Peri-event Time Histograms Research Articles

OPETH: Open Source Solution for Real-Time Peri-Event Time Histogram Based on Open Ephys.

Single cell electrophysiology remains one of the most widely used approaches of systems neuroscience. Decisions made by the experimenter during electrophysiology recording largely determine recording quality, duration of the project and value of the collected data. Therefore, online feedback aiding these decisions can lower monetary and time investment, and substantially speed up projects as well as allow novel studies otherwise not possible due to prohibitively low throughput. Real-time feedback is especially important in studies that involve optogenetic cell type identification by enabling a systematic search for neurons of interest. However, such tools are scarce and limited to costly commercial systems with high degree of specialization, which hitherto prevented wide-ranging benefits for the community. To address this, we present an open-source tool that enables online feedback during electrophysiology experiments and provides a Python interface for the widely used Open Ephys open source data acquisition system. Specifically, our software allows flexible online visualization of spike alignment to external events, called the online peri-event time histogram (OPETH). These external events, conveyed by digital logic signals, may indicate photostimulation time stamps for in vivo optogenetic cell type identification or the times of behaviorally relevant events during in vivo behavioral neurophysiology experiments. Therefore, OPETH allows real-time identification of genetically defined neuron types or behaviorally responsive populations. By allowing “hunting” for neurons of interest, OPETH significantly reduces experiment time and thus increases the efficiency of experiments that combine in vivo electrophysiology with behavior or optogenetic tagging of neurons.

Zolpidem Activation of Alpha 1-Containing GABAA Receptors Selectively Inhibits High Frequency Action Potential Firing of Cortical Neurons.

Introduction: High frequency neuronal activity in the cerebral cortex can be induced by noxious stimulation during surgery, brain injury or poisoning. In this scenario, it is essential to block cortical hyperactivity to protect the brain against damage, e.g., by using drugs that act as positive allosteric modulators at GABAA receptors. Yet, cortical neurons express multiple, functionally distinct GABAA receptor subtypes. Currently there is a lack of knowledge which GABAA receptor subtypes would be a good pharmacological target to reduce extensive cortical activity.Methods: Spontaneous action potential activity was monitored by performing extracellular recordings from organotypic neocortical slice cultures of wild type and GABAAR-α1(H101R) mutant mice. Phases of high neuronal activity were characterized using peri-event time histograms. Drug effects on within-up state firing rates were quantified via Hedges’ g.Results: We quantified the effects of zolpidem, a positive modulator of GABAA receptors harboring α1-subunits, and the experimental benzodiazepine SH-053-2′F-S-CH3, which preferably acts at α2/3/5- but spares α1-subunits. Both agents decreased spontaneous action potential activity but altered the firing patterns in different ways. Zolpidem reduced action potential firing during highly active network states. This action was abolished by flumazenil, suggesting that it was mediated by benzodiazepine-sensitive GABAA receptors. SH-053-2′F-S-CH3 also attenuated neuronal activity, but unlike zolpidem, failed to reduce high frequency firing. To confirm that zolpidem actions were indeed mediated via α1-dependent actions, it was evaluated in slices from wild type and α(H101R) knock-in mice. Inhibition of high frequency action potential firing was observed in slices from wild type but not mutant mice.Conclusion: Our results suggest that during episodes of scarce and high neuronal activity action potential firing of cortical neurons is controlled by different GABAA receptor subtypes. Exaggerated firing of cortical neurons is reduced by positive modulation of α1-, but not α2/3/5-subunit containing GABAA receptors.

