Spike Train Patterns Research Articles

On the Relation between Bursts and Dynamic Synapse Properties: A Modulation‐Based Ansatz

When entering a synapse, presynaptic pulse trains are filtered according to the recent pulse history at the synapse and also with respect to their own pulse time course. Various behavioral models have tried to reproduce these complex filtering properties. In particular, the quantal model of neurotransmitter release has been shown to be highly selective for particular presynaptic pulse patterns. However, since the original, pulse-iterative quantal model does not lend itself to mathematical analysis, investigations have only been carried out via simulations. In contrast, we derive a comprehensive explicit expression for the quantal model. We show the correlation between the parameters of this explicit expression and the preferred spike train pattern of the synapse. In particular, our analysis of the transmission of modulated pulse trains across a dynamic synapse links the original parameters of the quantal model to the transmission efficacy of two major spiking regimes, that is, bursting and constant-rate ones.

Synchronous Neuronal Processes

Event Abstract Back to Event Synchronous Neuronal Processes Epaminondas Rosa1*, Hans A. Braun2, Ulrike Feudel3 and Christian Finke3 1 Illinois State University, Department of Physics and Biocenter Oulu,, United States 2 Philipps-University Marburg, Germany 3 University of Oldenburg, Germany Neuronal synchrony is known for being the underlying state for disorders such as Parkinson's disease and epilepsy. At the same time, synchrony of neurons may be a desirable condition for sleeping. As the mechanism governing synchronization of neurons is not well understood, numerical studies focusing on neuronal synchronization processes may prove to be useful tools for unlocking the unknown mechanisms behind certain types of neurological pathologies.Here we present results of numerical neuronal synchronization studies based on the model equations developed by Huber and Braun [1]. It is a modified Hodgkin-Huxley model which exhibits all spike train patterns observed in electroreceptors from dogfish and catfish, and from facial cold receptors of the rat. The model allows the generation of a variety of neuronal dynamic states including regular and chaotic bursting as well as tonic firing, depending on different model parameters. For our synchronization study, the neurons have been coupled electronically. The results indicate a wealth of possibilities in terms of synchronization regimes and transitions, depending on a variety of conditions. In addition, we draw attention to dynamical systems other than neurons, both theoretical and experimental, displaying similar behaviors. [1] Chaos 10, 231 (2000). Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009. Presentation Type: Poster Presentation Topic: Computational neuroscience Citation: Rosa E, Braun HA, Feudel U and Finke C (2019). Synchronous Neuronal Processes. Front. Neuroinform. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.115 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 25 May 2009; Published Online: 09 May 2019. * Correspondence: Epaminondas Rosa, Illinois State University, Department of Physics and Biocenter Oulu,, Illinois, United States, erosa@ilstu.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Epaminondas Rosa Hans A Braun Ulrike Feudel Christian Finke Google Epaminondas Rosa Hans A Braun Ulrike Feudel Christian Finke Google Scholar Epaminondas Rosa Hans A Braun Ulrike Feudel Christian Finke PubMed Epaminondas Rosa Hans A Braun Ulrike Feudel Christian Finke Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Altered Information Processing in the Prefrontal Cortex of Huntington's Disease Mouse Models

Understanding cortical information processing in Huntington's disease (HD), a genetic neurological disorder characterized by prominent motor and cognitive abnormalities, is key to understanding the mechanisms underlying the HD behavioral phenotype. We recorded extracellular spike activity in two symptomatic, freely behaving mouse models: R6/2 transgenics, which are based on a CBA x C57BL/6 background and show robust behavioral symptoms, and HD knock-in (KI) mice, which have a 129sv background and express relatively mild behavioral signs. We focused on prefrontal cortex and assessed firing patterns of individually recorded neurons as well as the amount of synchrony between simultaneously recorded neuronal pairs. At the single-unit level, spike trains in R6/2 transgenics were less variable and had a faster rate than their corresponding wild-type (WT) littermates but showed significantly less bursting. In contrast, KI and WT firing patterns were closely matched. An assessment of both WTs revealed that the R6/2 and KI difference could not be explained by a difference in WT electrophysiology. Thus, the altered pattern of individual spike trains in R6/2 mice appears to parallel their aggressive form of symptom expression. Both WT lines, however, showed a high proportion of synchrony between neuronal pairs (>85%) that was significantly attenuated in both corresponding HD models (decreases of approximately 20% and approximately 30% in R6/2s and knock-ins, respectively). The loss of spike synchrony, regardless of symptom severity, suggests a population-level deficit in cortical information processing that underlies HD progression.

