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Journal articles on the topic 'Spike time synchrony'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Ciba, Manuel, Robert Bestel, Christoph Nick, et al. "Comparison of Different Spike Train Synchrony Measures Regarding Their Robustness to Erroneous Data From Bicuculline-Induced Epileptiform Activity." Neural Computation 32, no. 5 (2020): 887–911. http://dx.doi.org/10.1162/neco_a_01277.

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As synchronized activity is associated with basic brain functions and pathological states, spike train synchrony has become an important measure to analyze experimental neuronal data. Many measures of spike train synchrony have been proposed, but there is no gold standard allowing for comparison of results from different experiments. This work aims to provide guidance on which synchrony measure is best suited to quantify the effect of epileptiform-inducing substances (e.g., bicuculline, BIC) in in vitro neuronal spike train data. Spike train data from recordings are likely to suffer from erron

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Kreuz, Thomas, Daniel Chicharro, Conor Houghton, Ralph G. Andrzejak, and Florian Mormann. "Monitoring spike train synchrony." Journal of Neurophysiology 109, no. 5 (2013): 1457–72. http://dx.doi.org/10.1152/jn.00873.2012.

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Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional ne

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Eisenman, Lawrence N., Christine M. Emnett, Jayaram Mohan, Charles F. Zorumski, and Steven Mennerick. "Quantification of bursting and synchrony in cultured hippocampal neurons." Journal of Neurophysiology 114, no. 2 (2015): 1059–71. http://dx.doi.org/10.1152/jn.00079.2015.

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It is widely appreciated that neuronal networks exhibit patterns of bursting and synchrony that are not captured by simple measures such as average spike rate. These patterns can encode information or represent pathological behavior such as seizures. However, methods for quantifying bursting and synchrony are not agreed upon and can be confounded with spike rate measures. Previous validation has largely relied on in silico networks and single experimental conditions. How published measures of bursting and synchrony perform when applied to biological networks of varied average spike rate and su

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Kreuz, Thomas, Mario Mulansky, and Nebojsa Bozanic. "SPIKY: a graphical user interface for monitoring spike train synchrony." Journal of Neurophysiology 113, no. 9 (2015): 3432–45. http://dx.doi.org/10.1152/jn.00848.2014.

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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 visualiz

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Golomb, David. "Models of Neuronal Transient Synchrony During Propagation of Activity Through Neocortical Circuitry." Journal of Neurophysiology 79, no. 1 (1998): 1–12. http://dx.doi.org/10.1152/jn.1998.79.1.1.

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Golomb, David. Models of neuronal transient synchrony during propagation of activity through neorcortical circuitry. J. Neurophysiol. 79: 1–12, 1998. Stereotypic paroxysmal discharges that propagate in neocortical tissues after electrical stimulations are used as a probe for studying cortical circuitry. I use modeling to investigate the effects of sparse connectivity, heterogeneity of intrinsic neuronal properties, and synaptic noise on synchronization of evoked propagating neuronal discharges in a network of excitatory, regular spiking neurons with spatially decaying connectivity. The global

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Baker, S. N., and R. N. Lemon. "Computer Simulation of Post-Spike Facilitation in Spike-Triggered Averages of Rectified EMG." Journal of Neurophysiology 80, no. 3 (1998): 1391–406. http://dx.doi.org/10.1152/jn.1998.80.3.1391.

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Baker, S. N. and R. N. Lemon. Computer simulation of post-spike facilitation in spike-triggered averages of rectified EMG. J. Neurophysiol. 80: 1391–1406, 1998. When the spikes of a motor cortical cell are used to compile a spike-triggered average (STA) of rectified electromyographic (EMG) activity, a post-spike facilitation (PSF) is sometimes seen. This is generally thought to be indicative of direct corticomotoneuronal (CM) connections. However, it has been claimed that a PSF could be caused by synchronization between CM and non-CM cells. This study investigates the generation of PSF using a

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Stigen, Tyler, Per Danzl, Jeff Moehlis, and Theoden Netoff. "Controlling spike timing and synchrony in oscillatory neurons." Journal of Neurophysiology 105, no. 5 (2011): 2074–82. http://dx.doi.org/10.1152/jn.00898.2011.

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We describe an algorithm to control synchrony between two periodically firing neurons. The control scheme operates in real-time using a dynamic clamp platform. This algorithm is a low-impact stimulation method that brings the neurons toward the desired level of synchrony over the course of several neuron firing periods. As a proof of principle, we demonstrate the versatility of the algorithm using real-time conductance models and then show its performance with biological neurons of hippocampal region CA1 and entorhinal cortex.