Identifying and characterizing systematic temporally-lagged BOLD artifacts

Residual noise in the BOLD signal remains problematic for fMRI – particularly for techniques such as functional connectivity, where findings can be spuriously influenced by noise sources that can covary with individual differences. Many such potential noise sources – for instance, motion and respiration – can have a temporally lagged effect on the BOLD signal. Thus, here we present a tool for assessing residual lagged structure in the BOLD signal that is associated with nuisance signals, using a construction similar to a peri-event time histogram. Using this method, we find that framewise displacements – both large and very small – were followed by structured, prolonged, and global changes in the BOLD signal that depend on the magnitude of the preceding displacement and extend for tens of seconds. This residual lagged BOLD structure was consistent across datasets, and independently predicted considerable variance in the global cortical signal (as much as 30–40% in some subjects). Mean functional connectivity estimates varied similarly as a function of displacements occurring many seconds in the past, even after strict censoring. Similar patterns of residual lagged BOLD structure were apparent following respiratory fluctuations (which covaried with framewise displacements), implicating respiration as one likely mechanism underlying the displacement-linked structure observed. Global signal regression largely attenuates this artifactual structure. These findings suggest the need for caution in interpreting results of individual difference studies where noise sources might covary with the individual differences of interest, and highlight the need for further development of preprocessing techniques for mitigating such structure in a more nuanced and targeted manner.

Neuroprotective activity of hydroponic Teucrium polium following bilateral ovariectomy.

Ovariectomy is known as "surgical menopause" with decreased levels of estrogen in female rodents. Its reported risks and adverse effects include cognitive impairment. The action of hydroponic Teucrium polium on nucleus basalis of Meynert (bnM) neurons following 6 weeks of ovariectomy was carried out. The analysis of spike activity was observed by on-line selection and the use of a software package. Early and late tetanic, - posttetanic potentiation and depression of neurons to high frequency stimulation of hippocampus were studied. The complex averaged peri-event time and frequency histograms were constructed. The histochemical study of the activity of Са(2+)-dependent acid phosphatase was observed. In conditions of hydroponic Teucrium polium administration, positive changes in neurons and gain of metabolism leading to cellular survival were revealed. The administration of Teucrium polium elicited neurodegenerative changes in bnM.

Inhibition of propofol on single neuron and neuronal ensemble activity in prefrontal cortex of rats during working memory task

Working memory (WM) refers to the temporary storage and manipulation of information necessary for performance of complex cognitive tasks. There is a growing interest in whether and how propofol anesthesia inhibits WM function. The aim of this study is to investigate the possible inhibition mechanism of propofol anesthesia from the view of single neuron and neuronal ensemble activities. Adult SD rats were randomly divided into two groups: propofol group (0.9mgkg−1min−1, 2h via a tail vein catheter) and control group. All the rats were tested for working memory performances in a Y-maze-rewarded alternation task (a task of delayed non-matched-to-sample) at 24, 48, 72h after propofol anesthesia, and the behavior results of WM tasks were recorded at the same time. Spatio-temporal trains of action potentials were obtained from the original signals. Single neuron activity was characterized by peri-event time histograms analysis and neuron ensemble activities were characterized by Granger causality to describe the interactions within the neuron ensemble. The results show that: comparing with the control group, the percentage of neurons excited and related to WM was significantly decreased (p<0.01 in 24h, p<0.05 in 48h); the interactions within neuron ensemble were significantly weakened (p<0.01 in 24h, p<0.05 in 48h), whereas no significant difference in 72h (p>0.05), which were consistent with the behavior results. These findings could lead to improved understanding of the mechanism of anesthesia inhibition on WM functions from the view of single neuron activity and neuron ensemble interactions.

Inducing Gamma Oscillations and Precise Spike Synchrony by Operant Conditioning via Brain-Machine Interface

Neural oscillations in the low-gamma range (30-50 Hz) have been implicated in neuronal synchrony, computation, behavior, and cognition. Abnormal low-gamma activity, hypothesized to reflect impaired synchronization, has been evidenced in several brain disorders. Thus, understanding the relations between gamma oscillations, neuronal synchrony and behavior is a major research challenge. We used a brain-machine interface (BMI) to train monkeys to specifically increase low-gamma power in selected sites of motor cortex to move a cursor and obtain a reward. The monkeys learned to robustly generate oscillatory gamma waves, which were accompanied by a dramatic increase of spiking synchrony of highly precise spatiotemporal patterns. The findings link volitional control of LFP oscillations, neuronal synchrony, and the behavioral outcome. Subjects' ability to directly modulate specific patterns of neuronal synchrony provides a powerful approach for understanding neuronal processing in relation to behavior and for the use of BMIs in a clinical setting.