Subthreshold membrane-potential resonances shape spike-train patterns in the entorhinal cortex.

Many neurons exhibit subthreshold membrane-potential resonances, such that the largest voltage responses occur at preferred stimulation frequencies. Because subthreshold resonances are known to influence the rhythmic activity at the network level, it is vital to understand how they affect spike generation on the single-cell level. We therefore investigated both resonant and nonresonant neurons of rat entorhinal cortex. A minimal resonate-and-fire type model based on measured physiological parameters captures fundamental properties of neuronal firing statistics surprisingly well and helps to shed light on the mechanisms that shape spike patterns: 1) subthreshold resonance together with a spike-induced reset of subthreshold oscillations leads to spike clustering and 2) spike-induced dynamics influence the fine structure of interspike interval (ISI) distributions and are responsible for ISI correlations appearing at higher firing rates (> or =3 Hz). Both mechanisms are likely to account for the specific discharge characteristics of various cell types.

Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains

Experimental studies have observed Long Term synaptic Potentiation (LTP) when a presynaptic neuron fires shortly before a postsynaptic neuron, and Long Term Depression (LTD) when the presynaptic neuron fires shortly after, a phenomenon known as Spike Timing Dependant Plasticity (STDP). When a neuron is presented successively with discrete volleys of input spikes STDP has been shown to learn ‘early spike patterns’, that is to concentrate synaptic weights on afferents that consistently fire early, with the result that the postsynaptic spike latency decreases, until it reaches a minimal and stable value. Here, we show that these results still stand in a continuous regime where afferents fire continuously with a constant population rate. As such, STDP is able to solve a very difficult computational problem: to localize a repeating spatio-temporal spike pattern embedded in equally dense ‘distractor’ spike trains. STDP thus enables some form of temporal coding, even in the absence of an explicit time reference. Given that the mechanism exposed here is simple and cheap it is hard to believe that the brain did not evolve to use it.

Spike train signatures of retinal ganglion cell types

The mammalian retina deconstructs the visual world using parallel neural channels, embodied in the morphological and physiological types of ganglion cells. We sought distinguishing features of each cell type in the temporal pattern of their spikes. As a first step, conventional physiological properties were used to cluster cells in eight types by a statistical analysis. We then adapted a method of P. Reinagel et al. (1999: J. Neurophysiol., 81, 2558-2569) to define epochs within the spike train of each cell. The spike trains of many cells were found to contain robust patterns that are defined by the (averaged) timing of successive interspike intervals in brief activity epochs. The patterns were robust across four different types of visual stimulus. Although the patterns are conserved in different visual environments, they do not prevent the cell from signaling the strength of its response to a particular stimulus, which is expressed in the number of spikes contained in each coding epoch. Clustering based on the spike train patterns alone showed that the spike train patterns correspond, in most but not all cases, to cell types pre-defined by traditional criteria. That the congruence is less than perfect suggests that the typing of rabbit ganglion cells may need further refinement. Analysis of the spike train patterns may be useful in this regard and for distinguishing the many unidentified ganglion cell types that exist in other mammalian retinas.