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Trimper, John B., and Laura Lee Colgin. "Spike Time Synchrony in the Absence of Continuous Oscillations." Neuron 100, no. 3 (2018): 527–29. http://dx.doi.org/10.1016/j.neuron.2018.10.036.

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Lin (林佳霈), Chia-pei, Yueh-peng Chen (陳嶽鵬), and Chou P. Hung (洪洲伯). "Tuning and spontaneous spike time synchrony share a common structure in macaque inferior temporal cortex." Journal of Neurophysiology 112, no. 4 (2014): 856–69. http://dx.doi.org/10.1152/jn.00485.2013.

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Investigating the relationship between tuning and spike timing is necessary to understand how neuronal populations in anterior visual cortex process complex stimuli. Are tuning and spontaneous spike time synchrony linked by a common spatial structure (do some cells covary more strongly, even in the absence of visual stimulation?), and what is the object coding capability of this structure? Here, we recorded from spiking populations in macaque inferior temporal (IT) cortex under neurolept anesthesia. We report that, although most nearby IT neurons are weakly correlated, neurons with more simila

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Briggs, Barbara G., and Allan Tinker. "Synchronous monoecy in Ecdeiocoleaceae (Poales), in Western Australia." Australian Journal of Botany 62, no. 5 (2014): 391. http://dx.doi.org/10.1071/bt14138.

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The Western Australian plant family Ecdeiocoleaceae includes only three species but DNA data show them as the closest living sister-group of the Poaceae. Ecdeiocoleaceae are wind-pollinated and monoecious; spikes produce separate zones of pistillate and staminate flowers, in acropetal succession. Spikes of Ecdeiocolea have up to 45 flowers, with a sequence of zones up the spike, commonly pistillate–staminate–pistillate–staminate–pistillate, with potentially high fruit set in both of the lower pistillate zones. Rainfall in their habitats in semiarid south-western Australia is highly variable an

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Kreuz, Thomas, Daniel Chicharro, Martin Greschner, and Ralph G. Andrzejak. "Time-resolved and time-scale adaptive measures of spike train synchrony." Journal of Neuroscience Methods 195, no. 1 (2011): 92–106. http://dx.doi.org/10.1016/j.jneumeth.2010.11.020.

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Lang, Eric J., and Jack Rosenbluth. "Role of Myelination in the Development of a Uniform Olivocerebellar Conduction Time." Journal of Neurophysiology 89, no. 4 (2003): 2259–70. http://dx.doi.org/10.1152/jn.00922.2002.

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Purkinje cells generate simultaneous complex spikes as a result of olivocerebellar activity. This synchronization (to within 1 ms) is thought to result from electrotonic coupling of inferior olivary neurons. However, the distance from the inferior olive (IO) varies across the cerebellar cortex. Thus signals generated simultaneously at the IO should arrive asynchronously across the cerebellar cortex, unless the length differences are compensated for. Previously, it was shown that the conduction time from the IO to the cerebellar cortex remains nearly constant at ≈4 ms in the rat, implying the e

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Ciba, Manuel, Takuya Isomura, Yasuhiko Jimbo, Andreas Bahmer, and Christiane Thielemann. "Spike-contrast: A novel time scale independent and multivariate measure of spike train synchrony." Journal of Neuroscience Methods 293 (January 2018): 136–43. http://dx.doi.org/10.1016/j.jneumeth.2017.09.008.

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Taylor, Anna M., Julie W. Steege, and Roger M. Enoka. "Motor-Unit Synchronization Alters Spike-Triggered Average Force in Simulated Contractions." Journal of Neurophysiology 88, no. 1 (2002): 265–76. http://dx.doi.org/10.1152/jn.2002.88.1.265.

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The purpose of the study was to quantify the effect of motor-unit synchronization on the spike-triggered average forces of a population of motor units. Muscle force was simulated by defining mechanical and activation characteristics of the motor units, specifying motor neuron discharge times, and imposing various levels of motor-unit synchronization. The model comprised 120 motor units. Simulations were performed for motor units 5–120 to compare the spike-triggered average responses in the presence and absence of motor-unit synchronization with the motor-unit twitch characteristics defined in

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Neckelmann, Dag, Florin Amzica, and Mircea Steriade. "Spike-Wave Complexes and Fast Components of Cortically Generated Seizures. III. Synchronizing Mechanisms." Journal of Neurophysiology 80, no. 3 (1998): 1480–94. http://dx.doi.org/10.1152/jn.1998.80.3.1480.

