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Sample records for dopamine shape spiking

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

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    Moritz eAugustin

    2013-02-01

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

  2. Dopamine

    International Nuclear Information System (INIS)

    Walters, L.

    1983-01-01

    Dopamine is an important neurotransmittor in the central nervous system. The physiological function of the peripheral dopamine receptors is unknown, but they are of therapeutic importance as dopamine is used to improve renal blood flow in shocked patients. There are 4 dopamine receptors. The classification of these dopamine receptors has been made possible by research with radiopharmaceuticals. Dopamine sensitive adenylate cyclase is an inherent part of the dopamine-1-receptor. Dopamine-1-receptors are stimulated by micromolar (physiological) concentrations of dopamine and inhibited by micromolar (supratherapeutic) concentrations of the antipsychotic drugs. The vascular effect of dopamine is mediated through the dopamine-1-receptors. Dopamine-2-receptors are responsible for the effect of dopamine at the mesolimbic, nigrostriatal and chemoreceptortrigger areas. It is activated by micromolar concentrations of dopamine and blocked by nanomolar (therapeutic) concentrations of the anti-psychotic drugs. Dopamine-3-receptors are activated by nanomolar concentrations of dopamine and inhibited by micromolar concentrations of the antipsychotic drugs. They occur on presynaptic nerve terminals and have a negative feedback effect on the liberation of dopamine, noradrenaline and serotonin. The dopamine-4-receptors are activated by nanomolar concentrations of dopamine. These are the only dopamine receptors that could be responsible for effects in the hypophysis as only nanomolar concentrations of dopamine occur there. These receptors are blocked by nanomolar concentrations of the antipsychotic drugs

  3. Dopamine D4 receptor activation increases hippocampal gamma oscillations by enhancing synchronization of fast-spiking interneurons.

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    Richard Andersson

    Full Text Available BACKGROUND: Gamma oscillations are electric activity patterns of the mammalian brain hypothesized to serve attention, sensory perception, working memory and memory encoding. They are disrupted or altered in schizophrenic patients with associated cognitive deficits, which persist in spite of treatment with antipsychotics. Because cognitive symptoms are a core feature of schizophrenia it is relevant to explore signaling pathways that potentially regulate gamma oscillations. Dopamine has been reported to decrease gamma oscillation power via D1-like receptors. Based on the expression pattern of D4 receptors (D4R in hippocampus, and pharmacological effects of D4R ligands in animals, we hypothesize that they are in a position to regulate gamma oscillations as well. METHODOLOGY/PRINCIPAL FINDINGS: To address this hypothesis we use rat hippocampal slices and kainate-induced gamma oscillations. Local field potential recordings as well as intracellular recordings of pyramidal cells, fast-spiking and non-fast-spiking interneurons were carried out. We show that D4R activation with the selective ligand PD168077 increases gamma oscillation power, which can be blocked by the D4R-specific antagonist L745,870 as well as by the antipsychotic drug Clozapine. Pyramidal cells did not exhibit changes in excitatory or inhibitory synaptic current amplitudes, but inhibitory currents became more coherent with the oscillations after application of PD168077. Fast-spiking, but not non-fast spiking, interneurons, increase their action potential phase-coupling and coherence with regard to ongoing gamma oscillations in response to D4R activation. Among several possible mechanisms we found that the NMDA receptor antagonist AP5 also blocks the D4R mediated increase in gamma oscillation power. CONCLUSIONS/SIGNIFICANCE: We conclude that D4R activation affects fast-spiking interneuron synchronization and thereby increases gamma power by an NMDA receptor-dependent mechanism. This

  4. A feed-forward spiking model of shape-coding by IT cells

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    August eRomeo

    2014-05-01

    Full Text Available The ability to recognize a shape is linked to figure-ground organization. Cell preferences appear to be correlated across contrast-polarity reversals and mirror reversals of polygon displays, but not so much across figure-ground (FG reversals. Here we present a network structure which explains both shape-coding by IT cells and the suppression of responses to figure-ground reversed stimuli. In the model figure-ground discrimination is achieved much before shape discrimination, that is itself evidenced by the difference in the spiking onsets of a couple of cells selective for two image categories.

  5. Dynamic shaping of dopamine signals during probabilistic Pavlovian conditioning.

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    Hart, Andrew S; Clark, Jeremy J; Phillips, Paul E M

    2015-01-01

    Cue- and reward-evoked phasic dopamine activity during Pavlovian and operant conditioning paradigms is well correlated with reward-prediction errors from formal reinforcement learning models, which feature teaching signals in the form of discrepancies between actual and expected reward outcomes. Additionally, in learning tasks where conditioned cues probabilistically predict rewards, dopamine neurons show sustained cue-evoked responses that are correlated with the variance of reward and are maximal to cues predicting rewards with a probability of 0.5. Therefore, it has been suggested that sustained dopamine activity after cue presentation encodes the uncertainty of impending reward delivery. In the current study we examined the acquisition and maintenance of these neural correlates using fast-scan cyclic voltammetry in rats implanted with carbon fiber electrodes in the nucleus accumbens core during probabilistic Pavlovian conditioning. The advantage of this technique is that we can sample from the same animal and recording location throughout learning with single trial resolution. We report that dopamine release in the nucleus accumbens core contains correlates of both expected value and variance. A quantitative analysis of these signals throughout learning, and during the ongoing updating process after learning in probabilistic conditions, demonstrates that these correlates are dynamically encoded during these phases. Peak CS-evoked responses are correlated with expected value and predominate during early learning while a variance-correlated sustained CS signal develops during the post-asymptotic updating phase. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. The shaping of two distinct dendritic spikes by A-type voltage-gated K+ channels

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    Sungchil eYang

    2015-12-01

    Full Text Available Dendritic ion channels have been a subject of intense research in neuroscience because active ion channels in dendrites shape input signals. Ca2+-permeable channels including NMDA receptors (NMDARs have been implicated in supralinear dendritic integration, and the IA conductance in sublinear integration. Despite their essential roles in dendritic integration, it has remained uncertain whether these conductances coordinate with, or counteract, each other in the process of dendritic integration. To address this question, experiments were designed in hippocampal CA1 neurons with a recent 3D digital holography system that has shown excellent performance for spatial photoactivation. The results demonstrated a role of IA as a key contributor to two distinct dendritic spikes, low- and high-threshold Ca2+ spikes, through a preferential action of IA on Ca2+-permeable channel-mediated currents, over fast AMPAR-mediated currents. It is likely that the rapid kinetics of IA provides feed-forward inhibition to counteract the delayed Ca2+ channel-mediated dendritic excitability. This research reveals one dynamic ionic mechanism of dendritic integration, and may contribute to a new understanding of neuronal hyperexcitability embedded in several neural diseases such as epilepsy, fragile X syndrome and Alzheimer's disease.

  7. Inverted-U-shaped correlation between dopamine receptor availability in striatum and sensation seeking

    DEFF Research Database (Denmark)

    Gjedde, Albert; Kumakura, Yoshitaka; Cumming, Paul

    2010-01-01

    to dopamine concentrations. Higher dopamine occupancy and dopamine concentrations explain the motivation that drives afflicted individuals to seek sensations, in agreement with reduced protection against addictive behavior that is characteristic of individuals with low binding potentials....

  8. Chemical synthesis and characterization of hollow dopamine coated, pentagonal and flower shaped magnetic iron oxide nanoparticles

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    Riasat, Rabia; Kaynat, Sumbal

    2018-04-01

    Iron oxide nanoparticles have gained attention recently in the field of nanoscience and technology due to their unique physicochemical properties. We hereby chemically synthesized novel pentagonal flower shaped iron oxide nanoparticles by thermal decomposition of iron penta-carbonyl in a two way annealing process. Controlled oxidation by acid etching was performed for these nanoparticles. At first 13 nm core shell nanoparticles of iron oxide (Fe/Fe3O4) were synthesized at 120°C annealing temperature that act as template material. The core shell nanoparticles then converted into porous hollow core shell nanoparticles (PH Fe/ Fe3O4) in a two way annealing process of heating, first at 100°C then at 250°C and heating rate of 5°C was kept constant throughout the reaction time. X-Ray diffraction (XRD) was done for the phase confirmation of as synthesized nanoparticles. Transmission electron microscopy (TEM) and higher resolution transmission electron microscopy (HRTEM) clearly shows the flower like nanoparticles that are approx. 16 nm-18 nm in size having the 4-5 nm core of Fe and 1-2 nm of the pores in the shell while the cavity between the shell and core is about 2 nm and the shell is 4-5 nm in diameter according to the TEM micrographs. The as prepared nanoparticles were then surface functionalized by dopamine polymer to make them water dispersible. Fourier transform Infrared spectroscopy confirmed the dopamine coating on the nanoparticles and the magnetic saturation of 38 emu/g of nanoparticles was analyzed by vibrating sample magnetometer (VSM). Magnetic saturation persists in the dopamine coated nanoparticles. These nanoparticles were surface functionalized with dopamine and show dispersity in the aqueous media and can further be exploited in many nano-biotechnological applications including target specific therapeutic applications for several diseases.

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

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    Guillaume eLajoie

    2014-10-01

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

  10. Dopamine-signalled reward predictions generated by competitive excitation and inhibition in a spiking neural network model

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    Paul eChorley

    2011-05-01

    Full Text Available Dopaminergic neurons in the mammalian substantia nigra displaycharacteristic phasic responses to stimuli which reliably predict thereceipt of primary rewards. These responses have been suggested toencode reward prediction-errors similar to those used in reinforcementlearning. Here, we propose a model of dopaminergic activity in whichprediction error signals are generated by the joint action ofshort-latency excitation and long-latency inhibition, in a networkundergoing dopaminergic neuromodulation of both spike-timing dependentsynaptic plasticity and neuronal excitability. In contrast toprevious models, sensitivity to recent events is maintained by theselective modification of specific striatal synapses, efferent tocortical neurons exhibiting stimulus-specific, temporally extendedactivity patterns. Our model shows, in the presence of significantbackground activity, (i a shift in dopaminergic response from rewardto reward predicting stimuli, (ii preservation of a response tounexpected rewards, and (iii a precisely-timed below-baseline dip inactivity observed when expected rewards are omitted.

  11. Inverted-U shaped dopamine actions on human working memory and cognitive control

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    Cools, R; D’Esposito, M

    2011-01-01

    Brain dopamine has long been implicated in cognitive control processes, including working memory. However, the precise role of dopamine in cognition is not well understood, partly because there is large variability in the response to dopaminergic drugs both across different behaviors and across different individuals. We review evidence from a series of studies with experimental animals, healthy humans and patients with Parkinson’s disease, which highlight two important factors that contribute to this large variability. First, the existence of an optimum dopamine level for cognitive function implicates the need to take into account baseline levels of dopamine when isolating dopamine’s effects. Second, cognitive control is a multi-factorial phenomenon, requiring a dynamic balance between cognitive stability and cognitive flexibility. These distinct components might implicate the prefrontal cortex and the striatum respectively. Manipulating dopamine will thus have paradoxical consequences for distinct cognitive control processes depending on distinct basal or optimal levels of dopamine in different brain regions. PMID:21531388

  12. The role of degree distribution in shaping the dynamics in networks of sparsely connected spiking neurons

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    Alex eRoxin

    2011-03-01

    Full Text Available Neuronal network models often assume a fixed probability of connectionbetween neurons. This assumption leads to random networks withbinomial in-degree and out-degree distributions which are relatively narrow. Here I study the effect of broaddegree distributions on network dynamics by interpolating between abinomial and a truncated powerlaw distribution for the in-degree andout-degree independently. This is done both for an inhibitory network(I network as well as for the recurrent excitatory connections in anetwork of excitatory and inhibitory neurons (EI network. In bothcases increasing the width of the in-degree distribution affects theglobal state of the network by driving transitions betweenasynchronous behavior and oscillations. This effect is reproduced ina simplified rate model which includes the heterogeneity in neuronalinput due to the in-degree of cells. On the other hand, broadeningthe out-degree distribution is shown to increase the fraction ofcommon inputs to pairs of neurons. This leads to increases in theamplitude of the cross-correlation (CC of synaptic currents. In thecase of the I network, despite strong oscillatory CCs in the currents, CCs of the membrane potential are low due to filtering and reset effects, leading to very weak CCs of the spikecount. In the asynchronous regime ofthe EI network, broadening the out-degree increases the amplitude ofCCs in the recurrent excitatory currents, while CC of the totalcurrent is essentially unaffected as are pairwise spikingcorrelations. This is due to a dynamic balance between excitatoryand inhibitory synaptic currents. In the oscillatory regime, changesin the out-degree can have a large effect on spiking correlations andeven on the qualitative dynamical state of the network.

  13. Timed Synaptic Inhibition Shapes NMDA Spikes, Influencing Local Dendritic Processing and Global I/O Properties of Cortical Neurons

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    Michael Doron

    2017-11-01

    Full Text Available The NMDA spike is a long-lasting nonlinear phenomenon initiated locally in the dendritic branches of a variety of cortical neurons. It plays a key role in synaptic plasticity and in single-neuron computations. Combining dynamic system theory and computational approaches, we now explore how the timing of synaptic inhibition affects the NMDA spike and its associated membrane current. When impinging on its early phase, individual inhibitory synapses strongly, but transiently, dampen the NMDA spike; later inhibition prematurely terminates it. A single inhibitory synapse reduces the NMDA-mediated Ca2+ current, a key player in plasticity, by up to 45%. NMDA spikes in distal dendritic branches/spines are longer-lasting and more resilient to inhibition, enhancing synaptic plasticity at these branches. We conclude that NMDA spikes are highly sensitive to dendritic inhibition; sparse weak inhibition can finely tune synaptic plasticity both locally at the dendritic branch level and globally at the level of the neuron’s output.

  14. Electrocatalytic determination of dopamine in the presence of uric acid using an indenedione derivative and multiwall carbon nanotubes spiked in carbon paste electrode

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    Nasirizadeh, Navid, E-mail: nasirizadeh@yahoo.com [Scientific Society of Nanotechnology, Yazd Branch, Islamic Azad University, Yazd (Iran, Islamic Republic of); Department of Textile Engineering, Yazd Branch, Islamic Azad University, Yazd (Iran, Islamic Republic of); Shekari, Zahra [Scientific Society of Nanotechnology, Yazd Branch, Islamic Azad University, Yazd (Iran, Islamic Republic of); Zare, Hamid R. [Department of Chemistry, Yazd University, P.O. Box 89195-741, Yazd (Iran, Islamic Republic of); Makarem, Somayeh [Department of Chemistry, Faculty of Sciences, ShahidBeheshti University, G. C., P. O. Box 19839-4716, Tehran (Iran, Islamic Republic of)

    2013-04-01

    In the present study, a modified carbon paste electrode (CPE) containing multi-wall carbon nanotubes and an indenedione derivative(IMWCNT−CPE) was constructed and was successfully used for dopamine(DA) electrocatalytic oxidation and simultaneous determination of DA and uric acid (UA). Cyclic voltammograms of the IMWCNT−CPE show a pair of well-defined and reversible redox. The obtained results indicate that the peak potential of DA oxidation at IMWCNT−CPE shifted by about 65 and 185 mV toward the negative values compared with that at a MWCNT and indenedione modified CPE, respectively. The electron transfer coefficient, α, and the heterogeneous electron transfer rate constant, k′, for the oxidation of DA at IMWCNT−CPE were calculated 0.4 ± 0.01 and (1.13 ± 0.03) × 10{sup −3} cm s{sup −1}, respectively. Furthermore, differential pulse voltammetry (DPV) exhibits two linear dynamic ranges of 1.9–79.4 μM, and 79.4–714.3 μM and a detection limit of 0.52 μM for DA determination. Then IMWCNT−CPE was applied to the simultaneous determination of DA and UA with DPV. Finally, the activity of the modified electrode was also investigated for determination of DA and UA in real samples, such as injection solution of DA and urine, with satisfactory results. - Highlights: ► According to referee's comment we have omitted references 33–35. ► Fig. 1 of the revised manuscript was improved based on referee comment. ► We have calculated the effective areas of MWCNT−CPE and unmodified CPE. ► Differential pulse voltammetry was used to estimate the quantitative parameters. ► Based on referee comment, the necessary corrections at the references list were mad.

  15. Space-Time Dynamics of Membrane Currents Evolve to Shape Excitation, Spiking, and Inhibition in the Cortex at Small and Large Scales

    DEFF Research Database (Denmark)

    Roland, Per E.

    2017-01-01

    positions. After transition to active spiking states, larger structured zones with active spiking neurons appear, propagating through the cortical network, driving it into various forms of widespread excitation, and engaging the network from microscopic scales to whole cortical areas. At each engaged...... cortical site, the amount of excitation in the network, after a delay, becomes matched by an equal amount of space-time fine-tuned inhibition that might be instrumental in driving the dynamics toward perception and action....

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

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

    2016-01-01

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

  17. Influence of phasic and tonic dopamine release on receptor activation

    DEFF Research Database (Denmark)

    Dreyer, Jakob Kristoffer Kisbye; Herrik, Kjartan F; Berg, Rune W

    2010-01-01

    Tonic and phasic dopamine release is implicated in learning, motivation, and motor functions. However, the relationship between spike patterns in dopaminergic neurons, the extracellular concentration of dopamine, and activation of dopamine receptors remains unresolved. In the present study, we...... develop a computational model of dopamine signaling that give insight into the relationship between the dynamics of release and occupancy of D(1) and D(2) receptors. The model is derived from first principles using experimental data. It has no free parameters and offers unbiased estimation...

  18. Deep Spiking Networks

    NARCIS (Netherlands)

    O'Connor, P.; Welling, M.

    2016-01-01

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

  19. Statistical properties of superimposed stationary spike trains.

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

    2012-06-01

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

  20. Decoding spikes in a spiking neuronal network

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

    2004-06-04

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

  1. Decoding spikes in a spiking neuronal network

    International Nuclear Information System (INIS)

    Feng Jianfeng; Ding, Mingzhou

    2004-01-01

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

  2. Barbed micro-spikes for micro-scale biopsy

    Science.gov (United States)

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

    2005-06-01

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

  3. Automatic EEG spike detection.

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    Harner, Richard

    2009-10-01

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

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

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

    2016-01-01

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

  5. Consensus-Based Sorting of Neuronal Spike Waveforms.

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

    2016-01-01

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

  6. Spike Code Flow in Cultured Neuronal Networks.

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    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.

  7. Spike Code Flow in Cultured Neuronal Networks

    Directory of Open Access Journals (Sweden)

    Shinichi Tamura

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Nicholas V. Swindale

    2014-02-01

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

  9. State-dependent spike and local field synchronization between motor cortex and substantia nigra in hemiparkinsonian rats.

    Science.gov (United States)

    Brazhnik, Elena; Cruz, Ana V; Avila, Irene; Wahba, Marian I; Novikov, Nikolay; Ilieva, Neda M; McCoy, Alex J; Gerber, Colin; Walters, Judith R

    2012-06-06

    Excessive beta frequency oscillatory and synchronized activity has been reported in the basal ganglia of parkinsonian patients and animal models of the disease. To gain insight into processes underlying this activity, this study explores relationships between oscillatory activity in motor cortex and basal ganglia output in behaving rats after dopamine cell lesion. During inattentive rest, 7 d after lesion, increases in motor cortex-substantia nigra pars reticulata (SNpr) coherence emerged in the 8-25 Hz range, with significant increases in local field potential (LFP) power in SNpr but not motor cortex. In contrast, during treadmill walking, marked increases in both motor cortex and SNpr LFP power, as well as coherence, emerged in the 25-40 Hz band with a peak frequency at 30-35 Hz. Spike-triggered waveform averages showed that 77% of SNpr neurons, 77% of putative cortical interneurons, and 44% of putative pyramidal neurons were significantly phase-locked to the increased cortical LFP activity in the 25-40 Hz range. Although the mean lag between cortical and SNpr LFPs fluctuated around zero, SNpr neurons phase-locked to cortical LFP oscillations fired, on average, 17 ms after synchronized spiking in motor cortex. High coherence between LFP oscillations in cortex and SNpr supports the view that cortical activity facilitates entrainment and synchronization of activity in basal ganglia after loss of dopamine. However, the dramatic increases in cortical power and relative timing of phase-locked spiking in these areas suggest that additional processes help shape the frequency-specific tuning of the basal ganglia-thalamocortical network during ongoing motor activity.

  10. Solving constraint satisfaction problems with networks of spiking neurons

    Directory of Open Access Journals (Sweden)

    Zeno eJonke

    2016-03-01

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

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

    Science.gov (United States)

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

    2013-05-01

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

  12. Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex.

    Directory of Open Access Journals (Sweden)

    George L Chadderdon

    Full Text Available Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint "forearm" to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1, no learning (0, or punishment (-1, corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.

  13. Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex.

    Science.gov (United States)

    Chadderdon, George L; Neymotin, Samuel A; Kerr, Cliff C; Lytton, William W

    2012-01-01

    Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint "forearm" to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1), no learning (0), or punishment (-1), corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.

  14. An Unsupervised Online Spike-Sorting Framework.

    Science.gov (United States)

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

    2016-08-01

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

  15. The variational spiked oscillator

    International Nuclear Information System (INIS)

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

    1992-08-01

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

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

    Science.gov (United States)

    Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

    2016-01-01

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

  17. Implementing Signature Neural Networks with Spiking Neurons.

    Science.gov (United States)

    Carrillo-Medina, José Luis; Latorre, Roberto

    2016-01-01

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

  18. Spiking neural network for recognizing spatiotemporal sequences of spikes

    International Nuclear Information System (INIS)

    Jin, Dezhe Z.

    2004-01-01

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

  19. Dopamine transporters govern diurnal variation in extracellular dopamine tone

    OpenAIRE

    Ferris, Mark J.; España, Rodrigo A.; Locke, Jason L.; Konstantopoulos, Joanne K.; Rose, Jamie H.; Chen, Rong; Jones, Sara R.

    2014-01-01

    The mechanism for diurnal (i.e., light/dark) oscillations in extracellular dopamine tone in mesolimbic and nigrostriatal systems is unknown. This is because, unlike other neurotransmitter systems, variation in dopamine tone does not correlate with variation in dopamine cell firing. The current research pinpoints the dopamine transporter as a critical governor of diurnal variation in both extracellular dopamine tone and the intracellular availability of releasable dopamine. These data describe...

  20. SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

    Science.gov (United States)

    Zenke, Friedemann; Ganguli, Surya

    2018-04-13

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

  1. A novel automated spike sorting algorithm with adaptable feature extraction.

    Science.gov (United States)

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

    2012-10-15

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

  2. Coronavirus spike-receptor interactions

    NARCIS (Netherlands)

    Mou, H.

    2015-01-01

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

  3. Mapping spikes to sensations

    Directory of Open Access Journals (Sweden)

    Maik Christopher Stüttgen

    2011-11-01

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

  4. Molecular and functional differences in voltage-activated sodium currents between GABA projection neurons and dopamine neurons in the substantia nigra

    OpenAIRE

    Ding, Shengyuan; Wei, Wei; Zhou, Fu-Ming

    2011-01-01

    GABA projection neurons (GABA neurons) in the substantia nigra pars reticulata (SNr) and dopamine projection neurons (DA neurons) in substantia nigra pars compacta (SNc) have strikingly different firing properties. SNc DA neurons fire low-frequency, long-duration spikes, whereas SNr GABA neurons fire high-frequency, short-duration spikes. Since voltage-activated sodium (NaV) channels are critical to spike generation, the different firing properties raise the possibility that, compared with DA...

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

    Science.gov (United States)

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

    2000-01-01

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

  6. Orthobunyavirus ultrastructure and the curious tripodal glycoprotein spike.

    Directory of Open Access Journals (Sweden)

    Thomas A Bowden

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

  7. Wavelet analysis of epileptic spikes

    Science.gov (United States)

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

    2003-05-01

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

  8. Wavelet analysis of epileptic spikes

    CERN Document Server

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

    2003-01-01

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

  9. Dopamine-imprinted monolithic column for capillary electrochromatography.

    Science.gov (United States)

    Aşır, Süleyman; Sarı, Duygu; Derazshamshir, Ali; Yılmaz, Fatma; Şarkaya, Koray; Denizli, Adil

    2017-11-01

    A dopamine-imprinted monolithic column was prepared and used in capillary electrochromatography as stationary phase for the first time. Dopamine was selectively separated from aqueous solution containing the competitor molecule norepinephrine, which is similar in size and shape to the template molecule. Morphology of the dopamine-imprinted column was observed by scanning electron microscopy. The influence of the organic solvent content of mobile phase, applied pressure and pH of the mobile phase on the recognition of dopamine by the imprinted monolithic column has been evaluated, and the imprinting effect in the dopamine-imprinted monolithic polymer was verified. Developed dopamine-imprinted monolithic column resulted in excellent separation of dopamine from structurally related competitor molecule, norepinephrine. Separation was achieved in a short period of 10 min, with the electrophoretic mobility of 5.81 × 10 -5  m 2 V -1 s -1 at pH 5.0 and 500 mbar pressure. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. Salsolinol facilitates glutamatergic transmission to dopamine neurons in the posterior ventral tegmental area of rats.

    Directory of Open Access Journals (Sweden)

    Guiqin Xie

    Full Text Available Although in vivo evidence indicates that salsolinol, the condensation product of acetaldehyde and dopamine, has properties that may contribute to alcohol abuse, the underlying mechanisms have not been fully elucidated. We have reported previously that salsolinol stimulates dopamine neurons in the posterior ventral tegmental area (p-VTA partly by reducing inhibitory GABAergic transmission, and that ethanol increases glutamatergic transmission to VTA-dopamine neurons via the activation of dopamine D(1 receptors (D(1Rs. In this study, we tested the hypothesis that salsolinol stimulates dopamine neurons involving activation of D(1Rs. By using whole-cell recordings on p-VTA-dopamine neurons in acute brain slices of rats, we found that salsolinol-induced increase in spike frequency of dopamine neurons was substantially attenuated by DL-2-amino-5-phosphono-valeric acid and 6, 7-dinitroquinoxaline-2, 3-dione, the antagonists of glutamatergic N-Methyl-D-aspartic acid and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors. Moreover, salsolinol increased the amplitude of evoked excitatory postsynaptic currents (EPSCs and the frequency but not the amplitude of spontaneous EPSCs. Additionally, SKF83566, a D(1R antagonist attenuated the salsolinol-induced facilitation of EPSCs and of spontaneous firing of dopamine neurons. Our data reveal that salsolinol enhances glutamatergic transmission onto dopamine neurons via activation of D(1Rs at the glutamatergic afferents in dopamine neurons, which contributes to salsolinol's stimulating effect on p-VTA dopamine neurons. This appears to be a novel mechanism which contributes toward rewarding properties of salsolinol.

  11. Striatal dopamine release codes uncertainty in pathological gambling

    DEFF Research Database (Denmark)

    Linnet, Jakob; Mouridsen, Kim; Peterson, Ericka

    2012-01-01

    Two mechanisms of midbrain and striatal dopaminergic projections may be involved in pathological gambling: hypersensitivity to reward and sustained activation toward uncertainty. The midbrain—striatal dopamine system distinctly codes reward and uncertainty, where dopaminergic activation is a linear...... function of expected reward and an inverse U-shaped function of uncertainty. In this study, we investigated the dopaminergic coding of reward and uncertainty in 18 pathological gambling sufferers and 16 healthy controls. We used positron emission tomography (PET) with the tracer [11C]raclopride to measure...... dopamine release, and we used performance on the Iowa Gambling Task (IGT) to determine overall reward and uncertainty. We hypothesized that we would find a linear function between dopamine release and IGT performance, if dopamine release coded reward in pathological gambling. If, on the other hand...

  12. Striatal dopamine release codes uncertainty in pathological gambling

    DEFF Research Database (Denmark)

    Linnet, Jakob; Mouridsen, Kim; Peterson, Ericka

    2012-01-01

    Two mechanisms of midbrain and striatal dopaminergic projections may be involved in pathological gambling: hypersensitivity to reward and sustained activation toward uncertainty. The midbrain-striatal dopamine system distinctly codes reward and uncertainty, where dopaminergic activation is a linear...... function of expected reward and an inverse U-shaped function of uncertainty. In this study, we investigated the dopaminergic coding of reward and uncertainty in 18 pathological gambling sufferers and 16 healthy controls. We used positron emission tomography (PET) with the tracer [(11)C......]raclopride to measure dopamine release, and we used performance on the Iowa Gambling Task (IGT) to determine overall reward and uncertainty. We hypothesized that we would find a linear function between dopamine release and IGT performance, if dopamine release coded reward in pathological gambling. If, on the other hand...

  13. Dopamine hypothesis of mania

    OpenAIRE

    Cookson, John

    2014-01-01

    s­of­the­Speakers­/­Konuşmacı­leriThe discovery of dopamine and its pathwaysDopamine (DA) was first synthesized in 1910 from 3,4-dihydroxy phenyl alanine (DOPA) by Barger and Ewens at Wellcome Laboratories in London. It is a cathecholamine and in the 1940s Blaschko in Cambridge proposed that DA was a precursor in synthesis of the cat-echolamine neurotransmitters noradrenaline (norepinephrine) and adrenaline (epinephrine). In 1957 it was shown to be present in the brain with other catecholamin...

  14. Amphetamine Paradoxically Augments Exocytotic Dopamine Release and Phasic Dopamine Signals

    Science.gov (United States)

    Daberkow, DP; Brown, HD; Bunner, KD; Kraniotis, SA; Doellman, MA; Ragozzino, ME; Garris, PA; Roitman, MF

    2013-01-01

    Drugs of abuse hijack brain reward circuitry during the addiction process by augmenting action potential-dependent phasic dopamine release events associated with learning and goal-directed behavior. One prominent exception to this notion would appear to be amphetamine (AMPH) and related analogs, which are proposed instead to disrupt normal patterns of dopamine neurotransmission by depleting vesicular stores and promoting non-exocytotic dopamine efflux via reverse transport. This mechanism of AMPH action, though, is inconsistent with its therapeutic effects and addictive properties - which are thought to be reliant on phasic dopamine signaling. Here we used fast-scan cyclic voltammetry in freely moving rats to interrogate principal neurochemical responses to AMPH in the striatum and relate these changes to behavior. First, we showed that AMPH dose-dependently enhanced evoked dopamine responses to phasic-like current pulse trains for up to two hours. Modeling the data revealed that AMPH inhibited dopamine uptake but also unexpectedly potentiated vesicular dopamine release. Second, we found that AMPH increased the amplitude, duration and frequency of spontaneous dopamine transients, the naturally occurring, non-electrically evoked, phasic increases in extracellular dopamine. Finally, using an operant sucrose reward paradigm, we showed that low-dose AMPH augmented dopamine transients elicited by sucrose-predictive cues. However, operant behavior failed at high-dose AMPH, which was due to phasic dopamine hyperactivity and the decoupling of dopamine transients from the reward predictive cue. These findings identify up-regulation of exocytotic dopamine release as a key AMPH action in behaving animals and support a unified mechanism of abused drugs to activate phasic dopamine signaling. PMID:23303926

  15. Dopamine and anorexia nervosa.

    Science.gov (United States)

    Södersten, P; Bergh, C; Leon, M; Zandian, M

    2016-01-01

    We have suggested that reduced food intake increases the risk for anorexia nervosa by engaging mesolimbic dopamine neurons, thereby initially rewarding dieting. Recent fMRI studies have confirmed that dopamine neurons are activated in anorexia nervosa, but it is not clear whether this response is due to the disorder or to its resulting nutritional deficit. When the body senses the shortage of nutrients, it rapidly shifts behavior toward foraging for food as a normal physiological response and the mesolimbic dopamine neurons may be involved in that process. On the other hand, the altered dopamine status of anorexics has been suggested to result from a brain abnormality that underlies their complex emotional disorder. We suggest that the outcomes of the treatments that emerge from that perspective remain poor because they target the mental symptoms that are actually the consequences of the food deprivation that accompanies anorexia. On the other hand, a method that normalizes the disordered eating behavior of anorexics results in much better physiological, behavioral, and emotional outcomes. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Dopamins renale virkninger

    DEFF Research Database (Denmark)

    Olsen, Niels Vidiendal

    1990-01-01

    is frequently employed in cases of acute oliguric renal failure but the results available concerning the therapeutic effect are frequently retrospective and uncontrolled. The results suggest that early treatment with 1-3 micrograms/kg/min dopamine combined with furosemide can postpone or possibly render...

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

    OpenAIRE

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

    2013-01-01

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

  18. Characterizing neural activities evoked by manual acupuncture through spiking irregularity measures

    International Nuclear Information System (INIS)

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

    2013-01-01

    The neural system characterizes information in external stimulations by different spiking patterns. In order to examine how neural spiking patterns are related to acupuncture manipulations, experiments are designed in such a way that different types of manual acupuncture (MA) manipulations are taken at the ‘Zusanli’ point of experimental rats, and the induced electrical signals in the spinal dorsal root ganglion are detected and recorded. The interspike interval (ISI) statistical histogram is fitted by the gamma distribution, which has two parameters: one is the time-dependent firing rate and the other is a shape parameter characterizing the spiking irregularities. The shape parameter is the measure of spiking irregularities and can be used to identify the type of MA manipulations. The coefficient of variation is mostly used to measure the spike time irregularity, but it overestimates the irregularity in the case of pronounced firing rate changes. However, experiments show that each acupuncture manipulation will lead to changes in the firing rate. So we combine four relatively rate-independent measures to study the irregularity of spike trains evoked by different types of MA manipulations. Results suggest that the MA manipulations possess unique spiking statistics and characteristics and can be distinguished according to the spiking irregularity measures. These studies have offered new insights into the coding processes and information transfer of acupuncture. (interdisciplinary physics and related areas of science and technology)

  19. Firing properties of dopamine neurons in freely moving dopamine-deficient mice: Effects of dopamine receptor activation and anesthesia

    OpenAIRE

    Robinson, Siobhan; Smith, David M.; Mizumori, Sheri J. Y.; Palmiter, Richard D.

    2004-01-01

    To examine the regulation of midbrain dopamine neurons, recordings were obtained from single neurons of freely moving, genetically engineered dopamine-deficient (DD) mice. DD mice were tested without dopamine signaling (basal state) and with endogenous dopamine signaling (after L-dopa administration). In the basal state, when dopamine concentration in DD mice is

  20. Computing with Spiking Neuron Networks

    NARCIS (Netherlands)

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

    2012-01-01

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

  1. Learning Universal Computations with Spikes

    Science.gov (United States)

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

    2016-01-01

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

  2. Differential and distributed effects of dopamine neuromodulations on resting-state network connectivity

    NARCIS (Netherlands)

    Cole, D.M.; Beckmann, Christian; Oei, N.Y.L.; Both, S.; van Gerven, J.M.A.; Rombouts, S.A.R.B.

    2013-01-01

    Dopaminergic medications, used to treat neurochemical pathology and resultant symptoms in neuropsychiatric disorders, are of mixed efficacy and regularly associated with behavioural side effects. The possibility that dopamine exerts both linear and nonlinear ('inverted U-shaped') effects on

  3. Differential and distributed effects of dopamine neuromodulations on resting-state network connectivity

    NARCIS (Netherlands)

    Cole, D.M.; Beckmann, C.F.; Oei, N.Y.L.; Both, S.; van Gerven, J.M.A.; Rombouts, S.A.R.B.

    Dopaminergic medications, used to treat neurochemical pathology and resultant symptoms in neuropsychiatric disorders, are of mixed efficacy and regularly associated with behavioural side effects. The possibility that dopamine exerts both linear and nonlinear ('inverted U-shaped') effects on

  4. Automatic spike sorting using tuning information.

    Science.gov (United States)

    Ventura, Valérie

    2009-09-01

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

  5. NEW DOPAMINE AGONISTS IN CARDIOVASCULAR THERAPY

    NARCIS (Netherlands)

    GIRBES, ARJ; VANVELDHUISEN, DJ; SMIT, AJ

    1992-01-01

    Dopamine, a naturally occurring catecholamine, has been extensively used in intensive care for many years. Dopamine stimulates different types of adrenergic receptors: alpha-1 and -2, beta-1 and -2, and dopamine-1 and -2. The renal effects of dopamine are the result of dopamine-1 receptor (DA1)

  6. Growth of dopamine crystals

    Energy Technology Data Exchange (ETDEWEB)

    Patil, Vidya, E-mail: vidya.patil@ruparel.edu; Patki, Mugdha, E-mail: mugdha.patki@ruparel.edu [D. G. Ruparel College, Senapati Bapat Marg, Mahim, Mumbai – 400 016 (India)

    2016-05-06

    Many nonlinear optical (NLO) crystals have been identified as potential candidates in optical and electro-optical devices. Use of NLO organic crystals is expected in photonic applications. Hence organic nonlinear optical materials have been intensely investigated due to their potentially high nonlinearities, and rapid response in electro-optic effect compared to inorganic NLO materials. There are many methods to grow organic crystals such as vapor growth method, melt growth method and solution growth method. Out of these methods, solution growth method is useful in providing constraint free crystal. Single crystals of Dopamine have been grown by evaporating the solvents from aqueous solution. Crystals obtained were of the size of orders of mm. The crystal structure of dopamine was determined using XRD technique. Images of crystals were obtained using FEG SEM Quanta Series under high vacuum and low KV.

  7. Epileptiform spike detection via convolutional neural networks

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  8. AgRP neurons regulate development of dopamine neuronal plasticity and nonfood-associated behaviors

    Science.gov (United States)

    Dietrich, Marcelo O; Bober, Jeremy; Ferreira, Jozélia G; Tellez, Luis A; Mineur, Yann S; Souza, Diogo O; Gao, Xiao-Bing; Picciotto, Marina R; Araújo, Ivan; Liu, Zhong-Wu; Horvath, Tamas L

    2012-01-01

    It is not known whether behaviors unrelated to feeding are affected by hypothalamic regulators of hunger. We found that impairment of Agouti-related protein (AgRP) circuitry by either Sirt1 knockdown in AgRP-expressing neurons or early postnatal ablation of these neurons increased exploratory behavior and enhanced responses to cocaine. In AgRP circuit–impaired mice, ventral tegmental dopamine neurons exhibited enhanced spike timing–dependent long-term potentiation, altered amplitude of miniature postsynaptic currents and elevated dopamine in basal forebrain. Thus, AgRP neurons determine the set point of the reward circuitry and associated behaviors. PMID:22729177

  9. Neuronal coding and spiking randomness

    Czech Academy of Sciences Publication Activity Database

    Košťál, Lubomír; Lánský, Petr; Rospars, J. P.

    2007-01-01

    Roč. 26, č. 10 (2007), s. 2693-2988 ISSN 0953-816X R&D Projects: GA MŠk(CZ) LC554; GA AV ČR(CZ) 1ET400110401; GA AV ČR(CZ) KJB100110701 Grant - others:ECO-NET(FR) 112644PF Institutional research plan: CEZ:AV0Z50110509 Keywords : spike train * variability * neurovědy Subject RIV: FH - Neurology Impact factor: 3.673, year: 2007

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  11. Radioiodinated ligands for dopamine receptors

    International Nuclear Information System (INIS)

    Kung, H.F.

