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.

Kinetics of fast short-term depression are matched to spike train statistics to reduce noise.

Science.gov (United States)

Khanbabaie, Reza; Nesse, William H; Longtin, Andre; Maler, Leonard

2010-06-01

Short-term depression (STD) is observed at many synapses of the CNS and is important for diverse computations. We have discovered a form of fast STD (FSTD) in the synaptic responses of pyramidal cells evoked by stimulation of their electrosensory afferent fibers (P-units). The dynamics of the FSTD are matched to the mean and variance of natural P-unit discharge. FSTD exhibits switch-like behavior in that it is immediately activated with stimulus intervals near the mean interspike interval (ISI) of P-units (approximately 5 ms) and recovers immediately after stimulation with the slightly longer intervals (>7.5 ms) that also occur during P-unit natural and evoked discharge patterns. Remarkably, the magnitude of evoked excitatory postsynaptic potentials appear to depend only on the duration of the previous ISI. Our theoretical analysis suggests that FSTD can serve as a mechanism for noise reduction. Because the kinetics of depression are as fast as the natural spike statistics, this role is distinct from previously ascribed functional roles of STD in gain modulation, synchrony detection or as a temporal filter.

Fast gamma oscillations are generated intrinsically in CA1 without the involvement of fast-spiking basket cells.

Science.gov (United States)

Craig, Michael T; McBain, Chris J

2015-02-25

Information processing in neuronal networks relies on the precise synchronization of ensembles of neurons, coordinated by the diverse family of inhibitory interneurons. Cortical interneurons can be usefully parsed by embryonic origin, with the vast majority arising from either the caudal or medial ganglionic eminences (CGE and MGE). Here, we examine the activity of hippocampal interneurons during gamma oscillations in mouse CA1, using an in vitro model where brief epochs of rhythmic activity were evoked by local application of kainate. We found that this CA1 KA-evoked gamma oscillation was faster than that in CA3 and, crucially, did not appear to require the involvement of fast-spiking basket cells. In contrast to CA3, we also found that optogenetic inhibition of pyramidal cells in CA1 did not significantly affect the power of the oscillation, suggesting that excitation may not be essential for gamma genesis in this region. We found that MGE-derived interneurons were generally more active than CGE interneurons during CA1 gamma, although a group of CGE-derived interneurons, putative trilaminar cells, were strongly phase-locked with gamma oscillations and, together with MGE-derived axo-axonic and bistratified cells, provide attractive candidates for being the driver of this locally generated, predominantly interneuron-driven model of gamma oscillations. Copyright © 2015 the authors 0270-6474/15/353616-09$15.00/0.

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

International Nuclear Information System (INIS)

Shimazaki, Hideaki

2013-01-01

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

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)

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.

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.

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.

Activity-Dependent Plasticity of Spike Pauses in Cerebellar Purkinje Cells

Directory of Open Access Journals (Sweden)

Giorgio Grasselli

2016-03-01

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

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

International Nuclear Information System (INIS)

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

2015-01-01

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

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

Science.gov (United States)

Thanaraj, Palani; Parvathavarthini, B

2017-06-01

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

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

DEFF Research Database (Denmark)

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

2008-01-01

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

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

Science.gov (United States)

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

2015-04-30

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

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

International Nuclear Information System (INIS)

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

2011-01-01

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

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

DEFF Research Database (Denmark)

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

2017-01-01

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

Energy consumption in Hodgkin–Huxley type fast spiking neuron model exposed to an external electric field

Directory of Open Access Journals (Sweden)

K. Usha

2016-09-01

Full Text Available This paper evaluates the change in metabolic energy required to maintain the signalling activity of neurons in the presence of an external electric field. We have analysed the Hodgkin–Huxley type conductance based fast spiking neuron model as electrical circuit by changing the frequency and amplitude of the applied electric field. The study has shown that, the presence of electric field increases the membrane potential, electrical energy supply and metabolic energy consumption. As the amplitude of applied electric field increases by keeping a constant frequency, the membrane potential increases and consequently the electrical energy supply and metabolic energy consumption increases. On increasing the frequency of the applied field, the peak value of membrane potential after depolarization gradually decreases as a result electrical energy supply decreases which results in a lower rate of hydrolysis of ATP molecules.

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

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

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

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.

Low blood glucose precipitates spike-and-wave activity in genetically predisposed animals.

Science.gov (United States)

Reid, Christopher A; Kim, Tae Hwan; Berkovic, Samuel F; Petrou, Steven

2011-01-01

Absence epilepsies are common, with a major genetic contribution to etiology. Certain environmental factors can influence absence occurrence but a complete understanding of absence precipitation is lacking. Herein we investigate if lowering blood glucose increases spike-wave activity in mouse models with varying seizure susceptibility. Three mouse models were used: an absence seizure model based on the knockin of a human GABA(A) γ2(R43Q) mutation (DBA(R43Q)), the spike-wave discharge (SWD)-prone DBA/2J strain, and the seizure resistant C57Bl/6 strain. Electrocorticography (ECoG) studies were recorded to determine SWDs during hypoglycemia induced by insulin or overnight fasting. An insulin-mediated reduction in blood glucose levels to 4 mm (c.a. 40% reduction) was sufficient to double SWD occurrence in the DBA(R43Q) model and in the SWD-prone DBA/2J mouse strain. Larger reductions in blood glucose further increased SWDs in both these models. However, even with large reductions in blood glucose, no discharges were observed in the seizure-resistant C57Bl/6 mouse strain. Injection of glucose reversed the impact of insulin on SWDs in the DBA(R43Q) model, supporting a reduction in blood glucose as the modulating influence. Overnight fasting reduced blood glucose levels to 4.5 mm (c.a. 35% reduction) and, like insulin, caused a doubling in occurrence of SWDs. Low blood glucose can precipitate SWDs in genetically predisposed animal models and should be considered as a potential environmental risk factor in patients with absence epilepsy. Wiley Periodicals, Inc. © 2010 International League Against Epilepsy.

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

DEFF Research Database (Denmark)

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

2008-01-01

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

Sharp boron spikes in silicon grown at reduced and atmospheric pressure by fast-gas-switching CVD

NARCIS (Netherlands)

Vink, A.T.; Roksnoer, P.J.; Maes, J.W.F.M.; Vriezema, C.J.; IJzendoorn, van L.J.; Zalm, P.C.

1990-01-01

Boron doping spikes in Si were grown by fast-gas-switching CVD at 800 and 850°C using Si2H6 and B2H6 in 0.03, 0.1 and 1 atm H2 as the carrier gas. The B2H6 doping gas was added for 2 s by two methods, namely during growth or as a flush while the Si2H6 flow was interrupted. High-resolution SIMS

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.

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.

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

International Nuclear Information System (INIS)

Zhe, Sun; Micheletto, Ruggero

2016-01-01

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

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

Science.gov (United States)

Zhe, Sun; Micheletto, Ruggero

2016-07-01

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

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

Science.gov (United States)

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

2004-01-01

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

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

A thermal spike analysis of low energy ion activated surface processes

International Nuclear Information System (INIS)

Gilmore, G.M.; Haeri, A.; Sprague, J.A.

1989-01-01

This paper reports a thermal spike analysis utilized to predict the time evolution of energy propagation through a solid resulting from energetic particle impact. An analytical solution was developed that can predict the number of surface excitations such as desorption, diffusion or chemical reaction activated by an energetic particle. The analytical solution is limited to substrates at zero Kelvin and to materials with constant thermal diffusivities. These limitations were removed by developing a computer numerical integration of the propagation of the thermal spike through the solid and the subsequent activation of surface processes

How adaptation shapes spike rate oscillations in recurrent neuronal networks

Directory of Open Access Journals (Sweden)

Moritz eAugustin

2013-02-01

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

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

Science.gov (United States)

Hoseini, Mahmood S; Wessel, Ralf

2016-01-01

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

Spiking in auditory cortex following thalamic stimulation is dominated by cortical network activity

Science.gov (United States)

Krause, Bryan M.; Raz, Aeyal; Uhlrich, Daniel J.; Smith, Philip H.; Banks, Matthew I.

2014-01-01

The state of the sensory cortical network can have a profound impact on neural responses and perception. In rodent auditory cortex, sensory responses are reported to occur in the context of network events, similar to brief UP states, that produce “packets” of spikes and are associated with synchronized synaptic input (Bathellier et al., 2012; Hromadka et al., 2013; Luczak et al., 2013). However, traditional models based on data from visual and somatosensory cortex predict that ascending sensory thalamocortical (TC) pathways sequentially activate cells in layers 4 (L4), L2/3, and L5. The relationship between these two spatio-temporal activity patterns is unclear. Here, we used calcium imaging and electrophysiological recordings in murine auditory TC brain slices to investigate the laminar response pattern to stimulation of TC afferents. We show that although monosynaptically driven spiking in response to TC afferents occurs, the vast majority of spikes fired following TC stimulation occurs during brief UP states and outside the context of the L4>L2/3>L5 activation sequence. Specifically, monosynaptic subthreshold TC responses with similar latencies were observed throughout layers 2–6, presumably via synapses onto dendritic processes located in L3 and L4. However, monosynaptic spiking was rare, and occurred primarily in L4 and L5 non-pyramidal cells. By contrast, during brief, TC-induced UP states, spiking was dense and occurred primarily in pyramidal cells. These network events always involved infragranular layers, whereas involvement of supragranular layers was variable. During UP states, spike latencies were comparable between infragranular and supragranular cells. These data are consistent with a model in which activation of auditory cortex, especially supragranular layers, depends on internally generated network events that represent a non-linear amplification process, are initiated by infragranular cells and tightly regulated by feed-forward inhibitory

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

Science.gov (United States)

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

2007-05-29

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

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.)

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.

Delayed Maturation of Fast-Spiking Interneurons Is Rectified by Activation of the TrkB Receptor in the Mouse Model of Fragile X Syndrome.

Science.gov (United States)

Nomura, Toshihiro; Musial, Timothy F; Marshall, John J; Zhu, Yiwen; Remmers, Christine L; Xu, Jian; Nicholson, Daniel A; Contractor, Anis

2017-11-22

Fragile X syndrome (FXS) is a neurodevelopmental disorder that is a leading cause of inherited intellectual disability, and the most common known cause of autism spectrum disorder. FXS is broadly characterized by sensory hypersensitivity and several developmental alterations in synaptic and circuit function have been uncovered in the sensory cortex of the mouse model of FXS ( Fmr1 KO). GABA-mediated neurotransmission and fast-spiking (FS) GABAergic interneurons are central to cortical circuit development in the neonate. Here we demonstrate that there is a delay in the maturation of the intrinsic properties of FS interneurons in the sensory cortex, and a deficit in the formation of excitatory synaptic inputs on to these neurons in neonatal Fmr1 KO mice. Both these delays in neuronal and synaptic maturation were rectified by chronic administration of a TrkB receptor agonist. These results demonstrate that the maturation of the GABAergic circuit in the sensory cortex is altered during a critical developmental period due in part to a perturbation in BDNF-TrkB signaling, and could contribute to the alterations in cortical development underlying the sensory pathophysiology of FXS. SIGNIFICANCE STATEMENT Fragile X (FXS) individuals have a range of sensory related phenotypes, and there is growing evidence of alterations in neuronal circuits in the sensory cortex of the mouse model of FXS ( Fmr1 KO). GABAergic interneurons are central to the correct formation of circuits during cortical critical periods. Here we demonstrate a delay in the maturation of the properties and synaptic connectivity of interneurons in Fmr1 KO mice during a critical period of cortical development. The delays both in cellular and synaptic maturation were rectified by administration of a TrkB receptor agonist, suggesting reduced BDNF-TrkB signaling as a contributing factor. These results provide evidence that the function of fast-spiking interneurons is disrupted due to a deficiency in neurotrophin

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

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

Science.gov (United States)

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

2016-01-01

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

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.

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.

Effects of potassium concentration on firing patterns of low-calcium epileptiform activity in anesthetized rat hippocampus: inducing of persistent spike activity.

Science.gov (United States)

Feng, Zhouyan; Durand, Dominique M

2006-04-01

It has been shown that a low-calcium high-potassium solution can generate ictal-like epileptiform activity in vitro and in vivo. Moreover, during status epileptiform activity, the concentration of [K+]o increases, and the concentration of [Ca2+]o decreases in brain tissue. Therefore we tested the hypothesis that long-lasting persistent spike activity, similar to one of the patterns of status epilepticus, could be generated by a high-potassium, low-calcium solution in the hippocampus in vivo. Artificial cerebrospinal fluid was perfused over the surface of the exposed left dorsal hippocampus of anesthetized rats. A stimulating electrode and a recording probe were placed in the CA1 region. By elevating K+ concentration from 6 to 12 mM in the perfusate solution, the typical firing pattern of low-calcium ictal bursts was transformed into persistent spike activity in the CA1 region with synaptic transmission being suppressed by calcium chelator EGTA. The activity was characterized by double spikes repeated at a frequency approximately 4 Hz that could last for >1 h. The analysis of multiple unit activity showed that both elevating [K+]o and lowering [Ca2+]o decreased the inhibition period after the response of paired-pulse stimulation, indicating a suppression of the after-hyperpolarization (AHP) activity. These results suggest that persistent status epilepticus-like spike activity can be induced by nonsynaptic mechanisms when synaptic transmission is blocked. The unique double-spike pattern of this activity is presumably caused by higher K+ concentration augmenting the frequency of typical low-calcium nonsynaptic burst activity.

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.

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.

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

Characterization of reliability of spike timing in spinal interneurons during oscillating inputs

DEFF Research Database (Denmark)

Beierholm, Ulrik; Nielsen, Carsten D.; Ryge, Jesper

2001-01-01

that interneurons can respond with a high reliability of spike timing, but only by combining fast and slow oscillations is it possible to obtain a high reliability of firing during rhythmic locomotor movements. Theoretical analysis of the rotation number provided new insights into the mechanism for obtaining......The spike timing in rhythmically active interneurons in the mammalian spinal locomotor network varies from cycle to cycle. We tested the contribution from passive membrane properties to this variable firing pattern, by measuring the reliability of spike timing, P, in interneurons in the isolated...... the analysis we used a leaky integrate and fire (LIF) model with a noise term added. The LIF model was able to reproduce the experimentally observed properties of P as well as the low-pass properties of the membrane. The LIF model enabled us to use the mathematical theory of nonlinear oscillators to analyze...

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.

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.

Representation of the body in the lateral striatum of the freely moving rat: Fast Spiking Interneurons respond to stimulation of individual body parts.

Science.gov (United States)

Kulik, Julianna M; Pawlak, Anthony P; Kalkat, Manraj; Coffey, Kevin R; West, Mark O

2017-02-15

Numerous studies have shown that certain types of striatal interneurons play a crucial role in selection and regulation of striatal output. Striatal Fast-Spiking Interneurons (FSIs) are parvalbumin positive, GABAergic interneurons that constitute less than 1% of the total striatal population. It is becoming increasingly evident that these sparsely distributed neurons exert a strong inhibitory effect on Medium Spiny projection Neurons (MSNs). MSNs in lateral striatum receive direct synaptic input from regions of cortex representing discrete body parts, and show phasic increases in activity during touch or movement of specific body parts. In the present study, we sought to determine whether lateral striatal FSIs identified by their electrophysiological properties, i.e., short-duration spike and fast firing rate (FR), display body part sensitivity similar to that exhibited by MSNs. During video recorded somatosensorimotor exams, each individual body part was stimulated and responses of single neurons were observed and quantified. Individual FSIs displayed patterns of activity related selectively to stimulation of a discrete body part. Most patterns of activity were similar to those exhibited by typical MSNs, but some phasic decreases were observed. These results serve as evidence that some striatal FSIs process information related to discrete body parts and participate in sensorimotor processing by striatal networks that contribute to motor output. Parvalbumin positive, striatal FSIs are hypothesized to play an important role in behavior by inhibiting MSNs. We asked a fundamental question regarding information processed during behavior by FSIs: whether FSIs, which preferentially occupy the sensorimotor portion of the striatum, process activity of discrete body parts. Our finding that they do, in a selective manner similar to MSNs, begins to reveal the types of phasic signals that FSI feed forward to projection neurons during striatal processing of cortical input

A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation

Science.gov (United States)

Fiebig, Florian

2017-01-01

A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations. Here we test the hypothesis that a recently identified fast-expressing form of Hebbian synaptic plasticity (associative short-term potentiation) is a possible mechanism for WM encoding and maintenance. Our simulations using a spiking neural network model of cortex reproduce a range of cognitive memory effects in the classical multi-item WM task of encoding and immediate free recall of word lists. Memory reactivation in the model occurs in discrete oscillatory bursts rather than as sustained activity. We relate dynamic network activity as well as key synaptic characteristics to electrophysiological measurements. Our findings support the hypothesis that fast Hebbian short-term potentiation is a key WM mechanism. SIGNIFICANCE STATEMENT Working memory (WM) is a key component of cognition. Hypotheses about the neural mechanism behind WM are currently under revision. Reflecting recent findings of fast Hebbian synaptic plasticity in cortex, we test whether a cortical spiking neural network model with such a mechanism can learn a multi-item WM task (word list learning). We show that our model can reproduce human cognitive phenomena and achieve comparable memory performance in both free and cued recall while being simultaneously compatible with experimental data on structure, connectivity, and

GABAergic activities control spike timing- and frequency-dependent long-term depression at hippocampal excitatory synapses

Directory of Open Access Journals (Sweden)

Makoto Nishiyama

2010-06-01

Full Text Available GABAergic interneuronal network activities in the hippocampus control a variety of neural functions, including learning and memory, by regulating θ and γ oscillations. How these GABAergic activities at pre- and post-synaptic sites of hippocampal CA1 pyramidal cells differentially contribute to synaptic function and plasticity during their repetitive pre- and post-synaptic spiking at θ and γ oscillations is largely unknown. We show here that activities mediated by postsynaptic GABAARs and presynaptic GABABRs determine, respectively, the spike timing- and frequency-dependence of activity-induced synaptic modifications at Schaffer collateral-CA1 excitatory synapses. We demonstrate that both feedforward and feedback GABAAR-mediated inhibition in the postsynaptic cell controls the spike timing-dependent long-term depression of excitatory inputs (“e-LTD” at the θ frequency. We also show that feedback postsynaptic inhibition specifically causes e-LTD of inputs that induce small postsynaptic currents (<70 pA with LTP timing, thus enforcing the requirement of cooperativity for induction of long-term potentiation at excitatory inputs (“e-LTP”. Furthermore, under spike-timing protocols that induce e-LTP and e-LTD at excitatory synapses, we observed parallel induction of LTP and LTD at inhibitory inputs (“i-LTP” and “i-LTD” to the same postsynaptic cells. Finally, we show that presynaptic GABABR-mediated inhibition plays a major role in the induction of frequency-dependent e-LTD at α and β frequencies. These observations demonstrate the critical influence of GABAergic interneuronal network activities in regulating the spike timing and frequency dependences of long-term synaptic modifications in the hippocampus.

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

Directory of Open Access Journals (Sweden)

Risako Kato

2016-11-01

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

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.

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.

Fast-Spiking Interneurons Supply Feedforward Control of Bursting, Calcium, and Plasticity for Efficient Learning.

Science.gov (United States)

Owen, Scott F; Berke, Joshua D; Kreitzer, Anatol C

2018-02-08

Fast-spiking interneurons (FSIs) are a prominent class of forebrain GABAergic cells implicated in two seemingly independent network functions: gain control and network plasticity. Little is known, however, about how these roles interact. Here, we use a combination of cell-type-specific ablation, optogenetics, electrophysiology, imaging, and behavior to describe a unified mechanism by which striatal FSIs control burst firing, calcium influx, and synaptic plasticity in neighboring medium spiny projection neurons (MSNs). In vivo silencing of FSIs increased bursting, calcium transients, and AMPA/NMDA ratios in MSNs. In a motor sequence task, FSI silencing increased the frequency of calcium transients but reduced the specificity with which transients aligned to individual task events. Consistent with this, ablation of FSIs disrupted the acquisition of striatum-dependent egocentric learning strategies. Together, our data support a model in which feedforward inhibition from FSIs temporally restricts MSN bursting and calcium-dependent synaptic plasticity to facilitate striatum-dependent sequence learning. Copyright © 2018 Elsevier Inc. All rights reserved.

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

Directory of Open Access Journals (Sweden)

Bertrand Fontaine

2014-04-01

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

The Effects of Context and Attention on Spiking Activity in Human Early Visual Cortex.

Science.gov (United States)

Self, Matthew W; Peters, Judith C; Possel, Jessy K; Reithler, Joel; Goebel, Rainer; Ris, Peterjan; Jeurissen, Danique; Reddy, Leila; Claus, Steven; Baayen, Johannes C; Roelfsema, Pieter R

2016-03-01

Here we report the first quantitative analysis of spiking activity in human early visual cortex. We recorded multi-unit activity from two electrodes in area V2/V3 of a human patient implanted with depth electrodes as part of her treatment for epilepsy. We observed well-localized multi-unit receptive fields with tunings for contrast, orientation, spatial frequency, and size, similar to those reported in the macaque. We also observed pronounced gamma oscillations in the local-field potential that could be used to estimate the underlying spiking response properties. Spiking responses were modulated by visual context and attention. We observed orientation-tuned surround suppression: responses were suppressed by image regions with a uniform orientation and enhanced by orientation contrast. Additionally, responses were enhanced on regions that perceptually segregated from the background, indicating that neurons in the human visual cortex are sensitive to figure-ground structure. Spiking responses were also modulated by object-based attention. When the patient mentally traced a curve through the neurons' receptive fields, the accompanying shift of attention enhanced neuronal activity. These results demonstrate that the tuning properties of cells in the human early visual cortex are similar to those in the macaque and that responses can be modulated by both contextual factors and behavioral relevance. Our results, therefore, imply that the macaque visual system is an excellent model for the human visual cortex.

The Effects of Context and Attention on Spiking Activity in Human Early Visual Cortex.

