Experience-Dependent Development of Feature-Selective Synchronization in the Primary Visual Cortex.

Early visual experience is essential for the maturation of visual functions in which the primary visual cortex plays crucial roles. The extraction of visual features based on response selectivity of individual neurons, a fundamental process in the cortex, is basically established by eye opening in rodents, suggesting that visual experience is required for the development of neural functions other than feature extraction. Here, we show that synchronized firing, which is important for visual information processing, occurs selectively in adjacent neurons sharing similar orientation or spatial frequency preferences in layers 2-4 (upper layer) of rat visual cortex. This feature-selective spike synchrony was rudimentary when the eyes opened and became prominent during the first few weeks after eye opening only in the presence of pattern vision. In contrast, synchronization in layers 5-6 (lower layer) was almost independent of orientation similarity and more weakly dependent on spatial frequency similarity compared with upper layer synchrony. Lower layer synchronization was strengthened during development after eye opening independently of visual experience as a whole. However, the feature selectivity of synchronization was regulated by visual inputs, whereas the inputs without contours were sufficient for this regulation. Therefore, we speculate that feature-selective synchronization in the upper layer may convey detailed information on visual objects to the higher-order cortex, whereas weakly feature-selective synchronization in the lower layer may covey rather rough visual information to the subcortical areas or higher-order cortex. A major role of visual experience may be to establish the specific neural circuits underlying highly feature-selective synchronization.SIGNIFICANCE STATEMENT The neuronal mechanisms underlying experience-dependent improvement of visual functions still remain unresolved. In this study, we investigated whether early visual experience contributes to the development of synchronized neural firing in the primary visual cortex, which plays important roles in visual information processing. We found that synchronized firing depends more remarkably on the similarity of preferred visual stimuli in the upper than lower layer neurons. Pattern vision during development was required for the establishment of spike synchrony in the upper but not the lower layer. These findings provide a new view regarding the role of sensory experience in the functional development of the cortex and the differences in the modes of information processing in the upper and lower cortical layers.

Methods for identification of spike patterns in massively parallel spike trains.

Temporally, precise correlations between simultaneously recorded neurons have been interpreted as signatures of cell assemblies, i.e., groups of neurons that form processing units. Evidence for this hypothesis was found on the level of pairwise correlations in simultaneous recordings of few neurons. Increasing the number of simultaneously recorded neurons increases the chances to detect cell assembly activity due to the larger sample size. Recent technological advances have enabled the recording of 100 or more neurons in parallel. However, these massively parallel spike train data require novel statistical tools to be analyzed for correlations, because they raise considerable combinatorial and multiple testing issues. Recently, various of such methods have started to develop. First approaches were based on population or pairwise measures of synchronization, and later led to methods for the detection of various types of higher-order synchronization and of spatio-temporal patterns. The latest techniques combine data mining with analysis of statistical significance. Here, we give a comparative overview of these methods, of their assumptions and of the types of correlations they can detect.

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

UNLABELLED: 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. SIGNIFICANCE STATEMENT: 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 a method to extend this type of investigation to larger data. Here, we apply it to simultaneous recordings of hundreds of neurons from the motor cortex of macaque monkeys performing a motor task. Our analysis reveals groups of neurons selectively synchronizing their activity in relation to behavior, which sheds new light on the role of synchrony in information processing in the cerebral cortex.

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

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.

Spike synchrony reveals emergence of proto-objects in visual cortex.

Neurons at early stages of the visual cortex signal elemental features, such as pieces of contour, but how these signals are organized into perceptual objects is unclear. Theories have proposed that spiking synchrony between these neurons encodes how features are grouped (binding-by-synchrony), but recent studies did not find the predicted increase in synchrony with binding. Here we propose that features are grouped to "proto-objects" by intrinsic feedback circuits that enhance the responses of the participating feature neurons. This hypothesis predicts synchrony exclusively between feature neurons that receive feedback from the same grouping circuit. We recorded from neurons in macaque visual cortex and used border-ownership selectivity, an intrinsic property of the neurons, to infer whether or not two neurons are part of the same grouping circuit. We found that binding produced synchrony between same-circuit neurons, but not between other pairs of neurons, as predicted by the grouping hypothesis. In a selective attention task, synchrony emerged with ignored as well as attended objects, and higher synchrony was associated with faster behavioral responses, as would be expected from early grouping mechanisms that provide the structure for object-based processing. Thus, synchrony could be produced by automatic activation of intrinsic grouping circuits. However, the binding-related elevation of synchrony was weak compared with its random fluctuations, arguing against synchrony as a code for binding. In contrast, feedback grouping circuits encode binding by modulating the response strength of related feature neurons. Thus, our results suggest a novel coding mechanism that might underlie the proto-objects of perception.

Neural mechanisms of selective attention in the somatosensory system.

Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates.

Static frequency tuning accounts for changes in neural synchrony evoked by transient communication signals.

Although communication signals often vary continuously on the underlying signal parameter, they are perceived as distinct categories. We here report the opposite case where an electrocommunication signal is encoded in four distinct regimes, although the behavior described to date does not show distinct categories. In particular, we studied the encoding of chirps by P-unit afferents in the weakly electric fish Apteronotus leptorhynchus. These fish generate an electric organ discharge that oscillates at a certain individual-specific frequency. The interaction of two fish in communication contexts leads to the emergence of a beating amplitude modulation (AM) at the frequency difference between the two individual signals. This frequency difference represents the social context of the encounter. Chirps are transient increases of the fish's frequency leading to transient changes in the frequency of the AM. We stimulated the cells with the same chirp on different, naturally occurring backgrounds beats. The P-units responded either by synchronization or desynchronization depending on the background. Although the duration of a chirp is often shorter than a full cycle of the AM it elicits, the distinct responses of the P-units to the chirp can be predicted solely from the frequency of the AM based on the static frequency tuning of the cells.

Spike synchronization in cat primary visual cortex depends on similarity of surround-suppression magnitude.

In the primary visual cortex (V1), the spike synchronization seen in neuron pairs with non-overlapping receptive fields can be explained by similarities in their preferred orientation (PO). However, this is not true for pairs with overlapping receptive fields, as they can still exhibit spike synchronization even if their POs are only weakly correlated. Here, we investigated the relationship between spike synchronization and suppressive modulation derived from classical receptive-field surround (surround suppression). We found that layer 4 and layer 2/3 pairs exhibited mainly asymmetric spike synchronization that had non-zero time-lags and was dependent on both the similarity of the PO and the strength of surround suppression. In contrast, layer 2/3 and layer 2/3 pairs showed mainly symmetric spike synchronization that had zero time-lag and was dependent on the similarity of the strength of surround suppression but not on the similarity in POs. From these results, we propose that in cat V1 there exists a functional network that mainly depends on the similarity in surround suppression, and that in layer 2/3 neurons the network maintains surround suppression that is primarily inherited from layer 4 neurons.

A prefrontal network model operating near steady and oscillatory states links spike desynchronization and synaptic deficits in schizophrenia.

Schizophrenia results in part from a failure of prefrontal networks but we lack full understanding of how disruptions at a synaptic level cause failures at the network level. This is a crucial gap in our understanding because it prevents us from discovering how genetic mutations and environmental risks that alter synaptic function cause prefrontal network to fail in schizophrenia. To address that question, we developed a recurrent spiking network model of prefrontal local circuits that can explain the link between NMDAR synaptic and 0-lag spike synchrony deficits we recently observed in a pharmacological monkey model of prefrontal network failure in schizophrenia. We analyze how the balance between AMPA and NMDA components of recurrent excitation and GABA inhibition in the network influence oscillatory spike synchrony to inform the biological data. We show that reducing recurrent NMDAR synaptic currents prevents the network from shifting from a steady to oscillatory state in response to extrinsic inputs such as might occur during behavior. These findings strongly parallel dynamic modulation of 0-lag spike synchrony we observed between neurons in monkey prefrontal cortex during behavior, as well as the suppression of this 0-lag spiking by administration of NMDAR antagonists. As such, our cortical network model provides a plausible mechanism explaining the link between NMDAR synaptic and 0-lag spike synchrony deficits observed in a pharmacological monkey model of prefrontal network failure in schizophrenia.

Schizophrenia is a long-term mental health condition that can cause a person to see, hear or believe things that are not real. Although researchers do not fully understand the causes of schizophrenia, it is known to disrupt synapses, which connect neurons in the brain to form circuits that carry out a specific function when activated. This disruption alters the pattern of activity among the neurons, distorting the way that information is processed and leading to symptoms. Development of schizophrenia is thought to be due to interactions between many factors, including genetic makeup, changes in how the brain matures during development, and environmental stress. Despite animal studies revealing how neural circuits can fail at the level of individual cells, it remains difficult to predict or understand the complex ways that this damage affects advanced brain functions. Previous research in monkeys showed that mimicking schizophrenia using a drug that blocks a particular type of synapse prevented neurons from coordinating their activity. However, this did not address how synaptic and cellular changes lead to disrupted neural circuits. To better understand this, Crowe et al. developed a computational model of neural circuits to study how they respond to synapse disruption. To replicate the brain, the model consisted of two types of neurons ­ those that activate connecting cells in response to received signals and those that suppress them. This model could replicate the complex network behavior that causes brain cells to respond to sensory inputs. Increasing the strength of inputs to the network caused it to switch from a state in which the cells fired independently to one where the cells fired at the same time. As was previously seen in monkeys, blocking a particular type of synapse thought to be involved in schizophrenia prevented the cells from coordinating their signaling. The findings suggest that schizophrenia-causing factors can reduce the ability of neurons to fire at the same instant. Disrupting this process could lead to weaker and fewer synapses forming during brain development or loss of synapses in adults. If that is the case, and scientists can understand how factors combine to trigger this process, the mechanism of coordinated activity failure revealed by the model could help identify treatments that prevent or reverse the synapse disruption seen in schizophrenia.