    1994-01-01

    The dopamine receptor system is important for normal brain function; it is also the apparent action site for various neuroleptic drugs for the treatment of schizophrenia and other metal disorders. In the past few years radioiodinated ligands for single photon emission tomography (SPECT) have been successfully developed and tested in humans: [ 123 I]TISCH for D1 dopamine receptors; [ 123 I]IBZM, epidepride, IBF and FIDA2, four iodobenzamide derivatives, for D2/D3 dopamine receptors. In addition, [ 123 I]β-CIT (RTI-55) and IPT, cocaine derivatives, for the dopamine reuptake site are potentially useful for diagnosis of loss of dopamine neurons. The first iodinated ligand, (R)trans-7-OH-PIPAT, for D3 dopamine receptors, was synthesized and characterized with cloned cell lines (Spodoptera frugiperda, Sf9) expressing the D2 and D3 dopamine receptors and with rat basal forebrain membrane preparations. Most of the known iodobenzamides displayed similar potency in binding to both D2 and D3 dopamine receptors expressed in the cell lines. Initial studies appear to suggest that by fine tuning the structures it may be possible to develop agents specific for D2 and D3 dopamine receptors. It is important to investigate D2/D3 selectivity for this series of potent ligands

  12. Information transmission with spiking Bayesian neurons

    International Nuclear Information System (INIS)

    Lochmann, Timm; Deneve, Sophie

    2008-01-01

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

  13. Role of network dynamics in shaping spike timing reliability

    International Nuclear Information System (INIS)

    Bazhenov, Maxim; Rulkov, Nikolai F.; Fellous, Jean-Marc; Timofeev, Igor

    2005-01-01

    We study the reliability of cortical neuron responses to periodically modulated synaptic stimuli. Simple map-based models of two different types of cortical neurons are constructed to replicate the intrinsic resonances of reliability found in experimental data and to explore the effects of those resonance properties on collective behavior in a cortical network model containing excitatory and inhibitory cells. We show that network interactions can enhance the frequency range of reliable responses and that the latter can be controlled by the strength of synaptic connections. The underlying dynamical mechanisms of reliability enhancement are discussed

  14. Spike sorting based upon machine learning algorithms (SOMA).

    Science.gov (United States)

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

    2007-02-15

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

  15. Methamphetamine Increases Locomotion and Dopamine Transporter Activity in Dopamine D5 Receptor-Deficient Mice

    OpenAIRE

    Hayashizaki, Seiji; Hirai, Shinobu; Ito, Yumi; Honda, Yoshiko; Arime, Yosefu; Sora, Ichiro; Okado, Haruo; Kodama, Tohru; Takada, Masahiko

    2013-01-01

    Dopamine regulates the psychomotor stimulant activities of amphetamine-like substances in the brain. The effects of dopamine are mediated through five known dopamine receptor subtypes in mammals. The functional relevance of D5 dopamine receptors in the central nervous system is not well understood. To determine the functional relevance of D5 dopamine receptors, we created D5 dopamine receptor-deficient mice and then used these mice to assess the roles of D5 dopamine receptors in the behaviora...

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

    Science.gov (United States)

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

    2017-01-01

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

  17. Dopamine Oxidation and Autophagy

    Directory of Open Access Journals (Sweden)

    Patricia Muñoz

    2012-01-01

    Full Text Available The molecular mechanisms involved in the neurodegenerative process of Parkinson's disease remain unclear. Currently, there is a general agreement that mitochondrial dysfunction, α-synuclein aggregation, oxidative stress, neuroinflammation, and impaired protein degradation are involved in the neurodegeneration of dopaminergic neurons containing neuromelanin in Parkinson's disease. Aminochrome has been proposed to play an essential role in the degeneration of dopaminergic neurons containing neuromelanin by inducing mitochondrial dysfunction, oxidative stress, the formation of neurotoxic α-synuclein protofibrils, and impaired protein degradation. Here, we discuss the relationship between the oxidation of dopamine to aminochrome, the precursor of neuromelanin, autophagy dysfunction in dopaminergic neurons containing neuromelanin, and the role of dopamine oxidation to aminochrome in autophagy dysfunction in dopaminergic neurons. Aminochrome induces the following: (i the formation of α-synuclein protofibrils that inactivate chaperone-mediated autophagy; (ii the formation of adducts with α- and β-tubulin, which induce the aggregation of the microtubules required for the fusion of autophagy vacuoles and lysosomes.

  18. Motor control by precisely timed spike patterns

    DEFF Research Database (Denmark)

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

    2017-01-01

    whether the information in spike timing actually plays a role in brain function. By examining the activity of individual motor units (the muscle fibers innervated by a single motor neuron) and manipulating patterns of activation of these neurons, we provide both correlative and causal evidence......A fundamental problem in neuroscience is understanding how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditional theories assume that this information is conveyed by the total number of spikes fired within a specified time...... interval (spike rate), recent studies have shown that additional information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear...

  19. A Theory of Material Spike Formation in Flow Separation

    Science.gov (United States)

    Serra, Mattia; Haller, George

    2017-11-01

    We develop a frame-invariant theory of material spike formation during flow separation over a no-slip boundary in two-dimensional flows with arbitrary time dependence. This theory identifies both fixed and moving separation, is effective also over short-time intervals, and admits a rigorous instantaneous limit. Our theory is based on topological properties of material lines, combining objectively stretching- and rotation-based kinematic quantities. The separation profile identified here serves as the theoretical backbone for the material spike from its birth to its fully developed shape, and remains hidden to existing approaches. Finally, our theory can be used to rigorously explain the perception of off-wall separation in unsteady flows, and more importantly, provide the conditions under which such a perception is justified. We illustrate our results in several examples including steady, time-periodic and unsteady analytic velocity fields with flat and curved boundaries, and an experimental dataset.

  20. Comparison of degradation between indigenous and spiked bisphenol A and triclosan in a biosolids amended soil

    Energy Technology Data Exchange (ETDEWEB)

    Langdon, Kate A., E-mail: Kate.Langdon@csiro.au [School of Agriculture, Food and Wine and Waite Research Institute, University of Adelaide, South Australia, 5005, Adelaide (Australia); Water for a Healthy Country Research Flagship, Commonwealth Scientific and Industrial Research Organisation (CSIRO), PMB 2, Glen Osmond, South Australia, 5064, Adelaide (Australia); Warne, Michael StJ. [Water for a Healthy Country Research Flagship, Commonwealth Scientific and Industrial Research Organisation (CSIRO), PMB 2, Glen Osmond, South Australia, 5064, Adelaide (Australia); Smernik, Ronald J. [School of Agriculture, Food and Wine and Waite Research Institute, University of Adelaide, South Australia, 5005, Adelaide (Australia); Shareef, Ali; Kookana, Rai S. [Water for a Healthy Country Research Flagship, Commonwealth Scientific and Industrial Research Organisation (CSIRO), PMB 2, Glen Osmond, South Australia, 5064, Adelaide (Australia)

    2013-03-01

    This study compared the degradation of indigenous bisphenol A (BPA) and triclosan (TCS) in a biosolids-amended soil, to the degradation of spiked labelled surrogates of the same compounds (BPA-d{sub 16} and TCS-{sup 13}C{sub 12}). The aim was to determine if spiking experiments accurately predict the degradation of compounds in biosolids-amended soils using two different types of biosolids, a centrifuge dried biosolids (CDB) and a lagoon dried biosolids (LDB). The rate of degradation of the compounds was examined and the results indicated that there were considerable differences between the indigenous and spiked compounds. These differences were more marked for BPA, for which the indigenous compound was detectable throughout the study, whereas the spiked compound decreased to below the detection limit prior to the study completion. The rate of degradation for the indigenous BPA was approximately 5-times slower than that of the spiked BPA-d{sub 16}. The indigenous and spiked TCS were both detectable throughout the study, however, the shape of the degradation curves varied considerably, particularly in the CDB treatment. These findings show that spiking experiments may not be suitable to predict the degradation and persistence of organic compounds following land application of biosolids. - Highlights: ► Degradation of indigenous and spiked compounds from biosolids were compared. ► Differences were observed for both the rate and pattern of degradation. ► Spiked bisphenol A entirely degraded however the indigenous compound remained. ► TCS was detectable during the experiment however the degradation patterns varied. ► Spiking experiments may not be suitable to predict degradation of organic compounds.

  1. Comparison of degradation between indigenous and spiked bisphenol A and triclosan in a biosolids amended soil

    International Nuclear Information System (INIS)

    Langdon, Kate A.; Warne, Michael StJ.; Smernik, Ronald J.; Shareef, Ali; Kookana, Rai S.

    2013-01-01

    This study compared the degradation of indigenous bisphenol A (BPA) and triclosan (TCS) in a biosolids-amended soil, to the degradation of spiked labelled surrogates of the same compounds (BPA-d 16 and TCS- 13 C 12 ). The aim was to determine if spiking experiments accurately predict the degradation of compounds in biosolids-amended soils using two different types of biosolids, a centrifuge dried biosolids (CDB) and a lagoon dried biosolids (LDB). The rate of degradation of the compounds was examined and the results indicated that there were considerable differences between the indigenous and spiked compounds. These differences were more marked for BPA, for which the indigenous compound was detectable throughout the study, whereas the spiked compound decreased to below the detection limit prior to the study completion. The rate of degradation for the indigenous BPA was approximately 5-times slower than that of the spiked BPA-d 16 . The indigenous and spiked TCS were both detectable throughout the study, however, the shape of the degradation curves varied considerably, particularly in the CDB treatment. These findings show that spiking experiments may not be suitable to predict the degradation and persistence of organic compounds following land application of biosolids. - Highlights: ► Degradation of indigenous and spiked compounds from biosolids were compared. ► Differences were observed for both the rate and pattern of degradation. ► Spiked bisphenol A entirely degraded however the indigenous compound remained. ► TCS was detectable during the experiment however the degradation patterns varied. ► Spiking experiments may not be suitable to predict degradation of organic compounds

  2. Multineuronal Spike Sequences Repeat with Millisecond Precision

    Directory of Open Access Journals (Sweden)

    Koki eMatsumoto

    2013-06-01

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

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  4. Comparison of degradation between indigenous and spiked bisphenol A and triclosan in a biosolids amended soil.

    Science.gov (United States)

    Langdon, Kate A; Warne, Michael Stj; Smernik, Ronald J; Shareef, Ali; Kookana, Rai S

    2013-03-01

    This study compared the degradation of indigenous bisphenol A (BPA) and triclosan (TCS) in a biosolids-amended soil, to the degradation of spiked labelled surrogates of the same compounds (BPA-d16 and TCS-(13)C12). The aim was to determine if spiking experiments accurately predict the degradation of compounds in biosolids-amended soils using two different types of biosolids, a centrifuge dried biosolids (CDB) and a lagoon dried biosolids (LDB). The rate of degradation of the compounds was examined and the results indicated that there were considerable differences between the indigenous and spiked compounds. These differences were more marked for BPA, for which the indigenous compound was detectable throughout the study, whereas the spiked compound decreased to below the detection limit prior to the study completion. The rate of degradation for the indigenous BPA was approximately 5-times slower than that of the spiked BPA-d16. The indigenous and spiked TCS were both detectable throughout the study, however, the shape of the degradation curves varied considerably, particularly in the CDB treatment. These findings show that spiking experiments may not be suitable to predict the degradation and persistence of organic compounds following land application of biosolids. Copyright © 2013 Elsevier B.V. All rights reserved.

  5. Spike voltage topography in temporal lobe epilepsy.

    Science.gov (United States)

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

    2016-07-15

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

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

    Directory of Open Access Journals (Sweden)

    Nicolas Frémaux

    2013-04-01

    Full Text Available 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.

  7. Prefrontal Dopamine in Associative Learning and Memory

    Science.gov (United States)

    Puig, M. Victoria; Antzoulatos, Evan G.; Miller, Earl K.

    2014-01-01

    Learning to associate specific objects or actions with rewards and remembering the associations are everyday tasks crucial for our flexible adaptation to the environment. These higher-order cognitive processes depend on the prefrontal cortex (PFC) and frontostriatal circuits that connect areas in the frontal lobe with the striatum in the basal ganglia. Both structures are densely innervated by dopamine (DA) afferents that originate in the midbrain. Although the activity of DA neurons is thought to be important for learning, the exact role of DA transmission in frontostriatal circuits during learning-related tasks is still unresolved. Moreover, the neural substrates of this modulation are poorly understood. Here, we review our recent work in monkeys utilizing local pharmacology of DA agents in the PFC to investigate the cellular mechanisms of DA modulation of associative learning and memory. We show that blocking both D1 and D2 receptors in the lateral PFC impairs learning of new stimulus-response associations and cognitive flexibility, but not the memory of highly familiar associations. In addition, D2 receptors may also contribute to motivation. The learning deficits correlated with reductions of neural information about the associations in PFC neurons, alterations in global excitability and spike synchronization, and exaggerated alpha and beta neural oscillations. Our findings provide new insights into how DA transmission modulate associative learning and memory processes in frontostriatal systems. PMID:25241063

  8. Prefrontal dopamine in associative learning and memory.

    Science.gov (United States)

    Puig, M V; Antzoulatos, E G; Miller, E K

    2014-12-12

    Learning to associate specific objects or actions with rewards and remembering the associations are everyday tasks crucial for our flexible adaptation to the environment. These higher-order cognitive processes depend on the prefrontal cortex (PFC) and frontostriatal circuits that connect areas in the frontal lobe with the striatum in the basal ganglia. Both structures are densely innervated by dopamine (DA) afferents that originate in the midbrain. Although the activity of DA neurons is thought to be important for learning, the exact role of DA transmission in frontostriatal circuits during learning-related tasks is still unresolved. Moreover, the neural substrates of this modulation are poorly understood. Here, we review our recent work in monkeys utilizing local pharmacology of DA agents in the PFC to investigate the cellular mechanisms of DA modulation of associative learning and memory. We show that blocking both D1 and D2 receptors in the lateral PFC impairs learning of new stimulus-response associations and cognitive flexibility, but not the memory of highly familiar associations. In addition, D2 receptors may also contribute to motivation. The learning deficits correlated with reductions of neural information about the associations in PFC neurons, alterations in global excitability and spike synchronization, and exaggerated alpha and beta neural oscillations. Our findings provide new insights into how DA transmission modulates associative learning and memory processes in frontostriatal systems. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

  9. Neuropharmacology of novel dopamine modulators

    NARCIS (Netherlands)

    Beek, Erik Tomas te

    2014-01-01

    De neurotransmitter dopamine speelt een essentiële rol in diverse neurofysiologische functies en is betrokken bij de pathofysiologie van diverse neuropsychiatrische aandoeningen, waaronder de ziekte van Parkinson, schizofrenie, drugsverslaving en hyperprolactinemie. De huidige

  10. Dopamine signaling: target in glioblastoma

    Czech Academy of Sciences Publication Activity Database

    Bartek, Jiří; Hodný, Zdeněk

    2014-01-01

    Roč. 5, č. 5 (2014), 1116-1117 ISSN 1949-2553 Institutional support: RVO:68378050 Keywords : Dopamine signaling * glioblastoma * MAPK Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 6.359, year: 2014

  11. Dopamine reward prediction error coding.

    Science.gov (United States)

    Schultz, Wolfram

    2016-03-01

    Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards-an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware.

  12. Bayesian population decoding of spiking neurons.

    Science.gov (United States)

    Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias

    2009-01-01

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

  13. Bayesian population decoding of spiking neurons

    Directory of Open Access Journals (Sweden)

    Sebastian Gerwinn

    2009-10-01

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

  14. Linking investment spikes and productivity growth

    NARCIS (Netherlands)

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

    2013-01-01

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

  15. Mimickers of generalized spike and wave discharges.

    Science.gov (United States)

    Azzam, Raed; Bhatt, Amar B

    2014-06-01

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

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

    Science.gov (United States)

    Fukami, Tadanori; Shimada, Takamasa; Ishikawa, Bunnoshin

    2018-06-01

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

  17. Stochastic Variational Learning in Recurrent Spiking Networks

    Directory of Open Access Journals (Sweden)

    Danilo eJimenez Rezende

    2014-04-01

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

  18. Spiking neural P systems with multiple channels.

    Science.gov (United States)

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

    2017-11-01

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

  19. Stochastic variational learning in recurrent spiking networks.

    Science.gov (United States)

    Jimenez Rezende, Danilo; Gerstner, Wulfram

    2014-01-01

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

  20. Linking structure and activity in nonlinear spiking networks.

    Directory of Open Access Journals (Sweden)

    Gabriel Koch Ocker

    2017-06-01

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

  1. Linking structure and activity in nonlinear spiking networks.

    Science.gov (United States)

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

    2017-06-01

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

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

    Science.gov (United States)

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

    2016-08-01

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

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

    International Nuclear Information System (INIS)

    Jin, Dezhe Z

    2008-01-01

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

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

    Science.gov (United States)

    Wennberg, Richard; Cheyne, Douglas

    2014-05-01

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

  5. Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  6. Memristors Empower Spiking Neurons With Stochasticity

    KAUST Repository

    Al-Shedivat, Maruan

    2015-06-01

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

  7. Spike Bursts from an Excitable Optical System

    Science.gov (United States)

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

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

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

    Science.gov (United States)

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

    2016-01-01

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

  9. NEUROTRANSMITTERS AND IMMUNITY: 1. DOPAMINE

    Directory of Open Access Journals (Sweden)

    Lucian Hritcu

    2007-08-01

    Full Text Available Dopamine is one of the principal neurotransmitters in the central nervous system (CNC, and its neuronal pathways are involved in several key functions such as behavior (Hefco et al., 2003a,b, control of movement, endocrine regulation, immune response (Fiserova et al., 2002; Levite et al., 2001, Hritcu et al., 2006a,b,c, and cardiovascular function. Dopamine has at least five G-protein, coupled receptor subtypes, D1-D5, each arising from a different gene (Sibley et al., 1993. Traditionally, these receptors have been classified into D1-like (the D1 and D5 and D2-like (D2, D3 and D4 receptors subtypes, primarily according to their ability to stimulate or inhibit adenylate cyclase, respectively, and to their pharmacological characteristics (Seeman et al., 1993. Receptors for dopamine (particularly of D2 subclass are the primary therapeutic target in a number of neuropathological disorders including schizophrenia, Parkinson’s disease and Huntington’s chorea (Seeman et al., 1987. Neither dopamine by itself, nor dopaminergic agonists by themselves, has been shown to activate T cell function. Nevertheless, lymphocytes are most probably exposed to dopamine since the primary and secondary lymphoid organs of various mammals are markedly innervated, and contain nerve fibers which stain for tyrosine hydroxylase (Weihe et al., 1991, the enzyme responsible for dopamine synthesis. Moreover, cathecolamines and their metabolites are present in single lymphocytes and in extracts of T and B cell clones, and pharmacological inhibition of tyrosine hydroxylase reduces catecholamine levels, suggesting catecholamine synthesis by lymphocytes (Bergquist et al., 1994. The existence of putative dopamine receptors of D2, D3, D4 and D5 subtypes on immune cells has been proposed of several authors, primarily on the basis of dopaminergic ligand binding assays and specific mRNA expression as monitored by reverse transcription-PCR. Several experiments evoked the idea of a

  10. Dopamine, reward learning, and active inference

    Directory of Open Access Journals (Sweden)

    Thomas eFitzgerald

    2015-11-01

    Full Text Available Temporal difference learning models propose phasic dopamine signalling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behaviour. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

  11. Dopamine, reward learning, and active inference.

    Science.gov (United States)

    FitzGerald, Thomas H B; Dolan, Raymond J; Friston, Karl

    2015-01-01

    Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

  12. Behavioural effects of chemogenetic dopamine neuron activation

    NARCIS (Netherlands)

    Boekhoudt, L

    2016-01-01

    Various psychiatric disorders, including schizophrenia, attention-deficit/hyperactivity disorder (ADHD) and major depressive disorder, have been associated with altered dopamine signalling in the brain. However, it remains unclear which specific changes in dopamine activity are related to specific

  13. Molecular Mechanisms of Dopamine Receptor Mediated Neuroprotection

    National Research Council Canada - National Science Library

    Sealfon, Stuart

    2000-01-01

    ... of the cellular changes characteristic of this process. Evidence from our laboratory and others suggest that activation of dopamine receptors can oppose the induction of apoptosis in dopamine neurons...

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

    Science.gov (United States)

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

    2018-05-10

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

  15. Memristors Empower Spiking Neurons With Stochasticity

    KAUST Repository

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

    2015-01-01

    Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning

  16. Fitting neuron models to spike trains

    Directory of Open Access Journals (Sweden)

    Cyrille eRossant

    2011-02-01

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

  17. Frequency of Rolandic Spikes in ADHD

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2003-10-01

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

  18. THE POLITICAL CRITIQUE OF SPIKE Lee's Bamboozled

    African Journals Online (AJOL)

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

  19. Differential Regulation of Action Potential Shape and Burst-Frequency Firing by BK and Kv2 Channels in Substantia Nigra Dopaminergic Neurons.

    Science.gov (United States)

    Kimm, Tilia; Khaliq, Zayd M; Bean, Bruce P

    2015-12-16

    Little is known about the voltage-dependent potassium currents underlying spike repolarization in midbrain dopaminergic neurons. Studying mouse substantia nigra pars compacta dopaminergic neurons both in brain slice and after acute dissociation, we found that BK calcium-activated potassium channels and Kv2 channels both make major contributions to the depolarization-activated potassium current. Inhibiting Kv2 or BK channels had very different effects on spike shape and evoked firing. Inhibiting Kv2 channels increased spike width and decreased the afterhyperpolarization, as expected for loss of an action potential-activated potassium conductance. BK inhibition also increased spike width but paradoxically increased the afterhyperpolarization. Kv2 channel inhibition steeply increased the slope of the frequency-current (f-I) relationship, whereas BK channel inhibition had little effect on the f-I slope or decreased it, sometimes resulting in slowed firing. Action potential clamp experiments showed that both BK and Kv2 current flow during spike repolarization but with very different kinetics, with Kv2 current activating later and deactivating more slowly. Further experiments revealed that inhibiting either BK or Kv2 alone leads to recruitment of additional current through the other channel type during the action potential as a consequence of changes in spike shape. Enhancement of slowly deactivating Kv2 current can account for the increased afterhyperpolarization produced by BK inhibition and likely underlies the very different effects on the f-I relationship. The cross-regulation of BK and Kv2 activation illustrates that the functional role of a channel cannot be defined in isolation but depends critically on the context of the other conductances in the cell. This work shows that BK calcium-activated potassium channels and Kv2 voltage-activated potassium channels both regulate action potentials in dopamine neurons of the substantia nigra pars compacta. Although both

  20. Inferring oscillatory modulation in neural spike trains.

    Science.gov (United States)

    Arai, Kensuke; Kass, Robert E

    2017-10-01

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

  1. Anticipating Activity in Social Media Spikes

    OpenAIRE

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

    2014-01-01

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

  2. Building functional networks of spiking model neurons.

    Science.gov (United States)

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

    2016-03-01

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

  3. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

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

    2016-01-01

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

  4. Cellular programming and reprogramming: sculpting cell fate for the production of dopamine neurons for cell therapy.

    Science.gov (United States)

    Aguila, Julio C; Hedlund, Eva; Sanchez-Pernaute, Rosario

    2012-01-01

    Pluripotent stem cells are regarded as a promising cell source to obtain human dopamine neurons in sufficient amounts and purity for cell replacement therapy. Importantly, the success of clinical applications depends on our ability to steer pluripotent stem cells towards the right neuronal identity. In Parkinson disease, the loss of dopamine neurons is more pronounced in the ventrolateral population that projects to the sensorimotor striatum. Because synapses are highly specific, only neurons with this precise identity will contribute, upon transplantation, to the synaptic reconstruction of the dorsal striatum. Thus, understanding the developmental cell program of the mesostriatal dopamine neurons is critical for the identification of the extrinsic signals and cell-intrinsic factors that instruct and, ultimately, determine cell identity. Here, we review how extrinsic signals and transcription factors act together during development to shape midbrain cell fates. Further, we discuss how these same factors can be applied in vitro to induce, select, and reprogram cells to the mesostriatal dopamine fate.

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

    Science.gov (United States)

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

    2012-08-08

    Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike

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

    Directory of Open Access Journals (Sweden)

    Oliynyk Andriy

    2012-08-01

    Full Text Available Abstract Background Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Results Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting, which is designed to optimize: (i fast and accurate detection, (ii offline sorting and (iii online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com using LabVIEW (National Instruments, USA. We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is

  7. Visually Evoked Spiking Evolves While Spontaneous Ongoing Dynamics Persist

    Science.gov (United States)

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

    2016-01-01

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

  8. Non-orthogonally transitive G2 spike solution

    International Nuclear Information System (INIS)

    Lim, Woei Chet

    2015-01-01

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

  9. Spiking, Bursting, and Population Dynamics in a Network of Growth Transform Neurons.

    Science.gov (United States)

    Gangopadhyay, Ahana; Chakrabartty, Shantanu

    2017-04-27

    This paper investigates the dynamical properties of a network of neurons, each of which implements an asynchronous mapping based on polynomial growth transforms. In the first part of this paper, we present a geometric approach for visualizing the dynamics of the network where each of the neurons traverses a trajectory in a dual optimization space, whereas the network itself traverses a trajectory in an equivalent primal optimization space. We show that as the network learns to solve basic classification tasks, different choices of primal-dual mapping produce unique but interpretable neural dynamics like noise shaping, spiking, and bursting. While the proposed framework is general enough, in this paper, we demonstrate its use for designing support vector machines (SVMs) that exhibit noise-shaping properties similar to those of ΣΔ modulators, and for designing SVMs that learn to encode information using spikes and bursts. It is demonstrated that the emergent switching, spiking, and burst dynamics produced by each neuron encodes its respective margin of separation from a classification hyperplane whose parameters are encoded by the network population dynamics. We believe that the proposed growth transform neuron model and the underlying geometric framework could serve as an important tool to connect well-established machine learning algorithms like SVMs to neuromorphic principles like spiking, bursting, population encoding, and noise shaping.

  10. Psychostimulants affect dopamine transmission through both dopamine transporter-dependent and independent mechanisms

    Science.gov (United States)

    dela Peña, Ike; Gevorkiana, Ruzanna; Shi, Wei-Xing

    2015-01-01

    The precise mechanisms by which cocaine and amphetamine-like psychostimulants exert their reinforcing effects are not yet fully defined. It is widely believed, however, that these drugs produce their effects by enhancing dopamine neurotransmission in the brain, especially in limbic areas such as the nucleus accumbens, by inducing dopamine transporter-mediated reverse transport and/or blocking dopamine reuptake though the dopamine transporter. Here, we present the evidence that aside from dopamine transporter, non-dopamine transporter-mediated mechanisms also participate in psychostimulant-induced dopamine release and contribute to the behavioral effects of these drugs, such as locomotor activation and reward. Accordingly, psychostimulants could increase norepinephrine release in the prefrontal cortex, the latter then alters the firing pattern of dopamine neurons resulting in changes in action potential-dependent dopamine release. These alterations would further affect the temporal pattern of dopamine release in the nucleus accumbens, thereby modifying information processing in that area. Hence, a synaptic input to a nucleus accumbens neuron may be enhanced or inhibited by dopamine depending on its temporal relationship to dopamine release. Specific temporal patterns of dopamine release may also be required for certain forms of synaptic plasticity in the nucleus accumbens. Together, these effects induced by psychostimulants, mediated through a non-dopamine transporter-mediated mechanism involving norepinephrine and the prefrontal cortex, may also contribute importantly to the reinforcing properties of these drugs. PMID:26209364

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

    Directory of Open Access Journals (Sweden)

    Vernon eLawhern

    2011-05-01

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

  12. Spiking Neurons for Analysis of Patterns

    Science.gov (United States)

    Huntsberger, Terrance

    2008-01-01

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

  13. Magnetic restricted-access microspheres for extraction of adrenaline, dopamine and noradrenaline from biological samples

    International Nuclear Information System (INIS)

    Xiao, Deli; Liu, Shubo; Liang, Liyun; Bi, Yanping

    2016-01-01

    Epoxy propyl bonded magnetic microspheres were prepared by atomic layer deposition using Fe 3 O 4 -SiO 2 microspheres as a core support material. Then, a restricted-access magnetic sorbent was prepared that contains diol groups on the external surface and m-aminophenylboronic acid groups on the internal surface. This kind of microspheres achieved excellent specific adsorption of the ortho-dihydroxy compounds (dopamine, adrenaline and noradrenaline). Following desorption with sorbitol, the ortho-dihydroxy compounds were quantified by HPLC. The limits of detection for dopamine, adrenaline and noradrenaline were 0.074, 0.053 and 0.095 μg mL −1 , respectively. Recoveries from spiked mice serum samples range from 80.2 to 89.1 %. (author)

  14. The electric potential of tripolar spikes

    Energy Technology Data Exchange (ETDEWEB)

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

    2010-02-22

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

  15. The electric potential of tripolar spikes

    International Nuclear Information System (INIS)

    Nocera, L.

    2010-01-01

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

  16. Trace element ink spiking for signature authentication

    International Nuclear Information System (INIS)

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

    2008-01-01

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

  17. A Novel and Simple Spike Sorting Implementation.

    Science.gov (United States)

    Petrantonakis, Panagiotis C; Poirazi, Panayiota

    2017-04-01

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

  18. Increased brain dopamine and dopamine receptors in schizophrenia

    International Nuclear Information System (INIS)

    Mackay, A.V.; Iversen, L.L.; Rossor, M.; Spokes, E.; Bird, E.; Arregui, A.; Creese, I.; Synder, S.H.

    1982-01-01

    In postmortem samples of caudate nucleus and nucleus accumbens from 48 schizophrenic patients, there were significant increases in both the maximum number of binding sites (Bmax) and the apparent dissociation constant (KD) for tritiated spiperone. The increase in apparent KD probably reflects the presence of residual neuroleptic drugs, but changes in Bmax for tritiated spiperone reflect genuine changes in receptor numbers. The increases in receptors were seen only in patients in whom neuroleptic medication had been maintained until the time of death, indicating that they may be entirely iatrogenic. Dopamine measurements for a larger series of schizophrenic and control cases (n greater than 60) show significantly increased concentrations in both the nucleus accumbens and caudate nucleus. The changes in dopamine were not obviously related to neuroleptic medication and, unlike the receptor changes, were most severe in younger patients

  19. Spike timing precision of neuronal circuits.

    Science.gov (United States)

    Kilinc, Deniz; Demir, Alper

    2018-04-17

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

  20. Peripheral Dopamine in Restless Legs Syndrome

    Directory of Open Access Journals (Sweden)

    Ulrike H. Mitchell

    2018-03-01

    Full Text Available Objective/BackgroundRestless Legs Syndrome (RLS is a dopamine-dependent disorder characterized by a strong urge to move. The objective of this study was to evalulate blood levels of dopamine and other catecholamines and blood D2-subtype dopamine receptors (D2Rs in RLS.Patients/MethodsDopamine levels in blood samples from age-matched unmedicated RLS subjects, medicated RLS subjects and Controls were evaluated with high performance liquid chromatography and dopamine D2R white blood cell (WBC expression levels were determined with fluorescence-activated cell sorting and immunocytochemistry.ResultsBlood plasma dopamine levels, but not norepinepherine or epinephrine levels, were significantly increased in medicated RLS subjects vs unmedicated RLS subjects and Controls. The percentage of lymphocytes and monocytes expressing D2Rs differed between Control, RLS medicated and RLS unmedicated subjects. Total D2R expression in lymphocytes, but not monocytes, differed between Control, RLS medicated and RLS unmedicated subjects. D2Rs in lymphocytes, but not monocytes, were sensitive to dopamine in Controls only.ConclusionDownregulation of WBCs D2Rs occurs in RLS. This downregulation is not reversed by medication, although commonly used RLS medications increase plasma dopamine levels. The insensitivity of monocytes to dopamine levels, but their downregulation in RLS, may reflect their utility as a biomarker for RLS and perhaps brain dopamine homeostasis.

  1. Evoking prescribed spike times in stochastic neurons

    Science.gov (United States)

    Doose, Jens; Lindner, Benjamin

    2017-09-01

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

  2. Temporal Correlations and Neural Spike Train Entropy

    International Nuclear Information System (INIS)

    Schultz, Simon R.; Panzeri, Stefano

    2001-01-01

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

  3. Goal-Directed Decision Making with Spiking Neurons.

    Science.gov (United States)

    Friedrich, Johannes; Lengyel, Máté

    2016-02-03

    Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules

  4. Impacts of stress and sex hormones on dopamine neurotransmission in the adolescent brain.

    Science.gov (United States)

    Sinclair, Duncan; Purves-Tyson, Tertia D; Allen, Katherine M; Weickert, Cynthia Shannon

    2014-04-01

    Adolescence is a developmental period of complex neurobiological change and heightened vulnerability to psychiatric illness. As a result, understanding factors such as sex and stress hormones which drive brain changes in adolescence, and how these factors may influence key neurotransmitter systems implicated in psychiatric illness, is paramount. In this review, we outline the impact of sex and stress hormones at adolescence on dopamine neurotransmission, a signaling pathway which is critical to healthy brain function and has been implicated in psychiatric illness. We review normative developmental changes in dopamine, sex hormone, and stress hormone signaling during adolescence and throughout postnatal life, then highlight the interaction of sex and stress hormones and review their impacts on dopamine neurotransmission in the adolescent brain. Adolescence is a time of increased responsiveness to sex and stress hormones, during which the maturing dopaminergic neural circuitry is profoundly influenced by these factors. Testosterone, estrogen, and glucocorticoids interact with each other and have distinct, brain region-specific impacts on dopamine neurotransmission in the adolescent brain, shaping brain maturation and cognitive function in adolescence and adulthood. Some effects of stress/sex hormones on cortical and subcortical dopamine parameters bear similarities with dopaminergic abnormalities seen in schizophrenia, suggesting a possible role for sex/stress hormones at adolescence in influencing risk for psychiatric illness via modulation of dopamine neurotransmission. Stress and sex hormones may prove useful targets in future strategies for modifying risk for psychiatric illness.

  5. Improved SpikeProp for Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Falah Y. H. Ahmed

    2013-01-01

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

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

    Science.gov (United States)

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

    2012-08-01

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

  7. Olfactory bulb short axon cell release of GABA and dopamine produces a temporally biphasic inhibition-excitation response in external tufted cells.

    Science.gov (United States)

    Liu, Shaolin; Plachez, Celine; Shao, Zuoyi; Puche, Adam; Shipley, Michael T

    2013-02-13

    Evidence for coexpression of two or more classic neurotransmitters in neurons has increased, but less is known about cotransmission. Ventral tegmental area (VTA) neurons corelease dopamine (DA), the excitatory transmitter glutamate, and the inhibitory transmitter GABA onto target cells in the striatum. Olfactory bulb (OB) short axon cells (SACs) form interglomerular connections and coexpress markers for DA and GABA. Using an optogenetic approach, we provide evidence that mouse OB SACs release both GABA and DA onto external tufted cells (ETCs) in other glomeruli. Optical activation of channelrhodopsin specifically expressed in DAergic SACs produced a GABA(A) receptor-mediated monosynaptic inhibitory response, followed by DA-D(1)-like receptor-mediated excitatory response in ETCs. The GABA(A) receptor-mediated hyperpolarization activates I(h) current in ETCs; synaptically released DA increases I(h), which enhances postinhibitory rebound spiking. Thus, the opposing actions of synaptically released GABA and DA are functionally integrated by I(h) to generate an inhibition-to-excitation "switch" in ETCs. Consistent with the established role of I(h) in ETC burst firing, we show that endogenous DA release increases ETC spontaneous bursting frequency. ETCs transmit sensory signals to mitral/tufted output neurons and drive intraglomerular inhibition to shape glomerulus output to downstream olfactory networks. GABA and DA cotransmission from SACs to ETCs may play a key role in regulating output coding across the glomerular array.

  8. Dopamine agents for hepatic encephalopathy

    DEFF Research Database (Denmark)

    Junker, Anders Ellekær; Als-Nielsen, Bodil; Gluud, Christian

    2014-01-01

    BACKGROUND: Patients with hepatic encephalopathy may present with extrapyramidal symptoms and changes in basal ganglia. These changes are similar to those seen in patients with Parkinson's disease. Dopamine agents (such as bromocriptine and levodopa, used for patients with Parkinson's disease) have...... therefore been assessed as a potential treatment for patients with hepatic encephalopathy. OBJECTIVES: To evaluate the beneficial and harmful effects of dopamine agents versus placebo or no intervention for patients with hepatic encephalopathy. SEARCH METHODS: Trials were identified through the Cochrane...... hepatic encephalopathy that were published during 1979 to 1982 were included. Three trials assessed levodopa, and two trials assessed bromocriptine. The mean daily dose was 4 grams for levodopa and 15 grams for bromocriptine. The median duration of treatment was 14 days (range seven to 56 days). None...

  9. Dopamine reward prediction error coding

    OpenAIRE

    Schultz, Wolfram

    2016-01-01

    Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards?an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less...

  10. PRESYNAPTIC DOPAMINE MODULATION BY STIMULANT SELF ADMINISTRATION

    Science.gov (United States)

    España, Rodrigo A.; Jones, Sara R.

    2013-01-01

    The mesolimbic dopamine system is an essential participant in the initiation and modulation of various forms of goal-directed behavior, including drug reinforcement and addiction processes. Dopamine neurotransmission is increased by acute administration of all drugs of abuse, including the stimulants cocaine and amphetamine. Chronic exposure to these drugs via voluntary self-administration provides a model of stimulant abuse that is useful in evaluating potential behavioral and neurochemical adaptations that occur during addiction. This review describes commonly used methodologies to measure dopamine and baseline parameters of presynaptic dopamine regulation, including exocytotic release and reuptake through the dopamine transporter in the nucleus accumbens core, as well as dramatic adaptations in dopamine neurotransmission and drug sensitivity that occur with acute non-contingent and chronic, contingent self-administration of cocaine and amphetamine. PMID:23277050

  11. Physics of volleyball: Spiking with a purpose

    Science.gov (United States)

    Behroozi, F.

    1998-05-01

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

  12. Spike-timing theory of working memory.

    Directory of Open Access Journals (Sweden)

    Botond Szatmáry

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

  13. Dopamine Receptor-Specific Contributions to the Computation of Value.

    Science.gov (United States)

    Burke, Christopher J; Soutschek, Alexander; Weber, Susanna; Raja Beharelle, Anjali; Fehr, Ernst; Haker, Helene; Tobler, Philippe N

    2018-05-01

    Dopamine is thought to play a crucial role in value-based decision making. However, the specific contributions of different dopamine receptor subtypes to the computation of subjective value remain unknown. Here we demonstrate how the balance between D1 and D2 dopamine receptor subtypes shapes subjective value computation during risky decision making. We administered the D2 receptor antagonist amisulpride or placebo before participants made choices between risky options. Compared with placebo, D2 receptor blockade resulted in more frequent choice of higher risk and higher expected value options. Using a novel model fitting procedure, we concurrently estimated the three parameters that define individual risk attitude according to an influential theoretical account of risky decision making (prospect theory). This analysis revealed that the observed reduction in risk aversion under amisulpride was driven by increased sensitivity to reward magnitude and decreased distortion of outcome probability, resulting in more linear value coding. Our data suggest that different components that govern individual risk attitude are under dopaminergic control, such that D2 receptor blockade facilitates risk taking and expected value processing.

