages but accumulated at AISs of CG cells from P21 to P40. By P40–P65, both Nav1.6 and Nav1.2 co-localized at CG cell AISs. By comparing Na+ currents in mature CG cells (P66–P74) from wild-type and CG-specific Scn8a KO mice, we found that transient and resurgent Na+ currents were not modified in the absence of Nav1.6 whereas persistent Na+ current was strongly reduced. Action potentials in conditional Scn8a KO CG cells showed no alteration in threshold and overshoot, but had a faster repolarization phase and larger post-spike hyperpolarization. In addition, although Scn8a KO CG cells kept their ability to fire action potentials at very high frequency, they displayed increased interspike-interval variability and firing irregularity in response to sustained depolarization. We conclude that Nav1.6 channels at axon initial segments contribute to persistent Na+ current and ensure a high degree of temporal precision in repetitive firing of CG cells. PMID:20173079

  8. Analog modulation of spike-evoked transmission in CA3 circuits is determined by axonal Kv1.1 channels in a time-dependent manner.

    PubMed

    Bialowas, Andrzej; Rama, Sylvain; Zbili, Mickaël; Marra, Vincenzo; Fronzaroli-Molinieres, Laure; Ankri, Norbert; Carlier, Edmond; Debanne, Dominique

    2015-02-01

    Synaptic transmission usually depends on action potentials (APs) in an all-or-none (digital) fashion. Recent studies indicate, however, that subthreshold presynaptic depolarization may facilitate spike-evoked transmission, thus creating an analog modulation of spike-evoked synaptic transmission, also called analog-digital (AD) synaptic facilitation. Yet, the underlying mechanisms behind this facilitation remain unclear. We show here that AD facilitation at rat CA3-CA3 synapses is time-dependent and requires long presynaptic depolarization (5-10 s) for its induction. This depolarization-induced AD facilitation (d-ADF) is blocked by the specific Kv1.1 channel blocker dendrotoxin-K. Using fast voltage-imaging of the axon, we show that somatic depolarization used for induction of d-ADF broadened the AP in the axon through inactivation of Kv1.1 channels. Somatic depolarization enhanced spike-evoked calcium signals in presynaptic terminals, but not basal calcium. In conclusion, axonal Kv1.1 channels determine glutamate release in CA3 neurons in a time-dependent manner through the control of the presynaptic spike waveform. PMID:25394682

  9. Nicotine Exposure during Adolescence Leads to Short- and Long-Term Changes in Spike Timing-Dependent Plasticity in Rat Prefrontal Cortex

    PubMed Central

    Goriounova, Natalia A.; Mansvelder, Huibert D.

    2013-01-01

    Adolescence is a critical period of brain development during which maturation of areas involved in cognitive functioning, such as the medial prefrontal cortex (mPFC), is still ongoing. Tobacco smoking during this age can compromise the normal course of prefrontal development and lead to cognitive impairments in later life. Recently, we reported that nicotine exposure during adolescence results in a short-term increase and lasting reduction in synaptic mGluR2 levels in the rat mPFC, causing attention deficits during adulthood. It is unknown how changed synaptic mGluR2 levels after adolescent nicotine exposure affect the ability of mPFC synapses to undergo long-term synaptic plasticity. Here, we addressed this question. To model nicotine exposure, adolescent (P34–P43) or adult (P60–P69) rats were treated with nicotine injections three times per day for 10 d. We found that, both during acute activation of nicotinic receptors in the adolescent mPFC as well as immediately following nicotine treatment during adolescence, long-term plasticity in response to timed presynaptic and postsynaptic activity (tLTP) was strongly reduced. In contrast, in the mPFC of adult rats 5 weeks after they received nicotine treatment during adolescence, but not during adulthood, tLTP was increased. Short-and long-term adaptation of mPFC synaptic plasticity after adolescent nicotine exposure could be explained by changed mGluR2 signaling. Blocking mGluR2s augmented tLTP, whereas activating mGluR2s reduced tLTP. Our findings suggest neuronal mechanisms by which exposure to nicotine during adolescence alters the rules for spike timing-dependent plasticity in prefrontal networks that may explain the observed deficits in cognitive performance in later life. PMID:22855798

  10. Reducing employee travelling time through smart commuting

    NASA Astrophysics Data System (ADS)

    Rahman, A. N. N. A.; Yusoff, Z. M.; Aziz, I. S.; Omar, D.

    2014-02-01

    Extremely congested roads will definitely delay the arrival time of each trip.This certainly impacted the journey of employees. Tardiness at the workplace has become a perturbing issue for companies where traffic jams are the most common worker excuses. A depressing consequence on daily life and productivity of the employee occurs. The issues of commuting distance between workplace and resident area become the core point of this research. This research will emphasize the use of Geographical Information System (GIS) technique to explore the distance parameter to the employment area and will focus on the accessibility pattern of low-cost housing. The research methodology consists of interview sessions and a questionnaire to residents of low-cost housing areas in Melaka Tengah District in Malaysia. The combination of these processes will show the criteria from the selected parameter for each respondent from their resident area to the employment area. This will further help in the recommendation of several options for a better commute or improvement to the existing routes and public transportations system. Thus enhancing quality of life for employees and helping to reduce stress, decrease lateness, absenteeism and improving productivity in workplace.

  11. The homeodomain transcription factor TaHDZipI-2 from wheat regulates frost tolerance, flowering time and spike development in transgenic barley.

    PubMed

    Kovalchuk, Nataliya; Chew, William; Sornaraj, Pradeep; Borisjuk, Nikolai; Yang, Nannan; Singh, Rohan; Bazanova, Natalia; Shavrukov, Yuri; Guendel, Andre; Munz, Eberhard; Borisjuk, Ljudmilla; Langridge, Peter; Hrmova, Maria; Lopato, Sergiy

    2016-07-01

    Homeodomain leucine zipper class I (HD-Zip I) transcription factors (TFs) play key roles in the regulation of plant growth and development under stresses. Functions of the TaHDZipI-2 gene isolated from the endosperm of developing wheat grain were revealed. Molecular characterization of TaHDZipI-2 protein included studies of its dimerisation, protein-DNA interactions and gene activation properties using pull-down assays, in-yeast methods and transient expression assays in wheat cells. The analysis of TaHDZipI-2 gene functions was performed using transgenic barley plants. It included comparison of developmental phenotypes, yield components, grain quality, frost tolerance and the levels of expression of potential target genes in transgenic and control plants. Transgenic TaHDZipI-2 lines showed characteristic phenotypic features that included reduced growth rates, reduced biomass, early flowering, light-coloured leaves and narrowly elongated spikes. Transgenic lines produced 25-40% more seeds per spike than control plants, but with 50-60% smaller grain size. In vivo lipid imaging exposed changes in the distribution of lipids between the embryo and endosperm in transgenic seeds. Transgenic lines were significantly more tolerant to frost than control plants. Our data suggest the role of TaHDZipI-2 in controlling several key processes underlying frost tolerance, transition to flowering and spike development. PMID:26990681

  12. Dopamine Modulates Spike Timing-Dependent Plasticity and Action Potential Properties in CA1 Pyramidal Neurons of Acute Rat Hippocampal Slices

    PubMed Central

    Edelmann, Elke; Lessmann, Volkmar

    2011-01-01

    Spike timing-dependent plasticity (STDP) is a cellular model of Hebbian synaptic plasticity which is believed to underlie memory formation. In an attempt to establish a STDP paradigm in CA1 of acute hippocampal slices from juvenile rats (P15–20), we found that changes in excitability resulting from different slice preparation protocols correlate with the success of STDP induction. Slice preparation with sucrose containing ACSF prolonged rise time, reduced frequency adaptation, and decreased latency of action potentials in CA1 pyramidal neurons compared to preparation in conventional ASCF, while other basal electrophysiological parameters remained unaffected. Whereas we observed prominent timing-dependent long-term potentiation (t-LTP) to 171 ± 10% of controls in conventional ACSF, STDP was absent in sucrose prepared slices. This sucrose-induced STDP deficit could not be rescued by stronger STDP paradigms, applying either more pre- and/or postsynaptic stimuli, or by a higher stimulation frequency. Importantly, slice preparation with sucrose containing ACSF did not eliminate theta-burst stimulation induced LTP in CA1 in field potential recordings in our rat hippocampal slices. Application of dopamine (for 10–20 min) to sucrose prepared slices completely rescued t-LTP and recovered action potential properties back to levels observed in ACSF prepared slices. Conversely, acute inhibition of D1 receptor signaling impaired t-LTP in ACSF prepared slices. No similar restoring effect for STDP as seen with dopamine was observed in response to the β-adrenergic agonist isoproterenol. ELISA measurements demonstrated a significant reduction of endogenous dopamine levels (to 61.9 ± 6.9% of ACSF values) in sucrose prepared slices. These results suggest that dopamine signaling is involved in regulating the efficiency to elicit STDP in CA1 pyramidal neurons. PMID:22065958

  13. An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

    PubMed Central

    Natora, Michal; Boucsein, Clemens; Munk, Matthias H. J.; Obermayer, Klaus

    2009-01-01

    For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes. PMID:19499318

  14. Three Policies to Reduce Time to Degree

    ERIC Educational Resources Information Center

    Johnson, Nate

    2011-01-01

    The old saying that time is money is nowhere more true than in U.S. higher education. Time is measured two ways in academia--by the calendar and by the credit hour. Both can be costly, whether in the form of tuition, taxpayer subsidies, or the wages students lose with each additional term enrolled. For many years, in credit terms, the standard for…

  15. Light gas gun with reduced timing jitter

    SciTech Connect

    Laabs, Gary W.; Funk, David J.; Asay, Blaine W.

    1996-12-01

    A gas gun having a prepressurized projectile held in place with a glass rod in compression is described. The glass rod is destroyed with an explosive at a precise time which allows a restraining pin to be moved by pneumatic means and free the projectile.

  16. Reducing waiting time at security checkpoints

    SciTech Connect

    Landauer, E.G.; Becker, L.C.

    1988-08-01

    The authors were asked to improve the time it took a queue of cars to go through a security gate every morning and afternoon. Each individual passing through the gate was required to show a security badge to a guard. The goal was to improve processing time and to eliminate a safety problem at the gate without increasing the required resources. This problem required quick resolution. The project was initiated using queuing theory. After collecting data, the authors determined that a simulation model would be more appropriate. The study demonstrated that a significant improvement could be seen when the same number of guards worked two parallel traffic lines leading to the single gate, in place of the security guards working in a serial process from a single line of traffic. 7 tabs., 4 figs.

  17. Reducing Peak Demand by Time Zone Divisions

    NASA Astrophysics Data System (ADS)

    Chakrabarti, A.

    2014-09-01

    For a large country like India, the electrical power demand is also large and the infrastructure cost for power is the largest among all the core sectors of economy. India has an emerging economy which requires high rate of growth of infrastructure in the power generation, transmission and distribution. The current peak demand in the country is approximately 1,50,000 MW which shall have a planned growth of at least 50 % over the next five years (Seventeenth Electric Power Survey of India, Central Electricity Authority, Government of India, March 2007). By implementing the time zone divisions each comprising of an integral number of contiguous states based on their total peak demand and geographical location, the total peak demand of the nation can be significantly cut down by spreading the peak demand of various states over time. The projected reduction in capital expenditure over a plan period of 5 years is substantial. Also, the estimated reduction in operations expenditure cannot be ignored.

  18. Desynchronization of fast-spiking interneurons reduces β-band oscillations and imbalance in firing in the dopamine-depleted striatum.

    PubMed

    Damodaran, Sriraman; Cressman, John R; Jedrzejewski-Szmek, Zbigniew; Blackwell, Kim T

    2015-01-21

    Oscillations in the β-band (8-30 Hz) that emerge in the output nuclei of the basal ganglia during Parkinson's disease, along with an imbalanced activation of the direct and indirect pathways, have been linked to the hypokinetic motor output associated with the disease. Although dopamine depletion causes a change in cellular and network properties in the striatum, it is unclear whether abnormal activity measured in the globus pallidus and substantia nigra pars reticulata is caused by abnormal striatal activity. Here we use a computational network model of medium spiny neurons (MSNs)-fast-spiking interneurons (FSIs), based on data from several mammalian species, and find that robust β-band oscillations and imbalanced firing emerge from implementation of changes to cellular and circuit properties caused by dopamine depletion. These changes include a reduction in connections between MSNs, a doubling of FSI inhibition to D2 MSNs, an increase in D2 MSN dendritic excitability, and a reduction in D2 MSN somatic excitability. The model reveals that the reduced decorrelation between MSNs attributable to weakened lateral inhibition enables the strong influence of synchronous FSIs on MSN firing and oscillations. Weakened lateral inhibition also produces an increased sensitivity of MSN output to cortical correlation, a condition relevant to the parkinsonian striatum. The oscillations of FSIs, in turn, are strongly modulated by fast electrical transmission between FSIs through gap junctions. These results suggest that pharmaceuticals that desynchronize FSI activity may provide a novel treatment for the enhanced β-band oscillations, imbalanced firing, and motor dysfunction in Parkinson's disease. PMID:25609629

  19. Desynchronization of Fast-Spiking Interneurons Reduces β-Band Oscillations and Imbalance in Firing in the Dopamine-Depleted Striatum

    PubMed Central

    Damodaran, Sriraman; Cressman, John R.; Jedrzejewski-Szmek, Zbigniew

    2015-01-01

    Oscillations in the β-band (8–30 Hz) that emerge in the output nuclei of the basal ganglia during Parkinson's disease, along with an imbalanced activation of the direct and indirect pathways, have been linked to the hypokinetic motor output associated with the disease. Although dopamine depletion causes a change in cellular and network properties in the striatum, it is unclear whether abnormal activity measured in the globus pallidus and substantia nigra pars reticulata is caused by abnormal striatal activity. Here we use a computational network model of medium spiny neurons (MSNs)—fast-spiking interneurons (FSIs), based on data from several mammalian species, and find that robust β-band oscillations and imbalanced firing emerge from implementation of changes to cellular and circuit properties caused by dopamine depletion. These changes include a reduction in connections between MSNs, a doubling of FSI inhibition to D2 MSNs, an increase in D2 MSN dendritic excitability, and a reduction in D2 MSN somatic excitability. The model reveals that the reduced decorrelation between MSNs attributable to weakened lateral inhibition enables the strong influence of synchronous FSIs on MSN firing and oscillations. Weakened lateral inhibition also produces an increased sensitivity of MSN output to cortical correlation, a condition relevant to the parkinsonian striatum. The oscillations of FSIs, in turn, are strongly modulated by fast electrical transmission between FSIs through gap junctions. These results suggest that pharmaceuticals that desynchronize FSI activity may provide a novel treatment for the enhanced β-band oscillations, imbalanced firing, and motor dysfunction in Parkinson's disease. PMID:25609629

  20. Quiet Spike (TM) Build-Up Ground Vibration Testing Approach

    NASA Technical Reports Server (NTRS)

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

    2007-01-01

    Flight tests of Gulfstream Aerospace Corporation s Quiet Spike(TM) hardware were recently completed on the NASA Dryden Flight Research Center F-15B airplane. NASA Dryden uses a modified F-15B airplane as a testbed aircraft to cost-effectively fly flight research experiments that are typically mounted underneath the F-15B airplane, along the fuselage centerline. For the Quiet Spike(TM) experiment, however, instead of a centerline mounting, a relatively long forward-pointing boom was attached to the radar bulkhead of the F-15B airplane. The Quiet Spike(TM) experiment is a stepping-stone to airframe structural morphing technologies designed to mitigate the sonic-boom strength of business jets over land. The Quiet Spike(TM) boom is a concept in which an aircraft's noseboom would be extended prior to supersonic acceleration. This morphing effectively lengthens the aircraft, thus reducing the peak sonic-boom amplitude, but is also expected to partition the otherwise strong bow shock into a series of reduced-strength, noncoalescing shocklets. Prior to flying the Quiet Spike(TM) experiment on the F-15B airplane several ground vibration tests were required to understand the Quiet Spike(TM) modal characteristics and coupling effects with the F-15B airplane. However, due to the flight hardware availability and compressed schedule requirements, a "traditional" ground vibration test of the mated F-15B Quiet Spike(TM) ready-for-flight configuration did not leave sufficient time available for the finite element model update and flutter analyses before flight testing. Therefore, a "nontraditional" ground vibration testing approach was taken. This paper provides an overview of each phase of the "nontraditional" ground vibration testing completed for the Quiet Spike(TM) project which includes the test setup details, instrumentation layout, and modal results obtained in support of the structural dynamic modeling and flutter analyses.

  1. Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system.

    PubMed

    Diamond, A; Schmuker, M; Berna, A Z; Trowell, S; Nowotny, Thomas

    2016-04-01

    In many application domains, conventional e-noses are frequently outperformed in both speed and accuracy by their biological counterparts. Exploring potential bio-inspired improvements, we note a number of neuronal network models have demonstrated some success in classifying static datasets by abstracting the insect olfactory system. However, these designs remain largely unproven in practical settings, where sensor data is real-time, continuous, potentially noisy, lacks a precise onset signal and accurate classification requires the inclusion of temporal aspects into the feature set. This investigation therefore seeks to inform and develop the potential and suitability of biomimetic classifiers for use with typical real-world sensor data. Taking a generic classifier design inspired by the inhibition and competition in the insect antennal lobe, we apply it to identifying 20 individual chemical odours from the timeseries of responses of metal oxide sensors. We show that four out of twelve available sensors and the first 30 s (10%) of the sensors' continuous response are sufficient to deliver 92% accurate classification without access to an odour onset signal. In contrast to previous approaches, once training is complete, sensor signals can be fed continuously into the classifier without requiring discretization. We conclude that for continuous data there may be a conceptual advantage in using spiking networks, in particular where time is an essential component of computation. Classification was achieved in real time using a GPU-accelerated spiking neural network simulator developed in our group. PMID:26891474

  2. Impact of calcium-activated potassium channels on NMDA spikes in cortical layer 5 pyramidal neurons.

    PubMed

    Bock, Tobias; Stuart, Greg J

    2016-03-01

    Active electrical events play an important role in shaping signal processing in dendrites. As these events are usually associated with an increase in intracellular calcium, they are likely to be under the control of calcium-activated potassium channels. Here, we investigate the impact of calcium-activated potassium channels onN-methyl-d-aspartate (NMDA) receptor-dependent spikes, or NMDA spikes, evoked by glutamate iontophoresis onto basal dendrites of cortical layer 5 pyramidal neurons. We found that small-conductance calcium-activated potassium channels (SK channels) act to reduce NMDA spike amplitude but at the same time, also decrease the iontophoretic current required for their generation. This SK-mediated decrease in NMDA spike threshold was dependent on R-type voltage-gated calcium channels and indicates a counterintuitive, excitatory effect of SK channels on NMDA spike generation, whereas the capacity of SK channels to suppress NMDA spike amplitude is in line with the expected inhibitory action of potassium channels on dendritic excitability. Large-conductance calcium-activated potassium channels had no significant impact on NMDA spikes, indicating that these channels are either absent from basal dendrites or not activated by NMDA spikes. These experiments reveal complex and opposing interactions among NMDA receptors, SK channels, and voltage-gated calcium channels in basal dendrites of cortical layer 5 pyramidal neurons during NMDA spike generation, which are likely to play an important role in regulating the way these neurons integrate the thousands of synaptic inputs they receive. PMID:26936985

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

    PubMed

    Banerjee, Arunava

    2016-05-01

    We derive a synaptic weight update rule for learning temporally precise spike train-to-spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing and avoids invoking concepts pertaining to spike rates or probabilistic models of spiking. The derivation is founded on two innovations. First, an error functional is proposed that compares the spike train emitted by the output neuron of the network to the desired spike train by way of their putative impact on a virtual postsynaptic neuron. This formulation sidesteps the need for spike alignment and leads to closed-form solutions for all quantities of interest. Second, virtual assignment of weights to spikes rather than synapses enables a perturbation analysis of individual spike times and synaptic weights of the output, as well as all intermediate neurons in the network, which yields the gradients of the error functional with respect to the said entities. Learning proceeds via a gradient descent mechanism that leverages these quantities. Simulation experiments demonstrate the efficacy of the proposed learning framework. The experiments also highlight asymmetries between synapses on excitatory and inhibitory neurons. PMID:26942750

  4. The electromagnetic spike solutions

    NASA Astrophysics Data System (ADS)

    Nungesser, Ernesto; Lim, Woei Chet

    2013-12-01

    The aim of this paper is to use the existing relation between polarized electromagnetic Gowdy spacetimes and vacuum Gowdy spacetimes to find explicit solutions for electromagnetic spikes by a procedure which has been developed by one of the authors for gravitational spikes. We present new inhomogeneous solutions which we call the EME and MEM electromagnetic spike solutions.

  5. Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces

    NASA Astrophysics Data System (ADS)

    Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I.; Shenoy, Krishna V.; Boahen, Kwabena

    2013-06-01

    Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system’s robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

  6. An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks

    PubMed Central

    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. PMID:27044001

  7. An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.

    PubMed

    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. PMID:27044001

  8. Multiscale analysis of neural spike trains.

    PubMed

    Ramezan, Reza; Marriott, Paul; Chenouri, Shojaeddin

    2014-01-30

    This paper studies the multiscale analysis of neural spike trains, through both graphical and Poisson process approaches. We introduce the interspike interval plot, which simultaneously visualizes characteristics of neural spiking activity at different time scales. Using an inhomogeneous Poisson process framework, we discuss multiscale estimates of the intensity functions of spike trains. We also introduce the windowing effect for two multiscale methods. Using quasi-likelihood, we develop bootstrap confidence intervals for the multiscale intensity function. We provide a cross-validation scheme, to choose the tuning parameters, and study its unbiasedness. Studying the relationship between the spike rate and the stimulus signal, we observe that adjusting for the first spike latency is important in cross-validation. We show, through examples, that the correlation between spike trains and spike count variability can be multiscale phenomena. Furthermore, we address the modeling of the periodicity of the spike trains caused by a stimulus signal or by brain rhythms. Within the multiscale framework, we introduce intensity functions for spike trains with multiplicative and additive periodic components. Analyzing a dataset from the retinogeniculate synapse, we compare the fit of these models with the Bayesian adaptive regression splines method and discuss the limitations of the methodology. Computational efficiency, which is usually a challenge in the analysis of spike trains, is one of the highlights of these new models. In an example, we show that the reconstruction quality of a complex intensity function demonstrates the ability of the multiscale methodology to crack the neural code. PMID:23996238

  9. An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm

    PubMed Central

    Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

    2015-01-01

    A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction. PMID:26287193

  10. MASPINN: novel concepts for a neuroaccelerator for spiking neural networks

    NASA Astrophysics Data System (ADS)

    Schoenauer, T.; Mehrtash, N.; Jahnke, Andreas; Klar, H.

    1999-03-01

    We present the basic architecture of a Memory Optimized Accelerator for Spiking Neural Networks. The accelerator architecture exploits two novel concepts for an efficient computation of spiking neural networks: weight caching and a compressed memory organization. These concepts allow a further parallelization in processing and reduce bandwidth requirements on accelerator's components. Therefore, they pave the way to dedicated digital hardware for real-time computation of more complex networks of pulse-coded neurons in the order of 106 neurons. The programmable neuron model which the accelerator is based on is described extensively. This shall encourage a discussion and suggestions on features which would be desirable to add to the current model.

  11. A Re-Examination of Hebbian-Covariance Rules and Spike Timing-Dependent Plasticity in Cat Visual Cortex in vivo

    PubMed Central

    Frégnac, Yves; Pananceau, Marc; René, Alice; Huguet, Nazyed; Marre, Olivier; Levy, Manuel; Shulz, Daniel E.

    2010-01-01

    Spike timing-dependent plasticity (STDP) is considered as an ubiquitous rule for associative plasticity in cortical networks in vitro. However, limited supporting evidence for its functional role has been provided in vivo. In particular, there are very few studies demonstrating the co-occurrence of synaptic efficiency changes and alteration of sensory responses in adult cortex during Hebbian or STDP protocols. We addressed this issue by reviewing and comparing the functional effects of two types of cellular conditioning in cat visual cortex. The first one, referred to as the “covariance” protocol, obeys a generalized Hebbian framework, by imposing, for different stimuli, supervised positive and negative changes in covariance between postsynaptic and presynaptic activity rates. The second protocol, based on intracellular recordings, replicated in vivo variants of the theta-burst paradigm (TBS), proven successful in inducing long-term potentiation in vitro. Since it was shown to impose a precise correlation delay between the electrically activated thalamic input and the TBS-induced postsynaptic spike, this protocol can be seen as a probe of causal (“pre-before-post”) STDP. By choosing a thalamic region where the visual field representation was in retinotopic overlap with the intracellularly recorded cortical receptive field as the afferent site for supervised electrical stimulation, this protocol allowed to look for possible correlates between STDP and functional reorganization of the conditioned cortical receptive field. The rate-based “covariance protocol” induced significant and large amplitude changes in receptive field properties, in both kitten and adult V1 cortex. The TBS STDP-like protocol produced in the adult significant changes in the synaptic gain of the electrically activated thalamic pathway, but the statistical significance of the functional correlates was detectable mostly at the population level. Comparison of our observations with the

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

    PubMed Central

    Resnik, Andrey; Celikel, Tansu; Englitz, Bernhard

    2016-01-01

    Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information. PMID:27304526

  13. Approximation of the first passage time density of a Wiener process to an exponentially decaying boundary by two-piecewise linear threshold. Application to neuronal spiking activity.

    PubMed

    Tamborrino, Massimiliano

    2016-06-01

    The first passage time density of a diffusion process to a time varying threshold is of primary interest in different fields. Here, we consider a Brownian motion in presence of an exponentially decaying threshold to model the neuronal spiking activity. Since analytical expressions of the first passage time density are not available, we propose to approximate the curved boundary by means of a continuous two-piecewise linear threshold. Explicit expressions for the first passage time density towards the new boundary are provided. First, we introduce different approximating linear thresholds. Then, we describe how to choose the optimal one minimizing the distance to the curved boundary, and hence the error in the corresponding passage time density. Theoretical means, variances and coefficients of variation given by our method are compared with empirical quantities from simulated data. Moreover, a further comparison with firing statistics derived under the assumption of a small amplitude of the time-dependent change in the threshold, is also carried out. Finally, maximum likelihood and moment estimators of the parameters of the model are derived and applied on simulated data. PMID:27106189

  14. Cluster-based spike detection algorithm adapts to interpatient and intrapatient variation in spike morphology.

    PubMed

    Nonclercq, Antoine; Foulon, Martine; Verheulpen, Denis; De Cock, Cathy; Buzatu, Marga; Mathys, Pierre; Van Bogaert, Patrick

    2012-09-30

    Visual quantification of interictal epileptiform activity is time consuming and requires a high level of expert's vigilance. This is especially true for overnight recordings of patient suffering from epileptic encephalopathy with continuous spike and waves during slow-wave sleep (CSWS) as they can show tens of thousands of spikes. Automatic spike detection would be attractive for this condition, but available algorithms have methodological limitations related to variation in spike morphology both between patients and within a single recording. We propose a fully automated method of interictal spike detection that adapts to interpatient and intrapatient variation in spike morphology. The algorithm works in five steps. (1) Spikes are detected using parameters suitable for highly sensitive detection. (2) Detected spikes are separated into clusters. (3) The number of clusters is automatically adjusted. (4) Centroids are used as templates for more specific spike detections, therefore adapting to the types of spike morphology. (5) Detected spikes are summed. The algorithm was evaluated on EEG samples from 20 children suffering from epilepsy with CSWS. When compared to the manual scoring of 3 EEG experts (3 records), the algorithm demonstrated similar performance since sensitivity and selectivity were 0.3% higher and 0.4% lower, respectively. The algorithm showed little difference compared to the manual scoring of another expert for the spike-and-wave index evaluation in 17 additional records (the mean absolute difference was 3.8%). This algorithm is therefore efficient for the count of interictal spikes and determination of a spike-and-wave index. PMID:22850558

  15. Kinetic properties and functional dynamics of sodium channels during repetitive spiking in a slow pacemaker neuron

    PubMed Central

    Milescu, Lorin S.; Yamanishi, Tadashi; Ptak, Krzysztof; Smith, Jeffrey C.

    2010-01-01

    We examined the kinetic properties of voltage-gated Na+ channels and their contribution to the repetitive spiking activity of medullary raphé neurons, which exhibit slow pacemaking and strong spiking adaptation. The study is based on a combination of whole-cell patch clamp, modeling and real-time computation. Na+ currents were recorded from neurons in brain slices obtained from male and female neonatal rats, using voltage-clamp protocols designed to reduce space-clamp artifacts and to emphasize functionally relevant kinetic features. A detailed kinetic model was formulated to explain the broad range of transient and stationary voltage-dependent properties exhibited by Na+ currents. The model was tested by injecting via dynamic clamp a model-based current as a substitute for the native TTX-sensitive Na+ currents, which were pharmacologically blocked. The model-based current reproduced well the native spike shape and spiking frequency. The dynamics of Na+ channels during repetitive spiking were indirectly examined through this model. By comparing the spiking activities generated with different kinetic models in dynamic clamp experiments, we determined that state-dependent slow inactivation contributes significantly to spiking adaptation. Through real-time manipulation of the model-based current, we established that suprathreshold Na+ current mainly controls spike shape, whereas subthreshold Na+ current modulates spiking frequency and contributes to the pacemaking mechanism. Since the model-based current was injected in the soma, the results also suggest that somatic Na+ channels are sufficient to establish the essential spiking properties of raphé neurons in vitro. PMID:20826674

  16. Intracoronary brachytherapy following drug-eluting stent failure It's still not time to hang up the spikes{exclamation_point}

    SciTech Connect

    Angiolillo, Dominick J.; Sabate, Manel; Jimenez-Quevedo, Pilar; Alfonso, Fernando; Galvan, Carmen; Fernandez, Jose Miguel; Hernandez-Antolin, Rosana; Escaned, Javier; Banuelos, Camino; Moreno, Raul; Macaya, Carlos

    2003-12-01

    Drug-eluting stents (DES) have significantly reduced the incidence of restenosis. Although the results obtained with these novel antiproliferative devices are encouraging, recent reports have shown that DES are not completely immune from restenosis. Therefore, the broad use of DES has inevitably led to a major issue: treatment of DES failure. Intracoronary brachytherapy (IBT) represents an important advancement for treatment of in-stent restenosis (ISR) and has led to important pathophysiological insight on the restenotic process. To date, IBT, when properly used, still represents the gold standard for treatment of ISR. However, experience with IBT is for treatment of ISR occurring with bare metal stents (BMS). Whether IBT may be used with the same safety and efficacy profile as an adjunctive treatment for ISR following DES implantation is still unknown. In this article, we report the outcome of a series of patients with DES failure treated with IBT. IBT for treatment of DES failure was shown to be both safe and efficient and, therefore, until ISR exists, IBT still remains an important player in this growing and even more challenging setting.

  17. Implementing spiking neural networks for real-time signal-processing and control applications: a model-validated FPGA approach.

    PubMed

    Pearson, Martin J; Pipe, A G; Mitchinson, B; Gurney, K; Melhuish, C; Gilhespy, I; Nibouche, M

    2007-09-01

    In this paper, we present two versions of a hardware processing architecture for modeling large networks of leaky-integrate-and-fire (LIF) neurons; the second version provides performance enhancing features relative to the first. Both versions of the architecture use fixed-point arithmetic and have been implemented using a single field-programmable gate array (FPGA). They have successfully simulated networks of over 1000 neurons configured using biologically plausible models of mammalian neural systems. The neuroprocessor has been designed to be employed primarily for use on mobile robotic vehicles, allowing bio-inspired neural processing models to be integrated directly into real-world control environments. When a neuroprocessor has been designed to act as part of the closed-loop system of a feedback controller, it is imperative to maintain strict real-time performance at all times, in order to maintain integrity of the control system. This resulted in the reevaluation of some of the architectural features of existing hardware for biologically plausible neural networks (NNs). In addition, we describe a development system for rapidly porting an underlying model (based on floating-point arithmetic) to the fixed-point representation of the FPGA-based neuroprocessor, thereby allowing validation of the hardware architecture. The developmental system environment facilitates the cooperation of computational neuroscientists and engineers working on embodied (robotic) systems with neural controllers, as demonstrated by our own experience on the Whiskerbot project, in which we developed models of the rodent whisker sensory system. PMID:18220195

  18. Reducing Design Cycle Time and Cost Through Process Resequencing

    NASA Technical Reports Server (NTRS)

    Rogers, James L.

    2004-01-01

    In today's competitive environment, companies are under enormous pressure to reduce the time and cost of their design cycle. One method for reducing both time and cost is to develop an understanding of the flow of the design processes and the effects of the iterative subcycles that are found in complex design projects. Once these aspects are understood, the design manager can make decisions that take advantage of decomposition, concurrent engineering, and parallel processing techniques to reduce the total time and the total cost of the design cycle. One software tool that can aid in this decision-making process is the Design Manager's Aid for Intelligent Decomposition (DeMAID). The DeMAID software minimizes the feedback couplings that create iterative subcycles, groups processes into iterative subcycles, and decomposes the subcycles into a hierarchical structure. The real benefits of producing the best design in the least time and at a minimum cost are obtained from sequencing the processes in the subcycles.

  19. Development of triplex real-time PCR and detection of Toxoplasma gondii DNA in infected mice tissues and spiked human samples.

    PubMed

    Rahumatullah, A; Khoo, B Y; Noordin, R

    2015-06-01

    Toxoplasma gondii is an important pathogen in veterinary and human medicine. In this study, a new multiplex TaqMan real-time PCR for detection of T. gondii DNA was developed. This assay consisted of new sets of primers and probes which targeted B1 gene and ITS-1 region of T. gondii, with Vibrio cholera gene as internal control. The B1 gene primers were designed to detect T. gondii RH strain, while the ITS-1 region primers detected most T. gondii strains. Specificity test using common protozoal and bacterial DNA revealed that the assay was very specific to T. gondii. Standard curves constructed using human body fluids spiked with T. gondii (RH and ME49 strains) showed that the sensitivity of the assay was one parasite, with R² value of 0.975 to 0.999 and efficiency of 97% to 99% for all types of samples. The assay performed on DNA extracted from tissues of mice infected with T. gondii showed that liver contained the highest parasite load for both strains of T. gondii. The multiplex real-time PCR developed in this study would be potentially useful for detection of T. gondii in human and animal samples. PMID:26691266

  20. Time-resolved luminescence screening of antibiotics in tissue matrices without centrifugation and filtration: spiked recovery studies.