Background firing rate affects the signal transfer of behavior locked input patterns from Purkinje cells to the cerebellar nuclei

Multielectrode recordings from awake behaving mice revealed rhythmic modulation both in Purkinje cell (PC) simple spike (SS) activity and the cerebellar nuclei (CN) neuron activity that is phase locked to the breathing rhythm. In the present work we were interested in studying how the different levels of background SS activity may affect the transfer of rhythmic SS modulation from PCs to the CN using a multicompartmental CN neuron model. We created a population of fifty artificial gamma distributed PC spike trains and gave them to the CN neuron model as inputs with 5nS conductance strength [1]. The mean firing rate for a given input was based on the rate histogram distribution constructed from our experimental PC recordings in awake mice, and ranged from 28 to 170 Hz. An identical slow fluctuation in the background firing rate for all 50 inputs was used, reflecting a high correlation of rates observed in vivo as quantified using a Bayesian rate estimation method [2]. The breathing associated rhythmic modulation estimated from a peri-event time histogram of a PC recording was normalized to a % change from mean rate and convolved with baseline firing rate at the timing of recorded breaths. The synaptic strength of the mossy fiber excitatory inputs was tuned so that the model output gave the best match to both the statistical properties and behavior related modulation of the experimental CN recordings. This match turned out to be excellent, as the mean ISI (recording 29.47, model 29.49), CV (1.072 vs. 0.926), and CV2 (0.74 vs. 0.75) were near identical. The mean breathing related rhythmic modulation in the CN simulation turned out to be higher (60 %) than in the applied PC input trains (12 %). We then separated the breathing modulations obtained in the model output based on whether they occurred at low or high background firing rate. We observed a decrease in modulation depth from 70% to 30% when the baseline firing rate was high in the CN. Similar results were observed in experimental recordings (a decrease from 60% to 30%) which also correlated with an decrease in PC firing rate and a decrease in rhythmicity of breathing during such episodes. To probe the cause of the inverse relationship between CN background rate and breathing modulation depth we used a PC input consisting of a 100 s ramp increase in rate with a fixed breathing modulation depth. This simulation revealed a linear inverse firing rate transformation between PC and CN rates, which also explains the dependence of breathing modulation on background rate. Essentially, since at high rates the absolute breathing related PC rate modulation is higher, this gets translated to greater relative rate modulation of CN neurons at their resulting low mean firing rate. Based on our findings we conclude that the background firing rate of PCs strongly affects the behavioral modulation of the output from the CN. This effect may be important in the cerebellar control or coordination of rhythmic motor behaviors such as breathing, licking, swallowing, and whisking.

Encoding of temporal intervals in the rat hindlimb sensorimotor cortex

The gradual buildup of neural activity over experimentally imposed delay periods, termed climbing activity, is well documented and is a potential mechanism by which interval time is encoded by distributed cortico-thalamico-striatal networks in the brain. Additionally, when multiple delay periods are incorporated, this activity has been shown to scale its rate of climbing proportional to the delay period. However, it remains unclear whether these patterns of activity occur within areas of motor cortex dedicated to hindlimb movement. Moreover, the effects of behavioral training (e.g., motor tasks) under different reward conditions but with similar behavioral output are not well addressed. To address this, we recorded activity from the hindlimb sensorimotor cortex (HLSMC) of two groups of rats performing a skilled hindlimb press task. In one group, rats were trained only to a make a valid press within a finite window after cue presentation for reward (non-interval trained, nIT; n = 5), while rats in the second group were given duration-specific cues in which they had to make presses of either short or long duration to receive reward (interval trained, IT; n = 6). Using perievent time histogram (PETH) analyses, we show that cells recorded from both groups showed climbing activity during the task in similar proportions (35% IT and 47% nIT), however, only climbing activity from IT rats was temporally scaled to press duration. Furthermore, using single trial decoding techniques (Wiener filter), we show that press duration can be inferred using climbing activity from IT animals (R = 0.61) significantly better than nIT animals (R = 0.507, p < 0.01), suggesting IT animals encode press duration through temporally scaled climbing activity. Thus, if temporal intervals are behaviorally relevant then the activity of climbing neurons is temporally scaled to encode the passage of time.

