An accurate measure of the instantaneous discharge probability, with application to unitary joint-even analysis

Hypoxia evokes a sequence of raphe-pontomedullary network operations for inspiratory drive amplification and gasping

Hypoxia can trigger a sequence of breathing-related behaviors, from augmentation to apneusis to apnea and gasping. Gasping is an autoresuscitative behavior that, via large tidal volumes and altered intrathoracic pressure, can enhance coronary perfusion, carotid blood flow, and sympathetic activity, and thereby coordinate cardiac and respiratory functions. We tested the hypotheses that hypoxia-evoked gasps are amplified through a disinhibitory microcircuit within the inspiratory neuron chain and that this drive is distributed via an efference copy mechanism. This generates coordinated gasplike discharges concurrently in other circuits of the raphe-pontomedullary respiratory network. Data were obtained from six decerebrate, vagotomized, neuromuscularly blocked, and artificially ventilated adult cats. Arterial blood pressure, phrenic nerve activity, end-tidal CO2, and other parameters were monitored. Hypoxia was produced by ventilation with a gas mixture of 5% O2 in nitrogen. Neuron spike trains were recorded at multiple pontomedullary sites simultaneously and evaluated for firing rate modulations and short-timescale correlations indicative of functional connectivity. Experimental perturbations evoked reconfiguration of raphe-pontomedullary circuits during initial augmentation, apneusis and augmented bursts, apnea, and gasping. Functional connectivity, altered firing rates, efference copy of gasp drive, and coordinated incremental blood pressure increases support a distributed brain stem network model for amplification and broadcasting of inspiratory drive during autoresuscitative gasping. Gasping begins with a reduction in inhibition by expiratory neurons and an initial loss of inspiratory drive during hypoxic apnea and culminates in autoresuscitative efforts. NEW & NOTEWORTHY Severe hypoxia evokes a sequence of breathing-related behaviors culminating in gasping. We report firing rate modulations and short-timescale correlations in spike trains recorded simultaneously in the raphe-pontomedullary respiratory network during hypoxia. Our findings support a disinhibitory microcircuit and a distributed efference copy mechanism for amplification of gasping. Coordinated increments in blood pressure lead to a model for autoresuscitative bootstrapping of peripheral chemoreceptor reflexes, breathing, and sympathetic activity, complementing and extending prior work.

Hypoxia evokes a sequence of raphe-pontomedullary network operations for inspiratory drive amplification and gasping

Hypoxia can trigger a sequence of breathing-related behaviors, from tachypnea to apneusis to apnea and gasping, an autoresuscitative behavior that, via large tidal volumes and altered intrathoracic pressure, can enhance coronary perfusion, carotid blood flow, and sympathetic activity, and thereby coordinate cardiac and respiratory functions. We tested the hypothesis that hypoxia-evoked gasps are amplified through a disinhibitory microcircuit within the inspiratory neuron chain and a distributed efference copy mechanism that generates coordinated gasp-like discharges concurrently in other circuits of the raphe-pontomedullary respiratory network. Data were obtained from 6 decerebrate, vagotomized, neuromuscularly-blocked, and artificially ventilated adult cats. Arterial blood pressure, phrenic nerve activity, end-tidal CO2, and other parameters were monitored. Hypoxia was produced by ventilation with a gas mixture of 5% O2 in nitrogen (N2). Neuron spike trains were recorded at multiple pontomedullary sites simultaneously and evaluated for firing rate modulations and short-time scale correlations indicative of functional connectivity. Experimental perturbations evoked reconfiguration of raphe-pontomedullary circuits during tachypnea, apneusis and augmented bursts, apnea, and gasping. The functional connectivity, altered firing rates, efference copy of gasp drive, and coordinated step increments in blood pressure reported here support a distributed brain stem network model for amplification and broadcasting of inspiratory drive during autoresuscitative gasping that begins with a reduction in inhibition by expiratory neurons and an initial loss of inspiratory drive during hypoxic apnea.

Chemical suppression of harmaline-induced body tremor yields recovery of pairwise neuronal coherence in cerebellar nuclei neurons

Neuronal oscillations occur in health and disease; however, their characteristics can differ across conditions. During voluntary movement in freely moving rats, cerebellar nuclei (CN) neurons display intermittent but coherent oscillations in the theta frequency band (4-12 Hz). However, in the rat harmaline model of essential tremor, a disorder attributed to cerebellar malfunction, CN neurons display aberrant oscillations concomitantly with the emergence of body tremor. To identify the oscillation features that may underlie the emergence of body tremor, we analyzed neuronal activity recorded chronically from the rat CN under three conditions: in freely behaving animals, in harmaline-treated animals, and during chemical suppression of the harmaline-induced body tremor. Suppression of body tremor did not restore single neuron firing characteristics such as firing rate, the global and local coefficients of variation, the likelihood of a neuron to fire in bursts or their tendency to oscillate at a variety of dominant frequencies. Similarly, the fraction of simultaneously recorded neuronal pairs oscillating at a similar dominant frequency (<1 Hz deviation) and the mean frequency deviation within pairs remained similar to the harmaline condition. Moreover, the likelihood that pairs of CN neurons would co-oscillate was not only significantly lower than that measured in freely moving animals, but was significantly worse than chance. By contrast, the chemical suppression of body tremor fully restored pairwise neuronal coherence; that is, unlike in the harmaline condition, pairs of neurons that oscillated at the same time and frequency displayed high coherence, as in the controls. We suggest that oscillation coherence in CN neurons is essential for the execution of smooth movement and its loss likely underlies the emergence of body tremor.

Cerebellar nuclei neurons display aberrant oscillations during harmaline-induced tremor

Essential tremor, a common, debilitating motor disorder, is thought to be caused by cerebellar malfunction. It has been shown that rhythmic Purkinje cell firing is both necessary and sufficient to induce body tremor. During tremor, cerebellar nuclei (CN) cells also display oscillatory activity. This study examined whether rhythmic activity in the CN characterizes the occurrence of body tremor, or alternatively, whether aberrant bursting activity underlies body tremor. Cerebellar nuclei activity was chronically recorded and analyzed in freely moving and in harmaline treated rats. CN neurons displayed rhythmic activity in both conditions, but the number of oscillatory neurons and the relative oscillation time were significantly higher under harmaline. The dominant frequencies of the oscillations were broadly distributed under harmaline and the likelihood that two simultaneously recorded neurons would co-oscillate and their oscillation coherence were significantly lower. It is argued that these alterations rather than neuronal rhythmicity per se underlie harmaline-induced body tremor.

Variability and Randomness of the Instantaneous Firing Rate

The apparent stochastic nature of neuronal activity significantly affects the reliability of neuronal coding. To quantify the encountered fluctuations, both in neural data and simulations, the notions of variability and randomness of inter-spike intervals have been proposed and studied. In this article we focus on the concept of the instantaneous firing rate, which is also based on the spike timing. We use several classical statistical models of neuronal activity and we study the corresponding probability distributions of the instantaneous firing rate. To characterize the firing rate variability and randomness under different spiking regimes, we use different indices of statistical dispersion. We find that the relationship between the variability of interspike intervals and the instantaneous firing rate is not straightforward in general. Counter-intuitively, an increase in the randomness (based on entropy) of spike times may either decrease or increase the randomness of instantaneous firing rate, in dependence on the neuronal firing model. Finally, we apply our methods to experimental data, establishing that instantaneous rate analysis can indeed provide additional information about the spiking activity.