'Fearful-place' coding in the amygdala-hippocampal network.

Animals seeking survival needs must be able to assess different locations of threats in their habitat. However, the neural integration of spatial and risk information essential for guiding goal-directed behavior remains poorly understood. Thus, we investigated simultaneous activities of fear-responsive basal amygdala (BA) and place-responsive dorsal hippocampus (dHPC) neurons as rats left the safe nest to search for food in an exposed space and encountered a simulated 'predator.' In this realistic situation, BA cells increased their firing rates and dHPC place cells decreased their spatial stability near the threat. Importantly, only those dHPC cells synchronized with the predator-responsive BA cells remapped significantly as a function of escalating risk location. Moreover, optogenetic stimulation of BA neurons was sufficient to cause spatial avoidance behavior and disrupt place fields. These results suggest a dynamic interaction of BA's fear signalling cells and dHPC's spatial coding cells as animals traverse safe-danger areas of their environment.

State-dependent modulation of sympathetic firing by α1-adrenoceptors requires constitutive PKC activity in the neonatal rat spinal cord.

The central adrenergic and noradrenergic neurotransmitter systems diffusively affect the operation of the spinal neural network and dynamically gauge central sympathetic outflow. Using in vitro splanchnic nerve-thoracic spinal cord preparations as an experimental model, this study examined the intraspinal α1-adrenoceptor-meidated modulation of sympathetic firing behaviors. Several sympathetic single-fiber activities were simultaneously recorded. Application of phenylephrine (Phe, an α1-adrenoceptor agonist) increased, decreased or did not affect spontaneous firing. A log-log plot of the change ratios of the average firing rates (AFR) versus their basal AFR displays a linear data distribution. Thus, the heterogeneity in α1-adrenoceptor-mediated responses is well described by a power law function. Phe-induced power-law firing modulation (plFM) was sensitive to prazosin (Prz, an α1-adrenoceptor antagonist). Heparin (Hep, a competitive IP3 receptor blocker) and chelerythrine (Che, a protein kinase C inhibitor) also caused plFM. Phe-induced plFM persisted in the presence of Hep; however, it was occluded by Che pretreatment. Pair-wise analysis of single-fiber activities revealed synchronous sympathetic discharges. Application of Phe, Hep or Che suppressed synchronous discharges in fiber pairs with apparent correlated firing (ACF) and induced or potentiated synchronous discharges in those without or with minimal ACF. Thus, the basal activities of the sympathetic preganglionic neurons participate in determining the responses mediated by the activation of α1-adrenoceptors. This deterministic factor, which is intrinsic to spinal neural networks, helps the supraspinal adrenergic and noradrenergic systems differentially control their widely distributed neural targets.

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

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.

Modulation of synchronous sympathetic firing behaviors by endogenous GABA(A) and glycine receptor-mediated activities in the neonatal rat spinal cord in vitro.

Delivering effective commands in the nervous systems require a temporal integration of neural activities such as synchronous firing. Although sympathetic nerve discharges are characterized by synchronous firing, its temporal structures and how it is modulated are largely unknown. This study used a collagenase-dissociated splanchnic sympathetic nerve-thoracic spinal cord preparation of neonatal rats in vitro as an experimental model. Several single-fiber activities were recorded simultaneously and verified by rigorous computational algorithms. Among 3763 fiber pairs that had spontaneous fiber activities, 382 fiber pairs had firing positively correlated. Their temporal relationship was quantitatively evaluated by cross-correlogram. On average, correlated firing in a fiber pair occurred in scales of ∼40ms lasting for ∼11ms. The relative frequency distribution curves of correlogram parametrical values pertinent to the temporal features were best described by trimodal Gaussians, suggesting a correlated firing originated from three or less sources. Applications of bicuculline or gabazine (noncompetitive or competitive GABA(A) receptor antagonist) and/or strychnine (noncompetitive glycine receptor antagonist) increased, decreased, or did not change individual fiber activities. Antagonist-induced enhancement and attenuation of correlated firing were demonstrated by a respective increase and decrease of the peak probability of the cross-correlograms. Heterogeneity in antagonistic responses suggests that the inhibitory neurotransmission mediated by GABA(A) and glycine receptors is not essential for but can serve as a neural substrate to modulate synchronous firing behaviors. Plausible neural mechanisms were proposed to explain the temporal structures of correlated firing between sympathetic fibers.