Conditional intensity/point process model of task-related prefrontal spiking: effects of performance

The spiking activity of neurons in the prefrontal associational cortex (PFC) of animals engaged in cognitive tasks is posited to reflect diverse set of independent cognitive and behavioral processes required for the successful attainment of future goals. Additionally, a neural representation of past information (i.e. spike history) is likely critical for performance in these delayed-response tasks of working memory. In the current study, we used a conditional intensity - generalized linear model (CI-GLM) statistical framework to determine the degree to which spike history and components of a delayed-response task predict PFC neural spiking as opposed to traditional peri-event time histogram (PETH) analysis approaches. The final CI-GLM model takes the form,, such that the conditional intensity function λ(t|Ht) is predicted by a generalized linear model from a series covariates where (µ) is the intercept of the equation representing the background level of activity, represents an autoregressive process of order (K), and is the design matrix of each event-state (r) for each sample in time (t) of the behavioral task and βr are the estimated weighting parameters. The discretized time for increasing spiking history durations is represented as ΔNt–k with γk representing the autoregressive coefficients. We found that in rats performing a PFC-dependent delayed-alternation task of working memory, the vast majority of PFC neurons participate in the representation of more than one task-related event-state (multiplexer neurons). Additionally, the size of functional neural groups or cell assemblies was critically linked to the level of behavioral performance (Fig. ​(Fig.1).1). Poor behavioral performance was not solely associated with an attenuation of spiking activity, but instead the activation of inappropriate assemblies during error trials may actively direct the animal towards an incorrect choice. Furthermore, we demonstrate that spike history improves predictions of neural activity within the PFC. In particular, the event-state most strongly associated with spike history effects was when animals revealed the decision to enter a particular maze arm. Combined, these and other results suggest that PFC spiking activity may reflect an overarching action-plan or policy rather than multiple independent cognitive processes. Figure 1 Heterogeneous plPFC neurons participate in and multiplex multiple functional ensembles. Plotted are the percentages of neurons for which task event-states (see Supplemental Material, Fig. S4) associated with this task significantly predicts the spiking ...

Basal Ganglia Preferentially Encode Context Dependent Choice in a Two-Armed Bandit Task

Decision is a self-generated phenomenon, which is hard to track with standard time averaging methods, such as peri-event time histograms (PETHs), used in behaving animals. Reasons include variability in duration of events within a task and uneven reaction time of animals. We have developed a temporal normalization method where PETHs were juxtaposed all along task events and compared between neurons. We applied this method to neurons recorded in striatum and GPi of behaving monkeys involved in a choice task. We observed a significantly higher homogeneity of neuron activity profile distributions in GPi than in striatum. Focusing on the period of the task during which the decision was taken, we showed that approximately one quarter of all recorded neurons exhibited tuning functions. These so-called coding neurons had average firing rates that varied as a function of the value of both presented cues, a combination here referred to as context, and/or value of the chosen cue. The tuning functions were used to build a simple maximum likelihood estimation model, which revealed that (i) GPi neurons are more efficient at encoding both choice and context than striatal neurons and (ii) context prediction rates were higher than those for choice. Furthermore, the mutual information between choice or context values and decision period average firing rate was higher in GPi than in striatum. Considered together, these results suggest a convergence process of the global information flow between striatum and GPi, preferentially involving context encoding, which could be used by the network to perform decision-making.