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Neckelmann, Dag, Florin Amzica, and Mircea Steriade. Spike-wave complexes and fast components of cortically generated seizures. III. Synchronizing mechanisms. J. Neurophysiol. 80: 1480–1494, 1998. The intracortical and thalamocortical synchronization of spontaneously occurring or bicuculline-induced seizures, consisting of spike-wave (SW) or polyspike-wave (PSW) complexes at 2–3 Hz and fast runs at 10–15 Hz, was investigated in cats under ketamine-xylazine anesthesia. We used single and dual simultaneous intracellular recordings from cortical areas 5 and 7, and extracellular recordings of unit

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Hansel, D., G. Mato, and C. Meunier. "Synchrony in Excitatory Neural Networks." Neural Computation 7, no. 2 (1995): 307–37. http://dx.doi.org/10.1162/neco.1995.7.2.307.

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Synchronization properties of fully connected networks of identical oscillatory neurons are studied, assuming purely excitatory interactions. We analyze their dependence on the time course of the synaptic interaction and on the response of the neurons to small depolarizations. Two types of responses are distinguished. In the first type, neurons always respond to small depolarization by advancing the next spike. In the second type, an excitatory postsynaptic potential (EPSP) received after the refractory period delays the firing of the next spike, while an EPSP received at a later time advances

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Steriade, M., and F. Amzica. "Dynamic coupling among neocortical neurons during evoked and spontaneous spike-wave seizure activity." Journal of Neurophysiology 72, no. 5 (1994): 2051–69. http://dx.doi.org/10.1152/jn.1994.72.5.2051.

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1. We investigated the development from patterns of electroencephalogram (EEG) synchronization to paroxysms consisting of spike-wave (SW) complexes at 2–4 Hz or to seizures at higher frequencies (7–15 Hz). We used multisite, simultaneous EEG, extracellular, and intracellular recordings from various neocortical areas and thalamic nuclei of anesthetized cats. 2. The seizures were observed in 25% of experimental animals, all maintained under ketamine and xylazine anesthesia, and were either induced by thalamocortical volleys and photic stimulation or occurred spontaneously. Out of unit and field

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Satuvuori, Eero, Mario Mulansky, Nebojsa Bozanic, et al. "Measures of spike train synchrony for data with multiple time scales." Journal of Neuroscience Methods 287 (August 2017): 25–38. http://dx.doi.org/10.1016/j.jneumeth.2017.05.028.

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Sarpeshkar, Rahul, and Micah O'Halloran. "Scalable Hybrid Computation with Spikes." Neural Computation 14, no. 9 (2002): 2003–38. http://dx.doi.org/10.1162/089976602320263971.

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We outline a hybrid analog-digital scheme for computing with three important features that enable it to scale to systems of large complexity: First, like digital computation, which uses several one-bit precise logical units to collectively compute a precise answer to a computation, the hybrid scheme uses several moderate-precision analog units to collectively compute a precise answer to a computation. Second, frequent discrete signal restoration of the analog information prevents analog noise and offset from degrading the computation. And, third, a state machine enables complex computations to

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Roy, A., P. N. Steinmetz, S. S. Hsiao, K. O. Johnson, and E. Niebur. "Synchrony: A Neural Correlate of Somatosensory Attention." Journal of Neurophysiology 98, no. 3 (2007): 1645–61. http://dx.doi.org/10.1152/jn.00522.2006.

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We investigated whether synchrony between neuronal spike trains is affected by the animal's attentional state. Cross-correlation functions between pairs of spike trains in the second somatosensory cortex (SII) of three macaque monkeys trained to switch attention between a visual task and a tactile task were computed. We previously showed that the majority of recorded neuron pairs (66%) in SII cortex fire synchronously while the animals performed either task and that in a subset of neuron pairs (17%), the degree of synchrony was affected by the animal's attentional state. Of the neuron pairs th

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Shahbaba, Babak, Bo Zhou, Shiwei Lan, Hernando Ombao, David Moorman, and Sam Behseta. "A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons." Neural Computation 26, no. 9 (2014): 2025–51. http://dx.doi.org/10.1162/neco_a_00631.

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We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their cofiring (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1s (spike) and 0s (silence) for each neuron is modeled using the logistic function of a continuous latent variable with a gaussian process prior. For multiple neurons, the corresponding marginal distributions are coupled to their joint probability distribution using a parametric copula model. The advantages of our approach

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Ito, Hiroyuki, Pedro E. Maldonado, and Charles M. Gray. "Dynamics of Stimulus-Evoked Spike Timing Correlations in the Cat Lateral Geniculate Nucleus." Journal of Neurophysiology 104, no. 6 (2010): 3276–92. http://dx.doi.org/10.1152/jn.01000.2009.