  14. Developmental Vitamin D (DVD) Deficiency Reduces Nurr1 and TH Expression in Post-mitotic Dopamine Neurons in Rat Mesencephalon.

    Science.gov (United States)

    Luan, Wei; Hammond, Luke Alexander; Cotter, Edmund; Osborne, Geoffrey William; Alexander, Suzanne Adele; Nink, Virginia; Cui, Xiaoying; Eyles, Darryl Walter

    2018-03-01

    Developmental vitamin D (DVD) deficiency has been proposed as an important risk factor for schizophrenia. Our previous study using Sprague Dawley rats found that DVD deficiency disrupted the ontogeny of mesencephalic dopamine neurons by decreasing the mRNA level of a crucial differentiation factor of dopamine cells, the nuclear receptor related 1 protein (Nurr1). However, it remains unknown whether this reflects a reduction in dopamine cell number or in Nurr1 expression. It is also unclear if any particular subset of developing dopamine neurons in the mesencephalon is selectively affected. In this study, we employed state-of-the-art spinning disk confocal microscopy optimized for the imaging of tissue sections and 3D segmentation to assess post-mitotic dopamine cells on a single-cell basis in the rat mesencephalon at embryonic day 15. Our results showed that DVD deficiency did not alter the number, morphology, or positioning of post-mitotic dopamine cells. However, the ratio of Nurr1+TH+ cells in the substantia nigra pars compacta (SNc) compared with the ventral tegmental area (VTA) was increased in DVD-deficient embryos. In addition, the expression of Nurr1 in immature dopamine cells and mature dopamine neurons in the VTA was decreased in DVD-deficient group. Tyrosine hydroxylase was selectively reduced in SNc of DVD-deficient mesencephalon. We conclude that DVD deficiency induced early alterations in mesencephalic dopamine development may in part explain the abnormal dopamine-related behaviors found in this model. Our findings may have broader implications for how certain environmental risk factors for schizophrenia may shape the ontogeny of dopaminergic systems and by inference increase the risk of schizophrenia.

  15. Spiking Neural P Systems with Communication on Request.

    Science.gov (United States)

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

    2017-12-01

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

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

    Institute of Scientific and Technical Information of China (English)

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

    2009-01-01

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

  17. Dopamine plasma clearance is increased in piglets compared to neonates during continuous dopamine infusion

    DEFF Research Database (Denmark)

    Rasmussen, Martin B; Gramsbergen, Jan Bert; Eriksen, Vibeke Ramsgaard

    2018-01-01

    pharmacokinetics. METHODS: Arterial blood samples were drawn from six neonates admitted to the neonatal intensive care unit of Copenhagen University Hospital and 20 newborn piglets during continuous dopamine infusion. Furthermore, to estimate the piglet plasma dopamine half-life, blood samples were drawn at 2.......5-minute intervals after the dopamine infusion was discontinued. The plasma dopamine content was analysed by high-performance liquid chromatography with electrochemical detection. RESULTS: The dopamine displayed first-order kinetics in piglets and had a half-life of 2.5 minutes, while the median plasma...

  18. Dopamine treatment and cognitive functioning in individuals with Parkinson's disease: the "cognitive flexibility" hypothesis seems to work.

    Science.gov (United States)

    Costa, Alberto; Peppe, Antonella; Mazzù, Ilenia; Longarzo, Mariachiara; Caltagirone, Carlo; Carlesimo, Giovanni A

    2014-01-01

    Previous data suggest that (i) dopamine modulates the ability to implement nonroutine schemata and update operations (flexibility processes) and that (ii) dopamine-related improvement may be related to baseline dopamine levels in target pathways (inverted U-shaped hypothesis). To investigate above hypotheses in individuals with Parkinson's disease (PD). Twenty PD patients were administered tasks varying as to flexibility load in two treatment conditions: (i) "off" condition, about 18 hours after dopamine dose and (ii) "on" condition, after dopamine administration. PD patients were separated into two groups: low performers (i.e., performance on Digit Span Backward below the sample mean) and high performers (i.e., performance above the mean). Twenty healthy individuals performed the tasks in two sessions without taking drugs. Passing from the "off" to the "on" state, only low performer PD patients significantly improved their performance on high-flexibility measures (interference condition of the Stroop test; P flexibility tasks. These findings document that high-flexibility processes are sensitive to dopamine neuromodulation in the early phases of PD. This is in line with the hypothesis that striatal dopamine pathways, affected early by PD, are precociously implicated in the expression of cognitive disorders in these individuals.

  19. Oxytocin, Motivation and the Role of Dopamine

    Science.gov (United States)

    Love, Tiffany M.

    2013-01-01

    The hypothalamic neuropeptide oxytocin has drawn the attention of scientists for more than a century. The understanding of the function of oxytocin has expanded dramatically over the years from a simple peptide adept at inducing uterine contractions and milk ejection to a complex neuromodulator with a capacity to shape human social behavior. Decades of research have outlined oxytocin’s ability to enhance intricate social activities ranging from pair bonding, sexual activity, affiliative preferences, and parental behaviors. The precise neural mechanisms underlying oxytocin’s influence on such behaviors have just begun to be understood. Research suggests that oxytocin interacts closely with the neural pathways responsible for processing motivationally relevant stimuli. In particular, oxytocin appears to impact dopaminergic activity within the mesocorticolimbic dopamine system, which is crucial not only for reward and motivated behavior but also for the expression of affiliative behaviors. Though most of the work performed in this area has been done using animal models, several neuroimaging studies suggest similar relationships may be observed in humans. In order to introduce this topic further, this paper will review the recent evidence that oxytocin may exert some of its social-behavioral effects through its impact on motivational networks. PMID:23850525

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

    DEFF Research Database (Denmark)

    Jensen, Hans Grinsted; Anderson, Kym

    2017-01-01

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

  1. The Mutation Frequency in Different Spike Categories in Barley

    DEFF Research Database (Denmark)

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

    1964-01-01

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

  2. Error-backpropagation in temporally encoded networks of spiking neurons

    NARCIS (Netherlands)

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

    2000-01-01

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

  3. Computational systems analysis of dopamine metabolism.

    Directory of Open Access Journals (Sweden)

    Zhen Qi

    2008-06-01

    Full Text Available A prominent feature of Parkinson's disease (PD is the loss of dopamine in the striatum, and many therapeutic interventions for the disease are aimed at restoring dopamine signaling. Dopamine signaling includes the synthesis, storage, release, and recycling of dopamine in the presynaptic terminal and activation of pre- and post-synaptic receptors and various downstream signaling cascades. As an aid that might facilitate our understanding of dopamine dynamics in the pathogenesis and treatment in PD, we have begun to merge currently available information and expert knowledge regarding presynaptic dopamine homeostasis into a computational model, following the guidelines of biochemical systems theory. After subjecting our model to mathematical diagnosis and analysis, we made direct comparisons between model predictions and experimental observations and found that the model exhibited a high degree of predictive capacity with respect to genetic and pharmacological changes in gene expression or function. Our results suggest potential approaches to restoring the dopamine imbalance and the associated generation of oxidative stress. While the proposed model of dopamine metabolism is preliminary, future extensions and refinements may eventually serve as an in silico platform for prescreening potential therapeutics, identifying immediate side effects, screening for biomarkers, and assessing the impact of risk factors of the disease.

  4. Hardware implementation of stochastic spiking neural networks.

    Science.gov (United States)

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

    2012-08-01

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

  5. Evolving spiking networks with variable resistive memories.

    Science.gov (United States)

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

    2014-01-01

    Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types.

  6. Visualizing spikes in source-space

    DEFF Research Database (Denmark)

    Beniczky, Sándor; Duez, Lene; Scherg, Michael

    2016-01-01

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

  7. Spiked instantons from intersecting D-branes

    Directory of Open Access Journals (Sweden)

    Nikita Nekrasov

    2017-01-01

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

  8. Stochastic synchronization in finite size spiking networks

    Science.gov (United States)

    Doiron, Brent; Rinzel, John; Reyes, Alex

    2006-09-01

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

  9. Non-singular spiked harmonic oscillator

    International Nuclear Information System (INIS)

    Aguilera-Navarro, V.C.; Guardiola, R.

    1990-01-01

    A perturbative study of a class of non-singular spiked harmonic oscillators defined by the hamiltonian H = d sup(2)/dr sup(2) + r sup(2) + λ/r sup(α) in the domain [0,∞] is carried out, in the two extremes of a weak coupling and a strong coupling regimes. A path has been found to connect both expansions for α near 2. (author)

  10. A Fully Automated Approach to Spike Sorting.

    Science.gov (United States)

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

    2017-09-13

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

  11. The transfer function of neuron spike.

    Science.gov (United States)

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

    2015-08-01

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

  12. Basalt FRP Spike Repairing of Wood Beams

    Directory of Open Access Journals (Sweden)

    Luca Righetti

    2015-08-01

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

  13. Cellular Programming and Reprogramming: Sculpting Cell Fate for the Production of Dopamine Neurons for Cell Therapy

    Directory of Open Access Journals (Sweden)

    Julio C. Aguila

    2012-01-01

    success of clinical applications depends on our ability to steer pluripotent stem cells towards the right neuronal identity. In Parkinson disease, the loss of dopamine neurons is more pronounced in the ventrolateral population that projects to the sensorimotor striatum. Because synapses are highly specific, only neurons with this precise identity will contribute, upon transplantation, to the synaptic reconstruction of the dorsal striatum. Thus, understanding the developmental cell program of the mesostriatal dopamine neurons is critical for the identification of the extrinsic signals and cell-intrinsic factors that instruct and, ultimately, determine cell identity. Here, we review how extrinsic signals and transcription factors act together during development to shape midbrain cell fates. Further, we discuss how these same factors can be applied in vitro to induce, select, and reprogram cells to the mesostriatal dopamine fate.

  14. Learning by stimulation avoidance: A principle to control spiking neural networks dynamics.

    Science.gov (United States)

    Sinapayen, Lana; Masumori, Atsushi; Ikegami, Takashi

    2017-01-01

    Learning based on networks of real neurons, and learning based on biologically inspired models of neural networks, have yet to find general learning rules leading to widespread applications. In this paper, we argue for the existence of a principle allowing to steer the dynamics of a biologically inspired neural network. Using carefully timed external stimulation, the network can be driven towards a desired dynamical state. We term this principle "Learning by Stimulation Avoidance" (LSA). We demonstrate through simulation that the minimal sufficient conditions leading to LSA in artificial networks are also sufficient to reproduce learning results similar to those obtained in biological neurons by Shahaf and Marom, and in addition explains synaptic pruning. We examined the underlying mechanism by simulating a small network of 3 neurons, then scaled it up to a hundred neurons. We show that LSA has a higher explanatory power than existing hypotheses about the response of biological neural networks to external simulation, and can be used as a learning rule for an embodied application: learning of wall avoidance by a simulated robot. In other works, reinforcement learning with spiking networks can be obtained through global reward signals akin simulating the dopamine system; we believe that this is the first project demonstrating sensory-motor learning with random spiking networks through Hebbian learning relying on environmental conditions without a separate reward system.

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

    Directory of Open Access Journals (Sweden)

    Qiang Yu

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

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

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

    2016-04-01

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

  18. Dopamine Agonists and Pathologic Behaviors

    Directory of Open Access Journals (Sweden)

    Brendan J. Kelley

    2012-01-01

    Full Text Available The dopamine agonists ropinirole and pramipexole exhibit highly specific affinity for the cerebral dopamine D3 receptor. Use of these medications in Parkinson’s disease has been complicated by the emergence of pathologic behavioral patterns such as hypersexuality, pathologic gambling, excessive hobbying, and other circumscribed obsessive-compulsive disorders of impulse control in people having no history of such disorders. These behavioral changes typically remit following discontinuation of the medication, further demonstrating a causal relationship. Expression of the D3 receptor is particularly rich within the limbic system, where it plays an important role in modulating the physiologic and emotional experience of novelty, reward, and risk assessment. Converging neuroanatomical, physiological, and behavioral science data suggest the high D3 affinity of these medications as the basis for these behavioral changes. These observations suggest the D3 receptor as a therapeutic target for obsessive-compulsive disorder and substance abuse, and improved understanding of D3 receptor function may aid drug design of future atypical antipsychotics.

  19. Dopamine beta-hydroxylase deficiency

    Directory of Open Access Journals (Sweden)

    Senard Jean-Michel

    2006-03-01

    Full Text Available Abstract Dopamine beta-hydroxylase (DβH deficiency is a very rare form of primary autonomic failure characterized by a complete absence of noradrenaline and adrenaline in plasma together with increased dopamine plasma levels. The prevalence of DβH deficiency is unknown. Only a limited number of cases with this disease have been reported. DβH deficiency is mainly characterized by cardiovascular disorders and severe orthostatic hypotension. First symptoms often start during a complicated perinatal period with hypotension, muscle hypotonia, hypothermia and hypoglycemia. Children with DβH deficiency exhibit reduced ability to exercise because of blood pressure inadaptation with exertion and syncope. Symptoms usually worsen progressively during late adolescence and early adulthood with severe orthostatic hypotension, eyelid ptosis, nasal stuffiness and sexual disorders. Limitation in standing tolerance, limited ability to exercise and traumatic morbidity related to falls and syncope may represent later evolution. The syndrome is caused by heterogeneous molecular alterations of the DBH gene and is inherited in an autosomal recessive manner. Restoration of plasma noradrenaline to the normal range can be achieved by therapy with the synthetic precursor of noradrenaline, L-threo-dihydroxyphenylserine (DOPS. Oral administration of 100 to 500 mg DOPS, twice or three times daily, increases blood pressure and reverses the orthostatic intolerance.

  20. Supervised Learning Based on Temporal Coding in Spiking Neural Networks.

    Science.gov (United States)

    Mostafa, Hesham

    2017-08-01

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

  1. Spikes and matter inhomogeneities in massless scalar field models

    International Nuclear Information System (INIS)

    Coley, A A; Lim, W C

    2016-01-01

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

  2. Dopamine-transporter SPECT and Dopamine-D2-receptor SPECT in basal ganglia diseases

    International Nuclear Information System (INIS)

    Hesse, S.; Barthel, H.; Seese, A.; Sabri, O.

    2007-01-01

    The basal ganglia comprise a group of subcortical nuclei, which are essential for motor control. Dysfunction of these areas, especially in dopaminergic transmission, results in disordered movement and neurological diseases such as Parkinson's disease, Wilson's disease, or Huntington disease. Positron emission tomography and single photon emission computed tomography (SPECT) have enhanced the understanding of the underlying pathophysiology, but they much more contribute to the early differential diagnosis of patients suffering from Parkinsonian syndrome in routine care. The present article provides dopamine transporter and D 2 receptor SPECT findings in selected movement disorders. (orig.)

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

    Science.gov (United States)

    Ponulak, Filip; Kasinski, Andrzej

    2011-01-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  5. Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses.

    Directory of Open Access Journals (Sweden)

    Gabriel Koch Ocker

    2015-08-01

    Full Text Available The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure.

  6. Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses.

    Science.gov (United States)

    Ocker, Gabriel Koch; Litwin-Kumar, Ashok; Doiron, Brent

    2015-08-01

    The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure.

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

    DEFF Research Database (Denmark)

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

    2006-01-01

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

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

    Science.gov (United States)

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

    2017-03-07

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

  9. Dopamine receptors in human gastrointestinal mucosa

    International Nuclear Information System (INIS)

    Hernandez, D.E.; Mason, G.A.; Walker, C.H.; Valenzuela, J.E.

    1987-01-01

    Dopamine is a putative enteric neurotransmitter that has been implicated in exocrine secretory and motility functions of the gastrointestinal tract of several mammalian species including man. This study was designed to determine the presence of dopamine binding sites in human gastric and duodenal mucosa and to describe certain biochemical characteristics of these enteric receptor sites. The binding assay was performed in triplicate with tissue homogenates obtained from healthy volunteers of both sexes using 3 H-dopamine as a ligand. The extent of nonspecific binding was determined in the presence of a 100-fold excess of unlabeled dopamine. Scatchard analysis performed with increasing concentrations of 3 H-dopamine (20-500 nM) revealed a single class of saturable dopamine binding sites in gastric and duodenal mucosa. The results of this report demonstrate the presence of specific dopamine receptors in human gastric and duodenal mucosa. These biochemical data suggest that molecular abnormalities of these receptor sites may be operative in the pathogenesis of important gastrointestinal disorders. 33 references, 2 figures

  10. Decreased prefrontal cortical dopamine transmission in alcoholism.

    Science.gov (United States)

    Narendran, Rajesh; Mason, Neale Scott; Paris, Jennifer; Himes, Michael L; Douaihy, Antoine B; Frankle, W Gordon

    2014-08-01

    Basic studies have demonstrated that optimal levels of prefrontal cortical dopamine are critical to various executive functions such as working memory, attention, inhibitory control, and risk/reward decisions, all of which are impaired in addictive disorders such as alcoholism. Based on this and imaging studies of alcoholism that have demonstrated less dopamine in the striatum, the authors hypothesized decreased dopamine transmission in the prefrontal cortex in persons with alcohol dependence. To test this hypothesis, amphetamine and [11C]FLB 457 positron emission tomography were used to measure cortical dopamine transmission in 21 recently abstinent persons with alcohol dependence and 21 matched healthy comparison subjects. [11C]FLB 457 binding potential, specific compared to nondisplaceable uptake (BPND), was measured in subjects with kinetic analysis using the arterial input function both before and after 0.5 mg kg-1 of d-amphetamine. Amphetamine-induced displacement of [11C]FLB 457 binding potential (ΔBPND) was significantly smaller in the cortical regions in the alcohol-dependent group compared with the healthy comparison group. Cortical regions that demonstrated lower dopamine transmission in the alcohol-dependent group included the dorsolateral prefrontal cortex, medial prefrontal cortex, orbital frontal cortex, temporal cortex, and medial temporal lobe. The results of this study, for the first time, unambiguously demonstrate decreased dopamine transmission in the cortex in alcoholism. Further research is necessary to understand the clinical relevance of decreased cortical dopamine as to whether it is related to impaired executive function, relapse, and outcome in alcoholism.

  11. Stereoselectivity of presynaptic autoreceptors modulating dopamine release

    International Nuclear Information System (INIS)

    Arbilla, S.; Langer, S.Z.

    1981-01-01

    The effects of the (R)- and (S)-enantiomers of sulpiride and butaclamol were studied on the spontaneous and field stimulation-evoked release of total radioactivity from slices of rabbit caudate nucleus prelabelled with [ 3 H]dopamine. (S)-Sulpiride in concentrations ranging from 0.01-1μM enhanced the electrically evoked release of [ 3 H]dopamine while (R)-sulpiride was 10 times less potent than (S)-sulpiride. Exposure to (S)-butaclamol (0.1-1 μM) but not to (R)-butaclamol (0.1-10μM) enhanced the field-stimulated release of [ 3 H]dopamine. The facilitatory effects of (S)- and (R)-sulpiride and (S)-butaclamol on the stimulated release of the labelled neurotransmitter were observed under conditions in which these drugs did not modify the spontaneous outflow of radioactivity. Only the active enantiomers of sulpiride and butaclamol antagonized the inhibition by apomorphine (1μM) of the stimulated release of [ 3 H]dopamine. Our results indicate that the presynaptic inhibitory dopamine autoreceptors modulating the stimulation-evoked release of [ 3 H]dopamine in the caudate nucleus are, like the classical postsynaptic dopamine receptors, chemically stereoselective. (Auth.)

  12. TRH regulates action potential shape in cerebral cortex pyramidal neurons.

    Science.gov (United States)

    Rodríguez-Molina, Víctor; Patiño, Javier; Vargas, Yamili; Sánchez-Jaramillo, Edith; Joseph-Bravo, Patricia; Charli, Jean-Louis

    2014-07-07

    Thyrotropin releasing hormone (TRH) is a neuropeptide with a wide neural distribution and a variety of functions. It modulates neuronal electrophysiological properties, including resting membrane potential, as well as excitatory postsynaptic potential and spike frequencies. We explored, with whole-cell patch clamp, TRH effect on action potential shape in pyramidal neurons of the sensorimotor cortex. TRH reduced spike and after hyperpolarization amplitudes, and increased spike half-width. The effect varied with dose, time and cortical layer. In layer V, 0.5µM of TRH induced a small increase in spike half-width, while 1 and 5µM induced a strong but transient change in spike half-width, and amplitude; after hyperpolarization amplitude was modified at 5µM of TRH. Cortical layers III and VI neurons responded intensely to 0.5µM TRH; layer II neurons response was small. The effect of 1µM TRH on action potential shape in layer V neurons was blocked by G-protein inhibition. Inhibition of the activity of the TRH-degrading enzyme pyroglutamyl peptidase II (PPII) reproduced the effect of TRH, with enhanced spike half-width. Many cortical PPII mRNA+ cells were VGLUT1 mRNA+, and some GAD mRNA+. These data show that TRH regulates action potential shape in pyramidal cortical neurons, and are consistent with the hypothesis that PPII controls its action in this region. Copyright © 2014 Elsevier B.V. All rights reserved.

  13. Human dopamine receptor and its uses

    Energy Technology Data Exchange (ETDEWEB)

    Civelli, Olivier (Portland, OR); Van Tol, Hubert Henri-Marie (Toronto, CA)

    1999-01-01

    The present invention is directed toward the isolation, characterization and pharmacological use of the human D4 dopamine receptor. The nucleotide sequence of the gene corresponding to this receptor and alleleic variant thereof are provided by the invention. The invention also includes recombinant eukaryotic expression constructs capable of expressing the human D4 dopamine receptor in cultures of transformed eukaryotic cells. The invention provides cultures of transformed eukaryotic cells which synthesize the human D4 dopamine receptor, and methods for characterizing novel psychotropic compounds using such cultures.

  14. Spike-based population coding and working memory.

    Directory of Open Access Journals (Sweden)

    Martin Boerlin

    2011-02-01

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

  15. Turning skin into dopamine neurons

    Institute of Scientific and Technical Information of China (English)

    Malin Parmar; Johan Jakobsson

    2011-01-01

    The possibility to generate neurons from fibroblasts became a reality with the development of iPS technology a few years ago.By reprogramming somatic cells using transcription factor (TF) overexpression,it is possible to generate pluripotent stem cells that then can be differentiated into any somatic cell type including various subtypes of neurons.This raises the possibility of using donor-matched or even patientspecific cells for cell therapy of neurological disorders such as Parkinson's disease (PD),Huntington's disease and stroke.Supporting this idea,dopamine neurons,which are the cells dying in PD,derived from human iPS cells have been demonstrated to survive transplantation and reverse motor symptoms in animal models of PD [1].

  16. Size-controlled gold nanoparticles obtained from electrodeposited amidoferrocenylpoly(propyleneimine) dendrimer-templates for the electrochemical sensing of dopamine

    Science.gov (United States)

    Villena, Carlos; Bravo, Marta; Alonso, Beatriz; Casado, Carmen M.; Losada, José; García Armada, M. Pilar

    2017-10-01

    Nanometer-scale gold particles exhibit size-dependent electronic properties with important sensing and biosensing applications. In the same way, a lot of analytes show some type of surface-sensitive reaction and the electrode material has a strong influence on the catalytic activity. In this work we study the kinetics and electrochemistry of electrodes with size controlled gold nanoparticles, obtained by electrodeposited amidoferrocenylpoly(propyleneimine) dendrimers of two generations as templates, and the kinetics and the analytical response to the oxidation of dopamine. We demonstrate that the four-types of modified electrodes show good catalytic responses toward the oxidation of dopamine via different processes in relation with the absence or presence of gold nanoparticles and their size. The best response was obtained with the largest nanoparticles, obtained with the first generation dendrimer-template at 0.3 V vs. SCE, with three linear ranges (0-70, 70-600 and 600-1000 μM), with sensitivities 585.7; 466.0 and 314.3 μA/mM cm2, and limit of detection of 0.01 μM. The effect of interfering substances has been studied by differential pulse voltammetry and the developed sensor has been successfully used for the determination of dopamine in a commercial dopamine hydrochloride injection and in spiked Human urine.

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

    Directory of Open Access Journals (Sweden)

    Drew Benjamin Sinha

    2014-01-01

    Full Text Available 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 transformations, we modeled feed-forward networks of 7-22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity 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.

  18. Detection of dopamine neurotransmission in 'real time'

    Directory of Open Access Journals (Sweden)

    Rajendra D Badgaiyan

    2013-07-01

    Full Text Available Current imaging techniques have limited ability to detect neurotransmitters released during brain processing. It is a critical limitation because neurotransmitters have significant control over the brain activity. In this context, recent development of single-scan dynamic molecular imaging technique is important because it allows detection, mapping, and measurement of dopamine released in the brain during task performance. The technique exploits the competition between endogenously released dopamine and its receptor ligand for occupancy of receptor sites. Dopamine released during task performance is detected by dynamically measuring concentration of intravenously injected radiolabeled ligand using a positron emission tomography camera. Based on the ligand concentration, values of receptor kinetic parameters are estimated. These estimates allow detection of dopamine released in the human brain during task performance.

  19. DOPA, norepinephrine, and dopamine in rat tissues

    DEFF Research Database (Denmark)

    Eldrup, E; Richter, Erik; Christensen, N J

    1989-01-01

    We studied the effect of unilateral sympathectomy on rat quadriceps and gastrocnemius muscle concentrations of endogenous dihydroxyphenylalanine (DOPA), dopamine (DA), and norepinephrine (NE) and assessed the relationships between these catecholamines in several rat tissues. Catecholamines were...

  20. Eliminating thermal violin spikes from LIGO noise

    Energy Technology Data Exchange (ETDEWEB)

    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.

  1. Eliminating thermal violin spikes from LIGO noise

    International Nuclear Information System (INIS)

    Santamore, D. H.; Levin, Yuri

    2001-01-01

    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 ∼2 x 10 -13 cm/Hz. This sensitivity is two orders of magnitude greater than that of currently available sensors

  2. Phase Diagram of Spiking Neural Networks

    Directory of Open Access Journals (Sweden)

    Hamed eSeyed-Allaei

    2015-03-01

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

  3. Communication through resonance in spiking neuronal networks.

    Science.gov (United States)

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

    2014-08-01

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

  4. Spike Pattern Recognition for Automatic Collimation Alignment

    CERN Document Server

    Azzopardi, Gabriella; Salvachua Ferrando, Belen Maria; Mereghetti, Alessio; Redaelli, Stefano; CERN. Geneva. ATS Department

    2017-01-01

    The LHC makes use of a collimation system to protect its sensitive equipment by intercepting potentially dangerous beam halo particles. The appropriate collimator settings to protect the machine against beam losses relies on a very precise alignment of all the collimators with respect to the beam. The beam center at each collimator is then found by touching the beam halo using an alignment procedure. Until now, in order to determine whether a collimator is aligned with the beam or not, a user is required to follow the collimator’s BLM loss data and detect spikes. A machine learning (ML) model was trained in order to automatically recognize spikes when a collimator is aligned. The model was loosely integrated with the alignment implementation to determine the classification performance and reliability, without effecting the alignment process itself. The model was tested on a number of collimators during this MD and the machine learning was able to output the classifications in real-time.

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

    International Nuclear Information System (INIS)

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

    2012-01-01

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

  6. The local field potential reflects surplus spike synchrony

    DEFF Research Database (Denmark)

    Denker, Michael; Roux, Sébastien; Lindén, Henrik

    2011-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2013-01-01

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

  8. A new supervised learning algorithm for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

    2013-06-01

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

  9. Propagation of spiking regularity and double coherence resonance in feedforward networks.

    Science.gov (United States)

    Men, Cong; Wang, Jiang; Qin, Ying-Mei; Deng, Bin; Tsang, Kai-Ming; Chan, Wai-Lok

    2012-03-01

    We investigate the propagation of spiking regularity in noisy feedforward networks (FFNs) based on FitzHugh-Nagumo neuron model systematically. It is found that noise could modulate the transmission of firing rate and spiking regularity. Noise-induced synchronization and synfire-enhanced coherence resonance are also observed when signals propagate in noisy multilayer networks. It is interesting that double coherence resonance (DCR) with the combination of synaptic input correlation and noise intensity is finally attained after the processing layer by layer in FFNs. Furthermore, inhibitory connections also play essential roles in shaping DCR phenomena. Several properties of the neuronal network such as noise intensity, correlation of synaptic inputs, and inhibitory connections can serve as control parameters in modulating both rate coding and the order of temporal coding.

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

    Science.gov (United States)

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

    2015-01-01

    Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

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

    Directory of Open Access Journals (Sweden)

    Daniel ede Santos-Sierra

    2015-11-01

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

  12. Dopamine reward prediction error responses reflect marginal utility.

    Science.gov (United States)

    Stauffer, William R; Lak, Armin; Schultz, Wolfram

    2014-11-03

    Optimal choices require an accurate neuronal representation of economic value. In economics, utility functions are mathematical representations of subjective value that can be constructed from choices under risk. Utility usually exhibits a nonlinear relationship to physical reward value that corresponds to risk attitudes and reflects the increasing or decreasing marginal utility obtained with each additional unit of reward. Accordingly, neuronal reward responses coding utility should robustly reflect this nonlinearity. In two monkeys, we measured utility as a function of physical reward value from meaningful choices under risk (that adhered to first- and second-order stochastic dominance). The resulting nonlinear utility functions predicted the certainty equivalents for new gambles, indicating that the functions' shapes were meaningful. The monkeys were risk seeking (convex utility function) for low reward and risk avoiding (concave utility function) with higher amounts. Critically, the dopamine prediction error responses at the time of reward itself reflected the nonlinear utility functions measured at the time of choices. In particular, the reward response magnitude depended on the first derivative of the utility function and thus reflected the marginal utility. Furthermore, dopamine responses recorded outside of the task reflected the marginal utility of unpredicted reward. Accordingly, these responses were sufficient to train reinforcement learning models to predict the behaviorally defined expected utility of gambles. These data suggest a neuronal manifestation of marginal utility in dopamine neurons and indicate a common neuronal basis for fundamental explanatory constructs in animal learning theory (prediction error) and economic decision theory (marginal utility). Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  13. Multimodal imaging of spike propagation: a technical case report.

    Science.gov (United States)

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

    2012-06-01

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

  14. Dopamine versus noradrenaline in septic shock

    Directory of Open Access Journals (Sweden)

    Bo Xu

    2011-10-01

    Full Text Available BackgroundThe ‘Surviving Sepsis’ Campaign guidelines recommend theuse of dopamine or noradrenaline as the first vasopressor inseptic shock. However, information that guides clinicians inchoosing between dopamine and noradrenaline as the firstvasopressor in patients with septic shock is limited.ObjectiveThis article presents a review of the literature regarding theuse of dopamine versus noradrenaline in patients with septicshock.ResultsTwo randomised controlled trials (RCT and two largeprospective cohort studies were analysed. RCT data showeddopamine was associated with increased arrhythmic events.One cohort study found dopamine was associated with higher30-day mortality. The other cohort study found noradrenalinewas associated with higher 28-day mortality.DiscussionData on the use of dopamine versus noradrenaline in patientswith septic shock is limited. Following the recent SOAP IIstudy, there is now strong evidence that the use of dopaminein septic shock is associated with significantly morecardiovascular adverse events, compared tonoradrenaline.ConclusionNoradrenaline should be used as the initial vasopressor inseptic shock to avoid the arrhythmic events associatedwith dopamine.

  15. Generalized activity equations for spiking neural network dynamics

    Directory of Open Access Journals (Sweden)

    Michael A Buice

    2013-11-01

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

  16. In-reactor creep of zirconium alloys by thermal spikes

    International Nuclear Information System (INIS)

    Ibrahim, E.F.

    1975-01-01

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

  17. Surfing a spike wave down the ventral stream.

    Science.gov (United States)

    VanRullen, Rufin; Thorpe, Simon J

    2002-10-01

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

  18. AMORE Mo-99 Spike Test Results

    Energy Technology Data Exchange (ETDEWEB)

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

    2017-09-27

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

  19. Dynamic Control of Synchronous Activity in Networks of Spiking Neurons.

    Directory of Open Access Journals (Sweden)

    Axel Hutt

    Full Text Available Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system's response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles.

  20. Dynamic Control of Synchronous Activity in Networks of Spiking Neurons.

    Science.gov (United States)

    Hutt, Axel; Mierau, Andreas; Lefebvre, Jérémie

    Oscillatory brain activity is believed to play a central role in neural coding. Accumulating evidence shows that features of these oscillations are highly dynamic: power, frequency and phase fluctuate alongside changes in behavior and task demands. The role and mechanism supporting this variability is however poorly understood. We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. We show that afferent noise, mimicking inputs to the neurons, causes smoothing of the system's response function, displacing equilibria and altering the stability of oscillatory states. Our analysis further shows that these noise-induced changes cause a shift of the peak frequency of synchronous oscillations that scales with input intensity, leading the network towards critical states. We lastly discuss the extension of these principles to periodic stimulation, in which externally applied driving signals can trigger analogous phenomena. Our results reveal one possible mechanism involved in shaping oscillatory activity in the brain and associated control principles.

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

    Science.gov (United States)

    Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

    2010-01-01

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

  2. Dopamine D(1) receptor-mediated control of striatal acetylcholine release by endogenous dopamine.

    Science.gov (United States)

    Acquas, E; Di Chiara, G

    1999-10-27

    The role of dopamine D(1) and D(2) receptors in the control of acetylcholine release in the dorsal striatum by endogenous dopamine was investigated by monitoring with microdialysis the effect of the separate or combined administration of the dopamine D(1) receptor antagonist, SCH 39166 ¿(-)-trans-6,7,7a,8,9, 13b-exahydro-3-chloro-2-hydroxy-N-methyl-5H-benzo-[d]-nap hto-[2, 1b]-azepine hydrochloride¿ (50 microg/kg subcutaneous (s.c.)), of the dopamine D(2)/D(3) receptor agonist, quinpirole (trans-(-)-4aR, 4a,5,6,7,8,8a,9-octahydro-5-propyl-1H-pyrazolo-(3,4-g)-quinoline hydrochloride) (5 and 10 microg/kg s.c.), and of the D(3) receptor selective agonist, PD 128,907 [S(+)-(4aR,10bR)-3,4,4a, 10b-tetrahydro-4-propyl-2H,5H-[1]benzopyrano-[4,3-b]-1,4-oxazin -9-ol hydrochloride] (50 microg/kg s.c.), on in vivo dopamine and acetylcholine release. Microdialysis was performed with a Ringer containing low concentrations (0.01 microM) of the acetylcholinesterase inhibitor, neostigmine. Quinpirole (10 microg/kg s.c.) decreased striatal dopamine and acetylcholine release. Administration of PD 128,907 (50 microg/kg) decreased dopamine but failed to affect acetylcholine release. SCH 39166 (50 microg/kg s.c.) stimulated dopamine release and reduced acetylcholine release. Pretreatment with quinpirole reduced (5 microg/kg s.c.) or completely prevented (10 microg/kg s.c.) the stimulation of dopamine release elicited by SCH 39166 (50 microg/kg s.c.); on the other hand, pretreatment with quinpirole (5 and 10 microg/kg) potentiated the reduction of striatal acetylcholine release induced by SCH 39166 (50 microg/kg s.c.). Similarly, pretreatment with PD 128,907 (50 microg/kg) which prevented the increase of dopamine release induced by SCH 39166 (50 microg/kg), potentiated the reduction of striatal acetylcholine transmission elicited by SCH 39166. Thus, pretreatment with low doses of quinpirole or PD 128,907 influences in opposite manner the effect of SCH 39166 on striatal dopamine and

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

    Science.gov (United States)

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

    2014-04-01

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

  4. A propane price spike nails users

    International Nuclear Information System (INIS)

    Milke, M.

    1997-01-01

    The increase in price for propane was discussed. In 1993, propane cost about 5 cents per litre; by December 1996, the price has risen to 27 cents wholesale, while retail prices for auto propane reached 40 cents per litre. As a result, farmers and fleet operators are considering switching to an alternative energy supply. The five factors which may have played a role in the propane price spike were described. These included a cold winter which lowered inventories, a Pemex gas plant in Mexico which had been damaged by fire, forcing Mexico to import natural gas and natural gas liquids from the USA, the failure of propane distributors to restock during the summer months in the hope of lower prices, and increased cost of competing fuels in the face of increased demand. It was noted that these factors are transitory, which could mean better prices this summer

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

    Directory of Open Access Journals (Sweden)

    Taghavi Kani M

    2011-02-01

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

  6. Addiction: beyond dopamine reward circuitry.

    Science.gov (United States)

    Volkow, Nora D; Wang, Gene-Jack; Fowler, Joanna S; Tomasi, Dardo; Telang, Frank

    2011-09-13

    Dopamine (DA) is considered crucial for the rewarding effects of drugs of abuse, but its role in addiction is much less clear. This review focuses on studies that used PET to characterize the brain DA system in addicted subjects. These studies have corroborated in humans the relevance of drug-induced fast DA increases in striatum [including nucleus accumbens (NAc)] in their rewarding effects but have unexpectedly shown that in addicted subjects, drug-induced DA increases (as well as their subjective reinforcing effects) are markedly blunted compared with controls. In contrast, addicted subjects show significant DA increases in striatum in response to drug-conditioned cues that are associated with self-reports of drug craving and appear to be of a greater magnitude than the DA responses to the drug. We postulate that the discrepancy between the expectation for the drug effects (conditioned responses) and the blunted pharmacological effects maintains drug taking in an attempt to achieve the expected reward. Also, whether tested during early or protracted withdrawal, addicted subjects show lower levels of D2 receptors in striatum (including NAc), which are associated with decreases in baseline activity in frontal brain regions implicated in salience attribution (orbitofrontal cortex) and inhibitory control (anterior cingulate gyrus), whose disruption results in compulsivity and impulsivity. These results point to an imbalance between dopaminergic circuits that underlie reward and conditioning and those that underlie executive function (emotional control and decision making), which we postulate contributes to the compulsive drug use and loss of control in addiction.

  7. Imaging dopamine transmission in schizophrenia

    International Nuclear Information System (INIS)

    Laruelle, M.