Blood pressure drives multispectral tuning of inspiration via a linked-loop neural network

The respiratory motor pattern is coordinated with cardiovascular system regulation. Inspiratory drive and respiratory phase durations are tuned by blood pressure and baroreceptor reflexes. We hypothesized that perturbations of systemic arterial blood pressure modulate inspiratory drive through a raphe-pontomedullary network. In 15 adult decerebrate vagotomized neuromuscular-blocked cats, we used multielectrode arrays to record the activities of 704 neurons within the medullary ventral respiratory column, pons, and raphe areas during baroreceptor-evoked perturbations of breathing, as measured by altered peak activity in integrated efferent phrenic nerve activity and changes in respiratory phase durations. Blood pressure was transiently (30 s) elevated or reduced by inflations of an embolectomy catheter in the descending aorta or inferior vena cava. S-transform time-frequency representations were calculated for multiunit phrenic nerve activity and some spike trains to identify changes in rhythmic activity during perturbations. Altered firing rates in response to either or both conditions were detected for 474 of 704 tested cells. Spike trains of 17,805 neuron pairs were evaluated for short-time scale correlational signatures indicative of functional connectivity with standard cross-correlation analysis, supplemented with gravitational clustering; ∼70% of tested (498 of 704) and responding neurons (333 of 474) were involved in a functional correlation with at least one other cell. Changes in high-frequency oscillations in the spiking of inspiratory neurons and the evocation or resetting of slow quasi-periodic fluctuations in the respiratory motor pattern associated with oscillations of arterial pressure were observed. The results support a linked-loop pontomedullary network architecture for multispectral tuning of inspiration.NEW & NOTEWORTHY The brain network that supports cardiorespiratory coupling remains poorly understood. Using multielectrode arrays, we tested the hypothesis that blood pressure and baroreceptor reflexes "tune" the breathing motor pattern via a raphe-pontomedullary network. Neuron responses to changes in arterial pressure and identified functional connectivity, together with altered high frequency and slow Lundberg B-wave oscillations, support a model with linked recurrent inhibitory loops that stabilize the respiratory network and provide a path for transmission of baroreceptor signals.

Online Detection of Multiple Stimulus Changes Based on Single Neuron Interspike Intervals

Nervous systems need to detect stimulus changes based on their neuronal responses without using any additional information on the number, times, and types of stimulus changes. Here, two relatively simple, biologically realistic change point detection methods are compared with two common analysis methods. The four methods are applied to intra- and extracellularly recorded responses of a single cricket interneuron (AN2) to acoustic simulation. Solely based on these recorded responses, the methods should detect an unknown number of different types of sound intensity in- and decreases shortly after their occurrences. For this task, the methods rely on calculating an adjusting interspike interval (ISI). Both simple methods try to separate responses to intensity in- or decreases from activity during constant stimulation. The Pure-ISI method performs this task based on the distribution of the ISI, while the ISI-Ratio method uses the ratio of actual and previous ISI. These methods are compared to the frequently used Moving-Average method, which calculates mean and standard deviation of the instantaneous spike rate in a moving interval. Additionally, a classification method provides the upper limit of the change point detection performance that can be expected for the cricket interneuron responses. The classification learns the statistical properties of the actual and previous ISI during stimulus changes and constant stimulation from a training data set. The main results are: (1) The Moving-Average method requires a stable activity in a long interval to estimate the previous activity, which was not always given in our data set. (2) The Pure-ISI method can reliably detect stimulus intensity increases when the neuron bursts, but it fails to identify intensity decreases. (3) The ISI-Ratio method detects stimulus in- and decreases well, if the spike train is not too noisy. (4) The classification method shows good performance for the detection of stimulus in- and decreases. But due to the statistical learning, this method tends to confuse responses to constant stimulation with responses triggered by a stimulus change. Our results suggest that stimulus change detection does not require computationally costly mechanisms. Simple nervous systems like the cricket's could effectively apply ISI-Ratios to solve this fundamental task.

Review: Methods of firing rate estimation

Neuronal firing rate is traditionally defined as the number of spikes per time window. The concept is essential for the rate coding hypothesis, which is still the most commonly investigated scenario in neuronal activity analysis. The estimation of dynamically changing firing rate from neural data can be challenging due to the variability of spike times, even under identical external conditions; hence a wide range of statistical measures have been employed to solve this particular problem. In this paper, we review established firing rate estimation methods, briefly summarize the technical aspects of each approach and discuss their practical applications.

Statistics of inverse interspike intervals: The instantaneous firing rate revisited

The rate coding hypothesis is the oldest and still one of the most accepted and investigated scenarios in neuronal activity analyses. However, the actual neuronal firing rate, while informally understood, can be mathematically defined in several different ways. These definitions yield distinct results; even their average values may differ dramatically for the simplest neuronal models. Such an inconsistency, together with the importance of "firing rate," motivates us to revisit the classical concept of the instantaneous firing rate. We confirm that different notions of firing rate can in fact be compatible, at least in terms of their averages, by carefully discerning the time instant at which the neuronal activity is observed. Two general cases are distinguished: either the inspection time is synchronised with a reference time or with the neuronal spiking. The statistical properties of the instantaneous firing rate, including parameter estimation, are analyzed, and compatibility with the intuitively understood concept is demonstrated.

Carotid chemoreceptors tune breathing via multipath routing: reticular chain and loop operations supported by parallel spike train correlations

We tested the hypothesis that carotid chemoreceptors tune breathing through parallel circuit paths that target distinct elements of an inspiratory neuron chain in the ventral respiratory column (VRC). Microelectrode arrays were used to monitor neuronal spike trains simultaneously in the VRC, peri-nucleus tractus solitarius (p-NTS)-medial medulla, the dorsal parafacial region of the lateral tegmental field (FTL-pF), and medullary raphe nuclei together with phrenic nerve activity during selective stimulation of carotid chemoreceptors or transient hypoxia in 19 decerebrate, neuromuscularly blocked, and artificially ventilated cats. Of 994 neurons tested, 56% had a significant change in firing rate. A total of 33,422 cell pairs were evaluated for signs of functional interaction; 63% of chemoresponsive neurons were elements of at least one pair with correlational signatures indicative of paucisynaptic relationships. We detected evidence for postinspiratory neuron inhibition of rostral VRC I-Driver (pre-Bötzinger) neurons, an interaction predicted to modulate breathing frequency, and for reciprocal excitation between chemoresponsive p-NTS neurons and more downstream VRC inspiratory neurons for control of breathing depth. Chemoresponsive pericolumnar tonic expiratory neurons, proposed to amplify inspiratory drive by disinhibition, were correlationally linked to afferent and efferent "chains" of chemoresponsive neurons extending to all monitored regions. The chains included coordinated clusters of chemoresponsive FTL-pF neurons with functional links to widespread medullary sites involved in the control of breathing. The results support long-standing concepts on brain stem network architecture and a circuit model for peripheral chemoreceptor modulation of breathing with multiple circuit loops and chains tuned by tegmental field neurons with quasi-periodic discharge patterns. NEW & NOTEWORTHY We tested the long-standing hypothesis that carotid chemoreceptors tune the frequency and depth of breathing through parallel circuit operations targeting the ventral respiratory column. Responses to stimulation of the chemoreceptors and identified functional connectivity support differential tuning of inspiratory neuron burst duration and firing rate and a model of brain stem network architecture incorporating tonic expiratory "hub" neurons regulated by convergent neuronal chains and loops through rostral lateral tegmental field neurons with quasi-periodic discharge patterns.