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Precisely synchronized neuronal activity has been commonly observed in the mammalian visual pathway. Spike timing correlations in the lateral geniculate nucleus (LGN) often take the form of phase synchronized oscillations in the high gamma frequency range. To study the relations between oscillatory activity, synchrony, and their time-dependent properties, we recorded activity from multiple single units in the cat LGN under stimulation by stationary spots of light. Autocorrelation analysis showed that approximately one third of the cells exhibited oscillatory firing with a mean frequency ∼80 Hz

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Miranda-Domínguez, Óscar, and Theoden I. Netoff. "Parameterized phase response curves for characterizing neuronal behaviors under transient conditions." Journal of Neurophysiology 109, no. 9 (2013): 2306–16. http://dx.doi.org/10.1152/jn.00942.2012.

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Phase response curves (PRCs) are a simple model of how a neuron's spike time is affected by synaptic inputs. PRCs are useful in predicting how networks of neurons behave when connected. One challenge in estimating a neuron's PRCs experimentally is that many neurons do not have stationary firing rates. In this article we introduce a new method to estimate PRCs as a function of firing rate of the neuron. We call the resulting model a parameterized PRC (pPRC). Experimentally, we perturb the neuron applying a current with two parts: 1) a current held constant between spikes but changed at the onse

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Frerking, M., J. Schulte, S. P. Wiebe, and U. Stäubli. "Spike Timing in CA3 Pyramidal Cells During Behavior: Implications for Synaptic Transmission." Journal of Neurophysiology 94, no. 2 (2005): 1528–40. http://dx.doi.org/10.1152/jn.00108.2005.

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Spike timing is thought to be an important mechanism for transmitting information in the CNS. Recent studies have emphasized millisecond precision in spike timing to allow temporal summation of rapid synaptic signals. However, spike timing over slower time scales could also be important, through mechanisms including activity-dependent synaptic plasticity or temporal summation of slow postsynaptic potentials (PSPs) such as those mediated by kainate receptors. To determine the extent to which these slower mechanisms contribute to information processing, it is first necessary to understand the pr

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Heck, D. H., W. T. Thach, and J. G. Keating. "On-beam synchrony in the cerebellum as the mechanism for the timing and coordination of movement." Proceedings of the National Academy of Sciences 104, no. 18 (2007): 7658–63. http://dx.doi.org/10.1073/pnas.0609966104.

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In trained reaching rats, we recorded simple spikes of pairs of Purkinje cells that, with respect to each other, were either aligned on a beam of shared parallel fibers or instead were located off beam. Rates of simple spike firing in both on-beam and off-beam Purkinje cell pairs commonly showed great variety in depth of modulation during reaching behavior. But with respect to timing, on-beam Purkinje cell pairs had simple spikes that were tightly time-locked to each other (either delayed or simultaneous) and to movement, despite the variability in rate. By contrast, off-beam Purkinje cell pai

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Lindsey, B. G., Y. M. Hernandez, K. F. Morris, R. Shannon, and G. L. Gerstein. "Dynamic reconfiguration of brain stem neural assemblies: respiratory phase-dependent synchrony versus modulation of firing rates." Journal of Neurophysiology 67, no. 4 (1992): 923–30. http://dx.doi.org/10.1152/jn.1992.67.4.923.

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1. The objective of this work was to determine whether configurations of midline brain stem neural assemblies change during the respiratory cycle. 2. Spike trains of several single neurons were recorded simultaneously in anesthetized, paralyzed, bilaterally vagotomized, artificially ventilated cats. Data were analyzed with cross-correlational and gravity methods. 3. Sequential samples from each of eight groups of neurons known to contain synchronously discharging neurons exhibited temporal variations in that synchrony. 4. Gravity analysis of short (less than 200-s) samples of spike train data

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Kelly, Ryan C., and Robert E. Kass. "A Framework for Evaluating Pairwise and Multiway Synchrony Among Stimulus-Driven Neurons." Neural Computation 24, no. 8 (2012): 2007–32. http://dx.doi.org/10.1162/neco_a_00307.