    1998-01-01

    Over the last ten years, several positron emission tomography (PET) and single photon computerized tomography (SPECT) studies of the dopamine (DA) system in patients with schizophrenia were performed to test the hypothesis that DA hyperactivity is associated with this illness. In this paper are reviewed the results of fifteen brain imaging studies comparing indices of DA function in drug naive or drug free patients with schizophrenia and healthy controls: thirteen studies included measurements of Da D 2 receptor density, two studies compared amphetamine-induced DA release, and two studies measured DOPA decarboxylase activity, an enzyme involved in DA synthesis. It was conducted a meta-analysis of the studies measuring D 2 receptor density parameters, under the assumption that all tracers labeled the same population of D 2 receptors. This analysis revealed that, compared to healthy controls, patients with schizophrenia present a significant but mild elevation of D 2 receptor density parameters and a significant larger variability of these indices. It was found no statistical evidence that studies performed with radiolabeled butyrophenones detected a larger increase in D 2 receptor density parameters than studies performed with other radioligands, such as benzamides. Studies of presynaptic activity revealed an increase in DA transmission response to amphetamine challenge, and an increase in DOPA decarboxylase activity. Together, these data are compatible with both pre- and post-synaptic alterations of DA transmission in schizophrenia. Future studies should aim at a better characterization of these alterations, and at defining their role in the pathophysiology of the illness

  8. Immunomodulatory Effects Mediated by Dopamine

    Science.gov (United States)

    Alvarez-Herrera, Samantha; Pérez-Sánchez, Gilberto; Becerril-Villanueva, Enrique; Cruz-Fuentes, Carlos; Flores-Gutierrez, Enrique Octavio; Quintero-Fabián, Saray

    2016-01-01

    Dopamine (DA), a neurotransmitter in the central nervous system (CNS), has modulatory functions at the systemic level. The peripheral and central nervous systems have independent dopaminergic system (DAS) that share mechanisms and molecular machinery. In the past century, experimental evidence has accumulated on the proteins knowledge that is involved in the synthesis, reuptake, and transportation of DA in leukocytes and the differential expression of the D1-like (D1R and D5R) and D2-like receptors (D2R, D3R, and D4R). The expression of these components depends on the state of cellular activation and the concentration and time of exposure to DA. Receptors that are expressed in leukocytes are linked to signaling pathways that are mediated by changes in cAMP concentration, which in turn triggers changes in phenotype and cellular function. According to the leukocyte lineage, the effects of DA are associated with such processes as respiratory burst, cytokine and antibody secretion, chemotaxis, apoptosis, and cytotoxicity. In clinical conditions such as schizophrenia, Parkinson disease, Tourette syndrome, and multiple sclerosis (MS), there are evident alterations during immune responses in leukocytes, in which changes in DA receptor density have been observed. Several groups have proposed that these findings are useful in establishing clinical status and clinical markers. PMID:27795960

  9. Immunomodulatory Effects Mediated by Dopamine

    Directory of Open Access Journals (Sweden)

    Rodrigo Arreola

    2016-01-01

    Full Text Available Dopamine (DA, a neurotransmitter in the central nervous system (CNS, has modulatory functions at the systemic level. The peripheral and central nervous systems have independent dopaminergic system (DAS that share mechanisms and molecular machinery. In the past century, experimental evidence has accumulated on the proteins knowledge that is involved in the synthesis, reuptake, and transportation of DA in leukocytes and the differential expression of the D1-like (D1R and D5R and D2-like receptors (D2R, D3R, and D4R. The expression of these components depends on the state of cellular activation and the concentration and time of exposure to DA. Receptors that are expressed in leukocytes are linked to signaling pathways that are mediated by changes in cAMP concentration, which in turn triggers changes in phenotype and cellular function. According to the leukocyte lineage, the effects of DA are associated with such processes as respiratory burst, cytokine and antibody secretion, chemotaxis, apoptosis, and cytotoxicity. In clinical conditions such as schizophrenia, Parkinson disease, Tourette syndrome, and multiple sclerosis (MS, there are evident alterations during immune responses in leukocytes, in which changes in DA receptor density have been observed. Several groups have proposed that these findings are useful in establishing clinical status and clinical markers.

  10. Addiction: Beyond dopamine reward circuitry

    International Nuclear Information System (INIS)

    Volkow, N.D.; Wang, G.-J.; Fowler, J.S.; Tomasi, D.; Telang, F.

    2011-01-01

    Dopamine (DA) is considered crucial for the rewarding effects of drugs of abuse, but its role in addiction is much less clear. This review focuses on studies that used PET to characterize the brain DA system in addicted subjects. These studies have corroborated in humans the relevance of drug-induced fast DA increases in striatum [including nucleus accumbens (NAc)] in their rewarding effects but have unexpectedly shown that in addicted subjects, drug-induced DA increases (as well as their subjective reinforcing effects) are markedly blunted compared with controls. In contrast, addicted subjects show significant DA increases in striatum in response to drug-conditioned cues that are associated with self-reports of drug craving and appear to be of a greater magnitude than the DA responses to the drug. We postulate that the discrepancy between the expectation for the drug effects (conditioned responses) and the blunted pharmacological effects maintains drug taking in an attempt to achieve the expected reward. Also, whether tested during early or protracted withdrawal, addicted subjects show lower levels of D2 receptors in striatum (including NAc), which are associated with decreases in baseline activity in frontal brain regions implicated in salience attribution (orbitofrontal cortex) and inhibitory control (anterior cingulate gyrus), whose disruption results in compulsivity and impulsivity. These results point to an imbalance between dopaminergic circuits that underlie reward and conditioning and those that underlie executive function (emotional control and decision making), which we postulate contributes to the compulsive drug use and loss of control in addiction.

  11. Dopamine, behavioral economics, and effort

    Directory of Open Access Journals (Sweden)

    John D Salamone

    2009-09-01

    Full Text Available Abstract. There are numerous problems with the hypothesis that brain dopamine (DA systems, particularly in the nucleus accumbens, directly mediate the rewarding or primary motivational characteristics of natural stimuli such as food. Research and theory related to the functions of mesolimbic DA are undergoing a substantial conceptual restructuring, with the traditional emphasis on hedonia and primary reward yielding to other concepts and lines of inquiry. The present review is focused upon the involvement of nucleus accumbens DA in behavioral activation and effort-related processes. Viewed from the framework of behavioral economics, the effects of accumbens DA depletions and antagonism on food-reinforced behavior are highly dependent upon the work requirements of the instrumental task, and DA depleted rats are more sensitive to increases in response costs (i.e., ratio requirements. Moreover, interference with accumbens DA transmission exerts a powerful influence over effort-related choice behavior. Rats with accumbens DA depletions or antagonism reallocate their instrumental behavior away from food-reinforced tasks that have high response requirements, and instead these rats select a less-effortful type of food-seeking behavior. Nucleus accumbens DA and adenosine interact in the regulation of effort-related functions, and other brain structures (anterior cingulate cortex, amygdala, ventral pallidum also are involved. Studies of the brain systems regulating effort-based processes may have implications for understanding drug abuse, as well as energy-related disorders such as psychomotor slowing, fatigue or anergia in depression and other neurological disorders.

  12. Addiction: Beyond dopamine reward circuitry

    Energy Technology Data Exchange (ETDEWEB)

    Volkow, N.D.; Wang, G.; Volkow, N.D.; Wang, G.-J.; Fowler, J.S.; Tomasi, D.; Telang, F.

    2011-09-13

    Dopamine (DA) is considered crucial for the rewarding effects of drugs of abuse, but its role in addiction is much less clear. This review focuses on studies that used PET to characterize the brain DA system in addicted subjects. These studies have corroborated in humans the relevance of drug-induced fast DA increases in striatum [including nucleus accumbens (NAc)] in their rewarding effects but have unexpectedly shown that in addicted subjects, drug-induced DA increases (as well as their subjective reinforcing effects) are markedly blunted compared with controls. In contrast, addicted subjects show significant DA increases in striatum in response to drug-conditioned cues that are associated with self-reports of drug craving and appear to be of a greater magnitude than the DA responses to the drug. We postulate that the discrepancy between the expectation for the drug effects (conditioned responses) and the blunted pharmacological effects maintains drug taking in an attempt to achieve the expected reward. Also, whether tested during early or protracted withdrawal, addicted subjects show lower levels of D2 receptors in striatum (including NAc), which are associated with decreases in baseline activity in frontal brain regions implicated in salience attribution (orbitofrontal cortex) and inhibitory control (anterior cingulate gyrus), whose disruption results in compulsivity and impulsivity. These results point to an imbalance between dopaminergic circuits that underlie reward and conditioning and those that underlie executive function (emotional control and decision making), which we postulate contributes to the compulsive drug use and loss of control in addiction.

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

    Directory of Open Access Journals (Sweden)

    Weiliang eChen

    2011-03-01

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

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

    DEFF Research Database (Denmark)

    Proietti, Tomasso; Haldrup, Niels; Knapik, Oskar

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

  15. The Nature of Power Spikes: a regime-switch approach

    NARCIS (Netherlands)

    C.M. de Jong (Cyriel)

    2005-01-01

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

  16. No WIMP mini-spikes in dwarf spheroidal galaxies

    NARCIS (Netherlands)

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

    2015-01-01

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

  17. Spiking and bursting patterns of fractional-order Izhikevich model

    Science.gov (United States)

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

    2018-03-01

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

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

    DEFF Research Database (Denmark)

    Kardos, J; Szilágyi, N; Juhász, G

    1998-01-01

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

  19. Accelerated spike resampling for accurate multiple testing controls.

    Science.gov (United States)

    Harrison, Matthew T

    2013-02-01

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

  20. Causal Inference and Explaining Away in a Spiking Network

    Science.gov (United States)

    Moreno-Bote, Rubén; Drugowitsch, Jan

    2015-01-01

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

  1. Cytoplasmic tail of coronavirus spike protein has intracellular

    Indian Academy of Sciences (India)

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

  2. Pressurized water reactor iodine spiking behavior under power transient conditions

    International Nuclear Information System (INIS)

    Ho, J.C.

    1992-01-01

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

  3. Recent progress in multi-electrode spike sorting methods.

    Science.gov (United States)

    Lefebvre, Baptiste; Yger, Pierre; Marre, Olivier

    2016-11-01

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

  4. Unsupervised spike sorting based on discriminative subspace learning.

    Science.gov (United States)

    Keshtkaran, Mohammad Reza; Yang, Zhi

    2014-01-01

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

  5. A supervised learning rule for classification of spatiotemporal spike patterns.

    Science.gov (United States)

    Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

    2016-08-01

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

  6. Bidirectional modulation of goal-directed actions by prefrontal cortical dopamine.

    Science.gov (United States)

    Hitchcott, Paul K; Quinn, Jennifer J; Taylor, Jane R

    2007-12-01

    Instrumental actions are a vital cognitive asset that endows an organism with sensitivity to the consequences of its behavior. Response-outcome feedback allows responding to be shaped in order to maximize beneficial, and minimize detrimental, outcomes. Lesions of the medial prefrontal cortex (mPFC) result in behavior that is insensitive to changes in outcome value in animals and compulsive behavior in several human psychopathologies. Such insensitivity to changes in outcome value is a defining characteristic of instrumental habits: responses that are controlled by antecedent stimuli rather than goal expectancy. Little is known regarding the neurochemical substrates mediating this sensitivity. The present experiments used sensitivity to posttraining outcome devaluation to index the action-habit status of instrumental responding. Infusions of dopamine into the ventral mPFC (vmPFC), but not dorsal mPFC, restored outcome sensitivity bidirectionally-decreasing responding following outcome devaluation and increasing responding when the outcome was not devalued. This bidirectionality makes the possibility that these infusions nonspecifically dysregulated vmPFC dopamine transmission unlikely. VmPFC dopamine promoted instrumental responding appropriate to outcome value. Reinforcer consumption data indicated that this was not a consequence of altered sensitivity to the reinforcer itself. We suggest that vmPFC dopamine reengages attentional processes underlying goal-directed behavior.

  7. Dopamine-induced apoptosis in human neuronal cells: inhibition by nucleic acides antisense to the dopamine transporter

    International Nuclear Information System (INIS)

    Porat, S.; Gabbay, M.; Tauber, M.; Ratovitski, T.; Blinder, E.; Simantov, R.

    1996-01-01

    Human neuroblastoma NMB cells take up [ 3 H]dopamine in a selective manner indicating that dopamine transporters are responsible for this uptake. These cells were therefore used as a model to study dopamine neurotoxicity, and to elucidate the role of dopamine transporters in controlling cell death. Treatment with 0.05-0.4 mM dopamine changed cells' morphology within 4 h, accompanied by retraction of processes, shrinkage, apoptosis-like atrophy, accumulation of apoptotic particles, DNA fragmentation and cell death. Cycloheximide inhibited dopamine's effect, suggesting that induction of apoptosis by dopamine was dependent upon protein synthesis. Dopamine cytotoxicity, monitored morphologically by flow cytometric analysis, and by lactate dehydrogenase released, was blocked by cocaine but not by the noradrenaline and serotonin uptake blockers desimipramine and imipramine, respectively. Attempting to inhibit dopamine transport and toxicity in a drug-free and highly selective way, three 18-mer dopamine transporter antisense phosphorothioate oligonucleotides (numbers 1, 2 and 3) and a new plasmid vector expressing the entire rat dopamine transporter complementary DNA in the antisense orientation were prepared and tested. Antisense phosphorothioate oligonucleotide 3 inhibited [ 3 H]dopamine uptake in a time- and dose-dependent manner. Likewise, transient transfection of NMB cells with the plasmid expressing dopamine transporter complementary DNA in the antisense orientation partially blocked [ 3 H]dopamine uptake. Antisense phosphorothioate oligonucleotide 3 also decreased, dose-dependently, the toxic effect of dopamine and 6-hydroxydopamine. Western blot analysis with newly prepared anti-human dopamine transporter antibodies showed that antisense phosphorothioate oligonucleotide 3 decreased the transporter protein level. These studies contribute to better understand the mechanism of dopamine-induced apoptosis and neurotoxicity. (Copyright (c) 1996 Elsevier Science B

  8. How the modified method of orbit quality assessment works for Oort spike comets?

    Science.gov (United States)

    Królikowska, Małgorzata; Dybczyński, Piotr A.

    2018-03-01

    We present a brief overview of the effectiveness of the modified method of a quality of orbit estimation proposed by us a few years ago. Having now a complete sample of 100 Oort spike comets with large-perihelion distances, we show that it was justified to introduce more restricted conditions separating the individual quality classes as well as introducing a new quality class containing orbits of the excellent quality, marked by us as 1a+. To enrich the perception, we provided a complete collection of visual time-distributions of positional data sets used by us for an orbit determination (see Appendix). We show that modern positional measurements of large-perihelion Oort spike comets should be carried out for at least three years around perihelion (three-four oppositions) to be almost certain that the derived orbit will be of the highest quality (1a+ class). Our results strongly support an expectation that in a near future it will be possible to study a shape of 1/aori-distribution of the Oort spike comets in a great detail basing only on the highest quality orbits, having 1/aori-uncertainties well below 5 . 10-6 au-1.

  9. Converging effects of Ginkgo biloba extract at the level of transmitter release, NMDA and sodium currents and dendritic spikes.

    Science.gov (United States)

    Szasz, Bernadett K; Lenkey, Nora; Barth, Albert M; Mike, Arpad; Somogyvari, Zsolt; Farkas, Orsolya; Lendvai, Balazs

    2008-08-01

    In this study, an attempt was made to integrate the effects of GINKGO BILOBA extract (GBE) in different experimental systems (IN VITRO cochlea, brain slice preparations and cortical cell culture) to elucidate whether these processes converge to promote neuroprotection or interfere with normal neural function. GBE increased the release of dopamine in the cochlea. NMDA-evoked currents were dose-dependently inhibited by rapid GBE application in cultured cortical cells. GBE moderately inhibited Na+ channels at depolarised holding potential in cortical cells. These inhibitory effects by GBE may sufficiently contribute to the prevention of excitotoxic damage in neurons. However, these channels also interact with memory formation at the cellular level. The lack of effect by GBE on dendritic spike initiation in neocortical layer 5 pyramidal neurons indicates that the integrative functions may remain intact during the inhibitory actions of GBE.

  10. Dopamine Gene Profiling to Predict Impulse Control and Effects of Dopamine Agonist Ropinirole.

    Science.gov (United States)

    MacDonald, Hayley J; Stinear, Cathy M; Ren, April; Coxon, James P; Kao, Justin; Macdonald, Lorraine; Snow, Barry; Cramer, Steven C; Byblow, Winston D

    2016-07-01

    Dopamine agonists can impair inhibitory control and cause impulse control disorders for those with Parkinson disease (PD), although mechanistically this is not well understood. In this study, we hypothesized that the extent of such drug effects on impulse control is related to specific dopamine gene polymorphisms. This double-blind, placebo-controlled study aimed to examine the effect of single doses of 0.5 and 1.0 mg of the dopamine agonist ropinirole on impulse control in healthy adults of typical age for PD onset. Impulse control was measured by stop signal RT on a response inhibition task and by an index of impulsive decision-making on the Balloon Analogue Risk Task. A dopamine genetic risk score quantified basal dopamine neurotransmission from the influence of five genes: catechol-O-methyltransferase, dopamine transporter, and those encoding receptors D1, D2, and D3. With placebo, impulse control was better for the high versus low genetic risk score groups. Ropinirole modulated impulse control in a manner dependent on genetic risk score. For the lower score group, both doses improved response inhibition (decreased stop signal RT) whereas the lower dose reduced impulsiveness in decision-making. Conversely, the higher score group showed a trend for worsened response inhibition on the lower dose whereas both doses increased impulsiveness in decision-making. The implications of the present findings are that genotyping can be used to predict impulse control and whether it will improve or worsen with the administration of dopamine agonists.

  11. Cerebral vascular effects of hypovolemia and dopamine infusions

    DEFF Research Database (Denmark)

    Holst Hahn, Gitte; Heiring, Christian; Pryds, Ole

    2012-01-01

    Despite widespread use, effects of volume boluses and dopamine in hypotensive newborn infants remain controversial. We aimed to elucidate if hypovolemia alone impairs cerebral autoregulation (CA) and if dopamine affects cerebral vasculature.......Despite widespread use, effects of volume boluses and dopamine in hypotensive newborn infants remain controversial. We aimed to elucidate if hypovolemia alone impairs cerebral autoregulation (CA) and if dopamine affects cerebral vasculature....

  12. Dopamine, T cells and multiple sclerosis (MS).

    Science.gov (United States)

    Levite, Mia; Marino, Franca; Cosentino, Marco

    2017-05-01

    Dopamine is a key neurotransmitter that induces critical effects in the nervous system and in many peripheral organs, via 5 dopamine receptors (DRs): D1R-D5R. Dopamine also induces many direct and very potent effects on many DR-expressing immune cells, primarily T cells and dendritic cells. In this review, we focus only on dopamine receptors, effects and production in T cells. Dopamine by itself (at an optimal concentration of~0.1 nM) induces multiple function of resting normal human T cells, among them: T cell adhesion, chemotactic migration, homing, cytokine secretion and others. Interestingly, dopamine activates resting effector T cells (Teffs), but suppresses regulatory T cells (Tregs), and both effects lead eventually to Teff activation. Dopamine-induced effects on T cells are dynamic, context-sensitive and determined by the: T cell activation state, T cell type, DR type, and dopamine concentration. Dopamine itself, and also few dopaminergic molecules/ drugs that are in clinical use for cardiac, neurological and other non-immune indications, have direct effects on human T cells (summarized in this review). These dopaminergic drugs include: dopamine = intropin, L-DOPA, bromocriptine, pramipexole, pergolide, haloperidol, pimozide, and amantadine. Other dopaminergic drugs were not yet tested for their direct effects on T cells. Extensive evidence in multiple sclerosis (MS) and experimental autoimmune encephalomyelitis (EAE) show dopaminergic dysregulations in T cells in these diseases: D1-like DRs are decreased in Teffs of MS patients, and dopamine does not affect these cells. In contrast, D1-like DRs are increased in Tregs of MS patients, possibly causing functional Treg impairment in MS. Treatment of MS patients with interferon β (IFN-β) increases D1-like DRs and decreases D2-like DRs in Teffs, decreases D1-like DRs in Tregs, and most important: restores responsiveness of patient's Teffs to dopamine. DR agonists and antagonists confer some benefits in

  13. Noisy Spiking in Visual Area V2 of Amblyopic Monkeys.

    Science.gov (United States)

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

    2017-01-25

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

  14. ORAL IBOPAMINE SUBSTITUTION IN PATIENTS WITH INTRAVENOUS DOPAMINE DEPENDENCE

    NARCIS (Netherlands)

    GIRBES, ARJ; MILNER, AR; MCCLOSKEY, BV; ZWAVELING, JH; VANVELDHUISEN, DJ; ZIJLSTRA, JG; LIE, KI

    1995-01-01

    In a prospective open study we evaluated whether intravenous dopamine infusions can be safely switched to enterally administered ibopamine in dopamine-dependent patients. Six patients defined as being clinically stable, normovolaemic, but dopamine dependent, i.e. with repeated inability to stop

  15. The binding sites for cocaine and dopamine in the dopamine transporter overlap

    DEFF Research Database (Denmark)

    Beuming, Thijs; Kniazeff, Julie; Bergmann, Marianne L

    2008-01-01

    Cocaine is a widely abused substance with psychostimulant effects that are attributed to inhibition of the dopamine transporter (DAT). We present molecular models for DAT binding of cocaine and cocaine analogs constructed from the high-resolution structure of the bacterial transporter homolog Leu......T. Our models suggest that the binding site for cocaine and cocaine analogs is deeply buried between transmembrane segments 1, 3, 6 and 8, and overlaps with the binding sites for the substrates dopamine and amphetamine, as well as for benztropine-like DAT inhibitors. We validated our models by detailed...... inhibition of dopamine transport by cocaine....

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

    Science.gov (United States)

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

    2014-02-05

    The action potential activity of single cortical neurons can evoke measurable sensory effects, but it is not known how spiking parameters and neuronal subtypes affect the evoked sensations. Here, we examined the effects of spike train irregularity, spike frequency, and spike number on the detectability of single-neuron stimulation in rat somatosensory cortex. For regular-spiking, putative excitatory neurons, detectability increased with spike train irregularity and decreasing spike frequencies but was not affected by spike number. Stimulation of single, fast-spiking, putative inhibitory neurons led to a larger sensory effect compared to regular-spiking neurons, and the effect size depended only on spike irregularity. An ideal-observer analysis suggests that, under our experimental conditions, rats were using integration windows of a few hundred milliseconds or more. Our data imply that the behaving animal is sensitive to single neurons' spikes and even to their temporal patterning. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. Asynchronous Rate Chaos in Spiking Neuronal Circuits.

    Directory of Open Access Journals (Sweden)

    Omri Harish

    2015-07-01

    Full Text Available 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.

  18. Memory recall and spike-frequency adaptation

    Science.gov (United States)

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

    2016-05-01

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

  19. Phase diagram of spiking neural networks.

    Science.gov (United States)

    Seyed-Allaei, Hamed

    2015-01-01

    In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2%, 20% of neurons are inhibitory and 80% are excitatory. These common values are based on experiments, observations, and trials and errors, but here, I take a different perspective, inspired by evolution, I systematically simulate many networks, each with a different set of parameters, and then I try to figure out what makes the common values desirable. I stimulate networks with pulses and then measure their: dynamic range, dominant frequency of population activities, total duration of activities, maximum rate of population and the occurrence time of maximum rate. The results are organized in phase diagram. This phase diagram gives an insight into the space of parameters - excitatory to inhibitory ratio, sparseness of connections and synaptic weights. This phase diagram can be used to decide the parameters of a model. The phase diagrams show that networks which are configured according to the common values, have a good dynamic range in response to an impulse and their dynamic range is robust in respect to synaptic weights, and for some synaptic weights they oscillates in α or β frequencies, independent of external stimuli.

  20. Asynchronous Rate Chaos in Spiking Neuronal Circuits

    Science.gov (United States)

    Harish, Omri; Hansel, David

    2015-01-01

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

  1. Bursts generate a non-reducible spike-pattern code

    Directory of Open Access Journals (Sweden)

    Hugo G Eyherabide

    2009-05-01

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

  2. A Simple Deep Learning Method for Neuronal Spike Sorting

    Science.gov (United States)

    Yang, Kai; Wu, Haifeng; Zeng, Yu

    2017-10-01

    Spike sorting is one of key technique to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. The spike sorting in this case will increase the computational complexity of conventional sorting algorithms. In this paper, we will focus spike sorting on how to reduce the complexity, and introduce a deep learning algorithm, principal component analysis network (PCANet) to spike sorting. The introduced method starts from a conventional model and establish a Toeplitz matrix. Through the column vectors in the matrix, we trains a PCANet, where some eigenvalue vectors of spikes could be extracted. Finally, support vector machine (SVM) is used to sort spikes. In experiments, we choose two groups of simulated data from public databases availably and compare this introduced method with conventional methods. The results indicate that the introduced method indeed has lower complexity with the same sorting errors as the conventional methods.

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

    International Nuclear Information System (INIS)

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

    1990-01-01

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

  4. Predictive coding of dynamical variables in balanced spiking networks.

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

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

    2009-11-15

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

  6. Geomagnetic spikes on the core-mantle boundary

    Science.gov (United States)

    Davies, C. J.; Constable, C.

    2017-12-01

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

  7. Contribution of vesicular and cytosolic dopamine to the increased striatal dopamine efflux elicited by intrastriatal injection of SKF38393.

    NARCIS (Netherlands)

    Saigusa, T.; Aono, Y.; Sekino, R.; Uchida, T.; Takada, K.; Oi, Y.; Koshikawa, N.; Cools, A.R.

    2009-01-01

    Like dexamphetamine, SKF38393 induces an increase in striatal dopamine efflux which is insensitive for tetrodotoxin, Ca(2+) independent and prevented by a dopamine transporter inhibitor. The dexamphetamine-induced striatal dopamine efflux originates from both the reserpine-sensitive vesicular

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

    OpenAIRE

    Walton, Gerald; Potvin, L.

    2009-01-01

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

  9. Dopamine induces neutrophil apoptosis through a dopamine D-1 receptor-independent mechanism.

    LENUS (Irish Health Repository)

    Sookhai, S

    2012-02-03

    BACKGROUND: For the normal resolution of an acute inflammatory response, neutrophil (PMN) apoptosis is essential to maintain immune homeostasis and to limit inappropriate host tissue damage. A delay in PMN apoptosis has been implicated in the pathogenesis of the systemic inflammatory response syndrome (SIRS). Dopamine, a biogenic amine with known cardiovascular and neurotransmitter properties, is used in patients with SIRS to maintain hemodynamic stability. We sought to determine whether dopamine may also have immunoregulatory properties capable of influencing PMN apoptosis, function, and activation state in patients with SIRS. METHODS: PMNs were isolated from healthy volunteers and patients with SIRS and treated with varying doses of dopamine and a dopamine D-1 receptor agonist, fenoldopam. PMN apoptosis was assessed every 6 hours with use of propidium iodide DNA staining and PMN function was assessed with use of respiratory burst activity, phagocytosis ability, and CD11a, CD11b, and CD18 receptor expression as functional markers. RESULTS: There was a significant delay in PMN apotosis in patients with SIRS compared with controls. Treatment of isolated PMNs from both healthy controls and patients with SIRS with 10 and 100 mumol\\/L dopamine induced apoptosis. PMN ingestive and cytocidal capacity were both decreased in patients with SIRS compared with controls. Treatment with dopamine significantly increased phagocytic function. Fenoldopam did not induce PMN apoptosis. CONCLUSION: Our data demonstrate for the first time that dopamine induces PMN apoptosis and modulates PMN function both in healthy controls and in patients with SIRS. These results indicate that dopamine may be beneficial during SIRS through a nonhemodynamic PMN-dependent proapoptotic mechanism.

  10. Membrane permeable C-terminal dopamine transporter peptides attenuate amphetamine-evoked dopamine release

    DEFF Research Database (Denmark)

    Rickhag, Karl Mattias; Owens, WA; Winkler, Marie-Therese

    2013-01-01

    The dopamine transporter (DAT) is responsible for sequestration of extracellular dopamine (DA). The psychostimulant amphetamine (AMPH) is a DAT substrate, which is actively transported into the nerve terminal, eliciting vesicular depletion and reversal of DA transport via DAT. Here, we investigate......-terminal protein-protein interactions are critical for AMPH-evoked DA efflux and suggest that it may be possible to target protein-protein interactions to modulate transporter function and interfere with psychostimulant effects....

  11. Integrated workflows for spiking neuronal network simulations

    Directory of Open Access Journals (Sweden)

    Ján eAntolík

    2013-12-01

    Full Text Available The increasing availability of computational resources is enabling more detailed, realistic modelling in computational neuroscience, resulting in a shift towards more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This poses a challenge for existing tool chains, as the set of tools involved in a typical modeller's workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modellers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity.To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation specification, simulation execution, data storage, data analysis and visualisation into a single automated workflow, ensuring that all relevant metadata are available to all workflow components. It is based on several existing tools, including PyNN, Neo and Matplotlib. It offers a declarative way to specify models and recording configurations using hierarchically organised configuration files. Mozaik automatically records all data together with all relevant metadata about the experimental context, allowing automation of the analysis and visualisation stages. Mozaik has a modular architecture, and the existing modules are designed to be extensible with minimal programming effort. Mozaik increases the productivity of running virtual experiments on highly structured neuronal networks by automating the entire experimental cycle, while increasing the reliability of modelling studies by relieving the user from manual handling of the flow of metadata between the individual

  12. Operant conditioning of synaptic and spiking activity patterns in single hippocampal neurons.

    Science.gov (United States)

    Ishikawa, Daisuke; Matsumoto, Nobuyoshi; Sakaguchi, Tetsuya; Matsuki, Norio; Ikegaya, Yuji

    2014-04-02

    Learning is a process of plastic adaptation through which a neural circuit generates a more preferable outcome; however, at a microscopic level, little is known about how synaptic activity is patterned into a desired configuration. Here, we report that animals can generate a specific form of synaptic activity in a given neuron in the hippocampus. In awake, head-restricted mice, we applied electrical stimulation to the lateral hypothalamus, a reward-associated brain region, when whole-cell patch-clamped CA1 neurons exhibited spontaneous synaptic activity that met preset criteria. Within 15 min, the mice learned to generate frequently the excitatory synaptic input pattern that satisfied the criteria. This reinforcement learning of synaptic activity was not observed for inhibitory input patterns. When a burst unit activity pattern was conditioned in paired and nonpaired paradigms, the frequency of burst-spiking events increased and decreased, respectively. The burst reinforcement occurred in the conditioned neuron but not in other adjacent neurons; however, ripple field oscillations were concomitantly reinforced. Neural conditioning depended on activation of NMDA receptors and dopamine D1 receptors. Acutely stressed mice and depression model mice that were subjected to forced swimming failed to exhibit the neural conditioning. This learning deficit was rescued by repetitive treatment with fluoxetine, an antidepressant. Therefore, internally motivated animals are capable of routing an ongoing action potential series into a specific neural pathway of the hippocampal network.

  13. Regulation of Dopamine Uptake by Vasoactive Peptides in the Kidney

    Directory of Open Access Journals (Sweden)

    N. L. Rukavina Mikusic

    2016-01-01

    Full Text Available Considering the key role of renal dopamine in tubular sodium handling, we hypothesized that c-type natriuretic peptide (CNP and Ang-(1-7 may regulate renal dopamine availability in tubular cells, contributing to Na+, K+-ATPase inhibition. Present results show that CNP did not affect either 3H-dopamine uptake in renal tissue or Na+, K+-ATPase activity; meanwhile, Ang-(1-7 was able to increase 3H-dopamine uptake and decreased Na+, K+-ATPase activity in renal cortex. Ang-(1-7 and dopamine together decreased further Na+, K+-ATPase activity showing an additive effect on the sodium pump. In addition, hydrocortisone reversed Ang-(1-7-dopamine overinhibition on the enzyme, suggesting that this inhibition is closely related to Ang-(1-7 stimulation on renal dopamine uptake. Both anantin and cANP (4-23-amide did not modify CNP effects on 3H-dopamine uptake by tubular cells. The Mas receptor antagonist, A-779, blocked the increase elicited by Ang-(1-7 on 3H-dopamine uptake. The stimulatory uptake induced by Ang-(1-7 was even more pronounced in the presence of losartan, suggesting an inhibitory effect of Ang-(1-7 on AT1 receptors on 3H-dopamine uptake. By increasing dopamine bioavailability in tubular cells, Ang-(1-7 enhances Na+, K+-ATPase activity inhibition, contributing to its natriuretic and diuretic effects.

  14. Dopamine in heart failure and critical care

    NARCIS (Netherlands)

    Smit, AJ

    Dopamine is widely used in critical care to prevent renal function loss. Nevertheless sufficient evidence is still lacking of reduction in end points like mortality or renal replacement therapy. Dopaminergic treatment in chronic heart failure (CHF) has provided an example of unexpected adverse

  15. DOPAMINE EFFECT ON CARDIAC REMODELING IN EXPERIMENT

    Directory of Open Access Journals (Sweden)

    V. R. Veber

    2009-01-01

    Full Text Available Aim. To study morphologic changes in myocardium of Wistar rats caused by single and long term dopamine administration.Methods. In acute study dopamine 10 mkg/kg was administrated to 15 rats by a single intraperitoneal injection. The material was taken in 2, 6, 24 hours and in 1 month after drug administration. In chronic study dopamine 10 mkg/kg was administrated to 15 rats 3 times a day by intraperitoneal injections during 2 weeks. The material was taken just after the drug administration was stopped and in 1 month of animals keeping without stress and drug influences. Control group included 15 rats comparable with experimental animals in age and weight. They were keeped without stress and drug influences. Morphometric parameters of left and right ventricles were evaluated as well as density of cardiomyocytes, collagen, vessels and volume of extracellular space.Results. The enlargement of cardiac fibrosis is found both in acute, and in chronic study. In acute study cardiac fibrosis was located mainly in a right ventricle. In chronic study cardiac fibrosis was located in both ventricles, but also mainly in a right one.Conclusion. Significant morphological «asynchronism» of the left and right ventricles remodeling requires elaboration of methods of myocardium protection and cardiac function control during dopamine administration. 

  16. Oscillating from Neurosecretion to Multitasking Dopamine Neurons

    Directory of Open Access Journals (Sweden)

    David R. Grattan

    2016-04-01

    Full Text Available In this issue of Cell Reports, Stagkourakis et al. (2016 report that oscillating hypothalamic TIDA neurons, previously thought to be simple neurosecretory neurons controlling pituitary prolactin secretion, control dopamine output via autoregulatory mechanisms and thus could potentially regulate other physiologically important hypothalamic neuronal circuits.

  17. Antagonism of presynaptic dopamine receptors by phenothiazine drug metabolites

    International Nuclear Information System (INIS)

    Nowak, J.Z.; Arbilla, S.; Langer, S.Z.; Dahl, S.G.

    1990-01-01

    Electrically evoked release of dopamine from the caudate nucleus is reduced by the dopamine receptor agonists, apomorphine and bromocriptine, and facilitated by neuroleptic drugs, which act as dopamine autoreceptor antagonists. The potencies of chlorpromazine, fluphenazine, levomepromazine and their hydroxy-metabolites in modulating electrically evoked release of dopamine were examined by superfusion of rabbit caudate nucleus slices pre-incubated with 3 H-dopamine. O-Desmethyl levomepromazine, 3-hydroxy- and 7-hydroxy metabolites of chlorpromazine and levomepromazine facilitated electrically evoked release of 3 H-dopamine, having potencies similar to that of the parent compounds. 7-Hydroxy fluphenazine was less active than fluphenazine in this system. These results indicate that phenolic metabolites of chlorpromazine and levomepromazine, but not of fluphenazine, may contribute to effects of the drugs mediated by presynaptic dopamine receptors

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

    Science.gov (United States)

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

    2017-08-01

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

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

    Science.gov (United States)

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

    2006-07-01

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

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

    Science.gov (United States)

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

    2010-03-09

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

  1. Spike timing-dependent plasticity as the origin of the formation of clustered synaptic efficacy engrams

    Directory of Open Access Journals (Sweden)

    Nicolangelo L Iannella

    2010-07-01

    Full Text Available 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 fibres 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 organisation 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.

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

    Directory of Open Access Journals (Sweden)

    Wei Xu

    2016-12-01

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

  3. Stochastic optimal control of single neuron spike trains

    DEFF Research Database (Denmark)

    Iolov, Alexandre; Ditlevsen, Susanne; Longtin, Andrë

    2014-01-01

    stimulation of a neuron to achieve a target spike train under the physiological constraint to not damage tissue. Approach. We pose a stochastic optimal control problem to precisely specify the spike times in a leaky integrate-and-fire (LIF) model of a neuron with noise assumed to be of intrinsic or synaptic...... origin. In particular, we allow for the noise to be of arbitrary intensity. The optimal control problem is solved using dynamic programming when the controller has access to the voltage (closed-loop control), and using a maximum principle for the transition density when the controller only has access...... to the spike times (open-loop control). Main results. We have developed a stochastic optimal control algorithm to obtain precise spike times. It is applicable in both the supra-threshold and sub-threshold regimes, under open-loop and closed-loop conditions and with an arbitrary noise intensity; the accuracy...

  4. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

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

    2015-01-01

    . Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards

  5. A novel unsupervised spike sorting algorithm for intracranial EEG.

    Science.gov (United States)

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

    2011-01-01

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

  6. Emergent dynamics of spiking neurons with fluctuating threshold

    Science.gov (United States)

    Bhattacharjee, Anindita; Das, M. K.

    2017-05-01

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

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

    Directory of Open Access Journals (Sweden)

    Gordon ePipa

    2011-06-01

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

  8. Intranasal dopamine reduces in vivo [123I]FP-CIT binding to striatal dopamine transporter: correlation with behavioral changes and evidence for Pavlovian conditioned dopamine response

    OpenAIRE

    Maria A de Souza Silva; C. eMattern; C. eMattern; C.I. eDecheva; Joseph P. Huston; A. eSadile; M. eBeu; H.W. eMüller; Susanne eNikolaus

    2016-01-01

    Purpose: Dopamine (DA), which does not cross the blood-brain barrier, has central and behavioral effects when administered via the nasal route. Neither the mechanisms of central action of intranasal dopamine (IN-DA), nor its mechanisms of diffusion and transport into the brain are well understood. We here examined whether IN-DA application influences dopamine transporter (DAT) binding in the dorsal striatum and assessed the extent of binding in relation to motor and exploratory behaviors. We ...