Directory of Open Access Journals (Sweden)

Matthew W Self

2016-03-01

Full Text Available Here we report the first quantitative analysis of spiking activity in human early visual cortex. We recorded multi-unit activity from two electrodes in area V2/V3 of a human patient implanted with depth electrodes as part of her treatment for epilepsy. We observed well-localized multi-unit receptive fields with tunings for contrast, orientation, spatial frequency, and size, similar to those reported in the macaque. We also observed pronounced gamma oscillations in the local-field potential that could be used to estimate the underlying spiking response properties. Spiking responses were modulated by visual context and attention. We observed orientation-tuned surround suppression: responses were suppressed by image regions with a uniform orientation and enhanced by orientation contrast. Additionally, responses were enhanced on regions that perceptually segregated from the background, indicating that neurons in the human visual cortex are sensitive to figure-ground structure. Spiking responses were also modulated by object-based attention. When the patient mentally traced a curve through the neurons' receptive fields, the accompanying shift of attention enhanced neuronal activity. These results demonstrate that the tuning properties of cells in the human early visual cortex are similar to those in the macaque and that responses can be modulated by both contextual factors and behavioral relevance. Our results, therefore, imply that the macaque visual system is an excellent model for the human visual cortex.

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...

A novel role of dendritic gap junction and mechanisms underlying its interaction with thalamocortical conductance in fast spiking inhibitory neurons

Directory of Open Access Journals (Sweden)

Sun Qian-Quan

2009-10-01

Full Text Available Abstract Background Little is known about the roles of dendritic gap junctions (GJs of inhibitory interneurons in modulating temporal properties of sensory induced responses in sensory cortices. Electrophysiological dual patch-clamp recording and computational simulation methods were used in combination to examine a novel role of GJs in sensory mediated feed-forward inhibitory responses in barrel cortex layer IV and its underlying mechanisms. Results Under physiological conditions, excitatory post-junctional potentials (EPJPs interact with thalamocortical (TC inputs within an unprecedented few milliseconds (i.e. over 200 Hz to enhance the firing probability and synchrony of coupled fast-spiking (FS cells. Dendritic GJ coupling allows fourfold increase in synchrony and a significant enhancement in spike transmission efficacy in excitatory spiny stellate cells. The model revealed the following novel mechanisms: 1 rapid capacitive current (Icap underlies the activation of voltage-gated sodium channels; 2 there was less than 2 milliseconds in which the Icap underlying TC input and EPJP was coupled effectively; 3 cells with dendritic GJs had larger input conductance and smaller membrane response to weaker inputs; 4 synchrony in inhibitory networks by GJ coupling leads to reduced sporadic lateral inhibition and increased TC transmission efficacy. Conclusion Dendritic GJs of neocortical inhibitory networks can have very powerful effects in modulating the strength and the temporal properties of sensory induced feed-forward inhibitory and excitatory responses at a very high frequency band (>200 Hz. Rapid capacitive currents are identified as main mechanisms underlying interaction between two transient synaptic conductances.

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

Fast Na+ spike generation in dendrites of guinea-pig substantia nigra pars compacta neurons

DEFF Research Database (Denmark)

Nedergaard, S; Hounsgaard, Jørn Dybkjær

1996-01-01

were not inhibited in the presence of glutamate receptor antagonists or during Ca2+ channel blockade. Blockers of gap junctional conductance (sodium propionate, octanol and halothane) did not affect the field-induced spikes. The spike generation was highly sensitive to changes in membrane conductance...

A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation.

Science.gov (United States)

Fiebig, Florian; Lansner, Anders

2017-01-04

A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations. Here we test the hypothesis that a recently identified fast-expressing form of Hebbian synaptic plasticity (associative short-term potentiation) is a possible mechanism for WM encoding and maintenance. Our simulations using a spiking neural network model of cortex reproduce a range of cognitive memory effects in the classical multi-item WM task of encoding and immediate free recall of word lists. Memory reactivation in the model occurs in discrete oscillatory bursts rather than as sustained activity. We relate dynamic network activity as well as key synaptic characteristics to electrophysiological measurements. Our findings support the hypothesis that fast Hebbian short-term potentiation is a key WM mechanism. Working memory (WM) is a key component of cognition. Hypotheses about the neural mechanism behind WM are currently under revision. Reflecting recent findings of fast Hebbian synaptic plasticity in cortex, we test whether a cortical spiking neural network model with such a mechanism can learn a multi-item WM task (word list learning). We show that our model can reproduce human cognitive phenomena and achieve comparable memory performance in both free and cued recall while being simultaneously compatible with experimental data on structure, connectivity, and neurophysiology of the underlying

Decimetric type III radio bursts and associated hard X-ray spikes

Science.gov (United States)

Dennis, B. R.; Benz, A. O.; Ranieri, M.; Simnett, G. M.

1984-01-01

For a relatively weak solar flare on August 6, 1981, at 10:32 UT, a detailed comparison is made between hard X-ray spikes and decimetric type III radio bursts. The hard X-ray observations are made at energies above 30 keV, and the radio data are obtained in the frequency range from 100 to 1000 MHz. The time resolution for all the data sets is approximately 0.1 s or better. The dynamic radio spectrum exhibits many fast drift type III radio bursts with both normal and reverse slope, whereas the X-ray time profile contains many well resolved short spikes with durations less than or equal to 1 s. Some of the X-ray spikes are seen to be associated in time with reverse-slope bursts, indicating either that the electron beams producing the radio burst contain two or three orders of magnitude more fast electrons than has previously been assumed or that the electron beams can induce the acceleration of additional electrons or occur in coincidence with this acceleration. A case is presented in which a normal slope radio burst at approximately 600 MHz occurs in coincidence with the peak of an X-ray spike to within 0.1 s.

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)

A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings.

Science.gov (United States)

Magri, Cesare; Whittingstall, Kevin; Singh, Vanessa; Logothetis, Nikos K; Panzeri, Stefano

2009-07-16

Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. The new toolbox presented here implements fast and data-robust computations of the most relevant

A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings

Directory of Open Access Journals (Sweden)

Logothetis Nikos K

2009-07-01

Full Text Available Abstract Background Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Results Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. Conclusion The new toolbox presented here implements fast

A prolongation of the postspike afterhyperpolarization following spike trains can partly explain the lower firing rates at derecruitment than those at recruitment

DEFF Research Database (Denmark)

Wienecke, Jacob; Zhang, Mengliang; Hultborn, Hans

2009-01-01

rates at derecruitment correlated with a change in the postspike afterhyperpolarization (AHP) after preceding spike trains? This question was investigated by intracellular recordings from cat motor neurons in both unanesthetized and anesthetized preparations. The firing frequencies at recruitment...... for the lower frequencies at derecruitment. This was independent of whether the current injection had activated persistent inward current (PIC; plateau potentials, secondary range firing). It was found that a preceding spike train could prolong the AHP duration following a subsequent spike. The lower rate...... from AHP duration in fast motoneurons and higher than expected in slow motoneurons. It is suggested that these deviations are explained by the presence of synaptic noise as well as recruitment of PICs below firing threshold. Thus synaptic noise may allow spike discharge even after the end of the AHP...

Spike-timing dependent plasticity in the striatum

Directory of Open Access Journals (Sweden)

Elodie Fino

2010-06-01

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

Effects of Hypocretin/Orexin and Major Transmitters of Arousal on Fast Spiking Neurons in Mouse Cortical Layer 6B.

Science.gov (United States)

Wenger Combremont, Anne-Laure; Bayer, Laurence; Dupré, Anouk; Mühlethaler, Michel; Serafin, Mauro

2016-08-01

Fast spiking (FS) GABAergic neurons are thought to be involved in the generation of high-frequency cortical rhythms during the waking state. We previously showed that cortical layer 6b (L6b) was a specific target for the wake-promoting transmitter, hypocretin/orexin (hcrt/orx). Here, we have investigated whether L6b FS cells were sensitive to hcrt/orx and other transmitters associated with cortical activation. Recordings were thus made from L6b FS cells in either wild-type mice or in transgenic mice in which GFP-positive GABAergic cells are parvalbumin positive. Whereas in a control condition hcrt/orx induced a strong increase in the frequency, but not amplitude, of spontaneous synaptic currents, in the presence of TTX, it had no effect at all on miniature synaptic currents. Hcrt/orx effect was thus presynaptic although not by an action on glutamatergic terminals but rather on neighboring cells. In contrast, noradrenaline and acetylcholine depolarized and excited these cells through a direct postsynaptic action. Neurotensin, which is colocalized in hcrt/orx neurons, also depolarized and excited these cells but the effect was indirect. Morphologically, these cells exhibited basket-like features. These results suggest that hcrt/orx, noradrenaline, acetylcholine, and neurotensin could contribute to high-frequency cortical activity through an action on L6b GABAergic FS cells. © The Author 2016. Published by Oxford University Press.

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)

Mouse neuroblastoma cell based model and the effect of epileptic events on calcium oscillations and neural spikes

Science.gov (United States)

Kim, Suhwan; Baek, Juyeong; Jung, Unsang; Lee, Sangwon; Jung, Woonggyu; Kim, Jeehyun; Kang, Shinwon

2013-05-01

Recently, Mouse neuroblastoma cells are considered as an attractive model for the study of human neurological and prion diseases, and intensively used as a model system in different areas. Among those areas, differentiation of neuro2a (N2A) cells, receptor mediated ion current, and glutamate induced physiological response are actively investigated. The reason for the interest to mouse neuroblastoma N2A cells is that they have a fast growing rate than other cells in neural origin with a few another advantages. This study evaluated the calcium oscillations and neural spikes recording of mouse neuroblastoma N2A cells in an epileptic condition. Based on our observation of neural spikes in mouse N2A cell with our proposed imaging modality, we report that mouse neuroblastoma N2A cells can be an important model related to epileptic activity studies. It is concluded that the mouse neuroblastoma N2A cells produce the epileptic spikes in vitro in the same way as produced by the neurons or the astrocytes. This evidence advocates the increased and strong level of neurotransmitters release by enhancement in free calcium using the 4-aminopyridine which causes the mouse neuroblastoma N2A cells to produce the epileptic spikes and calcium oscillation.

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.

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.

Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution

Science.gov (United States)

Rule, Michael E.; Vargas-Irwin, Carlos; Donoghue, John P.; Truccolo, Wilson

2015-01-01

Understanding the sources of variability in single-neuron spiking responses is an important open problem for the theory of neural coding. This variability is thought to result primarily from spontaneous collective dynamics in neuronal networks. Here, we investigate how well collective dynamics reflected in motor cortex local field potentials (LFPs) can account for spiking variability during motor behavior. Neural activity was recorded via microelectrode arrays implanted in ventral and dorsal premotor and primary motor cortices of non-human primates performing naturalistic 3-D reaching and grasping actions. Point process models were used to quantify how well LFP features accounted for spiking variability not explained by the measured 3-D reach and grasp kinematics. LFP features included the instantaneous magnitude, phase and analytic-signal components of narrow band-pass filtered (δ,θ,α,β) LFPs, and analytic signal and amplitude envelope features in higher-frequency bands. Multiband LFP features predicted single-neuron spiking (1ms resolution) with substantial accuracy as assessed via ROC analysis. Notably, however, models including both LFP and kinematics features displayed marginal improvement over kinematics-only models. Furthermore, the small predictive information added by LFP features to kinematic models was redundant to information available in fast-timescale (spiking history. Overall, information in multiband LFP features, although predictive of single-neuron spiking during movement execution, was redundant to information available in movement parameters and spiking history. Our findings suggest that, during movement execution, collective dynamics reflected in motor cortex LFPs primarily relate to sensorimotor processes directly controlling movement output, adding little explanatory power to variability not accounted by movement parameters. PMID:26157365

Persistence and storage of activity patterns in spiking recurrent cortical networks: modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine.

Science.gov (United States)

Palma, Jesse; Grossberg, Stephen; Versace, Massimiliano

2012-01-01

Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM). Theorems in the 1970's showed how feedback signal functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP) currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh) can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 ms or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all (WTA) stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners when the network

Persistence and storage of activity patterns in spiking recurrent cortical networks:Modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine

Directory of Open Access Journals (Sweden)

Jesse ePalma

2012-06-01

Full Text Available Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM. Theorems in the 1970’s showed how feedback signal functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 milliseconds or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners

Directional spike propagation in a recurrent network: dynamical firewall as anisotropic recurrent inhibition.

Science.gov (United States)

Samura, Toshikazu; Hayashi, Hatsuo

2012-09-01

It has been demonstrated that theta rhythm propagates along the septotemporal axis of the hippocampal CA1 of the rat running on a track, and it has been suggested that directional spike propagation in the hippocampal CA3 is reflected in CA1. In this paper, we show that directional spike propagation occurs in a recurrent network model in which neurons are connected locally and connection weights are modified through STDP. The recurrent network model consists of excitatory and inhibitory neurons, which are intrinsic bursting and fast spiking neurons developed by Izhikevich, respectively. The maximum length of connections from excitatory neurons is shorter in the horizontal direction than the vertical direction. Connections from inhibitory neurons have the same maximum length in both directions, and the maximum length of inhibitory connections is the same as that of excitatory connections in the vertical direction. When connection weights between excitatory neurons (E→E) were modified through STDP and those from excitatory neurons to inhibitory neurons (E→I) were constant, spikes propagated in the vertical direction as expected from the network structure. However, when E→I connection weights were modified through STDP, as well as E→E connection weights, spikes propagated in the horizontal direction against the above expectation. This paradoxical propagation was produced by strengthened E→I connections which shifted the timing of inhibition forward. When E→I connections are enhanced, the direction of effective inhibition changes from horizontal to vertical, as if a gate for spike propagation is opened in the horizontal direction and firewalls come out in the vertical direction. These results suggest that the advance of timing of inhibition caused by potentiation of E→I connections is influential in network activity and is an important element in determining the direction of spike propagation. Copyright © 2012 Elsevier Ltd. All rights reserved.

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.

A reanalysis of "Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons".

Science.gov (United States)

Engelken, Rainer; Farkhooi, Farzad; Hansel, David; van Vreeswijk, Carl; Wolf, Fred

2016-01-01

Neuronal activity in the central nervous system varies strongly in time and across neuronal populations. It is a longstanding proposal that such fluctuations generically arise from chaotic network dynamics. Various theoretical studies predict that the rich dynamics of rate models operating in the chaotic regime can subserve circuit computation and learning. Neurons in the brain, however, communicate via spikes and it is a theoretical challenge to obtain similar rate fluctuations in networks of spiking neuron models. A recent study investigated spiking balanced networks of leaky integrate and fire (LIF) neurons and compared their dynamics to a matched rate network with identical topology, where single unit input-output functions were chosen from isolated LIF neurons receiving Gaussian white noise input. A mathematical analogy between the chaotic instability in networks of rate units and the spiking network dynamics was proposed. Here we revisit the behavior of the spiking LIF networks and these matched rate networks. We find expected hallmarks of a chaotic instability in the rate network: For supercritical coupling strength near the transition point, the autocorrelation time diverges. For subcritical coupling strengths, we observe critical slowing down in response to small external perturbations. In the spiking network, we found in contrast that the timescale of the autocorrelations is insensitive to the coupling strength and that rate deviations resulting from small input perturbations rapidly decay. The decay speed even accelerates for increasing coupling strength. In conclusion, our reanalysis demonstrates fundamental differences between the behavior of pulse-coupled spiking LIF networks and rate networks with matched topology and input-output function. In particular there is no indication of a corresponding chaotic instability in the spiking network.

The Effects of Context and Attention on Spiking Activity in Human Early Visual Cortex

NARCIS (Netherlands)

Self, Matthew W.; Peters, Judith C.; Possel, Jessy K.; Reithler, Joel; Goebel, Rainer; Ris, Peterjan; Jeurissen, Danique; Reddy, Leila; Claus, Steven; Baayen, Johannes C.; Roelfsema, Pieter R.

2016-01-01

Here we report the first quantitative analysis of spiking activity in human early visual cortex. We recorded multi-unit activity from two electrodes in area V2/V3 of a human patient implanted with depth electrodes as part of her treatment for epilepsy. We observed well-localized multi-unit receptive

The Effects of Context and Attention on Spiking Activity in Human Early Visual Cortex

NARCIS (Netherlands)

Self, Matthew W; Peters, Judith C; Possel, Jessy K; Reithler, Joel; Goebel, Rainer; Ris, Peterjan; Jeurissen, Danique; Reddy, Leila; Claus, Steven; Baayen, Johannes C; Roelfsema, Pieter R

Here we report the first quantitative analysis of spiking activity in human early visual cortex. We recorded multi-unit activity from two electrodes in area V2/V3 of a human patient implanted with depth electrodes as part of her treatment for epilepsy. We observed well-localized multi-unit receptive

Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons.

Directory of Open Access Journals (Sweden)

Kyriaki Sidiropoulou

Full Text Available Proper functioning of working memory involves the expression of stimulus-selective persistent activity in pyramidal neurons of the prefrontal cortex (PFC, which refers to neural activity that persists for seconds beyond the end of the stimulus. The mechanisms which PFC pyramidal neurons use to discriminate between preferred vs. neutral inputs at the cellular level are largely unknown. Moreover, the presence of pyramidal cell subtypes with different firing patterns, such as regular spiking and intrinsic bursting, raises the question as to what their distinct role might be in persistent firing in the PFC. Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS and an intrinsic bursting (IB model neuron. We identify synaptic location within the basal dendrites as a feature of stimulus selectivity. Specifically, persistent activity-inducing stimuli consist of activated synapses that are located more distally from the soma compared to non-inducing stimuli, in both model cells. In addition, the action potential (AP latency and the first few inter-spike-intervals of the neuronal response can be used to reliably detect inducing vs. non-inducing inputs, suggesting a potential mechanism by which downstream neurons can rapidly decode the upcoming emergence of persistent activity. While the two model neurons did not differ in the coding features of persistent activity emergence, the properties of persistent activity, such as the firing pattern and the duration of temporally-restricted persistent activity were distinct. Collectively, our results pinpoint to specific features of the neuronal response to a given

Extraction and characterization of essential discharge patterns from multisite recordings of spiking ongoing activity.

Directory of Open Access Journals (Sweden)

Riccardo Storchi

Full Text Available Neural activation patterns proceed often by schemes or motifs distributed across the involved cortical networks. As neurons are correlated, the estimate of all possible dependencies quickly goes out of control. The complex nesting of different oscillation frequencies and their high non-stationariety further hamper any quantitative evaluation of spiking network activities. The problem is exacerbated by the intrinsic variability of neural patterns.Our technique introduces two important novelties and enables to insulate essential patterns on larger sets of spiking neurons and brain activity regimes. First, the sampling procedure over N units is based on a fixed spike number k in order to detect N-dimensional arrays (k-sequences, whose sum over all dimension is k. Then k-sequences variability is greatly reduced by a hierarchical separative clustering, that assigns large amounts of distinct k-sequences to few classes. Iterative separations are stopped when the dimension of each cluster comes to be smaller than a certain threshold. As threshold tuning critically impacts on the number of classes extracted, we developed an effective cost criterion to select the shortest possible description of our dataset. Finally we described three indexes (C,S,R to evaluate the average pattern complexity, the structure of essential classes and their stability in time.We validated this algorithm with four kinds of surrogated activity, ranging from random to very regular patterned. Then we characterized a selection of ongoing activity recordings. By the S index we identified unstable, moderatly and strongly stable patterns while by the C and the R indices we evidenced their non-random structure. Our algorithm seems able to extract interesting and non-trivial spatial dynamics from multisource neuronal recordings of ongoing and potentially stimulated activity. Combined with time-frequency analysis of LFPs could provide a powerful multiscale approach linking population

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

Science.gov (United States)

Nobukawa, Sou; Nishimura, Haruhiko; Yamanishi, Teruya

2018-01-10

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

Differential regulation of the excitability of prefrontal cortical fast-spiking interneurons and pyramidal neurons by serotonin and fluoxetine.

Directory of Open Access Journals (Sweden)

Ping Zhong

2011-02-01

Full Text Available Serotonin exerts a powerful influence on neuronal excitability. In this study, we investigated the effects of serotonin on different neuronal populations in prefrontal cortex (PFC, a major area controlling emotion and cognition. Using whole-cell recordings in PFC slices, we found that bath application of 5-HT dose-dependently increased the firing of FS (fast spiking interneurons, and decreased the firing of pyramidal neurons. The enhancing effect of 5-HT in FS interneurons was mediated by 5-HT₂ receptors, while the reducing effect of 5-HT in pyramidal neurons was mediated by 5-HT₁ receptors. Fluoxetine, the selective serotonin reuptake inhibitor, also induced a concentration-dependent increase in the excitability of FS interneurons, but had little effect on pyramidal neurons. In rats with chronic fluoxetine treatment, the excitability of FS interneurons was significantly increased, while pyramidal neurons remained unchanged. Fluoxetine injection largely occluded the enhancing effect of 5-HT in FS interneurons, but did not alter the reducing effect of 5-HT in pyramidal neurons. These data suggest that the excitability of PFC interneurons and pyramidal neurons is regulated by exogenous 5-HT in an opposing manner, and FS interneurons are the major target of Fluoxetine. It provides a framework for understanding the action of 5-HT and antidepressants in altering PFC network activity.

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)

Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks

Directory of Open Access Journals (Sweden)

Daniel ede Santos-Sierra

2015-11-01

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

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

Science.gov (United States)

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

2015-01-01

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

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.

Hybrid Spintronic-CMOS Spiking Neural Network with On-Chip Learning: Devices, Circuits, and Systems

Science.gov (United States)

Sengupta, Abhronil; Banerjee, Aparajita; Roy, Kaushik

2016-12-01

Over the past decade, spiking neural networks (SNNs) have emerged as one of the popular architectures to emulate the brain. In SNNs, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing-dependent plasticity mechanisms require the online programing of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike-transmission and programing-current paths. The spintronic synapse consists of a ferromagnet-heavy-metal heterostructure where the programing current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programing energy and fast programing times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in the transistor subthreshold region to form a network of spiking neurons that can be utilized for pattern-recognition problems.

Review of fast reactor activities

Energy Technology Data Exchange (ETDEWEB)

Balz, W [Commission of the European Communities, Brussels (Belgium)

1978-07-01

The Commission of the European Communities continued its activities on the following lines: activities aimed at preparing for commercialization of fast breeder reactors which are essentially performed in the frame of Fast Reactor Coordinating Committee (FRCC); the execution of its own research program in the Joint Research Center. The report covers activities of the FRCC, of the Safety Working Group (SWG), the Whole Core Accident Code (WAC) subgroup, Containment (CONT) subgroup, Codes and Standards Working Group (CSWG). Research and development activities are concerned with LMFBR safety, subassembly thermal hydraulics, fuel-coolant interactions, post-accident heat removal, dynamic load response, safety related material properties, utilization limits of fast breeder fuels, plutonium and actinide aspects of nuclear fuel cycle.