    PubMed

    Chen, Guoying; Smith, Emily; Qin, Feng; Liu, Linshu

    2006-05-01

    Analyses of chemical residues in animal tissue matrices require multistep sample preparation. To simplify this process, a methodology was developed that combines sorbent extraction and solid-matrix time-resolved luminescence (TRL); it was applied to tetracycline screening in milk. Reported here is an effort to extend its application to tissue matrices, illustrated by oxytetracycline (OTC) screening in catfish muscle. Extraction and enrichment are accomplished by immersing small C18 sorbent strips into tissue homogenates for 20 min, followed by a 3 min rinse in water and a 2 min dip in a reagent solution. After desiccation, TRL is measured directly on the sorbent surface. Tissue particulates no longer interfere via attenuation or scattering, rendering centrifugation and filtration unnecessary. The integrated TRL intensity shows a linear dependence on OTC concentration in the 0-8 microg/g range (R2 = 0.9992) with a 0.026 microg/g limit of detection. To screen OTC at 2 microg/g, the U.S. regulatory tolerance level, a threshold is established at x2-3sigma2, where x2 and sigma2 are the mean and standard deviation, respectively, of the TRL signals from 15 samples fortified at 2 microg/g. Among 45 blind samples randomly fortified at 0-4 microg/g, 41 were screened correctly and 4 negative samples were presumed positive. This method has the potential to improve throughput and save assay costs by eliminating acids, organic solvents, centrifugation, and filtration. PMID:16637677

  1. Stochastic variational learning in recurrent spiking networks

    PubMed Central

    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. PMID:24772078

  2. Spike Decomposition Technique: Modeling and Performance Tests

    NASA Astrophysics Data System (ADS)

    Nita, Gelu M.; Fleishman, Gregory D.; Gary, Dale E.

    2008-12-01

    We develop an automated technique for fitting the spectral components of solar microwave spike bursts, which are characterized by narrowband spectral features. The algorithm is especially useful for periods when the spikes occur in densely packed clusters, where the algorithm is capable of decomposing overlapping spike structures into individual spectral components. To test the performance and applicability limits of this data reduction tool, we perform comprehensive modeling of spike clusters characterized by various typical bandwidths, spike densities, and amplitude distributions. We find that, for a wide range of favorable combinations of input parameters, the algorithm is able to recover the characteristic features of the modeled distributions within reasonable confidence intervals. Having model-tested the algorithm against spike overlap, broadband spectral background, noise contamination, and possible malfunction of some spectral channels, we apply the technique to a spike cluster recorded by the Chinese Purple Mountain Observatory (PMO) spectrometer, operating above 4.5 GHz. We study the variation of the spike distribution parameters, such as amplitude, bandwidth, and related derived physical parameters, as a function of time. The method can be further applied to observations from other instruments and to other types of fine structures.

  3. Symmetric spike timing-dependent plasticity at CA3–CA3 synapses optimizes storage and recall in autoassociative networks

    PubMed Central

    Mishra, Rajiv K.; Kim, Sooyun; Guzman, Segundo J.; Jonas, Peter

    2016-01-01

    CA3–CA3 recurrent excitatory synapses are thought to play a key role in memory storage and pattern completion. Whether the plasticity properties of these synapses are consistent with their proposed network functions remains unclear. Here, we examine the properties of spike timing-dependent plasticity (STDP) at CA3–CA3 synapses. Low-frequency pairing of excitatory postsynaptic potentials (EPSPs) and action potentials (APs) induces long-term potentiation (LTP), independent of temporal order. The STDP curve is symmetric and broad (half-width ∼150 ms). Consistent with these STDP induction properties, AP–EPSP sequences lead to supralinear summation of spine [Ca2+] transients. Furthermore, afterdepolarizations (ADPs) following APs efficiently propagate into dendrites of CA3 pyramidal neurons, and EPSPs summate with dendritic ADPs. In autoassociative network models, storage and recall are more robust with symmetric than with asymmetric STDP rules. Thus, a specialized STDP induction rule allows reliable storage and recall of information in the hippocampal CA3 network. PMID:27174042

  4. Reducing preoperative fasting time: A trend based on evidence

    PubMed Central

    de Aguilar-Nascimento, José Eduardo; Dock-Nascimento, Diana Borges

    2010-01-01

    Preoperative fasting is mandatory before anesthesia to reduce the risk of aspiration. However, the prescribed 6-8 h of fasting is usually prolonged to 12-16 h for various reasons. Prolonged fasting triggers a metabolic response that precipitates gluconeogenesis and increases the organic response to trauma. Various randomized trials and meta-analyses have consistently shown that is safe to reduce the preoperative fasting time with a carbohydrate-rich drink up to 2 h before surgery. Benefits related to this shorter preoperative fasting include the reduction of postoperative gastrointestinal discomfort and insulin resistance. New formulas containing amino acids such as glutamine and other peptides are being studied and are promising candidates to be used to reduce preoperative fasting time. PMID:21160851

  5. Reducing current reversal time in electric motor control

    SciTech Connect

    Bredemann, Michael V

    2014-11-04

    The time required to reverse current flow in an electric motor is reduced by exploiting inductive current that persists in the motor when power is temporarily removed. Energy associated with this inductive current is used to initiate reverse current flow in the motor.

  6. Millisecond Radio Spikes in the Decimetric Band

    NASA Astrophysics Data System (ADS)

    dąbrowski, B. P.; Rudawy, P.; Karlický, M.

    2011-11-01

    We present the results of the analysis of thirteen events consisting of dm-spikes observed in Toruń between 15 March 2000 and 30 October 2001. The events were obtained with a very high time resolution (80 microseconds) radio spectrograph in the 1352 - 1490 MHz range. These data were complemented with observations from the radio spectrograph at Ondřejov in the 0.8 - 2.0 GHz band. We evaluated the basic characteristics of the individual spikes (duration, spectral width, and frequency drifts), as well as their groups and chains, the location of their emission sources, and the temporal correlations of the emissions with various phases of the associated solar flares. We found that the mean duration and spectral width of the radio spikes are equal to 0.036 s and 9.96 MHz, respectively. Distributions of the duration and spectral widths of the spikes have positive skewness for all investigated events. Each spike shows positive or negative frequency drift. The mean negative and positive drifts of the investigated spikes are equal to -776 MHz s-1 and 1608 MHz s-1, respectively. The emission sources of the dm-spikes are located mainly at disk center. We have noticed two kinds of chains, with and without frequency drifts. The mean durations of the chains vary between 0.067 s and 0.509 s, while their spectral widths vary between 7.2 MHz and 17.25 MHz. The mean duration of an individual spike observed in a chain was equal to 0.03 s. While we found some agreement between the global characteristics of the groups of spikes recorded with the two instruments located in Toruń and Ondřejov, we did not find any one-to-one relation between individual spikes.

  7. Millisecond Radio Spikes in the Decimetric Band

    NASA Astrophysics Data System (ADS)

    Dąbrowski, B. P.; Rudawy, P.; Karlický, M.

    We present the results of the analysis of thirteen events consisting of dm-spikes observed in Toruń between 15 March 2000 and 30 October 2001. The events were obtained with a very high time resolution (80 microseconds) radio spectrograph in the 1352 - 1490 MHz range. These data were complemented with observations from the radio spectrograph at Ondřejov in the 0.8 - 2.0 GHz band. We evaluated the basic characteristics of the individual spikes (duration, spectral width, and frequency drifts), as well as their groups and chains, the location of their emission sources, and the temporal correlations of the emissions with various phases of the associated solar flares. We found that the mean duration and spectral width of the radio spikes are equal to 0.036 s and 9.96 MHz, respectively. Distributions of the duration and spectral widths of the spikes have positive skewness for all investigated events. Each spike shows positive or negative frequency drift. The mean negative and positive drifts of the investigated spikes are equal to -776 MHz s-1 and 1608 MHz s-1, respectively. The emission sources of the dm-spikes are located mainly at disk center. We have noticed two kinds of chains, with and without frequency drifts. The mean durations of the chains vary between 0.067 s and 0.509 s, while their spectral widths vary between 7.2 MHz and 17.25 MHz. The mean duration of an individual spike observed in a chain was equal to 0.03 s. While we found some agreement between the global characteristics of the groups of spikes recorded with the two instruments located in Toruń and Ondřejov, we did not find any one-to-one relation between individual spikes.

  8. Effects of a reduced time-out interval on compliance with the time-out instruction.

    PubMed

    Donaldson, Jeanne M; Vollmer, Timothy R; Yakich, Theresa M; Van Camp, Carole

    2013-01-01

    Time-out is a negative punishment procedure that parents and teachers commonly use to reduce problem behavior; however, specific time-out parameters have not been evaluated adequately. One parameter that has received relatively little attention in the literature is the mode of administration (verbal or physical) of time-out. In this study, we evaluated a procedure designed to reduce problem behavior and increase compliance with the verbal instruction to go to time-out. Specifically, we reduced the time-out interval contingent on compliance with the time-out instruction. Six preschool-aged boys participated in the study. Time-out effectively reduced the problem behavior of all 6 participants, and the procedure to increase compliance with the time-out instruction was effective for 4 of 6 participants. PMID:24114153

  9. Spiking the Geomagnetic Field

    NASA Astrophysics Data System (ADS)

    Constable, C.; Davies, C. J.

    2015-12-01

    Geomagnetic field intensities corresponding to virtual axial dipole moments of up to 200 ZAm2, more than twice the modern value, have been inferred from archeomagnetic measurements on artifacts dated at or shortly after 1000 BC. Anomalously high values occur in the Levant and Georgia, but not in Bulgaria. The origin of this spike is believed to lie in Earth's core: however, its spatio-temporal characteristics and the geomagnetic processes responsible for such a feature remain a mystery. We show that a localized spike in the radial magnetic field at the core-mantle boundary (CMB) must necessarily contribute to the largest scale changes in Earth's surface field, namely the dipole. Even the limiting spike of a delta function at the CMB produces a minimum surface cap size of 60 degrees for a factor of two increase in paleointensity. Combined evidence from modern satellite and millennial scale field modeling suggests that the Levantine Spike is intimately associated with a strong increase in dipole moment prior to 1000 BC and likely the product of north-westward motion of concentrated near equatorial Asian flux patches like those seen in the modern field. New archeomagnetic studies are needed to confirm this interpretation. Minimum estimates of the power dissipated by the spike are comparable to independent estimates of the dissipation associated with the entire steady state geodynamo. This suggests that geomagnetic spikes are either associated with rapid changes in magnetic energy or strong Lorentz forces.

  10. Theta phase shift in spike timing and modulation of gamma oscillation: a dynamic code for spatial alternation during fixation in rat hippocampal area CA1

    PubMed Central

    Nishida, Hiroshi; David Redish, A.; Lauwereyns, Johan

    2014-01-01

    Although hippocampus is thought to perform various memory-related functions, little is known about the underlying dynamics of neural activity during a preparatory stage before a spatial choice. Here we focus on neural activity that reflects a memory-based code for spatial alternation, independent of current sensory and motor parameters. We recorded multiple single units and local field potentials in the stratum pyramidale of dorsal hippocampal area CA1 while rats performed a delayed spatial-alternation task. This task includes a 1-s fixation in a nose-poke port between selecting alternating reward sites and so provides time-locked enter-and-leave events. At the single-unit level, we concentrated on neurons that were specifically active during the 1-s fixation period, when the rat was ready and waiting for a cue to pursue the task. These neurons showed selective activity as a function of the alternation sequence. We observed a marked shift in the phase timing of the neuronal spikes relative to the theta oscillation, from the theta peak at the beginning of fixation to the theta trough at the end of fixation. The gamma-band local field potential also changed during the fixation period: the high-gamma power (60–90 Hz) decreased and the low-gamma power (30–45 Hz) increased toward the end. These two gamma components were observed at different phases of the ongoing theta oscillation. Taken together, our data suggest a switch in the type of information processing through the fixation period, from externally cued to internally generated. PMID:24478159

  11. Reducing implicit racial preferences: II. Intervention effectiveness across time.

    PubMed

    Lai, Calvin K; Skinner, Allison L; Cooley, Erin; Murrar, Sohad; Brauer, Markus; Devos, Thierry; Calanchini, Jimmy; Xiao, Y Jenny; Pedram, Christina; Marshburn, Christopher K; Simon, Stefanie; Blanchar, John C; Joy-Gaba, Jennifer A; Conway, John; Redford, Liz; Klein, Rick A; Roussos, Gina; Schellhaas, Fabian M H; Burns, Mason; Hu, Xiaoqing; McLean, Meghan C; Axt, Jordan R; Asgari, Shaki; Schmidt, Kathleen; Rubinstein, Rachel; Marini, Maddalena; Rubichi, Sandro; Shin, Jiyun-Elizabeth L; Nosek, Brian A

    2016-08-01

    Implicit preferences are malleable, but does that change last? We tested 9 interventions (8 real and 1 sham) to reduce implicit racial preferences over time. In 2 studies with a total of 6,321 participants, all 9 interventions immediately reduced implicit preferences. However, none were effective after a delay of several hours to several days. We also found that these interventions did not change explicit racial preferences and were not reliably moderated by motivations to respond without prejudice. Short-term malleability in implicit preferences does not necessarily lead to long-term change, raising new questions about the flexibility and stability of implicit preferences. (PsycINFO Database Record PMID:27454041

  12. Intra-spike crosslinking overcomes antibody evasion by HIV-1.

    PubMed

    Galimidi, Rachel P; Klein, Joshua S; Politzer, Maria S; Bai, Shiyu; Seaman, Michael S; Nussenzweig, Michel C; West, Anthony P; Bjorkman, Pamela J

    2015-01-29

    Antibodies developed during HIV-1 infection lose efficacy as the viral spike mutates. We postulated that anti-HIV-1 antibodies primarily bind monovalently because HIV's low spike density impedes bivalent binding through inter-spike crosslinking, and the spike structure prohibits bivalent binding through intra-spike crosslinking. Monovalent binding reduces avidity and potency, thus expanding the range of mutations permitting antibody evasion. To test this idea, we engineered antibody-based molecules capable of bivalent binding through intra-spike crosslinking. We used DNA as a "molecular ruler" to measure intra-epitope distances on virion-bound spikes and construct intra-spike crosslinking molecules. Optimal bivalent reagents exhibited up to 2.5 orders of magnitude increased potency (>100-fold average increases across virus panels) and identified conformational states of virion-bound spikes. The demonstration that intra-spike crosslinking lowers the concentration of antibodies required for neutralization supports the hypothesis that low spike densities facilitate antibody evasion and the use of molecules capable of intra-spike crosslinking for therapy or passive protection. PMID:25635457

  13. Measuring multiple spike train synchrony.

    PubMed

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

    2009-10-15

    Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods. PMID:19591867

  14. Reducing the contact time of a bouncing drop

    NASA Astrophysics Data System (ADS)

    Bird, James C.; Dhiman, Rajeev; Kwon, Hyuk-Min; Varanasi, Kripa K.

    2013-11-01

    Surfaces designed so that drops do not adhere to them but instead bounce off have received substantial attention because of their ability to stay dry, self-clean and resist icing. A drop striking a non-wetting surface of this type will spread out to a maximum diameter and then recoil to such an extent that it completely rebounds and leaves the solid material. The amount of time that the drop is in contact with the solid--the `contact time'--depends on the inertia and capillarity of the drop, internal dissipation and surface-liquid interactions. And because contact time controls the extent to which mass, momentum and energy are exchanged between drop and surface, it is often advantageous to minimize it. The conventional approach has been to minimize surface-liquid interactions that can lead to contact line pinning; but even in the absence of any surface interactions, drop hydrodynamics imposes a minimum contact time that was conventionally assumed to be attained with axisymmetrically spreading and recoiling drops. Here we demonstrate that it is possible to reduce the contact time below this theoretical limit by using superhydrophobic surfaces with a morphology that redistributes the liquid mass and thereby alters the drop hydrodynamics. We show theoretically and experimentally that this approach allows us to reduce the overall contact time between a bouncing drop and a surface below what was previously thought possible.

  15. Interictal spikes and epileptic seizures: their relationship and underlying rhythmicity.

    PubMed

    Karoly, Philippa J; Freestone, Dean R; Boston, Ray; Grayden, David B; Himes, David; Leyde, Kent; Seneviratne, Udaya; Berkovic, Samuel; O'Brien, Terence; Cook, Mark J

    2016-04-01

    We report on a quantitative analysis of electrocorticography data from a study that acquired continuous ambulatory recordings in humans over extended periods of time. The objectives were to examine patterns of seizures and spontaneous interictal spikes, their relationship to each other, and the nature of periodic variation. The recorded data were originally acquired for the purpose of seizure prediction, and were subsequently analysed in further detail. A detection algorithm identified potential seizure activity and a template matched filter was used to locate spikes. Seizure events were confirmed manually and classified as either clinically correlated, electroencephalographically identical but not clinically correlated, or subclinical. We found that spike rate was significantly altered prior to seizure in 9 out of 15 subjects. Increased pre-ictal spike rate was linked to improved predictability; however, spike rate was also shown to decrease before seizure (in 6 out of the 9 subjects). The probability distribution of spikes and seizures were notably similar, i.e. at times of high seizure likelihood the probability of epileptic spiking also increased. Both spikes and seizures showed clear evidence of circadian regulation and, for some subjects, there were also longer term patterns visible over weeks to months. Patterns of spike and seizure occurrence were highly subject-specific. The pre-ictal decrease in spike rate is not consistent with spikes promoting seizures. However, the fact that spikes and seizures demonstrate similar probability distributions suggests they are not wholly independent processes. It is possible spikes actively inhibit seizures, or that a decreased spike rate is a secondary symptom of the brain approaching seizure. If spike rate is modulated by common regulatory factors as seizures then spikes may be useful biomarkers of cortical excitability.media-1vid110.1093/brain/aww019_video_abstractaww019_video_abstract. PMID:26912639

  16. Spike detection using the continuous wavelet transform.

    PubMed

    Nenadic, Zoran; Burdick, Joel W

    2005-01-01

    This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution. PMID:15651566

  17. Prospective Coding by Spiking Neurons

    PubMed Central

    Brea, Johanni; Gaál, Alexisz Tamás; Senn, Walter

    2016-01-01

    Animals learn to make predictions, such as associating the sound of a bell with upcoming feeding or predicting a movement that a motor command is eliciting. How predictions are realized on the neuronal level and what plasticity rule underlies their learning is not well understood. Here we propose a biologically plausible synaptic plasticity rule to learn predictions on a single neuron level on a timescale of seconds. The learning rule allows a spiking two-compartment neuron to match its current firing rate to its own expected future discounted firing rate. For instance, if an originally neutral event is repeatedly followed by an event that elevates the firing rate of a neuron, the originally neutral event will eventually also elevate the neuron’s firing rate. The plasticity rule is a form of spike timing dependent plasticity in which a presynaptic spike followed by a postsynaptic spike leads to potentiation. Even if the plasticity window has a width of 20 milliseconds, associations on the time scale of seconds can be learned. We illustrate prospective coding with three examples: learning to predict a time varying input, learning to predict the next stimulus in a delayed paired-associate task and learning with a recurrent network to reproduce a temporally compressed version of a sequence. We discuss the potential role of the learning mechanism in classical trace conditioning. In the special case that the signal to be predicted encodes reward, the neuron learns to predict the discounted future reward and learning is closely related to the temporal difference learning algorithm TD(λ). PMID:27341100

  18. Parallel Processing of Distributed Video Coding to Reduce Decoding Time

    NASA Astrophysics Data System (ADS)

    Tonomura, Yoshihide; Nakachi, Takayuki; Fujii, Tatsuya; Kiya, Hitoshi

    This paper proposes a parallelized DVC framework that treats each bitplane independently to reduce the decoding time. Unfortunately, simple parallelization generates inaccurate bit probabilities because additional side information is not available for the decoding of subsequent bitplanes, which degrades encoding efficiency. Our solution is an effective estimation method that can calculate the bit probability as accurately as possible by index assignment without recourse to side information. Moreover, we improve the coding performance of Rate-Adaptive LDPC (RA-LDPC), which is used in the parallelized DVC framework. This proposal selects a fitting sparse matrix for each bitplane according to the syndrome rate estimation results at the encoder side. Simulations show that our parallelization method reduces the decoding time by up to 35[%] and achieves a bit rate reduction of about 10[%].

  19. Efficient Identification of Assembly Neurons within Massively Parallel Spike Trains

    PubMed Central

    Berger, Denise; Borgelt, Christian; Louis, Sebastien; Morrison, Abigail; Grün, Sonja

    2010-01-01

    The chance of detecting assembly activity is expected to increase if the spiking activities of large numbers of neurons are recorded simultaneously. Although such massively parallel recordings are now becoming available, methods able to analyze such data for spike correlation are still rare, as a combinatorial explosion often makes it infeasible to extend methods developed for smaller data sets. By evaluating pattern complexity distributions the existence of correlated groups can be detected, but their member neurons cannot be identified. In this contribution, we present approaches to actually identify the individual neurons involved in assemblies. Our results may complement other methods and also provide a way to reduce data sets to the “relevant” neurons, thus allowing us to carry out a refined analysis of the detailed correlation structure due to reduced computation time. PMID:19809521

  20. Survival-time statistics for sample space reducing stochastic processes

    NASA Astrophysics Data System (ADS)

    Yadav, Avinash Chand

    2016-04-01

    Stochastic processes wherein the size of the state space is changing as a function of time offer models for the emergence of scale-invariant features observed in complex systems. I consider such a sample-space reducing (SSR) stochastic process that results in a random sequence of strictly decreasing integers {x (t )},0 ≤t ≤τ , with boundary conditions x (0 )=N and x (τ ) = 1. This model is shown to be exactly solvable: PN(τ ) , the probability that the process survives for time τ is analytically evaluated. In the limit of large N , the asymptotic form of this probability distribution is Gaussian, with mean and variance both varying logarithmically with system size: <τ >˜lnN and στ2˜lnN . Correspondence can be made between survival-time statistics in the SSR process and record statistics of independent and identically distributed random variables.

  1. Spike Decomposition Technique: Modeling and Performance Tests

    NASA Astrophysics Data System (ADS)

    Nita, G. M.; Fleishman, G. D.; Gary, D. E.

    2008-05-01

    We develop an automated technique for fitting the spectral components of solar microwave spike bursts characterized by narrow-band (1-50~MHz) features of 1-10~ms duration, which are thought to be due to Electron-Cyclotron Maser emission. The algorithm is especially useful for periods when the spikes occur in densely packed clusters, where the algorithm is capable of decomposing overlapping spike structures into individual spectral components. To test the performance and applicability limits of this forward fitting algorithm, we perform comprehensive modeling of spike clusters characterized by various typical bandwidths, spike densities, and amplitude distributions. We find that, for a wide range of input parameters, the algorithm is able to recover the characteristic features of the modeled distributions within reasonable confidence intervals. Having model-tested the algorithm comprehensively against spike overlap, broadband spectral background, noise contamination, and possible contamination of cross-channel polarization, we apply the technique to observational data obtained from different instruments in different frequency ranges. Specifically, we studied spike clusters recorded by a Chinese Purple Mountain Observatory (PMO) spectrometer above 4.5 GHz and by Owens Valley Solar Array's FASR Subsystem Testbed instrument above 1 GHz. We study variation of the spike distribution parameters, such as amplitude, bandwidth and related derived physical parameters as a function of frequency and time. We discuss the implications of our results for the choice between competing models of spike generation and underlying physical processes. The method can be further applied to observations from other instruments and to other types of radio spectral fine structures. This work was supported in part by NSF grants AST-0607544 and ATM-0707319 and NASA grant NNG06GJ40G to New Jersey Institute of Technology.

  2. Reducing video frame rate increases remote optimal focus time

    NASA Technical Reports Server (NTRS)

    Haines, Richard F.

    1993-01-01

    Twelve observers made best optical focus adjustments to a microscope whose high-resolution pattern was video monitored and displayed first on a National Television System Committee (NTSC) analog color monitor and second on a digitally compressed computer monitor screen at frame rates ranging (in six steps) from 1.5 to 30 frames per second (fps). This was done to determine whether reducing the frame rate affects the image focus. Reducing frame rate has been shown to be an effective and acceptable means of reducing transmission bandwidth of dynamic video imagery sent from Space Station Freedom (SSF) to ground scientists. Three responses were recorded per trial: time to complete the focus adjustment, number of changes of focus direction, and subjective rating of final image quality. It was found that: the average time to complete the focus setting increases from 4.5 sec at 30 fps to 7.9 sec at 1.5 fps (statistical probability = 1.2 x 10(exp -7)); there is no significant difference in the number of changes in the direction of focus adjustment across these frame rates; and there is no significant change in subjectively determined final image quality across these frame rates. These data can be used to help pre-plan future remote optical-focus operations on SSF.

  3. Effects of a Reduced Time-Out Interval on Compliance with the Time-Out Instruction

    ERIC Educational Resources Information Center

    Donaldson, Jeanne M.; Vollmer, Timothy R.; Yakich, Theresa M.; Van Camp, Carole

    2013-01-01

    Time-out is a negative punishment procedure that parents and teachers commonly use to reduce problem behavior; however, specific time-out parameters have not been evaluated adequately. One parameter that has received relatively little attention in the literature is the mode of administration (verbal or physical) of time-out. In this study, we…

  4. Reducing the Time and Cost of Testing Engines

    NASA Technical Reports Server (NTRS)

    2004-01-01

    Producing a new aircraft engine currently costs approximately $1 billion, with 3 years of development time for a commercial engine and 10 years for a military engine. The high development time and cost make it extremely difficult to transition advanced technologies for cleaner, quieter, and more efficient new engines. To reduce this time and cost, NASA created a vision for the future where designers would use high-fidelity computer simulations early in the design process in order to resolve critical design issues before building the expensive engine hardware. To accomplish this vision, NASA's Glenn Research Center initiated a collaborative effort with the aerospace industry and academia to develop its Numerical Propulsion System Simulation (NPSS), an advanced engineering environment for the analysis and design of aerospace propulsion systems and components. Partners estimate that using NPSS has the potential to dramatically reduce the time, effort, and expense necessary to design and test jet engines by generating sophisticated computer simulations of an aerospace object or system. These simulations will permit an engineer to test various design options without having to conduct costly and time-consuming real-life tests. By accelerating and streamlining the engine system design analysis and test phases, NPSS facilitates bringing the final product to market faster. NASA's NPSS Version (V)1.X effort was a task within the Agency s Computational Aerospace Sciences project of the High Performance Computing and Communication program, which had a mission to accelerate the availability of high-performance computing hardware and software to the U.S. aerospace community for its use in design processes. The technology brings value back to NASA by improving methods of analyzing and testing space transportation components.

  5. Quiet Spike(TradeMark) Build-up Ground Vibration Testing Approach

    NASA Technical Reports Server (NTRS)

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

    2007-01-01

    Flight tests of Gulfstream Aerospace Corporation s Quiet Spike(TradeMark) hardware were recently completed on the NASA Dryden Flight Research Center F-15B airplane. NASA Dryden uses a modified F-15B airplane as a testbed aircraft to cost-effectively fly flight research experiments that are typically mounted underneath the F-15B airplane, along the fuselage centerline. For the Quiet Spike(TradeMark) experiment, however, instead of a centerline mounting, a relatively long forward-pointing boom was attached to the radar bulkhead of the F-15B airplane. The Quiet Spike(TradeMark) experiment is a stepping-stone to airframe structural morphing technologies designed to mitigate the sonic-boom strength of business jets over land. The Quiet Spike(TradeMark) boom is a concept in which an aircraft s noseboom would be extended prior to supersonic acceleration. This morphing effectively lengthens the aircraft, thus reducing the peak sonic-boom amplitude, but is also expected to partition the otherwise strong bow shock into a series of reduced-strength, noncoalescing shocklets. Prior to flying the Quiet Spike(TradeMark) experiment on the F-15B airplane several ground vibration tests were required to understand the Quiet Spike(TradeMark) modal characteristics and coupling effects with the F-15B airplane. However, due to the flight hardware availability and compressed schedule requirements, a "traditional" ground vibration test of the mated F-15B Quiet Spike(TradeMark) ready-for- flight configuration did not leave sufficient time available for the finite element model update and flutter analyses before flight testing. Therefore, a "nontraditional" ground vibration testing approach was taken. This paper provides an overview of each phase of the "nontraditional" ground vibration testing completed for the Quiet Spike(TradeMark) project which includes the test setup details, instrumentation layout, and modal results obtained in support of the structural dynamic modeling and flutter

  6. Reducing neural network training time with parallel processing

    NASA Technical Reports Server (NTRS)

    Rogers, James L., Jr.; Lamarsh, William J., II

    1995-01-01

    Obtaining optimal solutions for engineering design problems is often expensive because the process typically requires numerous iterations involving analysis and optimization programs. Previous research has shown that a near optimum solution can be obtained in less time by simulating a slow, expensive analysis with a fast, inexpensive neural network. A new approach has been developed to further reduce this time. This approach decomposes a large neural network into many smaller neural networks that can be trained in parallel. Guidelines are developed to avoid some of the pitfalls when training smaller neural networks in parallel. These guidelines allow the engineer: to determine the number of nodes on the hidden layer of the smaller neural networks; to choose the initial training weights; and to select a network configuration that will capture the interactions among the smaller neural networks. This paper presents results describing how these guidelines are developed.

  7. Neuronal Spike Trains and Stochastic Point Processes

    PubMed Central

    Perkel, Donald H.; Gerstein, George L.; Moore, George P.

    1967-01-01

    In a growing class of neurophysiological experiments, the train of impulses (“spikes”) produced by a nerve cell is subjected to statistical treatment involving the time intervals between spikes. The statistical techniques available for the analysis of single spike trains are described and related to the underlying mathematical theory, that of stochastic point processes, i.e., of stochastic processes whose realizations may be described as series of point events occurring in time, separated by random intervals. For single stationary spike trains, several orders of complexity of statistical treatment are described; the major distinction is that between statistical measures that depend in an essential way on the serial order of interspike intervals and those that are order-independent. The interrelations among the several types of calculations are shown, and an attempt is made to ameliorate the current nomenclatural confusion in this field. Applications, interpretations, and potential difficulties of the statistical techniques are discussed, with special reference to types of spike trains encountered experimentally. Next, the related types of analysis are described for experiments which involve repeated presentations of a brief, isolated stimulus. Finally, the effects of nonstationarity, e.g. long-term changes in firing rate, on the various statistical measures are discussed. Several commonly observed patterns of spike activity are shown to be differentially sensitive to such changes. A companion paper covers the analysis of simultaneously observed spike trains. PMID:4292791

  8. Millisecond solar radio spikes observed at 1420 MHz

    NASA Astrophysics Data System (ADS)

    Dabrowski, B. P.; Kus, A. J.

    We present results from observations of narrowband solar millisecond radio spikes at 1420 MHz. Observing data were collected between February 2000 and December 2001 with the 15-m radio telescope at the Centre for Astronomy Nicolaus Copernicus University in Torun, Poland, equipped with a radio spectrograph that covered the 1352-1490 MHz frequency band. The radio spectrograph has 3 MHz frequency resolution and 80 microsecond time resolution. We analyzed the individual radio spike duration, bandwidth and rate of frequency drift. A part of the observed spikes showed well-outlined subtle structures. On dynamic radio spectrograms of the investigated events we notice complex structures formed by numerous individual spikes known as chains of spikes and distinctly different structure of columns. Positions of active regions connected with radio spikes emission were investigated. It turns out that most of them are located near the center of the solar disk, suggesting strong beaming of the spikes emission.

  9. Analyzing multiple spike trains with nonparametric Granger causality.

    PubMed

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

    2009-08-01

    Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation. PMID:19137420

  10. Production of a microcapsule agent of chromate-reducing Lysinibacillus fusiformis ZC1 and its application in remediation of chromate-spiked soil.