  9. Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

    Science.gov (United States)

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

    2015-07-15

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

  10. Dopamine Modulates Option Generation for Behavior.

    Science.gov (United States)

    Ang, Yuen-Siang; Manohar, Sanjay; Plant, Olivia; Kienast, Annika; Le Heron, Campbell; Muhammed, Kinan; Hu, Michele; Husain, Masud

    2018-05-21

    Animals make innumerable decisions every day, each of which involves evaluating potential options for action. But how are options generated? Although much is now known about decision making when a fixed set of potential options is provided, surprisingly little progress has been made on self-generated options. Some researchers have proposed that such abilities might be modulated by dopamine. Here, we used a new measure of option generation that is quantitative, objective, and culture fair to investigate how humans generate different behavioral options. Participants were asked to draw as many different paths (options) as they could between two points within a fixed time. Healthy individuals (n = 96) exhibited a trade-off between uniqueness (how individually different their options were) and fluency (number of options), generating either many similar or few unique options. To assess influence of dopamine, we first examined patients with Parkinson's disease (n = 35) ON and OFF their dopaminergic medication and compared them to elderly healthy controls (n = 34). Then we conducted a double-blind, placebo-controlled crossover study of the D2 agonist cabergoline in healthy older people (n = 29). Across both studies, dopamine increased fluency but diminished overall uniqueness of options generated, due to the effect of fluency trading off with uniqueness. Crucially, however, when this trade-off was corrected for, dopamine was found to increase uniqueness for any given fluency. Three carefully designed control studies showed that performance on our option-generation task was not related to executing movements, planning actions, or selecting between generated options. These findings show that dopamine plays an important role in modulating option generation. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  11. A Dopamine Hypothesis of Autism Spectrum Disorder.

    Science.gov (United States)

    Pavăl, Denis

    2017-01-01

    Autism spectrum disorder (ASD) comprises a group of neurodevelopmental disorders characterized by social deficits and stereotyped behaviors. While several theories have emerged, the pathogenesis of ASD remains unknown. Although studies report dopamine signaling abnormalities in autistic patients, a coherent dopamine hypothesis which could link neurobiology to behavior in ASD is currently lacking. In this paper, we present such a hypothesis by proposing that autistic behavior arises from dysfunctions in the midbrain dopaminergic system. We hypothesize that a dysfunction of the mesocorticolimbic circuit leads to social deficits, while a dysfunction of the nigrostriatal circuit leads to stereotyped behaviors. Furthermore, we discuss 2 key predictions of our hypothesis, with emphasis on clinical and therapeutic aspects. First, we argue that dopaminergic dysfunctions in the same circuits should associate with autistic-like behavior in nonautistic subjects. Concerning this, we discuss the case of PANDAS (pediatric autoimmune neuropsychiatric disorder associated with streptococcal infections) which displays behaviors similar to those of ASD, presumed to arise from dopaminergic dysfunctions. Second, we argue that providing dopamine modulators to autistic subjects should lead to a behavioral improvement. Regarding this, we present clinical studies of dopamine antagonists which seem to have improving effects on autistic behavior. Furthermore, we explore the means of testing our hypothesis by using neuroreceptor imaging, which could provide comprehensive evidence for dopamine signaling dysfunctions in autistic subjects. Lastly, we discuss the limitations of our hypothesis. Along these lines, we aim to provide a dopaminergic model of ASD which might lead to a better understanding of the ASD pathogenesis. © 2017 S. Karger AG, Basel.

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

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-01

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

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

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-06

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

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

    Science.gov (United States)

    Kreuz, Thomas; Mulansky, Mario; Bozanic, Nebojsa

    2015-05-01

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

  15. Nonlinear evolution of single spike in Richtmyer-Meshkov instability

    International Nuclear Information System (INIS)

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

    2000-01-01

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

  16. Feature extraction using extrema sampling of discrete derivatives for spike sorting in implantable upper-limb neural prostheses.

    Science.gov (United States)

    Zamani, Majid; Demosthenous, Andreas

    2014-07-01

    Next generation neural interfaces for upper-limb (and other) prostheses aim to develop implantable interfaces for one or more nerves, each interface having many neural signal channels that work reliably in the stump without harming the nerves. To achieve real-time multi-channel processing it is important to integrate spike sorting on-chip to overcome limitations in transmission bandwidth. This requires computationally efficient algorithms for feature extraction and clustering suitable for low-power hardware implementation. This paper describes a new feature extraction method for real-time spike sorting based on extrema analysis (namely positive peaks and negative peaks) of spike shapes and their discrete derivatives at different frequency bands. Employing simulation across different datasets, the accuracy and computational complexity of the proposed method are assessed and compared with other methods. The average classification accuracy of the proposed method in conjunction with online sorting (O-Sort) is 91.6%, outperforming all the other methods tested with the O-Sort clustering algorithm. The proposed method offers a better tradeoff between classification error and computational complexity, making it a particularly strong choice for on-chip spike sorting.

  17. Cellular and circuit mechanisms maintain low spike co-variability and enhance population coding in somatosensory cortex

    Directory of Open Access Journals (Sweden)

    Cheng eLy

    2012-03-01

    Full Text Available The responses of cortical neurons are highly variable across repeated presentations of a stimulus. Understanding this variability is critical for theories of both sensory and motor processing, since response variance affects the accuracy of neural codes. Despite this influence, the cellular and circuit mechanisms that shape the trial-to-trial variability of population responses remain poorly understood. We used a combination of experimental and computational techniques to uncover the mechanisms underlying response variability of populations of pyramidal (E cells in layer 2/3 of rat whisker barrel cortex. Spike trains recorded from pairs of E-cells during either spontaneous activity or whisker deflected responses show similarly low levels of spiking co-variability, despite large differences in network activation between the two states. We developed network models that show how spike threshold nonlinearities dilutes E-cell spiking co-variability during spontaneous activity and low velocity whisker deflections. In contrast, during high velocity whisker deflections, cancelation mechanisms mediated by feedforward inhibition maintain low E-cell pairwise co-variability. Thus, the combination of these two mechanisms ensure low E-cell population variability over a wide range of whisker deflection velocities. Finally, we show how this active decorrelation of population variability leads to a drastic increase in the population information about whisker velocity. The canonical cellular and circuit components of our study suggest that low network variability over a broad range of neural states may generalize across the nervous system.

  18. Pharmacological characterization of the dopamine-sensitive adenylate cyclase in cockroach brain: evidence for a distinct dopamine receptor

    International Nuclear Information System (INIS)

    Orr, G.L.; Gole, J.W.D.; Notman, H.J.; Downer, R.G.H.

    1987-01-01

    Dopamine increases cyclic AMP production in crude membrane preparations of cockroach brain with plateaus in cyclic AMP production occurring between 1-10 μM and 10 mM. Maximal production of cyclic AMP is 2.25 fold greater than that of control values. Octopamine also increases cyclic AMP production with a Ka of 1.4 μM and maximal production 3.5 fold greater than that of control. 5-Hydroxytryptamine does not increase cyclic AMP production. The effects of octopamine and dopamine are fully additive. The vertebrate dopamine agonists ADTN and epinine stimulate the dopamine-sensitive adenylate cyclase (AC) with Ka values of 4.5 and 0.6 μM respectively and with maximal effectiveness 1.7 fold greater than that of control. The selective D 2 -dopamine agonist LY-171555 stimulates cyclic AMP production to a similar extent with a Ka of 50 μM. Other dopamine agonists have no stimulatory effects. With the exception of mianserin, 3 H-piflutixol is displaced from brain membranes by dopamine antagonists with an order of potency similar to that observed for the inhibition of dopamine-sensitive AC. The results indicate that the octopamine- and dopamine-sensitive AC in cockroach brain can be distinguished pharmacologically and the dopamine receptors coupled to AC have pharmacological characteristics distinct from vertebrate D 1 - and D 2 -dopamine receptors. 33 references, 3 figures, 2 tables

  19. Coincidence Detection Using Spiking Neurons with Application to Face Recognition

    Directory of Open Access Journals (Sweden)

    Fadhlan Kamaruzaman

    2015-01-01

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

  20. The Omega-Infinity Limit of Single Spikes

    CERN Document Server

    Axenides, Minos; Linardopoulos, Georgios

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

  1. Comparison of Classifier Architectures for Online Neural Spike Sorting.

    Science.gov (United States)

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

    2017-04-01

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

  2. Automatic fitting of spiking neuron models to electrophysiological recordings

    Directory of Open Access Journals (Sweden)

    Cyrille Rossant

    2010-03-01

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

  3. Absolute Ca Isotopic Measurement Using an Improved Double Spike Technique

    Directory of Open Access Journals (Sweden)

    Jason Jiun-San Shen

    2009-01-01

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

  4. Heads for learning, tails for memory: Reward, reinforcement and a role of dopamine in determining behavioural relevance across multiple timescales

    Directory of Open Access Journals (Sweden)

    Mathieu eBaudonnat

    2013-10-01

    Full Text Available Dopamine has long been tightly associated with aspects of reinforcement learning and motivation in simple situations where there are a limited number of stimuli to guide behaviour and constrained range of outcomes. In naturalistic situations, however, there are many potential cues and foraging strategies that could be adopted, and it is critical that animals determine what might be behaviourally relevant in such complex environments. This requires not only detecting discrepancies with what they have recently experienced, but also identifying similarities with past experiences stored in memory. Here, we review what role dopamine might play in determining how and when to learn about the world, and how to develop choice policies appropriate to the situation faced. We discuss evidence that dopamine is shaped by motivation and memory and in turn shapes reward-based memory formation. In particular, we suggest that hippocampal-striatal-dopamine networks may interact to determine how surprising the world is and to either inhibit or promote actions at time of behavioural uncertainty.

  5. Determination of dopamine using a glassy carbon electrode modified with a graphene and carbon nanotube hybrid decorated with molybdenum disulfide flowers

    International Nuclear Information System (INIS)

    Mani, Veerappan; Govindasamy, Mani; Chen, Shen-Ming; Karthik, Raj; Huang, Sheng-Tung

    2016-01-01

    We describe a hybrid material that consists of molybdenum sulfide flowers placed on graphene nanosheets and multiwalled carbon nanotubes (GNS-CNTs/MoS_2). It was deposited on a glassy carbon electrode (GCE) which then is well suited for sensitive and selective determination of dopamine. The GNS-CNTs/MoS_2 nanocomposite was prepared by a hydrothermal method and characterized by scanning electron and transmission emission microscopies, energy-dispersive X-ray spectroscopy, cyclic voltammetry, differential pulse voltammetry and electrochemical impedance spectroscopy. Electrochemical studies show the composite to possess excellent electrochemical properties such as a large electrochemically active surface, high capacitance current, a wide potential window, high conductivity and large porosity. The electrode displays excellent electrocatalytic ability to oxidize dopamine. The modified GCE, best operated at a working potential as low as 0.15 V (vs. Ag/AgCl), responds linearly to dopamine in the 100 nM to 100 μM concentration range. The detection limit is 50 nM, and the sensitivity is 10.81 (± 0.26) μA⋅μM"−"1⋅cm"−"2. The sensor has good selectivity, appreciable stability, repeatability and reproducibility. It was applied to the determination of dopamine in (spiked) biological and pharmaceutical samples. (author)

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

    Science.gov (United States)

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

    2012-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Zedong Bi

    2016-08-01

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

  8. Voltammetric sensing of paracetamole, dopamine and 4-aminophenol at a glassy carbon electrode coated with gold nanoparticles and an organophillic layered double hydroxide

    International Nuclear Information System (INIS)

    Yin, H.; Shang, K.; Meng, X.; Ai, S.

    2011-01-01

    A differential pulse voltammetric method was developed for the simultaneous determination of paracetamole, 4-aminophenol and dopamine at pH 7.0 using a glassy carbon electrode (GCE) coated with gold nanoparticles (AuNPs) and a layered double hydroxide sodium modified with dodecyl sulfate (SDS-LDH). The modified electrode displays excellent redox activity towards paracetamole, and the redox current is increased (and the corresponding over-potential decreased) compared to those of the bare GCE, the AuNPs-modified GCE, and the SDS-LDH-modified GCE. The modified electrode enables the determination of paracetamole in the concentration range from 0.5 to 400 μM, with a detection limit of 0.13 μM (at an S/N of 3). The sensor was successfully applied to the simultaneous determination of paracetamole and dopamine, and of paracetamole and 4-aminophenol, respectively, in pharmaceutical tablets and in spiked human serum samples. (author)

  9. Dopamine receptor activation increases HIV entry into primary human macrophages.

    Directory of Open Access Journals (Sweden)

    Peter J Gaskill

    Full Text Available Macrophages are the primary cell type infected with HIV in the central nervous system, and infection of these cells is a major component in the development of neuropathogenesis and HIV-associated neurocognitive disorders. Within the brains of drug abusers, macrophages are exposed to increased levels of dopamine, a neurotransmitter that mediates the addictive and reinforcing effects of drugs of abuse such as cocaine and methamphetamine. In this study we examined the effects of dopamine on HIV entry into primary human macrophages. Exposure to dopamine during infection increased the entry of R5 tropic HIV into macrophages, irrespective of the concentration of the viral inoculum. The entry pathway affected was CCR5 dependent, as antagonizing CCR5 with the small molecule inhibitor TAK779 completely blocked entry. The effect was dose-dependent and had a steep threshold, only occurring above 108 M dopamine. The dopamine-mediated increase in entry required dopamine receptor activation, as it was abrogated by the pan-dopamine receptor antagonist flupenthixol, and could be mediated through both subtypes of dopamine receptors. These findings indicate that the effects of dopamine on macrophages may have a significant impact on HIV pathogenesis. They also suggest that drug-induced increases in CNS dopamine may be a common mechanism by which drugs of abuse with distinct modes of action exacerbate neuroinflammation and contribute to HIV-associated neurocognitive disorders in infected drug abusers.

  10. Dopamine Receptor Activation Increases HIV Entry into Primary Human Macrophages

    Science.gov (United States)

    Gaskill, Peter J.; Yano, Hideaki H.; Kalpana, Ganjam V.; Javitch, Jonathan A.; Berman, Joan W.

    2014-01-01

    Macrophages are the primary cell type infected with HIV in the central nervous system, and infection of these cells is a major component in the development of neuropathogenesis and HIV-associated neurocognitive disorders. Within the brains of drug abusers, macrophages are exposed to increased levels of dopamine, a neurotransmitter that mediates the addictive and reinforcing effects of drugs of abuse such as cocaine and methamphetamine. In this study we examined the effects of dopamine on HIV entry into primary human macrophages. Exposure to dopamine during infection increased the entry of R5 tropic HIV into macrophages, irrespective of the concentration of the viral inoculum. The entry pathway affected was CCR5 dependent, as antagonizing CCR5 with the small molecule inhibitor TAK779 completely blocked entry. The effect was dose-dependent and had a steep threshold, only occurring above 108 M dopamine. The dopamine-mediated increase in entry required dopamine receptor activation, as it was abrogated by the pan-dopamine receptor antagonist flupenthixol, and could be mediated through both subtypes of dopamine receptors. These findings indicate that the effects of dopamine on macrophages may have a significant impact on HIV pathogenesis. They also suggest that drug-induced increases in CNS dopamine may be a common mechanism by which drugs of abuse with distinct modes of action exacerbate neuroinflammation and contribute to HIV-associated neurocognitive disorders in infected drug abusers. PMID:25268786

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

    NARCIS (Netherlands)

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

    2000-01-01

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

  12. Joint Probability-Based Neuronal Spike Train Classification

    Directory of Open Access Journals (Sweden)

    Yan Chen

    2009-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  14. Dual roles for spike signaling in cortical neural populations

    Directory of Open Access Journals (Sweden)

    Dana eBallard

    2011-06-01

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

  15. NOVEL FLUORESCENT PROBES FOR THE DOPAMINE TRANSPORTER

    DEFF Research Database (Denmark)

    Cha, J; Vægter, Christian Bjerggaard; Adkins, Erica

    -reactive rhodamine red derivatives. The resulting N-substituted (JHC 1-64) and 2-substituted (JHC 1-53) ligands showed high affinity binding to DAT expressed in HEK 293 cells (Ki= 6.4 and 29 nM, respectively). Their ability to selectively label the DAT was demonstrated by confocal laser scanning microscopy of HEK......To enable visualization of the dopamine transporter (DAT) through fluorescence technologies we have synthesized a novel series of fluorescently tagged analogs of cocaine. Previous structure-activity relationship (SAR) studies have demonstrated that the dopamine transporter (DAT) can tolerate...... in untransfected control cells. The possibility of using these ligands for direct labeling of the DAT in living cells represents a new and important approach for understanding cellular targeting and trafficking of the DAT. Moreover, these fluorescent ligands might also provide the molecular tools...

  16. A metric space approach to the information capacity of spike trains

    OpenAIRE

    HOUGHTON, CONOR JAMES; GILLESPIE, JAMES

    2010-01-01

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

  17. Clinical usefulness of dopamine transporter imaging

    International Nuclear Information System (INIS)

    Kim, Jong Min; Kim, Yu Kyeong; Kim, Sang Eun; Jeon, Beom S.

    2007-01-01

    Imaging of the dopamine transporter (DAT) provides a marker for the integrity of presynaptic nigrostriatal dopaminergic system. DAT density is reduced in Parkinson disease, multiple system atrophy, and progressive supranuclear palsy. In patients with suspicious parkinsonism, normal DAT imaging suggests an alternative diagnosis such as essential tremor, vascular parkinsonism, or drug-induced parkinsonism. DAT imaging is a useful tool to aid clinician's differential diagnosis in parkinsonism

  18. Dopamine Signaling in reward-related behaviors

    OpenAIRE

    Baik, Ja-Hyun

    2013-01-01

    Dopamine (DA) regulates emotional and motivational behavior through the mesolimbic dopaminergic pathway. Changes in DA mesolimbic neurotransmission have been found to modify behavioral responses to various environmental stimuli associated with reward behaviors. Psychostimulants, drugs of abuse, and natural reward such as food can cause substantial synaptic modifications to the mesolimbic DA system. Recent studies using optogenetics and DREADDs, together with neuron-specific or circuit-specifi...

  19. Dopamine D2 receptors photolabeled by iodo-azido-clebopride.

    Science.gov (United States)

    Niznik, H B; Dumbrille-Ross, A; Guan, J H; Neumeyer, J L; Seeman, P

    1985-04-19

    Iodo-azido-clebopride, a photoaffinity compound for dopamine D2 receptors, had high affinity for canine brain striatal dopamine D2 receptors with a dissociation constant (Kd) of 14 nM. Irradiation of striatal homogenate with iodo-azido-clebopride irreversibly inactivated 50% of dopamine D2 receptors at 20 nM (as indicated by subsequent [3H]spiperone binding). Dopamine agonists and antagonists prevented this photo-inactivation with the appropriate rank-order of potency. Striatal dopamine D1, serotonin (S2), alpha 1- and beta-adrenoceptors were not significantly inactivated following irradiation with iodo-azido-clebopride. Thus, iodo-azido-clebopride is a selective photoaffinity probe for dopamine D2 receptors, the radiolabelled form of which may aid in the molecular characterization of these proteins.

  20. Neuronal Depolarization Drives Increased Dopamine Synaptic Vesicle Loading via VGLUT.

    Science.gov (United States)

    Aguilar, Jenny I; Dunn, Matthew; Mingote, Susana; Karam, Caline S; Farino, Zachary J; Sonders, Mark S; Choi, Se Joon; Grygoruk, Anna; Zhang, Yuchao; Cela, Carolina; Choi, Ben Jiwon; Flores, Jorge; Freyberg, Robin J; McCabe, Brian D; Mosharov, Eugene V; Krantz, David E; Javitch, Jonathan A; Sulzer, David; Sames, Dalibor; Rayport, Stephen; Freyberg, Zachary

    2017-08-30

    The ability of presynaptic dopamine terminals to tune neurotransmitter release to meet the demands of neuronal activity is critical to neurotransmission. Although vesicle content has been assumed to be static, in vitro data increasingly suggest that cell activity modulates vesicle content. Here, we use a coordinated genetic, pharmacological, and imaging approach in Drosophila to study the presynaptic machinery responsible for these vesicular processes in vivo. We show that cell depolarization increases synaptic vesicle dopamine content prior to release via vesicular hyperacidification. This depolarization-induced hyperacidification is mediated by the vesicular glutamate transporter (VGLUT). Remarkably, both depolarization-induced dopamine vesicle hyperacidification and its dependence on VGLUT2 are seen in ventral midbrain dopamine neurons in the mouse. Together, these data suggest that in response to depolarization, dopamine vesicles utilize a cascade of vesicular transporters to dynamically increase the vesicular pH gradient, thereby increasing dopamine vesicle content. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. The multiplicity of the D-1 dopamine receptor

    International Nuclear Information System (INIS)

    Mailman, R.B.; Klits, C.D.; Lewis, M.H.; Rollema, H.; Schulz, D.W.; Wyrick, S.

    1986-01-01

    The authors have sought to address two questions of some neuropharmacological importance in this chapter. First, they examine the nature of mechanisms by which dopamine initiates many psychopharmacological effects and, second, they study the possibility of designing highly specific drugs targeted only at a selected subpopulation of dopamine receptors. Effects of SCH23390 and haloperidol on concentrations of dopamine, DOPAC, and HVA in various rat brain regions are shown. In addition, the effects of SCH23390 on the in vivo binding of dipropyl-5, 6-ADTN are shown. Differential distribution of a dopamine sensitive adenylate cyclase and ( 3 H)-SCH23390 binding sites are examined. A model is presented of D 1 dopamine receptors in membrane, illustrating the lack of identity of some of the ( 3 H)-SCH23390 binding sites with the dopamine receptor linked to stimulation of cAMP synthesis

  2. Noise-enhanced coding in phasic neuron spike trains.

    Science.gov (United States)

    Ly, Cheng; Doiron, Brent

    2017-01-01

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

  3. Spike morphology in blast-wave-driven instability experiments

    International Nuclear Information System (INIS)

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

    2010-01-01

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

  4. Financial time series prediction using spiking neural networks.

    Science.gov (United States)

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

    2014-01-01

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

  5. Multiplexed Spike Coding and Adaptation in the Thalamus

    Directory of Open Access Journals (Sweden)

    Rebecca A. Mease

    2017-05-01

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

  6. Scaling of spiking and humping in keyhole welding

    Energy Technology Data Exchange (ETDEWEB)

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

    2011-06-22

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

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

    Science.gov (United States)

    Halliday, D M; Rosenberg, J R

    2017-04-24

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

  8. Linking unfounded beliefs to genetic dopamine availability

    Science.gov (United States)

    Schmack, Katharina; Rössler, Hannes; Sekutowicz, Maria; Brandl, Eva J.; Müller, Daniel J.; Petrovic, Predrag; Sterzer, Philipp

    2015-01-01

    Unfounded convictions involving beliefs in the paranormal, grandiosity ideas or suspicious thoughts are endorsed at varying degrees among the general population. Here, we investigated the neurobiopsychological basis of the observed inter-individual variability in the propensity toward unfounded beliefs. One hundred two healthy individuals were genotyped for four polymorphisms in the COMT gene (rs6269, rs4633, rs4818, and rs4680, also known as val158met) that define common functional haplotypes with substantial impact on synaptic dopamine degradation, completed a questionnaire measuring unfounded beliefs, and took part in a behavioral experiment assessing perceptual inference. We found that greater dopamine availability was associated with a stronger propensity toward unfounded beliefs, and that this effect was statistically mediated by an enhanced influence of expectations on perceptual inference. Our results indicate that genetic differences in dopaminergic neurotransmission account for inter-individual differences in perceptual inference linked to the formation and maintenance of unfounded beliefs. Thus, dopamine might be critically involved in the processes underlying one's interpretation of the relationship between the self and the world. PMID:26483654

  9. Linking unfounded beliefs to genetic dopamine availability

    Directory of Open Access Journals (Sweden)

    Katharina eSchmack

    2015-09-01

    Full Text Available Unfounded convictions involving beliefs in the paranormal, grandiosity ideas or suspicious thoughts are endorsed at varying degrees among the general population. Here, we investigated the neurobiopsychological basis of the observed inter-individual variability in the propensity towards unfounded beliefs. 109 healthy individuals were genotyped for four polymorphisms in the COMT gene (rs6269, rs4633, rs4818 and rs4680, also known as val158met that define common functional haplotypes with substantial impact on synaptic dopamine degradation, completed a questionnaire measuring unfounded beliefs, and took part in a behavioural experiment assessing perceptual inference. We found that greater dopamine availability was associated with a stronger propensity towards unfounded beliefs, and that this effect was mediated by an enhanced influence of expectations on perceptual inference. Our results indicate that genetic differences in dopaminergic neurotransmission account for inter-individual differences in perceptual inference linked to the formation and maintenance of unfounded beliefs. Thus, dopamine might be critically involved in the processes underlying one's interpretation of the relationship between the self and the world.

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

    Directory of Open Access Journals (Sweden)

    Zedong eBi

    2016-02-01

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

  11. ILLICIT DOPAMINE TRANSIENTS: RECONCILING ACTIONS OF ABUSED DRUGS

    OpenAIRE

    Covey, Dan P.; Roitman, Mitchell F.; Garris, Paul A.

    2014-01-01

    Phasic increases in brain dopamine are required for cue-directed reward seeking. While compelling within the framework of appetitive behavior, the view that illicit drugs hijack reward circuits by hyper-activating these dopamine transients is inconsistent with established psychostimulant pharmacology. However, recent work reclassifying amphetamine (AMPH), cocaine, and other addictive dopamine-transporter inhibitors (DAT-Is) supports transient hyper-activation as a unifying hypothesis of abuse...

  12. The dopamine transporter: role in neurotoxicity and human disease

    International Nuclear Information System (INIS)

    Bannon, Michael J.

    2005-01-01

    The dopamine transporter (DAT) is a plasma membrane transport protein expressed exclusively within a small subset of CNS neurons. It plays a crucial role in controlling dopamine-mediated neurotransmission and a number of associated behaviors. This review focuses on recent data elucidating the role of the dopamine transporter in neurotoxicity and a number of CNS disorders, including Parkinson disease, drug abuse, and attention deficit hyperactivity disorder (ADHD)

  13. The dopamine transporter: role in neurotoxicity and human disease

    Energy Technology Data Exchange (ETDEWEB)

    Bannon, Michael J [Department of Psychiatry and Behavioral Neuroscience, Pharmacology, and Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201 (United States)

    2005-05-01

    The dopamine transporter (DAT) is a plasma membrane transport protein expressed exclusively within a small subset of CNS neurons. It plays a crucial role in controlling dopamine-mediated neurotransmission and a number of associated behaviors. This review focuses on recent data elucidating the role of the dopamine transporter in neurotoxicity and a number of CNS disorders, including Parkinson disease, drug abuse, and attention deficit hyperactivity disorder (ADHD)

  14. CRYSTAL STRUCTURE OF HUMAN DOPAMINE BETA-HYDROXYLASE

    DEFF Research Database (Denmark)

    2017-01-01

    A crystalline form of dopamine β-hydroxylase is provided. X-ray crystallography reveals the space group and cell dimensions, as well as the atomic coordinates. The information can be used for identifying one or more modulators of dopamine β-hydroxylase, which can then be chemically synthesised...... and used in treatment. A process for preparing the crystalline form of human dopamine β-hydroxylase is also provided....

  15. Practical Approach for the Clinical Use of Dopamine Transporter Imaging

    International Nuclear Information System (INIS)

    Kim, Jae Seung

    2008-01-01

    Dopamine transporter imaging is useful in the diagnosis of Parkinson's disease and the most successful technique in the clinical use of neuroreceptor imaging. Recently, several radiopharmaceuticals including I-123 FP-CIT, Tc-99m TRODAT, and F-18 FP-CIT for dopamine transporter imaging have been approved for the routine clinical use in several European countries, Taiwan and Korea, respectively. This review summarized the practical issue for the routine clinical examination of dopamine transporter imaging

  16. Successful treatment of dopamine dysregulation syndrome with dopamine D2 partial agonist antipsychotic drug

    Directory of Open Access Journals (Sweden)

    Mizushima Jin

    2012-07-01

    Full Text Available Abstract Dopamine dysregulation syndrome (DDS consists of a series of complications such as compulsive use of dopaminergic medications, aggressive or hypomanic behaviors during excessive use, and withdrawal states characterized by dysphoria and anxiety, caused by long-term dopaminergic treatment in patients with Parkinson’s disease (PD. Although several ways to manage DDS have been suggested, there has been no established treatment that can manage DDS without deterioration of motor symptoms. In this article, we present a case of PD in whom the administration of the dopamine D2 partial agonistic antipsychotic drug aripiprazole improved DDS symptoms such as craving and compulsive behavior without worsening of motor symptoms. Considering the profile of this drug as a partial agonist at D2 receptors, it is possible that it exerts its therapeutic effect on DDS by modulating the dysfunctional dopamine system.

  17. Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

    Science.gov (United States)

    Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

    2018-02-01

    Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

  18. Development of the SPIKE code for analysis of the sodium-water reaction

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, Sung Tai; Park, Jin Ho; Choi, Jong Hyeun; Kim, Tae Joon [Korea Atomic Energy Research Institute, Taejon (Korea)

    1998-08-01

    In the secondary loop of liquid metal reactors, including SG, water leak into sodium causes the sudden increase of pressure by the H{sub 2} and heat generated from reaction. At few miliseconds after leak, a sharp and short-lived increase of pressure is generated and its propagation depends on the acoustic constraint characteristics of secondary loop. As increasing leak amount of water, another pressure increase is caused by H{sub 2} and its transients depends on the resistance of pressure opening system, such as rupture disc. For prediction of the transients of initial spike pressure, a code of SPIKE was developed. The code was based on the following simplifications and assumptions: combination of total and half release of H{sub 2} rate, spherical shape of H{sub 2} bubble, compressible and Newtonian fluid for sodium. The program was built in FOTRAN language and consisted of 5 modules. Several sample calculations were performed to test the code and to determine the scale down factor of experimental facilities for experimental verification of the code: parameter study of the variables in chemical reaction model, comparison study with results calculated by superposition methods for simple piping structures, comparison study with results calculated by previous researchers, and calculation for KALIMER models of various size. With these calculation results, the generally predicted phenomena of sodium water reaction can be explained and the calculated ones by SPIKE code were well agreed with the previous study. And the scale down factor can be determined. (author). 88 refs., 99 figs., 39 tabs.

  19. Spatial Frequency Selectivity Is Impaired in Dopamine D2 Receptor Knockout Mice

    Science.gov (United States)

    Souza, Bruno Oliveira Ferreira; Abou Rjeili, Mira; Quintana, Clémentine; Beaulieu, Jean M.; Casanova, Christian

    2018-01-01

    Dopamine is a neurotransmitter implicated in several brain functions, including vision. In the present study, we investigated the impacts of the lack of D2 dopamine receptors on the structure and function of the primary visual cortex (V1) of D2-KO mice using optical imaging of intrinsic signals. Retinotopic maps were generated in order to measure anatomo-functional parameters such as V1 shape, cortical magnification factor, scatter, and ocular dominance. Contrast sensitivity and spatial frequency selectivity (SF) functions were computed from responses to drifting gratings. When compared to control mice, none of the parameters of the retinotopic maps were affected by D2 receptor loss of function. While the contrast sensitivity function of D2-KO mice did not differ from their wild-type counterparts, SF selectivity function was significantly affected as the optimal SF and the high cut-off frequency (p D2-KO than in WT mice. These findings show that the lack of function of D2 dopamine receptors had no influence on cortical structure whereas it had a significant impact on the spatial frequency selectivity and high cut-off. Taken together, our results suggest that D2 receptors play a specific role on the processing of spatial features in early visual cortex while they do not seem to participate in its development. PMID:29379422

  20. Homeostatic mechanisms in dopamine synthesis and release: a mathematical model

    Directory of Open Access Journals (Sweden)

    Nijhout H Frederik

    2009-09-01

    Full Text Available Abstract Background Dopamine is a catecholamine that is used as a neurotransmitter both in the periphery and in the central nervous system. Dysfunction in various dopaminergic systems is known to be associated with various disorders, including schizophrenia, Parkinson's disease, and Tourette's syndrome. Furthermore, microdialysis studies have shown that addictive drugs increase extracellular dopamine and brain imaging has shown a correlation between euphoria and psycho-stimulant-induced increases in extracellular dopamine 1. These consequences of dopamine dysfunction indicate the importance of maintaining dopamine functionality through homeostatic mechanisms that have been attributed to the delicate balance between synthesis, storage, release, metabolism, and reuptake. Methods We construct a mathematical model of dopamine synthesis, release, and reuptake and use it to study homeostasis in single dopaminergic neuron terminals. We investigate the substrate inhibition of tyrosine hydroxylase by tyrosine, the consequences of the rapid uptake of extracellular dopamine by the dopamine transporters, and the effects of the autoreceoptors on dopaminergic function. The main focus is to understand the regulation and control of synthesis and release and to explicate and interpret experimental findings. Results We show that the substrate inhibition of tyrosine hydroxylase by tyrosine stabilizes cytosolic and vesicular dopamine against changes in tyrosine availability due to meals. We find that the autoreceptors dampen the fluctuations in extracellular dopamine caused by changes in tyrosine hydroxylase expression and changes in the rate of firing. We show that short bursts of action potentials create significant dopamine signals against the background of tonic firing. We explain the observed time courses of extracellular dopamine responses to stimulation in wild type mice and mice that have genetically altered dopamine transporter densities and the observed

  1. Cross-hemispheric dopamine projections have functional significance

    Science.gov (United States)

    Fox, Megan E.; Mikhailova, Maria A.; Bass, Caroline E.; Takmakov, Pavel; Gainetdinov, Raul R.; Budygin, Evgeny A.; Wightman, R. Mark

    2016-01-01

    Dopamine signaling occurs on a subsecond timescale, and its dysregulation is implicated in pathologies ranging from drug addiction to Parkinson’s disease. Anatomic evidence suggests that some dopamine neurons have cross-hemispheric projections, but the significance of these projections is unknown. Here we report unprecedented interhemispheric communication in the midbrain dopamine system of awake and anesthetized rats. In the anesthetized rats, optogenetic and electrical stimulation of dopamine cells elicited physiologically relevant dopamine release in the contralateral striatum. Contralateral release differed between the dorsal and ventral striatum owing to differential regulation by D2-like receptors. In the freely moving animals, simultaneous bilateral measurements revealed that dopamine release synchronizes between hemispheres and intact, contralateral projections can release dopamine in the midbrain of 6-hydroxydopamine–lesioned rats. These experiments are the first, to our knowledge, to show cross-hemispheric synchronicity in dopamine signaling and support a functional role for contralateral projections. In addition, our data reveal that psychostimulants, such as amphetamine, promote the coupling of dopamine transients between hemispheres. PMID:27298371

  2. Dopamine and dopamine receptor D1 associated with decreased social interaction.

    Science.gov (United States)

    Liu, Qiang; Shi, Jieyun; Lin, Rongfei; Wen, Tieqiao

    2017-05-01

    Deficits in social interaction are hallmarks of neurological and psychiatric disorders. However, its underlying mechanism is still unclear. Here, we show that the loss of dendritic cell factor 1 (Dcf1) in the nervous system of mice induces social interaction deficiency, autism-like behaviour, and influences social interaction via the dopamine system. Dopamine receptor D1 agonist rescues this social cognition phenotype, and improves short-term plasticity. Together, this study presents a new genetic mechanism that affects social interaction and may provide a new way to improve positive social interaction and treat autism spectrum disorders. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Comparison of spike-sorting algorithms for future hardware implementation.

    Science.gov (United States)

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

    2008-01-01

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

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

    DEFF Research Database (Denmark)

    Jensen, Hans Grinsted; Anderson, Kym

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

  5. Character recognition from trajectory by recurrent spiking neural networks.

    Science.gov (United States)

    Jiangrong Shen; Kang Lin; Yueming Wang; Gang Pan

    2017-07-01

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

  6. Evaluation of the uranium double spike technique for environmental monitoring

    International Nuclear Information System (INIS)

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

    1998-01-01

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

  7. A Hybrid Setarx Model for Spikes in Tight Electricity Markets

    Directory of Open Access Journals (Sweden)

    Carlo Lucheroni

    2012-01-01

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

  8. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan

    2015-04-01

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

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

    Science.gov (United States)

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

    2017-05-01

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

  10. Demonstration of conjugated dopamine in monkey CSF by gas chromatography-mass spectrometry

    Energy Technology Data Exchange (ETDEWEB)

    Elchisak, M A; Powers, K H; Ebert, M H

    1982-09-01

    A method for measuring unconjugated and conjugated dopamine in body tissues and fluids is described. Conjugated dopamine was hydrolyzed in acid to unconjugated dopamine, separated from the sample matrix by alumina chromatography, and assayed by gas chromatography-mass spectrometry. Conjugated dopamine was detected in greater concentrations than unconjugated dopamine in CSF taken from lateral ventricle or thecal sac of the Rhesus monkey. Haloperidol administration did not increase the levels of conjugated dopamine in lumbar CSF.

  11. Presence and function of dopamine transporter (DAT in stallion sperm: dopamine modulates sperm motility and acrosomal integrity.

    Directory of Open Access Journals (Sweden)

    Javier A Urra

    Full Text Available Dopamine is a catecholamine with multiple physiological functions, playing a key role in nervous system; however its participation in reproductive processes and sperm physiology is controversial. High dopamine concentrations have been reported in different portions of the feminine and masculine reproductive tract, although the role fulfilled by this catecholamine in reproductive physiology is as yet unknown. We have previously shown that dopamine type 2 receptor is functional in boar sperm, suggesting that dopamine acts as a physiological modulator of sperm viability, capacitation and motility. In the present study, using immunodetection methods, we revealed the presence of several proteins important for the dopamine uptake and signalling in mammalian sperm, specifically monoamine transporters as dopamine (DAT, serotonin (SERT and norepinephrine (NET transporters in equine sperm. We also demonstrated for the first time in equine sperm a functional dopamine transporter using 4-[4-(Dimethylaminostyryl]-N-methylpyridinium iodide (ASP(+, as substrate. In addition, we also showed that dopamine (1 mM treatment in vitro, does not affect sperm viability but decreases total and progressive sperm motility. This effect is reversed by blocking the dopamine transporter with the selective inhibitor vanoxerine (GBR12909 and non-selective inhibitors of dopamine reuptake such as nomifensine and bupropion. The effect of dopamine in sperm physiology was evaluated and we demonstrated that acrosome integrity and thyrosine phosphorylation in equine sperm is significantly reduced at high concentrations of this catecholamine. In summary, our results revealed the presence of monoamine transporter DAT, NET and SERT in equine sperm, and that the dopamine uptake by DAT can regulate sperm function, specifically acrosomal integrity and sperm motility.

  12. Spike and burst coding in thalamocortical relay cells.

    Directory of Open Access Journals (Sweden)

    Fleur Zeldenrust

    2018-02-01

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

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

    Science.gov (United States)

    Sanlı, Ceyda; Lambiotte, Renaud

    2015-01-01

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

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

    OpenAIRE

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

    1995-01-01

    The effects of sleep deprivation were studied on the occurrence of spike-wave discharges in the electroencephalogram of rats of the epileptic WAG/Rij strain, a model for absence epilepsy. This was done before, during and after a period of 12 hours of near total sleep deprivation. A substantial increase in the number of spike-wave discharges was found during the first 4 hours of the deprivation period, whereas in the following deprivation hours epileptic activity returned to baseline values. I...

  15. Spike propagation in driven chain networks with dominant global inhibition

    International Nuclear Information System (INIS)

    Chang Wonil; Jin, Dezhe Z.