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Several authors have previously discussed the use of log-linear models, often called maximum entropy models, for analyzing spike train data to detect synchrony. The usual log-linear modeling techniques, however, do not allow time-varying firing rates that typically appear in stimulus-driven (or action-driven) neurons, nor do they incorporate non-Poisson history effects or covariate effects. We generalize the usual approach, combining point-process regression models of individual neuron activity with log-linear models of multiway synchronous interaction. The methods are illustrated with results

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Tateno, T., and H. P. C. Robinson. "Rate Coding and Spike-Time Variability in Cortical Neurons With Two Types of Threshold Dynamics." Journal of Neurophysiology 95, no. 4 (2006): 2650–63. http://dx.doi.org/10.1152/jn.00683.2005.

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Neurons and dynamical models of spike generation display two different classes of threshold behavior: type 1 [firing frequency vs. current ( f– I) relationship is continuous at threshold] and type 2 (discontinuous f– I). With steady current or conductance stimulation, regular-spiking (RS) pyramidal neurons and fast-spiking (FS) inhibitory interneurons in layer 2/3 of somatosensory cortex exhibit type 1 and type 2 threshold behaviors, respectively. We compared the postsynaptic firing variability of type 1 RS and type 2 FS cells, during naturalistic, fluctuating conductance input. In RS neurons,

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Masuda, Naoki, and Kazuyuki Aihara. "Self-Organizing Dual Coding Based on Spike-Time-Dependent Plasticity." Neural Computation 16, no. 3 (2004): 627–63. http://dx.doi.org/10.1162/089976604772744938.

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It has been a matter of debate how firing rates or spatiotemporal spike patterns carry information in the brain. Recent experimental and theoretical work in part showed that these codes, especially a population rate code and a synchronous code, can be dually used in a single architecture. However, we are not yet able to relate the role of firing rates and synchrony to the spatiotemporal structure of inputs and the architecture of neural networks. In this article, we examine how feedforward neural networks encode multiple input sources in the firing patterns. We apply spike-time-dependent plast

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Swadlow, Harvey A., Irina N. Beloozerova, and Mikhail G. Sirota. "Sharp, Local Synchrony Among Putative Feed-Forward Inhibitory Interneurons of Rabbit Somatosensory Cortex." Journal of Neurophysiology 79, no. 2 (1998): 567–82. http://dx.doi.org/10.1152/jn.1998.79.2.567.

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Swadlow, Harvey A., Irina N. Beloozerova, and Mikhail G. Sirota. Sharp, local synchrony among putative feed-forward inhibitory interneurons of rabbit somatosensory cortex. J. Neurophysiol. 79: 567–582, 1998. Many suspected inhibitory interneurons (SINs) of primary somatosensory cortex (S1) receive a potent monosynaptic thalamic input (thalamocortical SINs, SINstc). It has been proposed that nearly all such SINstc of a S1 barrel column (BC) receive excitatory synaptic input from each member of a subpopulation of neurons within the topographically aligned ventrobasal (VB) thalamic barreloid. Suc

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Baker, S. N., R. Spinks, A. Jackson, and R. N. Lemon. "Synchronization in Monkey Motor Cortex During a Precision Grip Task. I. Task-Dependent Modulation in Single-Unit Synchrony." Journal of Neurophysiology 85, no. 2 (2001): 869–85. http://dx.doi.org/10.1152/jn.2001.85.2.869.

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Neural synchronization in the cortex, and its potential role in information coding, has attracted much recent attention. In this study, we have recorded long spike trains (mean, 33,000 spikes) simultaneously from multiple single neurons in the primary motor cortex (M1) of two conscious macaque monkeys performing a precision grip task. The task required the monkey to use its index finger and thumb to move two spring-loaded levers into a target, hold them there for 1 s, and release for a food reward. Synchrony was analyzed using a time-resolved cross-correlation method, normalized using an estim

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Ventura, Valérie, Can Cai, and Robert E. Kass. "Trial-to-Trial Variability and Its Effect on Time-Varying Dependency Between Two Neurons." Journal of Neurophysiology 94, no. 4 (2005): 2928–39. http://dx.doi.org/10.1152/jn.00644.2004.

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The joint peristimulus time histogram (JPSTH) and cross-correlogram provide a visual representation of correlated activity for a pair of neurons, and the way this activity may increase or decrease over time. In a companion paper we showed how a Bootstrap evaluation of the peaks in the smoothed diagonals of the JPSTH may be used to establish the likely validity of apparent time-varying correlation. As noted in earlier studies by Brody and Ben-Shaul et al., trial-to-trial variation can confound correlation and synchrony effects. In this paper we elaborate on that observation, and present a metho

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Gibson, Jay R., Michael Beierlein, and Barry W. Connors. "Functional Properties of Electrical Synapses Between Inhibitory Interneurons of Neocortical Layer 4." Journal of Neurophysiology 93, no. 1 (2005): 467–80. http://dx.doi.org/10.1152/jn.00520.2004.