    PubMed

    Huang, Jun; Li, Jingxin; Wang, Gejiao

    2016-01-01

    Lysinibacillus fusiformis ZC1 is an efficient Cr(VI)-reducing bacterium that can transform the toxic and soluble chromate [Cr(VI)] form to the less toxic and precipitated chromite form [Cr(III)]. As such, this strain might be applicable for bioremediation of Cr(VI) in soil by reducing its bioavailability. The study objective was to prepare a microcapsule agent of strain ZC1 for bioremediation of Cr(VI)-contaminated soil. Using a single-factor orthogonal array design, the optimal fermentation medium was obtained and consisted of 6 g/L corn flour, 12 g/L soybean flour, 8 g/L NH4Cl and 6 g/L CaCl2. After enlarged fermentation, the cell and spore densities were 5.9 × 10(9) and 1.7 × 10(8) cfu/mL, respectively. The fermentation products were collected and embedded with 1 % gum arabic and 1 % sorbitol as the microcapsule carriers and were subsequently spray-dried. Strain ZC1 exhibited viable cell counts of (3.6 ± 0.44) × 10(10) cfu/g dw after 50-day storage at room temperature. In simulated soil bioremediation experiments, 67 % of Cr(VI) was reduced in 5 days with the inoculation of this microcapsule agent, and the Cr(VI) concentration was below the soil Cr(VI) standard level. The results demonstrated that the microcapsule agent of strain ZC1 is efficient for bioremediation of Cr(VI)-contaminated soil. PMID:27218011

  11. Test statistics for the identification of assembly neurons in parallel spike trains.

    PubMed

    Picado Muiño, David; Borgelt, Christian

    2015-01-01

    In recent years numerous improvements have been made in multiple-electrode recordings (i.e., parallel spike-train recordings) and spike sorting to the extent that nowadays it is possible to monitor the activity of up to hundreds of neurons simultaneously. Due to these improvements it is now potentially possible to identify assembly activity (roughly understood as significant synchronous spiking of a group of neurons) from these recordings, which-if it can be demonstrated reliably-would significantly improve our understanding of neural activity and neural coding. However, several methodological problems remain when trying to do so and, among them, a principal one is the combinatorial explosion that one faces when considering all potential neuronal assemblies, since in principle every subset of the recorded neurons constitutes a candidate set for an assembly. We present several statistical tests to identify assembly neurons (i.e., neurons that participate in a neuronal assembly) from parallel spike trains with the aim of reducing the set of neurons to a relevant subset of them and this way ease the task of identifying neuronal assemblies in further analyses. These tests are an improvement of those introduced in the work by Berger et al. (2010) based on additional features like spike weight or pairwise overlap and on alternative ways to identify spike coincidences (e.g., by avoiding time binning, which tends to lose information). PMID:25866503

  12. Macroscopic Description for Networks of Spiking Neurons

    NASA Astrophysics Data System (ADS)

    Montbrió, Ernest; Pazó, Diego; Roxin, Alex

    2015-04-01

    A major goal of neuroscience, statistical physics, and nonlinear dynamics is to understand how brain function arises from the collective dynamics of networks of spiking neurons. This challenge has been chiefly addressed through large-scale numerical simulations. Alternatively, researchers have formulated mean-field theories to gain insight into macroscopic states of large neuronal networks in terms of the collective firing activity of the neurons, or the firing rate. However, these theories have not succeeded in establishing an exact correspondence between the firing rate of the network and the underlying microscopic state of the spiking neurons. This has largely constrained the range of applicability of such macroscopic descriptions, particularly when trying to describe neuronal synchronization. Here, we provide the derivation of a set of exact macroscopic equations for a network of spiking neurons. Our results reveal that the spike generation mechanism of individual neurons introduces an effective coupling between two biophysically relevant macroscopic quantities, the firing rate and the mean membrane potential, which together govern the evolution of the neuronal network. The resulting equations exactly describe all possible macroscopic dynamical states of the network, including states of synchronous spiking activity. Finally, we show that the firing-rate description is related, via a conformal map, to a low-dimensional description in terms of the Kuramoto order parameter, called Ott-Antonsen theory. We anticipate that our results will be an important tool in investigating how large networks of spiking neurons self-organize in time to process and encode information in the brain.

  13. How a short stay unit can reduce children's waiting times.

    PubMed

    Gray, Constance; Christensen, Martin

    2016-06-01

    Admitting children to emergency departments (EDs) often places them in an environment better suited to the treatment of adult patients. These children are often triaged and treated as adults, resulting in children being given the wrong triage categories and having their treatment delayed. EDs have problems giving drugs to children, staff are unfamiliar with children's emergency care, and children find EDs frightening. A paediatric emergency short stay unit (PESSU) was opened at Caboolture Hospital, Queensland, Australia, in January 2014. Admission to the PESSU has significantly reduced waiting times for children arriving at the ED and enabled specialist nursing and medical care to be provided quickly. This has been supported by the development of the paediatric flow nurse role ( Gray et al 2016 ). PMID:27266752

  14. Remotely operated guideposts reduce drilling time and costs

    SciTech Connect

    Watkins, S.S.; Beato, C.L. ); Vetter, V.H. )

    1990-03-01

    This paper reports remotely operated guideposts used to establish, release, and re-establish guidelines for a template installation in 1,758 ft (536 m) of water in the Gulf of Mexico. The guideposts were used to reduce the drilling-template weight, to improve accessibility of the remotely operated vehicle (ROV) around the well slots, and to reposition the blowout preventer (BOP) stack on a new well without tripping the stack to the surface. Before field installation, procedures were developed and the guideposts and running tools were function tested. Wet tests verified the reliability of the ROV and guidepost interface. This testing contributed to the successful use of guideposts. Drilling-template costs dropped significantly. The approach helped save about 36 days of drilling time. The guideposts were also used to tie back the tendons and production risers for the tension-leg well platform (TLWP).

  15. Spikes in quantum trajectories

    NASA Astrophysics Data System (ADS)

    Tilloy, Antoine; Bauer, Michel; Bernard, Denis

    2015-11-01

    A quantum system subjected to a strong continuous monitoring undergoes quantum jumps. This very-well-known fact hides a neglected subtlety: sharp scale-invariant fluctuations invariably decorate the jump process, even in the limit where the measurement rate is very large. This article is devoted to the quantitative study of these remaining fluctuations, which we call spikes, and to a discussion of their physical status. We start by introducing a classical model where the origin of these fluctuations is more intuitive, and then jump to the quantum realm where their existence is less intuitive. We compute the exact distribution of the spikes for a continuously monitored qubit. We conclude by discussing their physical and operational relevance.

  16. Simultaneous recording of brain extracellular glucose, spike and local field potential in real time using an implantable microelectrode array with nano-materials

    NASA Astrophysics Data System (ADS)

    Wei, Wenjing; Song, Yilin; Fan, Xinyi; Zhang, Song; Wang, Li; Xu, Shengwei; Wang, Mixia; Cai, Xinxia

    2016-03-01

    Glucose is the main substrate for neurons in the central nervous system. In order to efficiently characterize the brain glucose mechanism, it is desirable to determine the extracellular glucose dynamics as well as the corresponding neuroelectrical activity in vivo. In the present study, we fabricated an implantable microelectrode array (MEA) probe composed of platinum electrochemical and electrophysiology microelectrodes by standard micro electromechanical system (MEMS) processes. The MEA probe was modified with nano-materials and implanted in a urethane-anesthetized rat for simultaneous recording of striatal extracellular glucose, local field potential (LFP) and spike on the same spatiotemporal scale when the rat was in normoglycemia, hypoglycemia and hyperglycemia. During these dual-mode recordings, we observed that increase of extracellular glucose enhanced the LFP power and spike firing rate, while decrease of glucose had an opposite effect. This dual mode MEA probe is capable of examining specific spatiotemporal relationships between electrical and chemical signaling in the brain, which will contribute significantly to improve our understanding of the neuron physiology.

  17. Simultaneous recording of brain extracellular glucose, spike and local field potential in real time using an implantable microelectrode array with nano-materials.

    PubMed

    Wei, Wenjing; Song, Yilin; Fan, Xinyi; Zhang, Song; Wang, Li; Xu, Shengwei; Wang, Mixia; Cai, Xinxia

    2016-03-18

    Glucose is the main substrate for neurons in the central nervous system. In order to efficiently characterize the brain glucose mechanism, it is desirable to determine the extracellular glucose dynamics as well as the corresponding neuroelectrical activity in vivo. In the present study, we fabricated an implantable microelectrode array (MEA) probe composed of platinum electrochemical and electrophysiology microelectrodes by standard micro electromechanical system (MEMS) processes. The MEA probe was modified with nano-materials and implanted in a urethane-anesthetized rat for simultaneous recording of striatal extracellular glucose, local field potential (LFP) and spike on the same spatiotemporal scale when the rat was in normoglycemia, hypoglycemia and hyperglycemia. During these dual-mode recordings, we observed that increase of extracellular glucose enhanced the LFP power and spike firing rate, while decrease of glucose had an opposite effect. This dual mode MEA probe is capable of examining specific spatiotemporal relationships between electrical and chemical signaling in the brain, which will contribute significantly to improve our understanding of the neuron physiology. PMID:26871752

  18. Development and validation of a spike detection and classification algorithm aimed at implementation on hardware devices.

    PubMed

    Biffi, E; Ghezzi, D; Pedrocchi, A; Ferrigno, G

    2010-01-01

    Neurons cultured in vitro on MicroElectrode Array (MEA) devices connect to each other, forming a network. To study electrophysiological activity and long term plasticity effects, long period recording and spike sorter methods are needed. Therefore, on-line and real time analysis, optimization of memory use and data transmission rate improvement become necessary. We developed an algorithm for amplitude-threshold spikes detection, whose performances were verified with (a) statistical analysis on both simulated and real signal and (b) Big O Notation. Moreover, we developed a PCA-hierarchical classifier, evaluated on simulated and real signal. Finally we proposed a spike detection hardware design on FPGA, whose feasibility was verified in terms of CLBs number, memory occupation and temporal requirements; once realized, it will be able to execute on-line detection and real time waveform analysis, reducing data storage problems. PMID:20300592

  19. Feedback stabilizes propagation of synchronous spiking in cortical neural networks.

    PubMed

    Moldakarimov, Samat; Bazhenov, Maxim; Sejnowski, Terrence J

    2015-02-24

    Precisely timed action potentials related to stimuli and behavior have been observed in the cerebral cortex. However, information carried by the precise spike timing has to propagate through many cortical areas, and noise could disrupt millisecond precision during the transmission. Previous studies have demonstrated that only strong stimuli that evoke a large number of spikes with small dispersion of spike times can propagate through multilayer networks without degrading the temporal precision. Here we show that feedback projections can increase the number of spikes in spike volleys without degrading their temporal precision. Feedback also increased the range of spike volleys that can propagate through multilayer networks. Our work suggests that feedback projections could be responsible for the reliable propagation of information encoded in spike times through cortex, and thus could serve as an attentional mechanism to regulate the flow of information in the cortex. Feedback projections may also participate in generating spike synchronization that is engaged in cognitive behaviors by the same mechanisms described here for spike propagation. PMID:25675531

  20. Mikkelson sweep/spike chisel plow shovel

    SciTech Connect

    Not Available

    1992-01-01

    Profitability comparisons are reported between the Mikkelson Sweep/Spike Chisel Plow Shovel standard sweeps. This evaluation covers the first year of testing of the new Sweep/Spike design. The data are not averaged over treatments due to significant interaction between treatments and environmental factors. The cost of fuel, fall and spring, to perform the various treatments ranged from $1.27 to $3.36 per acre. Use of the sweep/spike shovel always reduced total fuel cost. Savings varied from $0.11 to $0.71 per acre depending on prior treatment. This means there will be money saved, to off-set expenses, when converting present chisel plows or for special options on new chisel plows, needed for use of the sweep/spike shovel. A summary of 1991--1992 energy measurements. They indicate that more power will be required to pull a chisel plow equipped with the sweep/spike shovel. A larger tractor, narrower chisel plow and/or slower speed will be required to avoid the wheel slippage problems encountered on soft or wet field surfaces.

  1. The Role of Spike Temporal Latencies in Artificial Olfaction

    NASA Astrophysics Data System (ADS)

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

    2011-09-01

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

  2. Competitive STDP-based spike pattern learning.

    PubMed

    Masquelier, Timothée; Guyonneau, Rudy; Thorpe, Simon J

    2009-05-01

    Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in equally dense distracter spike trains can be robustly detected and learned by a single neuron equipped with spike-timing-dependent plasticity (STDP) (Masquelier, Guyonneau, & Thorpe, 2008). To be precise, the neuron becomes selective to successive coincidences of the pattern. Here we extend this scheme to a more realistic scenario with multiple repeating patterns and multiple STDP neurons "listening" to the incoming spike trains. These "listening" neurons are in competition: as soon as one fires, it strongly inhibits the others through lateral connections (one-winner-take-all mechanism). This tends to prevent the neurons from learning the same (parts of the) repeating patterns, as shown in simulations. Instead, the population self-organizes, trying to cover the different patterns or coding one pattern by the successive firings of several neurons, and a powerful distributed coding scheme emerges. Taken together, these results illustrate how the brain could easily encode and decode information in the spike times, a theory referred to as temporal coding, and how STDP could play a key role by detecting repeating patterns and generating selective response to them. PMID:19718815

  3. Reduced Scan Time 3D FLAIR using Modulated Inversion and Repetition Time

    PubMed Central

    Gai, Neville D.; Butman, John A.

    2014-01-01

    Purpose To design and evaluate a new reduced scan time 3D FLuid Attenuated Inversion Recovery (FLAIR) sequence. Materials and Methods The 3D FLAIR sequence was modified so that the repetition time was modulated in a predetermined smooth fashion (3D mFLAIR). Inversion times were adjusted accordingly to maintain CSF suppression. Simulations were performed to determine SNR for gray matter (GM), white matter (WM) and CSF. Fourteen volunteers were imaged using the modified and product sequence. SNR measurements were performed in GM, WM and CSF. Mean value and the 95% confidence interval ([CI]) were assessed. Scan time for the 3D FLAIR and 3D mFLAIR sequences was measured. Results There was no statistically significant difference in the SNR measured in GM (P value = 0.5; mean SNR = 42.8 [CI]: 38.2-45.5 vs 42.2 [CI]: 38.3-46.1 for 3D FLAIR and 3D mFLAIR, respectively) and WM (P value = 0.25; mean SNR = 32.1 [CI]: 30.3-33.8 vs 32.9 [CI]: 31.1-34.7). Scan time reduction greater than 30% was achieved for the given parameter set with the 3D mFLAIR sequence. Conclusion Scan time for 3D FLAIR can be effectively reduced by modulating repetition and inversion time in a predetermined fashion while maintaining the SNR and CNR of a constant TR sequence. PMID:24979311

  4. Spiking Neurons for Analysis of Patterns

    NASA Technical Reports Server (NTRS)

    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

  5. Spike Code Flow in Cultured Neuronal Networks.

    PubMed

    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. PMID:27217825

  6. Spike Code Flow in Cultured Neuronal Networks

    PubMed Central

    Tamura, Shinichi; Nishitani, Yoshi; 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. PMID:27217825

  7. When self-affirmations reduce defensiveness: timing is key.

    PubMed

    Critcher, Clayton R; Dunning, David; Armor, David A

    2010-07-01

    Research on self-affirmation has shown that simple reminders of self-integrity reduce people's tendency to respond defensively to threat. Recent research has suggested it is irrelevant whether the self-affirmation exercise takes place before or after the threat or the individual's defensive response to it, supposedly because the meaning of threats is continuously reprocessed. However, four experiments revealed that affirmations may be effective only when introduced prior to the initiation of a defensive response. Affirmations introduced before threatening feedback reduced defensive responding; affirming after a threat was effective in reducing defensiveness only if the defensive conclusion had yet to be reached. Even though threats may activate a defensive motivation, the authors' results suggest that defensive responses may not be spontaneous and may be prompted only when suggested by the dependent measures themselves. This explains why some affirmations positioned after threats are effective in reducing defensiveness. Implications for self-affirmation theory are discussed. PMID:20505163

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

    PubMed

    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. PMID:27065785

  9. Solving Constraint Satisfaction Problems with Networks of Spiking Neurons

    PubMed Central

    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. PMID:27065785

  10. Radioxenon spiked air.

    PubMed

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

    2015-12-01

    Four of the radioactive xenon isotopes ((131m)Xe, (133m)Xe, (133)Xe and (135)Xe) with half-lives ranging from 9 h to 12 days are produced from nuclear fission and can be detected from days to weeks following their production and release. Being inert gases, they are readily transported through the atmosphere. Sources for release of radioactive xenon isotopes include operating nuclear reactors via leaks in fuel rods, medical isotope production facilities, and nuclear weapons' detonations. They are not normally released from fuel reprocessing due to the short half-lives. The Comprehensive Nuclear-Test-Ban Treaty has led to creation of the International Monitoring System. The International Monitoring System, when fully implemented, will consist of one component with 40 stations monitoring radioactive xenon around the globe. Monitoring these radioactive xenon isotopes is important to the Comprehensive Nuclear-Test-Ban Treaty in determining whether a seismically detected event is or is not a nuclear detonation. A variety of radioactive xenon quality control check standards, quantitatively spiked into various gas matrices, could be used to demonstrate that these stations are operating on the same basis in order to bolster defensibility of data across the International Monitoring System. This paper focuses on Idaho National Laboratory's capability to produce three of the xenon isotopes in pure form and the use of the four xenon isotopes in various combinations to produce radioactive xenon spiked air samples that could be subsequently distributed to participating facilities. PMID:26318775

  11. Radioxenon spiked air

    DOE PAGESBeta

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

    2015-08-27

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

  12. Spiking models for level-invariant encoding.

    PubMed

    Brette, Romain

    2011-01-01

    Levels of ecological sounds vary over several orders of magnitude, but the firing rate and membrane potential of a neuron are much more limited in range. In binaural neurons of the barn owl, tuning to interaural delays is independent of level differences. Yet a monaural neuron with a fixed threshold should fire earlier in response to louder sounds, which would disrupt the tuning of these neurons. How could spike timing be independent of input level? Here I derive theoretical conditions for a spiking model to be insensitive to input level. The key property is a dynamic change in spike threshold. I then show how level invariance can be physiologically implemented, with specific ionic channel properties. It appears that these ingredients are indeed present in monaural neurons of the sound localization pathway of birds and mammals. PMID:22291634

  13. Nonsmooth dynamics in spiking neuron models

    NASA Astrophysics Data System (ADS)

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

    2012-11-01

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

  14. Solar Flare Hard X-ray Spikes Observed by RHESSI: a Statistical Study

    NASA Astrophysics Data System (ADS)

    Cheng, Jianxia; Qiu, J.; Ding, M.; Wang, H.

    2013-07-01

    Hard X-ray (HXR) spikes refer to fine time structures on timescales of seconds to milliseconds in high-energy HXR emission profiles during solar flare eruptions. We present a preliminary statistical investigation of temporal and spectral properties of HXR spikes. Using a three-sigma spike selection rule, we detected 184 spikes in 94 out of 322 flares with significant counts at given photon energies, which were detected from demodulated HXR light curves obtained by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). About one fifth of these spikes are also detected at photon energies higher than 100 keV. The statistical properties of the spikes are as follows. (1) HXR spikes are produced in both impulsive flares and long-duration flares with nearly the same occurrence rates. Ninety percent of the spikes occur during the rise phase of the flares, and about 70% occur around the peak times of the flares. (2) The time durations of the spikes vary from 0.2 to 2 s, with the mean being 1.0 s, which is not dependent on photon energies. The spikes exhibit symmetric time profiles with no significant difference between rise and decay times.(3) Among the most energetic spikes, nearly all of them have harder count spectra than their underlying slow-varying components. There is also a weak indication that spikes exhibiting time lags in high-energy emissions tend to have harder spectra than spikes with time lags in low-energy emissions.

  15. The Structure and Precision of Retinal Spike Trains

    NASA Astrophysics Data System (ADS)

    Berry, Michael J.; Warland, David K.; Meister, Markus

    1997-05-01

    Assessing the reliability of neuronal spike trains is fundamental to an understanding of the neural code. We measured the reproducibility of retinal responses to repeated visual stimuli. In both tiger salamander and rabbit, the retinal ganglion cells responded to random flicker with discrete, brief periods of firing. For any given cell, these firing events covered only a small fraction of the total stimulus time, often less than 5%. Firing events were very reproducible from trial to trial: the timing jitter of individual spikes was as low as 1 msec, and the standard deviation in spike count was often less than 0.5 spikes. Comparing the precision of spike timing to that of the spike count showed that the timing of a firing event conveyed several times more visual information than its spike count. This sparseness and precision were general characteristics of ganglion cell responses, maintained over the broad ensemble of stimulus waveforms produced by random flicker, and over a range of contrasts. Thus, the responses of retinal ganglion cells are not properly described by a firing probability that varies continuously with the stimulus. Instead, these neurons elicit discrete firing events that may be the fundamental coding symbols in retinal spike trains.

  16. Kv7 channels regulate pairwise spiking covariability in health and disease.

    PubMed

    Ocker, Gabriel Koch; Doiron, Brent

    2014-07-15

    Low-threshold M currents are mediated by the Kv7 family of potassium channels. Kv7 channels are important regulators of spiking activity, having a direct influence on the firing rate, spike time variability, and filter properties of neurons. How Kv7 channels affect the joint spiking activity of populations of neurons is an important and open area of study. Using a combination of computational simulations and analytic calculations, we show that the activation of Kv7 conductances reduces the covariability between spike trains of pairs of neurons driven by common inputs. This reduction is beyond that explained by the lowering of firing rates and involves an active cancellation of common fluctuations in the membrane potentials of the cell pair. Our theory shows that the excess covariance reduction is due to a Kv7-induced shift from low-pass to band-pass filtering of the single neuron spike train response. Dysfunction of Kv7 conductances is related to a number of neurological diseases characterized by both elevated firing rates and increased network-wide correlations. We show how changes in the activation or strength of Kv7 conductances give rise to excess correlations that cannot be compensated for by synaptic scaling or homeostatic modulation of passive membrane properties. In contrast, modulation of Kv7 activation parameters consistent with pharmacological treatments for certain hyperactivity disorders can restore normal firing rates and spiking correlations. Our results provide key insights into how regulation of a ubiquitous potassium channel class can control the coordination of population spiking activity. PMID:24790164

  17. Masking of short stimuli by noises with spiked spectra: II. Time summation and frequency selectivity of hearing in a narrow frequency range

    NASA Astrophysics Data System (ADS)

    Rimskaya-Korsakova, L. K.; Lalayants, M. R.; Supin, A. Ya.; Tavartkiladze, G. A.

    2011-03-01

    The thresholds of masking of short high-frequency pulses with either different durations (1.25-25 ms) and similar central frequency or different central frequencies (3.6-4.4 kHz) but similar durations were measured to reveal manifestations of the properties of peripheral encoding in auditory perception. Noises with a spiked amplitude spectrum structure were used as maskers. The central frequency and the frequency band of a masker were 4 and 1 kHz, respectively. The central frequencies of a stimulus and a masker being equal, the noise the central frequency of which coincided with the frequency corresponding to a dip of an indented spectrum was called an off(rip)-frequency masker. Owing to the off(rip)-masker, stimuli-induced masking thresholds were formed taking into account excitation in a narrow region of a basila membrane and auditory nerve fibers with characteristic frequencies from a narrow range. High-frequency pulses with an envelope in the form of the Gaussian function and sinusoidal filling were used as stimuli. At masker levels of 30 dB above the auditory threshold, frequencies of off(rip)-masker spectra spikes of 500-2000 Hz, and a central stimulus frequency of 4 kHz, the thresholds of tonal stimuli (25 ms in duration) masking in two out of three probationers were higher than the thresholds of masking of compact stimuli (1.25 ms in duration). In the third probationer, on the contrary, the thresholds of tonal stimuli masking were lower than the thresholds of compact stimuli masking. At masker levels of 50 dB, individual threshold differences disappeared. The obtained results were interpreted in the context of implementation of different methods of auditory encoding of the intensity. The methods were based on either the average frequency of auditory nerve pulsations or the number of fibers participating in the response. The interpretation was also carried out in the context of revealing manifestations of nonlinear properties of basila membrane displacements

  18. Large seals fabricated from small segments reduce procurement lead time

    NASA Technical Reports Server (NTRS)

    Daniels, C. M.; Hanes, V. D.

    1966-01-01

    Large diameter seals are fabricated from narrow strip stock welded in segments to form a complete ring. This technique could be used to reduce the cost of critical, large diameter seals in the heating and ventilating industry, petrochemical industry, and marine fabrication industry.

  19. Hierarchical spike clustering analysis for investigation of interneuron heterogeneity.

    PubMed

    Boehlen, Anne; Heinemann, Uwe; Henneberger, Christian

    2016-04-21

    Action potentials represent the output of a neuron. Especially interneurons display a variety of discharge patterns ranging from regular action potential firing to prominent spike clustering or stuttering. The mechanisms underlying this heterogeneity remain incompletely understood. We established hierarchical cluster analysis of spike trains as a measure of spike clustering. A clustering index was calculated from action potential trains recorded in the whole-cell patch clamp configuration from hippocampal (CA1, stratum radiatum) and entorhinal (medial entorhinal cortex, layer 2) interneurons in acute slices and simulated data. Prominent, region-dependent, but also variable spike clustering was detected using this measure. Further analysis revealed a strong positive correlation between spike clustering and membrane potentials oscillations but an inverse correlation with neuronal resonance. Furthermore, clustering was more pronounced when the balance between fast-activating K(+) currents, assessed by the spike repolarisation time, and hyperpolarization-activated currents, gauged by the size of the sag potential, was shifted in favour of fast K(+) currents. Simulations of spike clustering confirmed that variable ratios of fast K(+) and hyperpolarization-activated currents could underlie different degrees of spike clustering and could thus be crucial for temporally structuring interneuron spike output. PMID:26987719

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

    PubMed Central

    Swindale, Nicholas V.; Spacek, Martin A.

    2014-01-01

    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 min. 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 (PCA). Hence spike sorting based on least-squares matching to templates may be unreliable. Our methods should be applicable to tetrodes and scalable to larger multi-electrode arrays (MEAs). PMID:24574979

  1. Reducing EnergyPlus Run Time For Code Compliance Tools

    SciTech Connect

    Athalye, Rahul A.; Gowri, Krishnan; Schultz, Robert W.; Glazer, Jason

    2014-09-12

    Integration of the EnergyPlus ™ simulation engine into performance-based code compliance software raises a concern about simulation run time, which impacts timely feedback of compliance results to the user. EnergyPlus annual simulations for proposed and code baseline building models, and mechanical equipment sizing result in simulation run times beyond acceptable limits. This paper presents a study that compares the results of a shortened simulation time period using 4 weeks of hourly weather data (one per quarter), to an annual simulation using full 52 weeks of hourly weather data. Three representative building types based on DOE Prototype Building Models and three climate zones were used for determining the validity of using a shortened simulation run period. Further sensitivity analysis and run time comparisons were made to evaluate the robustness and run time savings of using this approach. The results of this analysis show that the shortened simulation run period provides compliance index calculations within 1% of those predicted using annual simulation results, and typically saves about 75% of simulation run time.

  2. Radioxenon spiked air

    SciTech Connect

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

    2015-08-27

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

  3. Description of a small commutation spike filter for d. c. magnet power supplies

    SciTech Connect

    Visser, A.T.

    1990-10-01

    This note describes a small spike filter used to remove SCR commutation spikes from the dc output voltage of magnet power supplies. Removal of these spikes reduces the noise problems in electronic detectors installed in the same area. The filter is small enough to be mounted inside a power supply.

  4. A new class of metrics for spike trains.

    PubMed

    Rusu, Cătălin V; Florian, Răzvan V

    2014-02-01

    The distance between a pair of spike trains, quantifying the differences between them, can be measured using various metrics. Here we introduce a new class of spike train metrics, inspired by the Pompeiu-Hausdorff distance, and compare them with existing metrics. Some of our new metrics (the modulus-metric and the max-metric) have characteristics that are qualitatively different from those of classical metrics like the van Rossum distance or the Victor and Purpura distance. The modulus-metric and the max-metric are particularly suitable for measuring distances between spike trains where information is encoded in bursts, but the number and the timing of spikes inside a burst do not carry information. The modulus-metric does not depend on any parameters and can be computed using a fast algorithm whose time depends linearly on the number of spikes in the two spike trains. We also introduce localized versions of the new metrics, which could have the biologically relevant interpretation of measuring the differences between spike trains as they are perceived at a particular moment in time by a neuron receiving these spike trains. PMID:24206385

  5. The time course of natural scene perception with reduced attention.

    PubMed

    Groen, Iris I A; Ghebreab, Sennay; Lamme, Victor A F; Scholte, H Steven

    2016-02-01

    Attention is thought to impose an informational bottleneck on vision by selecting particular information from visual scenes for enhanced processing. Behavioral evidence suggests, however, that some scene information is extracted even when attention is directed elsewhere. Here, we investigated the neural correlates of this ability by examining how attention affects electrophysiological markers of scene perception. In two electro-encephalography (EEG) experiments, human subjects categorized real-world scenes as manmade or natural (full attention condition) or performed tasks on unrelated stimuli in the center or periphery of the scenes (reduced attention conditions). Scene processing was examined in two ways: traditional trial averaging was used to assess the presence of a categorical manmade/natural distinction in event-related potentials, whereas single-trial analyses assessed whether EEG activity was modulated by scene statistics that are diagnostic of naturalness of individual scenes. The results indicated that evoked activity up to 250 ms was unaffected by reduced attention, showing intact categorical differences between manmade and natural scenes and strong modulations of single-trial activity by scene statistics in all conditions. Thus initial processing of both categorical and individual scene information remained intact with reduced attention. Importantly, however, attention did have profound effects on later evoked activity; full attention on the scene resulted in prolonged manmade/natural differences, increased neural sensitivity to scene statistics, and enhanced scene memory. These results show that initial processing of real-world scene information is intact with diminished attention but that the depth of processing of this information does depend on attention. PMID:26609116

  6. Increased commuting to school time reduces sleep duration in adolescents.

    PubMed

    Pereira, Erico Felden; Moreno, Claudia; Louzada, Fernando Mazzilli

    2014-02-01

    Active travel to school has been referred to as one way of increasing the level of daily physical exercise, but the actual impacts on student's general health are not clear. Recently, a possible association between active travel to school and the duration of sleep was suggested. Thus, the aim was of this study to investigate the associations between the type of transportation and travel time to school, the time in bed and sleepiness in the classroom of high school students. Information on sleeping habits and travel to school of 1126 high school students were analyzed, where 55.1% were girls with an average age of 16.24 (1.39) years old, in Santa Maria Municipality, Rio Grande do Sul, Brazil. Multiple linear regression and adjusted prevalence rates analyses were carried out. The frequency of active travel found was 61.8%. Associations between time in bed, sleepiness in the classroom and the type of transportation (active or passive) were not identified. Nevertheless, the time in bed was inversely associated with the travel time (p = 0.036) and with a phase delay. In the adjusted analysis, active travel was more incident for the students of schools in the suburbs (PR: 1.68; CI: 1.40-2.01) in comparison with the students of schools in the center. Therefore, longer trips were associated with a reduction of sleep duration of morning and night groups. Interventions concerning active travel to school must be carried out cautiously in order not to cause a reduction of the sleeping time. PMID:24152062

  7. Causal Inference and Explaining Away in a Spiking Network

    PubMed Central

    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

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

    PubMed Central

    Mulansky, Mario; Bozanic, Nebojsa

    2015-01-01

    Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface 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. PMID:25744888

  9. The effect of spiked boots on logger safety, productivity and workload.

    PubMed

    Kirk, P; Parker, R

    1994-04-01

    Analysis of 1657 lost-time logging accidents in the New Zealand logging industry (1985-1991) indicates that 17.5% were as a result of slips, trips and falls and a total of 2870 days were lost. Most (56%) of these slipping, tripping and falling accidents occurred in the felling and delimbing phase of the logging operation, where 37% of the workforce are employed. In an attempt to reduce the number of slipping injuries to loggers employed in felling and delimbing, a study of the effectiveness of spike-soled (caulk) boots was undertaken. Four loggers were intensively observed at work, by continuous time-study methods, while wearing their conventional rubber-soled boots and then spike-soled boots. The number of slips, work methods used, physiological workload and productivity were compared for loggers wearing the two footwear types. Results indicated that spike-soled boots were associated with a significant reduction in the frequency of slips and had no adverse effect on work methods, physiological workload or productivity. Spike-soled boots are now being promoted for use by loggers in New Zealand as a simple method to reduce slipping, tripping and falling accidents. PMID:15676957

  10. A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings

    PubMed Central

    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. PMID:23671583

  11. Corticospinal excitability is reduced in a simple reaction time task requiring complex timing.

    PubMed

    Kennefick, Michael; Maslovat, Dana; Chua, Romeo; Carlsen, Anthony N

    2016-07-01

    Increasing the complexity of a movement has been shown to result in longer simple reaction time (RT), which has been attributed to sequencing or timing requirements following the go-signal. However, RT differences may also be due to differences in corticospinal excitability (CE) as previous studies have found an enhanced excitatory state of corticospinal neurons in complex tasks. Transcranial magnetic stimulation (TMS) was used in the present study to probe the excitability of the motor pathway during the simple RT interval for single (simple) versus multiple (complex) key press responses. Premotor RT data indicated that participants responded significantly (p<.001) faster in the simple task compared to the complex task, confirming response complexity was manipulated appropriately. Analysis of the CE data indicated that motor evoked potential (MEP) amplitudes increased with time following the go-signal in both conditions and that MEP amplitudes in the simple task were significantly larger than those in the complex task when evoked within 75ms of movement onset (p=.009). These findings suggest that the rate of increase for initiation-related neural activation is reduced for complex as compared to simple movements, which may partially explain differences in RT. PMID:27064075

  12. Reduced time delay for gravitational waves with dark matter emulators

    NASA Astrophysics Data System (ADS)

    Desai, S.; Kahya, E. O.; Woodard, R. P.

    2008-06-01

    We discuss the implications for gravitational wave detectors of a class of modified gravity theories which dispense with the need for dark matter. These models, which are known as dark matter emulators, have the property that weak gravitational waves couple to the metric that would follow from general relativity without dark matter whereas ordinary particles couple to a combination of the metric and other fields which reproduces the result of general relativity with dark matter. We show that there is an appreciable difference in the Shapiro delays of gravitational waves and photons or neutrinos from the same source, with the gravitational waves always arriving first. We compute the expected time lags for GRB 070201, for SN 1987a and for Sco-X1. We estimate the probable error by taking account of the uncertainty in position, and by using three different dark matter profiles.