Functional connectivity in raphé-pontomedullary circuits supports active suppression of breathing during hypocapnic apnea

Hyperventilation is a common feature of disordered breathing. Apnea ensues if CO2 drive is sufficiently reduced. We tested the hypothesis that medullary raphé, ventral respiratory column (VRC), and pontine neurons have functional connectivity and persistent or evoked activities appropriate for roles in the suppression of drive and rhythm during hyperventilation and apnea. Phrenic nerve activity, arterial blood pressure, end-tidal CO2, and other parameters were monitored in 10 decerebrate, vagotomized, neuromuscularly-blocked, and artificially ventilated cats. Multielectrode arrays recorded spiking activity of 649 neurons. Loss and return of rhythmic activity during passive hyperventilation to apnea were identified with the S-transform. Diverse fluctuating activity patterns were recorded in the raphé-pontomedullary respiratory network during the transition to hypocapnic apnea. The firing rates of 160 neurons increased during apnea; the rates of 241 others decreased or stopped. VRC inspiratory neurons were usually the last to cease firing or lose rhythmic activity during the transition to apnea. Mayer wave-related oscillations (0.04-0.1 Hz) in firing rate were also disrupted during apnea. Four-hundred neurons (62%) were elements of pairs with at least one hyperventilation-responsive neuron and a correlational signature of interaction identified by cross-correlation or gravitational clustering. Our results support a model with distinct groups of chemoresponsive raphé neurons contributing to hypocapnic apnea through parallel processes that incorporate disfacilitation and active inhibition of inspiratory motor drive by expiratory neurons. During apnea, carotid chemoreceptors can evoke rhythm reemergence and an inspiratory shift in the balance of reciprocal inhibition via suppression of ongoing tonic expiratory neuron activity.

Peripheral chemoreceptors tune inspiratory drive via tonic expiratory neuron hubs in the medullary ventral respiratory column network

Models of brain stem ventral respiratory column (VRC) circuits typically emphasize populations of neurons, each active during a particular phase of the respiratory cycle. We have proposed that "tonic" pericolumnar expiratory (t-E) neurons tune breathing during baroreceptor-evoked reductions and central chemoreceptor-evoked enhancements of inspiratory (I) drive. The aims of this study were to further characterize the coordinated activity of t-E neurons and test the hypothesis that peripheral chemoreceptors also modulate drive via inhibition of t-E neurons and disinhibition of their inspiratory neuron targets. Spike trains of 828 VRC neurons were acquired by multielectrode arrays along with phrenic nerve signals from 22 decerebrate, vagotomized, neuromuscularly blocked, artificially ventilated adult cats. Forty-eight of 191 t-E neurons fired synchronously with another t-E neuron as indicated by cross-correlogram central peaks; 32 of the 39 synchronous pairs were elements of groups with mutual pairwise correlations. Gravitational clustering identified fluctuations in t-E neuron synchrony. A network model supported the prediction that inhibitory populations with spike synchrony reduce target neuron firing probabilities, resulting in offset or central correlogram troughs. In five animals, stimulation of carotid chemoreceptors evoked changes in the firing rates of 179 of 240 neurons. Thirty-two neuron pairs had correlogram troughs consistent with convergent and divergent t-E inhibition of I cells and disinhibitory enhancement of drive. Four of 10 t-E neurons that responded to sequential stimulation of peripheral and central chemoreceptors triggered 25 cross-correlograms with offset features. The results support the hypothesis that multiple afferent systems dynamically tune inspiratory drive in part via coordinated t-E neurons.

The potential of multilateral analyses of neuronal activities in future brain-machine interface research

Current brain-machine interfaces are based on the implicit assumption that information encoded by neuronal activities does not change despite some recent physiological studies indicating that information encoded by neuronal activities changes. Here, we highlight the necessity for advanced decoding of neuronal activities. Especially, we discuss the advantages of multilateral analyses of neuronal activities, including synchronization and variability.

Practice improves motor control in older adults by increasing the motor unit modulation from 13 to 30 Hz

Practice of a motor task decreases motor output variability in older adults and is associated with adaptations of discharge activity of single motor units. In this study we were interested in the practice-induced modulation of multiple motor units within 13-30 Hz because theoretically it enhances the timing of active motoneurons. Our purpose, therefore, was to determine the neural adaptation of multiple motor units and related improvements in movement control following practice. Nine healthy older adults (65-85 yr) performed 40 practice trials of a sinusoidal task (0.12 Hz) with their index finger (10° range of motion). Multi-motor unit activity was recorded intramuscularly from the first dorsal interosseus muscle. The mean spike rate (MSR), spike rate variability (CV(ISI)), and frequency modulation (5-60 Hz) of the spike rate were calculated from the multi-motor unit activity and were correlated with movement accuracy and variability of index finger position. A decrease in movement trajectory variability was associated with an increase in MSR (R(2) = 0.58), a decrease in CV(ISI) (R(2) = 0.58), and an increase in total power within a 13- to 30-Hz band (R(2) = 0.48). The increase in total power within a 13- to 30-Hz band was associated significantly (P < 0.005) with an increase in MSR (R(2) = 0.75) and the decrease in CV(ISI) (R(2) = 0.70). We demonstrate that practice-induced improvements in movement control are associated with changes in activity of multiple motor units. These findings suggest that practice-induced improvements in movement steadiness of older adults are associated with changes in the modulation of the motoneuron pool from 13 to 30 Hz.

Detection of bursts and pauses in spike trains

Midbrain dopaminergic neurons in vivo exhibit a wide range of firing patterns. They normally fire constantly at a low rate, and speed up, firing a phasic burst when reward exceeds prediction, or pause when an expected reward does not occur. Therefore, the detection of bursts and pauses from spike train data is a critical problem when studying the role of phasic dopamine (DA) in reward related learning, and other DA dependent behaviors. However, few statistical methods have been developed that can identify bursts and pauses simultaneously. We propose a new statistical method, the Robust Gaussian Surprise (RGS) method, which performs an exhaustive search of bursts and pauses in spike trains simultaneously. We found that the RGS method is adaptable to various patterns of spike trains recorded in vivo, and is not influenced by baseline firing rate, making it applicable to all in vivo spike trains where baseline firing rates vary over time. We compare the performance of the RGS method to other methods of detecting bursts, such as the Poisson Surprise (PS), Rank Surprise (RS), and Template methods. Analysis of data using the RGS method reveals potential mechanisms underlying how bursts and pauses are controlled in DA neurons.