Review of fast reactor activities

International Nuclear Information System (INIS)

Balz, W.

1978-01-01

The Commission of the European Communities continued its activities on the following lines: activities aimed at preparing for commercialization of fast breeder reactors which are essentially performed in the frame of Fast Reactor Coordinating Committee (FRCC); the execution of its own research program in the Joint Research Center. The report covers activities of the FRCC, of the Safety Working Group (SWG), the Whole Core Accident Code (WAC) subgroup, Containment (CONT) subgroup, Codes and Standards Working Group (CSWG). Research and development activities are concerned with LMFBR safety, subassembly thermal hydraulics, fuel-coolant interactions, post-accident heat removal, dynamic load response, safety related material properties, utilization limits of fast breeder fuels, plutonium and actinide aspects of nuclear fuel cycle

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.

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

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

Directory of Open Access Journals (Sweden)

Christian Albers

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

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

Science.gov (United States)

Albers, Christian; Westkott, Maren; Pawelzik, Klaus

2016-01-01

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

Intermittent Theta-Burst Transcranial Magnetic Stimulation Alters Electrical Properties of Fast-Spiking Neocortical Interneurons in an Age-Dependent Fashion

Directory of Open Access Journals (Sweden)

Kathrin eHoppenrath

2016-03-01

Full Text Available Modulation of human cortical excitability by repetitive transcranial magnetic stimulation (rTMS appears to be in part related to changed activity of inhibitory systems. Our own studies showed that intermittent theta-burst stimulation (iTBS applied via rTMS to rat cortex primarily affects the parvalbumin-expressing (PV fast-spiking interneurons (FSIs, evident via a strongly reduced PV expression. We further found the iTBS effect on PV to be age-dependent since no reduction in PV could be induced before the perineuronal nets (PNNs of FSIs start to grow around postnatal day 30. To elucidate possible iTBS-induced changes in the electrical properties of FSIs and cortical network activity during cortical critical period, we performed ex vivo – in vitro whole-cell patch clamp recordings from pre-labelled FSIs in the current study. FSIs of verum iTBS-treated rats displayed a higher excitability than sham-treated controls at PD29-38, evident as higher rates of induced action potential firing at low current injections (100-200 pA and a more depolarized resting membrane potential. This effect was absent in younger (PD26-28 and older animals (PD40-62. Slices of verum iTBS-treated rats further showed higher rates of spontaneous EPSCs. Based on these and previous findings we conclude that FSIs are particularly sensitive to theta-burst stimulation during early cortical development, when FSIs show an activity-driven step of maturation which is paralleled by intense growth of the PNNs and subsequent closure of the cortical critical period. Although to be proven further, rTMS may be a possible early intervention to compensate for hypo-activity related mal-development of cortical neuronal circuits.

Intermittent Theta-Burst Transcranial Magnetic Stimulation Alters Electrical Properties of Fast-Spiking Neocortical Interneurons in an Age-Dependent Fashion.

Science.gov (United States)

Hoppenrath, Kathrin; Härtig, Wolfgang; Funke, Klaus

2016-01-01

Modulation of human cortical excitability by repetitive transcranial magnetic stimulation (rTMS) appears to be in part related to changed activity of inhibitory systems. Our own studies showed that intermittent theta-burst stimulation (iTBS) applied via rTMS to rat cortex primarily affects the parvalbumin-expressing (PV) fast-spiking interneurons (FSIs), evident via a strongly reduced PV expression. We further found the iTBS effect on PV to be age-dependent since no reduction in PV could be induced before the perineuronal nets (PNNs) of FSIs start to grow around postnatal day (PD) 30. To elucidate possible iTBS-induced changes in the electrical properties of FSIs and cortical network activity during cortical critical period, we performed ex vivo-in vitro whole-cell patch clamp recordings from pre-labeled FSIs in the current study. FSIs of verum iTBS-treated rats displayed a higher excitability than sham-treated controls at PD29-38, evident as higher rates of induced action potential firing at low current injections (100-200 pA) and a more depolarized resting membrane potential. This effect was absent in younger (PD26-28) and older animals (PD40-62). Slices of verum iTBS-treated rats further showed higher rates of spontaneous excitatory postsynaptic currents (sEPSCs). Based on these and previous findings we conclude that FSIs are particularly sensitive to TBS during early cortical development, when FSIs show an activity-driven step of maturation which is paralleled by intense growth of the PNNs and subsequent closure of the cortical critical period. Although to be proven further, rTMS may be a possible early intervention to compensate for hypo-activity related mal-development of cortical neuronal circuits.

Treatment characteristics of various sediment components spiked with 2-chlorobiphenyl using reactive activated carbon.

Science.gov (United States)

Choi, Hyeok

2018-04-05

Previously, the concept of reactive activated carbon (RAC), where the porous structure of activated carbon (AC) is impregnated with palladized zerovalent iron, has been proposed to be effective to adsorb and dechlorinate polychlorinated biphenyls (PCBs). To explain the low dechlorination of PCBs bound to actual aquatic sediments under remediation with RAC, this study investigated the role of various solid organic and inorganic sediment components in adsorbing and desorbing PCBs. Detailed fate and transport mechanism of 2-chlorinated biphenyl (2-ClBP) spiked to sediment components, including kaolin, montmorillonite (MMT), coal, graphite, AC, and their mixture, was revealed. Adsorption and holding capability of sediment components toward 2-ClBP strongly influenced amount of spiked 2-ClBP, amount of desorbed 2-ClBP, overall dechlorination of 2-ClBP to biphenyl (BP), and eventual partitioning of 2-ClBP and BP to water, sediment component, and RAC. Order of the amount of spiked 2-ClBP to sediment components after drying, following AC > mixture > coal > graphite > kaolin > MMT, was in agreements (in opposite direction) with order of the amount of desorbed 2-ClBP and order of overall 2-ClBP dechlorination. Substantial role of organic components in aquatic sediments for holding 2-ClBP and thus preventing it from dechlorination on RAC was proven. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Seizure-induced alterations in fast-spiking basket cell GABA currents modulate frequency and coherence of gamma oscillation in network simulations

International Nuclear Information System (INIS)

Proddutur, Archana; Yu, Jiandong; Elgammal, Fatima S.; Santhakumar, Vijayalakshmi

2013-01-01

Gamma frequency oscillations have been proposed to contribute to memory formation and retrieval. Fast-spiking basket cells (FS-BCs) are known to underlie development of gamma oscillations. Fast, high amplitude GABA synapses and gap junctions have been suggested to contribute to gamma oscillations in FS-BC networks. Recently, we identified that, apart from GABAergic synapses, FS-BCs in the hippocampal dentate gyrus have GABAergic currents mediated by extrasynaptic receptors. Our experimental studies demonstrated two specific changes in FS-BC GABA currents following experimental seizures [Yu et al., J. Neurophysiol. 109, 1746 (2013)]: increase in the magnitude of extrasynaptic (tonic) GABA currents and a depolarizing shift in GABA reversal potential (E GABA ). Here, we use homogeneous networks of a biophysically based model of FS-BCs to examine how the presence of extrasynaptic GABA conductance (g GABA-extra ) and experimentally identified, seizure-induced changes in g GABA-extra and E GABA influence network activity. Networks of FS-BCs interconnected by fast GABAergic synapses developed synchronous firing in the dentate gamma frequency range (40–100 Hz). Systematic investigation revealed that the biologically realistic range of 30 to 40 connections between FS-BCs resulted in greater coherence in the gamma frequency range when networks were activated by Poisson-distributed dendritic synaptic inputs rather than by homogeneous somatic current injections, which were balanced for FS-BC firing frequency in unconnected networks. Distance-dependent conduction delay enhanced coherence in networks with 30–40 FS-BC interconnections while inclusion of gap junctional conductance had a modest effect on coherence. In networks activated by somatic current injections resulting in heterogeneous FS-BC firing, increasing g GABA-extra reduced the frequency and coherence of FS-BC firing when E GABA was shunting (−74 mV), but failed to alter average FS-BC frequency when E GABA

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.

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...

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...

Review of fast reactor activities

International Nuclear Information System (INIS)

1982-01-01

A description of some highlights of the activities performed by the Commission of the European Communities in the field of fast reactors is given. They fall into two categories: coordinating and harmonizing activities and research activities. The former are essentially performed in the frame of the Fast Reactor Coordinating Committee (FRCC), the latter in the Commission's Joint Research Center and to some extent under contract in research centers of the Member States

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.

Statistical properties of superimposed stationary spike trains.

Science.gov (United States)

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

2012-06-01

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

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.

The detection of intestinal spike activity on surface electroenterograms

Energy Technology Data Exchange (ETDEWEB)

Ye-Lin, Y; Garcia-Casado, J; Martinez-de-Juan, J L; Prats-Boluda, G [Instituto interuniversitario de investigacion en bioingenierIa y tecnologIa orientada al ser humano (I3BH), Universidad Politecnica de Valencia, Camino de Vera, s/n, Ed. 8E, Acceso N, 2a, planta 46022 Valencia (Spain); Ponce, J L [Department of Surgery, Hospital Universitario La Fe de Valencia, Avenida Campanar n0. 51, 46009 Valencia (Spain)], E-mail: yiye@eln.upv.es, E-mail: jgarciac@eln.upv.es, E-mail: jlmartinez@eln.upv.es, E-mail: geprabo@eln.upv.es, E-mail: drjlponce@ono.com

2010-02-07

Myoelectrical recording could provide an alternative technique for assessing intestinal motility, which is a topic of great interest in gastroenterology since many gastrointestinal disorders are associated with intestinal dysmotility. The pacemaker activity (slow wave, SW) of the electroenterogram (EEnG) has been detected in abdominal surface recordings, although the activity related to bowel contractions (spike bursts, SB) has to date only been detected in experimental models with artificially favored electrical conductivity. The aim of the present work was to assess the possibility of detecting SB activity in abdominal surface recordings under physiological conditions. For this purpose, 11 recording sessions of simultaneous internal and external myolectrical signals were conducted on conscious dogs. Signal analysis was carried out in the spectral domain. The results show that in periods of intestinal contractile activity, high-frequency components of EEnG signals can be detected on the abdominal surface in addition to SW activity. The energy between 2 and 20 Hz of the surface myoelectrical recording presented good correlation with the internal intestinal motility index (0.64 {+-} 0.10 for channel 1 and 0.57 {+-} 0.11 for channel 2). This suggests that SB activity can also be detected in canine surface EEnG recording.

The detection of intestinal spike activity on surface electroenterograms

International Nuclear Information System (INIS)

Ye-Lin, Y; Garcia-Casado, J; Martinez-de-Juan, J L; Prats-Boluda, G; Ponce, J L

2010-01-01

Myoelectrical recording could provide an alternative technique for assessing intestinal motility, which is a topic of great interest in gastroenterology since many gastrointestinal disorders are associated with intestinal dysmotility. The pacemaker activity (slow wave, SW) of the electroenterogram (EEnG) has been detected in abdominal surface recordings, although the activity related to bowel contractions (spike bursts, SB) has to date only been detected in experimental models with artificially favored electrical conductivity. The aim of the present work was to assess the possibility of detecting SB activity in abdominal surface recordings under physiological conditions. For this purpose, 11 recording sessions of simultaneous internal and external myolectrical signals were conducted on conscious dogs. Signal analysis was carried out in the spectral domain. The results show that in periods of intestinal contractile activity, high-frequency components of EEnG signals can be detected on the abdominal surface in addition to SW activity. The energy between 2 and 20 Hz of the surface myoelectrical recording presented good correlation with the internal intestinal motility index (0.64 ± 0.10 for channel 1 and 0.57 ± 0.11 for channel 2). This suggests that SB activity can also be detected in canine surface EEnG recording.

Sensory information in local field potentials and spikes from visual and auditory cortices: time scales and frequency bands.

Science.gov (United States)

Belitski, Andrei; Panzeri, Stefano; Magri, Cesare; Logothetis, Nikos K; Kayser, Christoph

2010-12-01

Studies analyzing sensory cortical processing or trying to decode brain activity often rely on a combination of different electrophysiological signals, such as local field potentials (LFPs) and spiking activity. Understanding the relation between these signals and sensory stimuli and between different components of these signals is hence of great interest. We here provide an analysis of LFPs and spiking activity recorded from visual and auditory cortex during stimulation with natural stimuli. In particular, we focus on the time scales on which different components of these signals are informative about the stimulus, and on the dependencies between different components of these signals. Addressing the first question, we find that stimulus information in low frequency bands (50 Hz), in contrast, is scale dependent, and is larger when the energy is averaged over several hundreds of milliseconds. Indeed, combined analysis of signal reliability and information revealed that the energy of slow LFP fluctuations is well related to the stimulus even when considering individual or few cycles, while the energy of fast LFP oscillations carries information only when averaged over many cycles. Addressing the second question, we find that stimulus information in different LFP bands, and in different LFP bands and spiking activity, is largely independent regardless of time scale or sensory system. Taken together, these findings suggest that different LFP bands represent dynamic natural stimuli on distinct time scales and together provide a potentially rich source of information for sensory processing or decoding brain activity.

A camel-derived MERS-CoV with a variant spike protein cleavage site and distinct fusion activation properties

Science.gov (United States)

Millet, Jean Kaoru; Goldstein, Monty E; Labitt, Rachael N; Hsu, Hung-Lun; Daniel, Susan; Whittaker, Gary R

2016-01-01

Middle East respiratory syndrome coronavirus (MERS-CoV) continues to circulate in both humans and camels, and the origin and evolution of the virus remain unclear. Here we characterize the spike protein of a camel-derived MERS-CoV (NRCE-HKU205) identified in 2013, early in the MERS outbreak. NRCE-HKU205 spike protein has a variant cleavage motif with regard to the S2′ fusion activation site—notably, a novel substitution of isoleucine for the otherwise invariant serine at the critical P1′ cleavage site position. The substitutions resulted in a loss of furin-mediated cleavage, as shown by fluorogenic peptide cleavage and western blot assays. Cell–cell fusion and pseudotyped virus infectivity assays demonstrated that the S2′ substitutions decreased spike-mediated fusion and viral entry. However, cathepsin and trypsin-like protease activation were retained, albeit with much reduced efficiency compared with the prototypical EMC/2012 human strain. We show that NRCE-HKU205 has more limited fusion activation properties possibly resulting in more restricted viral tropism and may represent an intermediate in the complex pattern of MERS-CoV ecology and evolution. PMID:27999426

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

Science.gov (United States)

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

2017-02-04

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

Seizure-induced alterations in fast-spiking basket cell GABA currents modulate frequency and coherence of gamma oscillation in network simulations

Energy Technology Data Exchange (ETDEWEB)

Proddutur, Archana; Yu, Jiandong; Elgammal, Fatima S. [Department of Neurology and Neurosciences, New Jersey Medical School, Rutgers, Newark, New Jersey 07103 (United States); Santhakumar, Vijayalakshmi, E-mail: santhavi@njms.rutgers.edu [Department of Neurology and Neurosciences, New Jersey Medical School, Rutgers, Newark, New Jersey 07103 (United States); Department of Pharmacology and Physiology, New Jersey Medical School, Rutgers, Newark, New Jersey 07103 (United States)

2013-12-15

Gamma frequency oscillations have been proposed to contribute to memory formation and retrieval. Fast-spiking basket cells (FS-BCs) are known to underlie development of gamma oscillations. Fast, high amplitude GABA synapses and gap junctions have been suggested to contribute to gamma oscillations in FS-BC networks. Recently, we identified that, apart from GABAergic synapses, FS-BCs in the hippocampal dentate gyrus have GABAergic currents mediated by extrasynaptic receptors. Our experimental studies demonstrated two specific changes in FS-BC GABA currents following experimental seizures [Yu et al., J. Neurophysiol. 109, 1746 (2013)]: increase in the magnitude of extrasynaptic (tonic) GABA currents and a depolarizing shift in GABA reversal potential (E{sub GABA}). Here, we use homogeneous networks of a biophysically based model of FS-BCs to examine how the presence of extrasynaptic GABA conductance (g{sub GABA-extra}) and experimentally identified, seizure-induced changes in g{sub GABA-extra} and E{sub GABA} influence network activity. Networks of FS-BCs interconnected by fast GABAergic synapses developed synchronous firing in the dentate gamma frequency range (40–100 Hz). Systematic investigation revealed that the biologically realistic range of 30 to 40 connections between FS-BCs resulted in greater coherence in the gamma frequency range when networks were activated by Poisson-distributed dendritic synaptic inputs rather than by homogeneous somatic current injections, which were balanced for FS-BC firing frequency in unconnected networks. Distance-dependent conduction delay enhanced coherence in networks with 30–40 FS-BC interconnections while inclusion of gap junctional conductance had a modest effect on coherence. In networks activated by somatic current injections resulting in heterogeneous FS-BC firing, increasing g{sub GABA-extra} reduced the frequency and coherence of FS-BC firing when E{sub GABA} was shunting (−74 mV), but failed to alter average

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...

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.

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.

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.

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

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

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

Science.gov (United States)

Desroches, Mathieu; Krupa, Martin; Rodrigues, Serafim

2016-09-01

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

Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition.

Science.gov (United States)

Kasabov, Nikola; Dhoble, Kshitij; Nuntalid, Nuttapod; Indiveri, Giacomo

2013-05-01

On-line learning and recognition of spatio- and spectro-temporal data (SSTD) is a very challenging task and an important one for the future development of autonomous machine learning systems with broad applications. Models based on spiking neural networks (SNN) have already proved their potential in capturing spatial and temporal data. One class of them, the evolving SNN (eSNN), uses a one-pass rank-order learning mechanism and a strategy to evolve a new spiking neuron and new connections to learn new patterns from incoming data. So far these networks have been mainly used for fast image and speech frame-based recognition. Alternative spike-time learning methods, such as Spike-Timing Dependent Plasticity (STDP) and its variant Spike Driven Synaptic Plasticity (SDSP), can also be used to learn spatio-temporal representations, but they usually require many iterations in an unsupervised or semi-supervised mode of learning. This paper introduces a new class of eSNN, dynamic eSNN, that utilise both rank-order learning and dynamic synapses to learn SSTD in a fast, on-line mode. The paper also introduces a new model called deSNN, that utilises rank-order learning and SDSP spike-time learning in unsupervised, supervised, or semi-supervised modes. The SDSP learning is used to evolve dynamically the network changing connection weights that capture spatio-temporal spike data clusters both during training and during recall. The new deSNN model is first illustrated on simple examples and then applied on two case study applications: (1) moving object recognition using address-event representation (AER) with data collected using a silicon retina device; (2) EEG SSTD recognition for brain-computer interfaces. The deSNN models resulted in a superior performance in terms of accuracy and speed when compared with other SNN models that use either rank-order or STDP learning. The reason is that the deSNN makes use of both the information contained in the order of the first input spikes

Decoding spikes in a spiking neuronal network

Energy Technology Data Exchange (ETDEWEB)

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

2004-06-04

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

Decoding spikes in a spiking neuronal network

International Nuclear Information System (INIS)

Feng Jianfeng; Ding, Mingzhou

2004-01-01

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

Low and high gamma oscillations in rat ventral striatum have distinct relationships to behavior, reward, and spiking activity on a learned spatial decision task

Directory of Open Access Journals (Sweden)

Matthijs A A Van Der Meer

2009-06-01

Full Text Available Local field potential (LFP oscillations in the brain reflect organization thought to be important for perception, attention, movement, and memory. In the basal ganglia, including dorsal striatum, dysfunctional LFP states are associated with Parkinson’s disease, while in healthy subjects, dorsal striatal LFPs have been linked to decision-making processes. However, LFPs in ventral striatum have been less studied. We report that in rats running a spatial decision task, prominent gamma-50 (45-55 Hz and gamma-80 (70-85 Hz oscillations in ventral striatum had distinct relationships to behavior, task events, and spiking activity. Gamma-50 power increased sharply following reward delivery and before movement initiation, while in contrast, gamma-80 power ramped up gradually to reward locations. Gamma-50 power was low and contained little structure during early learning, but rapidly developed a stable pattern, while gamma-80 power was initially high before returning to a stable level within a similar timeframe. Putative fast-spiking interneurons (FSIs showed phase, firing rate, and coherence relationships with gamma-50 and gamma-80, indicating that the observed LFP patterns are locally relevant. Furthermore, in a number of FSIs such relationships were specific to gamma-50 or gamma-80, suggesting that partially distinct FSI populations mediate the effects of gamma-50 and gamma-80.

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

Science.gov (United States)

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

2008-09-01

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

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

Science.gov (United States)

Masurkar, Arjun V.; Chen, Wei R.

2015-01-01

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

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.

Efficient Architecture for Spike Sorting in Reconfigurable Hardware

Science.gov (United States)

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

2013-01-01

This paper presents a novel hardware architecture for fast spike sorting. The architecture is able to perform both the feature extraction and clustering in hardware. The generalized Hebbian algorithm (GHA) and fuzzy C-means (FCM) algorithm are used for feature extraction and clustering, respectively. The employment of GHA allows efficient computation of principal components for subsequent clustering operations. The FCM is able to achieve near optimal clustering for spike sorting. Its performance is insensitive to the selection of initial cluster centers. The hardware implementations of GHA and FCM feature low area costs and high throughput. In the GHA architecture, the computation of different weight vectors share the same circuit for lowering the area costs. Moreover, in the FCM hardware implementation, the usual iterative operations for updating the membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. To show the effectiveness of the circuit, the proposed architecture is physically implemented by field programmable gate array (FPGA). It is embedded in a System-on-Chip (SOC) platform for performance measurement. Experimental results show that the proposed architecture is an efficient spike sorting design for attaining high classification correct rate and high speed computation. PMID:24189331

Efficient Architecture for Spike Sorting in Reconfigurable Hardware

Directory of Open Access Journals (Sweden)

Sheng-Ying Lai

2013-11-01

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

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

Science.gov (United States)

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

2001-03-01

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

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

Science.gov (United States)

Masurkar, Arjun V; Chen, Wei R

2012-02-01

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

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.