Convergent approaches toward the study of multisensory perception.

Classical analytical approaches for examining multisensory processing in individual neurons have relied heavily on changes in mean firing rate to assess the presence and magnitude of multisensory interaction. However, neurophysiological studies within individual sensory systems have illustrated that important sensory and perceptual information is encoded in forms that go beyond these traditional spike-based measures. Here we review analytical tools as they are used within individual sensory systems (auditory, somatosensory, and visual) to advance our understanding of how sensory cues are effectively integrated across modalities (e.g., audiovisual cues facilitating speech processing). Specifically, we discuss how methods used to assess response variability (Fano factor, or FF), local field potentials (LFPs), current source density (CSD), oscillatory coherence, spike synchrony, and receiver operating characteristics (ROC) represent particularly promising tools for understanding the neural encoding of multisensory stimulus features. The utility of each approach and how it might optimally be applied toward understanding multisensory processing is placed within the context of exciting new data that is just beginning to be generated. Finally, we address how underlying encoding mechanisms might shape-and be tested alongside with-the known behavioral and perceptual benefits that accompany multisensory processing.

Statistical evaluation of synchronous spike patterns extracted by frequent item set mining.

We recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of any superset (closed frequent item sets). The number of patterns found by FIM makes direct statistical tests infeasible due to severe multiple testing. To overcome this issue, we proposed to test the significance not of individual patterns, but instead of their signatures, defined as the pairs of pattern size z and support c. Here, we derive in detail a statistical test for the significance of the signatures under the null hypothesis of full independence (pattern spectrum filtering, PSF) by means of surrogate data. As a result, injected spike patterns that mimic assembly activity are well detected, yielding a low false negative rate. However, this approach is prone to additionally classify patterns resulting from chance overlap of real assembly activity and background spiking as significant. These patterns represent false positives with respect to the null hypothesis of having one assembly of given signature embedded in otherwise independent spiking activity. We propose the additional method of pattern set reduction (PSR) to remove these false positives by conditional filtering. By employing stochastic simulations of parallel spike trains with correlated activity in form of injected spike synchrony in subsets of the neurons, we demonstrate for a range of parameter settings that the analysis scheme composed of FIM, PSF and PSR allows to reliably detect active assemblies in massively parallel spike trains.

Dysregulated Neuronal Activity Patterns Implicate Corticostriatal Circuit Dysfunction in Multiple Rodent Models of Huntington's Disease.

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder that targets the corticostriatal system and results in progressive deterioration of cognitive, emotional, and motor skills. Although cortical and striatal neurons are widely studied in animal models of HD, there is little information on neuronal function during expression of the HD behavioral phenotype. To address this knowledge gap, we used chronically implanted micro-wire bundles to record extracellular spikes and local field potentials (LFPs) in truncated (R6/1 and R6/2) and full-length (knock-in, KI) mouse models as well as in transgenic HD rats (tgHD rats) behaving in an open-field arena. Spike activity was recorded in the striatum of all models and in prefrontal cortex (PFC) of R6/2 and KI mice, and in primary motor cortex (M1) of R6/2 mice. We also recorded LFP activity in R6/2 striatum. All HD models exhibited altered neuronal activity relative to wild-type (WT) controls. Although there was no consistent effect on firing rate across models and brain areas, burst firing was reduced in striatum, PFC, and M1 of R6/2 mice, and in striatum of KI mice. Consistent with a decline in bursting, the inter-spike-interval coefficient of variation was reduced in all regions of all models, except PFC of KI mice and striatum of tgHD rats. Among simultaneously recorded neuron pairs, correlated firing was reduced in all brain regions of all models, while coincident bursting, which measures the temporal overlap between bursting pairs, was reduced in striatum of all models as well as in M1 of R6/2s. Preliminary analysis of striatal LFPs revealed aberrant behavior-related oscillations in the delta to theta range and in gamma activity. Collectively, our results indicate that disrupted corticostriatal processing occurs across multiple HD models despite differences in the severity of the behavioral phenotype. Efforts aimed at normalizing corticostriatal activity may hold the key to developing new HD therapeutics.

Higher Order Spike Synchrony in Prefrontal Cortex during Visual Memory.

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 seven sites exhibit performance dependent modulation of their spike synchronization.

Surrogate spike train generation through dithering in operational time.

Detecting the excess of spike synchrony and testing its significance can not be done analytically for many types of spike trains and relies on adequate surrogate methods. The main challenge for these methods is to conserve certain features of the spike trains, the two most important being the firing rate and the inter-spike interval statistics. In this study we make use of operational time to introduce generalizations to spike dithering and propose two novel surrogate methods which conserve both features with high accuracy. Compared to earlier approaches, the methods show an improved robustness in detecting excess synchrony between spike trains.