Conditional modeling and the jitter method of spike resampling

The existence and role of fine-temporal structure in the spiking activity of central neurons is the subject of an enduring debate among physiologists. To a large extent, the problem is a statistical one: what inferences can be drawn from neurons monitored in the absence of full control over their presynaptic environments? In principle, properly crafted resampling methods can still produce statistically correct hypothesis tests. We focus on the approach to resampling known as jitter. We review a wide range of jitter techniques, illustrated by both simulation experiments and selected analyses of spike data from motor cortical neurons. We rely on an intuitive and rigorous statistical framework known as conditional modeling to reveal otherwise hidden assumptions and to support precise conclusions. Among other applications, we review statistical tests for exploring any proposed limit on the rate of change of spiking probabilities, exact tests for the significance of repeated fine-temporal patterns of spikes, and the construction of acceptance bands for testing any purported relationship between sensory or motor variables and synchrony or other fine-temporal events.

The fate of spontaneous synchronous rhythms on the cerebrocerebellar loop

How does the cerebellum participate in neocortical rhythms? Neocortical signals destined for the cerebellum are integrated in the pontine nuclei (PN) with cerebellar output signals via a direct, reciprocal feedback loop with the cerebellar nuclei (CN). The present study investigated the fate of two spontaneously occurring rhythms in rat neocortex under ketamine anesthesia-slow wave activity at around 1 Hz and gamma oscillations-within this pontonuclear feedback loop. Coordinated oscillatory neuronal activity was studied using simultaneous multineuron recordings in primary motor cortex (M1), PN, and lateral CN. It was revealed that slow burst firing-known in neocortex as "up and down states"-is readily conveyed within the pontonuclear feedback loop and thus engages the entire cerebropontocerebellothalamic loop. In contrast, gamma band synchronous oscillations reached only the PN under the present experimental conditions. Surprisingly, many CN single units were actually found to oscillate in the gamma range, but they completely failed to synchronize with other units in either CN or PN. These results show firstly that slow concerted activity can readily engage the entire cerebrocerebellar loop. Secondly, they raise the possibility that fast gamma oscillations may be incompatible with cerebellar processing and get blocked out. Future studies in behaving animals are needed to answer the question whether signals coded in gamma band frequency are converted to another carrier code using the feedback control exerted by the pontonuclear loop.

Evidence of multistability in a realistic computer simulation of hippocampus subfield CA1

The manner in which hippocampus processes neural signals is thought to be central to the memory encoding process. A theoretically oriented literature has suggested that this is carried out via "attractors" or distinctive spatio-temporal patterns of activity. However, these ideas have not been thoroughly investigated using computational models featuring both realistic single-cell physiology and detailed cell-to-cell connectivity. Here we present a 452 cell simulation based on Traub et al.'s pyramidal cell [Traub RD, Jefferys JG, Miles R, Whittington MA, Toth K. A branching dendritic model of a rodent CA3 pyramidal neurone. J Physiol (Lond) 1994;481:79-95] and interneuron [Traub RD, Miles R, Pyramidal cell-to-inhibitory cell spike transduction explicable by active dendritic conductances in inhibitory cell. J Comput Neurosci 1995;2:291-8] models, incorporating patterns of synaptic connectivity based on an extensive review of the neuroanatomic literature. When stimulated with a one second physiologically realistic input, our simulated tissue shows the ability to hold activity on-line for several seconds; furthermore, its spiking activity, as measured by frequency and interspike interval (ISI) distributions, resembles that of in vivo hippocampus. An interesting emergent property of the system is its tendency to transition from stable state to stable state, a behavior consistent with recent experimental findings [Sasaki T, Matsuki N, Ikegaya Y. Metastability of active CA3 networks. J Neurosci 2007;27:517-28]. Inspection of spike trains and simulated blockade of K(AHP) channels suggest that this is mediated by spike frequency adaptation. This finding, in conjunction with studies showing that apamin, a K(AHP) channel blocker, enhances the memory consolidation process in laboratory animals, suggests the formation of stable attractor states is central to the process by which memories are encoded. Ways that this methodology could shed light on the etiology of mental illness, such as schizophrenia, are discussed.

Maximum decoding abilities of temporal patterns and synchronized firings: application to auditory neurons responding to click trains and amplitude modulated white noise

Simultaneous recordings of an increasing number of neurons have recently become available, but few methods have been proposed to handle this activity. Here, we extract and investigate all the possible temporal neural activity patterns based on synchronized firings of neurons recorded on multiple electrodes, or based on bursts of single-electrode activity in cat primary auditory cortex. We apply this to responses to periodic click trains or sinusoïdal amplitude modulated noise by obtaining for each pattern its temporal modulation transfer function. An algorithm that maximizes the mutual information between all patterns and stimuli subsequently leads to the identification of patterns that optimally decode modulation frequency (MF). We show that stimulus information contained in multi-electrode synchronized firing is not redundant with single-electrode firings and leads to improved efficiency of MF decoding. We also show that the combined use of firing rate and temporal codes leads to a better discrimination of the MF.

Unbiased estimation of precise temporal correlations between spike trains

A key issue in systems neuroscience is the contribution of precise temporal inter-neuronal interactions to information processing in the brain, and the main analytical tool used for studying pair-wise interactions is the cross-correlation histogram (CCH). Although simple to generate, a CCH is influenced by multiple factors in addition to precise temporal correlations between two spike trains, thus complicating its interpretation. A Monte-Carlo-based technique, the jittering method, has been suggested to isolate the contribution of precise temporal interactions to neural information processing. Here, we show that jittering spike trains is equivalent to convolving the CCH derived from the original trains with a finite window and using a Poisson distribution to estimate probabilities. Both procedures over-fit the original spike trains and therefore the resulting statistical tests are biased and have low power. We devise an alternative method, based on convolving the CCH with a partially hollowed window, and illustrate its utility using artificial and real spike trains. The modified convolution method is unbiased, has high power, and is computationally fast. We recommend caution in the use of the jittering method and in the interpretation of results based on it, and suggest using the modified convolution method for detecting precise temporal correlations between spike trains.

Data-driven significance estimation for precise spike correlation

The mechanisms underlying neuronal coding and, in particular, the role of temporal spike coordination are hotly debated. However, this debate is often confounded by an implicit discussion about the use of appropriate analysis methods. To avoid incorrect interpretation of data, the analysis of simultaneous spike trains for precise spike correlation needs to be properly adjusted to the features of the experimental spike trains. In particular, nonstationarity of the firing of individual neurons in time or across trials, a spike train structure deviating from Poisson, or a co-occurrence of such features in parallel spike trains are potent generators of false positives. Problems can be avoided by including these features in the null hypothesis of the significance test. In this context, the use of surrogate data becomes increasingly important, because the complexity of the data typically prevents analytical solutions. This review provides an overview of the potential obstacles in the correlation analysis of parallel spike data and possible routes to overcome them. The discussion is illustrated at every stage of the argument by referring to a specific analysis tool (the Unitary Events method). The conclusions, however, are of a general nature and hold for other analysis techniques. Thorough testing and calibration of analysis tools and the impact of potentially erroneous preprocessing stages are emphasized.