Regular Patterns in Cerebellar Purkinje Cell Simple Spike Trains

BackgroundCerebellar Purkinje cells (PC) in vivo are commonly reported to generate irregular spike trains, documented by high coefficients of variation of interspike-intervals (ISI). In strong contrast, they fire very regularly in the in vitro slice preparation. We studied the nature of this difference in firing properties by focusing on short-term variability and its dependence on behavioral state.Methodology/Principal FindingsUsing an analysis based on CV2 values, we could isolate precise regular spiking patterns, lasting up to hundreds of milliseconds, in PC simple spike trains recorded in both anesthetized and awake rodents. Regular spike patterns, defined by low variability of successive ISIs, comprised over half of the spikes, showed a wide range of mean ISIs, and were affected by behavioral state and tactile stimulation. Interestingly, regular patterns often coincided in nearby Purkinje cells without precise synchronization of individual spikes. Regular patterns exclusively appeared during the up state of the PC membrane potential, while single ISIs occurred both during up and down states. Possible functional consequences of regular spike patterns were investigated by modeling the synaptic conductance in neurons of the deep cerebellar nuclei (DCN). Simulations showed that these regular patterns caused epochs of relatively constant synaptic conductance in DCN neurons.Conclusions/SignificanceOur findings indicate that the apparent irregularity in cerebellar PC simple spike trains in vivo is most likely caused by mixing of different regular spike patterns, separated by single long intervals, over time. We propose that PCs may signal information, at least in part, in regular spike patterns to downstream DCN neurons.

Stochastic Emergence of Repeating Cortical Motifs in Spontaneous Membrane Potential Fluctuations In Vivo

It was recently discovered that subthreshold membrane potential fluctuations of cortical neurons can precisely repeat during spontaneous activity, seconds to minutes apart, both in brain slices and in anesthetized animals. These repeats, also called cortical motifs, were suggested to reflect a replay of sequential neuronal firing patterns. We searched for motifs in spontaneous activity, recorded from the rat barrel cortex and from the cat striate cortex of anesthetized animals, and found numerous repeating patterns of high similarity and repetition rates. To test their significance, various statistics were compared between physiological data and three different types of stochastic surrogate data that preserve dynamical characteristics of the recorded data. We found no evidence for the existence of deterministically generated cortical motifs. Rather, the stochastic properties of cortical motifs suggest that they appear by chance, as a result of the constraints imposed by the coarse dynamics of subthreshold ongoing activity.

Spike-Timing Precision Underlies the Coding Efficiency of Auditory Receptor Neurons

Sensory systems must translate incoming signals quickly and reliably so that an animal can act successfully in its environment. Even at the level of receptor neurons, however, functional aspects of the sensory encoding process are not yet fully understood. Specifically, this concerns the question how stimulus features and neural response characteristics lead to an efficient transmission of sensory information. To address this issue, we have recorded and analyzed spike trains from grasshopper auditory receptors, while systematically varying the stimulus statistics. The stimulus variations profoundly influenced the efficiency of neural encoding. This influence was largely attributable to the presence of specific stimulus features that triggered remarkably precise spikes whose trial-to-trial timing variability was as low as 0.15 ms--one order of magnitude shorter than typical stimulus time scales. Precise spikes decreased the noise entropy of the spike trains, thereby increasing the rate of information transmission. In contrast, the total spike train entropy, which quantifies the variety of different spike train patterns, hardly changed when stimulus conditions were altered, as long as the neural firing rate remained the same. This finding shows that stimulus distributions that were transmitted with high information rates did not invoke additional response patterns, but instead displayed exceptional temporal precision in their neural representation. The acoustic stimuli that led to the highest information rates and smallest spike-time jitter feature pronounced sound-pressure deflections lasting for 2-3 ms. These upstrokes are reminiscent of salient structures found in natural grasshopper communication signals, suggesting that precise spikes selectively encode particularly important aspects of the natural stimulus environment.

Rhythmic Activity of Noisy Neural Circuits

Dynamical features of neural circuits generating different patterns of spike trains in the presence of noise are investigated: (i) We explore noise-induced multimode dynamics in excitable functional units. Multiple gain of regularity is found to be related to different frequency entrainments and to the appearance of additional time scales; (ii) We develop stochastic implementation of central pattern generator to show that excitable units can successfully replace pacemakers at the appropriate level of noise; (iii) We study the coexistence of beta and gamma rhythms in a local network of nerve cells. It is found that noise can cause coherent switchings between different oscillatory states.