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The existence of electrical synapses between GABAergic inhibitory interneurons in neocortex is well established, but their functional properties have not been described in detail. We made whole cell recordings from pairs of electrically coupled fast-spiking (FS) or low threshold–spiking (LTS) neurons, and filled some cells with biocytin for morphological reconstruction. Data were used to create compartmental cable models and to guide mathematical analysis. We analyzed the time course and amplitude of electrical postsynaptic potentials (ePSPs), the subthreshold events generated by presynaptic a

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Umeoka, Shuichi C., Hans O. Lüders, John P. Turnbull, Mohamad Z. Koubeissi, and Robert J. Maciunas. "Requirement of longitudinal synchrony of epileptiform discharges in the hippocampus for seizure generation: a pilot study." Journal of Neurosurgery 116, no. 3 (2012): 513–24. http://dx.doi.org/10.3171/2011.10.jns11261.

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Object The goal in this study was to assess the role of longitudinal hippocampal circuits in the generation of interictal and ictal activity in temporal lobe epilepsy (TLE) and to evaluate the effects of multiple hippocampal transections (MHT). Methods In 6 patients with TLE, the authors evaluated the synchrony of hippocampal interictal and ictal epileptiform discharges by using a cross-correlation analysis, and the effect of MHT on hippocampal interictal spikes was studied. Five of the 6 patients were studied with depth electrodes, and epilepsy surgery was performed in 4 patients (anterior te

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Zador, Anthony. "Impact of Synaptic Unreliability on the Information Transmitted by Spiking Neurons." Journal of Neurophysiology 79, no. 3 (1998): 1219–29. http://dx.doi.org/10.1152/jn.1998.79.3.1219.

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Zador, Anthony. Impact of synaptic unreliability on the information transmitted by spiking neurons. J. Neurophysiol. 79: 1219–1229, 1998. The spike generating mechanism of cortical neurons is highly reliable, able to produce spikes with a precision of a few milliseconds or less. The excitatory synapses driving these neurons are by contrast much less reliable, subject both to release failures and quantal fluctuations. This suggests that synapses represent the primary bottleneck limiting the faithful transmission of information through cortical circuitry. How does the capacity of a neuron to con

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Scholes, Chris, Stephen Coombes, Alan R. Palmer, William S. Rhode, Rob Mill, and Christian J. Sumner. "Precise spike-timing information in the brainstem is well aligned with the needs of communication and the perception of environmental sounds." PLOS Biology 23, no. 6 (2025): e3003213. https://doi.org/10.1371/journal.pbio.3003213.

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The dynamic fluctuations in the amplitude of sound, known as sound envelopes, are ubiquitous in natural sounds and convey information critical for the recognition of speech, and of sounds generally. We are perceptually most sensitive to slow modulations which are most common. However, previous studies of envelope coding in the brainstem found an under-representation of these slow, low-frequency, modulations. Specifically, the synchronization of spike times to the envelope was enhanced in some neuron types, forming channels specialized for envelope processing but tuned to a restricted range of

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Lee, Christopher M., Ahmad F. Osman, Maxim Volgushev, Monty A. Escabí, and Heather L. Read. "Neural spike-timing patterns vary with sound shape and periodicity in three auditory cortical fields." Journal of Neurophysiology 115, no. 4 (2016): 1886–904. http://dx.doi.org/10.1152/jn.00784.2015.

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Mammals perceive a wide range of temporal cues in natural sounds, and the auditory cortex is essential for their detection and discrimination. The rat primary (A1), ventral (VAF), and caudal suprarhinal (cSRAF) auditory cortical fields have separate thalamocortical pathways that may support unique temporal cue sensitivities. To explore this, we record responses of single neurons in the three fields to variations in envelope shape and modulation frequency of periodic noise sequences. Spike rate, relative synchrony, and first-spike latency metrics have previously been used to quantify neural sen

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Deister, Christopher A., Ramana Dodla, David Barraza, Hitoshi Kita, and Charles J. Wilson. "Firing rate and pattern heterogeneity in the globus pallidus arise from a single neuronal population." Journal of Neurophysiology 109, no. 2 (2013): 497–506. http://dx.doi.org/10.1152/jn.00677.2012.