    2009-01-01

    Spike propagation in chain networks is usually studied in the synfire regime, in which successive groups of neurons are synaptically activated sequentially through the unidirectional excitatory connections. Here we study the dynamics of chain networks with dominant global feedback inhibition that prevents the synfire activity. Neural activity is driven by suprathreshold external inputs. We analytically and numerically demonstrate that spike propagation along the chain is a unique dynamical attractor in a wide parameter regime. The strong inhibition permits a robust winner-take-all propagation in the case of multiple chains competing via the inhibition.

  16. Spiking neuron devices consisting of single-flux-quantum circuits

    International Nuclear Information System (INIS)

    Hirose, Tetsuya; Asai, Tetsuya; Amemiya, Yoshihito

    2006-01-01

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

  17. The thermal-spike model description of the ion-irradiated polyimide

    International Nuclear Information System (INIS)

    Sun Youmei; Zhang Chonghong; Zhu Zhiyong; Wang Zhiguang; Jin Yunfan; Liu Jie; Wang Ying

    2004-01-01

    To describe the role of electronic energy loss (dE/dX) e for chemical modification of polyimide (PI), multi-layer stacks (corresponding to different dE/dX) were irradiated by different swift heavy ions (1.158 GeV Fe 56 and 1.755 GeV Xe 136 ) under vacuum and at room temperature. Chemical changes of modified PI films were studied by Fourier transform infrared (FTIR) spectroscopy. The chain disruption rate of PI was investigated in the fluence range from 1 x 10 11 to 6 x 10 12 ions/cm 2 and a wider energy stopping power range (2.2-5.1 keV/nm for Fe 56 ions and 8.6-11.5 keV/nm for Xe 136 ions). Alkyne formation was observed over the electronic energy loss range of interest. By applying the saturated track model assumption (the damage process only occur in a cylinder of area σ), the mean degradation and alkyne formation radii in tracks were induced for Fe and Xe ion irradiation, respectively. The results were validated by the thermal-spike model. The analysis of the irradiated PI films shows that the predictions of the thermal-spike model of Szenes are in qualitative agreement with the curve shape of experimental results

  18. A stochastic-field description of finite-size spiking neural networks.

    Science.gov (United States)

    Dumont, Grégory; Payeur, Alexandre; Longtin, André

    2017-08-01

    Neural network dynamics are governed by the interaction of spiking neurons. Stochastic aspects of single-neuron dynamics propagate up to the network level and shape the dynamical and informational properties of the population. Mean-field models of population activity disregard the finite-size stochastic fluctuations of network dynamics and thus offer a deterministic description of the system. Here, we derive a stochastic partial differential equation (SPDE) describing the temporal evolution of the finite-size refractory density, which represents the proportion of neurons in a given refractory state at any given time. The population activity-the density of active neurons per unit time-is easily extracted from this refractory density. The SPDE includes finite-size effects through a two-dimensional Gaussian white noise that acts both in time and along the refractory dimension. For an infinite number of neurons the standard mean-field theory is recovered. A discretization of the SPDE along its characteristic curves allows direct simulations of the activity of large but finite spiking networks; this constitutes the main advantage of our approach. Linearizing the SPDE with respect to the deterministic asynchronous state allows the theoretical investigation of finite-size activity fluctuations. In particular, analytical expressions for the power spectrum and autocorrelation of activity fluctuations are obtained. Moreover, our approach can be adapted to incorporate multiple interacting populations and quasi-renewal single-neuron dynamics.

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

    Science.gov (United States)

    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

  20. Small heat-shock proteins and leaf cooling capacity account for the unusual heat tolerance of the central spike leaves in Agave tequilana var. Weber.

    Science.gov (United States)

    Luján, Rosario; Lledías, Fernando; Martínez, Luz María; Barreto, Rita; Cassab, Gladys I; Nieto-Sotelo, Jorge

    2009-12-01

    Agaves are perennial crassulacean acid metabolism (CAM) plants distributed in tropical and subtropical arid environments, features that are attractive for studying the heat-shock response. In agaves, the stress response can be analysed easily during leaf development, as they form a spirally shaped rosette, having the meristem surrounded by folded leaves in the centre (spike) and the unfolded and more mature leaves in the periphery. Here, we report that the spike of Agave tequilana is the most thermotolerant part of the rosette withstanding shocks of up to 55 degrees C. This finding was inconsistent with the patterns of heat-shock protein (Hsp) gene expression, as maximal accumulation of Hsp transcripts was at 44 degrees C in all sectors (spike, inner, middle and outer). However, levels of small HSP (sHSP)-CI and sHSP-CII proteins were conspicuously higher in spike leaves at all temperatures correlating with their thermotolerance. In addition, spike leaves showed a higher stomatal density and abated more efficiently their temperature several degrees below that of air. We propose that the greater capacity for leaf cooling during the day in response to heat stress, and the elevated levels of sHSPs, constitute part of a set of strategies that protect the SAM and folded leaves of A. tequilana from high temperatures.

  1. SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

    Science.gov (United States)

    Jimenez-Romero, Cristian; Johnson, Jeffrey

    2017-01-01

    The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

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

    Science.gov (United States)

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

    2013-01-01

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

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

    Science.gov (United States)

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

    2011-01-01

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

  4. The dopamine beta-hydroxylase inhibitor nepicastat increases dopamine release and potentiates psychostimulant-induced dopamine release in the prefrontal cortex.

    Science.gov (United States)

    Devoto, Paola; Flore, Giovanna; Saba, Pierluigi; Bini, Valentina; Gessa, Gian Luigi

    2014-07-01

    The dopamine-beta-hydroxylase inhibitor nepicastat has been shown to reproduce disulfiram ability to suppress the reinstatement of cocaine seeking after extinction in rats. To clarify its mechanism of action, we examined the effect of nepicastat, given alone or in association with cocaine or amphetamine, on catecholamine release in the medial prefrontal cortex and the nucleus accumbens, two key regions involved in the reinforcing and motivational effects of cocaine and in the reinstatement of cocaine seeking. Nepicastat effect on catecholamines was evaluated by microdialysis in freely moving rats. Nepicastat reduced noradrenaline release both in the medial prefrontal cortex and in the nucleus accumbens, and increased dopamine release in the medial prefrontal cortex but not in the nucleus accumbens. Moreover, nepicastat markedly potentiated cocaine- and amphetamine-induced extracellular dopamine accumulation in the medial prefrontal cortex but not in the nucleus accumbens. Extracellular dopamine accumulation produced by nepicastat alone or by its combination with cocaine or amphetamine was suppressed by the α2 -adrenoceptor agonist clonidine. It is suggested that nepicastat, by suppressing noradrenaline synthesis and release, eliminated the α2 -adrenoceptor mediated inhibitory mechanism that constrains dopamine release and cocaine- and amphetamine-induced dopamine release from noradrenaline or dopamine terminals in the medial prefrontal cortex. © 2012 The Authors, Addiction Biology © 2012 Society for the Study of Addiction.

  5. Central actions of a novel and selective dopamine antagonist

    International Nuclear Information System (INIS)

    Schulz, D.W.

    1985-01-01

    Receptors for the neurotransmitter dopamine traditionally have been divided into two subgroups: the D 1 class, which is linked to the stimulation of adenylate cyclase-activity, and the D 2 class which is not. There is much evidence suggesting that it is the D 2 class which is not. There is much evidence suggesting that it is the D 2 dopamine receptor that mediates the physiological and behavioral actions of dopamine in the intact animal. However, the benzazepine SCH23390 is a dopamine antagonist which has potent behavioral actions while displaying apparent neurochemical selectivity for the D 1 class of dopamine receptors. The purpose of this dissertation was to (1) confirm and characterize this selectivity, and (2) test certain hypothesis related to possible modes of action of SCH233390. The inhibition of adenylate cyclase by SCH23390 occurred via an action at the dopamine receptor only. A radiolabeled analog of SCH23390 displayed the receptor binding properties of a specific high-affinity ligand, and regional receptor densities were highly correlated with dopamine levels. The subcellular distribution of [ 3 H]-SCH23390 binding did not correspond completely with that of dopamine-stimulated adenylate cyclase. The neurochemical potency of SCH23390 as a D 1 receptor antagonist was preserved following parental administration. A variety of dopamine agonists and antagonists displayed a high correlation between their abilities to compete for [ 3 H]-SCH23390 binding in vitro and to act at an adenylate cyclase-linked receptor. Finally, the relative affinities of dopamine and SCH23390 for both D 1 receptors and [ 3 H]-SCH23390 binding sites were comparable. It is concluded that the behavioral effects of SCH23390 are mediated by actions at D 1 dopamine receptors only, and that the physiological importance of this class of receptors should be reevaluated

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

    Science.gov (United States)

    Caputi, Theodore L

    2018-05-03

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

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

    Directory of Open Access Journals (Sweden)

    Pietro Quaglio

    2017-05-01

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

  8. Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.

    Science.gov (United States)

    Pena, Rodrigo F O; Vellmer, Sebastian; Bernardi, Davide; Roque, Antonio C; Lindner, Benjamin

    2018-01-01

    Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as

  9. Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks

    Directory of Open Access Journals (Sweden)

    Rodrigo F. O. Pena

    2018-03-01

    Full Text Available Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i different neural subpopulations (e.g., excitatory and inhibitory neurons have different cellular or connectivity parameters; (ii the number and strength of the input connections are random (Erdős-Rényi topology and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of

  10. Interaction of structural analogs of dopamine, chlorpromazine and sulpiride with striatal dopamine receptors

    International Nuclear Information System (INIS)

    Wallace, R.A.

    1987-01-01

    The objectives of these studies were to determine if the nitrogen atom of dopaminergic agonists and antagonists drugs is required for interaction with the D-1 and D-2 dopamine receptors and whether the positively charged or uncharged molecular species interacts with these receptors. To address these issues, permanently charged analogs of dopamine, chlorpromazine and sulpiride were synthesized in which a dimethylsulfonium, dimethylselenonium or quaternary ammonium group replaced the amine group. Permanently uncharged analogs which contained a methylsulfide, methylselenide and sulfoxide group instead of an amine group were also synthesized. The interactions of these compounds with striatal dopamine receptors were studied. We found that the permanently charged dopamine analogs bound to the D-2 receptor of striatal membranes like conventional dopaminergic agonists and displayed agonist activity at the D-2 receptor regulating potassium-evoked [ 3 H] acetylcholine release. In contrast, the permanently uncharged analogs bound only to the high affinity state of the D-2 receptor and had neither agonist or antagonist activity

  11. Effects of alkylating agents on dopamine D(3) receptors in rat brain: selective protection by dopamine.

    Science.gov (United States)

    Zhang, K; Weiss, N T; Tarazi, F I; Kula, N S; Baldessarini, R J

    1999-11-13

    Dopamine D(3) receptors are structurally highly homologous to other D(2)-like dopamine receptors, but differ from them pharmacologically. D(3) receptors are notably resistant to alkylation by 1-ethoxycarbonyl-2-ethoxy-1,2-dihydroquinoline (EEDQ), which readily alkylates D(2) receptors. We compared EEDQ with N-(p-isothiocyanatophenethyl)spiperone (NIPS), a selective D(2)-like receptor alkylating agent, for effects on D(3) and D(2) receptors in rat brain using autoradiographic analysis. Neither agent occluded D(3) receptors in vivo at doses that produced substantial blockade of D(2) receptors, even after catecholamine-depleting pretreatments. In vitro, however, D(3) receptors were readily alkylated by both NIPS (IC(50)=40 nM) and EEDQ (IC(50)=12 microM). These effects on D(3) sites were blocked by nM concentrations of dopamine, whereas microM concentrations were required to protect D(2) receptors from the alkylating agents. The findings are consistent with the view that alkylation of D(3) receptors in vivo is prevented by its high affinity for even minor concentrations of endogenous dopamine.

  12. Serotonin-S2 and dopamine-D2 receptors are the same size in membranes

    International Nuclear Information System (INIS)

    Brann, M.R.

    1985-01-01

    Target size analysis was used to compare the sizes of serotonin-S2 and dopamine-D2 receptors in rat brain membranes. The sizes of these receptors were standardized by comparison with the muscarinic receptor, a receptor of known size. The number of serotonin-S2 receptors labeled with (3H)ketanserin or (3H)spiperone in frontal cortex decreased as an exponential function of radiation dose, and receptor affinity was not affected. The number of dopamine-D2 receptors labeled with (3H)spiperone in striatum also decreased as an exponential function of radiation dose, and D2 and S2 receptors were equally sensitive to radiation. In both striatum and frontal cortex, the number of muscarinic receptors labeled with (3H)QNB decreased as an exponential function of radiation dose, and were much less sensitive to radiation than S2 and D2 receptors. These data indicate that in rat brain membranes, S2 and D2 receptors are of similar size, and both molecules are much larger than the muscarinic receptor

  13. The binding sites for benztropines and dopamine in the dopamine transporter overlap

    DEFF Research Database (Denmark)

    Jensen, Heidi Bisgaard; Larsen, M Andreas B; Mazier, Sonia

    2011-01-01

    Analogs of benztropines (BZTs) are potent inhibitors of the dopamine transporter (DAT) but are less effective than cocaine as behavioral stimulants. As a result, there have been efforts to evaluate these compounds as leads for potential medication for cocaine addiction. Here we use computational...

  14. Syntaxin 1A interaction with the dopamine transporter promotes amphetamine-induced dopamine efflux

    DEFF Research Database (Denmark)

    Binda, Francesca; Dipace, Concetta; Bowton, Erica

    2008-01-01

    of the dopamine (DA) transporter (DAT) as the site of direct interaction with SYN1A. Amphetamine (AMPH) increases the association of SYN1A with human DAT (hDAT) in a heterologous expression system (hDAT cells) and with native DAT in murine striatal synaptosomes. Immunoprecipitation of DAT from the biotinylated...

  15. De novo mutation in the dopamine transporter gene associates dopamine dysfunction with autism spectrum disorder

    DEFF Research Database (Denmark)

    Hamilton, P J; Campbell, N G; Sharma, S

    2013-01-01

    De novo genetic variation is an important class of risk factors for autism spectrum disorder (ASD). Recently, whole-exome sequencing of ASD families has identified a novel de novo missense mutation in the human dopamine (DA) transporter (hDAT) gene, which results in a Thr to Met substitution...

  16. SPECT imaging of D2 dopamine receptors and endogenous dopamine release in mice

    NARCIS (Netherlands)

    Jongen, C.; De Bruin, K.; Beekman, F.J.; Booij, J.

    2008-01-01

    Purpose: The dopamine D2 receptor (D2R) is important in the mediation of addiction. [123I]iodobenzamide (IBZM), a SPECT ligand for the D2R, has been used for in vivo studies of D2R availability in humans, monkeys, and rats. Although mouse models are important in the study of addiction, [123I]IBZM

  17. CD-SEM metrology of spike detection on sub-40 nm contact holes

    Science.gov (United States)

    Momonoi, Yoshinori; Osabe, Taro; Yamaguchi, Atsuko; Mclellan Martin, Erin; Koyanagi, Hajime; Colburn, Matthew E.; Torii, Kazuyoshi

    2010-03-01

    In this work, for the purpose of contact-hole process control, new metrics for contact-hole edge roughness (CER) are being proposed. The metrics are correlated to lithographic process variation which result in increased electric fields; a primary driver of time-dependent dielectric breakdown (TDDB). Electric field strength at the tip of spoke-shaped CER has been simulated; and new hole-feature metrics have been introduced. An algorithm for defining critical features like spoke angle, spoke length, etc has been defined. In addition, a method for identifying at-risk holes has been demonstrated. The number of spike holes can determine slight defocus conditions that are not detected though the conventional CER metrics. The newly proposed metrics can identify contact holes with a propensity for increased electric field concentration and are expected to improve contact-hole reliability in the sub-40-nm contact-hole process.

  18. Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking Model with Reward Dependent Plasticity

    Directory of Open Access Journals (Sweden)

    Pierre Berthet

    2016-07-01

    Full Text Available The brain enables animals to behaviourally adapt in order to survive in a complex and dynamic environment, but how reward-oriented behaviours are achieved and computed by its underlying neural circuitry is an open question. To address this concern, we have developed a spiking model of the basal ganglia (BG that learns to dis-inhibit the action leading to a reward despite ongoing changes in the reward schedule. The architecture of the network features the two pathways commonly described in BG, the direct (denoted D1 and the indirect (denoted D2 pathway, as well as a loop involving striatum and the dopaminergic system. The activity of these dopaminergic neurons conveys the reward prediction error (RPE, which determines the magnitude of synaptic plasticity within the different pathways. All plastic connections implement a versatile four-factor learning rule derived from Bayesian inference that depends upon pre- and postsynaptic activity, receptor type and dopamine level. Synaptic weight updates occur in the D1 or D2 pathways depending on the sign of the RPE, and an efference copy informs upstream nuclei about the action selected. We demonstrate successful performance of the system in a multiple-choice learning task with a transiently changing reward schedule. We simulate lesioning of the various pathways and show that a condition without the D2 pathway fares worse than one without D1. Additionally, we simulate the degeneration observed in Parkinson’s disease (PD by decreasing the number of dopaminergic neurons during learning. The results suggest that the D1 pathway impairment in PD might have been overlooked. Furthermore, an analysis of the alterations in the synaptic weights shows that using the absolute reward value instead of the RPE leads to a larger change in D1.

  19. A memristive spiking neuron with firing rate coding

    Directory of Open Access Journals (Sweden)

    Marina eIgnatov

    2015-10-01

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

  20. Cochlear spike synchronization and neuron coincidence detection model

    Science.gov (United States)

    Bader, Rolf

    2018-02-01

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

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

    DEFF Research Database (Denmark)

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

    2009-01-01

    A proficiency test on incurred and spiked pesticide residues in wheat was organised in 2008. The test material was grown in 2007 and treated in the field with 14 pesticides formulations containing the active substances, alpha-cypermethrin, bifentrin, carbendazim, chlormequat, chlorpyrifos...

  2. Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems

    Directory of Open Access Journals (Sweden)

    Emre eNeftci

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Dan F M Goodman

    2010-11-01

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

  4. Thermal spike analysis of highly charged ion tracks

    International Nuclear Information System (INIS)

    Karlušić, M.; Jakšić, M.

    2012-01-01

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

  5. Bayesian Inference for Structured Spike and Slab Priors

    DEFF Research Database (Denmark)

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

    2014-01-01

    Sparse signal recovery addresses the problem of solving underdetermined linear inverse problems subject to a sparsity constraint. We propose a novel prior formulation, the structured spike and slab prior, which allows to incorporate a priori knowledge of the sparsity pattern by imposing a spatial...

  6. Effect of Rolandic Spikes on ADHD Impulsive Behavior

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2007-01-01

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

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

    NARCIS (Netherlands)

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

    1995-01-01

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

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

    Science.gov (United States)

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

    2017-03-01

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

  9. Spike Neural Models Part II: Abstract Neural Models

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2018-02-01

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

  10. Fast computation with spikes in a recurrent neural network

    International Nuclear Information System (INIS)

    Jin, Dezhe Z.; Seung, H. Sebastian

    2002-01-01

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

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

    Science.gov (United States)

    Gardner, Brian; Sporea, Ioana; Grüning, André

    2015-12-01

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

  12. Dynamics of directional coupling underlying spike-wave discharges

    NARCIS (Netherlands)

    Sysoeva, M.V.; Luttjohann, A.K.; Luijtelaar, E.L.J.M. van; Sysoev, I.V.

    2016-01-01

    Purpose: Spike and wave discharges (SWDs), generated within cortico-thalamo-cortical networks, are the electroencephalographic biomarker of absence epilepsy. The current work aims to identify mechanisms of SWD initiation, maintenance and termination by the analyses of dynamics and directionality of

  13. Breathing, spiking and chaos in a laser with injected signal

    Energy Technology Data Exchange (ETDEWEB)

    Lugiato, L A; Narducci, L M

    1983-06-01

    The behavior of a laser driven by an injected cw field detuned from the operating laser frequency is considered. The analysis covers the entire range of incident power levels from zero to the injection locking threshold. In this domain, the output intensity exhibits regular and chaotic oscillations, a period doubling cascade in reverse order, envelope breathing and spiking.

  14. Inhibitory Synaptic Plasticity - Spike timing dependence and putative network function.

    Directory of Open Access Journals (Sweden)

    Tim P Vogels

    2013-07-01

    Full Text Available While the plasticity of excitatory synaptic connections in the brain has been widely studied, the plasticity of inhibitory connections is much less understood. Here, we present recent experimental and theoretical □ndings concerning the rules of spike timing-dependent inhibitory plasticity and their putative network function. This is a summary of a workshop at the COSYNE conference 2012.

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

    Science.gov (United States)

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

    2013-01-01

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

  16. Spiking Activity of a LIF Neuron in Distributed Delay Framework

    Directory of Open Access Journals (Sweden)

    Saket Kumar Choudhary

    2016-06-01

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

  17. Dopamine-independent locomotor actions of amphetamines in a novel acute mouse model of Parkinson disease.

    Directory of Open Access Journals (Sweden)

    2005-08-01

    Full Text Available Brain dopamine is critically involved in movement control, and its deficiency is the primary cause of motor symptoms in Parkinson disease. Here we report development of an animal model of acute severe dopamine deficiency by using mice lacking the dopamine transporter. In the absence of transporter-mediated recycling mechanisms, dopamine levels become entirely dependent on de novo synthesis. Acute pharmacological inhibition of dopamine synthesis in these mice induces transient elimination of striatal dopamine accompanied by the development of a striking behavioral phenotype manifested as severe akinesia, rigidity, tremor, and ptosis. This phenotype can be reversed by administration of the dopamine precursor, L-DOPA, or by nonselective dopamine agonists. Surprisingly, several amphetamine derivatives were also effective in reversing these behavioral abnormalities in a dopamine-independent manner. Identification of dopamine transporter- and dopamine-independent locomotor actions of amphetamines suggests a novel paradigm in the search for prospective anti-Parkinsonian drugs.

  18. Retinal dopamine mediates multiple dimensions of light-adapted vision.

    Science.gov (United States)

    Jackson, Chad R; Ruan, Guo-Xiang; Aseem, Fazila; Abey, Jane; Gamble, Karen; Stanwood, Greg; Palmiter, Richard D; Iuvone, P Michael; McMahon, Douglas G

    2012-07-04

    Dopamine is a key neuromodulator in the retina and brain that supports motor, cognitive, and visual function. Here, we developed a mouse model on a C57 background in which expression of the rate-limiting enzyme for dopamine synthesis, tyrosine hydroxylase, is specifically disrupted in the retina. This model enabled assessment of the overall role of retinal dopamine in vision using electrophysiological (electroretinogram), psychophysical (optokinetic tracking), and pharmacological techniques. Significant disruptions were observed in high-resolution, light-adapted vision caused by specific deficits in light responses, contrast sensitivity, acuity, and circadian rhythms in this retinal dopamine-depleted mouse model. These global effects of retinal dopamine on vision are driven by the differential actions of dopamine D1 and D4 receptors on specific retinal functions and appear to be due to the ongoing bioavailability of dopamine rather than developmental effects. Together, our data indicate that dopamine is necessary for the circadian nature of light-adapted vision as well as optimal contrast detection and acuity.

  19. Layered reward signalling through octopamine and dopamine in Drosophila.

    Science.gov (United States)

    Burke, Christopher J; Huetteroth, Wolf; Owald, David; Perisse, Emmanuel; Krashes, Michael J; Das, Gaurav; Gohl, Daryl; Silies, Marion; Certel, Sarah; Waddell, Scott

    2012-12-20

    Dopamine is synonymous with reward and motivation in mammals. However, only recently has dopamine been linked to motivated behaviour and rewarding reinforcement in fruitflies. Instead, octopamine has historically been considered to be the signal for reward in insects. Here we show, using temporal control of neural function in Drosophila, that only short-term appetitive memory is reinforced by octopamine. Moreover, octopamine-dependent memory formation requires signalling through dopamine neurons. Part of the octopamine signal requires the α-adrenergic-like OAMB receptor in an identified subset of mushroom-body-targeted dopamine neurons. Octopamine triggers an increase in intracellular calcium in these dopamine neurons, and their direct activation can substitute for sugar to form appetitive memory, even in flies lacking octopamine. Analysis of the β-adrenergic-like OCTβ2R receptor reveals that octopamine-dependent reinforcement also requires an interaction with dopamine neurons that control appetitive motivation. These data indicate that sweet taste engages a distributed octopamine signal that reinforces memory through discrete subsets of mushroom-body-targeted dopamine neurons. In addition, they reconcile previous findings with octopamine and dopamine and suggest that reinforcement systems in flies are more similar to mammals than previously thought.

  20. Free and conjugated dopamine in human ventricular fluid

    International Nuclear Information System (INIS)

    Sharpless, N.S.; Thal, L.J.; Wolfson, L.I.; Tabaddor, K.; Tyce, G.M.; Waltz, J.M.

    1981-01-01

    Free dopamine and an acid hydrolyzable conjugate of dopamine were measured in human ventricular fluid specimens with a radioenzymatic assay and by high performance liquid chromatography (HPLC) with electrochemical detection. Only trace amounts of free norepinephrine and dopamine were detected in ventricular fluid from patients with movement disorders. When the ventricular fluid was hydrolyzed by heating in HClO 4 or by lyophilization in dilute HClO 4 , however, a substantial amount of free dopamine was released. Values for free plus conjugated dopamine in ventricular fluid from patients who had never taken L-DOPA ranged from 139 to 340 pg/ml when determined by HPLC and from 223 to 428 pg/ml when measured radioenzymatically. The correlation coefficient for values obtained by the two methods in the same sample of CSF was 0.94 (P<0.001). Patients who had been treated with L-DOPA had higher levels of conjugated dopamine in their ventricular CSF which correlated inversely with the time between the last dose of L-DOPA and withdrawal of the ventricular fluid. Additionally, one patient with acute cerebral trauma had elevated levels of free norepinephrine and both free and conjugated dopamine in his ventricular fluid. Conjugation may be an important inactivation pathway for released dopamine in man. (Auth.)

  1. Dopamine D2 receptors in the pathophysiology of insulin resistance

    NARCIS (Netherlands)

    Leeuw van Weenen, Judith Elisabeth de

    2011-01-01

    Extensive literature links the dopamine receptor D2 to insulin resistance and diabetes mellitus type 2. However, many aspects of the functional relationship remain unclear. In this thesis we focused on unraveling the characteristics of the interplay between dopamine D2 receptors and glucose

  2. Novos agonistas dopaminérgicos

    Directory of Open Access Journals (Sweden)

    MATTOS JAMES PITÁGORAS DE

    1999-01-01

    Full Text Available Apresentamos breve revisão da literatura sobre os agonistas dopaminérgicos. Referimos os cinco receptores conhecidos e onde estão localizados, as vantagens e as desvantagens de sua utilização nos pacientes com a doença de Parkinson.Introduzidos com o objetivo principal de controlar as limitações da levodopa, aumentando a janela terapêutica, analisamos a farmacocinética, a eficácia e os efeitos colaterais da cabergolina, do ropinirole e do pramipexole.

  3. Graphene Oxide Modified Electrodes for Dopamine Sensing

    Directory of Open Access Journals (Sweden)

    M. Z. H. Khan

    2017-01-01

    Full Text Available Dopamine (DA is one of the most important catecholamine neurotransmitters that plays an important role in the central nervous, renal, hormonal, and cardiovascular systems. Since its discovery, tremendous effort has been made and various techniques have been developed for the DA detection. Recently, graphene-based materials have attracted a tremendous amount of attention due to their high sensitivity and rapid response towards effective detection of DA. This review focuses on current advances of graphene-based materials for DA detection based on recent articles published in the last five years.

  4. Linear shaped charge

    Energy Technology Data Exchange (ETDEWEB)

    Peterson, David; Stofleth, Jerome H.; Saul, Venner W.

    2017-07-11

    Linear shaped charges are described herein. In a general embodiment, the linear shaped charge has an explosive with an elongated arrowhead-shaped profile. The linear shaped charge also has and an elongated v-shaped liner that is inset into a recess of the explosive. Another linear shaped charge includes an explosive that is shaped as a star-shaped prism. Liners are inset into crevices of the explosive, where the explosive acts as a tamper.

  5. Illicit dopamine transients: reconciling actions of abused drugs.

    Science.gov (United States)

    Covey, Dan P; Roitman, Mitchell F; Garris, Paul A

    2014-04-01

    Phasic increases in brain dopamine are required for cue-directed reward seeking. Although compelling within the framework of appetitive behavior, the view that illicit drugs hijack reward circuits by hyperactivating these dopamine transients is inconsistent with established psychostimulant pharmacology. However, recent work reclassifying amphetamine (AMPH), cocaine, and other addictive dopamine-transporter inhibitors (DAT-Is) supports transient hyperactivation as a unifying hypothesis of abused drugs. We argue here that reclassification also identifies generating burst firing by dopamine neurons as a keystone action. Unlike natural rewards, which are processed by sensory systems, drugs act directly on the brain. Consequently, to mimic natural rewards and exploit reward circuits, dopamine transients must be elicited de novo. Of available drug targets, only burst firing achieves this essential outcome. Copyright © 2014 Elsevier Ltd. All rights reserved.

  6. ILLICIT DOPAMINE TRANSIENTS: RECONCILING ACTIONS OF ABUSED DRUGS

    Science.gov (United States)

    Covey, Dan P.; Roitman, Mitchell F.; Garris, Paul A.

    2014-01-01

    Phasic increases in brain dopamine are required for cue-directed reward seeking. While compelling within the framework of appetitive behavior, the view that illicit drugs hijack reward circuits by hyper-activating these dopamine transients is inconsistent with established psychostimulant pharmacology. However, recent work reclassifying amphetamine (AMPH), cocaine, and other addictive dopamine-transporter inhibitors (DAT-Is) supports transient hyper-activation as a unifying hypothesis of abused drugs. We argue here that reclassification also identifies generating burst firing by dopamine neurons as a keystone action. Unlike natural rewards, which are processed by sensory systems, drugs act directly on the brain. Consequently, to mimic natural reward and exploit reward circuits, dopamine transients must be elicited de novo. Of available drug targets, only burst firing achieves this essential outcome. PMID:24656971

  7. Dopamine release in ventral striatum of pathological gamblers losing money

    DEFF Research Database (Denmark)

    Linnet, J; Peterson, E; Doudet, D J

    2010-01-01

    Linnet J, Peterson E, Doudet DJ, Gjedde A, Møller A. Dopamine release in ventral striatum of pathological gamblers losing money. Objective: To investigate dopaminergic neurotransmission in relation to monetary reward and punishment in pathological gambling. Pathological gamblers (PG) often continue...... gambling despite losses, known as 'chasing one's losses'. We therefore hypothesized that losing money would be associated with increased dopamine release in the ventral striatum of PG compared with healthy controls (HC). Method: We used Positron Emission Tomography (PET) with [(11)C]raclopride to measure...... dopamine release in the ventral striatum of 16 PG and 15 HC playing the Iowa Gambling Task (IGT). Results: PG who lost money had significantly increased dopamine release in the left ventral striatum compared with HC. PG and HC who won money did not differ in dopamine release. Conclusion: Our findings...

  8. A causal link between prediction errors, dopamine neurons and learning.

    Science.gov (United States)

    Steinberg, Elizabeth E; Keiflin, Ronald; Boivin, Josiah R; Witten, Ilana B; Deisseroth, Karl; Janak, Patricia H

    2013-07-01

    Situations in which rewards are unexpectedly obtained or withheld represent opportunities for new learning. Often, this learning includes identifying cues that predict reward availability. Unexpected rewards strongly activate midbrain dopamine neurons. This phasic signal is proposed to support learning about antecedent cues by signaling discrepancies between actual and expected outcomes, termed a reward prediction error. However, it is unknown whether dopamine neuron prediction error signaling and cue-reward learning are causally linked. To test this hypothesis, we manipulated dopamine neuron activity in rats in two behavioral procedures, associative blocking and extinction, that illustrate the essential function of prediction errors in learning. We observed that optogenetic activation of dopamine neurons concurrent with reward delivery, mimicking a prediction error, was sufficient to cause long-lasting increases in cue-elicited reward-seeking behavior. Our findings establish a causal role for temporally precise dopamine neuron signaling in cue-reward learning, bridging a critical gap between experimental evidence and influential theoretical frameworks.

  9. Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

    Science.gov (United States)

    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.

  10. A photoaffinity ligand for dopamine D2 receptors: azidoclebopride.

    Science.gov (United States)

    Niznik, H B; Guan, J H; Neumeyer, J L; Seeman, P

    1985-02-01

    In order to label D2 dopamine receptors selectively and covalently by means of a photosensitive compound, azidoclebopride was synthesized directly from clebopride. The dissociation constant (KD) of clebopride for the D2 dopamine receptor (canine brain striatum) was 1.5 nM, while that for azidoclebopride was 21 nM. The affinities of both clebopride and azidoclebopride were markedly reduced in the absence of sodium chloride. In the presence of ultraviolet light, azidoclebopride inactivated D2 dopamine receptors irreversibly, as indicated by the inability of the receptors to bind [3H]spiperone. Maximal photoinactivation of about 60% of the D2 dopamine receptors occurred at 1 microM azidoclebopride; 30% of the receptors were inactivated at 80 nM azidoclebopride (pseudo-IC50). Dopamine agonists selectively protected the D2 receptors from being inactivated by azidoclebopride, the order of potency being (-)-N-n-propylnorapomorphine greater than apomorphine greater than (+/-)-6,7-dihydroxy-2-aminotetralin greater than (+)-N-n-propylnorapomorphine greater than dopamine greater than noradrenaline greater than serotonin. Similarly, dopaminergic antagonists prevented the photoinactivation of D2 receptors by azidoclebopride with the following order of potency: spiperone greater than (+)-butaclamol greater than haloperidol greater than clebopride greater than (-)-sulpiride greater than (-)-butaclamol. The degree of D2 dopamine receptor photoinduced inactivation by azidoclebopride was not significantly affected by scavengers such as p-aminobenzoic acid and dithiothreitol. Furthermore, irradiation of striatal membranes with a concentration of azidoclebopride sufficient to inactivate dopamine D2 receptors by 60% did not significantly reduce dopamine D1, serotonin (S2), benzodiazepine, alpha 1- or beta-noradrenergic receptors. This study describes the use of a novel and selective photoaffinity ligand for brain dopamine D2 receptors. The molecule, in radiolabeled form, may aid in the

  11. Temporal Profiles Dissociate Regional Extracellular Ethanol versus Dopamine Concentrations

    Science.gov (United States)

    2015-01-01

    In vivo monitoring of dopamine via microdialysis has demonstrated that acute, systemic ethanol increases extracellular dopamine in regions innervated by dopaminergic neurons originating in the ventral tegmental area and substantia nigra. Simultaneous measurement of dialysate dopamine and ethanol allows comparison of the time courses of their extracellular concentrations. Early studies demonstrated dissociations between the time courses of brain ethanol concentrations and dopaminergic responses in the nucleus accumbens (NAc) elicited by acute ethanol administration. Both brain ethanol and extracellular dopamine levels peak during the first 5 min following systemic ethanol administration, but the dopamine response returns to baseline while brain ethanol concentrations remain elevated. Post hoc analyses examined ratios of the dopamine response (represented as a percent above baseline) to tissue concentrations of ethanol at different time points within the first 25–30 min in the prefrontal cortex, NAc core and shell, and dorsomedial striatum following a single intravenous infusion of ethanol (1 g/kg). The temporal patterns of these “response ratios” differed across brain regions, possibly due to regional differences in the mechanisms underlying the decline of the dopamine signal associated with acute intravenous ethanol administration and/or to the differential effects of acute ethanol on the properties of subpopulations of midbrain dopamine neurons. This Review draws on neurochemical, physiological, and molecular studies to summarize the effects of acute ethanol administration on dopamine activity in the prefrontal cortex and striatal regions, to explore the potential reasons for the regional differences observed in the decline of ethanol-induced dopamine signals, and to suggest directions for future research. PMID:25537116

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

    NARCIS (Netherlands)

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

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

  13. CuO nanoparticle sensor for the electrochemical determination of dopamine

    International Nuclear Information System (INIS)

    Reddy, Sathish; Kumara Swamy, B.E.; Jayadevappa, H.

    2012-01-01

    Highlights: ► The MCPE prepared from flake-shaped CuO nanoparticles exhibits good electrocatalytic activity for DA compared with MCPE prepared from rod-shaped CuO nanoparticles. ► The MCPE prepared from SDS/polyglycine/flake-shaped CuO nanoparticles strong electrocatalytic enhancement of redox peak currents for DA and large peak potential separation between E AA − E DA . ► Analysis of DA shows linearly increase in anodic peak current in presence of excess ascorbic acid. ► Ease of preparation and good analytical response supports its claim for use as a potential dopamine sensor. - Abstract: In the present work, different shaped CuO nanoparticles were synthesized using cetyl trimethyl ammonium bromide (CTAB) and sodium dodecyl sulfate (SDS) in a co-precipitation method. The CuO nanoparticles were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), infrared absorption spectroscopy (IR) and UV–visible absorption spectroscopy (UV–vis). The prepared CuO nanoparticles were used for the preparation of modified carbon-paste electrodes (MCPE) for the electrochemical detection of dopamine (DA) at pH 6.0. The MCPE prepared from flake-shaped CuO nanoparticles exhibited an enhanced current response for DA. Electrochemical parameters, such as the surface area of the electrode, the heterogeneous rate constant (k s ) and the lower detection limit (5.5 × 10 −8 M), were calculated and compared with those of the MCPE prepared from rod-shaped CuO nanoparticles. The MCPE prepared from SDS/polyglycine/flake-shaped CuO nanoparticles exhibited a further improved current response for DA and a high selectivity (E AA − E DA = 0.28 V) for the simultaneous investigation of DA and ascorbic acid (AA) at pH 6.0. The modified carbon-paste electrochemical sensors were compared, and the MCPE prepared from SDS/polyglycine/flake-shaped CuO nanoparticles exhibited better performance than the MCPE prepared from CTAB/polyglycine/flake-shaped

  14. Dopamine receptors in the Parkinsonian brain

    Energy Technology Data Exchange (ETDEWEB)

    Rinne, U K; Loennberg, P; Koskinen, V [Turku Univ. (Finland). Dept. of Neurology

    1981-01-01

    Striatal dopamine receptors were studied in 44 patients with Parkinson disease by the radioligand-binding technique using /sup 3/H-spiroperidol. The specific binding of /sup 3/H-spiroperidol was either significantly increased or reduced in the caudate nucleus and putamen of parkinsonian patients without levodopa therapy. Scatchard analysis showed that there were corresponding changes in the receptor number, but no significant changes in the mean dissociation constant. The increased binding of /sup 3/H-spiroperidol in the basal ganglia was also found in parkinsonian patients suffering from psychotic episodes and treated with neuroleptic drugs. Normal and low binding of /sup 3/H-spiroperidol was found in patients treated with levodopa. Clinically, the patient with low binding were more disabled and had lost the beneficial response to levodopa. Thus in Parkinson disease in some patients a denervation supersensitivity seemed to develop and in some others a loss of postsynaptic dopamine receptor sites in the neostriatium. The latter alteration may contribute to the decreased response of parkinsonian patients to chronic levodopa therapy.