Extra Spike Formation in Sensory Neurons and the Disruption of Afferent Spike Patterning

The peculiar pseudounipolar geometry of primary sensory neurons can lead to ectopic generation of “extra spikes” in the region of the dorsal root ganglion potentially disrupting the fidelity of afferent signaling. We have used an explicit model of myelinated vertebrate sensory neurons to investigate the location and mechanism of extra spike formation, and its consequences for distortion of afferent impulse patterning. Extra spikes originate in the initial segment axon under conditions in which the soma spike becomes delayed and broadened. The broadened soma spike then re-excites membrane it has just passed over, initiating an extra spike which propagates outwards into the main conducting axon. Extra spike formation depends on cell geometry, electrical excitability, and the recent history of impulse activity. Extra spikes add to the impulse barrage traveling toward the spinal cord, but they also travel antidromically in the peripheral nerve colliding with and occluding normal orthodromic spikes. As a result there is no net increase in afferent spike number. However, extra spikes render firing more staccato by increasing the number of short and long interspike intervals in the train at the expense of intermediate intervals. There may also be more complex changes in the pattern of afferent spike trains, and hence in afferent signaling.

A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 2. Application to simultaneous single unit recordings

This study demonstrates the practical application of the pattern grouping algorithm (PGA), presented in the companion paper (Tetko IV, Villa AEP. A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 1. Detection of repeated patterns. J. Neurosci. Methods 2000; accompanying article), to data sets including up to 30 simultaneously recorded spike trains. The analysis of a large network of simulated neurons shows that the incidence of patterns cannot be simply related to an increase in firing rates obtained after Hebbian learning. Patterns that disappeared and reappeared in the thalamus of anesthetized rats when the cerebral cortex was reversibly inactivated suggest that widespread cell assemblies contribute to the generation and propagation of precisely timed activity. In an another experiment multiple spike trains were recorded from the temporal cortex of freely moving rats performing a complex two-choice discrimination task. The presence or absence of particular patterns in the period preceding the cue was associated with changes in reaction time. In conclusion, neuronal network interactions may generate spatiotemporal firing patterns detectable by PGA. We provide evidence of such patterned activity associated with specific animal's behavior, thus suggesting the existence of complex temporal coding schemes in the higher nervous centers of the brain.

A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 1. Detection of repeated patterns

The existence of precise temporal relations in sequences of spike intervals, referred to as ‘spatiotemporal patterns’, is suggested by brain theories that emphasize the role of temporal coding. Specific analytical methods able to assess the significance of such patterned activity are extremely important to establish its function for information processing in the brain. This study proposes a new method called ‘pattern grouping algorithm’ (PGA), designed to identify and evaluate the statistical significance of patterns which differ from each other by a defined and small jitter in spike timing of the order of few ms. The algorithm performs a pre-selection of template patterns with a fast computational approach, optimizes the jitter for each spike in the template and evaluates the statistical significance of the pattern group using three complementary statistical approaches. Simulated data sets characterized by various types of known non stationarities are used for validation of PGA and for comparison of its performance to other methods. Applications of PGA to experimental data sets of simultaneously recorded spike trains are described in a companion paper (Tetko IV, Villa AEP. A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 2. Application to simultaneous single unit recordings. J Neurosci Methods 2000; accompanying article).

Processing of auditory midbrain interspike intervals by model neurons.