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Intrinsic heterogeneity in networks of interconnected cells has profound effects on synchrony and spike-time reliability of network responses. Projection neurons of the globus pallidus (GPe) are interconnected by GABAergic inhibitory synapses and in vivo fire continuously but display significant rate and firing pattern heterogeneity. Despite being deprived of most of their synaptic inputs, GPe neurons in slices also fire continuously and vary greatly in their firing rate (1–70 spikes/s) and in regularity of their firing. We asked if this rate and pattern heterogeneity arises from separate cell

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Huang, Xin, and Stephen G. Lisberger. "Circuit mechanisms revealed by spike-timing correlations in macaque area MT." Journal of Neurophysiology 109, no. 3 (2013): 851–66. http://dx.doi.org/10.1152/jn.00775.2012.

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We recorded simultaneously from pairs of motion-sensitive neurons in the middle temporal cortex (MT) of macaque monkeys and used cross-correlations in the timing of spikes between neurons to gain insights into cortical circuitry. We characterized the time course and stimulus dependency of the cross-correlogram (CCG) for each pair of neurons and of the auto-correlogram (ACG) of the individual neurons. For some neuron pairs, the CCG showed negative flanks that emerged next to the central peak during stimulus-driven responses. Similar negative flanks appeared in the ACG of many neurons. Negative

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Funabashi, Masatoshi. "Synthetic Modeling of Autonomous Learning with a Chaotic Neural Network." International Journal of Bifurcation and Chaos 25, no. 04 (2015): 1550054. http://dx.doi.org/10.1142/s0218127415500546.

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We investigate the possible role of intermittent chaotic dynamics called chaotic itinerancy, in interaction with nonsupervised learnings that reinforce and weaken the neural connection depending on the dynamics itself. We first performed hierarchical stability analysis of the Chaotic Neural Network model (CNN) according to the structure of invariant subspaces. Irregular transition between two attractor ruins with positive maximum Lyapunov exponent was triggered by the blowout bifurcation of the attractor spaces, and was associated with riddled basins structure. We secondly modeled two autonomo

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Chang, E. Y., K. F. Morris, R. Shannon, and B. G. Lindsey. "Repeated Sequences of Interspike Intervals in Baroresponsive Respiratory Related Neuronal Assemblies of the Cat Brain Stem." Journal of Neurophysiology 84, no. 3 (2000): 1136–48. http://dx.doi.org/10.1152/jn.2000.84.3.1136.

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Many neurons exhibit spontaneous activity in the absence of any specific experimental perturbation. Patterns of distributed synchrony embedded in such activity have been detected in the brain stem, suggesting that it represents more than “baseline” firing rates subject only to being regulated up or down. This work tested the hypothesis that nonrandom sequences of impulses recur in baroresponsive respiratory-related brain stem neurons that are elements of correlational neuronal assemblies. In 15 Dial-urethan anesthetized vagotomized adult cats, neuronal impulses were monitored with microelectro

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Amzica, F., and M. Steriade. "Short- and long-range neuronal synchronization of the slow (< 1 Hz) cortical oscillation." Journal of Neurophysiology 73, no. 1 (1995): 20–38. http://dx.doi.org/10.1152/jn.1995.73.1.20.

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1. Multisite, extra- and intracellular recordings were carried out in cats under ketamine and xylazine anesthesia to assess the degree of synchrony and time relations among cellular activities in various neocortical fields during a slow (< 1 Hz) oscillation consisting of long-lasting depolarizing and hyperpolarizing phases. 2. Recordings were performed from visual areas 17, 18, 19, and 21, association suprasylvian areas 5 and 7, motor pericruciate areas 4 and 6, as well as some related thalamic territories, such as the lateral geniculate (LG), perigeniculate (PG), and rostral intralaminar n

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Cudmore, R. H., L. Fronzaroli-Molinieres, P. Giraud, and D. Debanne. "Spike-Time Precision and Network Synchrony Are Controlled by the Homeostatic Regulation of the D-Type Potassium Current." Journal of Neuroscience 30, no. 38 (2010): 12885–95. http://dx.doi.org/10.1523/jneurosci.0740-10.2010.

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Lindsey, B. G., L. S. Segers, K. F. Morris, Y. M. Hernandez, S. Saporta, and R. Shannon. "Distributed actions and dynamic associations in respiratory-related neuronal assemblies of the ventrolateral medulla and brain stem midline: evidence from spike train analysis." Journal of Neurophysiology 72, no. 4 (1994): 1830–51. http://dx.doi.org/10.1152/jn.1994.72.4.1830.