Quantifying neural coding of event timing

Single-neuron firing is often analyzed relative to an external event, such as successful task performance or the delivery of a stimulus. The perievent time histogram (PETH) examines how, on average, neural firing modulates before and after the alignment event. However, the PETH contains no information about the single-trial reliability of the neural response, which is important from the perspective of a target neuron. In this study, we propose the concept of using the neural activity to predict the timing of the occurrence of an event, as opposed to using the event to predict the neural response. We first estimate the likelihood of an observed spike train, under the assumption that it was generated by an inhomogeneous gamma process with rate profile similar to the PETH shifted by a small time. This is used to generate a probability distribution of the event occurrence, using Bayes’ rule. By an information theoretic approach, this method yields a single value (in bits) that quantifies the reduction in uncertainty regarding the time of an external event following observation of the spike train. We show that the approach is sensitive to the amplitude of a response, to the level of baseline firing, and to the consistency of a response between trials, all of which are factors that will influence a neuron's ability to code for the time of the event. The technique can provide a useful means not only of determining which of several behavioral events a cell encodes best, but also of permitting objective comparison of different cell populations.

Can spike coordination be differentiated from rate covariation?

There has been a long and lively debate on whether rate covariance and temporal coordination of spikes, regarded as potential origins for correlations in cortical spike signals, fulfill different roles in the cortical code. In this context, studies that report spike coordination have often been criticized for ignoring fast nonstationarities, which would result in wrongly assigned spike coordination. The underlying hypothesis of this critique is that spike coordination is essentially identical to rate covariation, only on a shorter timescale. This study investigates the validity of this critique. We provide a decomposition for the cross-correlation function of doubly stochastic point processes, where each of the components corresponds precisely to the concepts of dependence under investigation. This allows us to correct the correlation function for rate effects, which implies that spike coordination and rate covariation are statistically separable concepts of dependence. Furthermore, we present direct and intuitive model implementations of the discussed concepts and illustrate that their difference is not a matter of timescale. Analysis of data generated by our models and analytical description of the relevant estimators reveals, however, that spike coordination dramatically influences the accuracy of rate covariance estimation. As a consequence, extreme parameter combinations can lead to situations where the concept of dependence cannot be identified empirically. However, for a wide range of parameters, the concept of dependence underlying a given data set can be identified regardless of its timescale.

Discharge synchrony during the transition of behavioral goal representations encoded by discharge rates of prefrontal neurons

To investigate the temporal relationship between synchrony in the discharge of neuron pairs and modulation of the discharge rate, we recorded the neuronal activity of the lateral prefrontal cortex of monkeys performing a behavioral task that required them to plan an immediate goal of action to attain a final goal. Information about the final goal was retrieved via visual instruction signals, whereas information about the immediate goal was generated internally. The synchrony of neuron pair discharges was analyzed separately from changes in the firing rate of individual neurons during a preparatory period. We focused on neuron pairs that exhibited a representation of the final goal followed by a representation of the immediate goal at a later stage. We found that changes in synchrony and discharge rates appeared to be complementary at different phases of the behavioral task. Synchrony was maximized during a specific phase in the preparatory period corresponding to a transitional stage when the neuronal activity representing the final goal was replaced with that representing the immediate goal. We hypothesize that the transient increase in discharge synchrony is an indication of a process that facilitates dynamic changes in the prefrontal neural circuits in order to undergo profound state changes.

Measurement of variability dynamics in cortical spike trains

We propose a method for the time-resolved joint analysis of two related aspects of single neuron variability, the spiking irregularity measured by the squared coefficient of variation (CV(2)) of the ISIs and the trial-by-trial variability of the spike count measured by the Fano factor (FF). We provide a calibration of both estimators using the theory of renewal processes, and verify it for spike trains recorded in vitro. Both estimators exhibit a considerable bias for short observations that count less than about 5-10 spikes on average. The practical difficulty of measuring the CV(2) in rate modulated data can be overcome by a simple procedure of spike train demodulation which was tested in numerical simulations and in real spike trains. We propose to test neuronal spike trains for deviations from the null-hypothesis FF=CV(2). We show that cortical pyramidal neurons, recorded under controlled stationary input conditions in vitro, comply with this assumption. Performing a time-resolved joint analysis of CV(2) and FF of a single unit recording from the motor cortex of a behaving monkey we demonstrate how the dynamic change of their quantitative relation can be interpreted with respect to neuron intrinsic and extrinsic factors that influence cortical variability in vivo. Finally, we discuss the effect of several additional factors such as serial interval correlation and refractory period on the empiric relation of FF and CV(2).

Inference of intrinsic spiking irregularity based on the Kullback-Leibler information

We have recently established an empirical Bayes method that extracts both the intrinsic irregularity and the time-dependent rate from a spike sequence [Koyama, S., Shinomoto, S., 2005. Empirical Bayes interpretations of random point events. J. Phys. A: Math. Gen. 38, L531-L537]. In the present paper, we examine an alternative method based on the more fundamental principle of minimizing the Kullback-Leibler information from the original distribution of spike sequences to a model distribution. Not only the empirical Bayes method but also the Kullback-Leibler information method exhibits a switch of the most plausible interpretation of the spikes between (I) being derived irregularly from a nearly constant rate, and (II) being derived rather regularly from a significantly fluctuating rate. The model distributions selected by both methods are similar for the same spike sequences derived from a given rate-fluctuating gamma process.

Linear Encoding of Muscle Activity in Primary Motor Cortex and Cerebellum

The activity of neurons in primary motor cortex (M1) and cerebellum is known to correlate with extrinsic movement parameters, including hand position and velocity. Relatively few studies have addressed the encoding of intrinsic parameters, such as muscle activity. Here we applied a generalized regression analysis to describe the relationship of neurons in M1 and cerebellar dentate nucleus to electromyographic (EMG) activity from hand and forearm muscles, during performance of precision grip by macaque monkeys. We showed that cells in both M1 and dentate encode muscle activity in a linear fashion, and that EMG signals provide predictions of neural discharge that are equally accurate to those from kinematic information under these task conditions. Neural activity in M1 was significantly more correlated with both EMG and kinematic signals than was activity in dentate nucleus. Furthermore, the analysis enabled us to look at the temporal properties of muscle encoding. Cells were broadly tuned to muscle activity as a function of the lag between spiking and EMG and there was considerable heterogeneity in the optimal delay among individual neurons. However, a single lag (40 ms) was generally sufficient to provide good fits. Finally, incorporating spike history effects in our model offered no advantage in predicting novel spike trains, reinforcing the simple nature of the muscle encoding that we observed here.