A question central to sensory processing is how signal information is encoded and processed by single neurons. Stimulus features can be represented through rate coding (via firing rate), temporal coding (via firing synchronization to temporal periodicities), or temporal encoding (via intricate patterns of spike trains). Of the three, examples of temporal encoding are the least documented. One region in which temporal encoding is currently being explored is the auditory midbrain. Midbrain neurons in the plainfin midshipman generate different interspike interval (ISI) distributions depending on the frequencies of the concurrent vocal signals. However, these distributions differ only along certain lengths of ISIs, so that any neurons trying to distinguish the distributions would have to respond selectively to specific ISI ranges. We used this empirical observation as a realistic challenge with which to explore the plausibility of ISI-tuned neurons that could validate this form of temporal encoding. The resulting modeled cells--point neurons optimized through multidimensional searching--were successfully tuned to discriminate patterns in specific ranges of ISIs. Achieving this task, particularly with simplified neurons, strengthens the credibility of ISI coding in the brain and lends credence to its role in auditory processing.

A Model of Frequency Coding in the Central Auditory Nervous System

A phenomenological model for neural coding in the central auditory system is presented. This model is based on average rate-place codes and the hypothesis is that the rate-place code present in the population of low spontaneous rate nerve fibres is adequate to account for frequency discrimination thresholds across the entire audible frequency range. The activity of a population of nerve fibres in response to an input pure tone is calculated and a neural spike train pattern is generated. An optimal central observer estimates the input frequency from the spike train pattern. The model output is the frequency difference limen at the specific input frequency, determined from the estimated input frequency. It is shown that a rate-place code can account for psychoacoustically observed frequency difference limens. The model also supports the hypothesis that a human listener does not make full use of all the information relevant to frequency that is available in auditory nerve spike trains.

Recordings of human polymodal single C-fiber afferents following mechanical and argon-laser heat stimulation of inflamed skin.

Activity in single C-fiber afferents, whose cutaneous receptive fields were on the dorsal side of the foot (n=10), were recorded in the peroneal nerve of healthy voluntary subjects. Characterization of the fibers with respect to thresholds and field areas were made before and after cutaneous inflammation, which was induced with mustard oil. To test the nociceptive heat modality, a high-intensity argon laser was used and single 200-ms light pulses were focused onto the skin. The mechanical properties were tested with von Frey-type filaments. In the uninflamed skin, heat and mechanical stimulation activated single C-fibers in matching skin areas. The areas were all within the receptive field borders defined by electrical cutaneous stimulation. The mustard-oil-induced cutaneous inflammation was subjectively reported by the subjects as being moderately painful. In six of the units, a spontaneous activity was induced in the sample of ten previously non-active units. Before the inflammation, the 200-ms argon-laser pulse evoked a highly reproducible pattern of spike-trains. Following inflammation, this pattern was reproducible, but appeared with a significantly reduced activation rate despite the same energy being delivered to the skin both before and after the inflammation. A reduction in slope of the stimulus-response relationship was also observed after inflammation. Following inflammation, changes occurred with expansion both of the mechanical- and heat-receptive fields. The expansion was delineated by the areas defined by electrical stimulation. Following inflammation, the threshold to heat was decreased, but that to mechanical stimuli was not. No relation was detected between the threshold change and the degree of receptive-field expansion. The subjective pain reported changed following inflammation with an increase in the perceived pain in relation to the recorded action potentials, which emphasizes the importance of either an increase in sensitivity in the central nervous system or an increase in peripheral spatial summation after inflammation.