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1. Considerable evidence indicates that neurons in the brain stem midline and ventrolateral medulla participate in the control of breathing. This work was undertaken to detect and evaluate evidence for functional links that coordinate the parallel operations of neurons distributed in these two domains. 2. Data were from 51 Dial-urethan-anesthetized, bilaterally vagotomized, paralyzed, artificially ventilated cats. Planar arrays of tungsten microelectrodes were used to monitor simultaneously spike trains in two or three of the following regions: n. raphe obscurus-n. raphe pallidus, n. raphe mag

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Lagorce, Xavier, and Ryad Benosman. "STICK: Spike Time Interval Computational Kernel, a Framework for General Purpose Computation Using Neurons, Precise Timing, Delays, and Synchrony." Neural Computation 27, no. 11 (2015): 2261–317. http://dx.doi.org/10.1162/neco_a_00783.

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There has been significant research over the past two decades in developing new platforms for spiking neural computation. Current neural computers are primarily developed to mimic biology. They use neural networks, which can be trained to perform specific tasks to mainly solve pattern recognition problems. These machines can do more than simulate biology; they allow us to rethink our current paradigm of computation. The ultimate goal is to develop brain-inspired general purpose computation architectures that can breach the current bottleneck introduced by the von Neumann architecture. This wor

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Lindsey, B. G., Y. M. Hernandez, K. F. Morris, R. Shannon, and G. L. Gerstein. "Respiratory-related neural assemblies in the brain stem midline." Journal of Neurophysiology 67, no. 4 (1992): 905–22. http://dx.doi.org/10.1152/jn.1992.67.4.905.

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1. The initial objective of this study was to determine whether respiratory-related neural assemblies exist in the brain stem midline. A second goal was to seek evidence for concurrent relationships among the neurons that could generate the detected synchrony. 2. Experiments were conducted on anesthetized, paralyzed, bilaterally vagotomized, artificially ventilated cats. Spike trains of four to nine simultaneously monitored neurons were recorded in the regions of n. raphe obscurus-n. raphe pallidus and n. raphe magnus. 3. Data were analyzed with cycle-triggered histograms, cross-correlograms,

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Cui, Jianxia, Carmen C. Canavier, and Robert J. Butera. "Functional Phase Response Curves: A Method for Understanding Synchronization of Adapting Neurons." Journal of Neurophysiology 102, no. 1 (2009): 387–98. http://dx.doi.org/10.1152/jn.00037.2009.

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Phase response curves (PRCs) for a single neuron are often used to predict the synchrony of mutually coupled neurons. Previous theoretical work on pulse-coupled oscillators used single-pulse perturbations. We propose an alternate method in which functional PRCs (fPRCs) are generated using a train of pulses applied at a fixed delay after each spike, with the PRC measured when the phasic relationship between the stimulus and the subsequent spike in the neuron has converged. The essential information is the dependence of the recovery time from pulse onset until the next spike as a function of the

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Goldwyn, Joshua H., and John Rinzel. "Neuronal coupling by endogenous electric fields: cable theory and applications to coincidence detector neurons in the auditory brain stem." Journal of Neurophysiology 115, no. 4 (2016): 2033–51. http://dx.doi.org/10.1152/jn.00780.2015.

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The ongoing activity of neurons generates a spatially and time-varying field of extracellular voltage ( Ve). This Ve field reflects population-level neural activity, but does it modulate neural dynamics and the function of neural circuits? We provide a cable theory framework to study how a bundle of model neurons generates Ve and how this Ve feeds back and influences membrane potential ( Vm). We find that these “ephaptic interactions” are small but not negligible. The model neural population can generate Ve with millivolt-scale amplitude, and this Ve perturbs the Vm of “nearby” cables and effe

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Wang, Yingxue, and Shih-Chii Liu. "Multilayer Processing of Spatiotemporal Spike Patterns in a Neuron with Active Dendrites." Neural Computation 22, no. 8 (2010): 2086–112. http://dx.doi.org/10.1162/neco.2010.06-09-1030.

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With the advent of new experimental evidence showing that dendrites play an active role in processing a neuron's inputs, we revisit the question of a suitable abstraction for the computing function of a neuron in processing spatiotemporal input patterns. Although the integrative role of a neuron in relation to the spatial clustering of synaptic inputs can be described by a two-layer neural network, no corresponding abstraction has yet been described for how a neuron processes temporal input patterns on the dendrites. We address this void using a real-time aVLSI (analog very-large-scale-integra

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Kobayashi, K., Y. Ohtsuka, E. Oka, and S. Ohtahara. "Primary and secondary bilateral synchrony in epilepsy: differentiation by estimation of interhemispheric small time differences during short spike-wave activity." Electroencephalography and Clinical Neurophysiology 83, no. 2 (1992): 93–103. http://dx.doi.org/10.1016/0013-4694(92)90022-a.

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