Automatic EEG spike detection.

Science.gov (United States)

Harner, Richard

2009-10-01

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

Review of fast reactor activities in India

International Nuclear Information System (INIS)

Paranjpe, S.R.

1982-01-01

A review of fast reactor activities in India is introduced. One stage of construction of the Fast Breeder Test Reactor (FBTR) and design studies for 500MWe Prototype Fast Breeder Reactor (PFBR) are briefly summarized. The emphasis is on fast reactor physics, materials studies, radiochemistry, and the safety and fuel reprocessing programme

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.

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

Directory of Open Access Journals (Sweden)

Liang Meng

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

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.

Copula Regression Analysis of Simultaneously Recorded Frontal Eye Field and Inferotemporal Spiking Activity during Object-Based Working Memory

Science.gov (United States)

Hu, Meng; Clark, Kelsey L.; Gong, Xiajing; Noudoost, Behrad; Li, Mingyao; Moore, Tirin

2015-01-01

Inferotemporal (IT) neurons are known to exhibit persistent, stimulus-selective activity during the delay period of object-based working memory tasks. Frontal eye field (FEF) neurons show robust, spatially selective delay period activity during memory-guided saccade tasks. We present a copula regression paradigm to examine neural interaction of these two types of signals between areas IT and FEF of the monkey during a working memory task. This paradigm is based on copula models that can account for both marginal distribution over spiking activity of individual neurons within each area and joint distribution over ensemble activity of neurons between areas. Considering the popular GLMs as marginal models, we developed a general and flexible likelihood framework that uses the copula to integrate separate GLMs into a joint regression analysis. Such joint analysis essentially leads to a multivariate analog of the marginal GLM theory and hence efficient model estimation. In addition, we show that Granger causality between spike trains can be readily assessed via the likelihood ratio statistic. The performance of this method is validated by extensive simulations, and compared favorably to the widely used GLMs. When applied to spiking activity of simultaneously recorded FEF and IT neurons during working memory task, we observed significant Granger causality influence from FEF to IT, but not in the opposite direction, suggesting the role of the FEF in the selection and retention of visual information during working memory. The copula model has the potential to provide unique neurophysiological insights about network properties of the brain. PMID:26063909

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

Science.gov (United States)

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

2016-01-01

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

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

Science.gov (United States)

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

2006-02-01

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

Selective fatigue of fast motor units after electrically elicited muscle contractions.

Science.gov (United States)

Hamada, Taku; Kimura, Tetsuya; Moritani, Toshio

2004-10-01

The aim of the present study was to elucidate the electrophysiological manifestations of selective fast motor unit (MU) activation by electrical stimulation (ES) of knee extensor muscles. In six male subjects, test contraction measurement at 40% maximal voluntary contraction (MVC) was performed before and at every 5 min (5, 10, 15 and 20 min) during 20-min low intensity intermittent exercise of either ES or voluntary contractions (VC) at 10% MVC (5-s isometric contraction and 5-s rest cycles). Both isolated intramuscular MU spikes obtained from three sets of bipolar fine-wire electrodes and surface electromyogram (EMG) were simultaneously recorded and were analyzed by means of a computer-aided intramuscular spike amplitude-frequency analysis and frequency power spectral analysis, respectively. Results indicated that mean MU spike amplitude, particularly those MUs with relatively large amplitude, was significantly reduced while those MUs with small spike amplitude increased their firing rate during the 40% MVC test contraction after the ES. This was accompanied by the increased amplitude of surface EMG (rmsEMG). However, no such significant changes in the intramuscular and surface EMGs were observed after VC. These findings indicated differential MU activation patterns in terms of MU recruitment and rate coding characteristics during ES and VC, respectively. Our data strongly suggest the possibility of "an inverse size principle" of MU recruitment during ES.

Population activity statistics dissect subthreshold and spiking variability in V1.

Science.gov (United States)

Bányai, Mihály; Koman, Zsombor; Orbán, Gergő

2017-07-01

variability. Our work shows that stimulus-dependent changes in pairwise but not in single-cell statistics can differentiate between two widely used models of neuronal variability. Contrasting model predictions with neuronal data provides hints on the noise sources in spiking and provides constraints on statistical models of population activity. Copyright © 2017 the American Physiological Society.

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.

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.

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.

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

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

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.

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

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

International Nuclear Information System (INIS)

Yoshioka, Masahiko

2002-01-01

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

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

A Feedforward Inhibitory Circuit Mediated by CB1-Expressing Fast-Spiking Interneurons in the Nucleus Accumbens.

Science.gov (United States)

Wright, William J; Schlüter, Oliver M; Dong, Yan

2017-04-01

The nucleus accumbens (NAc) gates motivated behaviors through the functional output of principle medium spiny neurons (MSNs), whereas dysfunctional output of NAc MSNs contributes to a variety of psychiatric disorders. Fast-spiking interneurons (FSIs) are sparsely distributed throughout the NAc, forming local feedforward inhibitory circuits. It remains elusive how FSI-based feedforward circuits regulate the output of NAc MSNs. Here, we investigated a distinct subpopulation of NAc FSIs that express the cannabinoid receptor type-1 (CB1). Using a combination of paired electrophysiological recordings and pharmacological approaches, we characterized and compared feedforward inhibition of NAc MSNs from CB1 + FSIs and lateral inhibition from recurrent MSN collaterals. We observed that CB1 + FSIs exerted robust inhibitory control over a large percentage of nearby MSNs in contrast to local MSN collaterals that provided only sparse and weak inhibitory input to their neighboring MSNs. Furthermore, CB1 + FSI-mediated feedforward inhibition was preferentially suppressed by endocannabinoid (eCB) signaling, whereas MSN-mediated lateral inhibition was unaffected. Finally, we demonstrated that CB1 + FSI synapses onto MSNs are capable of undergoing experience-dependent long-term depression in a voltage- and eCB-dependent manner. These findings demonstrated that CB1 + FSIs are a major source of local inhibitory control of MSNs and a critical component of the feedforward inhibitory circuits regulating the output of the NAc.

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.

Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis.

Science.gov (United States)

Leibig, Christian; Wachtler, Thomas; Zeck, Günther

2016-09-15

Unsupervised identification of action potentials in multi-channel extracellular recordings, in particular from high-density microelectrode arrays with thousands of sensors, is an unresolved problem. While independent component analysis (ICA) achieves rapid unsupervised sorting, it ignores the convolutive structure of extracellular data, thus limiting the unmixing to a subset of neurons. Here we present a spike sorting algorithm based on convolutive ICA (cICA) to retrieve a larger number of accurately sorted neurons than with instantaneous ICA while accounting for signal overlaps. Spike sorting was applied to datasets with varying signal-to-noise ratios (SNR: 3-12) and 27% spike overlaps, sampled at either 11.5 or 23kHz on 4365 electrodes. We demonstrate how the instantaneity assumption in ICA-based algorithms has to be relaxed in order to improve the spike sorting performance for high-density microelectrode array recordings. Reformulating the convolutive mixture as an instantaneous mixture by modeling several delayed samples jointly is necessary to increase signal-to-noise ratio. Our results emphasize that different cICA algorithms are not equivalent. Spike sorting performance was assessed with ground-truth data generated from experimentally derived templates. The presented spike sorter was able to extract ≈90% of the true spike trains with an error rate below 2%. It was superior to two alternative (c)ICA methods (≈80% accurately sorted neurons) and comparable to a supervised sorting. Our new algorithm represents a fast solution to overcome the current bottleneck in spike sorting of large datasets generated by simultaneous recording with thousands of electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

Fluctuating inhibitory inputs promote reliable spiking at theta frequencies in hippocampal interneurons

Directory of Open Access Journals (Sweden)

Duluxan eSritharan

2012-05-01

Full Text Available Theta frequency (4-12 Hz rhythms in the hippocampus play important roles in learning and memory. CA1 interneurons located at the stratum lacunosum-moleculare and radiatum junction (LM/RAD are thought to contribute to hippocampal theta population activities by rhythmically pacing pyramidal cells with inhibitory postsynaptic potentials. This implies that LM/RAD cells need to fire reliably at theta frequencies in vivo. To determine whether this could occur, we use biophysically-based LM/RAD model cells and apply different cholinergic and synaptic inputs to simulate in vivo-like network environments. We assess spike reliabilities and spiking frequencies, identifying biophysical properties and network conditions that best promote reliable theta spiking. We find that synaptic background activities that feature large inhibitory, but not excitatory, fluctuations are essential. This suggests that strong inhibitory input to these cells is vital for them to be able to contribute to population theta activities. Furthermore, we find that Type I-like oscillator models produced by augmented persistent sodium currents (INap or diminished A type potassium currents (IA enhance reliable spiking at lower theta frequencies. These Type I-like models are also the most responsive to large inhibitory fluctuations and can fire more reliably under such conditions. In previous work, we showed that INap and IA are largely responsible for establishing LM/RAD cells’ subthreshold activities. Taken together with this study, we see that while both these currents are important for subthreshold theta fluctuations and reliable theta spiking, they contribute in different ways – INap to reliable theta spiking and subthreshold activity generation, and IA to subthreshold activities at theta frequencies. This suggests that linking subthreshold and suprathreshold activities should be done with consideration of both in vivo contexts and biophysical specifics.

Design of node record for fast active reflector

International Nuclear Information System (INIS)

Wu Wenqing; Luo Mingcheng; Tang Pengyi; Liu Jiajing; Wang Jian

2014-01-01

Active Reflector is the one of the innovations of Five hundred meter Aperture Spherical Telescope (FAST) whose performance touches on that of the overall telescope. Therefore a real time control system is needed by the Active Reflector System. In this paper, a new record type-node record is designed for EPICS-based active reflector control system of FAST, according to more than 2000 controlled node, which will be convenient for node management of IOC and prove the reusage of IOC codes. The record type is used in design of active reflector control system of FAST Miyun model. (authors)

Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone.

Directory of Open Access Journals (Sweden)

Felipe Gerhard

Full Text Available Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities.

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.

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.

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.

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.

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.

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.

Spike propagation through the dorsal root ganglia in an unmyelinated sensory neuron: a modeling study.

Science.gov (United States)

Sundt, Danielle; Gamper, Nikita; Jaffe, David B

2015-12-01

Unmyelinated C-fibers are a major type of sensory neurons conveying pain information. Action potential conduction is regulated by the bifurcation (T-junction) of sensory neuron axons within the dorsal root ganglia (DRG). Understanding how C-fiber signaling is influenced by the morphology of the T-junction and the local expression of ion channels is important for understanding pain signaling. In this study we used biophysical computer modeling to investigate the influence of axon morphology within the DRG and various membrane conductances on the reliability of spike propagation. As expected, calculated input impedance and the amplitude of propagating action potentials were both lowest at the T-junction. Propagation reliability for single spikes was highly sensitive to the diameter of the stem axon and the density of voltage-gated Na(+) channels. A model containing only fast voltage-gated Na(+) and delayed-rectifier K(+) channels conducted trains of spikes up to frequencies of 110 Hz. The addition of slowly activating KCNQ channels (i.e., KV7 or M-channels) to the model reduced the following frequency to 30 Hz. Hyperpolarization produced by addition of a much slower conductance, such as a Ca(2+)-dependent K(+) current, was needed to reduce the following frequency to 6 Hz. Attenuation of driving force due to ion accumulation or hyperpolarization produced by a Na(+)-K(+) pump had no effect on following frequency but could influence the reliability of spike propagation mutually with the voltage shift generated by a Ca(2+)-dependent K(+) current. These simulations suggest how specific ion channels within the DRG may contribute toward therapeutic treatments for chronic pain. Copyright © 2015 the American Physiological Society.

Fast reactor research activities in Brazil

International Nuclear Information System (INIS)

Menezes, A.

1998-01-01

Fast reactor activities in Brazil have the objective of establishing a consistent knowledge basis which can serve as a support for a future transitions to the activities more directly related to design, construction and operation of an experimental fast reactor, although its materialization is still far from being decided. Due to the present economic difficulties and uncertainties, the program is modest and all efforts have been directed towards its consolidation, based on the understanding that this class of reactors will play an important role in the future and Brazil needs to be minimally prepared. The text describes the present status of those activities, emphasizing the main progress made in 1996. (author)

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.

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

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

Directory of Open Access Journals (Sweden)

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.

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

Science.gov (United States)

Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

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

iRaster: a novel information visualization tool to explore spatiotemporal patterns in multiple spike trains.

Science.gov (United States)

Somerville, J; Stuart, L; Sernagor, E; Borisyuk, R

2010-12-15

Over the last few years, simultaneous recordings of multiple spike trains have become widely used by neuroscientists. Therefore, it is important to develop new tools for analysing multiple spike trains in order to gain new insight into the function of neural systems. This paper describes how techniques from the field of visual analytics can be used to reveal specific patterns of neural activity. An interactive raster plot called iRaster has been developed. This software incorporates a selection of statistical procedures for visualization and flexible manipulations with multiple spike trains. For example, there are several procedures for the re-ordering of spike trains which can be used to unmask activity propagation, spiking synchronization, and many other important features of multiple spike train activity. Additionally, iRaster includes a rate representation of neural activity, a combined representation of rate and spikes, spike train removal and time interval removal. Furthermore, it provides multiple coordinated views, time and spike train zooming windows, a fisheye lens distortion, and dissemination facilities. iRaster is a user friendly, interactive, flexible tool which supports a broad range of visual representations. This tool has been successfully used to analyse both synthetic and experimentally recorded datasets. In this paper, the main features of iRaster are described and its performance and effectiveness are demonstrated using various types of data including experimental multi-electrode array recordings from the ganglion cell layer in mouse retina. iRaster is part of an ongoing research project called VISA (Visualization of Inter-Spike Associations) at the Visualization Lab in the University of Plymouth. The overall aim of the VISA project is to provide neuroscientists with the ability to freely explore and analyse their data. The software is freely available from the Visualization Lab website (see www.plymouth.ac.uk/infovis). Copyright © 2010

A reanalysis of “Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons” [version 1; referees: 2 approved

Directory of Open Access Journals (Sweden)

Rainer Engelken

2016-08-01

Full Text Available Neuronal activity in the central nervous system varies strongly in time and across neuronal populations. It is a longstanding proposal that such fluctuations generically arise from chaotic network dynamics. Various theoretical studies predict that the rich dynamics of rate models operating in the chaotic regime can subserve circuit computation and learning. Neurons in the brain, however, communicate via spikes and it is a theoretical challenge to obtain similar rate fluctuations in networks of spiking neuron models. A recent study investigated spiking balanced networks of leaky integrate and fire (LIF neurons and compared their dynamics to a matched rate network with identical topology, where single unit input-output functions were chosen from isolated LIF neurons receiving Gaussian white noise input. A mathematical analogy between the chaotic instability in networks of rate units and the spiking network dynamics was proposed. Here we revisit the behavior of the spiking LIF networks and these matched rate networks. We find expected hallmarks of a chaotic instability in the rate network: For supercritical coupling strength near the transition point, the autocorrelation time diverges. For subcritical coupling strengths, we observe critical slowing down in response to small external perturbations. In the spiking network, we found in contrast that the timescale of the autocorrelations is insensitive to the coupling strength and that rate deviations resulting from small input perturbations rapidly decay. The decay speed even accelerates for increasing coupling strength. In conclusion, our reanalysis demonstrates fundamental differences between the behavior of pulse-coupled spiking LIF networks and rate networks with matched topology and input-output function. In particular there is no indication of a corresponding chaotic instability in the spiking network.

Attractors of relaxation discrete-time systems with chaotic dynamics on a fast time scale

International Nuclear Information System (INIS)

Maslennikov, Oleg V.; Nekorkin, Vladimir I.

2016-01-01

In this work, a new type of relaxation systems is considered. Their prominent feature is that they comprise two distinct epochs, one is slow regular motion and another is fast chaotic motion. Unlike traditionally studied slow-fast systems that have smooth manifolds of slow motions in the phase space and fast trajectories between them, in this new type one observes, apart the same geometric objects, areas of transient chaos. Alternating periods of slow regular motions and fast chaotic ones as well as transitions between them result in a specific chaotic attractor with chaos on a fast time scale. We formulate basic properties of such attractors in the framework of discrete-time systems and consider several examples. Finally, we provide an important application of such systems, the neuronal electrical activity in the form of chaotic spike-burst oscillations.

Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains

Directory of Open Access Journals (Sweden)

Rodrigo Cofré

2018-01-01

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

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

Science.gov (United States)

Tiganj, Z; Mboup, M

2012-12-01

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

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

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.

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

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.

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.

Bayesian population decoding of spiking neurons.

Science.gov (United States)

Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias

2009-01-01

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

Bayesian population decoding of spiking neurons

Directory of Open Access Journals (Sweden)

Sebastian Gerwinn

2009-10-01

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

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

Science.gov (United States)

Azami, Hamed; Escudero, Javier

2016-05-01

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

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

Science.gov (United States)

Khaliq, Zayd M; Raman, Indira M

2005-01-12

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

Parametric models to relate spike train and LFP dynamics with neural information processing.

Science.gov (United States)

Banerjee, Arpan; Dean, Heather L; Pesaran, Bijan

2012-01-01

Spike trains and local field potentials (LFPs) resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus-driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior. We obtained significant spike-field onset time correlations from single trials using a previously published data set where significantly strong correlation was only obtained through trial averaging. We also found that unified models extracted a stronger relationship between neural response latency and trial

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

Directory of Open Access Journals (Sweden)

Gregor Strobbe

2016-01-01

Full Text Available Electrical source imaging of interictal spikes observed in EEG recordings of patients with refractory epilepsy provides useful information to localize the epileptogenic focus during the presurgical evaluation. However, the selection of the time points or time epochs of the spikes in order to estimate the origin of the activity remains a challenge. In this study, we consider a Bayesian EEG source imaging technique for distributed sources, i.e. the multiple volumetric sparse priors (MSVP approach. The approach allows to estimate the time courses of the intensity of the sources corresponding with a specific time epoch of the spike. Based on presurgical averaged interictal spikes in six patients who were successfully treated with surgery, we estimated the time courses of the source intensities for three different time epochs: (i an epoch starting 50 ms before the spike peak and ending at 50% of the spike peak during the rising phase of the spike, (ii an epoch starting 50 ms before the spike peak and ending at the spike peak and (iii an epoch containing the full spike time period starting 50 ms before the spike peak and ending 230 ms after the spike peak. To identify the primary source of the spike activity, the source with the maximum energy from 50 ms before the spike peak till 50% of the spike peak was subsequently selected for each of the time windows. For comparison, the activity at the spike peaks and at 50% of the peaks was localized using the LORETA inversion technique and an ECD approach. Both patient-specific spherical forward models and patient-specific 5-layered finite difference models were considered to evaluate the influence of the forward model. Based on the resected zones in each of the patients, extracted from post-operative MR images, we compared the distances to the resection border of the estimated activity. Using the spherical models, the distances to the resection border for the MSVP approach and each of the different time

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.

Temporally coordinated spiking activity of human induced pluripotent stem cell-derived neurons co-cultured with astrocytes.

Science.gov (United States)

Kayama, Tasuku; Suzuki, Ikuro; Odawara, Aoi; Sasaki, Takuya; Ikegaya, Yuji

2018-01-01

In culture conditions, human induced-pluripotent stem cells (hiPSC)-derived neurons form synaptic connections with other cells and establish neuronal networks, which are expected to be an in vitro model system for drug discovery screening and toxicity testing. While early studies demonstrated effects of co-culture of hiPSC-derived neurons with astroglial cells on survival and maturation of hiPSC-derived neurons, the population spiking patterns of such hiPSC-derived neurons have not been fully characterized. In this study, we analyzed temporal spiking patterns of hiPSC-derived neurons recorded by a multi-electrode array system. We discovered that specific sets of hiPSC-derived neurons co-cultured with astrocytes showed more frequent and highly coherent non-random synchronized spike trains and more dynamic changes in overall spike patterns over time. These temporally coordinated spiking patterns are physiological signs of organized circuits of hiPSC-derived neurons and suggest benefits of co-culture of hiPSC-derived neurons with astrocytes. Copyright © 2017 Elsevier Inc. All rights reserved.

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

Directory of Open Access Journals (Sweden)

Qiang Yu

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

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

Science.gov (United States)

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

2013-01-01

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

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

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...

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

International Nuclear Information System (INIS)

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

2003-01-01

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

Consensus-Based Sorting of Neuronal Spike Waveforms.

Science.gov (United States)

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

2016-01-01

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

Temporal Pattern of Online Communication Spike Trains in Spreading a Scientific Rumor: How Often, Who Interacts with Whom?

Directory of Open Access Journals (Sweden)

Ceyda eSanli

2015-09-01

Full Text Available We study complex time series (spike trains of online user communication while spreading messages about the discovery of the Higgs boson in Twitter. We focus on online social interactions among users such as retweet, mention, and reply, and construct different types of active (performing an action and passive (receiving an action spike trains for each user. The spike trains are analyzed by means of local variation, to quantify the temporal behavior of active and passive users, as a function of their activity and popularity. We show that the active spike trains are bursty, independently of their activation frequency. For passive spike trains, in contrast, the local variation of popular users presents uncorrelated (Poisson random dynamics. We further characterize the correlations of the local variation in different interactions. We obtain high values of correlation, and thus consistent temporal behavior, between retweets and mentions, but only for popular users, indicating that creating online attention suggests an alignment in the dynamics of the two interactions.

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

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

Science.gov (United States)

Evans, Benjamin D; Stringer, Simon M

2012-01-01

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

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

Directory of Open Access Journals (Sweden)

Benjamin eEvans

2012-07-01

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

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

Science.gov (United States)

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

2013-01-01

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

ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

Science.gov (United States)

Canova, Carlos; Denker, Michael; Gerstein, George; Helias, Moritz

2016-01-01

With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity. PMID:27420734

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.

Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

Science.gov (United States)

Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

2011-11-01

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.

Relationship of light-output nonlinearities and light-output spikes in proton-bombarded stripe-geometry double-heterostructure (Al,Ga)As lasers

International Nuclear Information System (INIS)

Dixon, R.W.; Joyce, W.B.; Miller, R.C.