  13. Method for reducing response time in sensor measurement.

    PubMed

    Casas, Oscar; Rillo, Francesc I

    2009-08-01

    Transients are present in sensor and instrumentation systems. They are caused by energy transference and are typically modeled as first-order systems. If too long, transients may suppose a critical factor for these systems unless they are analyzed (e.g., in terms of consumption). This work presents a new method for estimating the final value of a first-order system transient. Since this problem may be harmful, mainly to autonomous systems, the method equations are composed of simple operations that can be implemented on microcontrollers or similar interfaces with low computational capacity. The experimental results validate the theoretical models of the method and prove that, when the system coexists with disturbances, it is possible to estimate the final value in around 60% of the time we would have to wait until reaching it, in the worst case scenario. PMID:19725676

  14. Mapping Spikes to Sensations

    PubMed Central

    Stüttgen, Maik C.; Schwarz, Cornelius; Jäkel, Frank

    2011-01-01

    Single-unit recordings conducted during perceptual decision-making tasks have yielded tremendous insights into the neural coding of sensory stimuli. In such experiments, detection or discrimination behavior (the psychometric data) is observed in parallel with spike trains in sensory neurons (the neurometric data). Frequently, candidate neural codes for information read-out are pitted against each other by transforming the neurometric data in some way and asking which code’s performance most closely approximates the psychometric performance. The code that matches the psychometric performance best is retained as a viable candidate and the others are rejected. In following this strategy, psychometric data is often considered to provide an unbiased measure of perceptual sensitivity. It is rarely acknowledged that psychometric data result from a complex interplay of sensory and non-sensory processes and that neglect of these processes may result in misestimating psychophysical sensitivity. This again may lead to erroneous conclusions regarding the adequacy of candidate neural codes. In this review, we first discuss requirements on the neural data for a subsequent neurometric-psychometric comparison. We then focus on different psychophysical tasks for the assessment of detection and discrimination performance and the cognitive processes that may underlie their execution. We discuss further factors that may compromise psychometric performance and how they can be detected or avoided. We believe that these considerations point to shortcomings in our understanding of the processes underlying perceptual decisions, and therefore offer potential for future research. PMID:22084627

  15. Spike processing with a graphene excitable laser.

    PubMed

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

    2016-01-01

    Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved "spiking" of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate a unified platform for spike processing with a graphene-coupled laser system. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation--fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system and also propose and simulate an analogous integrated device. The addition of graphene leads to a number of advantages which stem from its unique properties, including high absorption and fast carrier relaxation. These could lead to significant speed and efficiency improvements in unconventional laser processing devices, and ongoing research on graphene microfabrication promises compatibility with integrated laser platforms. PMID:26753897

  16. Perineuronal Nets Enhance the Excitability of Fast-Spiking Neurons.

    PubMed

    Balmer, Timothy S

    2016-01-01

    Perineuronal nets (PNNs) are specialized complexes of extracellular matrix molecules that surround the somata of fast-spiking neurons throughout the vertebrate brain. PNNs are particularly prevalent throughout the auditory brainstem, which transmits signals with high speed and precision. It is unknown whether PNNs contribute to the fast-spiking ability of the neurons they surround. Whole-cell recordings were made from medial nucleus of the trapezoid body (MNTB) principal neurons in acute brain slices from postnatal day 21 (P21) to P27 mice. PNNs were degraded by incubating slices in chondroitinase ABC (ChABC) and were compared to slices that were treated with a control enzyme (penicillinase). ChABC treatment did not affect the ability of MNTB neurons to fire at up to 1000 Hz when driven by current pulses. However, f-I (frequency-intensity) curves constructed by injecting Gaussian white noise currents superimposed on DC current steps showed that ChABC treatment reduced the gain of spike output. An increase in spike threshold may have contributed to this effect, which is consistent with the observation that spikes in ChABC-treated cells were delayed relative to control-treated cells. In addition, parvalbumin-expressing fast-spiking cortical neurons in >P70 slices that were treated with ChABC also had reduced excitability and gain. The development of PNNs around somata of fast-spiking neurons may be essential for fast and precise sensory transmission and synaptic inhibition in the brain. PMID:27570824

  17. Perineuronal Nets Enhance the Excitability of Fast-Spiking Neurons

    PubMed Central

    2016-01-01

    Perineuronal nets (PNNs) are specialized complexes of extracellular matrix molecules that surround the somata of fast-spiking neurons throughout the vertebrate brain. PNNs are particularly prevalent throughout the auditory brainstem, which transmits signals with high speed and precision. It is unknown whether PNNs contribute to the fast-spiking ability of the neurons they surround. Whole-cell recordings were made from medial nucleus of the trapezoid body (MNTB) principal neurons in acute brain slices from postnatal day 21 (P21) to P27 mice. PNNs were degraded by incubating slices in chondroitinase ABC (ChABC) and were compared to slices that were treated with a control enzyme (penicillinase). ChABC treatment did not affect the ability of MNTB neurons to fire at up to 1000 Hz when driven by current pulses. However, f–I (frequency–intensity) curves constructed by injecting Gaussian white noise currents superimposed on DC current steps showed that ChABC treatment reduced the gain of spike output. An increase in spike threshold may have contributed to this effect, which is consistent with the observation that spikes in ChABC-treated cells were delayed relative to control-treated cells. In addition, parvalbumin-expressing fast-spiking cortical neurons in >P70 slices that were treated with ChABC also had reduced excitability and gain. The development of PNNs around somata of fast-spiking neurons may be essential for fast and precise sensory transmission and synaptic inhibition in the brain. PMID:27570824

  18. Models of Emergency Departments for Reducing Patient Waiting Times

    PubMed Central

    Laskowski, Marek; McLeod, Robert D.; Friesen, Marcia R.; Podaima, Blake W.; Alfa, Attahiru S.

    2009-01-01

    In this paper, we apply both agent-based models and queuing models to investigate patient access and patient flow through emergency departments. The objective of this work is to gain insights into the comparative contributions and limitations of these complementary techniques, in their ability to contribute empirical input into healthcare policy and practice guidelines. The models were developed independently, with a view to compare their suitability to emergency department simulation. The current models implement relatively simple general scenarios, and rely on a combination of simulated and real data to simulate patient flow in a single emergency department or in multiple interacting emergency departments. In addition, several concepts from telecommunications engineering are translated into this modeling context. The framework of multiple-priority queue systems and the genetic programming paradigm of evolutionary machine learning are applied as a means of forecasting patient wait times and as a means of evolving healthcare policy, respectively. The models' utility lies in their ability to provide qualitative insights into the relative sensitivities and impacts of model input parameters, to illuminate scenarios worthy of more complex investigation, and to iteratively validate the models as they continue to be refined and extended. The paper discusses future efforts to refine, extend, and validate the models with more data and real data relative to physical (spatial–topographical) and social inputs (staffing, patient care models, etc.). Real data obtained through proximity location and tracking system technologies is one example discussed. PMID:19572015

  19. Reducing the Time Requirement of k-Means Algorithm

    PubMed Central

    Osamor, Victor Chukwudi; Adebiyi, Ezekiel Femi; Oyelade, Jelilli Olarenwaju; Doumbia, Seydou

    2012-01-01

    Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d. In k-means clustering, we are given a set of n data points in d-dimensional space Rd and an integer k. The problem is to determine a set of k points in Rd, called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this work, we develop a novel k-means algorithm, which is simple but more efficient than the traditional k-means and the recent enhanced k-means. Our new algorithm is based on the recently established relationship between principal component analysis and the k-means clustering. We provided the correctness proof for this algorithm. Results obtained from testing the algorithm on three biological data and six non-biological data (three of these data are real, while the other three are simulated) also indicate that our algorithm is empirically faster than other known k-means algorithms. We assessed the quality of our algorithm clusters against the clusters of a known structure using the Hubert-Arabie Adjusted Rand index (ARIHA). We found that when k is close to d, the quality is good (ARIHA>0.8) and when k is not close to d, the quality of our new k-means algorithm is excellent (ARIHA>0.9). In this paper, emphases are on the reduction of the time requirement of the k-means algorithm and its application to microarray data due to the desire to create a tool for clustering and malaria research. However, the new clustering algorithm can be used for other clustering needs as long as an appropriate measure of distance between the centroids and the members is used. This has been demonstrated in this work on six non-biological data. PMID:23239974

  20. Reducing the time requirement of k-means algorithm.

    PubMed

    Osamor, Victor Chukwudi; Adebiyi, Ezekiel Femi; Oyelade, Jelilli Olarenwaju; Doumbia, Seydou

    2012-01-01

    Traditional k-means and most k-means variants are still computationally expensive for large datasets, such as microarray data, which have large datasets with large dimension size d. In k-means clustering, we are given a set of n data points in d-dimensional space R(d) and an integer k. The problem is to determine a set of k points in R(d), called centers, so as to minimize the mean squared distance from each data point to its nearest center. In this work, we develop a novel k-means algorithm, which is simple but more efficient than the traditional k-means and the recent enhanced k-means. Our new algorithm is based on the recently established relationship between principal component analysis and the k-means clustering. We provided the correctness proof for this algorithm. Results obtained from testing the algorithm on three biological data and six non-biological data (three of these data are real, while the other three are simulated) also indicate that our algorithm is empirically faster than other known k-means algorithms. We assessed the quality of our algorithm clusters against the clusters of a known structure using the Hubert-Arabie Adjusted Rand index (ARI(HA)). We found that when k is close to d, the quality is good (ARI(HA)>0.8) and when k is not close to d, the quality of our new k-means algorithm is excellent (ARI(HA)>0.9). In this paper, emphases are on the reduction of the time requirement of the k-means algorithm and its application to microarray data due to the desire to create a tool for clustering and malaria research. However, the new clustering algorithm can be used for other clustering needs as long as an appropriate measure of distance between the centroids and the members is used. This has been demonstrated in this work on six non-biological data. PMID:23239974

  1. Geophone with depth sensitive spikes

    SciTech Connect

    Rice, J.A.; Houston, L.M.; Arevalo, R.

    1992-06-23

    This patent describes a geophone. It comprises a seismic sensitive element for sensing elastic motion and converting the motion to an electrical signal, a housing for enclosing the seismic element, and an elongated spike attachable to the housing.

  2. Wavelet analysis of epileptic spikes

    NASA Astrophysics Data System (ADS)

    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.

  3. Kinetic Control of Multiple Forms of Ca2+ Spikes by Inositol Trisphosphate in Pancreatic Acinar Cells

    PubMed Central

    Ito, Koichi; Miyashita, Yasushi; Kasai, Haruo

    1999-01-01

    The mechanisms of agonist-induced Ca2+ spikes have been investigated using a caged inositol 1,4,5-trisphosphate (IP3) and a low-affinity Ca2+ indicator, BTC, in pancreatic acinar cells. Rapid photolysis of caged IP3 was able to reproduce acetylcholine (ACh)-induced three forms of Ca2+ spikes: local Ca2+ spikes and submicromolar (<1 μM) and micromolar (1–15 μM) global Ca2+ spikes (Ca2+ waves). These observations indicate that subcellular gradients of IP3 sensitivity underlie all forms of ACh-induced Ca2+ spikes, and that the amplitude and extent of Ca2+ spikes are determined by the concentration of IP3. IP3-induced local Ca2+ spikes exhibited similar time courses to those generated by ACh, supporting a role for Ca2+-induced Ca2+ release in local Ca2+ spikes. In contrast, IP3- induced global Ca2+ spikes were consistently faster than those evoked with ACh at all concentrations of IP3 and ACh, suggesting that production of IP3 via phospholipase C was slow and limited the spread of the Ca2+ spikes. Indeed, gradual photolysis of caged IP3 reproduced ACh-induced slow Ca2+ spikes. Thus, local and global Ca2+ spikes involve distinct mechanisms, and the kinetics of global Ca2+ spikes depends on that of IP3 production particularly in those cells such as acinar cells where heterogeneity in IP3 sensitivity plays critical role. PMID:10427093

  4. Spike Penetration in Blast-wave-driven Instabilities

    NASA Astrophysics Data System (ADS)

    Drake, R. P.

    2012-01-01

    The problem of interest is the unstable growth of structure at density transitions affected by blast waves, which arise in natural environments such as core-collapse supernovae and in laboratory experiments. The resulting spikes of dense material, which penetrate the less dense material, develop broadened tips, but the degree of broadening varies substantially across both experiments and simulations. The variable broadening presumably produces variations in the drag experienced by the spike tips as they penetrate the less dense material. The present work has used semianalytic theory to address the question of how the variation in drag might affect the spike penetration, for cases in which the post-shock interface deceleration can be described by a power law in a normalized time variable. It did so by following the evolution of structure on the interface through the initial shock passage, the subsequent small-amplitude phase of Rayleigh-Taylor instability growth, and the later phase in which the spike growth involves the competition of buoyancy and drag. In all phases, the expansion of the system during its evolution was accounted for and was important. The calculated spike length is strongly affected by the drag attributed to spike tip broadening. One finds from such a calculation that it is not unreasonable for narrow spikes to keep up with the shock front of the blast wave. The implication is that the accuracy of prediction of spike penetration and consequent structure by simulations very likely depends on how accurately they treat the broadening of the spike tips and the associated drag. Experimental validation of spike morphology in simulations would be useful.

  5. SPIKE PENETRATION IN BLAST-WAVE-DRIVEN INSTABILITIES

    SciTech Connect

    Drake, R. P.

    2012-01-10

    The problem of interest is the unstable growth of structure at density transitions affected by blast waves, which arise in natural environments such as core-collapse supernovae and in laboratory experiments. The resulting spikes of dense material, which penetrate the less dense material, develop broadened tips, but the degree of broadening varies substantially across both experiments and simulations. The variable broadening presumably produces variations in the drag experienced by the spike tips as they penetrate the less dense material. The present work has used semianalytic theory to address the question of how the variation in drag might affect the spike penetration, for cases in which the post-shock interface deceleration can be described by a power law in a normalized time variable. It did so by following the evolution of structure on the interface through the initial shock passage, the subsequent small-amplitude phase of Rayleigh-Taylor instability growth, and the later phase in which the spike growth involves the competition of buoyancy and drag. In all phases, the expansion of the system during its evolution was accounted for and was important. The calculated spike length is strongly affected by the drag attributed to spike tip broadening. One finds from such a calculation that it is not unreasonable for narrow spikes to keep up with the shock front of the blast wave. The implication is that the accuracy of prediction of spike penetration and consequent structure by simulations very likely depends on how accurately they treat the broadening of the spike tips and the associated drag. Experimental validation of spike morphology in simulations would be useful.

  6. Generalized analog thresholding for spike acquisition at ultralow sampling rates.

    PubMed

    He, Bryan D; Wein, Alex; Varshney, Lav R; Kusuma, Julius; Richardson, Andrew G; Srinivasan, Lakshminarayan

    2015-07-01

    Efficient spike acquisition techniques are needed to bridge the divide from creating large multielectrode arrays (MEA) to achieving whole-cortex electrophysiology. In this paper, we introduce generalized analog thresholding (gAT), which achieves millisecond temporal resolution with sampling rates as low as 10 Hz. Consider the torrent of data from a single 1,000-channel MEA, which would generate more than 3 GB/min using standard 30-kHz Nyquist sampling. Recent neural signal processing methods based on compressive sensing still require Nyquist sampling as a first step and use iterative methods to reconstruct spikes. Analog thresholding (AT) remains the best existing alternative, where spike waveforms are passed through an analog comparator and sampled at 1 kHz, with instant spike reconstruction. By generalizing AT, the new method reduces sampling rates another order of magnitude, detects more than one spike per interval, and reconstructs spike width. Unlike compressive sensing, the new method reveals a simple closed-form solution to achieve instant (noniterative) spike reconstruction. The base method is already robust to hardware nonidealities, including realistic quantization error and integration noise. Because it achieves these considerable specifications using hardware-friendly components like integrators and comparators, generalized AT could translate large-scale MEAs into implantable devices for scientific investigation and medical technology. PMID:25904712

  7. Generalized analog thresholding for spike acquisition at ultralow sampling rates

    PubMed Central

    He, Bryan D.; Wein, Alex; Varshney, Lav R.; Kusuma, Julius; Richardson, Andrew G.

    2015-01-01

    Efficient spike acquisition techniques are needed to bridge the divide from creating large multielectrode arrays (MEA) to achieving whole-cortex electrophysiology. In this paper, we introduce generalized analog thresholding (gAT), which achieves millisecond temporal resolution with sampling rates as low as 10 Hz. Consider the torrent of data from a single 1,000-channel MEA, which would generate more than 3 GB/min using standard 30-kHz Nyquist sampling. Recent neural signal processing methods based on compressive sensing still require Nyquist sampling as a first step and use iterative methods to reconstruct spikes. Analog thresholding (AT) remains the best existing alternative, where spike waveforms are passed through an analog comparator and sampled at 1 kHz, with instant spike reconstruction. By generalizing AT, the new method reduces sampling rates another order of magnitude, detects more than one spike per interval, and reconstructs spike width. Unlike compressive sensing, the new method reveals a simple closed-form solution to achieve instant (noniterative) spike reconstruction. The base method is already robust to hardware nonidealities, including realistic quantization error and integration noise. Because it achieves these considerable specifications using hardware-friendly components like integrators and comparators, generalized AT could translate large-scale MEAs into implantable devices for scientific investigation and medical technology. PMID:25904712

  8. Estimating spiking irregularities under changing environments.

    PubMed

    Miura, Keiji; Okada, Masato; Amari, Shun-Ichi

    2006-10-01

    We considered a gamma distribution of interspike intervals as a statistical model for neuronal spike generation. A gamma distribution is a natural extension of the Poisson process taking the effect of a refractory period into account. The model is specified by two parameters: a time-dependent firing rate and a shape parameter that characterizes spiking irregularities of individual neurons. Because the environment changes over time, observed data are generated from a model with a time-dependent firing rate, which is an unknown function. A statistical model with an unknown function is called a semiparametric model and is generally very difficult to solve. We used a novel method of estimating functions in information geometry to estimate the shape parameter without estimating the unknown function. We obtained an optimal estimating function analytically for the shape parameter independent of the functional form of the firing rate. This estimation is efficient without Fisher information loss and better than maximum likelihood estimation. We suggest a measure of spiking irregularity based on the estimating function, which may be useful for characterizing individual neurons in changing environments. PMID:16907630

  9. Comparison of magnetoencephalographic spikes with and without concurrent electroencephalographic spikes in extratemporal epilepsy.

    PubMed

    Park, Hyeon-Mi; Nakasato, Nobukazu; Iwasaki, Masaki; Shamoto, Hiroshi; Tominaga, Teiji; Yoshimoto, Takashi

    2004-07-01

    Interictal spikes in patients with epilepsy may be detected by either electroencephalography (EEG) (E-spikes) or magnetoencephalography (MEG) (M-spikes), or both MEG and EEG (E/M-spikes). Localization and amplitude were compared between E/M-spikes and M-spikes in 7 adult patients with extratemporal epilepsy to evaluate the clinical significance of MEG spikes. MEG and EEG were simultaneously measured using a helmet-shaped MEG system with planar-type gradiometers and scalp electrodes of the international 10-20 system. Sources of E/M-spikes and M-spikes were estimated by an equivalent current dipole (ECD) model for MEG at peak latency. Each subject showed 9 to 20 (mean 13.4) E/M-spikes and 9 to 31 (mean 16.3) M-spikes. No subjects showed significant differences in the ECD locations between E/M- and M-spikes. ECD moments of the E/M-spikes were significantly larger in 2 patients and not significantly different in the other 5 patients. The similar localizations of E/M-spikes and M-spikes suggest that combination of MEG and EEG is useful to detect more interictal spikes in patients with extratemporal epilepsy. The smaller tendency of ECD amplitude of the M-spikes than E/M-spikes suggests that scalp EEG may overlook small tangential spikes due to background brain noise. Localization value of M-spikes is clinically equivalent to that of E/M-spikes. PMID:15240925

  10. Changes in complex spike activity during classical conditioning.

    PubMed

    Rasmussen, Anders; Jirenhed, Dan-Anders; Wetmore, Daniel Z; Hesslow, Germund

    2014-01-01

    The cerebellar cortex is necessary for adaptively timed conditioned responses (CRs) in eyeblink conditioning. During conditioning, Purkinje cells acquire pause responses or "Purkinje cell CRs" to the conditioned stimuli (CS), resulting in disinhibition of the cerebellar nuclei (CN), allowing them to activate motor nuclei that control eyeblinks. This disinhibition also causes inhibition of the inferior olive (IO), via the nucleo-olivary pathway (N-O). Activation of the IO, which relays the unconditional stimulus (US) to the cortex, elicits characteristic complex spikes in Purkinje cells. Although Purkinje cell activity, as well as stimulation of the CN, is known to influence IO activity, much remains to be learned about the way that learned changes in simple spike firing affects the IO. In the present study, we analyzed changes in simple and complex spike firing, in extracellular Purkinje cell records, from the C3 zone, in decerebrate ferrets undergoing training in a conditioning paradigm. In agreement with the N-O feedback hypothesis, acquisition resulted in a gradual decrease in complex spike activity during the conditioned stimulus, with a delay that is consistent with the long N-O latency. Also supporting the feedback hypothesis, training with a short interstimulus interval (ISI), which does not lead to acquisition of a Purkinje cell CR, did not cause a suppression of complex spike activity. In contrast, observations that extinction did not lead to a recovery in complex spike activity and the irregular patterns of simple and complex spike activity after the conditioned stimulus are less conclusive. PMID:25140129

  11. Quantitative Analysis of Calcium Spikes in Noisy Fluorescent Background

    PubMed Central

    Janicek, Radoslav; Hotka, Matej; Zahradníková, Alexandra; Zahradníková, Alexandra; Zahradník, Ivan

    2013-01-01

    Intracellular calcium signals are studied by laser-scanning confocal fluorescence microscopy. The required spatio-temporal resolution makes description of calcium signals difficult because of the low signal-to-noise ratio. We designed a new procedure of calcium spike analysis based on their fitting with a model. The accuracy and precision of calcium spike description were tested on synthetic datasets generated either with randomly varied spike parameters and Gaussian noise of constant amplitude, or with constant spike parameters and Gaussian noise of various amplitudes. Statistical analysis was used to evaluate the performance of spike fitting algorithms. The procedure was optimized for reliable estimation of calcium spike parameters and for dismissal of false events. A new algorithm was introduced that corrects the acquisition time of pixels in line-scan images that is in error due to sequential acquisition of individual pixels along the space coordinate. New software was developed in Matlab and provided for general use. It allows interactive dissection of temporal profiles of calcium spikes from x-t images, their fitting with predefined function(s) and acceptance of results on statistical grounds, thus allowing efficient analysis and reliable description of calcium signaling in cardiac myocytes down to the in situ function of ryanodine receptors. PMID:23741324

  12. Spatiotemporal spike encoding of a continuous external signal.

    PubMed

    Masuda, Naoki; Aihara, Kazuyuki

    2002-07-01

    Interspike intervals of spikes emitted from an integrator neuron model of sensory neurons can encode input information represented as a continuous signal from a deterministic system. If a real brain uses spike timing as a means of information processing, other neurons receiving spatiotemporal spikes from such sensory neurons must also be capable of treating information included in deterministic interspike intervals. In this article, we examine functions of neurons modeling cortical neurons receiving spatiotemporal spikes from many sensory neurons. We show that such neuron models can encode stimulus information passed from the sensory model neurons in the form of interspike intervals. Each sensory neuron connected to the cortical neuron contributes equally to the information collection by the cortical neuron. Although the incident spike train to the cortical neuron is a superimposition of spike trains from many sensory neurons, it need not be decomposed into spike trains according to the input neurons. These results are also preserved for generalizations of sensory neurons such as a small amount of leak, noise, inhomogeneity in firing rates, or biases introduced in the phase distributions. PMID:12079548

  13. Information geometry of interspike intervals in spiking neurons.

    PubMed

    Ikeda, Kazushi

    2005-12-01

    An information geometrical method is developed for characterizing or classifying neurons in cortical areas, whose spike rates fluctuate in time. Under the assumption that the interspike intervals of a spike sequence of a neuron obey a gamma process with a time-variant spike rate and a fixed shape parameter, we formulate the problem of characterization as a semiparametric statistical estimation, where the spike rate is a nuisance parameter. We derive optimal criteria from the information geometrical viewpoint when certain assumptions are added to the formulation, and we show that some existing measures, such as the coefficient of variation and the local variation, are expressed as estimators of certain functions under the same assumptions. PMID:16212769

  14. Sensitivity to Interaural Time Differences Conveyed in the Stimulus Envelope: Estimating Inputs of Binaural Neurons Through the Temporal Analysis of Spike Trains.

    PubMed

    Dietz, Mathias; Wang, Le; Greenberg, David; McAlpine, David

    2016-08-01

    Sound-source localization in the horizontal plane relies on detecting small differences in the timing and level of the sound at the two ears, including differences in the timing of the modulated envelopes of high-frequency sounds (envelope interaural time differences (ITDs)). We investigated responses of single neurons in the inferior colliculus (IC) to a wide range of envelope ITDs and stimulus envelope shapes. By a novel means of visualizing neural activity relative to different portions of the periodic stimulus envelope at each ear, we demonstrate the role of neuron-specific excitatory and inhibitory inputs in creating ITD sensitivity (or the lack of it) depending on the specific shape of the stimulus envelope. The underlying binaural brain circuitry and synaptic parameters were modeled individually for each neuron to account for neuron-specific activity patterns. The model explains the effects of envelope shapes on sensitivity to envelope ITDs observed in both normal-hearing listeners and in neural data, and has consequences for understanding how ITD information in stimulus envelopes might be maximized in users of bilateral cochlear implants-for whom ITDs conveyed in the stimulus envelope are the only ITD cues available. PMID:27294694

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

    NASA Astrophysics Data System (ADS)

    Kim, Hideaki; Shinomoto, Shigeru

    2012-11-01

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

  16. A model-based prediction of the calcium responses in the striatal synaptic spines depending on the timing of cortical and dopaminergic inputs and post-synaptic spikes.

    PubMed

    Nakano, Takashi; Yoshimoto, Junichiro; Doya, Kenji

    2013-01-01

    The dopamine-dependent plasticity of the cortico-striatal synapses is considered as the cellular mechanism crucial for reinforcement learning. The dopaminergic inputs and the calcium responses affect the synaptic plasticity by way of the signaling cascades within the synaptic spines. The calcium concentration within synaptic spines, however, is dependent on multiple factors including the calcium influx through ionotropic glutamate receptors, the intracellular calcium release by activation of metabotropic glutamate receptors, and the opening of calcium channels by EPSPs and back-propagating action potentials. Furthermore, dopamine is known to modulate the efficacies of NMDA receptors, some of the calcium channels, and sodium and potassium channels that affect the back propagation of action potentials. Here we construct an electric compartment model of the striatal medium spiny neuron with a realistic morphology and predict the calcium responses in the synaptic spines with variable timings of the glutamatergic and dopaminergic inputs and the postsynaptic action potentials. The model was validated by reproducing the responses to current inputs and could predict the electric and calcium responses to glutamatergic inputs and back-propagating action potential in the proximal and distal synaptic spines during up- and down-states. We investigated the calcium responses by systematically varying the timings of the glutamatergic and dopaminergic inputs relative to the action potential and found that the calcium response and the subsequent synaptic potentiation is maximal when the dopamine input precedes glutamate input and action potential. The prediction is not consistent with the hypothesis that the dopamine input provides the reward prediction error for reinforcement learning. The finding suggests that there is an unknown learning mechanisms at the network level or an unknown cellular mechanism for calcium dynamics and signaling cascades. PMID:24062681

  17. Spike processing with a graphene excitable laser

    PubMed Central

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

    2016-01-01

    Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved “spiking” of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate a unified platform for spike processing with a graphene-coupled laser system. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation—fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system and also propose and simulate an analogous integrated device. The addition of graphene leads to a number of advantages which stem from its unique properties, including high absorption and fast carrier relaxation. These could lead to significant speed and efficiency improvements in unconventional laser processing devices, and ongoing research on graphene microfabrication promises compatibility with integrated laser platforms. PMID:26753897

  18. The microwave spectrum of solar millisecond spikes

    NASA Technical Reports Server (NTRS)

    Staehli, M.; Magun, A.

    1986-01-01

    The microwave radiation from solar flares sometimes shows short and intensive spikes which are superimposed on the burst continuum. In order to determine the upper frequency limit of their occurrence and the circular polarization, a statistical analysis was performed on digital microwave observations from 3.2 to 92.5 GHz. Additionally, fine structures were investigated with a fast 32-channel spectrometer at 3.47 GHz. It was found that about 10 percent of the bursts show fine structures at 3.2 and 5.2 GHz, whereas none occurred above 8.4 GHz. Most of the observed spikes were very short and their bandwidth varied from below 0.5 MHz to more than 200 MHz. Simultaneous observations at two further frequencies showed no coincident spikes at the second and third harmonic. The observations can be explained by the theory of electron cyclotron masering if the observed bandwidths are determined by magnetic field inhomogeneities or if the rise times are independent of the source diameters. The latter would imply source sizes between 50 and 100 km.

  19. Spike processing with a graphene excitable laser

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

    Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved “spiking” of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate a unified platform for spike processing with a graphene-coupled laser system. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation—fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system and also propose and simulate an analogous integrated device. The addition of graphene leads to a number of advantages which stem from its unique properties, including high absorption and fast carrier relaxation. These could lead to significant speed and efficiency improvements in unconventional laser processing devices, and ongoing research on graphene microfabrication promises compatibility with integrated laser platforms.

  20. Time-resolved studies of particle effects in laser ablation inductively coupled plasma-mass spectrometry

    SciTech Connect

    Perdian, D.; Bajic, S.; Baldwin, D.; Houk, R.

    2007-11-13

    Time resolved signals in laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) are studied to determine the influence of experimental parameters on ICP-induced fractionation effects. Differences in sample composition and morphology, i.e., ablating brass, glass, or dust pellets, have a profound effect on the time resolved signal. Helium transport gas significantly decreases large positive signal spikes arising from large particles in the ICP. A binder for pellets also reduces the abundance and amplitude of spikes in the signal. MO{sup +} ions also yield signal spikes, but these MO{sup +} spikes generally occur at different times from their atomic ion counterparts.

  1. State space analysis of timing: exploiting task redundancy to reduce sensitivity to timing

    PubMed Central

    Cohen, Rajal G.

    2012-01-01

    Timing is central to many coordinated actions, and the temporal accuracy of central nervous system commands presents an important limit to skilled performance. Using target-oriented throwing in a virtual environment as an example task, this study presents a novel analysis that quantifies contributions of timing accuracy and shaping of hand trajectories to performance. Task analysis reveals that the result of a throw is fully determined by the projectile position and velocity at release; zero error can be achieved by a manifold of position and velocity combinations (solution manifold). Four predictions were tested. 1) Performers learn to release the projectile closer to the optimal moment for a given arm trajectory, achieving timing accuracy levels similar to those reported in other timing tasks (∼10 ms). 2) Performers develop a hand trajectory that follows the solution manifold such that zero error can be achieved without perfect timing. 3) Skilled performers exploit both routes to improvement more than unskilled performers. 4) Long-term improvement in skilled performance relies on continued optimization of the arm trajectory as timing limits are reached. Average and skilled subjects practiced for 6 and 15 days, respectively. In 6 days, both timing and trajectory alignment improved for all subjects, and skilled subjects showed an advantage in timing. With extended practice, performance continued to improve due to continued shaping of the trajectory, whereas timing accuracy reached an asymptote at 9 ms. We conclude that skilled subjects first maximize timing accuracy and then optimize trajectory shaping to compensate for intrinsic limitations of timing accuracy. PMID:22031769

  2. 22 CFR 401.19 - Reducing or extending time and dispensing with statements.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... 22 Foreign Relations 2 2010-04-01 2010-04-01 true Reducing or extending time and dispensing with statements. 401.19 Section 401.19 Foreign Relations INTERNATIONAL JOINT COMMISSION, UNITED STATES AND CANADA RULES OF PROCEDURE Applications § 401.19 Reducing or extending time and dispensing with statements....

  3. 22 CFR 401.19 - Reducing or extending time and dispensing with statements.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... 22 Foreign Relations 2 2011-04-01 2009-04-01 true Reducing or extending time and dispensing with statements. 401.19 Section 401.19 Foreign Relations INTERNATIONAL JOINT COMMISSION, UNITED STATES AND CANADA RULES OF PROCEDURE Applications § 401.19 Reducing or extending time and dispensing with statements....

  4. Estimating membrane voltage correlations from extracellular spike trains.

    PubMed

    Dorn, Jessy D; Ringach, Dario L

    2003-04-01

    The cross-correlation coefficient between neural spike trains is a commonly used tool in the study of neural interactions. Two well-known complications that arise in its interpretation are 1) modulations in the correlation coefficient may result solely from changes in the mean firing rate of the cells and 2) the mean firing rates of the neurons impose upper and lower bounds on the correlation coefficient whose absolute values differ by an order of magnitude or more. Here, we propose a model-based approach to the interpretation of spike train correlations that circumvents these problems. The basic idea of our proposal is to estimate the cross-correlation coefficient between the membrane voltages of two cells from their extracellular spike trains and use the resulting value as the degree of correlation (or association) of neural activity. This is done in the context of a model that assumes the membrane voltages of the cells have a joint normal distribution and spikes are generated by a simple thresholding operation. We show that, under these assumptions, the estimation of the correlation coefficient between the membrane voltages reduces to the calculation of a tetrachoric correlation coefficient (a measure of association in nominal data introduced by Karl Pearson) on a contingency table calculated from the spike data. Simulations of conductance-based leaky integrate-and-fire neurons indicate that, despite its simplicity, the technique yields very good estimates of the intracellular membrane voltage correlation from the extracellular spike trains in biologically realistic models. PMID:12686584

  5. Learning Universal Computations with Spikes

    PubMed Central

    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

  6. Making Time for Nature: Visual Exposure to Natural Environments Lengthens Subjective Time Perception and Reduces Impulsivity

    PubMed Central

    Berry, Meredith S.; Repke, Meredith A.; Nickerson, Norma P.; Conway, Lucian G.; Odum, Amy L.; Jordan, Kerry E.

    2015-01-01

    Impulsivity in delay discounting is associated with maladaptive behaviors such as overeating and drug and alcohol abuse. Researchers have recently noted that delay discounting, even when measured by a brief laboratory task, may be the best predictor of human health related behaviors (e.g., exercise) currently available. Identifying techniques to decrease impulsivity in delay discounting, therefore, could help improve decision-making on a global scale. Visual exposure to natural environments is one recent approach shown to decrease impulsive decision-making in a delay discounting task, although the mechanism driving this result is currently unknown. The present experiment was thus designed to evaluate not only whether visual exposure to natural (mountains, lakes) relative to built (buildings, cities) environments resulted in less impulsivity, but also whether this exposure influenced time perception. Participants were randomly assigned to either a natural environment condition or a built environment condition. Participants viewed photographs of either natural scenes or built scenes before and during a delay discounting task in which they made choices about receiving immediate or delayed hypothetical monetary outcomes. Participants also completed an interval bisection task in which natural or built stimuli were judged as relatively longer or shorter presentation durations. Following the delay discounting and interval bisection tasks, additional measures of time perception were administered, including how many minutes participants thought had passed during the session and a scale measurement of whether time "flew" or "dragged" during the session. Participants exposed to natural as opposed to built scenes were less impulsive and also reported longer subjective session times, although no differences across groups were revealed with the interval bisection task. These results are the first to suggest that decreased impulsivity from exposure to natural as opposed to built

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

    PubMed Central

    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. PMID:26648863

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

    PubMed

    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. PMID:26648863

  9. [Wooden spike orbital injury].