WaveSolution: a software for analyzing brain signals derived by multielectrodes

Event Abstract Back to Event WaveSolution: a software for analyzing brain signals derived by multielectrodes R. Csercsa1*, A. Magony2 and I. Ulbert1 1 Hungarian Academy of Sciences, Institute for Psychology, Hungary 2 University of Birmingham, School of Clinical and Experimental Medicine, United Kingdom The use of electrodes with multiple recording contacts for deriving brain electrical activity is becoming more and more widespread. It allows the experimenter to examine signals from several sites in the same time. Thus, a software dedicated to analyzing multielectrode recordings and exploiting the potentials in this method is of high importance. The two main types of brain activity that can be recorded from within the cortex are local field potentials (LFP), and single or multiunit activity (SUA, MUA). For SUA analysis, WaveSolution includes a spike sorting pack that contains several methods allocating spikes to single cells. For the analysis of LFP waves the software offers several signal manipulation methods such as epoching, averaging, Fourier transform, spectrogram; and other functions profiting from the multielectrode technique, like cross-correlation, coherence, or current source density analysis. In order to determine the instantaneous phase of an oscillation, WaveSolution calculates the Hilbert transform of the signal and allows the sorting of cycles by amplitude and length. These methods are primarily designed for the analysis of sleep slow oscillation. Another useful method is the peri-event time histogram that shows the timing of events (e.g. spikes) in relation to another event (e.g. external stimulus, or special phases of oscillation). Altogether, the collection of these methods was proven to be helpful in the analyses of brain activity detected by multielectrodes. Keywords: methods, Neuroscience Conference: 13th Conference of the Hungarian Neuroscience Society (MITT), Budapest, Hungary, 20 Jan - 22 Jan, 2011. Presentation Type: Abstract Topic: Methods Citation: Csercsa R, Magony A and Ulbert I (2011). WaveSolution: a software for analyzing brain signals derived by multielectrodes. Front. Neurosci. Conference Abstract: 13th Conference of the Hungarian Neuroscience Society (MITT). doi: 10.3389/conf.fnins.2011.84.00105 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 03 Mar 2011; Published Online: 23 Mar 2011. * Correspondence: Dr. R. Csercsa, Hungarian Academy of Sciences, Institute for Psychology, Budapest, Hungary, csercsa@cogpsyphy.hu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers R. Csercsa A. Magony I. Ulbert Google R. Csercsa A. Magony I. Ulbert Google Scholar R. Csercsa A. Magony I. Ulbert PubMed R. Csercsa A. Magony I. Ulbert Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Protective Effects of Hypothalamic Proline-Rich Peptide and Cobra Venom Naja Naja Oxiana on Dynamics of Vestibular Compensation Following Unilateral Labyrinthectomy

We tested the action of proline-rich peptide (PRP-1) and cobra venom Naja Naja Oxiana (NOX) on Deiters' nucleus neurons at 3rd, 15th and 35th days after unilateral labyrinthectomy (UL). Early and late tetanic, post-tetanic potentiation and depression of Deiters'neurons to bilateral high frequency stimulation of hypothalamic supraoptic and paraventricualar nuclei was studied. The analysis of spike activity was carried out by mean of on-line selection and special program. The complex averaged peri-event time and frequency histograms shows the increase of inhibitory and excitatory reactions of Deiters' neurons at early stage of vestibular compensation following PRP-1 and NOX injection, reaching the norm at the end of tests. In histochemical study the changes in Ca(2+)-dependent acidic phosphatase (AP) activity in neurons was discovered. It was shown that in UL animals the total disappearance or delay of decolorizing of Deiters' neurons lead to neurodegenerative pattern as cellular "shade". AP activity after UL and PRP-1 injection exerts more effective recovery of neurons in comparison with events, observed after the administration of NOX. The data of this study indicate that PRP-1 and NOX are protectors, which may successfully recover the disturbed vestibular functions.