1979-01-01

By using a fast photodetector and a simple spatial filtering technique, the relationship between the optical spike which frequently appears at the leading edge of a pulsed-GaAs-laser response and the optical nonlinearities called kinks has been investigated. It is suggested that the spike can be viewed consistently as a transition period during which the lasing mode distortion, which has been previously associated with kinks, occurs. It is the time taken for the kink to become established. During the spike, light emisson occurs with a spatial and angular intensity distribution consistent with the lasing which would be appropriate if the kink did not exist. It is shown that the experimental technique can also be used to investigate sustained laser optical-intensity oscillations

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.

Prolonged fasting activates Nrf2 in post-weaned elephant seals.

Science.gov (United States)

Vázquez-Medina, José Pablo; Soñanez-Organis, José G; Rodriguez, Ruben; Viscarra, Jose A; Nishiyama, Akira; Crocker, Daniel E; Ortiz, Rudy M

2013-08-01

Elephant seals naturally experience prolonged periods of absolute food and water deprivation (fasting). In humans, rats and mice, prolonged food deprivation activates the renin-angiotensin system (RAS) and increases oxidative damage. In elephant seals, prolonged fasting activates RAS without increasing oxidative damage likely due to an increase in antioxidant defenses. The mechanism leading to the upregulation of antioxidant defenses during prolonged fasting remains elusive. Therefore, we investigated whether prolonged fasting activates the redox-sensitive transcription factor Nrf2, which controls the expression of antioxidant genes, and if such activation is potentially mediated by systemic increases in RAS. Blood and skeletal muscle samples were collected from seals fasting for 1, 3, 5 and 7 weeks. Nrf2 activity and nuclear content increased by 76% and 167% at week 7. Plasma angiotensin II (Ang II) and transforming growth factor β (TGF-β) were 5000% and 250% higher at week 7 than at week 1. Phosphorylation of Smad2, an effector of Ang II and TGF signaling, increased by 120% at week 7 and by 84% in response to intravenously infused Ang II. NADPH oxidase 4 (Nox4) mRNA expression, which is controlled by smad proteins, increased 430% at week 7, while Nox4 protein expression, which can activate Nrf2, was 170% higher at week 7 than at week 1. These results demonstrate that prolonged fasting activates Nrf2 in elephant seals and that RAS stimulation can potentially result in increased Nox4 through Smad phosphorylation. The results also suggest that Nox4 is essential to sustain the hormetic adaptive response to oxidative stress in fasting seals.

Activation of mGluR5 induces spike afterdepolarization and enhanced excitability in medium spiny neurons of the nucleus accumbens by modulating persistent Na+ currents

Science.gov (United States)

D’Ascenzo, Marcello; Podda, Maria Vittoria; Fellin, Tommaso; Azzena, Gian Battista; Haydon, Philip; Grassi, Claudio

2009-01-01

The involvement of metabotropic glutamate receptors type 5 (mGluR5) in drug-induced behaviours is well-established but limited information is available on their functional roles in addiction-relevant brain areas like the nucleus accumbens (NAc). This study demonstrates that pharmacological and synaptic activation of mGluR5 increases the spike discharge of medium spiny neurons (MSNs) in the NAc. This effect was associated with the appearance of a slow afterdepolarization (ADP) which, in voltage-clamp experiments, was recorded as a slowly inactivating inward current. Pharmacological studies showed that ADP was elicited by mGluR5 stimulation via G-protein-dependent activation of phospholipase C and elevation of intracellular Ca2+ levels. Both ADP and spike aftercurrents were significantly inhibited by the Na+ channel-blocker, tetrodotoxin (TTX). Moreover, the selective blockade of persistent Na+ currents (INaP), achieved by NAc slice pre-incubation with 20 nm TTX or 10 μm riluzole, significantly reduced the ADP amplitude, indicating that this type of Na+ current is responsible for the mGluR5-dependent ADP. mGluR5 activation also produced significant increases in INaP, and the pharmacological blockade of this current prevented the mGluR5-induced enhancement of spike discharge. Collectively, these data suggest that mGluR5 activation upregulates INaP in MSNs of the NAc, thereby inducing an ADP that results in enhanced MSN excitability. Activation of mGluR5 will significantly alter spike firing in MSNs in vivo, and this effect could be an important mechanism by which these receptors mediate certain aspects of drug-induced behaviours. PMID:19433572

Review of fast reactor activities in India (1982-83)

International Nuclear Information System (INIS)

Paranjpe, S.R.

1983-01-01

A review of fast reactor activities in India in 1982-1983 is given. One stage of construction of Fast Breeder Test Reactor (FBTR) is briefly described. The emphasis is on design studies for the 500 MWe Prototype Fast Breeder Reactor (PFBR). The main features of this design are introduced

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

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

Science.gov (United States)

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

2011-10-01

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

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.

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.

Glycan shield and fusion activation of a deltacoronavirus spike glycoprotein fine-tuned for enteric infections.

Science.gov (United States)

Xiong, Xiaoli; Tortorici, M Alejandra; Snijder, Joost; Yoshioka, Craig; Walls, Alexandra C; Li, Wentao; McGuire, Andrew T; Rey, Félix A; Bosch, Berend-Jan; Veesler, David

2017-11-01

Coronaviruses recently emerged as major human pathogens causing outbreaks of severe acute respiratory syndrome and Middle-East respiratory syndrome. They utilize the spike (S) glycoprotein anchored in the viral envelope to mediate host attachment and fusion of the viral and cellular membranes to initiate infection. The S protein is a major determinant of the zoonotic potential of coronaviruses and is also the main target of the host humoral immune response. We report here the 3.5 Å resolution cryo-electron microscopy structure of the S glycoprotein trimer from the pathogenic porcine deltacoronavirus (PDCoV), which belongs to the recently identified delta genus. Structural and glycoproteomics data indicate that the glycans of PDCoV S are topologically conserved when compared with the human respiratory coronavirus HCoV-NL63 S, resulting in similar surface areas being shielded from neutralizing antibodies and implying that both viruses are under comparable immune pressure in their respective hosts. The structure further reveals a shortened S 2 ' activation loop, containing a reduced number of basic amino acids, which participates to rendering the spike largely protease-resistant. This property distinguishes PDCoV S from recently characterized betacoronavirus S proteins and suggests that the S protein of enterotropic PDCoV has evolved to tolerate the protease-rich environment of the small intestine and to fine-tune its fusion activation to avoid premature triggering and reduction of infectivity. IMPORTANCE Coronaviruses use transmembrane spike (S) glycoprotein trimers to promote host attachment and fusion of the viral and cellular membranes. We determined a near-atomic resolution cryo-electron microscopy structure of the S ectodomain trimer from the pathogenic porcine deltacoronavirus (PDCoV), which is responsible for diarrhea in piglets and has had devastating consequences for the swine industry worldwide. Structural and glycoproteomics data reveal that PDCoV S is

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.

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

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.

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

Directory of Open Access Journals (Sweden)

Aurel Vasile Martiniuc

2012-01-01

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

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

International Nuclear Information System (INIS)

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

2016-01-01

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

Wavelet analysis of epileptic spikes

Science.gov (United States)

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

2003-05-01

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

Wavelet analysis of epileptic spikes

CERN Document Server

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

2003-01-01

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

Metabolic adaptation to intermittent fasting is independent of peroxisome proliferator-activated receptor alpha

Directory of Open Access Journals (Sweden)

Guolin Li

2018-01-01

Full Text Available Background: Peroxisome proliferator-activated receptor alpha (PPARA is a major regulator of fatty acid oxidation and severe hepatic steatosis occurs during acute fasting in Ppara-null mice. Thus, PPARA is considered an important mediator of the fasting response; however, its role in other fasting regiments such as every-other-day fasting (EODF has not been investigated. Methods: Mice were pre-conditioned using either a diet containing the potent PPARA agonist Wy-14643 or an EODF regimen prior to acute fasting. Ppara-null mice were used to assess the contribution of PPARA activation during the metabolic response to EODF. Livers were collected for histological, biochemical, qRT-PCR, and Western blot analysis. Results: Acute fasting activated PPARA and led to steatosis, whereas EODF protected against fasting-induced hepatic steatosis without affecting PPARA signaling. In contrast, pretreatment with Wy-14,643 did activate PPARA signaling but did not ameliorate acute fasting-induced steatosis and unexpectedly promoted liver injury. Ppara ablation exacerbated acute fasting-induced hypoglycemia, hepatic steatosis, and liver injury in mice, whereas these detrimental effects were absent in response to EODF, which promoted PPARA-independent fatty acid metabolism and normalized serum lipids. Conclusions: These findings indicate that PPARA activation prior to acute fasting cannot ameliorate fasting-induced hepatic steatosis, whereas EODF induced metabolic adaptations to protect against fasting-induced steatosis without altering PPARA signaling. Therefore, PPARA activation does not mediate the metabolic adaptation to fasting, at least in preventing acute fasting-induced steatosis. Keywords: PPARA, PPARalpha, Intermittent fasting, Every-other-day fasting, Steatosis, Adaptive fasting response

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

DEFF Research Database (Denmark)

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

2018-01-01

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

Spike-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.

Attention deficit associated with early life interictal spikes in a rat model is improved with ACTH.

Directory of Open Access Journals (Sweden)

Amanda E Hernan

Full Text Available Children with epilepsy often present with pervasive cognitive and behavioral comorbidities including working memory impairments, attention deficit hyperactivity disorder (ADHD and autism spectrum disorder. These non-seizure characteristics are severely detrimental to overall quality of life. Some of these children, particularly those with epilepsies classified as Landau-Kleffner Syndrome or continuous spike and wave during sleep, have infrequent seizure activity but frequent focal epileptiform activity. This frequent epileptiform activity is thought to be detrimental to cognitive development; however, it is also possible that these IIS events initiate pathophysiological pathways in the developing brain that may be independently associated with cognitive deficits. These hypotheses are difficult to address due to the previous lack of an appropriate animal model. To this end, we have recently developed a rat model to test the role of frequent focal epileptiform activity in the prefrontal cortex. Using microinjections of a GABA(A antagonist (bicuculline methiodine delivered multiple times per day from postnatal day (p 21 to p25, we showed that rat pups experiencing frequent, focal, recurrent epileptiform activity in the form of interictal spikes during neurodevelopment have significant long-term deficits in attention and sociability that persist into adulthood. To determine if treatment with ACTH, a drug widely used to treat early-life seizures, altered outcome we administered ACTH once per day subcutaneously during the time of the induced interictal spike activity. We show a modest amelioration of the attention deficit seen in animals with a history of early life interictal spikes with ACTH, in the absence of alteration of interictal spike activity. These results suggest that pharmacological intervention that is not targeted to the interictal spike activity is worthy of future study as it may be beneficial for preventing or ameliorating adverse

The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.

Science.gov (United States)

Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun

2017-01-01

Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.

Metabolic adaptation to intermittent fasting is independent of peroxisome proliferator-activated receptor alpha.

Science.gov (United States)

Li, Guolin; Brocker, Chad N; Yan, Tingting; Xie, Cen; Krausz, Kristopher W; Xiang, Rong; Gonzalez, Frank J

2018-01-01

Peroxisome proliferator-activated receptor alpha (PPARA) is a major regulator of fatty acid oxidation and severe hepatic steatosis occurs during acute fasting in Ppara-null mice. Thus, PPARA is considered an important mediator of the fasting response; however, its role in other fasting regiments such as every-other-day fasting (EODF) has not been investigated. Mice were pre-conditioned using either a diet containing the potent PPARA agonist Wy-14643 or an EODF regimen prior to acute fasting. Ppara-null mice were used to assess the contribution of PPARA activation during the metabolic response to EODF. Livers were collected for histological, biochemical, qRT-PCR, and Western blot analysis. Acute fasting activated PPARA and led to steatosis, whereas EODF protected against fasting-induced hepatic steatosis without affecting PPARA signaling. In contrast, pretreatment with Wy-14,643 did activate PPARA signaling but did not ameliorate acute fasting-induced steatosis and unexpectedly promoted liver injury. Ppara ablation exacerbated acute fasting-induced hypoglycemia, hepatic steatosis, and liver injury in mice, whereas these detrimental effects were absent in response to EODF, which promoted PPARA-independent fatty acid metabolism and normalized serum lipids. These findings indicate that PPARA activation prior to acute fasting cannot ameliorate fasting-induced hepatic steatosis, whereas EODF induced metabolic adaptations to protect against fasting-induced steatosis without altering PPARA signaling. Therefore, PPARA activation does not mediate the metabolic adaptation to fasting, at least in preventing acute fasting-induced steatosis. Published by Elsevier GmbH.

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

Directory of Open Access Journals (Sweden)

Laureline Logiaco

2015-08-01

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

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

Science.gov (United States)

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

2015-08-01

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

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

Directory of Open Access Journals (Sweden)

Arno Onken

2016-11-01

Full Text Available Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations, in their temporal dimension (temporal neural response variations, or in their combination (temporally coordinated neural population firing. Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together, temporal firing patterns (temporal activation of these groups of neurons and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial. We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling

Science.gov (United States)

Keshtkaran, Mohammad Reza; Yang, Zhi

2017-06-01

Objective. Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise-robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Approach. The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis and clustering using Gaussian mixture model with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results. Comparative results on publicly available simulated and real in vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such as principal component analysis or wavelets. Significance. By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.

Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task.

Science.gov (United States)

Torre, Emiliano; Quaglio, Pietro; Denker, Michael; Brochier, Thomas; Riehle, Alexa; Grün, Sonja

2016-08-10

The computational role of spike time synchronization at millisecond precision among neurons in the cerebral cortex is hotly debated. Studies performed on data of limited size provided experimental evidence that low-order correlations occur in relation to behavior. Advances in electrophysiological technology to record from hundreds of neurons simultaneously provide the opportunity to observe coordinated spiking activity of larger populations of cells. We recently published a method that combines data mining and statistical evaluation to search for significant patterns of synchronous spikes in massively parallel spike trains (Torre et al., 2013). The method solves the computational and multiple testing problems raised by the high dimensionality of the data. In the current study, we used our method on simultaneous recordings from two macaque monkeys engaged in an instructed-delay reach-to-grasp task to determine the emergence of spike synchronization in relation to behavior. We found a multitude of synchronous spike patterns aligned in both monkeys along a preferential mediolateral orientation in brain space. The occurrence of the patterns is highly specific to behavior, indicating that different behaviors are associated with the synchronization of different groups of neurons ("cell assemblies"). However, pooled patterns that overlap in neuronal composition exhibit no specificity, suggesting that exclusive cell assemblies become active during different behaviors, but can recruit partly identical neurons. These findings are consistent across multiple recording sessions analyzed across the two monkeys. Neurons in the brain communicate via electrical impulses called spikes. How spikes are coordinated to process information is still largely unknown. Synchronous spikes are effective in triggering a spike emission in receiving neurons and have been shown to occur in relation to behavior in a number of studies on simultaneous recordings of few neurons. We recently published

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...

Quantitative Comparative Analysis of the Bio-Active and Toxic Constituents of Leaves and Spikes of Schizonepeta tenuifolia at Different Harvesting Times

Directory of Open Access Journals (Sweden)

Anwei Ding

2011-10-01

Full Text Available A GC-MS-Selected Ion Monitoring (SIM detection method was developed for simultaneous determination of four monoterpenes: (--menthone, (+-pulegone, (--limonene and (+-menthofuran as the main bio-active and toxic constituents, and four other main compounds in the volatile oils of Schizonepeta tenuifolia (ST leaves and spikes at different harvesting times. The results showed that the method was simple, sensitive and reproducible, and that harvesting time was a possible key factor in influencing the quality of ST leaves, but not its spikes. The research might be helpful for determining the harvesting time of ST samples and establishing a validated method for the quality control of ST volatile oil and other relative products.

Layer-specific high-frequency spiking in the prefrontal cortex of awake rats

Directory of Open Access Journals (Sweden)

Zimbo Saroeni Raymond Maria Boudewijns

2013-06-01

Full Text Available Cortical pyramidal neurons show irregular in vivo action potential (AP spiking with high frequency bursts occurring on sparse background activity. Somatic APs can backpropagate from soma into basal and apical dendrites and locally generate dendritic calcium spikes. The critical AP frequency for generation of such dendritic calcium spikes can be very different depending on cell-type or brain area involved. Previously, it was shown in vitro that calcium electrogenesis can also be induced in L(ayer 5 pyramidal neurons of prefrontal cortex (PFC. It remains an open question whether somatic burst spiking and resulting dendritic calcium electrogenesis also occur in morphologically more compact L2/3 pyramidal neurons. Furthermore, it is not known whether critical frequencies that trigger dendritic calcium electrogenesis occur in PFC under awake conditions in vivo. Here, we addressed these issues and found that pyramidal neurons in both PFC L2/3 and L5 in awake rats spike APs in short bursts, but with different probabilities. The critical frequency for calcium electrogenesis in vitro was layer-specific and lower in L5 neurons compared to L2/3. Taking the in vitro critical frequency as predictive measure for dendritic electrogenesis during in vivo spontaneous activity, supracritical bursts in vivo were observed in a larger fraction of L5 neurons compared to L2/3 neurons but with similar incidence within these subpopulations. Together, these results show that in PFC of awake rats, AP spiking occurs at frequencies that are relevant for dendritic calcium electrogenesis and suggest that in awake rat PFC, dendritic calcium electrogenesis may be involved in neuronal computation.

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

Science.gov (United States)

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

2017-12-01

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

The IAEA Activities in the Field of Fast Reactors Technology Development

International Nuclear Information System (INIS)

Monti, Stefano

2011-01-01

Main activities of the IAEA Programme on Fast Reactor: Carry out Collaborative Research Projects (CRPs) of common interest to the TWG-FR Member States in the field of FRs and ADS; Secure Training and Education in the field of fast neutron system physics, technology and applications; Support Fast Reactor data retrieval and knowledge preservation activities in MSs; Provide support to IAEA Nuclear Safety and Security Department for preparation of fast reactor Safety standards / requirements / guides. IAEA TWG-FR Functions: Provide advice and guidance, and marshal support in their countries for implementation of IAEA’s programmatic activities in the area of advanced technologies and R&D for fast reactors and sub-critical hybrid systems for energy production and for utilization/transmutation of long-lived nuclides; Provide a forum for information and knowledge sharing on national and international development programs; Act as a link between IAEA’s activities in the specific area of the TWG-FR and national scientific communities, delivering information from and to national communities

Status of fast reactor activities in Brazil

International Nuclear Information System (INIS)

Menezes, Artur

1996-01-01

This text describes the present status of fast reactor activities in Brazil, emphasizing the strategies being used to preserve this reactor concept as a viable alternative for future electricity generation in the country. The program is mostly research-oriented and has the objective of establishing a consistent knowledge basis which can serve as a support for the transition to the activities more directly related to design, construction and operation of an experimental fast reactor. Due to the present economic difficulties, the program is still modest but it is gradually growing. A report which has been finalized in December, 1995 and submitted to the authorities indicates the existence of the grounds for enlarging and consolidating the program. (author)

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.

Fast negative feedback enables mammalian auditory nerve fibers to encode a wide dynamic range of sound intensities.

Directory of Open Access Journals (Sweden)

Mark Ospeck

Full Text Available Mammalian auditory nerve fibers (ANF are remarkable for being able to encode a 40 dB, or hundred fold, range of sound pressure levels into their firing rate. Most of the fibers are very sensitive and raise their quiescent spike rate by a small amount for a faint sound at auditory threshold. Then as the sound intensity is increased, they slowly increase their spike rate, with some fibers going up as high as ∼300 Hz. In this way mammals are able to combine sensitivity and wide dynamic range. They are also able to discern sounds embedded within background noise. ANF receive efferent feedback, which suggests that the fibers are readjusted according to the background noise in order to maximize the information content of their auditory spike trains. Inner hair cells activate currents in the unmyelinated distal dendrites of ANF where sound intensity is rate-coded into action potentials. We model this spike generator compartment as an attenuator that employs fast negative feedback. Input current induces rapid and proportional leak currents. This way ANF are able to have a linear frequency to input current (f-I curve that has a wide dynamic range. The ANF spike generator remains very sensitive to threshold currents, but efferent feedback is able to lower its gain in response to noise.

Spikes and matter inhomogeneities in massless scalar field models

International Nuclear Information System (INIS)

Coley, A A; Lim, W C

2016-01-01

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

Spiking neural P systems with multiple channels.

Science.gov (United States)

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

2017-11-01

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

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

On the genesis of spike-wave oscillations in a mean-field model of human thalamic and corticothalamic dynamics

International Nuclear Information System (INIS)

Rodrigues, Serafim; Terry, John R.; Breakspear, Michael

2006-01-01

In this Letter, the genesis of spike-wave activity-a hallmark of many generalized epileptic seizures-is investigated in a reduced mean-field model of human neural activity. Drawing upon brain modelling and dynamical systems theory, we demonstrate that the thalamic circuitry of the system is crucial for the generation of these abnormal rhythms, observing that the combination of inhibition from reticular nuclei and excitation from the cortical signal, interplay to generate the spike-wave oscillation. The mechanism revealed provides an explanation of why approaches based on linear stability and Heaviside approximations to the activation function have failed to explain the phenomena of spike-wave behaviour in mean-field models. A mathematical understanding of this transition is a crucial step towards relating spiking network models and mean-field approaches to human brain modelling

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.

Ablation of fast-spiking interneurons in the dorsal striatum, recapitulating abnormalities seen post-mortem in Tourette syndrome, produces anxiety and elevated grooming.

Science.gov (United States)

Xu, M; Li, L; Pittenger, C

2016-06-02

Tic disorders, including Tourette syndrome (TS), are thought to involve pathology of cortico-basal ganglia loops, but their pathology is not well understood. Post-mortem studies have shown a reduced number of several populations of striatal interneurons, including the parvalbumin-expressing fast-spiking interneurons (FSIs), in individuals with severe, refractory TS. We tested the causal role of this interneuronal deficit by recapitulating it in an otherwise normal adult mouse using a combination transgenic-viral cell ablation approach. FSIs were reduced bilaterally by ∼40%, paralleling the deficit found post-mortem. This did not produce spontaneous stereotypies or tic-like movements, but there was increased stereotypic grooming after acute stress in two validated paradigms. Stereotypy after amphetamine, in contrast, was not elevated. FSI ablation also led to increased anxiety-like behavior in the elevated plus maze, but not to alterations in motor learning on the rotorod or to alterations in prepulse inhibition, a measure of sensorimotor gating. These findings indicate that a striatal FSI deficit can produce stress-triggered repetitive movements and anxiety. These repetitive movements may recapitulate aspects of the pathophysiology of tic disorders. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Is the thermal-spike model consistent with experimentally determined electron temperature?