  15. Dopamine receptors in the Parkinsonian brain

    International Nuclear Information System (INIS)

    Rinne, U.K.; Loennberg, P.; Koskinen, V.

    1981-01-01

    Striatal dopamine receptors were studied in 44 patients with Parkinson disease by the radioligand-binding technique using 3 H-spiroperidol. The specific binding of 3 H-spiroperidol was either significantly increased or reduced in the caudate nucleus and putamen of parkinsonian patients without levodopa therapy. Scatchard analysis showed that there were corresponding changes in the receptor number, but no significant changes in the mean dissociation constant. The increased binding of 3 H-spiroperidol in the basal ganglia was also found in parkinsonian patients suffering from psychotic episodes and treated with neuroleptic drugs. Normal and low binding of 3 H-spiroperidol was found in patients treated with levodopa. Clinically, the patient with low binding were more disabled and had lost the beneficial response to levodopa. Thus in Parkinson disease in some patients a denervation supersensitivity seemed to develop and in some others a loss of postsynaptic dopamine receptor sites in the neostriatium. The latter alteration may contribute to the decreased response of parkinsonian patients to chronic levodopa therapy. (author)

  16. [Scans without Evidence of Dopamine Deficit (SWEDDs)].

    Science.gov (United States)

    Mukai, Yohei; Murata, Miho

    2016-01-01

    Dopamine transporter (DaT) single-photon emission computed tomography (SPECT) and [18F]fluoro-L-DOPA ([18F]DOPA) positron emission tomography (PET) facilitate the investigation of dopaminergic hypofunction in neurodegenerative diseases. DaT SPECT and [18F]DOPA PET have been adopted as survey tools in clinical trials. In a large study on Parkinson's disease, 4-15% of subjects clinically diagnosed with early-stage Parkinson's disease had normal dopaminergic functional imaging scans. These are called Scans without Evidence of Dopamine Deficit (SWEDDs), and are considered to represent a state different from Parkinson's disease. Neurological diseases that exhibit parkinsonism and have normal dopaminergic cells in the nigrostriatal system (e.g., essential tremor, psychogenic parkinsonism, DOPA-responsive dystonia, vascular parkinsonism, drug-induced parkinsonism, manganism, brain tumor, myoclonus-dystonia (DYT11), and fragile X syndrome) might be diagnosed with SWEDDs. True bradykinesia with fatigue or decrement may be useful for distinguishing between Parkinson's disease and SWEDDs. However, because SWEDDs encompass many diseases, their properties may not be uniform. In this review, we discuss DaT SPECT, the concept of SWEDDs, and differential diagnosis.

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

    Science.gov (United States)

    Song, Tao; Xu, Jinbang; Pan, Linqiang

    2015-12-01

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

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

    African Journals Online (AJOL)

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

  19. Genetically determined interaction between the dopamine transporter and the D2 receptor on prefronto-striatal activity and volume in humans.

    Science.gov (United States)

    Bertolino, Alessandro; Fazio, Leonardo; Di Giorgio, Annabella; Blasi, Giuseppe; Romano, Raffaella; Taurisano, Paolo; Caforio, Grazia; Sinibaldi, Lorenzo; Ursini, Gianluca; Popolizio, Teresa; Tirotta, Emanuele; Papp, Audrey; Dallapiccola, Bruno; Borrelli, Emiliana; Sadee, Wolfgang

    2009-01-28

    Dopamine modulation of neuronal activity during memory tasks identifies a nonlinear inverted-U shaped function. Both the dopamine transporter (DAT) and dopamine D(2) receptors (encoded by DRD(2)) critically regulate dopamine signaling in the striatum and in prefrontal cortex during memory. Moreover, in vitro studies have demonstrated that DAT and D(2) proteins reciprocally regulate each other presynaptically. Therefore, we have evaluated the genetic interaction between a DRD(2) polymorphism (rs1076560) causing reduced presynaptic D(2) receptor expression and the DAT 3'-VNTR variant (affecting DAT expression) in a large sample of healthy subjects undergoing blood oxygenation level-dependent (BOLD)-functional magnetic resonance imaging (MRI) during memory tasks and structural MRI. Results indicated a significant DRD(2)/DAT interaction in prefrontal cortex and striatum BOLD activity during both working memory and encoding of recognition memory. The differential effect on BOLD activity of the DAT variant was mostly manifest in the context of the DRD(2) allele associated with lower presynaptic expression. Similar results were also evident for gray matter volume in caudate. These interactions describe a nonlinear relationship between compound genotypes and brain activity or gray matter volume. Complementary data from striatal protein extracts from wild-type and D(2) knock-out animals (D2R(-/-)) indicate that DAT and D(2) proteins interact in vivo. Together, our results demonstrate that the interaction between genetic variants in DRD(2) and DAT critically modulates the nonlinear relationship between dopamine and neuronal activity during memory processing.

  20. Striatal dopamine D1 and D2 receptors: widespread influences on methamphetamine-induced dopamine and serotonin neurotoxicity.

    Science.gov (United States)

    Gross, Noah B; Duncker, Patrick C; Marshall, John F

    2011-11-01

    Methamphetamine (mAMPH) is an addictive psychostimulant drug that releases monoamines through nonexocytotic mechanisms. In animals, binge mAMPH dosing regimens deplete markers for monoamine nerve terminals, for example, dopamine and serotonin transporters (DAT and SERT), in striatum and cerebral cortex. Although the precise mechanism of mAMPH-induced damage to monoaminergic nerve terminals is uncertain, both dopamine D1 and D2 receptors are known to be important. Systemic administration of dopamine D1 or D2 receptor antagonists to rodents prevents mAMPH-induced damage to striatal dopamine nerve terminals. Because these studies employed systemic antagonist administration, the specific brain regions involved remain to be elucidated. The present study examined the contribution of dopamine D1 and D2 receptors in striatum to mAMPH-induced DAT and SERT neurotoxicities. In this experiment, either the dopamine D1 antagonist, SCH23390, or the dopamine D2 receptor antagonist, sulpiride, was intrastriatally infused during a binge mAMPH regimen. Striatal DAT and cortical, hippocampal, and amygdalar SERT were assessed as markers of mAMPH-induced neurotoxicity 1 week following binge mAMPH administration. Blockade of striatal dopamine D1 or D2 receptors during an otherwise neurotoxic binge mAMPH regimen produced widespread protection against mAMPH-induced striatal DAT loss and cortical, hippocampal, and amygdalar SERT loss. This study demonstrates that (1) dopamine D1 and D2 receptors in striatum, like nigral D1 receptors, are needed for mAMPH-induced striatal DAT reductions, (2) these same receptors are needed for mAMPH-induced SERT loss, and (3) these widespread influences of striatal dopamine receptor antagonists are likely attributable to circuits connecting basal ganglia to thalamus and cortex. Copyright © 2011 Wiley-Liss, Inc.

  1. α2A- and α2C-Adrenoceptors as Potential Targets for Dopamine and Dopamine Receptor Ligands.

    Science.gov (United States)

    Sánchez-Soto, Marta; Casadó-Anguera, Verònica; Yano, Hideaki; Bender, Brian Joseph; Cai, Ning-Sheng; Moreno, Estefanía; Canela, Enric I; Cortés, Antoni; Meiler, Jens; Casadó, Vicent; Ferré, Sergi

    2018-03-18

    The poor norepinephrine innervation and high density of Gi/o-coupled α 2A - and α 2C -adrenoceptors in the striatum and the dense striatal dopamine innervation have prompted the possibility that dopamine could be an effective adrenoceptor ligand. Nevertheless, the reported adrenoceptor agonistic properties of dopamine are still inconclusive. In this study, we analyzed the binding of norepinephrine, dopamine, and several compounds reported as selective dopamine D 2 -like receptor ligands, such as the D 3 receptor agonist 7-OH-PIPAT and the D 4 receptor agonist RO-105824, to α 2 -adrenoceptors in cortical and striatal tissue, which express α 2A -adrenoceptors and both α 2A - and α 2C -adrenoceptors, respectively. The affinity of dopamine for α 2 -adrenoceptors was found to be similar to that for D 1 -like and D 2 -like receptors. Moreover, the exogenous dopamine receptor ligands also showed high affinity for α 2A - and α 2C -adrenoceptors. Their ability to activate Gi/o proteins through α 2A - and α 2C -adrenoceptors was also analyzed in transfected cells with bioluminescent resonance energy transfer techniques. The relative ligand potencies and efficacies were dependent on the Gi/o protein subtype. Furthermore, dopamine binding to α 2 -adrenoceptors was functional, inducing changes in dynamic mass redistribution, adenylyl cyclase activity, and ERK1/2 phosphorylation. Binding events were further studied with computer modeling of ligand docking. Docking of dopamine at α 2A - and α 2C -adrenoceptors was nearly identical to its binding to the crystallized D 3 receptor. Therefore, we provide conclusive evidence that α 2A - and α 2C -adrenoceptors are functional receptors for norepinephrine, dopamine, and other previously assumed selective D 2 -like receptor ligands, which calls for revisiting previous studies with those ligands.

  2. Graphitic carbon nitride nanosheets doped graphene oxide for electrochemical simultaneous determination of ascorbic acid, dopamine and uric acid

    International Nuclear Information System (INIS)

    Zhang, Hanqiang; Huang, Qitong; Huang, Yihong; Li, Feiming; Zhang, Wuxiang; Wei, Chan; Chen, Jianhua; Dai, Pingwang; Huang, Lizhang; Huang, Zhouyi; Kang, Lianping; Hu, Shirong; Hao, Aiyou

    2014-01-01

    Graphical abstract: Schematic drawing of electrochemical oxidize AA, DA and UA on graphitic carbon nitride nanosheets-graphene oxide composite modified electrode. - Highlights: • Synthesize g-C 3 N 4 , GO and CNNS-GO composite. • CNNS-GO composite was the first time for simultaneous determination of AA, DA and UA. • CNNS-GO/GCE displays fantastic selectivity and sensitivity for AA, DA and UA. • CNNS-GO/GCE was applied to detect real sample with satisfactory results. - Abstract: Graphitic carbon nitride nanosheets with a graphite-like structure have strong covalent bonds between carbon and nitride atoms, and nitrogen atoms in the carbon architecture can accelerate the electron transfer and enhance electrical properties effectually. The graphitic carbon nitride nanosheets-graphene oxide composite was synthesized. And the electrochemical performance of the composite was investigated by cyclic voltammetry and differential pulse voltammetry ulteriorly. Due to the synergistic effects of layer-by-layer structures by π-π stacking or charge-transfer interactions, graphitic carbon nitride nanosheets-graphene oxide composite can improved conductivity, electro-catalytic and selective oxidation performance. The proposed graphitic carbon nitride nanosheets-graphene oxide composite modified electrode was employed for simultaneous determination of ascorbic acid, dopamine and uric acid in their mixture solution, it exhibited distinguished sensitivity, wide linear range and low detection limit. Moreover, the modified electrode was applied to detect urine and dopamine injection sample, and then the samples were spiked with certain concentration of three substances with satisfactory recovery results

  3. Measures of spike train synchrony for data with multiple time scales

    NARCIS (Netherlands)

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

    2017-01-01

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

  4. Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

    NARCIS (Netherlands)

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

    2016-01-01

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

  5. The role of dopamine receptors in the neurotoxicity of methamphetamine.

    Science.gov (United States)

    Ares-Santos, S; Granado, N; Moratalla, R

    2013-05-01

    Methamphetamine is a synthetic drug consumed by millions of users despite its neurotoxic effects in the brain, leading to loss of dopaminergic fibres and cell bodies. Moreover, clinical reports suggest that methamphetamine abusers are predisposed to Parkinson's disease. Therefore, it is important to elucidate the mechanisms involved in methamphetamine-induced neurotoxicity. Dopamine receptors may be a plausible target to prevent this neurotoxicity. Genetic inactivation of dopamine D1 or D2 receptors protects against the loss of dopaminergic fibres in the striatum and loss of dopaminergic neurons in the substantia nigra. Protection by D1 receptor inactivation is due to blockade of hypothermia, reduced dopamine content and turnover and increased stored vesicular dopamine in D1R(-/-) mice. However, the neuroprotective impact of D2 receptor inactivation is partially dependent on an effect on body temperature, as well as on the blockade of dopamine reuptake by decreased dopamine transporter activity, which results in reduced intracytosolic dopamine levels in D2R(-/-) mice. © 2013 The Association for the Publication of the Journal of Internal Medicine.

  6. The neurotropic parasite Toxoplasma gondii increases dopamine metabolism.

    Directory of Open Access Journals (Sweden)

    Emese Prandovszky

    Full Text Available The highly prevalent parasite Toxoplasma gondii manipulates its host's behavior. In infected rodents, the behavioral changes increase the likelihood that the parasite will be transmitted back to its definitive cat host, an essential step in completion of the parasite's life cycle. The mechanism(s responsible for behavioral changes in the host is unknown but two lines of published evidence suggest that the parasite alters neurotransmitter signal transduction: the disruption of the parasite-induced behavioral changes with medications used to treat psychiatric disease (specifically dopamine antagonists and identification of a tyrosine hydroxylase encoded in the parasite genome. In this study, infection of mammalian dopaminergic cells with T. gondii enhanced the levels of K+-induced release of dopamine several-fold, with a direct correlation between the number of infected cells and the quantity of dopamine released. Immunostaining brain sections of infected mice with dopamine antibody showed intense staining of encysted parasites. Based on these analyses, T. gondii orchestrates a significant increase in dopamine metabolism in neural cells. Tyrosine hydroxylase, the rate-limiting enzyme for dopamine synthesis, was also found in intracellular tissue cysts in brain tissue with antibodies specific for the parasite-encoded tyrosine hydroxylase. These observations provide a mechanism for parasite-induced behavioral changes. The observed effects on dopamine metabolism could also be relevant in interpreting reports of psychobehavioral changes in toxoplasmosis-infected humans.

  7. Dopamine induces soluble α-synuclein oligomers and nigrostriatal degeneration.

    Science.gov (United States)

    Mor, Danielle E; Tsika, Elpida; Mazzulli, Joseph R; Gould, Neal S; Kim, Hanna; Daniels, Malcolm J; Doshi, Shachee; Gupta, Preetika; Grossman, Jennifer L; Tan, Victor X; Kalb, Robert G; Caldwell, Kim A; Caldwell, Guy A; Wolfe, John H; Ischiropoulos, Harry

    2017-11-01

    Parkinson's disease (PD) is defined by the loss of dopaminergic neurons in the substantia nigra and the formation of Lewy body inclusions containing aggregated α-synuclein. Efforts to explain dopamine neuron vulnerability are hindered by the lack of dopaminergic cell death in α-synuclein transgenic mice. To address this, we manipulated both dopamine levels and α-synuclein expression. Nigrally targeted expression of mutant tyrosine hydroxylase with enhanced catalytic activity increased dopamine levels without damaging neurons in non-transgenic mice. In contrast, raising dopamine levels in mice expressing human A53T mutant α-synuclein induced progressive nigrostriatal degeneration and reduced locomotion. Dopamine elevation in A53T mice increased levels of potentially toxic α-synuclein oligomers, resulting in conformationally and functionally modified species. Moreover, in genetically tractable Caenorhabditis elegans models, expression of α-synuclein mutated at the site of interaction with dopamine prevented dopamine-induced toxicity. These data suggest that a unique mechanism links two cardinal features of PD: dopaminergic cell death and α-synuclein aggregation.

  8. Noncovalent Interactions between Dopamine and Regular and Defective Graphene.

    Science.gov (United States)

    Fernández, Ana C Rossi; Castellani, Norberto J

    2017-08-05

    The role of noncovalent interactions in the adsorption of biological molecules on graphene is a subject of fundamental interest regarding the use of graphene as a material for sensing and drug delivery. The adsorption of dopamine on regular graphene and graphene with monovacancies (GV) is theoretically studied within the framework of density functional theory. Several adsorption modes are considered, and notably those in which the dopamine molecule is oriented parallel or quasi-parallel to the surface are the more stable. The adsorption of dopamine on graphene implies an attractive interaction of a dispersive nature that competes with Pauli repulsion between the occupied π orbitals of the dopamine ring and the π orbitals of graphene. If dopamine adsorbs at the monovacancy in the A-B stacking mode, a hydrogen bond is produced between one of the dopamine hydroxy groups and one carbon atom around the vacancy. The electronic charge redistribution due to adsorption is consistent with an electronic drift from the graphene or GV surface to the dopamine molecule. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  9. Dopamine modulates metabolic rate and temperature sensitivity in Drosophila melanogaster.

    Directory of Open Access Journals (Sweden)

    Taro Ueno

    Full Text Available Homeothermal animals, such as mammals, maintain their body temperature by heat generation and heat dissipation, while poikilothermal animals, such as insects, accomplish it by relocating to an environment of their favored temperature. Catecholamines are known to regulate thermogenesis and metabolic rate in mammals, but their roles in other animals are poorly understood. The fruit fly, Drosophila melanogaster, has been used as a model system for the genetic studies of temperature preference behavior. Here, we demonstrate that metabolic rate and temperature sensitivity of some temperature sensitive behaviors are regulated by dopamine in Drosophila. Temperature-sensitive molecules like dTrpA1 and shi(ts induce temperature-dependent behavioral changes, and the temperature at which the changes are induced were lowered in the dopamine transporter-defective mutant, fumin. The mutant also displays a preference for lower temperatures. This thermophobic phenotype was rescued by the genetic recovery of the dopamine transporter in dopamine neurons. Flies fed with a dopamine biosynthesis inhibitor (3-iodo-L-tyrosine, which diminishes dopamine signaling, exhibited preference for a higher temperature. Furthermore, we found that the metabolic rate is up-regulated in the fumin mutant. Taken together, dopamine has functions in the temperature sensitivity of behavioral changes and metabolic rate regulation in Drosophila, as well as its previously reported functions in arousal/sleep regulation.

  10. Dopamine agonist withdrawal syndrome: implications for patient care.

    Science.gov (United States)

    Nirenberg, Melissa J

    2013-08-01

    Dopamine agonists are effective treatments for a variety of indications, including Parkinson's disease and restless legs syndrome, but may have serious side effects, such as orthostatic hypotension, hallucinations, and impulse control disorders (including pathological gambling, compulsive eating, compulsive shopping/buying, and hypersexuality). The most effective way to alleviate these side effects is to taper or discontinue dopamine agonist therapy. A subset of patients who taper a dopamine agonist, however, develop dopamine agonist withdrawal syndrome (DAWS), which has been defined as a severe, stereotyped cluster of physical and psychological symptoms that correlate with dopamine agonist withdrawal in a dose-dependent manner, cause clinically significant distress or social/occupational dysfunction, are refractory to levodopa and other dopaminergic medications, and cannot be accounted for by other clinical factors. The symptoms of DAWS include anxiety, panic attacks, dysphoria, depression, agitation, irritability, suicidal ideation, fatigue, orthostatic hypotension, nausea, vomiting, diaphoresis, generalized pain, and drug cravings. The severity and prognosis of DAWS is highly variable. While some patients have transient symptoms and make a full recovery, others have a protracted withdrawal syndrome lasting for months to years, and therefore may be unwilling or unable to discontinue DA therapy. Impulse control disorders appear to be a major risk factor for DAWS, and are present in virtually all affected patients. Thus, patients who are unable to discontinue dopamine agonist therapy may experience chronic impulse control disorders. At the current time, there are no known effective treatments for DAWS. For this reason, providers are urged to use dopamine agonists judiciously, warn patients about the risks of DAWS prior to the initiation of dopamine agonist therapy, and follow patients closely for withdrawal symptoms during dopamine agonist taper.

  11. Spikes matter for phase-locked bursting in inhibitory neurons

    Science.gov (United States)

    Jalil, Sajiya; Belykh, Igor; Shilnikov, Andrey

    2012-03-01

    We show that inhibitory networks composed of two endogenously bursting neurons can robustly display several coexistent phase-locked states in addition to stable antiphase and in-phase bursting. This work complements and enhances our recent result [Jalil, Belykh, and Shilnikov, Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.81.045201 81, 045201(R) (2010)] that fast reciprocal inhibition can synchronize bursting neurons due to spike interactions. We reveal the role of spikes in generating multiple phase-locked states and demonstrate that this multistability is generic by analyzing diverse models of bursting networks with various fast inhibitory synapses; the individual cell models include the reduced leech heart interneuron, the Sherman model for pancreatic beta cells, and the Purkinje neuron model.

  12. Reflex reading epilepsy: effect of linguistic characteristics on spike frequency.

    Science.gov (United States)

    Safi, Dima; Lassonde, Maryse; Nguyen, Dang Khoa; Denault, Carole; Macoir, Joël; Rouleau, Isabelle; Béland, Renée

    2011-04-01

    Reading epilepsy is a rare reflex epilepsy in which seizures are provoked by reading. Several cases have been described in the literature, but the pathophysiological processes vary widely and remain unclear. We describe a 42-year-old male patient with reading epilepsy evaluated using clinical assessments and continuous video/EEG recordings. We administered verbal, nonverbal, and reading tasks to determine factors precipitating seizures. Linguistic characteristics of the words were manipulated. Results indicated that reading-induced seizures were significantly more numerous than those observed during verbal and nonverbal tasks. In reading tasks, spike frequency significantly increased with involvement of the phonological reading route. Spikes were recorded predominantly in left parasagittal regions. Future cerebral imaging studies will enable us to visualize the spatial localization and temporal course of reading-induced seizures and brain activity involved in reading. A better understanding of reading epilepsy is crucial for reading rehabilitation in these patients. Copyright © 2011 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

    Ročková, Veronika; George, Edward I

    2014-08-01

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

  14. Adrenalectomy eliminates the extinction spike in autoshaping with rats.

    Science.gov (United States)

    Thomas, B L; Papini, M R

    2001-03-01

    Experiment 1, using rats, investigated the effect of adrenalectomy (ADX) on the invigoration of lever-contact performance that occurs in the autoshaping situation after a shift from acquisition to extinction (called the extinction spike). Groups of rats with ADX or sham operations were trained under spaced and massed conditions [average intertrial intervals (ITI) of either 15 or 90 s] for 10 sessions and then shifted to extinction. ADX did not affect acquisition training but it eliminated the extinction spike. Plasma corticosterone levels during acquisition were shown in Experiment 2 to be similar in rats trained under spaced or massed conditions. Adrenal participation in the emotional arousal induced by conditions of surprising nonreward (e.g., extinction) is discussed.

  15. Method for spiking soil samples with organic compounds

    DEFF Research Database (Denmark)

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

    2002-01-01

    We examined the harmful side effects on indigenous soil microorganisms of two organic solvents, acetone and dichloromethane, that are normally used for spiking of soil with polycyclic aromatic hydrocarbons for experimental purposes. The solvents were applied in two contamination protocols to either...... higher than in control soil, probably due mainly to release of predation from indigenous protozoa. In order to minimize solvent effects on indigenous soil microorganisms when spiking native soil samples with compounds having a low water solubility, we propose a common protocol in which the contaminant...... tagged with luxAB::Tn5. For both solvents, application to the whole sample resulted in severe side effects on both indigenous protozoa and bacteria. Application of dichloromethane to the whole soil volume immediately reduced the number of protozoa to below the detection limit. In one of the soils...

  16. Voltage spike detection in high field superconducting accelerator magnets

    Energy Technology Data Exchange (ETDEWEB)

    Orris, D.F.; Carcagno, R.; Feher, S.; Makulski, A.; Pischalnikov, Y.M.; /Fermilab

    2004-12-01

    A measurement system for the detection of small magnetic flux changes in superconducting magnets, which are due to either mechanical motion of the conductor or flux jump, has been developed at Fermilab. These flux changes are detected as small amplitude, short duration voltage spikes, which are {approx}15mV in magnitude and lasts for {approx}30 {micro}sec. The detection system combines an analog circuit for the signal conditioning of two coil segments and a fast data acquisition system for digitizing the results, performing threshold detection, and storing the resultant data. The design of the spike detection system along with the modeling results and noise analysis will be presented. Data from tests of high field Nb{sub 3}Sn magnets at currents up to {approx}20KA will also be shown.

  17. Voltage spike detection in high field superconducting accelerator magnets

    International Nuclear Information System (INIS)

    Orris, D.F.; Carcagno, R.; Feher, S.; Makulski, A.; Pischalnikov, Y.M.

    2004-01-01

    A measurement system for the detection of small magnetic flux changes in superconducting magnets, which are due to either mechanical motion of the conductor or flux jump, has been developed at Fermilab. These flux changes are detected as small amplitude, short duration voltage spikes, which are ∼15mV in magnitude and lasts for ∼30(micro)sec. The detection system combines an analog circuit for the signal conditioning of two coil segments and a fast data acquisition system for digitizing the results, performing threshold detection, and storing the resultant data. The design of the spike detection system along with the modeling results and noise analysis will be presented. Data from tests of high field Nb3Sn magnets at currents up to ∼20KA will also be shown

  18. Past, present and future of spike sorting techniques.

    Science.gov (United States)

    Rey, Hernan Gonzalo; Pedreira, Carlos; Quian Quiroga, Rodrigo

    2015-10-01

    Spike sorting is a crucial step to extract information from extracellular recordings. With new recording opportunities provided by the development of new electrodes that allow monitoring hundreds of neurons simultaneously, the scenario for the new generation of algorithms is both exciting and challenging. However, this will require a new approach to the problem and the development of a common reference framework to quickly assess the performance of new algorithms. In this work, we review the basic concepts of spike sorting, including the requirements for different applications, together with the problems faced by presently available algorithms. We conclude by proposing a roadmap stressing the crucial points to be addressed to support the neuroscientific research of the near future. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

    Lengler, Johannes; Steger, Angelika

    2017-08-01

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

  20. Analysis of voltage spikes in superconducting Nb3Sn magnets

    International Nuclear Information System (INIS)

    Rahimzadeh-Kalaleh, S.; Ambrosio, G.; Chlachidze, G.; Donnelly, C.

    2008-01-01

    Fermi National Accelerator Laboratory has been developing a new generation of superconducting accelerator magnets based on Niobium Tin (Nb 3 Sn). The performance of these magnets is influenced by thermo-magnetic instabilities, known as flux jumps, which can lead to premature trips of the quench detection system due to large voltage transients or quenches at low current. In an effort to better characterize and understand these instabilities, a system for capturing fast voltage transients was developed and used in recent tests of R and D model magnets. A new automated voltage spike analysis program was developed for the analysis of large amount of voltage-spike data. We report results from the analysis of large statistics data samples for short model magnets that were constructed using MJR and RRP strands having different sub-element size and structure. We then assess the implications for quench protection of Nb 3 Sn magnets

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

    Directory of Open Access Journals (Sweden)

    E. D. Belousova

    2012-01-01

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

  2. Supervised learning in spiking neural networks with FORCE training.

    Science.gov (United States)

    Nicola, Wilten; Clopath, Claudia

    2017-12-20

    Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.

  3. The Ripple Pond: Enabling Spiking Networks to See

    Directory of Open Access Journals (Sweden)

    Saeed eAfshar

    2013-11-01

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

  4. The ripple pond: enabling spiking networks to see.

    Science.gov (United States)

    Afshar, Saeed; Cohen, Gregory K; Wang, Runchun M; Van Schaik, André; Tapson, Jonathan; Lehmann, Torsten; Hamilton, Tara J

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Andreas Stöckel

    2017-08-01

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

  6. Planning Annuaulised hours when spike in demand exists

    Directory of Open Access Journals (Sweden)

    MR Sureshkumar

    2012-04-01

    Full Text Available Manpower planning using annualised hours is an effective tool where seasonal demand for staff in industry exists. In annualised hours (AH workers are contracted to work for a certain number of hours per year. The workers are associated with relative efficiency for different types of tasks. This paper proposes a Mixed Integer linear Programming (MILP model to solve an annualised working hours planning problem when spike in demand exists. The holiday weeks for the workers are considered as partially individualised. If a worker has been assigned with more than one type of working week in a week, this will be compensated with one or more holiday week. The performance of the model is demonstrated with an example. It can be seen that this type of modelling helps to meet the spikes in demand with less capacity shortage compared with one working week in a week.

  7. Asymptotics of empirical eigenstructure for high dimensional spiked covariance.

    Science.gov (United States)

    Wang, Weichen; Fan, Jianqing

    2017-06-01

    We derive the asymptotic distributions of the spiked eigenvalues and eigenvectors under a generalized and unified asymptotic regime, which takes into account the magnitude of spiked eigenvalues, sample size, and dimensionality. This regime allows high dimensionality and diverging eigenvalues and provides new insights into the roles that the leading eigenvalues, sample size, and dimensionality play in principal component analysis. Our results are a natural extension of those in Paul (2007) to a more general setting and solve the rates of convergence problems in Shen et al. (2013). They also reveal the biases of estimating leading eigenvalues and eigenvectors by using principal component analysis, and lead to a new covariance estimator for the approximate factor model, called shrinkage principal orthogonal complement thresholding (S-POET), that corrects the biases. Our results are successfully applied to outstanding problems in estimation of risks of large portfolios and false discovery proportions for dependent test statistics and are illustrated by simulation studies.

  8. Supervised learning with decision margins in pools of spiking neurons.

    Science.gov (United States)

    Le Mouel, Charlotte; Harris, Kenneth D; Yger, Pierre

    2014-10-01

    Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such "supervised learning", using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.

  9. Archeomagnetic Intensity Spikes: Global or Regional Geomagnetic Field Features?

    Directory of Open Access Journals (Sweden)

    Monika Korte

    2018-03-01

    Full Text Available Variations of the geomagnetic field prior to direct observations are inferred from archeo- and paleomagnetic experiments. Seemingly unusual variations not seen in the present-day and historical field are of particular interest to constrain the full range of core dynamics. Recently, archeomagnetic intensity spikes, characterized by very high field values that appear to be associated with rapid secular variation rates, have been reported from several parts of the world. They were first noted in data from the Levant at around 900 BCE. A recent re-assessment of previous and new Levantine data, involving a rigorous quality assessment, interprets the observations as an extreme local geomagnetic high with at least two intensity spikes between the 11th and 8th centuries BCE. Subsequent reports of similar features from Asia, the Canary Islands and Texas raise the question of whether such features might be common occurrences, or whether they might even be part of a global magnetic field feature. Here we use spherical harmonic modeling to test two hypotheses: firstly, whether the Levantine and other potential spikes might be associated with higher dipole field intensity than shown by existing global field models around 1,000 BCE, and secondly, whether the observations from different parts of the world are compatible with a westward drifting intense flux patch. Our results suggest that the spikes originate from intense flux patches growing and decaying mostly in situ, combined with stronger and more variable dipole moment than shown by previous global field models. Axial dipole variations no more than 60% higher than observed in the present field, probably within the range of normal geodynamo behavior, seem sufficient to explain the observations.

  10. Discriminating Sea Spikes in Incoherent Radar Measurements of Sea Clutter

    Science.gov (United States)

    2008-03-01

    het detecteren echter niet te verwachten dat bet gebruik van sea spikes te onderzoeken. Een van deze modellen zal leiden tot een Auteur (s) dergelijk...report I TNO-DV 2008 A067 6/33 Abbreviations CFAR Constant False-Alarm Rate CST Composite Surface Theory FFT Fast Fourier Transform PDF Probability Density...described by the composite surface theory (CST). This theory describes the sea surface as small Bragg-resonant capillary waves riding on top of

  11. Simulating large-scale spiking neuronal networks with NEST

    OpenAIRE

    Schücker, Jannis; Eppler, Jochen Martin

    2014-01-01

    The Neural Simulation Tool NEST [1, www.nest-simulator.org] is the simulator for spiking neural networkmodels of the HBP that focuses on the dynamics, size and structure of neural systems rather than on theexact morphology of individual neurons. Its simulation kernel is written in C++ and it runs on computinghardware ranging from simple laptops to clusters and supercomputers with thousands of processor cores.The development of NEST is coordinated by the NEST Initiative [www.nest-initiative.or...

  12. Dopamine does double duty in motivating cognitive effort

    Science.gov (United States)

    Westbrook, Andrew; Braver, Todd S.

    2015-01-01

    Cognitive control is subjectively costly, suggesting that engagement is modulated in relationship to incentive state. Dopamine appears to play key roles. In particular, dopamine may mediate cognitive effort by two broad classes of functions: 1) modulating the functional parameters of working memory circuits subserving effortful cognition, and 2) mediating value-learning and decision-making about effortful cognitive action. Here we tie together these two lines of research, proposing how dopamine serves “double duty”, translating incentive information into cognitive motivation. PMID:26889810

  13. Elevated Striatal Dopamine Function in Immigrants and Their Children: A Risk Mechanism for Psychosis

    OpenAIRE

    Egerton, A.; Howes, O. D.; Houle, S.; McKenzie, K.; Valmaggia, L. R.; Bagby, M. R.; Tseng, H-H; Bloomfield, M. A. P.; Kenk, M.; Bhattacharyya, S.; Suridjan, I.; Chaddock, C. A.; Winton-Brown, T. T.; Allen, P.; Rusjan, P.

    2017-01-01

    Migration is a major risk factor for schizophrenia but the neurochemical processes involved are unknown. One candidate mechanism is through elevations in striatal dopamine synthesis and release. The objective of this research was to determine whether striatal dopamine function is elevated in immigrants compared to nonimmigrants and the relationship with psychosis. Two complementary case–control studies of in vivo dopamine function (stress-induced dopamine release and dopamine synthesis capaci...

  14. Dopamine synthesis and dopamine receptor expression are disturbed in recurrent miscarriages.

    Science.gov (United States)

    Gratz, Michael J; Stavrou, Stavroula; Kuhn, Christina; Hofmann, Simone; Hermelink, Kerstin; Heidegger, Helene; Hutter, Stefan; Mayr, Doris; Mahner, Sven; Jeschke, Udo; Vattai, Aurelia

    2018-05-01

    l-dopa decarboxylase (DDC) is responsible for the synthesis of dopamine. Dopamine, which binds to the D 2 -dopamine receptor (D2R), plays an important role in the maintenance of pregnancy. Aim of our study was the analysis of DDC and D2R expression in placentas of spontaneous miscarriages (SMs) and recurrent miscarriages (RMs) in comparison to healthy controls. Patients with SM (n = 15) and RM (n = 15) were compared with patients from healthy pregnancies (n = 15) (pregnancy weeks 7-13 each). Placental tissue has been collected from SMs and RMs from the first trimester (Department of Gynaecology and Obstetrics, LMU Munich) and from abruptions (private practice, Munich). Placental cell lines, BeWo- and JEG-3 cells, were stimulated with the trace amines T 0 AM and T 1 AM in vitro . Levels of DDC and D2R in trophoblasts and the decidua were lower in RMs in comparison to healthy controls. Stimulation of BeWo cells with T 1 AM significantly reduced DDC mRNA and protein levels. Via double-immunofluorescence, a DDC-positive cell type beneath decidual stromal cells and foetal EVT in the decidua could be detected. Downregulation of DDC and D2R in trophoblasts of RMs reflects a reduced signal cascade of catecholamines on the foetal side. © 2018 The authors.

  15. Poisson-Like Spiking in Circuits with Probabilistic Synapses

    Science.gov (United States)

    Moreno-Bote, Rubén

    2014-01-01

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

  16. Emergent properties of interacting populations of spiking neurons

    Directory of Open Access Journals (Sweden)

    Stefano eCardanobile

    2011-12-01

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

  17. Emergent properties of interacting populations of spiking neurons.

    Science.gov (United States)

    Cardanobile, Stefano; Rotter, Stefan

    2011-01-01

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

  18. Efficient Architecture for Spike Sorting in Reconfigurable Hardware

    Directory of Open Access Journals (Sweden)

    Sheng-Ying Lai

    2013-11-01

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

  19. Efficient computation in networks of spiking neurons: simulations and theory

    International Nuclear Information System (INIS)

    Natschlaeger, T.

    1999-01-01

    One of the most prominent features of biological neural systems is that individual neurons communicate via short electrical pulses, the so called action potentials or spikes. In this thesis we investigate possible mechanisms which can in principle explain how complex computations in spiking neural networks (SNN) can be performed very fast, i.e. within a few 10 milliseconds. Some of these models are based on the assumption that relevant information is encoded by the timing of individual spikes (temporal coding). We will also discuss a model which is based on a population code and still is able to perform fast complex computations. In their natural environment biological neural systems have to process signals with a rich temporal structure. Hence it is an interesting question how neural systems process time series. In this context we explore possible links between biophysical characteristics of single neurons (refractory behavior, connectivity, time course of postsynaptic potentials) and synapses (unreliability, dynamics) on the one hand and possible computations on times series on the other hand. Furthermore we describe a general model of computation that exploits dynamic synapses. This model provides a general framework for understanding how neural systems process time-varying signals. (author)

  20. Efficient Architecture for Spike Sorting in Reconfigurable Hardware

    Science.gov (United States)

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

    2013-01-01

    This paper presents a novel hardware architecture for fast spike sorting. The architecture is able to perform both the feature extraction and clustering in hardware. The generalized Hebbian algorithm (GHA) and fuzzy C-means (FCM) algorithm are used for feature extraction and clustering, respectively. The employment of GHA allows efficient computation of principal components for subsequent clustering operations. The FCM is able to achieve near optimal clustering for spike sorting. Its performance is insensitive to the selection of initial cluster centers. The hardware implementations of GHA and FCM feature low area costs and high throughput. In the GHA architecture, the computation of different weight vectors share the same circuit for lowering the area costs. Moreover, in the FCM hardware implementation, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. To show the effectiveness of the circuit, the proposed architecture is physically implemented by field programmable gate array (FPGA). It is embedded in a System-on-Chip (SOC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient spike sorting design for attaining high classification correct rate and high speed computation. PMID:24189331

  1. Spike: Artificial intelligence scheduling for Hubble space telescope

    Science.gov (United States)

    Johnston, Mark; Miller, Glenn; Sponsler, Jeff; Vick, Shon; Jackson, Robert

    1990-01-01

    Efficient utilization of spacecraft resources is essential, but the accompanying scheduling problems are often computationally intractable and are difficult to approximate because of the presence of numerous interacting constraints. Artificial intelligence techniques were applied to the scheduling of the NASA/ESA Hubble Space Telescope (HST). This presents a particularly challenging problem since a yearlong observing program can contain some tens of thousands of exposures which are subject to a large number of scientific, operational, spacecraft, and environmental constraints. New techniques were developed for machine reasoning about scheduling constraints and goals, especially in cases where uncertainty is an important scheduling consideration and where resolving conflicts among conflicting preferences is essential. These technique were utilized in a set of workstation based scheduling tools (Spike) for HST. Graphical displays of activities, constraints, and schedules are an important feature of the system. High level scheduling strategies using both rule based and neural network approaches were developed. While the specific constraints implemented are those most relevant to HST, the framework developed is far more general and could easily handle other kinds of scheduling problems. The concept and implementation of the Spike system are described along with some experiments in adapting Spike to other spacecraft scheduling domains.