Discussion

This communication describes the new information that may be obtained by applying nonlinear analytical techniques to neurobiological time-series. Specifically, we consider the sequence of interspike intervals Ti (the "timing") of trains recorded from synaptically inhibited crayfish pacemaker neurons. As reported earlier, different postsynaptic spike train forms (sets of timings with shared properties) are generated by varying the average rate and/or pattern (implying interval dispersions and sequences) of presynaptic spike trains. When the presynaptic train is Poisson (independent exponentially distributed intervals), the form is "Poisson-driven" (unperturbed and lengthened intervals succeed each other irregularly). When presynaptic trains are pacemaker (intervals practically equal), forms are either "p:q locked" (intervals repeat periodically), "intermittent" (mostly almost locked but disrupted irregularly), "phase walk throughs" (intermittencies with briefer regular portions), or "messy" (difficult to predict or describe succinctly). Messy trains are either "erratic" (some intervals natural and others lengthened irregularly) or "stammerings" (intervals are integral multiples of presynaptic intervals). The individual spike train forms were analysed using attractor reconstruction methods based on the lagged coordinates provided by successive intervals from the time-series Ti. Numerous models were evaluated in terms of their predictive performance by a trial-and-error procedure: the most successful model was taken as best reflecting the true nature of the system's attractor. Each form was characterized in terms of its dimensionality, nonlinearity and predictability. (1) The dimensionality of the underlying dynamical attractor was estimated by the minimum number of variables (coordinates Ti) required to model acceptably the system's dynamics, i.e. by the system's degrees of freedom. Each model tested was based on a different number of Ti; the smallest number whose predictions were judged successful provided the best integer approximation of the attractor's true dimension (not necessarily an integer). Dimensionalities from three to five provided acceptable fits. (2) The degree of nonlinearity was estimated by: (i) comparing the correlations between experimental results and data from linear and nonlinear models, and (ii) tuning model nonlinearity via a distance-weighting function and identifying the either local or global neighborhood size. Lockings were compatible with linear models and stammerings were marginal; nonlinear models were best for Poisson-driven, intermittent and erratic forms. (3) Finally, prediction accuracy was plotted against increasingly long sequences of intervals forecast: the accuracies for Poisson-driven, locked and stammering forms were invariant, revealing irregularities due to uncorrelated noise, but those of intermittent and messy erratic forms decayed rapidly, indicating an underlying deterministic process. The excellent reconstructions possible for messy erratic and for some intermittent forms are especially significant because of their relatively low dimensionality (around 4), high degree of nonlinearity and prediction decay with time. This is characteristic of chaotic systems, and provides evidence that nonlinear couplings between relatively few variables are the major source of the apparent complexity seen in these cases. This demonstration of different dimensions, degrees of nonlinearity and predictabilities provides rigorous support for the categorization of different synaptically driven discharge forms proposed earlier on the basis of more heuristic criteria. This has significant implications. (1) It demonstrates that heterogeneous postsynaptic forms can indeed be induced by manipulating a few presynaptic variables. (2) Each presynaptic timing induces a form with characteristic dimensionality, thus breaking up the preparation into subsystems such that the physical variables in each operate as one

The significance of precisely replicating patterns in mammalian CNS spike trains

Neuronal spike trains from both single and multi-unit recordings often contain patterns such as doublets and triplets of spikes that precisely replicate themselves at a later time. The presence of such precisely replicating patterns can still be detected when the tolerance on interval replication is shortened to a fraction of a millisecond. In this context we examine here data taken from various parts of the central nervous systems of anesthetized rats, cats and monkeys. The relative abundance of replicating triplets varies from centre to centre, and is nearly always significantly greater than obtained in Monte-Carlo simulations of either a Poisson-like process or a renewal process having the same interspike interval distribution as the neuronal data. However, a remarkable exception is found in the activity of retinal ganglion cells. Significant deviations were found in the primary visual cortex and, even more so, in the lateral geniculate body and the mitral cells of the olfactory bulb. Using a fixed tolerance for the replication of intervals (0.5 ms) it is usually observed that replicating patterns are produced in excess (with respect to renewal process models) mostly in low firing rate episodes (≤100 Hz). However, using a tolerance that varies in direct proportion to the mean interval (i.e. as the reciprocal of the firing rate), one generally observes that replicating triplets occur with higher than expected frequency in comparable proportions at all firing rates. This observation suggests the existence of a scale invariance principle in these phenomena with respect to certain neuronal codes. In order to decrease the influence of the estimated neuronal firing rate on the results of the comparisons, we computed also the ratio NT2/ND3, of the number of replicating triplets to the number of doublets replicating three times, [Lestienne R. (1994) Proc. Soc. Neurosci. 20, 22; Lestienne R. (1996) Bio. Cybern. 74, 55–61] using both a fixed or a variable tolerance. In spike trains obeying a Poisson process, NT2/ND3 ratios should be nearly independent of the frequency, especially when using a variable tolerance. These studies supported previous results : significant deviations from the models are found in all the spike trains examined, except in the case of retinal ganglion cells, and the most significant deviations are found in recordings from the lateral geniculate nucleus and the mitral cells of the olfactory bulb. Removing spikes that belong to bursts having large “Poisson surprise” values [Legéndy C. R. and Salcman M. (1985) J. Neurophysiol. 53, 926-939] (except the very first spike of the burst) significantly decreases NT2/ND3 ratios in the record from the lateral geniculate nucleus, suggesting that in this case bursty episodes greatly contribute to the production of replicating patterns, but such a removal does not affect results from the piriform record. Finally, in both the lateral geniculate nucleus and in the mitral cells of the olfactory bulb records, perturbing the timing of spikes by applying to interspike intervals small jitters of uniform probability density with amplitude up to 3 ms, very significantly decrease NT2/ND3 ratios in these centres, but does not change much the NT2/ND3 ratios in other neuronal recordings. Implications of these findings for a possible role of precisely replicating patterns in temporal coding of neuronal information is discussed, as well as possible mechanisms for their production.