Selective Activation of Striatal Fast-Spiking Interneurons during Choice Execution

Basal ganglia circuits are essential for the organization and execution of voluntary actions. Within the striatum, fast-spiking interneurons (FSIs) are thought to tightly regulate the activity of medium-spiny projection neurons (MSNs) through feed-forward inhibition, yet few studies have investigated the functional contributions of FSIs in behaving animals. We recorded presumed MSNs and FSIs together with motor cortex and globus pallidus (GP) neurons, in rats performing a simple choice task. MSN activity was widely distributed across the task sequence, especially near reward receipt. By contrast, FSIs showed a coordinated pulse of increased activity as chosen actions were initiated, in conjunction with a sharp decrease in GP activity. Both MSNs and FSIs were direction selective, but neighboring MSNs and FSIs showed opposite selectivity. Our findings suggest that individual FSIs participate in local striatal information processing, but more global disinhibition of FSIs by GP is important for initiating chosen actions while suppressing unwanted alternatives.

Isolating Event-related Neuronal Responses by Deconvolution

Although many studies in neuroscience are based on comparing neuronal responses to single, isolated sensory or motor events, multiple events frequently occur in close temporal proximity in freely moving animals. This often obscures the precise temporal correlation between each event and the relevant brain activity. By simulating neuronal responses in multi-event tasks, we show that perievent time histograms (PETHs) greatly distort the underlying true responses. We propose a multi-event deconvolution method that can separate the contribution of each event to the overall neuronal activity. The improvements over PETH in analyzing real data are demonstrated using simulated data and a sample electrophysiological recording obtained from rats in a task involving responses to a reward predictive cue.

Differential Dynamics of Activity Changes in Dorsolateral and Dorsomedial Striatal Loops during Learning

The basal ganglia are implicated in a remarkable range of functions influencing emotion and cognition as well as motor behavior. Current models of basal ganglia function hypothesize that parallel limbic, associative, and motor cortico-basal ganglia loops contribute to this diverse set of functions, but little is yet known about how these loops operate and how their activities evolve during learning. To address these issues, we recorded simultaneously in sensorimotor and associative regions of the striatum as rats learned different versions of a conditional T-maze task. We found highly contrasting patterns of activity in these regions during task performance and found that these different patterns of structured activity developed concurrently, but with sharply different dynamics. Based on the region-specific dynamics of these patterns across learning, we suggest a working model whereby dorsomedial associative loops can modulate the access of dorsolateral sensorimotor loops to the control of action.

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.

SigTOOL: A MATLAB-based environment for sharing laboratory-developed software to analyze biological signals

This paper describes a software package, named sigTOOL, for processing biological signals. The package runs in the MATLAB programming environment and has been designed to promote the sharing of laboratory-developed software across the worldwide web. As proof-of-concept of the design of the system, sigTOOL has been used to build an analysis application for dealing with neuroscience data complete with a user-friendly graphical user interface which implements a range of waveform and spike-train analysis functions. The interface allows many commonly used neuroscience data file formats to be loaded (including those of Alpha Omega, Cambridge Electronic Design, Cyberkinetics Inc., Molecular Devices, Nex Technologies and Plexon Instruments). Waveform analysis functions selectable from the interface support waveform averaging (mean and median), auto- and cross-correlation, power spectral analysis, coherence estimation, digital filtering (feedback and feedforward) and resampling. Spike-train analyses include interspike interval distributions, Poincaré plots, event auto- and cross-correlations, spike-triggered averaging, stimulus driven and phase-related peri-event time histograms and rasters as well as frequencygrams. User-developed additions to sigTOOL that are archived and distributed electronically will be added to the sigTOOL interface on-the-fly, without the need to modify the core sigTOOL code. Full sigTOOL functionality will be provided to support the user-developed code, including the ability to record a user action history for batch processing of files and support for exporting the results of analyses to external graphics editing software and spreadsheet-based data processing packages.