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

    Directory of Open Access Journals (Sweden)

    Karim El-Laithy

    2011-01-01

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

  3. Enhanced polychronisation in a spiking network with metaplasticity

    Directory of Open Access Journals (Sweden)

    Mira eGuise

    2015-02-01

    Full Text Available Computational models of metaplasticity have usually focused on the modeling of single synapses (Shouval et al., 2002. In this paper we study the effect of metaplasticity on network behavior. Our guiding assumption is that the primary purpose of metaplasticity is to regulate synaptic plasticity, by increasing it when input is low and decreasing it when input is high. For our experiments we adopt a model of metaplasticity that demonstrably has this effect for a single synapse; our primary interest is in how metaplasticity thus defined affects network-level phenomena. We focus on a network-level phenomenon called polychronicity, that has a potential role in representation and memory. A network with polychronicity has the ability to produce non-synchronous but precisely timed sequences of neural firing events that can arise from strongly connected groups of neurons called polychronous neural groups (Izhikevich et al., 2004; Izhikevich, 2006a. Polychronous groups (PNGs develop readily when spiking networks are exposed to repeated spatio-temporal stimuli under the influence of spike-timing-dependent plasticity (STDP, but are sensitive to changes in synaptic weight distribution. We use a technique we have recently developed called Response Fingerprinting to show that PNGs formed in the presence of metaplasticity are significantly larger than those with no metaplasticity. A potential mechanism for this enhancement is proposed that links an inherent property of integrator type neurons called spike latency to an increase in the tolerance of PNG neurons to jitter in their inputs.

  4. Stochastic models for spike trains of single neurons

    CERN Document Server

    Sampath, G

    1977-01-01

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

  5. The Use Of Spikes Protocol In Cancer: An Integrative Review

    Directory of Open Access Journals (Sweden)

    Fernando Henrique de Sousa

    2017-03-01

    Full Text Available This is an integrative review which aimed to evaluate the use of the SPIKES protocol in Oncology. We selected articles published in Medline and CINAHL databases between 2005-2015, in English, with the descriptors defined by the Medical Subject Headings (MeSH:cancer, neoplasms, plus the uncontrolled descriptor: protocol spikes.  Six articles met the inclusion criteria and were analyzed in full, three thematic categories were established: aspects inherent to the health care professional; Aspects related to the patient and aspects related to the protocol. The main effects of the steps of SPIKES protocol can provide the strengthening of ties between health professionals and patients, and ensure the maintenance and quality of this relationship.  The results indicate an important limiting factor for effective doctor-patient relationship, the little training provided to medical professionals communication of bad news, verified by the difficulty reported in this moment through interviews in the analyzed studies.

  6. Economic impact on the Florida economy of energy price spikes

    International Nuclear Information System (INIS)

    Mory, J.F.

    1992-01-01

    A substantial disturbance in oil supplies is likely to generate a large price upsurge and a downturn in the level of economic activity. Each of these two effects diminishes demand by a certain amount. The specific price surge required to reduce demand to the lower level of supply can be calculated with an oil demand function and with empirical estimations of the association between price spikes and declines in economic activity. The first section presents an energy demand model for Florida, which provides the price and income elasticities needed. The second section includes theoretical explanations and empirical estimations of the relationship between price spikes and recessions. Based on historical evidence, it seems that Florida's and the nation's economic systems are very sensitive to oil price surges. As price spikes appear damaging to the economy, it could be expected that reductions in the price of oil are beneficial to the system. That is likely to be the case in the long run, but no empirical evidence of favorable short-term effects of oil price decreases was found. Several possible explanations and theoretical reasons are offered to explain this lack of association. The final section presents estimates of the effect of oil disruptions upon specific industries in Florida and the nation

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

    Science.gov (United States)

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

    2016-10-01

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

  8. Dopamine signaling in reward-related behaviors.

    Science.gov (United States)

    Baik, Ja-Hyun

    2013-01-01

    Dopamine (DA) regulates emotional and motivational behavior through the mesolimbic dopaminergic pathway. Changes in DA mesolimbic neurotransmission have been found to modify behavioral responses to various environmental stimuli associated with reward behaviors. Psychostimulants, drugs of abuse, and natural reward such as food can cause substantial synaptic modifications to the mesolimbic DA system. Recent studies using optogenetics and DREADDs, together with neuron-specific or circuit-specific genetic manipulations have improved our understanding of DA signaling in the reward circuit, and provided a means to identify the neural substrates of complex behaviors such as drug addiction and eating disorders. This review focuses on the role of the DA system in drug addiction and food motivation, with an overview of the role of D1 and D2 receptors in the control of reward-associated behaviors.

  9. Role of Dopamine Signaling in Drug Addiction.

    Science.gov (United States)

    Chen, Wan; Nong, Zhihuan; Li, Yaoxuan; Huang, Jianping; Chen, Chunxia; Huang, Luying

    2017-01-01

    Addiction is a chronic, relapsing disease of the brain that includes drug-induced compulsive seeking behavior and consumption of drugs. Dopamine (DA) is considered to be critical in drug addiction due to reward mechanisms in the midbrain. In this article, we review the major animal models in addictive drug experiments in vivo and in vitro. We discuss the relevance of the structure and pharmacological function of DA receptors. To improve the understanding of the role of DA receptors in reward pathways, specific brain regions, including the Ventral tegmental area, Nucleus accumbens, Prefrontal cortex, and Habenula, are highlighted. These factors contribute to the development of novel therapeutic targets that act at DA receptors. In addiction, the development of neuroimaging method will increase our understanding of the mechanisms underlying drug addiction. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  10. Dopamine Signaling in reward-related behaviors

    Directory of Open Access Journals (Sweden)

    Ja-Hyun eBaik

    2013-10-01

    Full Text Available Dopamine (DA regulates emotional and motivational behavior through the mesolimbic dopaminergic pathway. Changes in DAmesolimbic neurotransmission have been found to modify behavioral responses to various environmental stimuli associated with reward behaviors. Psychostimulants, drugs of abuse, and natural rewards such as food can cause substantial synaptic modifications to the mesolimbic DA system. Recent studies using optogenetics and DREADDs, together with neuron-specific or circuit-specific genetic manipulations have improved our understanding of DA signaling in the reward circuit, and provided a means to identify the neural substrates of complex behaviors such as drug addiction and eating disorders. This review focuses on the role of the DA system in drug addiction and food motivation, with an overview of the role of D1 and D2 receptors in the control of reward-associated behaviors.

  11. Prefrontal cortex, dopamine, and jealousy endophenotype.

    Science.gov (United States)

    Marazziti, Donatella; Poletti, Michele; Dell'Osso, Liliana; Baroni, Stefano; Bonuccelli, Ubaldo

    2013-02-01

    Jealousy is a complex emotion characterized by the perception of a threat of loss of something that the person values,particularly in reference to a relationship with a loved one, which includes affective, cognitive, and behavioral components. Neural systems and cognitive processes underlying jealousy are relatively unclear, and only a few neuroimaging studies have investigated them. The current article discusses recent empirical findings on delusional jealousy, which is the most severe form of this feeling, in neurodegenerative diseases. After reviewing empirical findings on neurological and psychiatric disorders with delusional jealousy, and after considering its high prevalence in patients with Parkinson's disease under dopamine agonist treatment, we propose a core neural network and core cognitive processes at the basis of (delusional) jealousy, characterizing this symptom as possible endophenotype. In any case,empirical investigation of the neural bases of jealousy is just beginning, and further studies are strongly needed to elucidate the biological roots of this complex emotion.

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

    Directory of Open Access Journals (Sweden)

    Gayathri Nattar Ranganathan

    2011-05-01

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

  13. Fast convergence of spike sequences to periodic patterns in recurrent networks

    International Nuclear Information System (INIS)

    Jin, Dezhe Z.

    2002-01-01

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

  14. Learning Touch Preferences with a Tactile Robot Using Dopamine Modulated STDP in a Model of Insular Cortex

    Directory of Open Access Journals (Sweden)

    Ting-Shuo eChou

    2015-07-01

    Full Text Available Neurorobots enable researchers to study how behaviors are produced by neural mechanisms in an uncertain, noisy, real-world environment. To investigate how the somatosensory system processes noisy, real-world touch inputs, we introduce a neurorobot called CARL-SJR, which has a full-body tactile sensory area. The design of CARL-SJR is such that it encourages people to communicate with it through gentle touch. CARL-SJR provides feedback to users by displaying bright colors on its surface. In the present study, we show that CARL-SJR is capable of learning associations between conditioned stimuli (CS; a color pattern on its surface and unconditioned stimuli (US; a preferred touch pattern by applying a spiking neural network (SNN with neurobiologically inspired plasticity. Specifically, we modeled the primary somatosensory cortex, prefrontal cortex, striatum, and the insular cortex, which is important for hedonic touch, to process noisy data generated directly from CARL-SJR’s tactile sensory area. To facilitate learning, we applied dopamine-modulated Spike Timing Dependent Plasticity (STDP to our simulated prefrontal cortex, striatum and insular cortex. To cope with noisy, varying inputs, the SNN was tuned to produce traveling waves of activity that carried spatiotemporal information. Despite the noisy tactile sensors, spike trains, and variations in subject hand swipes, the learning was quite robust. Further, the plasticity (i.e., STDP in primary somatosensory cortex and insular cortex in the incremental pathway of dopaminergic reward system allowed us to control CARL-SJR’s preference for touch direction without heavily pre-processed inputs. The emerged behaviors we found in this model match animal’s behaviors wherein they prefer touch in particular areas and directions. Thus, the results in this paper could serve as an explanation on the underlying neural mechanisms for developing tactile preferences and hedonic touch.

  15. Bursting as a source of non-linear determinism in the firing patterns of nigral dopamine neurons.

    Science.gov (United States)

    Jeong, Jaeseung; Shi, Wei-Xing; Hoffman, Ralph; Oh, Jihoon; Gore, John C; Bunney, Benjamin S; Peterson, Bradley S

    2012-11-01

    Nigral dopamine (DA) neurons in vivo exhibit complex firing patterns consisting of tonic single-spikes and phasic bursts that encode information for certain types of reward-related learning and behavior. Non-linear dynamical analysis has previously demonstrated the presence of a non-linear deterministic structure in complex firing patterns of DA neurons, yet the origin of this non-linear determinism remains unknown. In this study, we hypothesized that bursting activity is the primary source of non-linear determinism in the firing patterns of DA neurons. To test this hypothesis, we investigated the dimension complexity of inter-spike interval data recorded in vivo from bursting and non-bursting DA neurons in the chloral hydrate-anesthetized rat substantia nigra. We found that bursting DA neurons exhibited non-linear determinism in their firing patterns, whereas non-bursting DA neurons showed truly stochastic firing patterns. Determinism was also detected in the isolated burst and inter-burst interval data extracted from firing patterns of bursting neurons. Moreover, less bursting DA neurons in halothane-anesthetized rats exhibited higher dimensional spiking dynamics than do more bursting DA neurons in chloral hydrate-anesthetized rats. These results strongly indicate that bursting activity is the main source of low-dimensional, non-linear determinism in the firing patterns of DA neurons. This finding furthermore suggests that bursts are the likely carriers of meaningful information in the firing activities of DA neurons. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

  16. An Investigation of the Stoichiometry of Na+ Cotransport with Dopamine in Rat and Human Dopamine Transporters Expressed in Human Embryonic Kidney Cells

    National Research Council Canada - National Science Library

    Schumacher, Paul

    2001-01-01

    The neuronal membrane transporter for dopamine (DAT) is a member of the Na+ and Cl dependent family of transporters and concentrates dopamine intracellularly up to 106 fold over extracellular levels...

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

    Science.gov (United States)

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

    2017-07-01

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

  18. Pacemaker rate and depolarization block in nigral dopamine neurons: a somatic sodium channel balancing act

    Science.gov (United States)

    Tucker, Kristal R.; Huertas, Marco A.; Horn, John P.; Canavier, Carmen C.; Levitan, Edwin S.

    2012-01-01

    Midbrain dopamine (DA) neurons are slow intrinsic pacemakers that undergo depolarization (DP) block upon moderate stimulation. Understanding DP block is important because it has been correlated with the clinical efficacy of chronic antipsychotic drug treatment. Here we describe how voltage-gated sodium (NaV) channels regulate DP block and pacemaker activity in DA neurons of the substantia nigra using rat brain slices. The distribution, density and gating of NaV currents were manipulated by blocking native channels with tetrodotoxin and by creating virtual channels and anti-channels with dynamic clamp. Although action potentials initiate in the axon initial segment (AIS) and NaV channels are distributed in multiple dendrites, selective reduction of NaV channel activity in the soma was sufficient to decrease pacemaker frequency and increase susceptibility to DP block. Conversely, increasing somatic NaV current density raised pacemaker frequency and lowered susceptibility to DP block. Finally, when NaV currents were restricted to the soma, pacemaker activity occurred at abnormally high rates due to excessive local subthreshold NaV current. Together with computational simulations, these data show that both the slow pacemaker rate and the sensitivity to DP block that characterizes DA neurons result from the low density of somatic NaV channels. More generally, we conclude that the somatodendritic distribution of NaV channels is a major determinant of repetitive spiking frequency. PMID:23077037

  19. β-Cyclodextrin functionalised gold nanoclusters as luminescence probes for the ultrasensitive detection of dopamine.

    Science.gov (United States)

    Ban, Rui; Abdel-Halim, E S; Zhang, Jianrong; Zhu, Jun-Jie

    2015-02-21

    A novel luminescence probe based on mono-6-amino-β-cyclodextrin (NH2-β-CD) functionalised gold nanoclusters (β-CD-AuNC) was designed for dopamine (DA) detection. The NH2-β-CD molecules were conjugated onto the surface of 11-mercaptoundecanoic acid capped AuNCs (11-MUA-AuNC) via a carbodiimide coupling reaction. The integrity of the β-CD cavities was preserved on the surface of AuNCs and they retained their capability for molecular DA host-guest recognition. DA could be captured by the β-CD cavities to form an inclusion complex in which the oxidised DA could quench the fluorescence of the β-CD-AuNC probe by electron transfer. The probe could be used to quantify DA in the range of 5-1000 nM with a detection limit of 2 nM. This sensitivity was 1-2 orders of magnitude higher than that in previously reported methods. Interference by both ascorbic acid (AA) and uric acid (UA) was not observed. Therefore, the β-CD-AuNC probe could be directly used to determine the DA content in biological samples without further separation. This strategy was successfully applied to a DA assay in spiked human serum samples and it exhibited remarkable accuracy, sensitivity and selectivity.

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

    Science.gov (United States)

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

    2013-09-01

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

  1. Contamination spike simulation and measurement in a clean metal vapor laser

    International Nuclear Information System (INIS)

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

    1990-01-01

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

  2. Ion channel density regulates switches between regular and fast spiking in soma but not in axons.

    Directory of Open Access Journals (Sweden)

    Hugo Zeberg

    2010-04-01

    Full Text Available The threshold firing frequency of a neuron is a characterizing feature of its dynamical behaviour, in turn determining its role in the oscillatory activity of the brain. Two main types of dynamics have been identified in brain neurons. Type 1 dynamics (regular spiking shows a continuous relationship between frequency and stimulation current (f-I(stim and, thus, an arbitrarily low frequency at threshold current; Type 2 (fast spiking shows a discontinuous f-I(stim relationship and a minimum threshold frequency. In a previous study of a hippocampal neuron model, we demonstrated that its dynamics could be of both Type 1 and Type 2, depending on ion channel density. In the present study we analyse the effect of varying channel density on threshold firing frequency on two well-studied axon membranes, namely the frog myelinated axon and the squid giant axon. Moreover, we analyse the hippocampal neuron model in more detail. The models are all based on voltage-clamp studies, thus comprising experimentally measurable parameters. The choice of analysing effects of channel density modifications is due to their physiological and pharmacological relevance. We show, using bifurcation analysis, that both axon models display exclusively Type 2 dynamics, independently of ion channel density. Nevertheless, both models have a region in the channel-density plane characterized by an N-shaped steady-state current-voltage relationship (a prerequisite for Type 1 dynamics and associated with this type of dynamics in the hippocampal model. In summary, our results suggest that the hippocampal soma and the two axon membranes represent two distinct kinds of membranes; membranes with a channel-density dependent switching between Type 1 and 2 dynamics, and membranes with a channel-density independent dynamics. The difference between the two membrane types suggests functional differences, compatible with a more flexible role of the soma membrane than that of the axon membrane.

  3. A Computational Model to Investigate Astrocytic Glutamate Uptake Influence on Synaptic Transmission and Neuronal Spiking

    Directory of Open Access Journals (Sweden)

    Sushmita Lakshmi Allam

    2012-10-01

    Full Text Available Over the past decades, our view of astrocytes has switched from passive support cells to active processing elements in the brain. The current view is that astrocytes shape neuronal communication and also play an important role in many neurodegenerative diseases. Despite the growing awareness of the importance of astrocytes, the exact mechanisms underlying neuron-astrocyte communication and the physiological consequences of astrocytic-neuronal interactions remain largely unclear. In this work, we define a modeling framework that will permit to address unanswered questions regarding the role of astrocytes. Our computational model of a detailed glutamatergic synapse facilitates the analysis of neural system responses to various stimuli and conditions that are otherwise difficult to obtain experimentally, in particular the readouts at the sub-cellular level. In this paper, we extend a detailed glutamatergic synaptic model, to include astrocytic glutamate transporters. We demonstrate how these glial transporters, responsible for the majority of glutamate uptake, modulate synaptic transmission mediated by ionotropic AMPA and NMDA receptors at glutamatergic synapses. Furthermore, we investigate how these local signaling effects at the synaptic level are translated into varying spatio-temporal patterns of neuron firing. Paired pulse stimulation results reveal that the effect of astrocytic glutamate uptake is more apparent when the input inter-spike interval is sufficiently long to allow the receptors to recover from desensitization. These results suggest an important functional role of astrocytes in spike timing dependent processes and demand further investigation of the molecular basis of certain neurological diseases specifically related to alterations in astrocytic glutamate uptake, such as epilepsy.

  4. Genetics Home Reference: dopamine beta-hydroxylase deficiency

    Science.gov (United States)

    ... common features include an unusually large range of joint movement (hypermobility) and muscle weakness. Related Information What ... Dopamine beta-hydroxylase deficiency Washington Univeristy, St. Louis: Neuromuscular Disease Center Patient Support and Advocacy Resources (1 ...

  5. Missense dopamine transporter mutations associate with adult parkinsonism and ADHD

    DEFF Research Database (Denmark)

    Hansen, Freja H; Skjørringe, Tina; Yasmeen, Saiqa

    2014-01-01

    experiments suggested that the disrupted function of the DAT-Asp421Asn mutant is the result of compromised sodium binding, in agreement with Asp421 coordinating sodium at the second sodium site. For DAT-Asp421Asn, substrate efflux experiments revealed a constitutive, anomalous efflux of dopamine......Parkinsonism and attention deficit hyperactivity disorder (ADHD) are widespread brain disorders that involve disturbances of dopaminergic signaling. The sodium-coupled dopamine transporter (DAT) controls dopamine homeostasis, but its contribution to disease remains poorly understood. Here, we......-deoxy-glucose-PET/MRI (FDG-PET/MRI) scan, the patient suffered from progressive dopaminergic neurodegeneration. In heterologous cells, both DAT variants exhibited markedly reduced dopamine uptake capacity but preserved membrane targeting, consistent with impaired catalytic activity. Computational simulations and uptake...

  6. Selective response of dopamine in the presence of ascorbic acid ...

    African Journals Online (AJOL)

    Selective response of dopamine in the presence of ascorbic acid and uric acid at gold nanoparticles and multi-walled carbon nanotubes grafted with ethylene diamine tetraacetic acid modified electrode.

  7. Diversion of the melanin synthetic pathway by dopamine product

    African Journals Online (AJOL)

    acetylcysteine adducts of dopamine studied using quantum chemical ... cyclization reaction of dopaminoquinone which leads to the synthesis of melanin. ..... a hydrogen bond with the carbonyl oxygen (−O−H---O=C− and the second one points ...

  8. Could Dopamine Agonists Aid in Drug Development for Anorexia Nervosa?

    Science.gov (United States)

    Frank, Guido K. W.

    2014-01-01

    Anorexia nervosa is a severe psychiatric disorder most commonly starting during the teenage-years and associated with food refusal and low body weight. Typically there is a loss of menses, intense fear of gaining weight, and an often delusional quality of altered body perception. Anorexia nervosa is also associated with a pattern of high cognitive rigidity, which may contribute to treatment resistance and relapse. The complex interplay of state and trait biological, psychological, and social factors has complicated identifying neurobiological mechanisms that contribute to the illness. The dopamine D1 and D2 neurotransmitter receptors are involved in motivational aspects of food approach, fear extinction, and cognitive flexibility. They could therefore be important targets to improve core and associated behaviors in anorexia nervosa. Treatment with dopamine antagonists has shown little benefit, and it is possible that antagonists over time increase an already hypersensitive dopamine pathway activity in anorexia nervosa. On the contrary, application of dopamine receptor agonists could reduce circuit responsiveness, facilitate fear extinction, and improve cognitive flexibility in anorexia nervosa, as they may be particularly effective during underweight and low gonadal hormone states. This article provides evidence that the dopamine receptor system could be a key factor in the pathophysiology of anorexia nervosa and dopamine agonists could be helpful in reducing core symptoms of the disorder. This review is a theoretical approach that primarily focuses on dopamine receptor function as this system has been mechanistically better described than other neurotransmitters that are altered in anorexia nervosa. However, those proposed dopamine mechanisms in anorexia nervosa also warrant further study with respect to their interaction with other neurotransmitter systems, such as serotonin pathways. PMID:25988121

  9. PCBs Alter Dopamine Mediated Function in Aging Workers

    Science.gov (United States)

    2011-01-01

    PCBs Alter Dopamine Mediated Function in Aging Workers 5a. CONTRACT NUMBER 5b. GRANT NUMBER DAMD17-02-1-0173 5c. PROGRAM ELEMENT...hypothesized that occupational exposure to polychlorinated biphenyls (PCBs) reduces dopamine (DA) terminal densities in the basal ganglia. We found...motor function in women compared to similarly aged men with similar bone lead levels. These latter findings are the first to demonstrate a sexual

  10. SEP-225289 serotonin and dopamine transporter occupancy: a PET study.

    Science.gov (United States)

    DeLorenzo, Christine; Lichenstein, Sarah; Schaefer, Karen; Dunn, Judith; Marshall, Randall; Organisak, Lisa; Kharidia, Jahnavi; Robertson, Brigitte; Mann, J John; Parsey, Ramin V

    2011-07-01

    SEP-225289 is a novel compound that, based on in vitro potencies for transporter function, potentially inhibits reuptake at dopamine, norepinephrine, and serotonin transporters. An open-label PET study was conducted during the development of SEP-225289 to investigate its dopamine and serotonin transporter occupancy. Different single doses of SEP-225289 were administered to healthy volunteers in 3 cohorts: 8 mg (n = 7), 12 mg (n = 5), and 16 mg (n = 7). PET was performed before and approximately 24 h after oral administration of SEP-225289, to assess occupancy at trough levels. Dopamine and serotonin transporter occupancies were estimated from PET using (11)C-N-(3-iodoprop-2E-enyl)-2β-carbomethoxy-3β-(4-methylphenyl)nortropane ((11)C-PE2I) and (11)C-N,N-dimethyl-2-(2-amino-4-cyanophenylthio)benzylamine ((11)C-DASB), respectively. Plasma concentration of SEP-225289 was assessed before ligand injection, and subjects were monitored for adverse events. Average dopamine and serotonin transporter occupancies increased with increasing doses of SEP-225289. Mean dopamine and serotonin transporter occupancies were 33% ± 11% and 2% ± 13%, respectively, for 8 mg; 44% ± 4% and 9% ± 10%, respectively, for 12 mg; and 49% ± 7% and 14% ± 15%, respectively, for 16 mg. On the basis of the relationship between occupancy and plasma concentration, dopamine transporter IC(50) (the plasma concentration of drug at 50% occupancy) was determined (4.5 ng/mL) and maximum dopamine transporter occupancy was extrapolated (85%); however, low serotonin transporter occupancy prevented similar serotonin transporter calculations. No serious adverse events were reported. At the doses evaluated, occupancy of the dopamine transporter was significantly higher than that of the serotonin transporter, despite similar in vitro potencies, confirming that, in addition to in vitro assays, PET occupancy studies can be instrumental to the drug development process by informing early decisions about

  11. Developmental changes in human dopamine neurotransmission: cortical receptors and terminators

    Directory of Open Access Journals (Sweden)

    Rothmond Debora A

    2012-02-01

    Full Text Available Abstract Background Dopamine is integral to cognition, learning and memory, and dysfunctions of the frontal cortical dopamine system have been implicated in several developmental neuropsychiatric disorders. The dorsolateral prefrontal cortex (DLPFC is critical for working memory which does not fully mature until the third decade of life. Few studies have reported on the normal development of the dopamine system in human DLPFC during postnatal life. We assessed pre- and postsynaptic components of the dopamine system including tyrosine hydroxylase, the dopamine receptors (D1, D2 short and D2 long isoforms, D4, D5, catechol-O-methyltransferase, and monoamine oxidase (A and B in the developing human DLPFC (6 weeks -50 years. Results Gene expression was first analysed by microarray and then by quantitative real-time PCR. Protein expression was analysed by western blot. Protein levels for tyrosine hydroxylase peaked during the first year of life (p O-methyltransferase (p = 0.024 were significantly higher in neonates and infants as was catechol-O-methyltransferase protein (32 kDa, p = 0.027. In contrast, dopamine D1 receptor mRNA correlated positively with age (p = 0.002 and dopamine D1 receptor protein expression increased throughout development (p Conclusions We find distinct developmental changes in key components of the dopamine system in DLPFC over postnatal life. Those genes that are highly expressed during the first year of postnatal life may influence and orchestrate the early development of cortical neural circuitry while genes portraying a pattern of increasing expression with age may indicate a role in DLPFC maturation and attainment of adult levels of cognitive function.

  12. Could dopamine agonists aid in drug development for anorexia nervosa?

    Science.gov (United States)

    Frank, Guido K W

    2014-01-01

    Anorexia nervosa is a severe psychiatric disorder most commonly starting during the teenage-years and associated with food refusal and low body weight. Typically there is a loss of menses, intense fear of gaining weight, and an often delusional quality of altered body perception. Anorexia nervosa is also associated with a pattern of high cognitive rigidity, which may contribute to treatment resistance and relapse. The complex interplay of state and trait biological, psychological, and social factors has complicated identifying neurobiological mechanisms that contribute to the illness. The dopamine D1 and D2 neurotransmitter receptors are involved in motivational aspects of food approach, fear extinction, and cognitive flexibility. They could therefore be important targets to improve core and associated behaviors in anorexia nervosa. Treatment with dopamine antagonists has shown little benefit, and it is possible that antagonists over time increase an already hypersensitive dopamine pathway activity in anorexia nervosa. On the contrary, application of dopamine receptor agonists could reduce circuit responsiveness, facilitate fear extinction, and improve cognitive flexibility in anorexia nervosa, as they may be particularly effective during underweight and low gonadal hormone states. This article provides evidence that the dopamine receptor system could be a key factor in the pathophysiology of anorexia nervosa and dopamine agonists could be helpful in reducing core symptoms of the disorder. This review is a theoretical approach that primarily focuses on dopamine receptor function as this system has been mechanistically better described than other neurotransmitters that are altered in anorexia nervosa. However, those proposed dopamine mechanisms in anorexia nervosa also warrant further study with respect to their interaction with other neurotransmitter systems, such as serotonin pathways.

  13. Could Dopamine Agonists Aid in Drug Development for Anorexia Nervosa?

    Directory of Open Access Journals (Sweden)

    Guido eFrank

    2014-11-01

    Full Text Available Anorexia nervosa is a severe psychiatric disorder most commonly starting during the teenage years and associated with food refusal and low body weight. Typically there is a loss of menses, intense fear of gaining weight and an often delusional quality of altered body perception. Anorexia nervosa is also associated with a pattern of high cognitive rigidity, which may contribute to treatment resistance and relapse. The complex interplay of state and trait biological, psychological and social factors has complicated identifying neurobiological mechanisms that contribute to the illness. The dopamine D1 and D2 neurotransmitter receptors are involved in motivational aspects of food approach, fear extinction and cognitive flexibility. They could therefore be important targets to improve core and associated behaviors in anorexia nervosa. Treatment with dopamine antagonists has shown little benefit, and it is possible that antagonists over time increase an already hypersensitive dopamine pathway activity in anorexia nervosa. On the contrary, application of dopamine receptor agonists could reduce circuit responsiveness, facilitate fear extinction and improve cognitive flexibility in anorexia nervosa, as they may be particularly effective during underweight and low gonadal hormone states. This article provides evidence that the dopamine receptor system could be a key factor in the pathophysiology of anorexia nervosa and dopamine agonists could be helpful in reducing core symptoms of the disorder. This review is a theoretical approach that primarily focuses on dopamine receptor function as this system has been mechanistically better described than other neurotransmitters that are altered in anorexia nervosa. However, those proposed dopamine mechanisms in anorexia nervosa also warrant further study with respect to their interaction with other neurotransmitter systems, such as serotonin pathways.

  14. Dopamine and glucose, obesity and Reward Deficiency Syndrome

    Directory of Open Access Journals (Sweden)

    Kenneth eBlum

    2014-09-01

    Full Text Available Obesity and many well described eating disorders are accurately considered a global epidemic. The consequences of Reward Deficiency Syndrome, a genetic and epigenetic phenomena that involves the interactions of powerful neurotransmitters, are impairments of brain reward circuitry, hypodopaminergic function and abnormal craving behavior. Numerous sound neurochemical and genetic studies provide strong evidence that food addiction is similar to psychoactive drug addiction. Important facts which could translate to potential therapeutic targets espoused in this review include: 1 brain dopamine (DA production and use is stimulated by consumption of alcohol in large quantities or carbohydrates bingeing; 2 in the mesolimbic system the enkephalinergic neurons are in close proximity, to glucose receptors; 3 highly concentrated glucose activates the calcium channel to stimulate dopamine release from P12 cells; 4 blood glucose and cerebrospinal fluid concentrations of homovanillic acid, the dopamine metabolite, are significantly correlated and 5 2-deoxyglucose the glucose analogue, in pharmacological doses associates with enhanced dopamine turnover and causes acute glucoprivation. Evidence from animal studies and human fMRI support the hypothesis that multiple, but similar brain circuits are disrupted in obesity and drug dependence and DA-modulated reward circuits are involved in pathologic eating behaviors. Treatment for addiction to glucose and drugs alike, based on a consensus of neuroscience research, should incorporate dopamine agonist therapy, in contrast to current theories and practices that use dopamine antagonists. Until now, powerful dopamine-D2 agonists have failed clinically, due to chronic down regulation of D2 receptors instead, consideration of novel less powerful D2 agonists that up-regulate D2 receptors seems prudent. We encourage new strategies targeted at improving DA function in the treatment and prevention of obesity a subtype of

  15. Infantile parkinsonism-dystonia: a dopamine “transportopathy”

    OpenAIRE

    Blackstone, Craig

    2009-01-01

    The dopamine transporter (DAT) retrieves the neurotransmitter dopamine from the synaptic cleft at dopaminergic synapses. Variations in solute carrier family 6A, member 3 (SLC6A3/DAT1), the human gene encoding DAT, have been implicated in attention deficit hyperactivity and bipolar disorders, and DAT is a prominent site of action for drugs such as amphetamines and cocaine. In this issue of the JCI, Kurian et al. report that an autosomal recessive infantile parkinsonism-dystonia is caused by lo...

  16. Dopamine and Reward: The Anhedonia Hypothesis 30 years on

    OpenAIRE

    Wise, Roy A.

    2008-01-01

    The anhedonia hypothesis – that brain dopamine plays a critical role in the subjective pleasure associated with positive rewards – was intended to draw the attention of psychiatrists to the growing evidence that dopamine plays a critical role in the objective reinforcement and incentive motivation associated with food and water, brain stimulation reward, and psychomotor stimulant and opiate reward. The hypothesis called to attention the apparent paradox that neuroleptics, drugs used to treat ...

  17. Introducing Thermal Wave Transport Analysis (TWTA): A Thermal Technique for Dopamine Detection by Screen-Printed Electrodes Functionalized with Molecularly Imprinted Polymer (MIP) Particles.

    Science.gov (United States)

    Peeters, Marloes M; van Grinsven, Bart; Foster, Christopher W; Cleij, Thomas J; Banks, Craig E

    2016-04-26

    A novel procedure is developed for producing bulk modified Molecularly Imprinted Polymer (MIP) screen-printed electrodes (SPEs), which involves the direct mixing of the polymer particles within the screen-printed ink. This allowed reduction of the sample preparation time from 45 min to 1 min, and resulted in higher reproducibility of the electrodes. The samples are measured with a novel detection method, namely, thermal wave transport analysis (TWTA), relying on the analysis of thermal waves through a functional interface. As a first proof-of-principle, MIPs for dopamine are developed and successfully incorporated within a bulk modified MIP SPE. The detection limits of dopamine within buffer solutions for the MIP SPEs are determined via three independent techniques. With cyclic voltammetry this was determined to be 4.7 × 10(-6) M, whereas by using the heat-transfer method (HTM) 0.35 × 10(-6) M was obtained, and with the novel TWTA concept 0.26 × 10(-6) M is possible. This TWTA technique is measured simultaneously with HTM and has the benefits of reducing measurement time to less than 5 min and increasing effect size by nearly a factor of two. The two thermal methods are able to enhance dopamine detection by one order of magnitude compared to the electrochemical method. In previous research, it was not possible to measure neurotransmitters in complex samples with HTM, but with the improved signal-to-noise of TWTA for the first time, spiked dopamine concentrations were determined in a relevant food sample. In summary, novel concepts are presented for both the sensor functionalization side by employing screen-printing technology, and on the sensing side, the novel TWTA thermal technique is reported. The developed bio-sensing platform is cost-effective and suitable for mass-production due to the nature of screen-printing technology, which makes it very interesting for neurotransmitter detection in clinical diagnostic applications.

  18. D1 dopamine receptor is involved in shell formation in larvae of Pacific oyster Crassostrea gigas.

    Science.gov (United States)

    Liu, Zhaoqun; Wang, Lingling; Yan, Yunchen; Zheng, Yan; Ge, Wenjing; Li, Meijia; Wang, Weilin; Song, Xiaorui; Song, Linsheng

    2018-07-01

    Dopamine (DA), a significant member of catecholamines, is reported to induce biomineralization of calcium carbonate vaterite microspheres via dopamine receptor (DR) in bivalves, implying the modulation of dopaminergic system on shell formation during larval development. In this research, a homologue of D1 type DR (CgD1DR-1) was identified from oyster Crassostrea gigas, whose full length cDNA was 1197 bp. It was widely expressed in various tissues of C. gigas, with the significantly higher levels in hepatopancreas, mantle, muscle and gill. During developmental stages, the mRNA transcripts of CgD1DR-1 in D-shape larvae were obviously higher (p < 0.05) than those in trochophore and umbo larvae, and CO 2 exposure could inhibit the synthesis of DA and mRNA expression of CgD1DR-1. After cell transfection and DA treatment, intracellular cAMP in cells with the expression of CgD1DR-1 increased significantly (p < 0.05). Furthermore, the incubation with SCH 23390 for the blockage of CgD1DR-1 significantly restrained the expressions of six shell formation-related genes including CgTyrosinase-1, CgTyrosinase-3, CgChitinaseLP, CgAMC, CgBMP and CgBMPR in trochophore and D-shape larvae. These results jointly suggested that DA together with its receptor CgD1DR-1 might be involved in shell formation during oyster larval development from trochophore to D-shape larvae, and CO 2 -induced ocean acidification (OA) might influence marine bivalves by inhibiting the DA-D1DR pathway to prohibit their shell formation. Copyright © 2018 Elsevier Ltd. All rights reserved.

  19. In vivo neurochemical characterization of clothianidin induced striatal dopamine release.

    Science.gov (United States)

    Faro, L R F; Oliveira, I M; Durán, R; Alfonso, M

    2012-12-16

    Clothianidin (CLO) is a neonicotinoid insecticide with selective action on nicotinic acetylcholine receptors. The aim of this study was to determine the neurochemical basis for CLO-induced striatal dopamine release using the microdialysis technique in freely moving and conscious rats. Intrastriatal administration of CLO (3.5mM), produced an increase in both spontaneous (2462 ± 627% with respect to basal values) and KCl-evoked (4672 ± 706% with respect to basal values) dopamine release. This effect was attenuated in Ca(2+)-free medium, and was prevented in reserpine pre-treated animals or in presence of tetrodotoxin (TTX). To investigate the involvement of dopamine transporter (DAT), the effect of CLO was observed in presence of nomifensine. The coadministration of CLO and nomifensine produced an additive effect on striatal dopamine release. The results suggest that the effect of CLO on striatal dopamine release is predominantly mediated by an exocytotic mechanism, Ca(2+), vesicular and TTX-dependent and not by a mechanism mediated by dopamine transporter. Published by Elsevier Ireland Ltd.

  20. Dopamine in the medial amygdala network mediates human bonding.

    Science.gov (United States)

    Atzil, Shir; Touroutoglou, Alexandra; Rudy, Tali; Salcedo, Stephanie; Feldman, Ruth; Hooker, Jacob M; Dickerson, Bradford C; Catana, Ciprian; Barrett, Lisa Feldman

    2017-02-28

    Research in humans and nonhuman animals indicates that social affiliation, and particularly maternal bonding, depends on reward circuitry. Although numerous mechanistic studies in rodents demonstrated that maternal bonding depends on striatal dopamine transmission, the neurochemistry supporting maternal behavior in humans has not been described so far. In this study, we tested the role of central dopamine in human bonding. We applied a combined functional MRI-PET scanner to simultaneously probe mothers' dopamine responses to their infants and the connectivity between the nucleus accumbens (NAcc), the amygdala, and the medial prefrontal cortex (mPFC), which form an intrinsic network (referred to as the "medial amygdala network") that supports social functioning. We also measured the mothers' behavioral synchrony with their infants and plasma oxytocin. The results of this study suggest that synchronous maternal behavior is associated with increased dopamine responses to the mother's infant and stronger intrinsic connectivity within the medial amygdala network. Moreover, stronger network connectivity is associated with increased dopamine responses within the network and decreased plasma oxytocin. Together, these data indicate that dopamine is involved in human bonding. Compared with other mammals, humans have an unusually complex social life. The complexity of human bonding cannot be fully captured in nonhuman animal models, particularly in pathological bonding, such as that in autistic spectrum disorder or postpartum depression. Thus, investigations of the neurochemistry of social bonding in humans, for which this study provides initial evidence, are warranted.