Trial-to-trial variability and its effect on time-varying dependency between two neurons

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 method of estimating the time-dependent trial-to-trial variation in spike trains that may exceed the natural variation displayed by Poisson and non-Poisson point processes. The statistical problem is somewhat subtle because relatively few spikes per trial are available for estimating a firing-rate function that fluctuates over time. The method developed here decomposes the spike-train variability into a stimulus-related component and a trial-specific component, allowing many degrees of freedom to characterize the former while assuming a small number suffices to characterize the latter. The Bootstrap significance test of the companion paper is then modified to accommodate these general excitability effects. This methodology allows an investigator to assess whether excitability effects are constant or time-varying, and whether they are shared by two neurons. In data from two V1 neurons we find that highly statistically significant evidence of dependency disappears after adjustment for time-varying trial-to-trial variation.

Frequency Modulation of Motor Unit Discharge Has Task-Dependent Effects on Fluctuations in Motor Output

The rate of change in the fluctuations in motor output differs during the performance of fatiguing contractions that involve different types of loads. The purpose of this study was to examine the contribution of frequency modulation of motor unit discharge to the fluctuations in the motor output during sustained contractions with the force and position tasks. In separate tests with the upper arm vertical and the elbow flexed to 1.57 rad, the seated subjects maintained either a constant upward force at the wrist (force task) or a constant elbow angle (position task). The force and position tasks were performed in random order at a target force equal to 3.6 ± 2.1% (mean ± SD) of the maximal voluntary contraction (MVC) force above the recruitment threshold of an isolated motor unit from the biceps brachii. Each subject maintained the two tasks for an identical duration (161 ± 93 s) at a mean target force of 22.4 ± 13.6% MVC. As expected, the rate of increase in the fluctuations in motor output (force task: SD for detrended force; position task: SD for vertical acceleration) was greater for the position task than the force task ( P < 0.001). The amplitude of the coefficient of variation (CV) and the power spectra for motor unit discharge were similar between tasks ( P > 0.1) and did not change with time ( P > 0.1), and could not explain the different rates of increase in motor output fluctuations for the two tasks. Nonetheless, frequency modulation of motor unit discharge differed during the two tasks and predicted ( P < 0.001) both the CV for discharge rate (force task: 1–3, 12–13, and 14–15 Hz; position task: 0–1, and 1–2 Hz) and the fluctuations in motor output (force task: 5–6, 9–10, 12–13, and 14–15 Hz; position task: 6–7, 14–15, 17–19, 20–21, and 23–24 Hz). Frequency modulation of motor unit discharge rate differed for the force and position tasks and influenced the ability to sustain steady contractions.

The Neurophysiology of Functionally Meaningful Categories: Macaque Ventrolateral Prefrontal Cortex Plays a Critical Role in Spontaneous Categorization of Species-Specific Vocalizations

Neurophysiological studies in nonhuman primates have demonstrated that the prefrontal cortex (PFC) plays a critical role in the acquisition of learned categories following training. What is presently unclear is whether this cortical area also plays a role in spontaneous recognition and discrimination of natural categories. Here, we explore this possibility by recording from neurons in the PFC while rhesus listen to species-specific vocalizations that vary in terms of their social function and acoustic morphology. We found that ventral prefrontal cortex (vPFC) activity, on average, did not differentiate between food calls that were associated with the same functional category, despite having different acoustic properties. In contrast, vPFC activity differentiated between food calls associated with different functional classes and specifically, information about the quality and motivational value of the food. These results suggest that the vPFC is involved in the categorization of socially meaningful signals, thereby both extending its previously conceived role in the acquisition of learned categories and showing the significance of using natural categorical distinctions in the study of neural mechanisms.

Spatial distribution of inputs and local receptive field properties of a wide-field, looming sensitive neuron

The lobula giant movement detector (LGMD) in the locust visual system and its target neuron, the descending contralateral movement detector (DCMD), respond to approaching objects looming on a collision course with the animal. They thus provide a good model to study the cellular and network mechanisms underlying the sensitivity to this specific class of behaviorally relevant stimuli. We determined over an entire locust eye the density distribution of optical axes describing the spatial organization of local inputs to the visual system and compared it with the sensitivity distribution of the LGMD/DCMD to local motion stimuli. The density of optical axes peaks in the equatorial region of the frontal eye. Local motion sensitivity, however, peaks in the equatorial region of the caudolateral visual field and only correlates positively with the dorso-ventral density of optical axes. On local stimulation, both the velocity tuning and the response latency of the LGMD/DCMD depend on stimulus position within the visual field. Spatial and temporal integration experiments in which several local motion stimuli were activated either simultaneously or at fixed delays reveal that the LGMD processes local motion in a strongly sublinear way. Thus the neuron's integration properties seem to depend on several factors including its dendritic morphology, the local characteristics of afferent fiber inputs, and inhibition mediated by different pathways or by voltage-gated conductances. Our study shows that the selectivity of this looming sensitive neuron to approaching objects relies on more complex biophysical mechanisms than previously thought.

Reduction of single-neuron firing uncertainty by cortical ensembles during motor skill learning

Motor skill learning is usually characterized by shortening of response time and performance of faster, more stereotypical movements. However, little is known about the changes in neural activity that underlie these behavioral changes. Here we used chronically implanted electrode arrays to record neuronal activity in the rat primary motor cortex (MI) as animals learned to execute movements in two directions. Strong modulation of MI single-neuron activity was observed while movement duration of the animal decreased. Despite many learning-induced changes, the precision with which single neurons fire did not improve with learning. Hence, prediction of movement direction from single neurons was bounded. In contrast, prediction of movement direction using neuronal ensembles improved significantly with learning, suggesting that, with practice, neuronal ensembles learn to overcome the uncertainty introduced by single-neuron stochastic activity.

Mean Instantaneous Firing Frequency Is Always Higher Than the Firing Rate

Frequency coding is considered one of the most common coding strategies employed by neural systems. This fact leads, in experiments as well as in theoretical studies, to construction of so-called transfer functions, where the output firing frequency is plotted against the input intensity. The term firing frequency can be understood differently in different contexts. Basically, it means that the number of spikes over an interval of preselected length is counted and then divided by the length of the interval, but due to the obvious limitations, the length of observation cannot be arbitrarily long. Then firing frequency is defined as reciprocal to the mean interspike interval. In parallel, an instantaneous firing frequency can be defined as reciprocal to the length of current interspike interval, and by taking a mean of these, the definition can be extended to introduce the mean instantaneous firing frequency. All of these definitions of firing frequency are compared in an effort to contribute to a better understanding of the input-output properties of a neuron.

Synchrony between neurons with similar muscle fields in monkey motor cortex

Synchronous firing of motor cortex cells exhibiting postspike facilitation (PSF) or suppression (PSS) of hand muscle EMG was examined to investigate the relationship between synchrony and output connectivity. Recordings were made in macaque monkeys performing a precision grip task. Synchronization was assessed with cross-correlation histograms of the activity from 144 pairs of simultaneously recorded neurons, while spike-triggered averages of EMG defined the muscle field for each cell. Cell pairs with similar muscle fields showed greater synchronization than pairs with nonoverlapping fields. Furthermore, cells with opposing effects in the same muscles exhibited negative synchronization. We conclude that synchrony in motor cortex engages networks of neurons directly controlling the same muscle set, while inhibitory connections exist between neuronal populations with opposing output effects.