International Nuclear Information System (INIS)

Ajryan, Eh.A.; Fedorov, A.V.; Kostenko, B.F.

2000-01-01

Carbon K-Auger electron spectra from amorphous carbon foils induced by fast heavy ions are theoretically investigated. The high-energy tail of the Auger structure showing a clear projectile charge dependence is analyzed within the thermal-spike model framework as well as in the frame of another model taking into account some kinetic features of the process. A poor comparison results between theoretically and experimentally determined temperatures are suggested to be due to an improper account of double electron excitations or due to shake-up processes which leave the system in a more energetic initial state than a statically screened core hole

Fasting inhibits hepatic stellate cells activation and potentiates anti-cancer activity of Sorafenib in hepatocellular cancer cells.

Science.gov (United States)

Lo Re, Oriana; Panebianco, Concetta; Porto, Stefania; Cervi, Carlo; Rappa, Francesca; Di Biase, Stefano; Caraglia, Michele; Pazienza, Valerio; Vinciguerra, Manlio

2018-02-01

Hepatocellular carcinoma (HCC) has a poor outcome. Most HCCs develop in the context of liver fibrosis and cirrhosis caused by chronic inflammation. Short-term fasting approaches enhance the activity of chemotherapy in preclinical cancer models, other than HCC. Multi-tyrosine kinase inhibitor Sorafenib is the mainstay of treatment in HCC. However, its benefit is frequently short-lived. Whether fasting can alleviate liver fibrosis and whether combining fasting with Sorafenib is beneficial remains unknown. A 24 hr fasting (2% serum, 0.1% glucose)-induced changes on human hepatic stellate cells (HSC) LX-2 proliferation/viability/cell cycle were assessed by MTT and flow cytometry. Expression of lypolysaccharide (LPS)-induced activation markers (vimentin, αSMA) was evaluated by qPCR and immunoblotting. Liver fibrosis and inflammation were evaluated in a mouse model of steatohepatitis exposed to cycles of fasting, by histological and biochemical analyses. A 24 hr fasting-induced changes were also analyzed on the proliferation/viability/glucose uptake of human HCC cells exposed to Sorafenib. An expression panel of genes involved in survival, inflammation, and metabolism was examined by qPCR in HCC cells exposed to fasting and/or Sorafenib. Fasting decreased the proliferation and the activation of HSC. Repeated cycles of short term starvation were safe in mice but did not improve fibrosis. Fasting synergized with Sorafenib in hampering HCC cell growth and glucose uptake. Finally, fasting normalized the expression levels of genes which are commonly altered by Sorafenib in HCC cells. Fasting or fasting-mimicking diet diets should be evaluated in preclinical studies as a mean to potentiate the activity of Sorafenib in clinical use. © 2017 Wiley Periodicals, Inc.

Bursts generate a non-reducible spike-pattern code

Directory of Open Access Journals (Sweden)

Hugo G Eyherabide

2009-05-01

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

Stochastic Variational Learning in Recurrent Spiking Networks

Directory of Open Access Journals (Sweden)

Danilo eJimenez Rezende

2014-04-01

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

Stochastic variational learning in recurrent spiking networks.

Science.gov (United States)

Jimenez Rezende, Danilo; Gerstner, Wulfram

2014-01-01

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

Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems

Directory of Open Access Journals (Sweden)

Emre eNeftci

2014-01-01

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

Event-driven contrastive divergence for spiking neuromorphic systems.

Science.gov (United States)

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

2013-01-01

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

Computational modeling of seizure dynamics using coupled neuronal networks: factors shaping epileptiform activity.

Directory of Open Access Journals (Sweden)

Sebastien Naze

2015-05-01

Full Text Available Epileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion

Simple networks for spike-timing-based computation, with application to olfactory processing.

Science.gov (United States)

Brody, Carlos D; Hopfield, J J

2003-03-06

Spike synchronization across neurons can be selective for the situation where neurons are driven at similar firing rates, a "many are equal" computation. This can be achieved in the absence of synaptic interactions between neurons, through phase locking to a common underlying oscillatory potential. Based on this principle, we instantiate an algorithm for robust odor recognition into a model network of spiking neurons whose main features are taken from known properties of biological olfactory systems. Here, recognition of odors is signaled by spike synchronization of specific subsets of "mitral cells." This synchronization is highly odor selective and invariant to a wide range of odor concentrations. It is also robust to the presence of strong distractor odors, thus allowing odor segmentation within complex olfactory scenes. Information about odors is encoded in both the identity of glomeruli activated above threshold (1 bit of information per glomerulus) and in the analog degree of activation of the glomeruli (approximately 3 bits per glomerulus).

Systematic Regional Variations in Purkinje Cell Spiking Patterns

Science.gov (United States)

Xiao, Jianqiang; Cerminara, Nadia L.; Kotsurovskyy, Yuriy; Aoki, Hanako; Burroughs, Amelia; Wise, Andrew K.; Luo, Yuanjun; Marshall, Sarah P.; Sugihara, Izumi; Apps, Richard; Lang, Eric J.

2014-01-01

In contrast to the uniform anatomy of the cerebellar cortex, molecular and physiological studies indicate that significant differences exist between cortical regions, suggesting that the spiking activity of Purkinje cells (PCs) in different regions could also show distinct characteristics. To investigate this possibility we obtained extracellular recordings from PCs in different zebrin bands in crus IIa and vermis lobules VIII and IX in anesthetized rats in order to compare PC firing characteristics between zebrin positive (Z+) and negative (Z−) bands. In addition, we analyzed recordings from PCs in the A2 and C1 zones of several lobules in the posterior lobe, which largely contain Z+ and Z− PCs, respectively. In both datasets significant differences in simple spike (SS) activity were observed between cortical regions. Specifically, Z− and C1 PCs had higher SS firing rates than Z+ and A2 PCs, respectively. The irregularity of SS firing (as assessed by measures of interspike interval distribution) was greater in Z+ bands in both absolute and relative terms. The results regarding systematic variations in complex spike (CS) activity were less consistent, suggesting that while real differences can exist, they may be sensitive to other factors than the cortical location of the PC. However, differences in the interactions between SSs and CSs, including the post-CS pause in SSs and post-pause modulation of SSs, were also consistently observed between bands. Similar, though less strong trends were observed in the zonal recordings. These systematic variations in spontaneous firing characteristics of PCs between zebrin bands in vivo, raises the possibility that fundamental differences in information encoding exist between cerebellar cortical regions. PMID:25144311

Systematic regional variations in Purkinje cell spiking patterns.

Directory of Open Access Journals (Sweden)

Jianqiang Xiao

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

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.

The Limited Utility of Multiunit Data in Differentiating Neuronal Population Activity.

Directory of Open Access Journals (Sweden)

Corey J Keller

Full Text Available To date, single neuron recordings remain the gold standard for monitoring the activity of neuronal populations. Since obtaining single neuron recordings is not always possible, high frequency or 'multiunit activity' (MUA is often used as a surrogate. Although MUA recordings allow one to monitor the activity of a large number of neurons, they do not allow identification of specific neuronal subtypes, the knowledge of which is often critical for understanding electrophysiological processes. Here, we explored whether prior knowledge of the single unit waveform of specific neuron types is sufficient to permit the use of MUA to monitor and distinguish differential activity of individual neuron types. We used an experimental and modeling approach to determine if components of the MUA can monitor medium spiny neurons (MSNs and fast-spiking interneurons (FSIs in the mouse dorsal striatum. We demonstrate that when well-isolated spikes are recorded, the MUA at frequencies greater than 100Hz is correlated with single unit spiking, highly dependent on the waveform of each neuron type, and accurately reflects the timing and spectral signature of each neuron. However, in the absence of well-isolated spikes (the norm in most MUA recordings, the MUA did not typically contain sufficient information to permit accurate prediction of the respective population activity of MSNs and FSIs. Thus, even under ideal conditions for the MUA to reliably predict the moment-to-moment activity of specific local neuronal ensembles, knowledge of the spike waveform of the underlying neuronal populations is necessary, but not sufficient.

PROMINENCE ACTIVATION BY CORONAL FAST MODE SHOCK

Energy Technology Data Exchange (ETDEWEB)

Takahashi, Takuya [Department of Astronomy, Kyoto University, Sakyo, Kyoto, 606-8502 (Japan); Asai, Ayumi [Unit of Synergetic Studies for Space, Kyoto University, Yamashina, Kyoto 607-8471 (Japan); Shibata, Kazunari, E-mail: takahashi@kwasan.kyoto-u.ac.jp [Kwasan and Hida Observatories, Kyoto University, Yamashina, Kyoto 607-8471 (Japan)

2015-03-01

An X5.4 class flare occurred in active region NOAA11429 on 2012 March 7. The flare was associated with a very fast coronal mass ejection (CME) with a velocity of over 2500 km s{sup −1}. In the images taken with the Solar Terrestrial Relations Observatory-B/COR1, a dome-like disturbance was seen to detach from an expanding CME bubble and propagated further. A Type-II radio burst was also observed at the same time. On the other hand, in extreme ultraviolet images obtained by the Solar Dynamic Observatory/Atmospheric Imaging Assembly (AIA), the expanding dome-like structure and its footprint propagating to the north were observed. The footprint propagated with an average speed of about 670 km s{sup −1} and hit a prominence located at the north pole and activated it. During the activation, the prominence was strongly brightened. On the basis of some observational evidence, we concluded that the footprint in AIA images and the ones in COR1 images are the same, that is, the MHD fast mode shock front. With the help of a linear theory, the fast mode Mach number of the coronal shock is estimated to be between 1.11 and 1.29 using the initial velocity of the activated prominence. Also, the plasma compression ratio of the shock is enhanced to be between 1.18 and 2.11 in the prominence material, which we consider to be the reason for the strong brightening of the activated prominence. The applicability of linear theory to the shock problem is tested with a nonlinear MHD simulation.

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

International Nuclear Information System (INIS)

Amjady, Nima; Keynia, Farshid

2010-01-01

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

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.

ACTIV87 Fast neutron activation cross section file 1987

International Nuclear Information System (INIS)

Manokhin, V.N.; Pashchenko, A.B.; Plyaskin, V.I.; Bychkov, V.M.; Pronyaev, V.G.; Schwerer, O.

1989-10-01

This document summarizes the content of the Fast Neutron Activation Cross Section File based on data from different evaluated data libraries and individual evaluations in ENDF/B-5 format. The entire file or selective retrievals from it are available on magnetic tape, free of charge, from the IAEA Nuclear Data Section. (author)

Elemental analysis of fertilizer by fast neutron activation

International Nuclear Information System (INIS)

Bodart, F.; Deconninck, G.

1977-01-01

A simple and accurate technique has been developed to analyse commercial fertilizers for phosphorus, potassium, chlorine, magnesium and silicon. The method is based on fast-neutron activation using a neutron flux of 2x10 11 neutrons/second. The optimum analytical conditions are tabulated. After irradiation, the sample is measured on a conventional counting system including a Ge(Li) detector (10% efficiency and 2 keV resolution for 60 Co) and a multichannel analyser. Monitor foils radioactivity are measured separately at the same time with a 2''x2''NaI detector coupled with a single channel analyser and a scaler. Fast neutron activation has proved to be a fast, simple, reliable and low cost analytical technique for the determination of phosphorus, silicon, potassium, magnesium and chlorine in fertilizers. Not less than five phosphorus determinations are possible in one hour, while two potassium, magnesium and chlorine determinations are made at the same time. (T.G.)

Continuous detection of weak sensory signals in afferent spike trains: the role of anti-correlated interspike intervals in detection performance.

Science.gov (United States)

Goense, J B M; Ratnam, R

2003-10-01

An important problem in sensory processing is deciding whether fluctuating neural activity encodes a stimulus or is due to variability in baseline activity. Neurons that subserve detection must examine incoming spike trains continuously, and quickly and reliably differentiate signals from baseline activity. Here we demonstrate that a neural integrator can perform continuous signal detection, with performance exceeding that of trial-based procedures, where spike counts in signal- and baseline windows are compared. The procedure was applied to data from electrosensory afferents of weakly electric fish (Apteronotus leptorhynchus), where weak perturbations generated by small prey add approximately 1 spike to a baseline of approximately 300 spikes s(-1). The hypothetical postsynaptic neuron, modeling an electrosensory lateral line lobe cell, could detect an added spike within 10-15 ms, achieving near ideal detection performance (80-95%) at false alarm rates of 1-2 Hz, while trial-based testing resulted in only 30-35% correct detections at that false alarm rate. The performance improvement was due to anti-correlations in the afferent spike train, which reduced both the amplitude and duration of fluctuations in postsynaptic membrane activity, and so decreased the number of false alarms. Anti-correlations can be exploited to improve detection performance only if there is memory of prior decisions.

Linking investment spikes and productivity growth

NARCIS (Netherlands)

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

2013-01-01

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

A new supervised learning algorithm for spiking neurons.

Science.gov (United States)

Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

2013-06-01

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

European Union: Review of fast reactor related activities

International Nuclear Information System (INIS)

Goethem, G. van; Hugon, M.

1998-01-01

The European Commission (EC) continued its fast reactor research activities on the same lines as in the past, but with the main emphasis on partitioning and transmutation (P and T) of long-lived radionuclides. The work was carried out by research institutions in the Member States and by the EC Joint Research Centre (JRC) as cost shared actions. The JRC has also been performing its own programme through institutional and competitive research activities. The JRC institutes involved in these studies are the Institute of Systems, Informatics and Safety (ISIS) in Ispra (I), the Institute for Transuranium Elements (ITU) in Karlsruhe (D) and the Institute for Advanced Materials in Petten. This paper summarizes the main activities performed in the field of (i) fast reactor safety and of (ii) partitioning and transmutation. (author)

A customizable stochastic state point process filter (SSPPF) for neural spiking activity.

Science.gov (United States)

Xin, Yao; Li, Will X Y; Min, Biao; Han, Yan; Cheung, Ray C C

2013-01-01

Stochastic State Point Process Filter (SSPPF) is effective for adaptive signal processing. In particular, it has been successfully applied to neural signal coding/decoding in recent years. Recent work has proven its efficiency in non-parametric coefficients tracking in modeling of mammal nervous system. However, existing SSPPF has only been realized in commercial software platforms which limit their computational capability. In this paper, the first hardware architecture of SSPPF has been designed and successfully implemented on field-programmable gate array (FPGA), proving a more efficient means for coefficient tracking in a well-established generalized Laguerre-Volterra model for mammalian hippocampal spiking activity research. By exploring the intrinsic parallelism of the FPGA, the proposed architecture is able to process matrices or vectors with random size, and is efficiently scalable. Experimental result shows its superior performance comparing to the software implementation, while maintaining the numerical precision. This architecture can also be potentially utilized in the future hippocampal cognitive neural prosthesis design.

Solving constraint satisfaction problems with networks of spiking neurons

Directory of Open Access Journals (Sweden)

Zeno eJonke

2016-03-01

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

Surfing a spike wave down the ventral stream.

Science.gov (United States)

VanRullen, Rufin; Thorpe, Simon J

2002-10-01

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

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

Science.gov (United States)

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

2018-01-01

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

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

Directory of Open Access Journals (Sweden)

Kevin Joseph

2018-04-01

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

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

Measurements of fast ion spatial dynamics during magnetic activity in the RFP

Science.gov (United States)

Goetz, J. A.; Anderson, J. K.; Bonofiglo, P.; Kim, J.; McConnell, R.; Magee, R. M.

2017-10-01

Fast ions in the RFP are only weakly affected by a stochastic magnetic field and behave nearly classically in concentration too low to excite Alfvenic activity. At high fast ion concentration sourced by H-NBI in 300kA RFP discharges, a substantial drop in core-localized high pitch fast ions is observed during bursts of coupled EPM and IAE (magnetic island-induced Alfven eigenmode) activity (100-200kHz) through neutral particle analysis. Sourcing instead fast deuterium with NBI, the DD fusion products can measure the dynamics of the fast ion density profile. Both a collimated neutron detector and a new 3MeV fusion proton detector loaned by TriAlpha Energy measure the fast ion density profile with 5cm spatial resolution and 100 μs temporal resolution. In D-NBI, the bursting EPM is excited at slightly lower frequency and the IAE activity is nearly absent, likely due to an isotope effect and loss of wave-particle interaction. In these cases, neutral particle analysis shows little change in the core-localized high pitch fast ion content, and the fusion product profile indicates little change in the fast ion density profile, leaving unexplained the mechanism removing EPM drive. We measure a substantial redistribution of the fast ion profile due to strong lower-frequency ( 30kHz) MHD activity that accompanies the current profile relaxation in the RFP. Profile flattening is strongest in low bulk density discharges, which often occur with a total increase in global neutron flux from acceleration of the beam ions. Work supported by US DoE.

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.

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

Science.gov (United States)

2012-01-01

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

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

A fasting inducible switch modulates gluconeogenesis via activator/coactivator exchange

DEFF Research Database (Denmark)

Liu, Yi; Dentin, Renaud; Chen, Danica

2008-01-01

During early fasting, increases in skeletal muscle proteolysis liberate free amino acids for hepatic gluconeogenesis in response to pancreatic glucagon. Hepatic glucose output diminishes during the late protein-sparing phase of fasting, when ketone body production by the liver supplies compensatory...... expression through the dephosphorylation and nuclear shuttling of forkhead box O1 (FOXO1). Here we show that a fasting-inducible switch, consisting of the histone acetyltransferase p300 and the nutrient-sensing deacetylase sirtuin 1 (SIRT1), maintains energy balance in mice through the sequential induction...... of CRTC2 and FOXO1. After glucagon induction, CRTC2 stimulated gluconeogenic gene expression by an association with p300, which we show here is also activated by dephosphorylation at Ser 89 during fasting. In turn, p300 increased hepatic CRTC2 activity by acetylating it at Lys 628, a site that also...

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

Directory of Open Access Journals (Sweden)

Fernando Perez-Peña

2013-11-01

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

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

Science.gov (United States)

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

2013-01-01

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

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

Directory of Open Access Journals (Sweden)

James P Crutchfield

2015-08-01

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

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.

Stimulus-dependent spiking relationships with the EEG

Science.gov (United States)

Snyder, Adam C.

2015-01-01

The development and refinement of noninvasive techniques for imaging neural activity is of paramount importance for human neuroscience. Currently, the most accessible and popular technique is electroencephalography (EEG). However, nearly all of what we know about the neural events that underlie EEG signals is based on inference, because of the dearth of studies that have simultaneously paired EEG recordings with direct recordings of single neurons. From the perspective of electrophysiologists there is growing interest in understanding how spiking activity coordinates with large-scale cortical networks. Evidence from recordings at both scales highlights that sensory neurons operate in very distinct states during spontaneous and visually evoked activity, which appear to form extremes in a continuum of coordination in neural networks. We hypothesized that individual neurons have idiosyncratic relationships to large-scale network activity indexed by EEG signals, owing to the neurons' distinct computational roles within the local circuitry. We tested this by recording neuronal populations in visual area V4 of rhesus macaques while we simultaneously recorded EEG. We found substantial heterogeneity in the timing and strength of spike-EEG relationships and that these relationships became more diverse during visual stimulation compared with the spontaneous state. The visual stimulus apparently shifts V4 neurons from a state in which they are relatively uniformly embedded in large-scale network activity to a state in which their distinct roles within the local population are more prominent, suggesting that the specific way in which individual neurons relate to EEG signals may hold clues regarding their computational roles. PMID:26108954

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

Science.gov (United States)

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

2015-01-01

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

Fast neutron activation analysis in metallurgy

International Nuclear Information System (INIS)

Sterlinski, S.

1981-01-01

Article discusses the usage of a 14 MeV neutron generator for producing fast neutrons of different energies and intensities. A complete instrumental set-up for the neutron activation analysis (NAA) is given. In metallurgy the device is mainly used in the determination of oxygen and silicon in steel and non-ferrous metal, including different alloys

Status of fast reactor activities in Russia

International Nuclear Information System (INIS)

Poplavski, V.M.; Ashurko, Yu.M.; Zverev, K.V.

1998-01-01

This paper outlines state-of-the-art of the Russian nuclear power as of 1997 and its prospects for the nearest future. Results of the BR-10, BOR-60 and BN-600 reactors operation are described, as well as activity of the Russian institutions on scientific and technological support of the BN-350 reactor. Analysis of current status of the BN-800 reactor South-Urals NPP and Beloyarskaya NPP designs is given in brief, as well as prospects of their construction and possible ways of fast reactor technology improvement. Studies on fast reactors now under way in Russia are described. (author)

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.

Fast neutron spectra determination by threshold activation detectors using neural networks

International Nuclear Information System (INIS)

Kardan, M.R.; Koohi-Fayegh, R.; Setayeshi, S.; Ghiassi-Nejad, M.

2004-01-01

Neural network method was used for fast neutron spectra unfolding in spectrometry by threshold activation detectors. The input layer of the neural networks consisted of 11 neurons for the specific activities of neutron-induced nuclear reaction products, while the output layers were fast neutron spectra which had been subdivided into 6, 8, 10, 12, 15 and 20 energy bins. Neural network training was performed by 437 fast neutron spectra and corresponding threshold activation detector readings. The trained neural network have been applied for unfolding 50 spectra, which were not in training sets and the results were compared with real spectra and unfolded spectra by SANDII. The best results belong to 10 energy bin spectra. The neural network was also trained by detector readings with 5% uncertainty and the response of the trained neural network to detector readings with 5%, 10%, 15%, 20%, 25% and 50% uncertainty was compared with real spectra. Neural network algorithm, in comparison with other unfolding methods, is very fast and needless to detector response matrix and any prior information about spectra and also the outputs have low sensitivity to uncertainty in the activity measurements. The results show that the neural network algorithm is useful when a fast response is required with reasonable accuracy

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

Different propagation speeds of recalled sequences in plastic spiking neural networks

Science.gov (United States)

Huang, Xuhui; Zheng, Zhigang; Hu, Gang; Wu, Si; Rasch, Malte J.