    PubMed

    Kiel, R; Wiaux, C; Atipo-Tsiba, P W; Gottrau, P de

    2005-03-01

    A 71-year-old female patient fell in her garden, inducing a skin wound on the temporal left eyebrow. Skin disinfection and wound closure were performed elsewhere, an X-ray image did not reveal a foreign body. She was referred to our service three days later with a progressive left periorbital swelling. Clinical inspection demonstrated a painfully, fluctuant swelling around the wound with an inflammatory pseudoptosis of the left eye. Vision was reduced on the left eye; anterior and posterior segments of both eyes were unharmed. After opening the wound sutures a purulent liquid was drained and a wooden fragment was found, measuring 22 x 0.5 mm. Because of restriction of abduction of the left eye, magnetic resonance imaging (MRI) was performed, detecting another organic intraorbital foreign body and a fracture of the left medial orbital wall. Anterior orbitotomy was performed and a wooden fragment was removed, measuring 47 x 0.6 mm. Under administration of intravenous antibiotics vision and ocular motility recovered uneventfully. This case emphasizes the value of MRI in the diagnostics of retained wooden foreign bodies as well as the importance of a soigneuse inspection of skin wounds with a high risk for remaining foreign bodies. PMID:15785993

  10. Dual Roles for Spike Signaling in Cortical Neural Populations

    PubMed Central

    Ballard, Dana H.; Jehee, Janneke F. M.

    2011-01-01

    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 and exponential interval histograms. In addition, it makes testable predictions that follow from the γ latency coding. PMID:21687798

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

    PubMed

    Bi, Zedong; Zhou, Changsong

    2016-01-01

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

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

    PubMed Central

    Bi, Zedong; Zhou, Changsong

    2016-01-01

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

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

    PubMed Central

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

    2015-01-01

    A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code. PMID:26082710

  14. Eliminating thermal violin spikes from LIGO noise

    SciTech Connect

    Santamore, D. H.; Levin, Yuri

    2001-08-15

    We have developed a scheme for reducing LIGO suspension thermal noise close to violin-mode resonances. The idea is to monitor directly the thermally induced motion of a small portion of (a 'point' on) each suspension fiber, thereby recording the random forces driving the test-mass motion close to each violin-mode frequency. One can then suppress the thermal noise by optimally subtracting the recorded fiber motions from the measured motion of the test mass, i.e., from the LIGO output. The proposed method is a modification of an analogous but more technically difficult scheme by Braginsky, Levin and Vyatchanin for reducing broad-band suspension thermal noise. The efficiency of our method is limited by the sensitivity of the sensor used to monitor the fiber motion. If the sensor has no intrinsic noise (i.e. has unlimited sensitivity), then our method allows, in principle, a complete removal of violin spikes from the thermal-noise spectrum. We find that in LIGO-II interferometers, in order to suppress violin spikes below the shot-noise level, the intrinsic noise of the sensor must be less than {approx}2 x 10{sup -13} cm/Hz. This sensitivity is two orders of magnitude greater than that of currently available sensors.

  15. Spike Inference from Calcium Imaging Using Sequential Monte Carlo Methods

    PubMed Central

    Vogelstein, Joshua T.; Watson, Brendon O.; Packer, Adam M.; Yuste, Rafael; Jedynak, Bruno; Paninski, Liam

    2009-01-01

    Abstract As recent advances in calcium sensing technologies facilitate simultaneously imaging action potentials in neuronal populations, complementary analytical tools must also be developed to maximize the utility of this experimental paradigm. Although the observations here are fluorescence movies, the signals of interest—spike trains and/or time varying intracellular calcium concentrations—are hidden. Inferring these hidden signals is often problematic due to noise, nonlinearities, slow imaging rate, and unknown biophysical parameters. We overcome these difficulties by developing sequential Monte Carlo methods (particle filters) based on biophysical models of spiking, calcium dynamics, and fluorescence. We show that even in simple cases, the particle filters outperform the optimal linear (i.e., Wiener) filter, both by obtaining better estimates and by providing error bars. We then relax a number of our model assumptions to incorporate nonlinear saturation of the fluorescence signal, as well external stimulus and spike history dependence (e.g., refractoriness) of the spike trains. Using both simulations and in vitro fluorescence observations, we demonstrate temporal superresolution by inferring when within a frame each spike occurs. Furthermore, the model parameters may be estimated using expectation maximization with only a very limited amount of data (e.g., ∼5–10 s or 5–40 spikes), without the requirement of any simultaneous electrophysiology or imaging experiments. PMID:19619479

  16. A Spiking Neural Network System for Robust Sequence Recognition.

    PubMed

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

    2016-03-01

    This paper proposes a biologically plausible network architecture with spiking neurons for sequence recognition. This architecture is a unified and consistent system with functional parts of sensory encoding, learning, and decoding. This is the first systematic model attempting to reveal the neural mechanisms considering both the upstream and the downstream neurons together. The whole system is a consistent temporal framework, where the precise timing of spikes is employed for information processing and cognitive computing. Experimental results show that the system is competent to perform the sequence recognition, being robust to noisy sensory inputs and invariant to changes in the intervals between input stimuli within a certain range. The classification ability of the temporal learning rule used in the system is investigated through two benchmark tasks that outperform the other two widely used learning rules for classification. The results also demonstrate the computational power of spiking neurons over perceptrons for processing spatiotemporal patterns. In summary, the system provides a general way with spiking neurons to encode external stimuli into spatiotemporal spikes, to learn the encoded spike patterns with temporal learning rules, and to decode the sequence order with downstream neurons. The system structure would be beneficial for developments in both hardware and software. PMID:25879976

  17. Fusarium graminearum infection and deoxynivalenol concentrations during development of wheat spikes.

    PubMed

    Cowger, Christina; Arellano, Consuelo

    2013-05-01

    Fusarium head blight (FHB) affects whole spikes of small grain plants, yet little is known about how FHB develops following infection, or about the concentration or progression of the mycotoxin deoxynivalenol (DON) in non-grain spike tissues. Fusarium mycotoxin levels in whole small-grain spikes are of concern to producers of whole-crop silage, as well as users of straw containing chaff for animal bedding or winter livestock rations. A 2-year field experiment was performed in Kinston, NC to reveal the time course of FHB development. Eight winter wheat cultivars with varying levels of FHB resistance were used in the 2006 experiment, and four of them were used in 2007. Plots were spray-inoculated with Fusarium graminearum macroconidia at mid-anthesis. Four durations of post-anthesis mist were applied: 0, 10, 20, or 30 days. Spike samples were collected and bulked by plot at 15, 25, 35, 45, 55, and 65 days after anthesis (daa); samples were separated into grain, glume, and rachis fractions. Increasing durations of post-anthesis moisture elevated grain DON and reduced the effect of cultivar on DON, presumably by affecting the expression of resistance, in all spike tissues. Fusarium-damaged kernels increased from early kernel-hard to harvest-ripe in both years. Percent infected kernels increased from medium-milk to harvest-ripe. During grainfill, DON concentrations declined in grain but increased in rachises and glumes, peaking at early kernel-hard, before declining. Higher mean and maximum DON levels were observed in rachises and glumes than in grain. Estimated whole-spike DON peaked at early kernel-hard. In a high-FHB year, whole-plant harvest for forage should be conducted as early as possible. Straw that may be consumed by livestock could contain significant amounts of DON in chaff, and DON can be minimized if straw is sourced from low-symptom crops. Cultivar FHB resistance ratings and disease data should be useful in predicting whole-spike DON levels. Overall

  18. Reducing acquisition times in multidimensional NMR with a time-optimized Fourier encoding algorithm.

    PubMed

    Zhang, Zhiyong; Smith, Pieter E S; Frydman, Lucio

    2014-11-21

    Speeding up the acquisition of multidimensional nuclear magnetic resonance (NMR) spectra is an important topic in contemporary NMR, with central roles in high-throughput investigations and analyses of marginally stable samples. A variety of fast NMR techniques have been developed, including methods based on non-uniform sampling and Hadamard encoding, that overcome the long sampling times inherent to schemes based on fast-Fourier-transform (FFT) methods. Here, we explore the potential of an alternative fast acquisition method that leverages a priori knowledge, to tailor polychromatic pulses and customized time delays for an efficient Fourier encoding of the indirect domain of an NMR experiment. By porting the encoding of the indirect-domain to the excitation process, this strategy avoids potential artifacts associated with non-uniform sampling schemes and uses a minimum number of scans equal to the number of resonances present in the indirect dimension. An added convenience is afforded by the fact that a usual 2D FFT can be used to process the generated data. Acquisitions of 2D heteronuclear correlation NMR spectra on quinine and on the anti-inflammatory drug isobutyl propionic phenolic acid illustrate the new method's performance. This method can be readily automated to deal with complex samples such as those occurring in metabolomics, in in-cell as well as in in vivo NMR applications, where speed and temporal stability are often primary concerns. PMID:25416883

  19. Reducing acquisition times in multidimensional NMR with a time-optimized Fourier encoding algorithm

    PubMed Central

    Zhang, Zhiyong; Frydman, Lucio

    2014-01-01

    Speeding up the acquisition of multidimensional nuclear magnetic resonance (NMR) spectra is an important topic in contemporary NMR, with central roles in high-throughput investigations and analyses of marginally stable samples. A variety of fast NMR techniques have been developed, including methods based on non-uniform sampling and Hadamard encoding, that overcome the long sampling times inherent to schemes based on fast-Fourier-transform (FFT) methods. Here, we explore the potential of an alternative fast acquisition method that leverages a priori knowledge, to tailor polychromatic pulses and customized time delays for an efficient Fourier encoding of the indirect domain of an NMR experiment. By porting the encoding of the indirect-domain to the excitation process, this strategy avoids potential artifacts associated with non-uniform sampling schemes and uses a minimum number of scans equal to the number of resonances present in the indirect dimension. An added convenience is afforded by the fact that a usual 2D FFT can be used to process the generated data. Acquisitions of 2D heteronuclear correlation NMR spectra on quinine and on the anti-inflammatory drug isobutyl propionic phenolic acid illustrate the new method's performance. This method can be readily automated to deal with complex samples such as those occurring in metabolomics, in in-cell as well as in in vivo NMR applications, where speed and temporal stability are often primary concerns. PMID:25416883

  20. Reducing acquisition times in multidimensional NMR with a time-optimized Fourier encoding algorithm

    SciTech Connect

    Zhang, Zhiyong; Smith, Pieter E. S.; Frydman, Lucio

    2014-11-21

    Speeding up the acquisition of multidimensional nuclear magnetic resonance (NMR) spectra is an important topic in contemporary NMR, with central roles in high-throughput investigations and analyses of marginally stable samples. A variety of fast NMR techniques have been developed, including methods based on non-uniform sampling and Hadamard encoding, that overcome the long sampling times inherent to schemes based on fast-Fourier-transform (FFT) methods. Here, we explore the potential of an alternative fast acquisition method that leverages a priori knowledge, to tailor polychromatic pulses and customized time delays for an efficient Fourier encoding of the indirect domain of an NMR experiment. By porting the encoding of the indirect-domain to the excitation process, this strategy avoids potential artifacts associated with non-uniform sampling schemes and uses a minimum number of scans equal to the number of resonances present in the indirect dimension. An added convenience is afforded by the fact that a usual 2D FFT can be used to process the generated data. Acquisitions of 2D heteronuclear correlation NMR spectra on quinine and on the anti-inflammatory drug isobutyl propionic phenolic acid illustrate the new method's performance. This method can be readily automated to deal with complex samples such as those occurring in metabolomics, in in-cell as well as in in vivo NMR applications, where speed and temporal stability are often primary concerns.

  1. Inter-Laboratory Uranium Double-Spike Experiment

    SciTech Connect

    Russ, G. P

    1999-11-11

    In environmental samples, the major analytical limitation on the use of uranium {sup 238}U/{sup 235}U determinations as an indicator of uranium enrichment is mass dependent bias occurring during the measurement. The double-spike technique can be used to correct the data for this effect. The purpose of this experiment was to evaluate the variation of mass bias among several laboratories and to determine the extent to which the double-spike could be used to reduce analytical uncertainty. Four laboratories performed replicate analyses on each of three samples. Generally mass bias was determined to be small compared to the random scatter of the measurements, but in at least one case, the bias was > 1%. In 8 of 12 cases, intra-laboratory variance was reduced when the double-spike correction was applied. For all three samples, the inter-laboratory variance was decreased, though the decrease was small. Based on a reasonable assumption about the true isotopic compositions of the samples, the accuracy of 11 of the twelve analyses was improved by applying the double spike correction. When the double spike is used to correct for mass bias, the {sup 238}U/{sup 235}U accuracy is better than 1% even for samples as small as 1 ng. For 50 ng of uranium, 0.1% accuracy was achieved.

  2. Automatic Spike Sorting Using Tuning Information

    PubMed Central

    Ventura, Valérie

    2011-01-01

    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. PMID:19548802

  3. Quiet Spike Build-Up Ground Vibration Testing Approach

    NASA Technical Reports Server (NTRS)

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

    2007-01-01

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

  4. Estimating Neuronal Information: Logarithmic Binning of Neuronal Inter-Spike Intervals.

    PubMed

    Dorval, Alan D

    2011-02-01

    Neurons communicate via the relative timing of all-or-none biophysical signals called spikes. For statistical analysis, the time between spikes can be accumulated into inter-spike interval histograms. Information theoretic measures have been estimated from these histograms to assess how information varies across organisms, neural systems, and disease conditions. Because neurons are computational units that, to the extent they process time, work not by discrete clock ticks but by the exponential decays of numerous intrinsic variables, we propose that neuronal information measures scale more naturally with the logarithm of time. For the types of inter-spike interval distributions that best describe neuronal activity, the logarithm of time enables fewer bins to capture the salient features of the distributions. Thus, discretizing the logarithm of inter-spike intervals, as compared to the inter-spike intervals themselves, yields histograms that enable more accurate entropy and information estimates for fewer bins and less data. Additionally, as distribution parameters vary, the entropy and information calculated from the logarithm of the inter-spike intervals are substantially better behaved, e.g., entropy is independent of mean rate, and information is equally affected by rate gains and divisions. Thus, when compiling neuronal data for subsequent information analysis, the logarithm of the inter-spike intervals is preferred, over the untransformed inter-spike intervals, because it yields better information estimates and is likely more similar to the construction used by nature herself. PMID:24839390

  5. Automatic classification of penicillin-induced epileptic EEG spikes.

    PubMed

    Kortelainen, Jukka; Silfverhuth, Minna; Suominen, Kalervo; Sonkajarvi, Eila; Alahuhta, Seppo; Jantti, Ville; Seppanen, Tapio

    2010-01-01

    Penicillin-induced focal epilepsy is a well-known model in epilepsy research. In this model, epileptic activity is generated by delivering penicillin focally to the cortex. The drug induces interictal electroencephalographic (EEG) spikes which evolve in time and may later change to ictal discharges. This paper proposes a method for automatic classification of these interictal epileptic spikes using iterative K-means clustering. The method is shown to be able to detect different spike waveforms and describe their characteristic occurrence in time during penicillin-induced focal epilepsy. The study offers potential for future research by providing a method to objectively and quantitatively analyze the time sequence of interictal epileptic activity. PMID:21096740

  6. iSpike: a spiking neural interface for the iCub robot.

    PubMed

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

    2012-06-01

    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. PMID:22617339

  7. When less is more: non-monotonic spike sequence processing in neurons.

    PubMed

    Arnoldt, Hinrich; Chang, Shuwen; Jahnke, Sven; Urmersbach, Birk; Taschenberger, Holger; Timme, Marc

    2015-02-01

    Fundamental response properties of neurons centrally underly the computational capabilities of both individual nerve cells and neural networks. Most studies on neuronal input-output relations have focused on continuous-time inputs such as constant or noisy sinusoidal currents. Yet, most neurons communicate via exchanging action potentials (spikes) at discrete times. Here, we systematically analyze the stationary spiking response to regular spiking inputs and reveal that it is generically non-monotonic. Our theoretical analysis shows that the underlying mechanism relies solely on a combination of the discrete nature of the communication by spikes, the capability of locking output to input spikes and limited resources required for spike processing. Numerical simulations of mathematically idealized and biophysically detailed models, as well as neurophysiological experiments confirm and illustrate our theoretical predictions. PMID:25646860

  8. When Less Is More: Non-monotonic Spike Sequence Processing in Neurons

    PubMed Central

    Arnoldt, Hinrich; Chang, Shuwen; Jahnke, Sven; Urmersbach, Birk; Taschenberger, Holger; Timme, Marc

    2015-01-01

    Fundamental response properties of neurons centrally underly the computational capabilities of both individual nerve cells and neural networks. Most studies on neuronal input-output relations have focused on continuous-time inputs such as constant or noisy sinusoidal currents. Yet, most neurons communicate via exchanging action potentials (spikes) at discrete times. Here, we systematically analyze the stationary spiking response to regular spiking inputs and reveal that it is generically non-monotonic. Our theoretical analysis shows that the underlying mechanism relies solely on a combination of the discrete nature of the communication by spikes, the capability of locking output to input spikes and limited resources required for spike processing. Numerical simulations of mathematically idealized and biophysically detailed models, as well as neurophysiological experiments confirm and illustrate our theoretical predictions. PMID:25646860

  9. Simple In-Hospital Interventions to Reduce Door-to-CT Time in Acute Stroke

    PubMed Central

    Taheraghdam, Aliakbar; Rikhtegar, Reza; Mehrvar, Kaveh; Mehrara, Mehrdad; Hassasi, Rogayyeh; Aliyar, Hannane; Farzi, Mohammadamin; Hasaneh Tamar, Somayyeh

    2016-01-01

    Background. Intravenous tissue plasminogen activator, a time dependent therapy, can reduce the morbidity and mortality of acute ischemic stroke. This study was designed to assess the effect of simple in-hospital interventions on reducing door-to-CT (DTC) time and reaching door-to-needle (DTN) time of less than 60 minutes. Methods. Before any intervention, DTC time was recorded for 213 patients over a one-year period at our center. Five simple quality-improvement interventions were implemented, namely, call notification, prioritizing patients for CT scan, prioritizing patients for lab analysis, specifying a bed for acute stroke patients, and staff education. After intervention, over a course of 44 months, DTC time was recorded for 276 patients with the stroke code. Furthermore DTN time was recorded for 106 patients who were treated with IV thrombolytic therapy. Results. The median DTC time significantly decreased in the postintervention period comparing to the preintervention period [median (IQR); 20 (12–30) versus 75 (52.5–105), P < 0.001]. At the postintervention period, the median (IQR) DTN time was 55 (40–73) minutes and proportion of patients with DTN time less than 60 minutes was 62.4% (P < 0.001). Conclusion. Our interventions significantly reduced DTC time and resulted in an acceptable DTN time. These interventions are feasible in most hospitals and should be considered. PMID:27478641

  10. Reducing bias and analyzing variability in the time-left procedure.

    PubMed

    Trujano, R Emmanuel; Orduña, Vladimir

    2015-04-01

    The time-left procedure was designed to evaluate the psychophysical function for time. Although previous results indicated a linear relationship, it is not clear what role the observed bias toward the time-left option plays in this procedure and there are no reports of how variability changes with predicted indifference. The purposes of this experiment were to reduce bias experimentally, and to contrast the difference limen (a measure of variability around indifference) with predictions from scalar expectancy theory (linear timing) and behavioral economic model (logarithmic timing). A control group of 6 rats performed the original time-left procedure with C=60 s and S=5, 10,…, 50, 55 s, whereas a no-bias group of 6 rats performed the same conditions in a modified time-left procedure in which only a single response per choice trial was allowed. Results showed that bias was reduced for the no-bias group, observed indifference grew linearly with predicted indifference for both groups, and difference limen and Weber ratios decreased as expected indifference increased for the control group, which is consistent with linear timing, whereas for the no-bias group they remained constant, consistent with logarithmic timing. Therefore, the time-left procedure generates results consistent with logarithmic perceived time once bias is experimentally reduced. PMID:25659914

  11. Spike voltage topography in temporal lobe epilepsy.

    PubMed

    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. PMID:27288809

  12. Scaling of spiking and humping in keyhole welding

    NASA Astrophysics Data System (ADS)

    Wei, P. S.; Chuang, K. C.; DebRoy, T.; Ku, J. S.

    2011-06-01

    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.

  13. The Minimum bandwidth of narrowband spikes in solar flare decimetric radio waves

    NASA Astrophysics Data System (ADS)

    Messmer, Peter; Benz, Arnold O.

    2000-02-01

    The minimum and the mean bandwidth of individual narrowband spikes in two events in decimetric radio waves is determined by means of multi-resolution analysis. Spikes of a few tens of millisecond duration occur at decimetric/microwave wavelength in the particle acceleration phase of solar flares. A first method determines the dominant spike bandwidth scale based on their scalegram, the mean squared wavelet coefficient at each frequency scale. This allows to measure the scale bandwidth independently of heuristic spike selection criteria, e.g. manual selection. The major drawback is a low resolution in the bandwidth. To overcome this uncertainty, a feature detection algorithm and a criterion for spike shape in the time-frequency plane is applied to locate the spikes. In that case, the bandwidth is measured by fitting an assumed spike profile into the denoised data. The smallest FWHM bandwidth of spikes was found at 0.17% and 0.41% of the center frequency in the two events. Knowing the shortest relevant bandwidth of spikes, the slope of the Fourier power spectrum of this two events was determined and no resemblance to a Kolmogorov spectrum detected. Additionally the correlation between spike peak flux and bandwidth was examined.

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

    PubMed Central

    Sanlı, Ceyda; Lambiotte, Renaud

    2015-01-01

    We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamical properties, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media. PMID:26161650

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

    PubMed

    Sanlı, Ceyda; Lambiotte, Renaud

    2015-01-01

    We draw a parallel between hashtag time series and neuron spike trains. In each case, the process presents complex dynamic patterns including temporal correlations, burstiness, and all other types of nonstationarity. We propose the adoption of the so-called local variation in order to uncover salient dynamical properties, while properly detrending for the time-dependent features of a signal. The methodology is tested on both real and randomized hashtag spike trains, and identifies that popular hashtags present regular and so less bursty behavior, suggesting its potential use for predicting online popularity in social media. PMID:26161650

  16. Reduced time as a unified parameter determining fixity and free recovery of shape memory polymers.

    PubMed

    Yu, Kai; Ge, Qi; Qi, H Jerry

    2014-01-01

    Shape memory polymers are at the forefront of recent materials research. Although the basic concept has been known for decades, recent advances in the research of shape memory polymers demand a unified approach to predict the shape memory performance under different thermo-temporal conditions. Here we report such an approach to predict the shape fixity and free recovery of thermo-rheologically simple shape memory polymers. The results show that the influence of programming conditions to free recovery can be unified by a reduced programming time that uniquely determines shape fixity, which consequently uniquely determines the shape recovery with a reduced recovery time. Furthermore, using the time-temperature superposition principle, shape recoveries under different thermo-temporal conditions can be extracted from the shape recovery under the reduced recovery time. Finally, a shape memory performance map is constructed based on a few simple standard polymer rheology tests to characterize the shape memory performance of the polymer. PMID:24423789

  17. Effects of extraction and spiking procedures on the determination of incurred residues of oxytetracycline in cattle kidney.

    PubMed

    Cooper, A D; Tarbin, J A; Farrington, W H; Shearer, G

    1998-01-01

    The effects of different extraction and spiking procedures on the determination of incurred oxytetracycline residues in animal tissues have been investigated. The extraction procedures investigated--direct aqueous or organic solvent extraction, enzymic digestion or sonication--all gave similar results for incurred oxytetracycline concentration in cattle kidney after correction for spike recovery. There was therefore no evidence for binding or conjugation of oxytetracycline in this tissue. Highest recovery from spiked tissue was obtained using ethyl acetate as extractant. The effects of spiking procedure (spike contact time, spike solvent and tissue state) on recovery from spiked cattle kidney were also small, indicating that added oxytetracycline spike does not interact with the tissue. PMID:10209574

  18. Upper Limb Biomechanics During the Volleyball Serve and Spike

    PubMed Central

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

    2010-01-01

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

  19. Spike Train Probability Models for Stimulus-Driven Leaky Integrate-and-Fire Neurons

    PubMed Central

    Koyama, Shinsuke; Kass, Robert E.

    2009-01-01

    Mathematical models of neurons are widely used to improve understanding of neuronal spiking behavior. These models can produce artificial spike trains that resemble actual spike train data in important ways, but they are not very easy to apply to the analysis of spike train data. Instead, statistical methods based on point process models of spike trains provide a wide range of data-analytical techniques. Two simplified point process models have been introduced in the literature: the time-rescaled renewal process (TRRP) and the multiplicative inhomogeneous Markov interval (m-IMI) model. In this letter we investigate the extent to which the TRRP and m-IMI models are able to fit spike trains produced by stimulus-driven leaky integrate-and-fire (LIF) neurons. With a constant stimulus, the LIF spike train is a renewal process, and the m-IMI and TRRP models will describe accurately the LIF spike train variability. With a time-varying stimulus, the probability of spiking under all three of these models depends on both the experimental clock time relative to the stimulus and the time since the previous spike, but it does so differently for the LIF, m-IMI, and TRRP models. We assessed the distance between the LIF model and each of the two empirical models in the presence of a time-varying stimulus. We found that while lack of fit of a Poisson model to LIF spike train data can be evident even in small samples, the m-IMI and TRRP models tend to fit well, and much larger samples are required before there is statistical evidence of lack of fit of the m-IMI or TRRP models. We also found that when the mean of the stimulus varies across time, the m-IMI model provides a better fit to the LIF data than the TRRP, and when the variance of the stimulus varies across time, the TRRP provides the better fit. PMID:18336078

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

    PubMed Central

    Matthews, Brett A.; Clements, Mark A.

    2014-01-01

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

  1. Tunable Low Energy, Compact and High Performance Neuromorphic Circuit for Spike-Based Synaptic Plasticity

    PubMed Central

    Rahimi Azghadi, Mostafa; Iannella, Nicolangelo; Al-Sarawi, Said; Abbott, Derek

    2014-01-01

    Cortical circuits in the brain have long been recognised for their information processing capabilities and have been studied both experimentally and theoretically via spiking neural networks. Neuromorphic engineers are primarily concerned with translating the computational capabilities of biological cortical circuits, using the Spiking Neural Network (SNN) paradigm, into in silico applications that can mimic the behaviour and capabilities of real biological circuits/systems. These capabilities include low power consumption, compactness, and relevant dynamics. In this paper, we propose a new accelerated-time circuit that has several advantages over its previous neuromorphic counterparts in terms of compactness, power consumption, and capability to mimic the outcomes of biological experiments. The presented circuit simulation results demonstrate that, in comparing the new circuit to previous published synaptic plasticity circuits, reduced silicon area and lower energy consumption for processing each spike is achieved. In addition, it can be tuned in order to closely mimic the outcomes of various spike timing- and rate-based synaptic plasticity experiments. The proposed circuit is also investigated and compared to other designs in terms of tolerance to mismatch and process variation. Monte Carlo simulation results show that the proposed design is much more stable than its previous counterparts in terms of vulnerability to transistor mismatch, which is a significant challenge in analog neuromorphic design. All these features make the proposed design an ideal circuit for use in large scale SNNs, which aim at implementing neuromorphic systems with an inherent capability that can adapt to a continuously changing environment, thus leading to systems with significant learning and computational abilities. PMID:24551089

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

    PubMed

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

    2005-02-01

    Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance. PMID:15356183

  3. Measuring Time Costs in Interventions Designed to Reduce Behavior Problems Among Children and Youth

    PubMed Central

    Foster, E. Michael; Johnson‐Shelton, Deborah; Taylor, Ted K.

    2007-01-01

    The economic evaluation of psychosocial interventions is a growing area of research. Though time costs are central to the economist’s understanding of social costs, these costs generally have been ignored by prevention scientists. This article highlights the need to measure such costs and then reviews the principles economists use in valuing time. It then considers the specific time costs that often arise in interventions designed to reduce behavior problems among children and youth. These include classroom time devoted to program activities, the time of parents or other caregivers, the time of teachers (outside of the classroom), and the time of volunteers. We consider the economic principles that govern how economists value these inputs and then apply these principles to data from an evaluation of a prominent intervention in the field, the Incredible Years Program. We find that the time costs are potentially rather large and consider the implications for public policy of ignoring them. PMID:17592769

  4. A strategy for reducing turnaround time in design optimization using a distributed computer system

    NASA Technical Reports Server (NTRS)

    Young, Katherine C.; Padula, Sharon L.; Rogers, James L.

    1988-01-01

    There is a need to explore methods for reducing lengthly computer turnaround or clock time associated with engineering design problems. Different strategies can be employed to reduce this turnaround time. One strategy is to run validated analysis software on a network of existing smaller computers so that portions of the computation can be done in parallel. This paper focuses on the implementation of this method using two types of problems. The first type is a traditional structural design optimization problem, which is characterized by a simple data flow and a complicated analysis. The second type of problem uses an existing computer program designed to study multilevel optimization techniques. This problem is characterized by complicated data flow and a simple analysis. The paper shows that distributed computing can be a viable means for reducing computational turnaround time for engineering design problems that lend themselves to decomposition. Parallel computing can be accomplished with a minimal cost in terms of hardware and software.

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

    PubMed Central

    Bi, Zedong; Zhou, Changsong

    2016-01-01

    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 work

  6. Vibration driven vehicle inspired from grass spike

    PubMed Central

    Bai, Suo; Xu, Qi; Qin, Yong

    2013-01-01

    Searching and detecting in some harsh environments such as collapsed buildings, pipes, small cracks are crucial for human rescue and industrial detection, military surveillance etc. However, the drawbacks of traditional moving modes of current vehicles make them difficult to perform such tasks. So developing some new vehicles is urgent. Here, we report a Setaria viridis spike's interesting behavior on a vibrating track, and inspired by that phenomena we develop a concept for cargo delivery, and give a detailed discussion about its working mechanism. This vehicle can move on a wide range of smooth and rough surfaces. Moreover, its climbing capability in tilted and even vertical smooth pipe is also outstanding. These features make it suitable for search-rescue, military reconnaissance, etc. Finally, this vehicle can be reduced into micro/nano-scale, which makes it would play an important role in target-drug delivery, micro-electromechanical systems (MEMS). PMID:23677337

  7. Stochastic optimal control of single neuron spike trains

    NASA Astrophysics Data System (ADS)

    Iolov, Alexandre; Ditlevsen, Susanne; Longtin, André

    2014-08-01

    Objective. External control of spike times in single neurons can reveal important information about a neuron's sub-threshold dynamics that lead to spiking, and has the potential to improve brain-machine interfaces and neural prostheses. The goal of this paper is the design of optimal electrical stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic 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 of control degrades with increasing intensity of the noise. Simulations show that our algorithms produce the desired results for the LIF model, but also for the case where the neuron dynamics are given by more complex models than the LIF model. This is illustrated explicitly using the Morris-Lecar spiking neuron model, for which an LIF approximation is first obtained from a spike sequence using a previously published method. We further show that a related control strategy based on the assumption that there is no noise performs poorly in comparison to our noise-based strategies. The algorithms are numerically intensive and may require efficiency refinements to achieve real-time control; in particular, the open-loop context is more numerically demanding than the closed

  8. Does bacteriology laboratory automation reduce time to results and increase quality management?

    PubMed

    Dauwalder, O; Landrieve, L; Laurent, F; de Montclos, M; Vandenesch, F; Lina, G

    2016-03-01

    Due to reductions in financial and human resources, many microbiological laboratories have merged to build very large clinical microbiology laboratories, which allow the use of fully automated laboratory instruments. For clinical chemistry and haematology, automation has reduced the time to results and improved the management of laboratory quality. The aim of this review was to examine whether fully automated laboratory instruments for microbiology can reduce time to results and impact quality management. This study focused on solutions that are currently available, including the BD Kiestra™ Work Cell Automation and Total Lab Automation and the Copan WASPLab(®). PMID:26577142

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

    PubMed

    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. PMID:21499739

  10. Magnetoencephalographic signatures of insular epileptic spikes based on functional connectivity.

    PubMed

    Zerouali, Younes; Pouliot, Philippe; Robert, Manon; Mohamed, Ismail; Bouthillier, Alain; Lesage, Frédéric; Nguyen, Dang K

    2016-09-01

    Failure to recognize insular cortex seizures has recently been identified as a cause of epilepsy surgeries targeting the temporal, parietal, or frontal lobe. Such failures are partly due to the fact that current noninvasive localization techniques fare poorly in recognizing insular epileptic foci. Our group recently demonstrated that magnetoencephalography (MEG) is sensitive to epileptiform spikes generated by the insula. In this study, we assessed the potential of distributed source imaging and functional connectivity analyses to distinguish insular networks underlying the generation of spikes. Nineteen patients with operculo-insular epilepsy were investigated. Each patient underwent MEG as well as T1-weighted magnetic resonance imaging (MRI) as part of their standard presurgical evaluation. Cortical sources of MEG spikes were reconstructed with the maximum entropy on the mean algorithm, and their time courses served to analyze source functional connectivity. The results indicate that the anterior and posterior subregions of the insula have specific patterns of functional connectivity mainly involving frontal and parietal regions, respectively. In addition, while their connectivity patterns are qualitatively similar during rest and during spikes, couplings within these networks are much stronger during spikes. These results show that MEG can establish functional connectivity-based signatures that could help in the diagnosis of different subtypes of insular cortex epilepsy. Hum Brain Mapp 37:3250-3261, 2016. © 2016 Wiley Periodicals, Inc. PMID:27220112

  11. Population dynamics of interacting spiking neurons

    NASA Astrophysics Data System (ADS)

    Mattia, Maurizio; del Giudice, Paolo

    2002-11-01

    A dynamical equation is derived for the spike emission rate ν(t) of a homogeneous network of integrate-and-fire (IF) neurons in a mean-field theoretical framework, where the activity of the single cell depends both on the mean afferent current (the ``field'') and on its fluctuations. Finite-size effects are taken into account, by a stochastic extension of the dynamical equation for the ν their effect on the collective activity is studied in detail. Conditions for the local stability of the collective activity are shown to be naturally and simply expressed in terms of (the slope of) the single neuron, static, current-to-rate transfer function. In the framework of the local analysis, we studied the spectral properties of the time-dependent collective activity of the finite network in an asynchronous state; finite-size fluctuations act as an ongoing self-stimulation, which probes the spectral structure of the system on a wide frequency range. The power spectrum of ν exhibits modes ranging from very high frequency (depending on spike transmission delays), which are responsible for instability, to oscillations at a few Hz, direct expression of the diffusion process describing the population dynamics. The latter ``diffusion'' slow modes do not contribute to the stability conditions. Their characteristic times govern the transient response of the network; these reaction times also exhibit a simple dependence on the slope of the neuron transfer function. We speculate on the possible relevance of our results for the change in the characteristic response time of a neural population during the learning process which shapes the synaptic couplings, thereby affecting the slope of the transfer function. There is remarkable agreement of the theoretical predictions with simulations of a network of IF neurons with a constant leakage term for the membrane potential.