Symmetric temporal patterns in cortical spike trains during performance of a short-term memory task

The trion model is a highly structured representation of cortical organizationl which predicts families of symmetric spatial-temporal firing patterns inherent in cortical activity. The symmetries of these inherent firing patterns are used by the brain in short-term memory to perform higher level computations. In the present study, symmetric temporal patterns were searched for in spike trains recorded from cells in parietal cortex of a monkey performing a short-term memory task. A new method of analysis was used to map neuronal firing into sequences of integers representing relative levels of firing rate about the mean (i.e. -1, 0 and 1). The results of this analysis show families of patterns related by symmetry operations. These operations are: i. the interchanging of all the + l’s and -l’s in a given pattern sequence (CT symmetry), ii. the inverting of the temporal sequence of the mapping (T symmetry)1 and iii. the combination of the two previous operations (CT symmetry). Patterns of a given family are found across cells especially in the memory periods of the task; in most cases they reoccur within a given spike train. The pattern families predicted by the model and reported here should be further investigated in multiple microelectrode and EEG recordings. [Neural Res 1997; 19: 509-514]

Dorsal spinocerebellar tract neurons in the chronic intact cat during wakefulness and sleep: analysis of spontaneous spike activity

Relatively little is known about the transmission of ascending sensory information from lumbar levels across the behavioral states of sleep and wakefulness. The present study used extracellular recording methods in chronically instrumented intact behaving cats to monitor the activity of lumbar dorsal spinocerebellar tract (DSCT) neurons within Clarke's column during the states of wakefulness, quiet sleep, and active sleep. Clarke's column DSCT neurons were identified using antidromic identification and retrograde labeling techniques. The spontaneous spike rate and interspike interval data of DSCT neurons were quantified as a function of behavioral state. During wakefulness and quiet sleep, the spike rate of DSCT neurons was stable, and interspike interval histograms (ISIH) indicated a relatively high degree of regularity in DSCT neuronal spike train patterns. In contrast, during active sleep there was a marked reduction in the ongoing spike rate in a vast majority of cells tested. The magnitude of change in ISIHs and interspike interval data during active sleep depended in part on whether the reduction in cell firing was maintained or periodic throughout active sleep. Further suppression of spontaneous activity also was observed during intense rapid-eye-movement episodes of active sleep that were associated with clustered pontogeniculo-occipital wave and muscular twitches and jerks. After re-awakening, spontaneous spike activity of Clarke's column DSCT neurons resembled that recorded during previous episodes of wakefulness. These data provide evidence that ascending proprioceptive and exteroceptive sensory transmission through Clarke's column is diminished during the behavioral state of active sleep.