2015-03-01

Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in

Fast Reactor Knowledge Preservation Activities at the OECD Nuclear Energy Agency

International Nuclear Information System (INIS)

Hill, Ian

2013-01-01

• NEA is organizing significant activities are ongoing to preserve fast reactor information: – Integral experiments supporting fast reactors; – Often “use it, or lose it”; – Difficult to preserve everything, critical information is identified; – Organisation is a large but important task; – Electronic databases needed to manage the data. • Authoritative Handbooks and state of the art reports: – These tend to be the documents that last. • OECD/NEA will continue to support member countries in fast reactor knowledge preservation: – forum for exchange of information; – collaborative activities

Fast neutron activation analysis of Kalewa (Myanmar) coal

Energy Technology Data Exchange (ETDEWEB)

Myint, U; Naing, W [Yangon Univ. (Myanmar). Dept. of Chemistry

1994-06-01

Aluminium, silicon, copper, iron, magnesium and sulfur in Kalewa (Myanmar) coal were determined by fast neutron activation analysis. For activation a KAMAN A-710 Neutron Generator was used. Kalewa coal was found to be low in sulfur and relatively rich in iron. (author) 2 refs.; 1 fig.; 1 tab.

Fast neutron activation analysis of Kalewa (Myanmar) coal

International Nuclear Information System (INIS)

Myint, U.; Naing, W.

1994-01-01

Aluminium, silicon, copper, iron, magnesium and sulfur in Kalewa (Myanmar) coal were determined by fast neutron activation analysis. For activation a KAMAN A-710 Neutron Generator was used. Kalewa coal was found to be low in sulfur and relatively rich in iron. (author) 2 refs.; 1 fig.; 1 tab

Fluctuations and information filtering in coupled populations of spiking neurons with adaptation.

Science.gov (United States)

Deger, Moritz; Schwalger, Tilo; Naud, Richard; Gerstner, Wulfram

2014-12-01

Finite-sized populations of spiking elements are fundamental to brain function but also are used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasirenewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales.

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...

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.

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.

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.

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

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

Science.gov (United States)

Tezuka, Taro

2018-06-01

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

α-Oscillations in the monkey sensorimotor network influence discrimination performance by rhythmical inhibition of neuronal spiking.

Science.gov (United States)

Haegens, Saskia; Nácher, Verónica; Luna, Rogelio; Romo, Ranulfo; Jensen, Ole

2011-11-29

Extensive work in humans using magneto- and electroencephalography strongly suggests that decreased oscillatory α-activity (8-14 Hz) facilitates processing in a given region, whereas increased α-activity serves to actively suppress irrelevant or interfering processing. However, little work has been done to understand how α-activity is linked to neuronal firing. Here, we simultaneously recorded local field potentials and spikes from somatosensory, premotor, and motor regions while a trained monkey performed a vibrotactile discrimination task. In the local field potentials we observed strong activity in the α-band, which decreased in the sensorimotor regions during the discrimination task. This α-power decrease predicted better discrimination performance. Furthermore, the α-oscillations demonstrated a rhythmic relation with the spiking, such that firing was highest at the trough of the α-cycle. Firing rates increased with a decrease in α-power. These findings suggest that α-oscillations exercise a strong inhibitory influence on both spike timing and firing rate. Thus, the pulsed inhibition by α-oscillations plays an important functional role in the extended sensorimotor system.

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

DEFF Research Database (Denmark)

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

2006-01-01

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

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

Science.gov (United States)

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

2011-01-01

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

Spike Timing Matters in Novel Neuronal Code Involved in Vibrotactile Frequency Perception.

Science.gov (United States)

Birznieks, Ingvars; Vickery, Richard M

2017-05-22

Skin vibrations sensed by tactile receptors contribute significantly to the perception of object properties during tactile exploration [1-4] and to sensorimotor control during object manipulation [5]. Sustained low-frequency skin vibration (perception of frequency is still unknown. Measures based on mean spike rates of neurons in the primary somatosensory cortex are sufficient to explain performance in some frequency discrimination tasks [7-11]; however, there is emerging evidence that stimuli can be distinguished based also on temporal features of neural activity [12, 13]. Our study's advance is to demonstrate that temporal features are fundamental for vibrotactile frequency perception. Pulsatile mechanical stimuli were used to elicit specified temporal spike train patterns in tactile afferents, and subsequently psychophysical methods were employed to characterize human frequency perception. Remarkably, the most salient temporal feature determining vibrotactile frequency was not the underlying periodicity but, rather, the duration of the silent gap between successive bursts of neural activity. This burst gap code for frequency represents a previously unknown form of neural coding in the tactile sensory system, which parallels auditory pitch perception mechanisms based on purely temporal information where longer inter-pulse intervals receive higher perceptual weights than short intervals [14]. Our study also demonstrates that human perception of stimuli can be determined exclusively by temporal features of spike trains independent of the mean spike rate and without contribution from population response factors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fast neutron activation analysis by means of low voltage neutron generator

Directory of Open Access Journals (Sweden)

M.E. Medhat

Full Text Available A description of D-T neutron generator (NG is presented. This machine can be used for fast neutron activation analysis applied to determine some selected elements, especially light elements, in different materials. Procedure of neutron flux determination and efficiency calculation is described. Examples of testing some Egyptian natural cosmetics are given. Keywords: Neutron generator, Fast neutron activation analysis, Elemental analysis

Biophysical Insights into How Spike Threshold Depends on the Rate of Membrane Potential Depolarization in Type I and Type II Neurons.

Directory of Open Access Journals (Sweden)

Guo-Sheng Yi

Full Text Available Dynamic spike threshold plays a critical role in neuronal input-output relations. In many neurons, the threshold potential depends on the rate of membrane potential depolarization (dV/dt preceding a spike. There are two basic classes of neural excitability, i.e., Type I and Type II, according to input-output properties. Although the dynamical and biophysical basis of their spike initiation has been established, the spike threshold dynamic for each cell type has not been well described. Here, we use a biophysical model to investigate how spike threshold depends on dV/dt in two types of neuron. It is observed that Type II spike threshold is more depolarized and more sensitive to dV/dt than Type I. With phase plane analysis, we show that each threshold dynamic arises from the different separatrix and K+ current kinetics. By analyzing subthreshold properties of membrane currents, we find the activation of hyperpolarizing current prior to spike initiation is a major factor that regulates the threshold dynamics. The outward K+ current in Type I neuron does not activate at the perithresholds, which makes its spike threshold insensitive to dV/dt. The Type II K+ current activates prior to spike initiation and there is a large net hyperpolarizing current at the perithresholds, which results in a depolarized threshold as well as a pronounced threshold dynamic. These predictions are further attested in several other functionally equivalent cases of neural excitability. Our study provides a fundamental description about how intrinsic biophysical properties contribute to the threshold dynamics in Type I and Type II neurons, which could decipher their significant functions in neural coding.

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...

Effect of physical activities and obesity on Ramadan fasting among hypertensive patients

Directory of Open Access Journals (Sweden)

Nazeer Khan

2016-12-01

Full Text Available Objective: To find out the effect of physical activities and obesity among Ramadan fasting hypertensive patients of Karachi. Methods: 117 hypertensive patients were selected conveniently from the staff and faculty members of Dow University and other locations of Karachi. The inclusion criterion was the hypertensive patients with at least 20 days of fasting. The investigators visited three times (last ten days of Shaban, Ramadan and Shawwal for collection of data. A questionnaire was completed before clinical examination. Blood pressures were   measured 3 times in sitting position. 103 patients fasted at least 20 days. Results: The mean age of the 103 patients was 53.7±11.0 years. 11% participants could be considered as active using MET value of 600 and above. Mean sleeping hours decreased from 6.9 hours in Shaban to 6.3 hours in Ramadan. Mean systolic and diastolic blood pressures decreased from Shaban to Ramadan and bounced back in Shawwal for both ‘active’ and ‘inactive’ patients. However, it was statistically significant for ‘inactive’ patients only. Only mean SBP decreased significantly from Shaban to Ramadan for normal and overweight patients. Combined effect of physical activity, obesity, sleeping pattern and number of fasting days with repeated measure ANOVA showed that only number of fasting days was statistically significant. Conclusions: The study concludes that fasting does not harm anyway to the hypertensive patients. Nevertheless, it significantly reduces the systolic and diastolic blood pressures. Changes in physical activities, sleeping patterns, and weight reduction, except number days of fasting, do not affect on the fasting hypertensive patients.

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data

Directory of Open Access Journals (Sweden)

Evangelos Stromatias

2017-06-01

Full Text Available This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77% and Poker-DVS (100% real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

Science.gov (United States)

Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2017-01-01

This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

Fast neutron activation analysis using short-lived radionuclides

International Nuclear Information System (INIS)

Salma, I.; Zemplen-Papp, E.

1993-01-01

Fast neutron activation analysis experiments were performed to investigate the analytical possibilities and prospective utilization of short-lived activation products. A rapid pneumatic transfer system for use with neutron generators has been installed and applied for detecting radionuclides with a half-life from ∼300 ms to 20 s. The transport time for samples of total mass of 1-4 g is between 130 and 160 ms for pressurized air of 0.1-0.4 MPa. The reproducibility of transport times is less than 2%. The employed method of correcting time-dependent counting losses is based on the virtual pulse generator principle. The measuring equipment consists of CAMAC modules and a special gating circuit. Typical time distributions of counting losses are presented. The same 14 elements were studied by the conventional activation method (single irradiation and single counting) by both a typical pneumatic transport system (run time 3 s) and the fast pneumatic transport facility. Furthermore, the influence of the cyclic activation technique on the elemental sensitivities was investigated. (author) 15 refs.; 5 figs.; 3 tabs

Training Deep Spiking Neural Networks Using Backpropagation.

Science.gov (United States)

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

2016-01-01

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

Spike Code Flow in Cultured Neuronal Networks.

Science.gov (United States)

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

2016-01-01

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

Grain price spikes and beggar-thy-neighbor policy responses

DEFF Research Database (Denmark)

Jensen, Hans Grinsted; Anderson, Kym

2017-01-01

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

Effect of the sub-threshold periodic current forcing on the regularity and the synchronization of neuronal spiking activity

International Nuclear Information System (INIS)

Ozer, Mahmut; Uzuntarla, Muhammet; Agaoglu, Sukriye Nihal

2006-01-01

We first investigate the amplitude effect of the subthreshold periodic forcing on the regularity of the spiking events by using the coefficient of variation of interspike intervals. We show that the resonance effect in the coefficient of variation, which is dependent on the driving frequency for larger membrane patch sizes, disappears when the amplitude of the subthreshold forcing is decreased. Then, we demonstrate that the timings of the spiking events of a noisy and periodically driven neuron concentrate on a specific phase of the stimulus. We also show that increasing the intensity of the noise causes the phase probability density of the spiking events to get smaller values, and eliminates differences in the phase locking behavior of the neuron for different patch sizes

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

Science.gov (United States)

Ponulak, Filip; Kasinski, Andrzej

2011-01-01

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

Neuronal spike sorting based on radial basis function neural networks

Directory of Open Access Journals (Sweden)

Taghavi Kani M

2011-02-01

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

The investigation of fast neutron Threshold Activation Detectors (TAD)

International Nuclear Information System (INIS)

Gozani, T; King, M J; Stevenson, J

2012-01-01

The detection of fast neutrons is usually done by liquid hydrogenous organic scintillators, where the separation between the ever present gamma rays and neutrons is achieved by the pulse shape discrimination (PSD). In many practical situation the detection of fast neutrons has to be carried out while the intense source (be it neutrons, gamma rays or x-rays) that creates these neutrons, for example by the fission process, is present. This source, or ''flash'', usually blinds the neutron detectors and temporarily incapacitates them. By the time the detectors recover the prompt neutron signature does not exist. Thus to overcome the blinding background, one needs to search for processes whereby the desired signature, such as fission neutrons could in some way be measured long after the fission occurred and when the neutron detector is fully recovered from the overload. A new approach was proposed and demonstrated a good sensitivity for the detection of fast neutrons in adverse overload situations where normally it could not be done. A temporal separation of the fission event from the prompt neutrons detection is achieved via the activation process. The main idea, called Threshold Activation Detection (or detector)-TAD, is to find appropriate substances that can be selectively activated by the fission neutrons and not by the source radiation, and then measure the radioactively decaying activation products (typically beta and γ-rays) well after the source pulse has ended. The activation material should possess certain properties: a suitable half-life; an energy threshold below which the numerous source neutrons will not activate it (e.g. about 3 MeV); easily detectable activation products and has a usable cross section for the selected reaction. Ideally the substance would be part of the scintillator. There are several good candidates for TAD. The first one we have selected is based on fluorine. One of the major advantages of this element is the fact that it is a major

The investigation of fast neutron Threshold Activation Detectors (TAD)

Science.gov (United States)

Gozani, T.; King, M. J.; Stevenson, J.

2012-02-01

The detection of fast neutrons is usually done by liquid hydrogenous organic scintillators, where the separation between the ever present gamma rays and neutrons is achieved by the pulse shape discrimination (PSD). In many practical situation the detection of fast neutrons has to be carried out while the intense source (be it neutrons, gamma rays or x-rays) that creates these neutrons, for example by the fission process, is present. This source, or ``flash'', usually blinds the neutron detectors and temporarily incapacitates them. By the time the detectors recover the prompt neutron signature does not exist. Thus to overcome the blinding background, one needs to search for processes whereby the desired signature, such as fission neutrons could in some way be measured long after the fission occurred and when the neutron detector is fully recovered from the overload. A new approach was proposed and demonstrated a good sensitivity for the detection of fast neutrons in adverse overload situations where normally it could not be done. A temporal separation of the fission event from the prompt neutrons detection is achieved via the activation process. The main idea, called Threshold Activation Detection (or detector)-TAD, is to find appropriate substances that can be selectively activated by the fission neutrons and not by the source radiation, and then measure the radioactively decaying activation products (typically beta and γ-rays) well after the source pulse has ended. The activation material should possess certain properties: a suitable half-life; an energy threshold below which the numerous source neutrons will not activate it (e.g. about 3 MeV); easily detectable activation products and has a usable cross section for the selected reaction. Ideally the substance would be part of the scintillator. There are several good candidates for TAD. The first one we have selected is based on fluorine. One of the major advantages of this element is the fact that it is a major

Event-driven processing for hardware-efficient neural spike sorting

Science.gov (United States)

Liu, Yan; Pereira, João L.; Constandinou, Timothy G.

2018-02-01

Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.

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,

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.

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.

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

Directory of Open Access Journals (Sweden)

Zedong eBi

2016-02-01

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

Mimickers of generalized spike and wave discharges.

Science.gov (United States)

Azzam, Raed; Bhatt, Amar B

2014-06-01

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

Fast coding of orientation in primary visual cortex.

Directory of Open Access Journals (Sweden)

Oren Shriki

Full Text Available Understanding how populations of neurons encode sensory information is a major goal of systems neuroscience. Attempts to answer this question have focused on responses measured over several hundred milliseconds, a duration much longer than that frequently used by animals to make decisions about the environment. How reliably sensory information is encoded on briefer time scales, and how best to extract this information, is unknown. Although it has been proposed that neuronal response latency provides a major cue for fast decisions in the visual system, this hypothesis has not been tested systematically and in a quantitative manner. Here we use a simple 'race to threshold' readout mechanism to quantify the information content of spike time latency of primary visual (V1 cortical cells to stimulus orientation. We find that many V1 cells show pronounced tuning of their spike latency to stimulus orientation and that almost as much information can be extracted from spike latencies as from firing rates measured over much longer durations. To extract this information, stimulus onset must be estimated accurately. We show that the responses of cells with weak tuning of spike latency can provide a reliable onset detector. We find that spike latency information can be pooled from a large neuronal population, provided that the decision threshold is scaled linearly with the population size, yielding a processing time of the order of a few tens of milliseconds. Our results provide a novel mechanism for extracting information from neuronal populations over the very brief time scales in which behavioral judgments must sometimes be made.

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.

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.

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

Science.gov (United States)

Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

2011-01-01

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons. PMID:22219717

Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

Directory of Open Access Journals (Sweden)

Dejan Pecevski

2011-12-01

Full Text Available An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows ("explaining away" and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons.

Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons.

Science.gov (United States)

Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

2011-12-01

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows ("explaining away") and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons.

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

Science.gov (United States)

Najafi, Sarvenaz; Jalali, Mohsen

2016-06-01

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

The Mutation Frequency in Different Spike Categories in Barley

DEFF Research Database (Denmark)

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

1964-01-01

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

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.

Spike Bursts from an Excitable Optical System

Science.gov (United States)

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

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

Spike Code Flow in Cultured Neuronal Networks

Directory of Open Access Journals (Sweden)

Shinichi Tamura

2016-01-01

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

Complexity optimization and high-throughput low-latency hardware implementation of a multi-electrode spike-sorting algorithm.

Science.gov (United States)

Dragas, Jelena; Jackel, David; Hierlemann, Andreas; Franke, Felix

2015-03-01

Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction.

A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data.

Science.gov (United States)

Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

2017-06-01

Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

Commission of the European Communities - Review of fast reactor activities, March 1979

International Nuclear Information System (INIS)

Balz, W.

1979-01-01

The Commission of the European Communities pursued its activities on the lines described earlier. Besides the execution of a research programme in its Joint Research Centre the Commission endeavoured to support the development and demonstration of fast reactors. Most of the latter activities were performed in the frame of the Fast Reactor Coordinating Committee

On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs.

Science.gov (United States)

Gerhard, Felipe; Deger, Moritz; Truccolo, Wilson

2017-02-01

Point process generalized linear models (PP-GLMs) provide an important statistical framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic stability is essential when sampling from these models, as done in computational neuroscience to analyze statistical properties of neuronal dynamics and in neuro-engineering to implement closed-loop applications. Here we show, however, that despite passing common goodness-of-fit tests, PP-GLMs estimated from data are often unstable, leading to divergent firing rates. The inclusion of absolute refractory periods is not a satisfactory solution since the activity then typically settles into unphysiological rates. To address these issues, we derive a framework for determining the existence and stability of fixed points of the expected conditional intensity function (CIF) for general PP-GLMs. Specifically, in nonlinear Hawkes PP-GLMs, the CIF is expressed as a function of the previous spike history and exogenous inputs. We use a mean-field quasi-renewal (QR) approximation that decomposes spike history effects into the contribution of the last spike and an average of the CIF over all spike histories prior to the last spike. Fixed points for stationary rates are derived as self-consistent solutions of integral equations. Bifurcation analysis and the number of fixed points predict that the original models can show stable, divergent, and metastable (fragile) dynamics. For fragile models, fluctuations of the single-neuron dynamics predict expected divergence times after which rates approach unphysiologically high values. This metric can be used to estimate the probability of rates to remain physiological for given time periods, e.g., for simulation purposes. We demonstrate the use of the stability framework using simulated single-neuron examples and neurophysiological recordings. Finally, we show how to adapt PP-GLM estimation procedures to guarantee model stability. Overall, our results provide a

Hypocretin (orexin) regulates glutamate input to fast-spiking interneurons in layer V of the Fr2 region of the murine prefrontal cortex.

Science.gov (United States)

Aracri, Patrizia; Banfi, Daniele; Pasini, Maria Enrica; Amadeo, Alida; Becchetti, Andrea

2015-05-01

We studied the effect of hypocretin 1 (orexin A) in the frontal area 2 (Fr2) of the murine neocortex, implicated in the motivation-dependent goal-directed tasks. In layer V, hypocretin stimulated the spontaneous excitatory postsynaptic currents (EPSCs) on fast-spiking (FS) interneurons. The effect was accompanied by increased frequency of miniature EPSCs, indicating that hypocretin can target the glutamatergic terminals. Moreover, hypocretin stimulated the spontaneous inhibitory postsynaptic currents (IPSCs) on pyramidal neurons, with no effect on miniature IPSCs. This action was prevented by blocking 1) the ionotropic glutamatergic receptors; 2) the hypocretin receptor type 1 (HCRTR-1), with SB-334867. Finally, hypocretin increased the firing frequency in FS cells, and the effect was blocked when the ionotropic glutamate transmission was inhibited. Immunolocalization confirmed that HCRTR-1 is highly expressed in Fr2, particularly in layer V-VI. Conspicuous labeling was observed in pyramidal neuron somata and in VGLUT1+ glutamatergic terminals, but not in VGLUT2+ fibers (mainly thalamocortical afferents). The expression of HCRTR-1 in GABAergic structures was scarce. We conclude that 1) hypocretin regulates glutamate release in Fr2; 2) the effect presents a presynaptic component; 3) the peptide control of FS cells is indirect, and probably mediated by the regulation of glutamatergic input onto these cells. © The Author 2013. Published by Oxford University Press.

Shock initiation of explosives: Temperature spikes and growth spurts

Science.gov (United States)

Bassett, Will P.; Dlott, Dana D.

2016-08-01

When energetic materials are subjected to high-velocity impacts, the first steps in the shock-to-detonation transition are the creation, ignition, and growth of hot spots. We used 1-3.2 km s-1 laser-launched flyer plates to impact powdered octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine, a powerful explosive, and monitored hundreds of emission bursts with an apparatus that determined temperature and emissivity at all times. The time-dependent volume fraction of hot spots was determined by measuring the time-dependent emissivity. After the shock, most hot spots extinguished, but the survivors smoldered for hundreds of nanoseconds until their temperatures spiked, causing a hot spot growth spurt. Depending on the impact duration, the growth spurts could be as fast as 300 ns and as slow as 13 μs.