  12. Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals.

    PubMed

    Melia, Umberto; Clariá, Francesc; Vallverdú, Montserrat; Caminal, Pere

    2014-04-01

    To remove peak and spike artifacts in biological time series has represented a hard challenge in the last decades. Several methods have been implemented mainly based on adaptive filtering in order to solve this problem. This work presents an algorithm for removing peak and spike artifacts based on a threshold built on the analytic signal envelope. The algorithm was tested on simulated and real EEG signals that contain peak and spike artifacts with random amplitude and frequency occurrence. The performance of the filter was compared with commonly used adaptive filters. Three indexes were used for testing the performance of the filters: Correlation coefficient (ρ), mean of coherence function (C), and rate of absolute error (RAE). All these indexes were calculated between filtered signal and original signal without noise. It was found that the new proposed filter was able to reduce the amplitude of peak and spike artifacts with ρ>0.85, C>0.8, and RAE<0.5. These values were significantly better than the performance of LMS adaptive filter (ρ<0.85, C<0.6, and RAE>1). PMID:24365255

  13. Spike phase precession persists after transient intrahippocampal perturbation

    PubMed Central

    Zugaro, Michaël B; Monconduit, Lénaïc; Buzsáki, György

    2007-01-01

    Oscillatory spike timing in the hippocampus is regarded as a temporal coding mechanism for space, but the underlying mechanisms are poorly understood. To contrast the predictions of the different models of phase precession, we transiently turned off neuronal discharges for up to 250 ms and reset the phase of theta oscillations by stimulating the commissural pathway in rats. After recovery from silence, phase precession continued. The phase of spikes for the first theta cycle after the perturbation was more advanced than the phase of spikes for the last theta cycle just before the perturbation. These findings indicate that phase advancement that emerges within hippocampal circuitry may be updated at the beginning of each theta cycle by extrahippocampal inputs. PMID:15592464

  14. Implementation and evaluation of an interictal spike detector

    NASA Astrophysics Data System (ADS)

    Horak, Peter C.; Meisenhelter, Stephen; Testorf, Markus E.; Connolly, Andrew C.; Davis, Kathryn A.; Jobst, Barbara C.

    2015-09-01

    The detection of epileptiform activity, such as interictal spikes, in electrical brain recordings has important clinical and research applications. Identification of interictal spikes is often carried out manually by trained neurologists. It is a time-consuming process and can exhibit variability between experts. In this work, we develop and evaluate an automated spike detector. We implement a template-matching approach and improve its accuracy on one set of recordings using a supervised machine-learning algorithm. Evaluation with two independent datasets shows the template-matching detector to perform comparably with experts and the version augmented with a classifier. In one test dataset, variations of the detection threshold partially explain discrepancies between experts. In the other, the detector demonstrates similar behavior to an existing algorithm developed with this dataset.

  15. Reducing transport delay through improvements in real-time program flow

    NASA Technical Reports Server (NTRS)

    Smith, R. M.

    1992-01-01

    This paper describes the process of measuring and reducing software-transport delays through careful analysis, and modifications of real-time programs. A 737 program is analyzed and modified to improve the simulation overall transport delay by approximately 30 percent. The transport delay was improved through modification to the real-time program flow and the implementation of quaternions in the calculation of the math model.

  16. Two effective approaches to reduce data storage in reverse time migration

    NASA Astrophysics Data System (ADS)

    Sun, Weijia; Fu, Li-Yun

    2013-07-01

    Prestack reverse time migration (RTM) requires extensive data storage since it computes wavefields in forward time and accesses wavefields in reverse order. We first review several successful schemes that have been proposed to reduce data storage, but require more computational redundancies. We propose two effective strategies to reduce data storage during RTM. The first strategy is based on the Nyquist sampling theorem, which involves no extra computational cost. The fact is that the time sampling intervals required by numerical algorithms or given by field records is generally several times smaller than that satisfied by the Nyquist sampling theorem. Therefore, we can correlate the source wavefields with the receiver wavefields at the Nyquist time step, which helps decrease storage of time history. The second strategy is based on a lossless compression algorithm, which is widely used in computer science and information theory. The compression approach reduces storage significantly at a little computational cost. Numerical examples show that the two proposed strategies are effective and efficient.

  17. Time-restricted feeding of a high-fat diet reduces diet-induced obesity

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Reducing obesity may alleviate many medical complications including diabetes, cardiovascular disease and cancer. It has been suggested that obesity is contributed by the disruption of the circadian rhythms in addition to increased caloric intake. Restricting feeding to particular times of the day ma...

  18. Controllability discrepancy and irreducibility/reducibility of Floquet factorisations in linear continuous-time periodic systems

    NASA Astrophysics Data System (ADS)

    Zhou, Jun; Lu, Xinbiao; Qian, Huimin

    2016-09-01

    The paper reports interesting but unnoticed facts about irreducibility (resp., reducibility) of Flouqet factorisations and their harmonic implication in term of controllability in finite-dimensional linear continuous-time periodic (FDLCP) systems. Reducibility and irreducibility are attributed to matrix logarithm algorithms during computing Floquet factorisations in FDLCP systems, which are a pair of essential features but remain unnoticed in the Floquet theory so far. The study reveals that reducible Floquet factorisations may bring in harmonic waves variance into the Fourier analysis of FDLCP systems that in turn may alter our interpretation of controllability when the Floquet factors are used separately during controllability testing; namely, controllability interpretation discrepancy (or simply, controllability discrepancy) may occur and must be examined whenever reducible Floquet factorisations are involved. On the contrary, when irreducible Floquet factorisations are employed, controllability interpretation discrepancy can be avoided. Examples are included to illustrate such observations.

  19. Reduced rank models for travel time estimation of low order mode pulses.

    PubMed

    Chandrayadula, Tarun K; Wage, Kathleen E; Worcester, Peter F; Dzieciuch, Matthew A; Mercer, James A; Andrew, Rex K; Howe, Bruce M

    2013-10-01

    Mode travel time estimation in the presence of internal waves (IWs) is a challenging problem. IWs perturb the sound speed, which results in travel time wander and mode scattering. A standard approach to travel time estimation is to pulse compress the broadband signal, pick the peak of the compressed time series, and average the peak time over multiple receptions to reduce variance. The peak-picking approach implicitly assumes there is a single strong arrival and does not perform well when there are multiple arrivals due to scattering. This article presents a statistical model for the scattered mode arrivals and uses the model to design improved travel time estimators. The model is based on an Empirical Orthogonal Function (EOF) analysis of the mode time series. Range-dependent simulations and data from the Long-range Ocean Acoustic Propagation Experiment (LOAPEX) indicate that the modes are represented by a small number of EOFs. The reduced-rank EOF model is used to construct a travel time estimator based on the Matched Subspace Detector (MSD). Analysis of simulation and experimental data show that the MSDs are more robust to IW scattering than peak picking. The simulation analysis also highlights how IWs affect the mode excitation by the source. PMID:24116527

  20. Analysis of the effects of periodic forcing in the spike rate and spike correlation's in semiconductor lasers with optical feedback

    NASA Astrophysics Data System (ADS)

    Quintero-Quiroz, C.; Sorrentino, Taciano; Torrent, M. C.; Masoller, Cristina

    2016-04-01

    We study the dynamics of semiconductor lasers with optical feedback and direct current modulation, operating in the regime of low frequency fluctuations (LFFs). In the LFF regime the laser intensity displays abrupt spikes: the intensity drops to zero and then gradually recovers. We focus on the inter-spike-intervals (ISIs) and use a method of symbolic time-series analysis, which is based on computing the probabilities of symbolic patterns. We show that the variation of the probabilities of the symbols with the modulation frequency and with the intrinsic spike rate of the laser allows to identify different regimes of noisy locking. Simulations of the Lang-Kobayashi model are in good qualitative agreement with experimental observations.

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

    PubMed Central

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

    2015-01-01

    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. PMID:26266537

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

    PubMed

    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. PMID:26266537

  3. Practical solutions for reducing container ships' waiting times at ports using simulation model

    NASA Astrophysics Data System (ADS)

    Sheikholeslami, Abdorreza; Ilati, Gholamreza; Yeganeh, Yones Eftekhari

    2013-12-01

    The main challenge for container ports is the planning required for berthing container ships while docked in port. Growth of containerization is creating problems for ports and container terminals as they reach their capacity limits of various resources which increasingly leads to traffic and port congestion. Good planning and management of container terminal operations reduces waiting time for liner ships. Reducing the waiting time improves the terminal's productivity and decreases the port difficulties. Two important keys to reducing waiting time with berth allocation are determining suitable access channel depths and increasing the number of berths which in this paper are studied and analyzed as practical solutions. Simulation based analysis is the only way to understand how various resources interact with each other and how they are affected in the berthing time of ships. We used the Enterprise Dynamics software to produce simulation models due to the complexity and nature of the problems. We further present case study for berth allocation simulation of the biggest container terminal in Iran and the optimum access channel depth and the number of berths are obtained from simulation results. The results show a significant reduction in the waiting time for container ships and can be useful for major functions in operations and development of container ship terminals.

  4. Incorporating spike-rate adaptation into a rate code in mathematical and biological neurons.

    PubMed

    Ralston, Bridget N; Flagg, Lucas Q; Faggin, Eric; Birmingham, John T

    2016-06-01

    For a slowly varying stimulus, the simplest relationship between a neuron's input and output is a rate code, in which the spike rate is a unique function of the stimulus at that instant. In the case of spike-rate adaptation, there is no unique relationship between input and output, because the spike rate at any time depends both on the instantaneous stimulus and on prior spiking (the "history"). To improve the decoding of spike trains produced by neurons that show spike-rate adaptation, we developed a simple scheme that incorporates "history" into a rate code. We utilized this rate-history code successfully to decode spike trains produced by 1) mathematical models of a neuron in which the mechanism for adaptation (IAHP) is specified, and 2) the gastropyloric receptor (GPR2), a stretch-sensitive neuron in the stomatogastric nervous system of the crab Cancer borealis, that exhibits long-lasting adaptation of unknown origin. Moreover, when we modified the spike rate either mathematically in a model system or by applying neuromodulatory agents to the experimental system, we found that changes in the rate-history code could be related to the biophysical mechanisms responsible for altering the spiking. PMID:26888106

  5. Upward synaptic scaling is dependent on neurotransmission rather than spiking

    PubMed Central

    Fong, Ming-fai; Newman, Jonathan P.; Potter, Steve M.; Wenner, Peter

    2015-01-01

    Homeostatic plasticity encompasses a set of mechanisms that are thought to stabilize firing rates in neural circuits. The most widely studied form of homeostatic plasticity is upward synaptic scaling (upscaling), characterized by a multiplicative increase in the strength of excitatory synaptic inputs to a neuron as a compensatory response to chronic reductions in firing rate. While reduced spiking is thought to trigger upscaling, an alternative possibility is that reduced glutamatergic transmission generates this plasticity directly. However, spiking and neurotransmission are tightly coupled, so it has been difficult to determine their independent roles in the scaling process. Here we combined chronic multielectrode recording, closed-loop optogenetic stimulation, and pharmacology to show that reduced glutamatergic transmission directly triggers cell-wide synaptic upscaling. This work highlights the importance of synaptic activity in initiating signalling cascades that mediate upscaling. Moreover, our findings challenge the prevailing view that upscaling functions to homeostatically stabilize firing rates. PMID:25751516

  6. Solar flare hard X-ray spikes observed by RHESSI: a case study

    NASA Astrophysics Data System (ADS)

    Qiu, J.; Cheng, J. X.; Hurford, G. J.; Xu, Y.; Wang, H.

    2012-11-01

    Context. Fast-varying hard X-ray spikes of subsecond time scales were discovered by space telescopes in the 70s and 80s, and are also observed by the Ramaty High Energy Solar Spectroscopic Imager (RHESSI). These events indicate that the flare energy release is fragmented. Aims: In this paper, we analyze hard X-ray spikes observed by RHESSI to understand their temporal, spectral, and spatial properties. Methods: A recently developed demodulation code was applied to hard X-ray light curves in several energy bands observed by RHESSI. Hard X-ray spikes were selected from the demodulated flare light curves. We measured the spike duration, the energy-dependent time delay, and count spectral index of these spikes. We also located the hard X-ray source emitting these spikes from RHESSI mapping that was coordinated with imaging observations in visible and UV wavelengths. Results: We identify quickly varying structures of ≤ 1 s during the rise of hard X-rays in five flares. These hard X-ray spikes can be observed at photon energies over 100 keV. They exhibit sharp rise and decay with a duration (FWHM) of less than 1 s. Energy-dependent time lags are present in some spikes. It is seen that the spikes exhibit harder spectra than underlying components, typically by 0.5 in the spectral index when they are fitted to power-law distributions. RHESSI clean maps at 25-100 keV with an integration of 2 s centered on the peak of the spikes suggest that hard X-ray spikes are primarily emitted by double foot-point sources in magnetic fields of opposite polarities. With the RHESSI mapping resolution of ~4'', the hard X-ray spike maps do not exhibit detectable difference in the spatial structure from sources emitting underlying components. Coordinated high-resolution imaging UV and infrared observations confirm that hard X-ray spikes are produced in magnetic structures embedded in the same magnetic environment of the underlying components. The coordinated high-cadence TRACE UV

  7. Caffeine Reduces Reaction Time and Improves Performance in Simulated-Contest of Taekwondo

    PubMed Central

    Santos, Victor G. F.; Santos, Vander R. F.; Felippe, Leandro J. C.; Almeida, Jose W.; Bertuzzi, Rômulo; Kiss, Maria A. P. D. M.; Lima-Silva, Adriano E.

    2014-01-01

    The aim of this study was to investigate the effects of caffeine on reaction time during a specific taekwondo task and athletic performance during a simulated taekwondo contest. Ten taekwondo athletes ingested either 5 mg·kg−1 body mass caffeine or placebo and performed two combats (spaced apart by 20 min). The reaction-time test (five kicks “Bandal Tchagui”) was performed immediately prior to the first combat and immediately after the first and second combats. Caffeine improved reaction time (from 0.42 ± 0.05 to 0.37 ± 0.07 s) only prior to the first combat (P = 0.004). During the first combat, break times during the first two rounds were shorter in caffeine ingestion, followed by higher plasma lactate concentrations compared with placebo (P = 0.029 and 0.014, respectively). During the second combat, skipping-time was reduced, and relative attack times and attack/skipping ratio was increased following ingestion of caffeine during the first two rounds (all P < 0.05). Caffeine resulted in no change in combat intensity parameters between the first and second combat (all P > 0.05), but combat intensity was decreased following placebo (all P < 0.05). In conclusion, caffeine reduced reaction time in non-fatigued conditions and delayed fatigue during successive taekwondo combats. PMID:24518826

  8. Philosophy of the Spike: Rate-Based vs. Spike-Based Theories of the Brain

    PubMed Central

    Brette, Romain

    2015-01-01

    Does the brain use a firing rate code or a spike timing code? Considering this controversial question from an epistemological perspective, I argue that progress has been hampered by its problematic phrasing. It takes the perspective of an external observer looking at whether those two observables vary with stimuli, and thereby misses the relevant question: which one has a causal role in neural activity? When rephrased in a more meaningful way, the rate-based view appears as an ad hoc methodological postulate, one that is practical but with virtually no empirical or theoretical support. PMID:26617496

  9. Real-time, high frequency QRS electrocardiograph with reduced amplitude zone detection

    NASA Technical Reports Server (NTRS)

    Schlegel, Todd T. (Inventor); DePalma, Jude L. (Inventor); Moradi, Saeed (Inventor)

    2009-01-01

    Real time cardiac electrical data are received from a patient, manipulated to determine various useful aspects of the ECG signal, and displayed in real time in a useful form on a computer screen or monitor. The monitor displays the high frequency data from the QRS complex in units of microvolts, juxtaposed with a display of conventional ECG data in units of millivolts or microvolts. The high frequency data are analyzed for their root mean square (RMS) voltage values and the discrete RMS values and related parameters are displayed in real time. The high frequency data from the QRS complex are analyzed with imbedded algorithms to determine the presence or absence of reduced amplitude zones, referred to herein as ''RAZs''. RAZs are displayed as ''go, no-go'' signals on the computer monitor. The RMS and related values of the high frequency components are displayed as time varying signals, and the presence or absence of RAZs may be similarly displayed over time.

  10. Doping-Spike PtSi Schottky Infrared Detectors with Extended Cutoff Wavelengths

    NASA Technical Reports Server (NTRS)

    Lin, T. L.; Park, J. S.; Gunapala, S. D.; Jones, E. W.; Castillo, H. M. Del

    1994-01-01

    A technique incorporating a p+ doping spike at the silicide/Si interface to reduce the effective Schottky barrier of the silicide infrared detectors and thus extend the cutoff wavelength has been developed.

  11. Solar microwave millisecond spike at 2.84 GHz

    NASA Technical Reports Server (NTRS)

    Fu, Qi-Jun; Jin, Sheng-Zhen; Zhao, Ren-Yang; Zheng, Le-Ping; Liu, Yu-Ying; Li, Xiao-Cong; Wang, Shu-Lan; Chen, Zhi-Jun; Hu, Chu-Min

    1986-01-01

    Using the high time resolution of 1 ms, the data of solar microwave millisecond spike (MMS) event was recorded more than two hundred times at the frequency of 2.84 GHz at Beijing (Peking) Observatory since May 1981. A preliminary analysis was made. It can be seen from the data that the MMS-events have a variety of the fast activities such as the dispersed and isolated spikes, the clusters of the crowded spikes, the weak spikes superimposed on the noise background, and the phenomena of absorption. The marked differences from that observed with lower time resolution are presented. Using the data, a valuable statistical analysis was made. There are close correlations between MMS-events and hard X-ray bursts, and fast drifting bursts. The MMS events are highly dependent on the type of active regions and the magnetic field configuration. It seems to be crucial to find out the accurate positions on the active region where the MMS-events happen and to make co-operative observations at different bands during the special period when specific active regions appear on the solar disk.

  12. Audiovisual biofeedback improves image quality and reduces scan time for respiratory-gated 3D MRI

    NASA Astrophysics Data System (ADS)

    Lee, D.; Greer, P. B.; Arm, J.; Keall, P.; Kim, T.

    2014-03-01

    The purpose of this study was to test the hypothesis that audiovisual (AV) biofeedback can improve image quality and reduce scan time for respiratory-gated 3D thoracic MRI. For five healthy human subjects respiratory motion guidance in MR scans was provided using an AV biofeedback system, utilizing real-time respiratory motion signals. To investigate the improvement of respiratory-gated 3D MR images between free breathing (FB) and AV biofeedback (AV), each subject underwent two imaging sessions. Respiratory-related motion artifacts and imaging time were qualitatively evaluated in addition to the reproducibility of external (abdominal) motion. In the results, 3D MR images in AV biofeedback showed more anatomic information such as a clear distinction of diaphragm, lung lobes and sharper organ boundaries. The scan time was reduced from 401±215 s in FB to 334±94 s in AV (p-value 0.36). The root mean square variation of the displacement and period of the abdominal motion was reduced from 0.4±0.22 cm and 2.8±2.5 s in FB to 0.1±0.15 cm and 0.9±1.3 s in AV (p-value of displacement <0.01 and p-value of period 0.12). This study demonstrated that audiovisual biofeedback improves image quality and reduces scan time for respiratory-gated 3D MRI. These results suggest that AV biofeedback has the potential to be a useful motion management tool in medical imaging and radiation therapy procedures.

  13. Reducing Youth Screen Time: Qualitative Metasynthesis of Findings on Barriers and Facilitators

    PubMed Central

    Minges, Karl E.; Salmon, Jo; Dunstan, David W.; Owen, Neville; Chao, Ariana; Whittemore, Robin

    2015-01-01

    Objective An integrated perspective on the relevant qualitative findings on the experience of screen time in youth can inform the development of hypotheses to be tested in future research and can guide the development of interventions to decrease sedentary behavior. The purpose of this qualitative metasynthesis was to explore parent, youth, and educational professionals’ perceptions of barriers to, and facilitators of, reducing youth screen time. Method Qualitative metasynthesis techniques were used to analyze and synthesize 15 qualitative studies of screen time among youth (11–18 years) meeting inclusion criteria. The phrases, quotes, and/or author interpretations (i.e., theme or subtheme) were recorded in a data display matrix to facilitate article comparisons. Codes were collapsed into 23 categories of similar conceptual meaning and 3 overarching themes were derived using thematic analysis procedures. Results Study sample sizes ranged from 6 to 270 participants from 6 countries. Data collection methods included focus groups (n = 6), interviews (n = 4), focus group and interviews (n = 4), and naturalistic observation (n = 1) with youth and/or parents. Data analysis techniques included thematic analysis (n = 9), content analysis (n = 3), grounded theory (n = 1), observation (n = 1), and interpretive phenomenological analysis (n = 1). Three thematic categories were identified: (a) youth’s norms—screen time is an integral part of daily life, and facilitates opportunities for entertainment, social interaction, and escapism; (b) family dynamics and parental roles—parents are conflicted and send mixed messages about the appropriate uses and amounts of screen time; and, (c) resources and environment—engagement in screen time is dependent on school, community, neighborhood, and home environmental contexts. Conclusions Screen time is an established norm in many youth cultures, presenting barriers to behavior change. Parents recognize the importance of reducing

  14. Binary hologram based high speed zonal wavefront sensing with reduced estimation time

    NASA Astrophysics Data System (ADS)

    Pathak, Biswajit; Boruah, Bosanta R.

    2016-03-01

    Reduced wavefront estimation time in a Shack-Hartmann type wavefront sensor plays an important role in any high speed application of the sensor. Exploiting computer generated holography technique, one can generate an array of binary diffraction grating pattern to produce an array of focal spots, similar to that in a Shack Hartmann wavefront sensor (SHWS). The transmittance functions of each of such a grating pattern can be configured to produce a one dimensional (1D) array of focal spots of a desired order. In this paper, we show that the formation of 1D array, further facilitates in the process of single indexed wavefront estimation in its true sense that considerably reduces the wavefront estimation time.

  15. A "Last Word" on Ice Spikes.

    ERIC Educational Resources Information Center

    Perry, Helene F.

    1995-01-01

    Attempts an explanation of how "ice spikes" are formed. The spikes are upward protrusions of ice that occur when water expands as it cools in a rigid container of low thermal conductivity. Describes the results of an investigation and includes color photos. (LZ)

  16. Nursing students' time management, reducing stress and gaining satisfaction: a grounded theory study.

    PubMed

    Mirzaei, Tayebeh; Oskouie, Fatemeh; Rafii, Forough

    2012-03-01

    In the course of their studies, nursing students must learn many skills and acquire the knowledge required for their future profession. This study investigates how Iranian nursing students manage their time according to the circumstances and obstacles of their academic field. Research was conducted using the grounded theory method. Twenty-one nursing students were purposefully chosen as participants. Data was collected through semi-structured interviews and analyzed using the method suggested by Corbin and Strauss. One of the three processes that the nursing students used was "unidirectional time management." This pattern consists of accepting the nursing field, overcoming uncertainty, assessing conditions, feeling stress, and trying to reduce stress and create satisfaction. It was found that students allotted most of their time to academic tasks in an attempt to overcome their stress. The findings of this study indicate the need for these students to have time for the extra-curricular activities and responsibilities that are appropriate to their age. PMID:22293018

  17. Analytic treatment of source photon emission times to reduce noise in implicit Monte Carlo calculations

    SciTech Connect

    Trahan, Travis J.; Gentile, Nicholas A.

    2012-09-10

    Statistical uncertainty is inherent to any Monte Carlo simulation of radiation transport problems. In space-angle-frequency independent radiative transfer calculations, the uncertainty in the solution is entirely due to random sampling of source photon emission times. We have developed a modification to the Implicit Monte Carlo algorithm that eliminates noise due to sampling of the emission time of source photons. In problems that are independent of space, angle, and energy, the new algorithm generates a smooth solution, while a standard implicit Monte Carlo solution is noisy. For space- and angle-dependent problems, the new algorithm exhibits reduced noise relative to standard implicit Monte Carlo in some cases, and comparable noise in all other cases. In conclusion, the improvements are limited to short time scales; over long time scales, noise due to random sampling of spatial and angular variables tends to dominate the noise reduction from the new algorithm.

  18. Discrete-time reduced order neural observers for uncertain nonlinear systems.

    PubMed

    Alanis, Alma Y; Sanchez, Edgar N; Ricalde, Luis J

    2010-02-01

    This paper focusses on a novel discrete-time reduced order neural observer for nonlinear systems, which model is assumed to be unknown. This neural observer is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm, using a parallel configuration. This work includes the stability proof of the estimation error on the basis of the Lyapunov approach; to illustrate the applicability, simulation results for a nonlinear oscillator are included. PMID:20180251

  19. Time reducing exposure containing 18 fluorine flourodeoxyglucose master vial dispensing in hot lab: Omega technique.

    PubMed

    Rao, Vatturi Venkata Satya Prabhakar; Manthri, Ranadheer; Hemalatha, Pottumuthu; Kumar, Vuyyuru Navin; Azhar, Mohammad

    2016-01-01

    Hot lab dispensing of large doses of 18 fluorine fluorodeoxyglucose in master vials supplied from the cyclotrons requires high degrees of skill to handle high doses. Presently practiced conventional method of fractionating from the inverted tiltable vial pig mounted on a metal frame has its own limitations such as increasing isotope handling times and exposure to the technologist. Innovative technique devised markedly improves the fractionating efficiency along with speed, precision, and reduced dose exposure. PMID:27095872

  20. Time reducing exposure containing 18 fluorine flourodeoxyglucose master vial dispensing in hot lab: Omega technique

    PubMed Central

    Rao, Vatturi Venkata Satya Prabhakar; Manthri, Ranadheer; Hemalatha, Pottumuthu; Kumar, Vuyyuru Navin; Azhar, Mohammad

    2016-01-01

    Hot lab dispensing of large doses of 18 fluorine fluorodeoxyglucose in master vials supplied from the cyclotrons requires high degrees of skill to handle high doses. Presently practiced conventional method of fractionating from the inverted tiltable vial pig mounted on a metal frame has its own limitations such as increasing isotope handling times and exposure to the technologist. Innovative technique devised markedly improves the fractionating efficiency along with speed, precision, and reduced dose exposure. PMID:27095872

  1. How being busy can increase motivation and reduce task completion time.

    PubMed

    Wilcox, Keith; Laran, Juliano; Stephen, Andrew T; Zubcsek, Peter P

    2016-03-01

    This research tests the hypothesis that being busy increases motivation and reduces the time it takes to complete tasks for which people miss a deadline. This effect occurs because busy people tend to perceive that they are using their time effectively, which mitigates the sense of failure people have when they miss a task deadline. Studies 1 and 2 show that when people are busy, they are more motivated to complete a task after missing a deadline than those who are not busy, and that the perception that one is using time effectively mediates this effect. Studies 3 and 4 show that this process makes busy people more likely to complete real tasks than people who are not busy. Study 5 uses data from over half a million tasks submitted by thousands of users of a task management software application to show that busy people take less time to complete a task after they miss a deadline for completing it. The findings delineate the conditions under which being busy can mitigate the negative effects of missing a deadline and reduce the time it takes to complete tasks. (PsycINFO Database Record PMID:26963764

  2. A cost sensitive inpatient bed reservation approach to reduce emergency department boarding times.

    PubMed

    Qiu, Shanshan; Chinnam, Ratna Babu; Murat, Alper; Batarse, Bassam; Neemuchwala, Hakimuddin; Jordan, Will

    2015-03-01

    Emergency departments (ED) in hospitals are experiencing severe crowding and prolonged patient waiting times. A significant contributing factor is boarding delays where admitted patients are held in ED (occupying critical resources) until an inpatient bed is identified and readied in the admit wards. Recent research has suggested that if the hospital admissions of ED patients can be predicted during triage or soon after, then bed requests and preparations can be triggered early on to reduce patient boarding time. We propose a cost sensitive bed reservation policy that recommends optimal bed reservation times for patients. The policy relies on a classifier that estimates the probability that the ED patient will be admitted using the patient information collected and readily available at triage or right after. The policy is cost sensitive in that it accounts for costs associated with patient admission prediction misclassification as well as costs associated with incorrectly selecting the reservation time. Results from testing the proposed bed reservation policy using data from a VA Medical Center are very promising and suggest significant cost saving opportunities and reduced patient boarding times. PMID:24811547

  3. USING CENTER HOLE HEAT TRANSFER TO REDUCE FORMATION TIMES FOR CERAMIC WASTE FORMS FROM PYROPROCESSING

    SciTech Connect

    Kenneth J. Bateman; Charles W. Solbrig

    2006-07-01

    The waste produced from processing spent fuel from the EBR II reactor must be processed into a waste form suitable for long term storage in Yucca Mountain. The method chosen produces zeolite granules mixed with glass frit, which must then be converted into a solid. This is accomplished by loading it into a can and heating to 900 C in a furnace regulated at 915 C. During heatup to 900 C, the zeolite and glass frit react and consolidate to produce a sodalite monolith. The resultant ceramic waste form (CWF) is then cooled. The waste is 52 cm in diameter and initially 300 cm long but consolidates to 150 cm long during the heating process. After cooling it is then inserted in a 5-DHLW/DOE SNF Long Canister. Without intervention, the waste takes 82 hours to heat up to 900 C in a furnace designed to geometrically fit the cylindrical waste form. This paper investigates the reduction in heating times possible with four different methods of additional heating through a center hole. The hole size is kept small to maximize the amount of CWF that is processed in a single run. A hole radius of 1.82 cm was selected which removes only 1% of the CWF. A reference computation was done with a specified inner hole surface temperature of 915 C to provide a benchmark for the amount of improvement which can be made. It showed that the heatup time can potentially be reduced to 43 hours with center hole heating. The first method, simply pouring high temperature liquid aluminum into the hole, did not produce any noticeable effect on reducing heat up times. The second method, flowing liquid aluminum through the hole, works well as long as the velocity is high enough (2.5 cm/sec) to prevent solidification of the aluminum during the initial front movement of the aluminum into the center hole. The velocity can be reduced to 1 cm/sec after the initial front has traversed the ceramic. This procedure reduces the formation time to near that of the reference case. The third method, flowing a gas

  4. [On reinforcing-reducing of acupuncture and moxibustion in modern times].

    PubMed

    Yuan, Qing; Yu, Jin; Jin, Rui

    2007-04-01

    Systematize the expound of professor in , a famous scientist of acupuncture and moxibustion, about characteristics of reinforcing-reducing manipulations of acupuncture in modern times. Ancient reinforcing-reducing manipulations of acupuncture stress the cold-hot results under the needle, but for modern manipulations of acupuncture, the stimulating amount is important due to the influence of neurological theories, with soreness, numbness, distension and pain and other nervous response as standard. Professor Jin holds that ancient manipulation is responses of different layers of the body, and the action is in the deep layer,but the modern manipulation is in the superficial layer of the Jing-jin system, alternate application of the both manipulations can increase the therapeutic effect. However, especial attention should be paid to that reinforcing-reducing manipulations of acupuncture is not equal to stimulating amount, and it also is closely related with stimulating period, technique, and other comprehensive factors. Ancient reinforcing-reducing manipulations of acupuncture, cold-heat under the needle is whole comprehensive response, and is deep layer of reinforcing-reducing manipulations of acupuncture, which needs deeply be studied. PMID:17585681

  5. Event-driven contrastive divergence for spiking neuromorphic systems

    PubMed Central

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

    2014-01-01

    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. PMID:24574952

  6. Increasing temperature forcing reduces the Greenland Ice Sheet's response time scale

    NASA Astrophysics Data System (ADS)

    Applegate, Patrick J.; Parizek, Byron R.; Nicholas, Robert E.; Alley, Richard B.; Keller, Klaus

    2015-10-01

    Damages from sea level rise, as well as strategies to manage the associated risk, hinge critically on the time scale and eventual magnitude of sea level rise. Satellite observations and paleo-data suggest that the Greenland Ice Sheet (GIS) loses mass in response to increased temperatures, and may thus contribute substantially to sea level rise as anthropogenic climate change progresses. The time scale of GIS mass loss and sea level rise are deeply uncertain, and are often assumed to be constant. However, previous ice sheet modeling studies have shown that the time scale of GIS response likely decreases strongly with increasing temperature anomaly. Here, we map the relationship between temperature anomaly and the time scale of GIS response, by perturbing a calibrated, three-dimensional model of GIS behavior. Additional simulations with a profile, higher-order, ice sheet model yield time scales that are broadly consistent with those obtained using the three-dimensional model, and shed light on the feedbacks in the ice sheet system that cause the time scale shortening. Semi-empirical modeling studies that assume a constant time scale of sea level adjustment, and are calibrated to small preanthropogenic temperature and sea level changes, may underestimate future sea level rise. Our analysis suggests that the benefits of reducing greenhouse gas emissions, in terms of avoided sea level rise from the GIS, may be greatest if emissions reductions begin before large temperature increases have been realized. Reducing anthropogenic climate change may also allow more time for design and deployment of risk management strategies by slowing sea level contributions from the GIS.