Reduced Spike-Timing Reliability Correlates with the Emergence of Fast Ripples in the Rat Epileptic Hippocampus

Ripples are sharp-wave-associated field oscillations (100-300 Hz) recorded in the hippocampus during behavioral immobility and slow-wave sleep. In epileptic rats and humans, a different and faster oscillation (200-600 Hz), termed fast ripples, has been described. However, the basic mechanisms are unknown. Here, we propose that fast ripples emerge from a disorganized ripple pattern caused by unreliable firing in the epileptic hippocampus. Enhanced synaptic activity is responsible for the irregular bursting of CA3 pyramidal cells due to large membrane potential fluctuations. Lower field interactions and a reduced spike-timing reliability concur with decreased spatial synchronization and the emergence of fast ripples. Reducing synaptically driven membrane potential fluctuations improves both spike-timing reliability and spatial synchronization and restores ripples in the epileptic hippocampus. Conversely, a lower spike-timing reliability, with reduced potassium currents, is associated with ripple shuffling in normal hippocampus. Therefore, fast ripples may reflect a pathological desynchronization of the normal ripple pattern.

Nonparametric Modeling of Neural Point Processes via Stochastic Gradient Boosting Regression

Statistical nonparametric modeling tools that enable the discovery and approximation of functional forms (e.g., tuning functions) relating neural spiking activity to relevant covariates are desirable tools in neuroscience. In this article, we show how stochastic gradient boosting regression can be successfully extended to the modeling of spiking activity data while preserving their point process nature, thus providing a robust nonparametric modeling tool. We formulate stochastic gradient boosting in terms of approximating the conditional intensity function of a point process in discrete time and use the standard likelihood of the process to derive the loss function for the approximation problem. To illustrate the approach, we apply the algorithm to the modeling of primary motor and parietal spiking activity as a function of spiking history and kinematics during a two-dimensional reaching task. Model selection, goodness of fit via the time rescaling theorem, model interpretation via partial dependence plots, ranking of covariates according to their relative importance, and prediction of peri-event time histograms are illustrated and discussed. Additionally, we use the tenfold cross-validated log likelihood of the modeled neural processes (67 cells) to compare the performance of gradient boosting regression to two alternative approaches: standard generalized linear models (GLMs) and Bayesian P-splines with Markov chain Monte Carlo (MCMC) sampling. In our data set, gradient boosting outperformed both Bayesian P-splines (in approximately 90% of the cells) and GLMs (100%). Because of its good performance and computational efficiency, we propose stochastic gradient boosting regression as an off-the-shelf nonparametric tool for initial analyses of large neural data sets (e.g., more than 50 cells; more than 10(5) samples per cell) with corresponding multidimensional covariate spaces (e.g., more than four covariates). In the cases where a functional form might be amenable to a more compact representation, gradient boosting might also lead to the discovery of simpler, parametric models.

Hippocampal sharp wave bursts coincide with neocortical “up-state” transitions

The sleeping neocortex shows nested oscillatory activity in different frequency ranges, characterized by fluctuations between "up-states" and "down-states." High-density neuronal ensemble recordings in rats now reveal the interaction between synchronized activity in the hippocampus and neocortex: Electroencephalographic sharp waves in the hippocampus were more probable during down-states than during up-states, and tended to coincide with transitions from down-states to up-states. The form of cortical activity fluctuations and their interactions with sharp waves depend on sleep depth: In deeper sleep stages, characterized by strong neocortical oscillation in the delta range or slower (approximately 0.8-4 Hz), sharp-wave-triggered peri-event time histograms (PETH) are consistent with a longer duration for down-states than for up-states. In lighter sleep, the sharp-wave-triggered PETH suggested longer up-states than down-states. These results highlight the interplay in the hippocampal/neocortical loop: Decreased neocortical input during down-states may be a factor in generation of sharp waves. In turn, sharp waves may facilitate down-to-up transitions. This interplay may reflect joint memory trace reactivation in the hippocampus and in the neocortex, possibly contributing to consolidation of long-term memory: Off-line reactivation of recent neural activity patterns in the hippocampus occurs during 50-100-msec electroencephalographic sharp waves, corresponding to pyramidal-cell population bursts. The neocortical up-states starting in correspondence with sharp waves may be influenced by the reactivated information carried by the hippocampal sharp wave.