Comparing the Effect of Fasting and Physical Activity on Active and Non-active Males’ Body Composition, Serum Osmolarity Levels and Some Parameters of Electrolytes

Directory of Open Access Journals (Sweden)

M Nematy

2012-08-01

Full Text Available Introduction: Ever since there is insufficient and incoherent information about the effect of the Ramadan Fasting together with regular exercise on levels of serum osmolarity, and electrolytes concentration. The aim of this study was to compare the effect of fasting and physical activity on active and non-active males’ body composition, serum osmolarity levels and some parameters of electrolytes. Methods: Twenty six healthy males, who were selected by convenience sampling method, were divided into two (active and non-active groups. The Active group participated in football training for three sessions per week during the fasting month. All measurements were repeated on the first and last day of fasting month and were used to analyze the test results. Results: The average differences were significantly decreased in weight, BMI, WHR, mineral, total water in two groups (P≤0.05. There was a significant difference in average of BMI, WHR, body fat, mineral and total water between two groups (P≤0.05. Within-group mean differences in glucose, potassium, urine and albumin in both groups were significant (P≤0.05. Differences of serum osmolarity in between- and within-groups were not significant in both groups. While, glucose decreased significantly, the levels of the protein decreased, and urea increased significantly only in non active fasting group. Conclusion: According to these results, regular exercise together with the Ramadan fasting result in change in some serum osmolarity index, electrolytes and water. Therefore, it is necessary to protect the athletics against the malnutrition in Ramadan fasting by using the diet schedule and enough water.

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.

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.

Comparison of Classifier Architectures for Online Neural Spike Sorting.

Science.gov (United States)

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

2017-04-01

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

Automatic fitting of spiking neuron models to electrophysiological recordings

Directory of Open Access Journals (Sweden)

Cyrille Rossant

2010-03-01

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

Glucocorticoids activate the ATP-ubiquitin-dependent proteolytic system in skeletal muscle during fasting

Science.gov (United States)

Wing, S. S.; Goldberg, A. L.; Goldberger, A. L. (Principal Investigator)

1993-01-01

Glucocorticoids are essential for the increase in protein breakdown in skeletal muscle normally seen during fasting. To determine which proteolytic pathway(s) are activated upon fasting, leg muscles from fed and fasted normal rats were incubated under conditions that block or activate different proteolytic systems. After food deprivation (1 day), the nonlysosomal ATP-dependent process increased by 250%, as shown in experiments involving depletion of muscle ATP. Also, the maximal capacity of the lysosomal process increased 60-100%, but no changes occurred in the Ca(2+)-dependent or the residual energy-independent proteolytic processes. In muscles from fasted normal and adrenalectomized (ADX) rats, the protein breakdown sensitive to inhibitors of the lysosomal or Ca(2+)-dependent pathways did not differ. However, the ATP-dependent process was 30% slower in muscles from fasted ADX rats. Administering dexamethasone to these animals or incubating their muscles with dexamethasone reversed this defect. During fasting, when the ATP-dependent process rises, muscles show a two- to threefold increase in levels of ubiquitin (Ub) mRNA. However, muscles of ADX animals failed to show this response. Injecting dexamethasone into the fasted ADX animals increased muscle Ub mRNA within 6 h. Thus glucocorticoids activate the ATP-Ub-dependent proteolytic pathway in fasting apparently by enhancing the expression of components of this system such as Ub.

Computer simulations of the damage due to the passage of a heavy fast ion through diamond

International Nuclear Information System (INIS)

Sorkin, Anastasia; Adler, Joan; Kalish, Rafi

2004-01-01

Full Text:The present tight-binding molecular dynamics simulations of the structural modifications that result from the ''thermal spike'' that occurs during the passage of a heavy fast ion through a thin diamond or amorphous carbon layer, and the subsequent regrowth upon cooling. The thermal spike and cooling down are simulated by locally heating and then quenching a small region of carbon: surrounded either by diamond or by a mostly sp''3 bonded amorphous carbon network. For the case of the thermal spike in diamond Fe find that if the ''temperature'' (kinetic energy of the atoms) at the center of the thermal spike is high enough, an amorphous carbon region containing a large fraction of threefold coordinated C atoms (sp 2 bonded) remains within the diamond network after cooling. The structure of this amorphous layer depends very strongly on the ''temperature'' of heating and on the dimensions of the thermal spike. Scaling is found between curves of the dependence of the percentage of sp''2 bonded atoms in the region of the thermal spike on the heating ''temperature'' for different volumes. Justification of the validity of the' tight-binding approximation for these simulations will also be given

Commission of the European Communities review of fast reactor activities, March 1981

Energy Technology Data Exchange (ETDEWEB)

Balz, W [Commission of the European Communities, Brussels (Belgium)

1981-05-01

The Commission of the European Communities continued its activities in the field of fast reactors development essentially in the frame of the Fast Reactor Coordinating Committee (FRCC) and by execution of a Reactor Programme at its Joint Research Center (JRC). The study was concerned with introducing fast reactors into European Community, elaboration of preliminary safety criteria and guidelines for typical fast reactor accidents; codes and standards; LMFBR safety, fuel, fuel cycle safety.

Commission of the European Communities review of fast reactor activities, March 1981

International Nuclear Information System (INIS)

Balz, W.

1981-01-01

The Commission of the European Communities continued its activities in the field of fast reactors development essentially in the frame of the Fast Reactor Coordinating Committee (FRCC) and by execution of a Reactor Programme at its Joint Research Center (JRC). The study was concerned with introducing fast reactors into European Community, elaboration of preliminary safety criteria and guidelines for typical fast reactor accidents; codes and standards; LMFBR safety, fuel, fuel cycle safety

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.

Biophysical properties and computational modeling of calcium spikes in serotonergic neurons of the dorsal raphe nucleus.

Science.gov (United States)

Tuckwell, Henry C

2013-06-01

Serotonergic neurons of the dorsal raphe nuclei, with their extensive innervation of nearly the whole brain have important modulatory effects on many cognitive and physiological processes. They play important roles in clinical depression and other psychiatric disorders. In order to quantify the effects of serotonergic transmission on target cells it is desirable to construct computational models and to this end these it is necessary to have details of the biophysical and spike properties of the serotonergic neurons. Here several basic properties are reviewed with data from several studies since the 1960s to the present. The quantities included are input resistance, resting membrane potential, membrane time constant, firing rate, spike duration, spike and afterhyperpolarization (AHP) amplitude, spike threshold, cell capacitance, soma and somadendritic areas. The action potentials of these cells are normally triggered by a combination of sodium and calcium currents which may result in autonomous pacemaker activity. We here analyse the mechanisms of high-threshold calcium spikes which have been demonstrated in these cells the presence of TTX (tetrodotoxin). The parameters for calcium dynamics required to give calcium spikes are quite different from those for regular spiking which suggests the involvement of restricted parts of the soma-dendritic surface as has been found, for example, in hippocampal neurons. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

A metric space approach to the information capacity of spike trains

OpenAIRE

HOUGHTON, CONOR JAMES; GILLESPIE, JAMES

2010-01-01

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

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

Directory of Open Access Journals (Sweden)

Weiliang eChen

2011-03-01

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

Sialic Acid Binding Properties of Soluble Coronavirus Spike (S1 Proteins: Differences between Infectious Bronchitis Virus and Transmissible Gastroenteritis Virus

Directory of Open Access Journals (Sweden)

Christine Winter

2013-07-01

Full Text Available The spike proteins of a number of coronaviruses are able to bind to sialic acids present on the cell surface. The importance of this sialic acid binding ability during infection is, however, quite different. We compared the spike protein of transmissible gastroenteritis virus (TGEV and the spike protein of infectious bronchitis virus (IBV. Whereas sialic acid is the only receptor determinant known so far for IBV, TGEV requires interaction with its receptor aminopeptidase N to initiate infection of cells. Binding tests with soluble spike proteins carrying an IgG Fc-tag revealed pronounced differences between these two viral proteins. Binding of the IBV spike protein to host cells was in all experiments sialic acid dependent, whereas the soluble TGEV spike showed binding to APN but had no detectable sialic acid binding activity. Our results underline the different ways in which binding to sialoglycoconjugates is mediated by coronavirus spike proteins.

Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity

Science.gov (United States)

Mikkelsen, Kaare; Imparato, Alberto; Torcini, Alessandro

2013-05-01

The collective dynamics of excitatory pulse coupled neurons with spike-timing dependent plasticity is studied. The introduction of spike-timing dependent plasticity induces persistent irregular oscillations between strongly and weakly synchronized states, reminiscent of brain activity during slow-wave sleep. We explain the oscillations by a mechanism, the Sisyphus Effect, caused by a continuous feedback between the synaptic adjustments and the coherence in the neural firing. Due to this effect, the synaptic weights have oscillating equilibrium values, and this prevents the system from relaxing into a stationary macroscopic state.

Fast and reliable methods for extracting functional connectivity in large populations

DEFF Research Database (Denmark)

Roudi, Yasser; Tyrcha, Joanna; Hertz, John

2009-01-01

in that time bin, and 1 if it has emitted one spike or more. One then can construct an Ising model, P(s )=Z-1exp{h.s+sJs} for the spike patterns with the same means and pair correlations as the data, using Boltzmann learning, which is in principle exact.  The elements Jij , of the matrix J can be considered...... to be functional couplings. However, Boltzmann learning is prohibitively time-consuming for large networks. Here, we compare the results from five fast approximate methods for finding the couplings with those from Boltzmann learning.      We used data from a simulated network of spiking neurons operating...... in a balanced state of asynchronous firing with a mean rate of ~10 Hz for excitatory neurons. Employing a bin size of 10 ms, we performed Boltzmann learning to fit Ising models for populations of size N up to 200 excitatory neurons chosen randomly from the 800 in the simulated network.  We studied the following...

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.

Voltage-spike analysis for a free-running parallel inverter

Science.gov (United States)

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

1974-01-01

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

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

Comparative hygienic assessment of active ingredients content in the air environment after treatment of cereal spiked crops by combined fungicides.

Science.gov (United States)

Kondratiuk, Mykola; Blagaia, Anna; Pelo, Ihor

2018-01-01

Introduction: The quality of the air environment significantly affects the health of the population. Chemical plant protection products in the spring and summer time may be the main pollutants of the air environment in rural areas. Chemical plant protection products are dangerous substances of anthropogenic origin. If applying pesticides in high concentrations, the risk of poisoning by active ingredients of pesticide preparations in workers directly contacting with it increases. The aim: Comparative hygienic assessment of active ingredients content in the air environment after treatment of cereal spiked crops by combined fungicides was the aim of the work. Materials and methods: Active ingredients of the studied combined fungicides, samples of air, and swabs from workers' skin and stripes from overalls were materials of the research. Methods of full-scale in-field hygienic experiment, gas-liquid chromatography, high-performance liquid chromatography, as well as statistical and bibliographic methods were used in the research. Results and conclusions: Active ingredients of the studied combined fungicides were not detected in the working zone air and atmospheric air at the levels exceeding the limits of its detection by appropriate chromatography methods. Findings confirmed the air environment safety for agricultural workers and rural population if studied combined fungicides are applied following the hygienically approved suggested application rates and in accordance of good agricultural practice rules. However the possible complex risk for workers after certain studied fungicides application may be higher than acceptable due to the elevated values for dermal effects. The complex risk was higher than acceptable in еру case of aerial spraying of both studied fungicides, meanwhile only one combination of active ingredients revealed possible risk for workers applying fungicides by rod method of cereal spiked crops treatment.

Inferior Olive HCN1 Channels Coordinate Synaptic Integration and Complex Spike Timing

Directory of Open Access Journals (Sweden)

Derek L.F. Garden

2018-02-01

Full Text Available Cerebellar climbing-fiber-mediated complex spikes originate from neurons in the inferior olive (IO, are critical for motor coordination, and are central to theories of cerebellar learning. Hyperpolarization-activated cyclic-nucleotide-gated (HCN channels expressed by IO neurons have been considered as pacemaker currents important for oscillatory and resonant dynamics. Here, we demonstrate that in vitro, network actions of HCN1 channels enable bidirectional glutamatergic synaptic responses, while local actions of HCN1 channels determine the timing and waveform of synaptically driven action potentials. These roles are distinct from, and may complement, proposed pacemaker functions of HCN channels. We find that in behaving animals HCN1 channels reduce variability in the timing of cerebellar complex spikes, which serve as a readout of IO spiking. Our results suggest that spatially distributed actions of HCN1 channels enable the IO to implement network-wide rules for synaptic integration that modulate the timing of cerebellar climbing fiber signals.

Real-time radionuclide identification in γ-emitter mixtures based on spiking neural network

International Nuclear Information System (INIS)

Bobin, C.; Bichler, O.; Lourenço, V.; Thiam, C.; Thévenin, M.

2016-01-01

Portal radiation monitors dedicated to the prevention of illegal traffic of nuclear materials at international borders need to deliver as fast as possible a radionuclide identification of a potential radiological threat. Spectrometry techniques applied to identify the radionuclides contributing to γ-emitter mixtures are usually performed using off-line spectrum analysis. As an alternative to these usual methods, a real-time processing based on an artificial neural network and Bayes’ rule is proposed for fast radionuclide identification. The validation of this real-time approach was carried out using γ-emitter spectra ( 241 Am, 133 Ba, 207 Bi, 60 Co, 137 Cs) obtained with a high-efficiency well-type NaI(Tl). The first tests showed that the proposed algorithm enables a fast identification of each γ-emitting radionuclide using the information given by the whole spectrum. Based on an iterative process, the on-line analysis only needs low-statistics spectra without energy calibration to identify the nature of a radiological threat. - Highlights: • A fast radionuclide identification algorithm applicable in spectroscopic portal monitors is presented. • The proposed algorithm combines a Bayesian sequential approach and a spiking neural network. • The algorithm was validated using the mixture of γ-emitter spectra provided by a well-type NaI(Tl) detector. • The radionuclide identification process is implemented using the whole γ-spectrum without energy calibration.

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.

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.

Proteolytic activation of the SARS-coronavirus spike protein: cutting enzymes at the cutting edge of antiviral research.

Science.gov (United States)

Simmons, Graham; Zmora, Pawel; Gierer, Stefanie; Heurich, Adeline; Pöhlmann, Stefan

2013-12-01

The severe acute respiratory syndrome (SARS) pandemic revealed that zoonotic transmission of animal coronaviruses (CoV) to humans poses a significant threat to public health and warrants surveillance and the development of countermeasures. The activity of host cell proteases, which cleave and activate the SARS-CoV spike (S) protein, is essential for viral infectivity and constitutes a target for intervention. However, the identities of the proteases involved have been unclear. Pioneer studies identified cathepsins and type II transmembrane serine proteases as cellular activators of SARS-CoV and demonstrated that several emerging viruses might exploit these enzymes to promote their spread. Here, we will review the proteolytic systems hijacked by SARS-CoV for S protein activation, we will discuss their contribution to viral spread in the host and we will outline antiviral strategies targeting these enzymes. This paper forms part of a series of invited articles in Antiviral Research on "From SARS to MERS: 10years of research on highly pathogenic human coronaviruses.'' Copyright © 2013 Elsevier B.V. All rights reserved.

The electric potential of tripolar spikes

Energy Technology Data Exchange (ETDEWEB)

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

2010-02-22

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

The electric potential of tripolar spikes

International Nuclear Information System (INIS)

Nocera, L.

2010-01-01

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

A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data

Science.gov (United States)

Lieb, Florian; Stark, Hans-Georg; Thielemann, Christiane

2017-06-01

Objective. Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. Approach. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. Main results. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. Significance. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms.

Spike-timing dependent plasticity and the cognitive map.

Science.gov (United States)

Bush, Daniel; Philippides, Andrew; Husbands, Phil; O'Shea, Michael

2010-01-01

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

Spike-timing dependent plasticity and the cognitive map

Directory of Open Access Journals (Sweden)

Daniel eBush

2010-10-01

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

Non-fasting factor VII coagulant activity (FVII:C) increased by high-fat diet

DEFF Research Database (Denmark)

Bladbjerg, Else-Marie; Marckmann, P; Sandström, B

1994-01-01

:Bt/FVII:Am (a measure of FVII activation) increased from fasting levels on both diets, but most markedly on the high-fat diet. In contrast, FVII:Am (a measure of FVII protein) tended to decrease from fasting levels on both diets. FVII:C rose from fasting levels on the high-fat diet, but not on the low-fat diet....... The findings suggest that high-fat diets increase non-fasting FVII:C, and consequently may be associated with increased risk of thrombosis. Udgivelsesdato: 1994-Jun......Preliminary observations have suggested that non-fasting factor VII coagulant activity (FVII:C) may be related to the dietary fat content. To confirm this, we performed a randomised cross-over study. Seventeen young volunteers were served 2 controlled isoenergetic diets differing in fat content (20...

Learning to Recognize Actions From Limited Training Examples Using a Recurrent Spiking Neural Model

Science.gov (United States)

Panda, Priyadarshini; Srinivasa, Narayan

2018-01-01

A fundamental challenge in machine learning today is to build a model that can learn from few examples. Here, we describe a reservoir based spiking neural model for learning to recognize actions with a limited number of labeled videos. First, we propose a novel encoding, inspired by how microsaccades influence visual perception, to extract spike information from raw video data while preserving the temporal correlation across different frames. Using this encoding, we show that the reservoir generalizes its rich dynamical activity toward signature action/movements enabling it to learn from few training examples. We evaluate our approach on the UCF-101 dataset. Our experiments demonstrate that our proposed reservoir achieves 81.3/87% Top-1/Top-5 accuracy, respectively, on the 101-class data while requiring just 8 video examples per class for training. Our results establish a new benchmark for action recognition from limited video examples for spiking neural models while yielding competitive accuracy with respect to state-of-the-art non-spiking neural models. PMID:29551962

Voltage spikes in Nb3Sn and NbTi strands

International Nuclear Information System (INIS)

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

2005-01-01

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

A supervised learning rule for classification of spatiotemporal spike patterns.

Science.gov (United States)

Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

2016-08-01

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

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

Science.gov (United States)

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

2017-02-10

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

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.

Spike timing regulation on the millisecond scale by distributed synaptic plasticity at the cerebellum input stage: a simulation study

Directory of Open Access Journals (Sweden)

Jesus A Garrido

2013-05-01

Full Text Available The way long-term synaptic plasticity regulates neuronal spike patterns is not completely understood. This issue is especially relevant for the cerebellum, which is endowed with several forms of long-term synaptic plasticity and has been predicted to operate as a timing and a learning machine. Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular layer network. In response to mossy fiber bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. The weight at mossy fiber to granule cell synapses regulated the delay of the first spike and the weight at mossy fiber and parallel fiber to Golgi cell synapses regulated the duration of the time-window during which the first-spike could be emitted. Moreover, the weights of synapses controlling Golgi cell activation regulated the intensity of granule cell inhibition and therefore the number of spikes that could be emitted. First spike timing was regulated with millisecond precision and the number of spikes ranged from 0 to 3. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time scale and allows the cerebellar granular layer to flexibly control burst transmission along the mossy fiber pathway.

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

Science.gov (United States)

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

2012-01-01

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

Fractal characterization of acupuncture-induced spike trains of rat WDR neurons

International Nuclear Information System (INIS)

Chen, Yingyuan; Guo, Yi; Wang, Jiang; Hong, Shouhai; Wei, Xile; Yu, Haitao; Deng, Bin

2015-01-01

Highlights: •Fractal analysis is a valuable tool for measuring MA-induced neural activities. •In course of the experiments, the spike trains display different fractal properties. •The fractal properties reflect the long-term modulation of MA on WDR neurons. •The results may explain the long-lasting effects induced by acupuncture. -- Abstract: The experimental and the clinical studies have showed manual acupuncture (MA) could evoke multiple responses in various neural regions. Characterising the neuronal activities in these regions may provide more deep insights into acupuncture mechanisms. This paper used fractal analysis to investigate MA-induced spike trains of Wide Dynamic Range (WDR) neurons in rat spinal dorsal horn, an important relay station and integral component in processing acupuncture information. Allan factor and Fano factor were utilized to test whether the spike trains were fractal, and Allan factor were used to evaluate the scaling exponents and Hurst exponents. It was found that these two fractal exponents before and during MA were different significantly. During MA, the scaling exponents of WDR neurons were regulated in a small range, indicating a special fractal pattern. The neuronal activities were long-range correlated over multiple time scales. The scaling exponents during and after MA were similar, suggesting that the long-range correlations not only displayed during MA, but also extended to after withdrawing the needle. Our results showed that fractal analysis is a useful tool for measuring acupuncture effects. MA could modulate neuronal activities of which the fractal properties change as time proceeding. This evolution of fractal dynamics in course of MA experiments may explain at the level of neuron why the effect of MA observed in experiment and in clinic are complex, time-evolutionary, long-range even lasting for some time after stimulation

Predictive coding of dynamical variables in balanced spiking networks.

Science.gov (United States)

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

2013-01-01

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

A 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)

Spike timing analysis in neural networks with unsupervised synaptic plasticity

Science.gov (United States)

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

2013-01-01

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

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

Science.gov (United States)

Xu, Yan; Yang, Jing; Zhong, Shuiming

2017-09-01

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

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

Science.gov (United States)

Kazantsev, V B; Asatryan, S Yu

2011-09-01

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

Voltage spikes in Nb3Sn and NbTi strands

Energy Technology Data Exchange (ETDEWEB)

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

2005-09-01

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

Pressurized water reactor iodine spiking behavior under power transient conditions

International Nuclear Information System (INIS)

Ho, J.C.

1992-01-01

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

Fasting potentiates the anticancer activity of tyrosine kinase inhibitors by strengthening MAPK signaling inhibition

Science.gov (United States)

Caffa, Irene; D'Agostino, Vito; Damonte, Patrizia; Soncini, Debora; Cea, Michele; Monacelli, Fiammetta; Odetti, Patrizio; Ballestrero, Alberto; Provenzani, Alessandro; Longo, Valter D.; Nencioni, Alessio

2015-01-01

Tyrosine kinase inhibitors (TKIs) are now the mainstay of treatment in many types of cancer. However, their benefit is frequently short-lived, mandating the search for safe potentiation strategies. Cycles of fasting enhance the activity of chemo-radiotherapy in preclinical cancer models and dietary approaches based on fasting are currently explored in clinical trials. Whether combining fasting with TKIs is going to be potentially beneficial remains unknown. Here we report that starvation conditions increase the ability of commonly administered TKIs, including erlotinib, gefitinib, lapatinib, crizotinib and regorafenib, to block cancer cell growth, to inhibit the mitogen-activated protein kinase (MAPK) signaling pathway and to strengthen E2F-dependent transcription inhibition. In cancer xenografts models, both TKIs and cycles of fasting slowed tumor growth, but, when combined, these interventions were significantly more effective than either type of treatment alone. In conclusion, cycles of fasting or of specifically designed fasting-mimicking diets should be evaluated in clinical studies as a means to potentiate the activity of TKIs in clinical use. PMID:25909220