Information-geometric measure for neural spikes

This study introduces information-geometric measures to analyze neural firing patterns by taking not only the second-order but also higher-order interactions among neurons into account. Information geometry provides useful tools and concepts for this purpose, including the orthogonality of coordinate parameters and the Pythagoras relation in the Kullback-Leibler divergence. Based on this orthogonality, we show a novel method for analyzing spike firing patterns by decomposing the interactions of neurons of various orders. As a result, purely pairwise, triple-wise, and higher-order interactions are singled out. We also demonstrate the benefits of our proposal by using several examples.

Analysis of phase-locked oscillations in multi-channel single-unit spike activity with wavelet cross-spectrum

Electrophysiological measures of neural activity frequently display oscillatory patterns at various frequencies. Furthermore, these oscillatory patterns can become dynamically synchronized across a wide region of the brain in a task-dependent manner. In this study, phase-locked oscillations in simultaneously recorded spike trains were analyzed using the wavelet cross-spectrum. Adaptation of the existent methods of calculating wavelet cross-spectrum to spike train data was straightforward. In contrast, new methods were needed for evaluating the statistical significance of the cross-spectrum. Although a permutation test based on a large number of re-sampled cross-spectra can provide a reliable estimate of statistical significance, this was quite time-consuming. As an alternative, statistical significance was determined with a normal probability density function estimated from a small number of re-sampled cross-spectra. When applied to neuron pairs recorded in the primate supplementary motor area, the re-sampling procedure produced a reliable outcome even when it was based on as few as ten re-sampled cross-spectra. These results suggest that the wavelet analysis in combination with a re-sampling procedure provides a useful tool to examine the dynamic patterns of temporal correlation in cortical spike trains.

Taste-specific neuronal ensembles in the gustatory cortex of awake rats

In gustatory cortex, single-neuron activity reflects the multimodal processing of taste stimuli. Little is known, however, about the interactions between gustatory cortical (GC) neurons during tastant processing. Here, these interactions were characterized. It was found that 36% (85 of 237) of neuron pairs, including many (61%) in which one or both single units were not taste specific, produced significant cross-correlations (CCs) to a subset of tastants across a hundreds of milliseconds timescale. Significant CCs arose from the coupling between the firing rates of neurons as those rates changed through time. Such coupling significantly increased the amount of tastant-specific information contained in ensembles. These data suggest that taste-specific GC assemblies may transiently form and coevolve on a behaviorally appropriate timescale, contributing to rats' ability to discriminate tastants.

Unitary events in multiple single-neuron spiking activity: II. Nonstationary data

In order to detect members of a functional group (cell assembly) in simultaneously recorded neuronal spiking activity, we adopted the widely used operational definition that membership in a common assembly is expressed in near-simultaneous spike activity. Unitary event analysis, a statistical method to detect the significant occurrence of coincident spiking activity in stationary data, was recently developed (see the companion article in this issue). The technique for the detection of unitary events is based on the assumption that the underlying processes are stationary in time. This requirement, however, is usually not fulfilled in neuronal data. Here we describe a method that properly normalizes for changes of rate: the unitary events by moving window analysis (UEMWA). Analysis for unitary events is performed separately in overlapping time segments by sliding a window of constant width along the data. In each window, stationarity is assumed. Performance and sensitivity are demonstrated by use of simulated spike trains of independently firing neurons, into which coincident events are inserted. If cortical neurons organize dynamically into functional groups, the occurrence of near-simultaneous spike activity should be time varying and related to behavior and stimuli. UEMWA also accounts for these potentially interesting nonstationarities and allows locating them in time. The potential of the new method is illustrated by results from multiple single-unit recordings from frontal and motor cortical areas in awake, behaving monkey.

Statistical significance of coincident spikes: count-based versus rate-based statistics

Inspired by different conceptualizations of temporal neural coding schemes, there has been recent interest in the search for signs of precisely synchronized neural activity in the cortex. One method developed for this task is unitary-event analysis. This method tests multiple single neuron recordings for short epochs with significantly more coincident spikes than expected from independent neurons. We reformulated the statistical test underlying this method using a coincidence count distribution based on empirical spike counts rather than on estimated spike probabilities. In the case of two neurons, the requirement of stationary firing rates, originally imposed on both neurons, can be relaxed; only the rate of one neuron needs to be stationary, while the other may follow an arbitrary time course. By analytical calculations of the test power curves of the original and the revised method, we demonstrate that the test power can be increased by a factor of two or more in physiologically realistic regimes. In addition, we analyze the effective significance levels of both methods for neural firing rates ranging between 0.2 Hz and 30 Hz.

Unitary events in multiple single-neuron spiking activity: I. Detection and significance

It has been proposed that cortical neurons organize dynamically into functional groups (cell assemblies) by the temporal structure of their joint spiking activity. Here, we describe a novel method to detect conspicuous patterns of coincident joint spike activity among simultaneously recorded single neurons. The statistical significance of these unitary events of coincident joint spike activity is evaluated by the joint-surprise. The method is tested and calibrated on the basis of simulated, stationary spike trains of independently firing neurons, into which coincident joint spike events were inserted under controlled conditions. The sensitivity and specificity of the method are investigated for their dependence on physiological parameters (firing rate, coincidence precision, coincidence pattern complexity) and temporal resolution of the analysis. In the companion article in this issue, we describe an extension of the method, designed to deal with nonstationary firing rates.

Temporal precision and temporal drift in brain and behavior of zebra finch song

In the zebra finch forebrain nucleus robustus archistriatalis (RA), neurons burst during singing. We showed that the internal structure of spike bursts was regulated with a precision of circa 0.2 ms, and yielded alignment of acoustic features of song with a precision of circa 1 ms. In addition, interburst intervals and corresponding syllable durations displayed systematic variation within song (average elongation 0.3 ms/s song), and slower "drift" across songs. Systematic variation on even a coarser time scale might be difficult to detect in other systems, but could affect the analysis of temporal patterning. The close relationship between precise timing of individual spikes and stereotypic behavior suggests that song is represented in RA by a temporal code.

Determination of response latency and its application to normalization of cross-correlation measures

It is often of interest experimentally to assess how synchronization between two neurons changes following a stimulus or other behaviorally relevant marker. The joint peristimulus time histogram (JPSTH) achieves this, but assumes that changes in the cells' firing rate following the stimulus are stereotyped from one sweep to the next. Erroneous results can be generated if this is not the case. We here present a method to assess whether there are variations in response latency or amplitude from sweep to sweep. We then describe how the effects of response latency variation can be mitigated by realigning sweeps to their individual latencies. Three methods of detecting response latency are presented and their performance compared on simulated data. Finally, the effect on the JPSTH of sweep realignment using detected latencies is illustrated.