  7. Reduced Operating Time but Not Blood Loss With Cruciate Retaining Total Knee Arthroplasty

    PubMed Central

    Vermesan, Dinu; Trocan, Ilie; Prejbeanu, Radu; Poenaru, Dan V; Haragus, Horia; Gratian, Damian; Marrelli, Massimo; Inchingolo, Francesco; Caprio, Monica; Cagiano, Raffaele; Tatullo, Marco

    2015-01-01

    Background There is no consensus regarding the use of retaining or replacing cruciate implants for patients with limited deformity who undergo a total knee replacement. Scope of this paper is to evaluate whether a cruciate sparing total knee replacement could have a reduced operating time compared to a posterior stabilized implant. Methods For this purpose, we performed a randomized study on 50 subjects. All procedures were performed by a single surgeon in the same conditions to minimize bias and only knees with a less than 20 varus deviation and/or maximum 15° fixed flexion contracture were included. Results Surgery time was significantly shorter with the cruciate retaining implant (P = 0.0037). The mean duration for the Vanguard implant was 68.9 (14.7) and for the NexGen II Legacy was 80.2 (11.3). A higher range of motion, but no significant Knee Society Scores at 6 months follow-up, was used as controls. Conclusions In conclusion, both implants had the potential to assure great outcomes. However, if a decision has to be made, choosing a cruciate retaining procedure could significantly reduce the surgical time. When performed under tourniquet, this gain does not lead to reduced blood loss. PMID:25584102

  8. Spiking dynamics of interacting oscillatory neurons

    NASA Astrophysics Data System (ADS)

    Kazantsev, V. B.; Nekorkin, V. I.; Binczak, S.; Jacquir, S.; Bilbault, J. M.

    2005-06-01

    Spiking sequences emerging from dynamical interaction in a pair of oscillatory neurons are investigated theoretically and experimentally. The model comprises two unidirectionally coupled FitzHugh-Nagumo units with modified excitability (MFHN). The first (master) unit exhibits a periodic spike sequence with a certain frequency. The second (slave) unit is in its excitable mode and responds on the input signal with a complex (chaotic) spike trains. We analyze the dynamic mechanisms underlying different response behavior depending on interaction strength. Spiking phase maps describing the response dynamics are obtained. Complex phase locking and chaotic sequences are investigated. We show how the response spike trains can be effectively controlled by the interaction parameter and discuss the problem of neuronal information encoding.

  9. Reducing the ecological consequences of night-time light pollution: options and developments

    PubMed Central

    Gaston, Kevin J; Davies, Thomas W; Bennie, Jonathan; Hopkins, John

    2012-01-01

    1. Much concern has been expressed about the ecological consequences of night-time light pollution. This concern is most often focused on the encroachment of artificial light into previously unlit areas of the night-time environment, but changes in the spectral composition, duration and spatial pattern of light are also recognized as having ecological effects. 2. Here, we examine the potential consequences for organisms of five management options to reduce night-time light pollution. These are to (i) prevent areas from being artificially lit; (ii) limit the duration of lighting; (iii) reduce the ‘trespass’ of lighting into areas that are not intended to be lit (including the night sky); (iv) change the intensity of lighting; and (v) change the spectral composition of lighting. 3. Maintaining and increasing natural unlit areas is likely to be the most effective option for reducing the ecological effects of lighting. However, this will often conflict with other social and economic objectives. Decreasing the duration of lighting will reduce energy costs and carbon emissions, but is unlikely to alleviate many impacts on nocturnal and crepuscular animals, as peak times of demand for lighting frequently coincide with those in the activities of these species. Reducing the trespass of lighting will maintain heterogeneity even in otherwise well-lit areas, providing dark refuges that mobile animals can exploit. Decreasing the intensity of lighting will reduce energy consumption and limit both skyglow and the area impacted by high-intensity direct light. Shifts towards ‘whiter’ light are likely to increase the potential range of environmental impacts as light is emitted across a broader range of wavelengths. 4. Synthesis and applications. The artificial lightscape will change considerably over coming decades with the drive for more cost-effective low-carbon street lighting solutions and growth in the artificially lit area. Developing lighting strategies that minimize

  10. Characterizing neural activities evoked by manual acupuncture through spiking irregularity measures

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

    The neural system characterizes information in external stimulations by different spiking patterns. In order to examine how neural spiking patterns are related to acupuncture manipulations, experiments are designed in such a way that different types of manual acupuncture (MA) manipulations are taken at the ‘Zusanli’ point of experimental rats, and the induced electrical signals in the spinal dorsal root ganglion are detected and recorded. The interspike interval (ISI) statistical histogram is fitted by the gamma distribution, which has two parameters: one is the time-dependent firing rate and the other is a shape parameter characterizing the spiking irregularities. The shape parameter is the measure of spiking irregularities and can be used to identify the type of MA manipulations. The coefficient of variation is mostly used to measure the spike time irregularity, but it overestimates the irregularity in the case of pronounced firing rate changes. However, experiments show that each acupuncture manipulation will lead to changes in the firing rate. So we combine four relatively rate-independent measures to study the irregularity of spike trains evoked by different types of MA manipulations. Results suggest that the MA manipulations possess unique spiking statistics and characteristics and can be distinguished according to the spiking irregularity measures. These studies have offered new insights into the coding processes and information transfer of acupuncture.

  11. Reducing Dilution and Analysis Time in Online Comprehensive Two-Dimensional Liquid Chromatography by Active Modulation.

    PubMed

    Gargano, Andrea F G; Duffin, Mike; Navarro, Pablo; Schoenmakers, Peter J

    2016-02-01

    Online comprehensive two-dimensional liquid chromatography (LC × LC) offers ways to achieve high-performance separations in terms of peak capacity (exceeding 1000) and additional selectivity to realize applications that cannot be addressed with one-dimensional chromatography (1D-LC). However, the greater resolving power of LC × LC comes at the price of higher dilutions (thus, reduced sensitivity) and, often, long analysis times (>100 min). The need to preserve the separation attained in the first dimension ((1)D) causes greater dilution for LC × LC, in comparison with 1D-LC, and long analysis times to sample the (1)D with an adequate number of second dimension separations. A way to significantly reduce these downsides is to introduce a concentration step between the two chromatographic dimensions. In this work we present a possible active-modulation approach to concentrate the fractions of (1)D effluent. A typical LC × LC system is used with the addition of a dilution flow to decrease the strength of the (1)D effluent and a modulation unit that uses trap columns. The potential of this approach is demonstrated for the separation of tristyrylphenol ethoxylate phosphate surfactants, using a combination of hydrophilic interaction and reversed-phase liquid chromatography. The modified LC × LC system enabled us to halve the analysis time necessary to obtain a similar degree of separation efficiency with respect to UHPLC based LC × LC and of 5 times with respect to HPLC instrumentation (40 compared with 80 and 200 min, respectively), while at the same time reducing dilution (DF of 142, 299, and 1529, respectively) and solvent consumption per analysis (78, 120, and 800 mL, respectively). PMID:26709410

  12. Optimal testing input sets for reduced diagnosis time of nuclear power plant digital electronic circuits

    SciTech Connect

    Kim, D.S.; Seong, P.H. . Dept. of Nuclear Engineering)

    1994-02-01

    This paper describes the optimal testing input sets required for the fault diagnosis of the nuclear power plant digital electronic circuits. With the complicated systems such as very large scale integration (VLSI), nuclear power plant (NPP), and aircraft, testing is the major factor of the maintenance of the system. Particularly, diagnosis time grows quickly with the complexity of the component. In this research, for reduce diagnosis time the authors derived the optimal testing sets that are the minimal testing sets required for detecting the failure and for locating of the failed component. For reduced diagnosis time, the technique presented by Hayes fits best for the approach to testing sets generation among many conventional methods. However, this method has the following disadvantages: (a) it considers only the simple network (b) it concerns only whether the system is in failed state or not and does not provide the way to locate the failed component. Therefore the authors have derived the optimal testing input sets that resolve these problems by Hayes while preserving its advantages. When they applied the optimal testing sets to the automatic fault diagnosis system (AFDS) which incorporates the advanced fault diagnosis method of artificial intelligence technique, they found that the fault diagnosis using the optimal testing sets makes testing the digital electronic circuits much faster than that using exhaustive testing input sets; when they applied them to test the Universal (UV) Card which is a nuclear power plant digital input/output solid state protection system card, they reduced the testing time up to about 100 times.

  13. Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

    PubMed Central

    Gardner, Brian; Grüning, André

    2016-01-01

    Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule’s error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism. PMID:27532262

  14. Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

    PubMed

    Gardner, Brian; Grüning, André

    2016-01-01

    Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism. PMID:27532262

  15. Reduced hierarchical equations of motion in real and imaginary time: Correlated initial states and thermodynamic quantities

    SciTech Connect

    Tanimura, Yoshitaka

    2014-07-28

    For a system strongly coupled to a heat bath, the quantum coherence of the system and the heat bath plays an important role in the system dynamics. This is particularly true in the case of non-Markovian noise. We rigorously investigate the influence of system-bath coherence by deriving the reduced hierarchal equations of motion (HEOM), not only in real time, but also in imaginary time, which represents an inverse temperature. It is shown that the HEOM in real time obtained when we include the system-bath coherence of the initial thermal equilibrium state possess the same form as those obtained from a factorized initial state. We find that the difference in behavior of systems treated in these two manners results from the difference in initial conditions of the HEOM elements, which are defined in path integral form. We also derive HEOM along the imaginary time path to obtain the thermal equilibrium state of a system strongly coupled to a non-Markovian bath. Then, we show that the steady state hierarchy elements calculated from the real-time HEOM can be expressed in terms of the hierarchy elements calculated from the imaginary-time HEOM. Moreover, we find that the imaginary-time HEOM allow us to evaluate a number of thermodynamic variables, including the free energy, entropy, internal energy, heat capacity, and susceptibility. The expectation values of the system energy and system-bath interaction energy in the thermal equilibrium state are also evaluated.

  16. Does Enhancing Work-Time Control and Flexibility Reduce Turnover? A Naturally Occurring Experiment

    PubMed Central

    Moen, Phyllis; Kelly, Erin L.; Hill, Rachelle

    2011-01-01

    We investigate the turnover effects of an organizational innovation (ROWE—Results Only Work Environment) aimed at moving away from standard time practices to focus on results rather than time spent at work. To model rates of turnover, we draw on survey data from a sample of employees at a corporate headquarters (N = 775) and institutional records of turnover over eight months following the ROWE implementation. We find the odds of turnover are indeed lower for employees participating in the ROWE initiative, which offers employees greater work-time control and flexibility, and that this is the case regardless of employees’ gender, age, or family life stage. ROWE also moderates the turnover effects of organizational tenure and negative home-to-work spillover, physical symptoms, and job insecurity, with those in ROWE who report these situations generally less likely to leave the organization. Additionally, ROWE reduces turnover intentions among those remaining with the corporation. This research moves the “opting-out” argument from one of private troubles to an issue of greater employee work-time control and flexibility by showing that an organizational policy initiative can reduce turnover. PMID:21532909

  17. Use of Six Sigma Methodology to Reduce Appointment Lead-Time in Obstetrics Outpatient Department.

    PubMed

    Ortiz Barrios, Miguel A; Felizzola Jiménez, Heriberto

    2016-10-01

    This paper focuses on the issue of longer appointment lead-time in the obstetrics outpatient department of a maternal-child hospital in Colombia. Because of extended appointment lead-time, women with high-risk pregnancy could develop severe complications in their health status and put their babies at risk. This problem was detected through a project selection process explained in this article and to solve it, Six Sigma methodology has been used. First, the process was defined through a SIPOC diagram to identify its input and output variables. Second, six sigma performance indicators were calculated to establish the process baseline. Then, a fishbone diagram was used to determine the possible causes of the problem. These causes were validated with the aid of correlation analysis and other statistical tools. Later, improvement strategies were designed to reduce appointment lead-time in this department. Project results evidenced that average appointment lead-time reduced from 6,89 days to 4,08 days and the deviation standard dropped from 1,57 days to 1,24 days. In this way, the hospital will serve pregnant women faster, which represents a risk reduction of perinatal and maternal mortality. PMID:27580729

  18. Frimand Needle Holder Reduces Suturing Time and Surgical Stress When Suturing in Palm Grip.

    PubMed

    Frimand Rönnow, Carl-Fredrik; Jeppsson, Bengt; Thorlacius, Henrik

    2016-06-01

    Purpose The Frimand needle holder (FNH) was developed to facilitate palm grip suturing. In the present study, we wanted to examine the impact of the FNH compared with a conventional Hegar-styled needle holder (HSNH) on suture time and surgical stress. Methods Thirty-two surgeons were enrolled and they performed sets of 3 continuous sutures on a polyurethane pad with premarked insert and exit points and the time for suturing was measured. Surgical stress was quantified by having the surgeons to perform 10 release maneuvers with the FNH and the HSNH on a needle attached to a scale. The scale sent 5 values per second to a computer. The first measurement of each series was regarded as the starting weight and all subsequent measurements were either regarded as neutral, pressure or traction. The sum of these measurements represented total surgical stress. Results We found that all surgeons reduced their median suturing time by 16% when using FNH for palm grip suturing with no difference between junior and senior surgeons. Moreover, it was observed that FNH decreased median surgical stress by 62% for all surgeons performing palm grip suturing compared with a conventional HSNH. Conclusion We conclude that the FNH reduces suture time and surgical stress compared with HSNH when performing palm grip suturing. These findings warrant studies in surgical patients in order to evaluate the potential clinical impact of FNH. PMID:26474606

  19. A study of the feasibility of using slabbing to reduce tomosynthesis review time

    NASA Astrophysics Data System (ADS)

    Dustler, Magnus; Andersson, Martin; Förnvik, Daniel; Timberg, Pontus; Tingberg, Anders

    2013-03-01

    This study aimed to investigate whether decreasing the amount of slices in breast tomosynthesis (BT) image volumes reduce reading time. BT slices were combined into so-called slabs, by reconstructing thin slices and merging them into thicker slabs. Sets of slabs where created from 35 clinical BT volumes with malignant or benignant findings and from 50 BT volumes drawn from screening sets (without any prior review). The image sets were reviewed in two separate sessions while the review time was recorded. A total of five experienced radiologists were employed for the image review. Additionally a VGA study was performed to compare slabbed images with the originals in order to ensure that the image quality was not significantly degraded. One set of 27 pathological cases (13 masses and 14 microcalcification clusters) and one of 22 subtle lesions that had been missed on digital mammography but detected on BT were presented to an experienced radiologist and 2 medical physicists who rated the quality of the slabbed versions relative to the originals. The study could find no significant degradation in image quality when using 2 mm slabs instead of 1 mm slices. There was no significant decrease in reading time on clinical cases (P = .133), but on screening images there was a significant decrease of 7.7 +/- 9.6 s from an average level of 32.2 +/- 14.5 s (P < .0001). This suggests that increasing slab thickness can reduce the time radiologists spend studying normal images by 20%.

  20. The Global Spike: Conserved Dendritic Properties Enable Unique Ca2+ Spike Generation in Low-Threshold Spiking Neurons

    PubMed Central

    Connelly, William M.; Crunelli, Vincenzo

    2015-01-01

    Low-threshold Ca2+ spikes (LTS) are an indispensible signaling mechanism for neurons in areas including the cortex, cerebellum, basal ganglia, and thalamus. They have critical physiological roles and have been strongly associated with disorders including epilepsy, Parkinson's disease, and schizophrenia. However, although dendritic T-type Ca2+ channels have been implicated in LTS generation, because the properties of low-threshold spiking neuron dendrites are unknown, the precise mechanism has remained elusive. Here, combining data from fluorescence-targeted dendritic recordings and Ca2+ imaging from low-threshold spiking cells in rat brain slices with computational modeling, the cellular mechanism responsible for LTS generation is established. Our data demonstrate that key somatodendritic electrical conduction properties are highly conserved between glutamatergic thalamocortical neurons and GABAergic thalamic reticular nucleus neurons and that these properties are critical for LTS generation. In particular, the efficiency of soma to dendrite voltage transfer is highly asymmetric in low-threshold spiking cells, and in the somatofugal direction, these neurons are particularly electrotonically compact. Our data demonstrate that LTS have remarkably similar amplitudes and occur synchronously throughout the dendritic tree. In fact, these Ca2+ spikes cannot occur locally in any part of the cell, and hence we reveal that LTS are generated by a unique whole-cell mechanism that means they always occur as spatially global spikes. This all-or-none, global electrical and biochemical signaling mechanism clearly distinguishes LTS from other signals, including backpropagating action potentials and dendritic Ca2+/NMDA spikes, and has important consequences for dendritic function in low-threshold spiking neurons. SIGNIFICANCE STATEMENT Low-threshold Ca2+ spikes (LTS) are critical for important physiological processes, including generation of sleep-related oscillations, and are

  1. Team effort identifies opportunities to reduce wait times, improve safety for patients.

    PubMed

    2013-07-01

    The ED at Avera Marshall Regional Medical Center in Marshall, MN, has been able to implement a number of improvements in its throughput process by holding monthly "quick hits" meetings aimed at identifying opportunities for improvement and potential solutions. Among the improvements that grew out of this process is a 12-minute dent in the ED's average decision-to-admit times. Administrators say a collaborative culture has been key to keeping the meetings positive and productive. The "quick hits" meetings typically included nurses, an ED physician, two representatives from the hospital's quality department, and representatives from lab or X-ray as needed. The ED director scheduled the meetings during the morning hours when the ED is typically not busy, and the physician has time to attend. Decision-to-admit times were reduced by giving charge nurses an earlier notification when patients presenting to the ED were likely to be admitted. PMID:23828969

  2. Aligning work and circadian time in shift workers improves sleep and reduces circadian disruption.

    PubMed

    Vetter, Céline; Fischer, Dorothee; Matera, Joana L; Roenneberg, Till

    2015-03-30

    Sleep loss and circadian disruption-a state of misalignment between physiological functions and imposed sleep/wake behavior-supposedly play central roles in the etiology of shift work-related pathologies [1-4]. Circadian entrainment is, however, highly individual [5], resulting in different chronotypes [6, 7]. Chronotype in turn modulates the effects of working times: compared to late chronotypes, earlier ones sleep worse and shorter and show higher levels of circadian misalignment during night shifts, while late types experience more sleep and circadian disruption than early types when working morning shifts [8]. To promote sleep and reduce the mismatch between circadian and working time, we implemented a chronotype-adjusted (CTA) shift schedule in a factory. We abolished the most strenuous shifts for extreme chronotypes (i.e., mornings for late chronotypes, nights for early ones) and examined whether sleep duration and quality, social jetlag [9, 10], wellbeing, subjective stress perception, and satisfaction with leisure time improved in this schedule. Intermediate chronotypes (quartiles 2 and 3) served as a control group, still working morning (6:00-14:00), evening (14:00-22:00), and night (22:00-6:00) shifts, with two strenuous shifts (out of twelve per month) replaced by evening ones. We observed a significant increase of self-reported sleep duration and quality, along with increased wellbeing ratings on workdays among extreme chronotypes. The CTA schedule reduced overall social jetlag by 1 hr, did not alter stress levels, and increased satisfaction with leisure time (early types only). Chronotype-based schedules thus can reduce circadian disruption and improve sleep; potential long-term effects on health and economic indicators need to be elucidated in future studies. PMID:25772446

  3. High precision calcium isotope analysis using 42Ca-48Ca double-spike TIMS technique

    NASA Astrophysics Data System (ADS)

    Feng, L.; Zhou, L.; Gao, S.; Tong, S. Y.; Zhou, M. L.

    2014-12-01

    Double spike techniques are widely used for determining calcium isotopic compositions of natural samples. The most important factor controlling precision of the double spike technique is the choice of appropriate spike isotope pair, the composition of double spikes and the ratio of spike to sample(CSp/CN). We propose an optimal 42Ca-48Ca double spike protocol which yields the best internal precision for calcium isotopic composition determinations among all kinds of spike pairs and various spike compositions and ratios of spike to sample, as predicted by linear error propagation method. It is suggested to use spike composition of 42Ca/(42Ca+48Ca) = 0.44 mol/mol and CSp/(CN+ CSp)= 0.12mol/mol because it takes both advantages of the largest mass dispersion between 42Ca and 48Ca (14%) and lowest spike cost. Spiked samples were purified by pass through homemade micro-column filled with Ca special resin. K, Ti and other interference elements were completely separated, while 100% calcium was recovered with negligible blank. Data collection includes integration time, idle time, focus and peakcenter frequency, which were all carefully designed for the highest internal precision and lowest analysis time. All beams were automatically measured in a sequence by Triton TIMS so as to eliminate difference of analytical conditions between samples and standards, and also to increase the analytical throughputs. The typical internal precision of 100 duty cycles for one beam is 0.012‒0.015 ‰ (2δSEM), which agrees well with the predicted internal precision of 0.0124 ‰ (2δSEM). Our methods improve internal precisions by a factor of 2‒10 compared to previous methods of determination of calcium isotopic compositions by double spike TIMS. We analyzed NIST SRM 915a, NIST SRM 915b and Pacific Seawater as well as interspersed geological samples during two months. The obtained average δ44/40Ca (all relative to NIST SRM 915a) is 0.02 ± 0.02 ‰ (n=28), 0.72±0.04 ‰ (n=10) and 1

  4. A method to reduce response times in prehospital care: the motorcycle experience.

    PubMed

    Lin, C S; Chang, H; Shyu, K G; Liu, C Y; Lin, C C; Hung, C R; Chen, P H

    1998-11-01

    This study compared the response times of a motorcycle and a standard ambulance in a congested urban emergency medical services (EMS) setting. The study was performed in Taipei, Taiwan, a densely populated urban area. A basic life support (BLS) motorcycle (without defibrillation capability) and an advanced life support (ALS) ambulance were based at three study hospitals and simultaneously dispatched when there was a perceived need for ALS ambulance transport. Over a 3-month period, prehospital personnel evaluated 307 medical and trauma emergencies. Time data were insufficient for analysis in 33 cases, leaving a study population of 274. Response times of the motorcycle and the ambulance were prospectively assessed and compared. During rush hours, the response times of the motorcycle and ambulance were 4.9+/-3.0 minutes and 6.3+/-3.4 minutes (P < .05), respectively, and in non-rush hours, 4.2+/-2.1 minutes and 5.1+/-2.5 minutes (P < .05), respectively. Using motorcycles to transport EMTs to the emergency scene significantly reduced response time compared with a standard ambulance in a congested urban setting. Large prospective studies are required to determine the impact on patient outcome of shorter EMS response times using motorcycles. EMS motorcycles appear feasible and deserve consideration to help expedite prehospital care in other systems in densely populated cities. PMID:9827757

  5. Effectiveness of a Myocardial Infarction Protocol in Reducing Door-to-Ballon Time

    PubMed Central

    Correia, Luis Cláudio Lemos; Brito, Mariana; Kalil, Felipe; Sabino, Michael; Garcia, Guilherme; Ferreira, Felipe; Matos, Iracy; Jacobs, Peter; Ronzoni, Liliana; Noya-Rabelo, Márcia

    2013-01-01

    Background An adequate door-to-balloon time (<120 minutes) is the necessary condition for the efficacy of primary angioplasty in infarction to translate into effectiveness. Objective To describe the effectiveness of a quality of care protocol in reducing the door-to-balloon time. Methods Between May 2010 and August 2012, all individuals undergoing primary angioplasty in our hospital were analyzed. The door time was electronically recorded at the moment the patient took a number to be evaluated in the emergency room, which occurred prior to filling the check-in forms and to the triage. The balloon time was defined as the beginning of artery opening (introduction of the first device). The first 5 months of monitoring corresponded to the period of pre-implementation of the protocol. The protocol comprised the definition of a flowchart of actions from patient arrival at the hospital, the team's awareness raising in relation to the prioritization of time, and provision of a periodic feedback on the results and possible inadequacies. Results A total of 50 individuals were assessed. They were divided into five groups of 10 sequential patients (one group pre-and four groups post-protocol). The door-to-balloon time regarding the 10 cases recorded before protocol implementation was 200 ± 77 minutes. After protocol implementation, there was a progressive reduction of the door-to-balloon time to 142 ± 78 minutes in the first 10 patients, then to 150 ± 50 minutes, 131 ± 37 minutes and, finally, 116 ± 29 minutes in the three sequential groups of 10 patients, respectively. Linear regression between sequential patients and the door-to-balloon time (r = - 0.41) showed a regression coefficient of - 1.74 minutes. Conclusion The protocol implementation proved effective in the reduction of the door-to-balloon time. PMID:23702814

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

    PubMed

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

    2009-02-01

    In this paper, we introduce a network of spiking neurons devoted to navigation control. Three different examples, dealing with stimuli of increasing complexity, are investigated. In the first one, obstacle avoidance in a simulated robot is achieved through a network of spiking neurons. In the second example, a second layer is designed aiming to provide the robot with a target approaching system, making it able to move towards visual targets. Finally, a network of spiking neurons for navigation based on visual cues is introduced. In all cases, the robot was assumed to rely on some a priori known responses to low-level sensors (i.e., to contact sensors in the case of obstacles, to proximity target sensors in the case of visual targets, or to the visual target for navigation with visual cues). Based on their knowledge, the robot has to learn the response to high-level stimuli (i.e., range finder sensors or visual input). The biologically plausible paradigm of spike-timing-dependent plasticity (STDP) is included in the network to make the system able to learn high-level responses that guide navigation through a simple unstructured environment. The learning procedure is based on classical conditioning. PMID:19150797

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

    PubMed

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

    2016-01-01

    Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness. PMID:27573200

  8. How Can Monosynaptic Spike Transmission Be So Fast?

    NASA Astrophysics Data System (ADS)

    Platkiewicz, Jonathan; Amarasingham, Asohan

    There has been recently a great deal of interest in ``mapping the brain'', namely in establishing the precise structural organization of neural microcircuits. High-density extracellular recordings offer the unique opportunity to observe simultaneously the activity of hundreds of neurons with millisecond precision in the behaving mammal. Neural connectivity is typically inferred from this recording type by seeking the cell pairs that exhibit finely-timed spike correlation. There is however no widely-accepted biophysical justification for this procedure, nor is there much in the way of ``ground truth'' data that might validate these inferences. First, we showed that a millisecond spike correlation can be observed between monosynaptically connected neurons regardless of the timescale of the postsynaptic potential response. The demonstration is based on the theory of stochastic processes - in particular on an escape noise model - and numerical simulations of biophysical models of monosynaptic spike transfer. Second, using the developed biophysical models, we highlighted the relevance of nonparametric statistical methods, called ``jitter methods'', in connectivity analysis from spike trains, even in the face of extreme firing nonstationarity. Supported by NIH Grant R01MH102840 and DoD (HBCU/MI) Grant W911NF-15-R-0002.

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

    PubMed Central

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

    2016-01-01

    Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep. However, analyzing representations of sleep-associated hippocampal ensemble spike activity remains a great challenge. In contrast to wake, during sleep there is a complete absence of animal behavior, and the ensemble spike activity is sparse (low occurrence) and fragmental in time. To examine important issues encountered in sleep data analysis, we constructed synthetic sleep-like hippocampal spike data (short epochs, sparse and sporadic firing, compressed timescale) for detailed investigations. Based upon two Bayesian population-decoding methods (one receptive field-based, and the other not), we systematically investigated their representation power and detection reliability. Notably, the receptive-field-free decoding method was found to be well-tuned for hippocampal ensemble spike data in slow wave sleep (SWS), even in the absence of prior behavioral measure or ground truth. Our results showed that in addition to the sample length, bin size, and firing rate, number of active hippocampal pyramidal neurons are critical for reliable representation of the space as well as for detection of spatiotemporal reactivated patterns in SWS or quiet wakefulness. PMID:27573200

  10. Using Strahler's analysis to reduce up to 200-fold the run time of realistic neuron models

    NASA Astrophysics Data System (ADS)

    Marasco, Addolorata; Limongiello, Alessandro; Migliore, Michele

    2013-10-01

    The cellular mechanisms underlying higher brain functions/dysfunctions are extremely difficult to investigate experimentally, and detailed neuron models have proven to be a very useful tool to help these kind of investigations. However, realistic neuronal networks of sizes appropriate to study brain functions present the major problem of requiring a prohibitively high computational resources. Here, building on our previous work, we present a general reduction method based on Strahler's analysis of neuron morphologies. We show that, without any fitting or tuning procedures, it is possible to map any morphologically and biophysically accurate neuron model into an equivalent reduced version. Using this method for Purkinje cells, we demonstrate how run times can be reduced up to 200-fold, while accurately taking into account the effects of arbitrarily located and activated synaptic inputs.

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

    PubMed

    Strobbe, Gregor; Carrette, Evelien; López, José David; Montes Restrepo, Victoria; Van Roost, Dirk; Meurs, Alfred; Vonck, Kristl; Boon, Paul; Vandenberghe, Stefaan; van Mierlo, Pieter

    2016-01-01

    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 epochs were in

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

    PubMed Central

    Strobbe, Gregor; Carrette, Evelien; López, José David; Montes Restrepo, Victoria; Van Roost, Dirk; Meurs, Alfred; Vonck, Kristl; Boon, Paul; Vandenberghe, Stefaan; van Mierlo, Pieter

    2016-01-01

    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 epochs were in

  13. Time-restricted feeding reduces adiposity in mice fed a high-fat diet.

    PubMed

    Sundaram, Sneha; Yan, Lin

    2016-06-01

    Disruption of the circadian rhythm contributes to obesity. This study tested the hypothesis that time-restricted feeding (TRF) reduces high-fat diet-induced increase in adiposity. Male C57BL/6 mice were fed the AIN93G or the high-fat diet ad libitum (ad lib); TRF of the high-fat diet for 12 or 8hours during the dark cycle was initiated when high-fat diet-fed mice exhibited significant increases in body weight. Energy intake of the TRF 12-hour group was not different from that of the high-fat ad lib group, although that of the TRF 8-hour group was slightly but significantly lower. Restricted feeding of the high-fat diet reduced body fat mass and body weight compared with mice fed the high-fat diet ad lib. There were no differences in respiratory exchange ratio (RER) among TRF and high-fat ad lib groups, but the RER of these groups was lower than that of the AIN93G group. Energy expenditure of the TRF groups was slightly but significantly lower than that of the high-fat ad lib group. Plasma concentrations of ghrelin were increased in TRF groups compared with both AIN93G and high-fat ad lib groups. Elevations of plasma concentrations of insulin, leptin, monocyte chemoattractant protein-1, and tissue inhibitor metalloproteinase-1 by high-fat ad lib feeding were reduced by TRF to the levels of mice fed the AIN93G diet. In conclusion, TRF during the dark cycle reduces high-fat diet-induced increases in adiposity and proinflammatory cytokines. These results indicate that circadian timing of food intake may prevent obesity and abate obesity-related metabolic disturbance. PMID:27188906

  14. Medical alert management: a real-time adaptive decision support tool to reduce alert fatigue.

    PubMed

    Lee, Eva K; Wu, Tsung-Lin; Senior, Tal; Jose, James

    2014-01-01

    With the adoption of electronic medical records (EMRs), drug safety alerts are increasingly recognized as valuable tools for reducing adverse drug events and improving patient safety. However, even with proper tuning of the EMR alert parameters, the volume of unfiltered alerts can be overwhelming to users. In this paper, we design an adaptive decision support tool in which past cognitive overriding decisions of users are learned, adapted and used for filtering actions to be performed on current alerts. The filters are designed and learned based on a moving time window, number of alerts, overriding rates, and monthly overriding fluctuations. Using alerts from two separate years to derive filters and test performance, predictive accuracy rates of 91.3%-100% are achieved. The moving time window works better than a static training approach. It allows continuous learning and capturing of the most recent decision characteristics and seasonal variations in drug usage. The decision support system facilitates filtering of non-essential alerts and adaptively learns critical alerts and highlights them prominently to catch providers' attention. The tool can be plugged into an existing EMR system as an add-on, allowing real-time decision support to users without interfering with existing EMR functionalities. By automatically filtering the alerts, the decision support tool mitigates alert fatigue and allows users to focus resources on potentially vital alerts, thus reducing the occurrence of adverse drug events. PMID:25954391

  15. Methods of Optimal Control of Laser-Plasma Instabilities Using Spike Trains of Uneven Duration and Delay (STUD Pulses)

    NASA Astrophysics Data System (ADS)

    Afeyan, Bedros

    2013-10-01

    We have recently introduced and extensively studied a new adaptive method of LPI control. It promises to extend the effectiveness of laser as inertial fusion drivers by allowing active control of stimulated Raman and Brillouin scattering and crossed beam energy transfer. It breaks multi-nanosecond pulses into a series of picosecond (ps) time scale spikes with comparable gaps in between. The height and width of each spike as well as their separations are optimization parameters. In addition, the spatial speckle patterns are changed after a number of successive spikes as needed (from every spike to never). The combination of these parameters allows the taming of parametric instabilities to conform to any desired reduced reflectivity profile, within the bounds of the performance limitations of the lasers. Instead of pulse shaping on hydrodynamical time scales, far faster (from 1 ps to 10 ps) modulations of the laser profile will be needed to implement the STUD pulse program for full LPI control. We will show theoretical and computational evidence for the effectiveness of the STUD pulse program to control LPI. The physics of why STUD pulses work and how optimization can be implemented efficiently using statistical nonlinear optical models and techniques will be explained. We will also discuss a novel diagnostic system employing STUD pulses that will allow the boosted measurement of velocity distribution function slopes on a ps time scale in the small crossing volume of a pump and a probe beam. Various regimes from weak to strong coupling and weak to strong damping will be treated. Novel pulse modulation schemes and diagnostic tools based on time-lenses used in both microscope and telescope modes will be suggested for the execution of the STUD pule program. Work Supported by the DOE NNSA-OFES Joint Program on HEDLP and DOE OFES SBIR Phase I Grants.

  16. Leisure-Time Running Reduces All-Cause and Cardiovascular Mortality Risk

    PubMed Central

    Lee, Duck-chul; Pate, Russell R.; Lavie, Carl J.; Sui, Xuemei; Church, Timothy S.; Blair, Steven N.

    2014-01-01

    Background Although running is a popular leisure-time physical activity, little is known about the long-term effects of running on mortality. The dose-response relations between running, as well as the change in running behaviors over time and mortality remain uncertain. Objectives We examined the associations of running with all-cause and cardiovascular mortality risks in 55,137 adults, aged 18 to 100 years (mean age, 44). Methods Running was assessed on the medical history questionnaire by leisure-time activity. Results During a mean follow-up of 15 years, 3,413 all-cause and 1,217 cardiovascular deaths occurred. Approximately, 24% of adults participated in running in this population. Compared with non-runners, runners had 30% and 45% lower adjusted risks of all-cause and cardiovascular mortality, respectively, with a 3-year life expectancy benefit. In dose-response analyses, the mortality benefits in runners were similar across quintiles of running time, distance, frequency, amount, and speed, compared with non-runners. Weekly running even <51 minutes, <6 miles, 1-2 times, <506 metabolic equivalent-minutes, or <6 mph was sufficient to reduce risk of mortality, compared with not running. In the analyses of change in running behaviors and mortality, persistent runners had the most significant benefits with 29% and 50% lower risks of all-cause and cardiovascular mortality, respectively, compared with never-runners. Conclusions Running, even 5-10 minutes per day and slow speeds <6 mph, is associated with markedly reduced risks of death from all causes and cardiovascular disease. This study may motivate healthy but sedentary individuals to begin and continue running for substantial and attainable mortality benefits. PMID:25082581

  17. Input-output relation and energy efficiency in the neuron with different spike threshold dynamics

    PubMed Central

    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. PMID:26074810

  18. Asynchronous Rate Chaos in Spiking Neuronal Circuits.

    PubMed

    Harish, Omri; Hansel, David

    2015-07-01

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

  19. Spiking Neural P Systems with Neuron Division and Dissolution.

    PubMed

    Zhao, Yuzhen; Liu, Xiyu; Wang, Wenping

    2016-01-01

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

  20. An analysis of thermal response factors and how to reduce their computational time requirement

    NASA Technical Reports Server (NTRS)

    Wiese, M. R.

    1982-01-01

    Te RESFAC2 version of the Thermal Response Factor Program (RESFAC) is the result of numerous modifications and additions to the original RESFAC. These modifications and additions have significantly reduced the program's computational time requirement. As a result of this work, the program is more efficient and its code is both readable and understandable. This report describes what a thermal response factor is; analyzes the original matrix algebra calculations and root finding techniques; presents a new root finding technique and streamlined matrix algebra; supplies ten validation cases and their results.

  1. Effect of hydrothermal reaction time and alkaline conditions on the electrochemical properties of reduced graphene oxide

    NASA Astrophysics Data System (ADS)

    Vermisoglou, E. C.; Giannakopoulou, T.; Romanos, G.; Giannouri, M.; Boukos, N.; Lei, C.; Lekakou, C.; Trapalis, C.

    2015-12-01

    Reduced graphene oxide sheets (rGO) were prepared by hydrothermal treatment of aqueous dispersions of graphite oxide (GtO) applied for short (4 h) and prolonged reaction times (19-24 h). The effect of process duration as well as the alkaline conditions (pH ∼10) by addition of K2CO3 on the quality characteristics of the produced rGO materials was investigated. Both reduction and exfoliation occurred during this process as it was evidenced by FTIR and XRD data. SEM, TEM and HRTEM microscopy displayed highly exfoliated rGO materials. XPS verified that the re-establishment of the conjugated graphene network is more extensive for prolonged times of hydrothermal processing in accordance to Raman spectroscopy measurements. The sample produced under alkaline conditions bore fewer defects and almost 5 times higher BET surface area (∼181 m2/g) than the sample with no pH adjustment (∼34 m2/g) for the same hydrothermal reaction time (19 h), attributed to the developed microporosity. The specific capacitance of this material estimated by electrochemical impedance using three-electrode cell and KCl aqueous solution as an electrolyte was ∼400-500 F/g. When EDLC capacitors were fabricated from rGO materials the electrochemical testing in organic electrolyte i.e. TEABF4 in PC, revealed that the shortest hydrothermal reaction time (4 h) was more efficient resulting in capacitance around 60 F/g.

  2. Decoding of intracellular calcium spike trains

    NASA Astrophysics Data System (ADS)

    Prank, K.; Läer, L.; von zur Mühlen, A.; Brabant, G.; Schöfl, C.

    1998-04-01

    Cells respond to external signals, such as hormonal stimuli, by generating repetitive spikes in the intracellular free-calcium concentration ([Ca2+]i). These [Ca2+]i spikes, which can be modulated in their frequency and amplitude, regulate diverse cellular processes. Experimentally, [Ca2+]i can be assessed continuously in contrast to cellular responses represented by the activation of proteins. We propose a mathematical model that allows for the on-line decoding of [Ca2+]i spike trains into cellular responses represented by the activation of proteins.

  3. Spiking sediment with organochlorines for toxicity testing

    SciTech Connect

    Murdoch, M.H.; Chapman, P.M.; Norman, D.M.; Quintino, V.M.

    1997-07-01

    Sediment toxicity testing integrates responses to sediment variables and hence does not directly indicate cause and effect. One tool for determining cause and effect is sediment spiking, in which relatively uncontaminated sediment is amended with known amounts of contaminants, then tested for toxicity. However, sediment spiking methods vary considerably. The present study details appropriate methodologies (dry and wet spiking) for amending sediments with a range of organic contaminant concentrations, i.e., polychlorinated biphenyl (PCB). Target and actual concentrations were similar. A dose-response was determined, but PCB was not toxic in an acute sediment toxicity test. Chronic testing of these same sediments is reported in a companion article in this issue.

  4. Vibration (?) spikes during natural rain events

    NASA Technical Reports Server (NTRS)

    Short, David A.

    1994-01-01

    Limited analysis of optical rain gauge (ORG) data from shipboard and ground based sensors has shown the existence of spikes, possibly attributable to sensor vibration, while rain is occurring. An extreme example of this behavior was noted aboard the PRC#5 on the evening of December 24, 1992 as the ship began repositioning during a rain event in the TOGA/COARE IFA. The spikes are readily evident in the one-second resolution data, but may be indistinguishable from natural rain rate fluctuations in subsampled or averaged data. Such spikes result in increased rainfall totals.

  5. Passive dosing versus solvent spiking for controlling and maintaining hydrophobic organic compound exposure in the Microtox® assay.

    PubMed

    Smith, Kilian E C; Jeong, Yoonah; Kim, Jongwoon

    2015-11-01

    Microbial toxicity bioassays such as the Microtox® test are ubiquitously applied to measure the toxicity of chemicals and environmental samples. In many ways their operation is conducive to the testing of organic chemicals. They are of short duration, use glass cuvettes and take place at reduced temperatures in medium lacking sorbing components. All of these are expected to reduce sorptive and volatile losses, but particularly for hydrophobic organics the role of such losses in determining the bioassay response remains unclear. This study determined the response of the Microtox® test when using solvent spiking compared to passive dosing for introducing the model hydrophobic compounds acenaphthene, phenanthrene, fluoranthene and benzo(a)pyrene. Compared to solvent spiking, the apparent sensitivity of the Microtox® test with passive dosing was 3.4 and 12.4 times higher for acenaphthene and phenanthrene, respectively. Furthermore, fluoranthene only gave a consistent response with passive dosing. Benzo(a)pyrene did not result in a response with either spiking or passive dosing even at aqueous solubility. Such differences in the apparent sensitivity of the Microtox® test can be traced back to the precise definition of the dissolved exposure concentrations and the buffering of losses with passive dosing. This highlights the importance of exposure control even in simple and short-term microbial bioassays such as the Microtox® test. PMID:26117202

  6. Origin of Bursting through Homoclinic Spike Adding in a Neuron Model

    NASA Astrophysics Data System (ADS)

    Channell, Paul; Cymbalyuk, Gennady; Shilnikov, Andrey

    2007-03-01

    The origin of spike adding in bursting activity is studied in a reduced model of the leech heart interneuron. We show that, as the activation kinetics of the slow potassium current are shifted towards depolarized membrane potential values, the bursting phase accommodates incrementally more spikes into the train. This phenomenon is attested to be caused by the homoclinic bifurcations of a saddle periodic orbit setting the threshold between the tonic spiking and quiescent phases of the bursting. The fundamentals of the mechanism are revealed through the analysis of a family of the onto Poincaré return mappings.

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

    PubMed Central

    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

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

    PubMed

    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

  9. Does Reducing Time to Identification of Infectious Agents Reduce Incidence Rates of Norovirus in a Population Deployed to Southwest Asia?

    PubMed

    Thompson, Kip R; Mossel, Eric C; Federman, Belle; Claborn, David M

    2016-01-01

    During its deployment to Kuwait from 2011-2012, the 983rd Medical Detachment (Preventive Medicine) was augmented with a 4-person laboratory section which provided polymerase chain reaction capabilities not normally associated with an Army Level III preventive medicine detachment. Although common in many civilian laboratories, this was the first time this equipment was used by a deployed Level III Army preventive medicine detachment to identify an outbreak in this theater. It allowed rapid identification and description of a gastrointestinal disease outbreak caused by norovirus in Kuwait. The technology contributed to a decreased time required to identification of the causative agent (hours vs days) and thus the implementation of appropriate preventive measures. Based on this event, the authors suggest the addition of a modified laboratory section to the modified table of organization equipment for deployable preventive medicine detachments. PMID:27613209

  10. Variance-reduced particle simulation of the Boltzmann transport equation in the relaxation-time approximation.

    PubMed

    Radtke, Gregg A; Hadjiconstantinou, Nicolas G

    2009-05-01

    We present an efficient variance-reduced particle simulation technique for solving the linearized Boltzmann transport equation in the relaxation-time approximation used for phonon, electron, and radiative transport, as well as for kinetic gas flows. The variance reduction is achieved by simulating only the deviation from equilibrium. We show that in the limit of small deviation from equilibrium of interest here, the proposed formulation achieves low relative statistical uncertainty that is also independent of the magnitude of the deviation from equilibrium, in stark contrast to standard particle simulation methods. Our results demonstrate that a space-dependent equilibrium distribution improves the variance reduction achieved, especially in the collision-dominated regime where local equilibrium conditions prevail. We also show that by exploiting the physics of relaxation to equilibrium inherent in the relaxation-time approximation, a very simple collision algorithm with a clear physical interpretation can be formulated. PMID:19518597

  11. Preoperative computed tomography angiography for planning DIEP flap breast reconstruction reduces operative time and overall complications

    PubMed Central

    Rozen, Warren Matthew; Chowdhry, Muhammad; Band, Bassam; Ramakrishnan, Venkat V.; Griffiths, Matthew

    2016-01-01

    Background The approach and operative techniques associated with breast reconstruction have steadily been refined since its inception, with abdominal perforator-based flaps becoming the gold standard reconstructive option for women undergoing breast cancer surgery. The current study comprises a cohort of 632 patients, in whom specific operative times are recorded by a blinded observer, and aims to address the potential benefits seen with the use of computer tomography (CT) scanning preoperatively on operative outcomes, complications and surgical times. Methods A prospectively recorded, retrospective review was undertaken of patients undergoing autologous breast reconstruction with a DIEP flap at the St Andrews Centre over a 4-year period from 2010 to 2014. Computed tomography angiography (CTA) scanning of patients began in September 2012 and thus 2 time periods were compared: 2 years prior to the use of CTA scans and 2 years afterwards. For all patients, key variables were collected including patient demographics, operative times, flap harvest time, pedicle length, surgeon experience and complications. Results In group 1, comprising patients within the period prior to CTA scans, 265 patients underwent 312 flaps; whilst in group 2, the immediately following 2 years, 275 patients had 320 flaps. The use of preoperative CTA scans demonstrated a significant reduction in flap harvest time of 13 minutes (P<0.013). This significant time saving was seen in all flap modifications: unilateral, bilateral and bipedicled DIEP flaps. The greatest time saving was seen in bipedicle flaps, with a 35-minute time saving. The return to theatre rate significantly dropped from 11.2% to 6.9% following the use of CTA scans, but there was no difference in the total failure rate. Conclusions The study has demonstrated both a benefit to flap harvest time as well as overall operative times when using preoperative CTA. The use of CTA was associated with a significant reduction in complications

  12. State-of-the-Art Solar Simulator Reduces Measurement Time and Uncertainty (Fact Sheet)

    SciTech Connect

    Not Available

    2012-04-01

    One-Sun Multisource Solar Simulator (OSMSS) brings accurate energy-rating predictions that account for the nonlinear behavior of multijunction photovoltaic devices. The National Renewable Energy Laboratory (NREL) is one of only a few International Organization for Standardization (ISO)-accredited calibration labs in the world for primary and secondary reference cells and modules. As such, it is critical to seek new horizons in developing simulators and measurement methods. Current solar simulators are not well suited for accurately measuring multijunction devices. To set the electrical current to each junction independently, simulators must precisely tune the spectral content with no overlap between the wavelength regions. Current simulators do not have this capability, and the overlaps lead to large measurement uncertainties of {+-}6%. In collaboration with LabSphere, NREL scientists have designed and implemented the One-Sun Multisource Solar Simulator (OSMSS), which enables automatic spectral adjustment with nine independent wavelength regions. This fiber-optic simulator allows researchers and developers to set the current to each junction independently, reducing errors relating to spectral effects. NREL also developed proprietary software that allows this fully automated simulator to rapidly 'build' a spectrum under which all junctions of a multijunction device are current matched and behave as they would under a reference spectrum. The OSMSS will reduce the measurement uncertainty for multijunction devices, while significantly reducing the current-voltage measurement time from several days to minutes. These features will enable highly accurate energy-rating predictions that take into account the nonlinear behavior of multijunction photovoltaic devices.

  13. On some classes of sequential spiking neural p systems.

    PubMed

    Zhang, Xingyi; Zeng, Xiangxiang; Luo, Bin; Pan, Linqiang

    2014-05-01

    Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes; neurons work in parallel in the sense that each neuron that can fire should fire, but the work in each neuron is sequential in the sense that at most one rule can be applied at each computation step. In this work, with biological inspiration, we consider SN P systems with the restriction that at each step, one of the neurons (i.e., sequential mode) or all neurons (i.e., pseudo-sequential mode) with the maximum (or minimum) number of spikes among the neurons that are active (can spike) will fire. If an active neuron has more than one enabled rule, it nondeterministically chooses one of the enabled rules to be applied, and the chosen rule is applied in an exhaustive manner (a kind of local parallelism): the rule is used as many times as possible. This strategy makes the system sequential or pseudo-sequential from the global view of the whole network and locally parallel at the level of neurons. We obtain four types of SN P systems: maximum/minimum spike number induced sequential/pseudo-sequential SN P systems with exhaustive use of rules. We prove that SN P systems of these four types are all Turing universal as number-generating computation devices. These results illustrate that the restriction of sequentiality may have little effect on the computation power of SN P systems. PMID:24555456

  14. Value of increasing film processing time to reduce radiation dose during mammography

    SciTech Connect

    Skubic, S.E.; Yagan, R.; Oravec, D.; Shah, Z. )

    1990-12-01

    We systematically tested the effects on radiation dose and image quality of increasing the mammographic film processing time from the standard 90 sec to 3 min. Hurter and Driffield curves were obtained for a Kodak Min-R-OM1-SO177 screen-film combination processed with Kodak chemistry. Image contrast and radiation dose were measured for two tissue-equivalent breast phantoms. We also compared sequential pairs of mammograms, one processed at 90 sec and one at 3 min, from 44 patients on the basis of nine categories of image quality. Increased processing time reduced breast radiation dose by 30%, increased contrast by 11%, and produced slight overall gains in image quality. Simple modifications can convert a 90-sec processor to a 3-min unit. We recommend that implementation of extended processing be considered, especially by those centers that obtain a large number of screening mammograms. Three-minute film processing can reduce breast radiation dose by 30% and increase contrast by 11% without compromising image quality.

  15. Schizophrenia Spectrum Disorders Show Reduced Specificity and Less Positive Events in Mental Time Travel.

    PubMed

    Chen, Xing-Jie; Liu, Lu-Lu; Cui, Ji-Fang; Wang, Ya; Chen, An-Tao; Li, Feng-Hua; Wang, Wei-Hong; Zheng, Han-Feng; Gan, Ming-Yuan; Li, Chun-Qiu; Shum, David H K; Chan, Raymond C K

    2016-01-01

    Mental time travel refers to the ability to recall past events and to imagine possible future events. Schizophrenia (SCZ) patients have problems in remembering specific personal experiences in the past and imagining what will happen in the future. This study aimed to examine episodic past and future thinking in SCZ spectrum disorders including SCZ patients and individuals with schizotypal personality disorder (SPD) proneness who are at risk for developing SCZ. Thirty-two SCZ patients, 30 SPD proneness individuals, and 33 healthy controls participated in the study. The Sentence Completion for Events from the Past Test (SCEPT) and the Sentence Completion for Events in the Future Test were used to measure past and future thinking abilities. Results showed that SCZ patients showed significantly reduced specificity in recalling past and imagining future events, they generated less proportion of specific and extended events compared to healthy controls. SPD proneness individuals only generated less extended events compared to healthy controls. The reduced specificity was mainly manifested in imagining future events. Both SCZ patients and SPD proneness individuals generated less positive events than controls. These results suggest that mental time travel impairments in SCZ spectrum disorders and have implications for understanding their cognitive and emotional deficits. PMID:27507958

  16. Nonlinear Reduced-Order Analysis with Time-Varying Spatial Loading Distributions

    NASA Technical Reports Server (NTRS)

    Prezekop, Adam

    2008-01-01

    Oscillating shocks acting in combination with high-intensity acoustic loadings present a challenge to the design of resilient hypersonic flight vehicle structures. This paper addresses some features of this loading condition and certain aspects of a nonlinear reduced-order analysis with emphasis on system identification leading to formation of a robust modal basis. The nonlinear dynamic response of a composite structure subject to the simultaneous action of locally strong oscillating pressure gradients and high-intensity acoustic loadings is considered. The reduced-order analysis used in this work has been previously demonstrated to be both computationally efficient and accurate for time-invariant spatial loading distributions, provided that an appropriate modal basis is used. The challenge of the present study is to identify a suitable basis for loadings with time-varying spatial distributions. Using a proper orthogonal decomposition and modal expansion, it is shown that such a basis can be developed. The basis is made more robust by incrementally expanding it to account for changes in the location, frequency and span of the oscillating pressure gradient.

  17. Community treatment orders and reduced time in hospital: a nationwide study, 2007–2012

    PubMed Central

    Taylor, Mark; Macpherson, Melanie; Macleod, Callum; Lyons, Donald

    2016-01-01

    Aims and method Community treatment orders (CTOs) were introduced in Scotland in 2005, but are controversial owing to a lack of supportive randomised evidence. The non-randomised studies provide mixed results on their efficacy and utility. We aimed to examine hospital bed day usage across Scotland both before and after CTOs were initiated in a national cohort of patients, spanning 5 years. Results In total, 1558 individuals who were subject to a CTO between 2007 and 2012, of whom 63% were male, were included. After CTO initiation the number of hospital bed days fell, on average, from 66 to 39 per annum per patient. Those with a longer psychiatric history appeared to benefit more from a CTO, in terms of reduced time in hospital. Clinical implications Our data offer cautious support for the use of CTOs in routine practice, in terms of reducing time spent in psychiatric hospital. This finding is balanced by the more rigorous randomised studies which do not find any benefit to CTOs. PMID:27280031

  18. Community treatment orders and reduced time in hospital: a nationwide study, 2007-2012.

    PubMed

    Taylor, Mark; Macpherson, Melanie; Macleod, Callum; Lyons, Donald

    2016-06-01

    Aims and method Community treatment orders (CTOs) were introduced in Scotland in 2005, but are controversial owing to a lack of supportive randomised evidence. The non-randomised studies provide mixed results on their efficacy and utility. We aimed to examine hospital bed day usage across Scotland both before and after CTOs were initiated in a national cohort of patients, spanning 5 years. Results In total, 1558 individuals who were subject to a CTO between 2007 and 2012, of whom 63% were male, were included. After CTO initiation the number of hospital bed days fell, on average, from 66 to 39 per annum per patient. Those with a longer psychiatric history appeared to benefit more from a CTO, in terms of reduced time in hospital. Clinical implications Our data offer cautious support for the use of CTOs in routine practice, in terms of reducing time spent in psychiatric hospital. This finding is balanced by the more rigorous randomised studies which do not find any benefit to CTOs. PMID:27280031

  19. Schizophrenia Spectrum Disorders Show Reduced Specificity and Less Positive Events in Mental Time Travel

    PubMed Central

    Chen, Xing-jie; Liu, Lu-lu; Cui, Ji-fang; Wang, Ya; Chen, An-tao; Li, Feng-hua; Wang, Wei-hong; Zheng, Han-feng; Gan, Ming-yuan; Li, Chun-qiu; Shum, David H. K.; Chan, Raymond C. K.

    2016-01-01

    Mental time travel refers to the ability to recall past events and to imagine possible future events. Schizophrenia (SCZ) patients have problems in remembering specific personal experiences in the past and imagining what will happen in the future. This study aimed to examine episodic past and future thinking in SCZ spectrum disorders including SCZ patients and individuals with schizotypal personality disorder (SPD) proneness who are at risk for developing SCZ. Thirty-two SCZ patients, 30 SPD proneness individuals, and 33 healthy controls participated in the study. The Sentence Completion for Events from the Past Test (SCEPT) and the Sentence Completion for Events in the Future Test were used to measure past and future thinking abilities. Results showed that SCZ patients showed significantly reduced specificity in recalling past and imagining future events, they generated less proportion of specific and extended events compared to healthy controls. SPD proneness individuals only generated less extended events compared to healthy controls. The reduced specificity was mainly manifested in imagining future events. Both SCZ patients and SPD proneness individuals generated less positive events than controls. These results suggest that mental time travel impairments in SCZ spectrum disorders and have implications for understanding their cognitive and emotional deficits. PMID:27507958

  20. A nanoparticle formulation of disulfiram prolongs corneal residence time of the drug and reduces intraocular pressure.

    PubMed

    Nagai, Noriaki; Yoshioka, Chiaki; Mano, Yu; Tnabe, Wataru; Ito, Yoshimasa; Okamoto, Norio; Shimomura, Yoshikazu

    2015-03-01

    The goal in the search for successful therapies for glaucoma is the reduction of intraocular pressure (IOP), and the search for effective eye drops that reduce IOP is a high priority. We previously reported the potential of a 2-hydroxypropyl-β-cyclodextrin (HPβCD) solution containing 0.5% DSF (DSF solution) to provide effective anti-glaucoma treatment in eye drop form. In this study, we designed new ophthalmic formulations containing 0.5% DSF nanoparticles prepared by a bead mill method (DSFnano dispersion; particle size 183 ± 92 nm, mean ± S.D.), and compared the IOP-reducing effects of a DSFnano dispersion with those of a DSF solution. The high stability of the DSFnano dispersion was observed until 7 days after preparation, and the DSFnano dispersion showed high antimicrobial activity against Escherichia coli (ATCC 8739). In transcorneal penetration experiments using rabbit corneas, only diethyldithiocarbamate (DDC) was detected in the aqueous humor, while no DSF was detected. The DDC penetration level (area under the curve, AUC) and corneal residence time (mean residence time, MRT) of the DSFnano dispersion were approximately 1.45- and 1.44-fold higher than those of the DSF, respectively. Moreover, the IOP-reducing effects of the DSFnano dispersion were significantly greater than those of the DSF solution in rabbits (the IOP was enhanced by placing the rabbits in a dark room for 5 h). In addition, DSFnano dispersion are tolerated better by a corneal epithelial cell than DSF solution and commercially available timolol maleate eye drops. It is possible that dispersions containing DSF nanoparticles will provide new possibilities for the effective treatment of glaucoma, and that an ocular drug delivery system using drug nanoparticles may expand their usage as therapy in the ophthalmologic field. These findings provide significant information that can be used to design further studies aimed at developing anti-glaucoma drugs. PMID:25633346

  1. Reducing Time to First on Scene: An Ambulance-Community First Responder Scheme

    PubMed Central

    Campbell, Alan; Ellington, Matt

    2016-01-01

    The importance of early access to prehospital care has been demonstrated in many medical emergencies. This work aims to describe the potential time benefit of implementing a student Community First Responder scheme to support ambulance services in an inner-city setting in the United Kingdom. Twenty final and penultimate year medical students in the UK were trained in the “First Person on Scene” Business and Technology Education Council (BTEC) qualification. Over 12 months, they attended 89 emergency calls in an inner-city setting as Community First Responders (CFRs), alongside the West Midlands Ambulance Service, UK. At the end of this period, a qualitative survey investigated the perceived educational value of the scheme. The mean CFR response time across all calls was an average of 3 minutes and 8 seconds less than ambulance crew response times. The largest difference was to calls relating to falls (12 min). The difference varied throughout the day, peaking between 16:00 and 18:00. All questionnaire respondents stated that they felt more prepared in assessing and treating acutely unwell patients. In this paper, the authors present a symbiotic solution which has both reduced time to first on scene and provided training and experience in medical emergencies for senior medical students. PMID:27119024

  2. Use of Music Intervention for Reducing Anxiety and Promoting Satisfaction in First-Time Filipino Fathers.

    PubMed

    Labrague, Leodoro J; McEnroe-Petitte, Denise M

    2016-03-01

    Childbirth is an anxiety-provoking event in a man's life. Therefore, strategies to decrease paternal anxiety during childbirth are necessary. This study determined the effects of music and satisfaction of first-time Filipino fathers during childbirth. In the study, a prospective quasi-experimental design was utilized. Ninety-eight purposive samples of first-time fathers were included in the study, 50 were allocated in the experimental group (music group) and 48 in the control group (nonmusic group) during the months of August to October 2013. Paternal anxiety and satisfaction were measured using the State Trait Anxiety Inventory and the Visual Analogue Scale for Satisfaction, respectively. Results revealed that the first-time fathers in the experimental group had lower State Trait Anxiety Inventory scores (p < .05) and higher Visual Analogue Scale for Satisfaction scores (p < .05) than those in the control group. Findings of the study provide substantial evidence to support the use of music in reducing anxiety and promoting satisfaction among first-time fathers during childbirth. PMID:25432465

  3. Reducing Time to First on Scene: An Ambulance-Community First Responder Scheme.

    PubMed

    Campbell, Alan; Ellington, Matt

    2016-01-01

    The importance of early access to prehospital care has been demonstrated in many medical emergencies. This work aims to describe the potential time benefit of implementing a student Community First Responder scheme to support ambulance services in an inner-city setting in the United Kingdom. Twenty final and penultimate year medical students in the UK were trained in the "First Person on Scene" Business and Technology Education Council (BTEC) qualification. Over 12 months, they attended 89 emergency calls in an inner-city setting as Community First Responders (CFRs), alongside the West Midlands Ambulance Service, UK. At the end of this period, a qualitative survey investigated the perceived educational value of the scheme. The mean CFR response time across all calls was an average of 3 minutes and 8 seconds less than ambulance crew response times. The largest difference was to calls relating to falls (12 min). The difference varied throughout the day, peaking between 16:00 and 18:00. All questionnaire respondents stated that they felt more prepared in assessing and treating acutely unwell patients. In this paper, the authors present a symbiotic solution which has both reduced time to first on scene and provided training and experience in medical emergencies for senior medical students. PMID:27119024

  4. Paraoxon suppresses Ca(2+) spike and afterhyperpolarization in snail neurons: Relevance to the hyperexcitability induction.

    PubMed

    Vatanparast, Jafar; Janahmadi, Mahyar; Asgari, Ali Reza; Sepehri, Houri; Haeri-Rohani, Ali

    2006-04-14

    The effects of organophosphate (OP) paraoxon, active metabolite of parathion, were studied on the Ca(2+) and Ba(2+) spikes and on the excitability of the neuronal soma membranes of land snail (Caucasotachea atrolabiata). Paraoxon (0.3 muM) reversibly decreased the duration and amplitude of Ca(2+) and Ba(2+) spikes. It also reduced the duration and the amplitude of the afterhyperpolarization (AHP) that follows spikes, leading to a significant increase in the frequency of Ca(2+) spikes. Pretreatment with atropine and hexamethonium, selective blockers of muscarinic and nicotinic receptors, respectively, did not prevent the effects of paraoxon on Ca(2+) spikes. Intracellular injection of the calcium chelator BAPTA dramatically decreased the duration and amplitude of AHP and increased the duration and frequency of Ca(2+) spikes. In the presence of BAPTA, paraoxon decreased the duration of the Ca(2+) spikes without affecting their frequency. Apamin, a neurotoxin from bee venom, known to selectively block small conductance of calcium-activated potassium channels (SK), significantly decreased the duration and amplitude of the AHP, an effect that was associated with an increase in spike frequency. In the presence of apamin, bath application of paraoxon reduced the duration of Ca(2+) spike and AHP and increased the firing frequency of nerve cells. In summary, these data suggest that exposure to submicromolar concentration of paraoxon may directly affect membrane excitability. Suppression of Ca(2+) entry during the action potential would down regulate Ca(2+)-activated K(+) channels leading to a reduction of the AHP and an increase in cell firing. PMID:16566905

  5. Method for reducing peak phase current and decreasing staring time for an internal combustion engine having an induction machine

    DOEpatents

    Amey, David L.; Degner, Michael W.

    2002-01-01

    A method for reducing the starting time and reducing the peak phase currents for an internal combustion engine that is started using an induction machine starter/alternator. The starting time is reduced by pre-fluxing the induction machine and the peak phase currents are reduced by reducing the flux current command after a predetermined period of time has elapsed and concurrent to the application of the torque current command. The method of the present invention also provides a strategy for anticipating the start command for an internal combustion engine and determines a start strategy based on the start command and the operating state of the internal combustion engine.

  6. Sparse Data Analysis Strategy for Neural Spike Classification

    PubMed Central

    Vigneron, Vincent; Chen, Hsin

    2014-01-01

    Many of the multichannel extracellular recordings of neural activity consist of attempting to sort spikes on the basis of shared characteristics with some feature detection techniques. Then spikes can be sorted into distinct clusters. There are in general two main statistical issues: firstly, spike sorting can result in well-sorted units, but by with no means one can be sure that one is dealing with single units due to the number of neurons adjacent to the recording electrode. Secondly, the waveform dimensionality is reduced in a small subset of discriminating features. This shortening dimension effort was introduced as an aid to visualization and manual clustering, but also to reduce the computational complexity in automatic classification. We introduce a metric based on common neighbourhood to introduce sparsity in the dataset and separate data into more homogeneous subgroups. The approach is particularly well suited for clustering when the individual clusters are elongated (that is nonspherical). In addition it does need not to select the number of clusters, it is very efficient to visualize clusters in a dataset, it is robust to noise, it can handle imbalanced data, and it is fully automatic and deterministic. PMID:25101122

  7. Using Queuing Theory and Simulation Modelling to Reduce Waiting Times in An Iranian Emergency Department

    PubMed Central

    Haghighinejad, Hourvash Akbari; Kharazmi, Erfan; Hatam, Nahid; Yousefi, Sedigheh; Hesami, Seyed Ali; Danaei, Mina; Askarian, Mehrdad

    2016-01-01

    Background: Hospital emergencies have an essential role in health care systems. In the last decade, developed countries have paid great attention to overcrowding crisis in emergency departments. Simulation analysis of complex models for which conditions will change over time is much more effective than analytical solutions and emergency department (ED) is one of the most complex models for analysis. This study aimed to determine the number of patients who are waiting and waiting time in emergency department services in an Iranian hospital ED and to propose scenarios to reduce its queue and waiting time. Methods: This is a cross-sectional study in which simulation software (Arena, version 14) was used. The input information was extracted from the hospital database as well as through sampling. The objective was to evaluate the response variables of waiting time, number waiting and utilization of each server and test the three scenarios to improve them. Results: Running the models for 30 days revealed that a total of 4088 patients left the ED after being served and 1238 patients waited in the queue for admission in the ED bed area at end of the run (actually these patients received services out of their defined capacity). The first scenario result in the number of beds had to be increased from 81 to179 in order that the number waiting of the “bed area” server become almost zero. The second scenario which attempted to limit hospitalization time in the ED bed area to the third quartile of the serving time distribution could decrease the number waiting to 586 patients. Conclusion: Doubling the bed capacity in the emergency department and consequently other resources and capacity appropriately can solve the problem. This includes bed capacity requirement for both critically ill and less critically ill patients. Classification of ED internal sections based on severity of illness instead of medical specialty is another solution. PMID:26793727

  8. Extraction and Characterization of Essential Discharge Patterns from Multisite Recordings of Spiking Ongoing Activity

    PubMed Central

    Storchi, Riccardo; Biella, Gabriele E. M.; Liberati, Diego; Baselli, Giuseppe

    2009-01-01

    Background Neural activation patterns proceed often by schemes or motifs distributed across the involved cortical networks. As neurons are correlated, the estimate of all possible dependencies quickly goes out of control. The complex nesting of different oscillation frequencies and their high non-stationariety further hamper any quantitative evaluation of spiking network activities. The problem is exacerbated by the intrinsic variability of neural patterns. Methodology/Principal Findings Our technique introduces two important novelties and enables to insulate essential patterns on larger sets of spiking neurons and brain activity regimes. First, the sampling procedure over N units is based on a fixed spike number k in order to detect N-dimensional arrays (k-sequences), whose sum over all dimension is k. Then k-sequences variability is greatly reduced by a hierarchical separative clustering, that assigns large amounts of distinct k-sequences to few classes. Iterative separations are stopped when the dimension of each cluster comes to be smaller than a certain threshold. As threshold tuning critically impacts on the number of classes extracted, we developed an effective cost criterion to select the shortest possible description of our dataset. Finally we described three indexes (C,S,R) to evaluate the average pattern complexity, the structure of essential classes and their stability in time. Conclusions/Significance We validated this algorithm with four kinds of surrogated activity, ranging from random to very regular patterned. Then we characterized