Precise burst synchrony in the superior colliculus of the awake cat during moving stimulus presentation

This study aimed to characterize the synchrony that occurs between cell discharges in the superior colliculus of the awake cat. We trained cats to perform a visual fixation in the presence of a visual moving stimulus and then recorded 686 pairs of neighboring cells in the superior colliculus during task performance. A new method to assess the significance of precise discharge synchronization is described, which permits analysis of nonstationary data. Of 181 pairs with sufficient data for quantitative analysis, 125 showed a cross-correlation histogram (CCH) with features assessed as significant using this approach. CCHs frequently showed an isolated central peak (41 of 125) or a peak flanked by one or two troughs (68 of 125), and in a few cases an oscillatory pattern of approximately 65 Hz (16 of 125). This is in contrast to the oscillation frequency reported for the visual cortex and shows that oscillations in the superior colliculus probably arise from a cortex-independent mechanism. Our method also permits direct quantification of the correlation shift predictors, assessing precise time locking of spikes to the stimulus. Only 1 of 125 cross-correlation shift predictors had a significant central peak, meaning that most of the CCH features were not related to cell discharges time-locked to the stimulus presentation. Further investigation using a burst-jittering method showed that synchrony in the superior colliculus is attributable to precise synchronization of short bursts of spikes. Such synchrony could be related to the network dynamics and the common inhibitory feedback from local interneurons, which would act as temporal selectors of the cells with greatest or fastest response.

Synchronization in monkey motor cortex during a precision grip task. I. Task-dependent modulation in single-unit synchrony

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 estimate of the instantaneous firing rate of the cell. This was shown to be more reliable than methods using trial-averaged firing rate. A total of 375 neurons was recorded from the M1 hand area; 235 were identified as pyramidal tract neurons. Synchrony was weak [mean k' = 1.05 +/- 0.04 (SD)] but widespread among pairs of M1 neurons (218/1359 pairs with above-chance synchrony), including output neurons. Synchrony usually took the form of a broad central peak [average width, 18.7 +/- 8.7 (SD) ms]. There were marked changes during different phases of the task. As a population, synchrony was greatest during the steady hold period in striking contrast to the averaged cell firing rate, which was maximal when the animal was moving the levers into target. However, the modulation of synchrony during task performance showed considerable variation across individual cell pairs. Two types of synchrony were identified: oscillatory (with periodic side lobes in the cross-correlation) and nonoscillatory. Their relative contributions were quantified by filtering the cross-correlations to exclude either frequencies from 18 to 37 Hz or all higher and lower frequencies. At the peak of population synchrony during the hold period, about half (51.7% in one monkey, 56.2% in the other) of the synchronization was within this oscillatory bandwidth. This study provides strong support for assemblies of neurons being synchronized during specific phases of a complex task with potentially important consequences for both information processing within M1 and for the impact of M1 commands on target motoneurons.

Improvements to the sensitivity of gravitational clustering for multiple neuron recordings

We outline two improvements to the technique of gravitational clustering for detection of neuronal synchrony, which are capable of improving the method's detection of weak synchrony with limited data. The advantages of the enhancements are illustrated using data with known levels of synchrony and different interspike interval distributions. The novel simulation method described can easily generate such test data. An important dependence of the sensitivity of gravitational clustering to the interspike interval distribution of the analysed spike trains is described.

Statistical signs of common inhibitory feedback with delay

Cross-correlation histograms (CCHs) sometimes exhibit an isolated central peak flanked by two troughs. What can cause this pattern? The absence of CCH satellite peak makes an oscillatory common input doubtful. It is here shown using a simple counting model that a common inhibitory feedback with delay can account for this pattern.

Precise spatiotemporal repeating patterns in monkey primary and supplementary motor areas occur at chance levels

Precise spatiotemporal patterns in neural discharge are a possible mechanism for information encoding in the brain. Previous studies have found that such patterns repeat and appear to relate to key behavioral events. Whether these patterns occur above chance levels remains controversial. To address this question, we have made simultaneous recordings from between two and nine neurons in the primary motor cortex and supplementary motor area of three monkeys while they performed a precision grip task. Out of a total of 67 neurons, 46 were antidromically identified as pyramidal tract neurons. Sections of recordings 60 s long were searched for patterns involving three or more spikes that repeated at least twice. The allowed jitter for pattern repetition was 3 ms, and the pattern length was limited to 192 ms. In all 11 recordings analyzed, large numbers of repeating patterns were found. To assess the expected chance level of patterns, "surrogate" datasets were generated. These had the same moment-by-moment modulation in firing rate as the experimental spike trains, and matched their interspike interval distribution, but did not preserve the precise timing of individual spikes. The number of repeating patterns in 10 randomly generated surrogates was used to form 99% confidence limits on the repeating pattern count expected by chance. There was close agreement between these confidence limits and the number of patterns seen in the experimental data. Analysis of high complexity patterns was carried out in four long recordings (mean duration 23.2 min, mean number of neurons simultaneously recorded 7.5). This analysis logged only patterns composed of a larger number (7-11) of spikes. The number of patterns seen in the surrogate datasets showed a small but significant excess over those seen in the original experimental data; this is discussed in the context of surrogate generation. The occurrence of repeating patterns in the experimental data were strongly associated with particular phases of the precision grip task; however, a similar task dependence was seen for the surrogate data. When a repeating pattern was used as a template to find inexact matches, in which up to half of the component spikes could be missing, similar numbers of matches were found in experimental and surrogate data, and the time of occurrence of such matches showed the same task dependence. We conclude that the existence of precise repeating patterns in our data are not due to cortical mechanisms that favor this form of coding, since as many, if not more, patterns are produced by spike trains constructed only to modulate their firing rate in the same way as the experimental data, and to match the interspike interval histograms. The task dependence of pattern occurrence is explicable as an artifact of the modulation of neural firing rate. The consequences for theories of temporal coding in the cortex are discussed.

Dynamical changes and temporal precision of synchronized spiking activity in monkey motor cortex during movement preparation

Movement preparation is considered to be based on central processes which are responsible for improving motor performance. For instance, it has been shown that motor cortical neurones change their activity selectively in relation to prior information about movement parameters. However, it is not clear how groups of neurones dynamically organize their activity to cope with computational demands. The aim of the study was to compare the firing rate of multiple simultaneously recorded neurones with the interaction between them by describing not only the frequency of occurrence of epochs of significant synchronization, but also its modulation in time and its changes in temporal precision during an instructed delay. Multiple single-neurone activity was thus recorded in monkey motor cortex during the performance of two different delayed multi-directional pointing tasks. In order to detect conspicuous spike coincidences in simultaneously recorded spike trains by tolerating temporal jitter ranging from 0 to 20 ms and to calculate their statistical significance, a modified method of the 'Unitary Events' analysis was used. Two main results were obtained. First, simultaneously recorded neurones synchronize their spiking activity in a highly dynamic way. Synchronization becomes significant only during short periods (about 100 to 200 ms). Several such periods occurred during a behavioural trial more or less regularly. Second, in many pairs of neurones, the temporal precision of synchronous activity was highest at the end of the preparatory period. As a matter of fact, at the beginning of this period, after the presentation of the preparatory signal, neurones significantly synchronize their spiking activity, but with low temporal precision. As time advances, significant synchronization becomes more precise. Data indicate that not only the discharge rate is involved in preparatory processes, but also temporal aspects of neuronal activity as expressed in the precise synchronization of individual action potentials.