Sonja Grün

Modeling the Spatial Reach of the LFP

The local field potential (LFP) reflects activity of manyxa0neurons in the vicinity of the recording electrode and is therefore useful for studying local network dynamics. Much of the nature of the LFP is, however, still unknown. There are, for instance, contradicting reports on the spatial extent of the region generating the LFP. Here, we use a detailed biophysical modeling approach to investigate the size of the contributing region by simulating the LFP from a large number of neurons around the electrode. We find that the size of the generating region depends on the neuron morphology, the synapse distribution, and the correlation in synaptic activity. For uncorrelated activity, the LFP represents cells in a small region (within a radius of a few hundred micrometers). If the LFP contributions from different cells are correlated, the size of the generating region is determined by the spatial extent of the correlated activity.

Cross-frequency interaction of the eye-movement related LFP signals in V1 of freely viewing monkeys

Recent studies have emphasized the functional role of neuronal activity underlying oscillatory local field potential (LFP) signals during visual processing in natural conditions. While functionally relevant components in multiple frequency bands have been reported, little is known about whether and how these components interact with each other across the dominant frequency bands. We examined this phenomenon in LFP signals obtained from the primary visual cortex of monkeys performing voluntary saccadic eye movements (EMs) on still images of natural-scenes. We identified saccade-related changes in respect to power and phase in four dominant frequency bands: delta-theta (2–4 Hz), alpha-beta (10–13 Hz), low-gamma (20–40 Hz), and high-gamma (>100 Hz). The phase of the delta-theta band component is found to be entrained to the rhythm of the repetitive saccades, while an increment in the power of the alpha-beta and low-gamma bands were locked to the onset of saccades. The degree of the power modulation in these frequency bands is positively correlated with the degree of the phase-locking of the delta-theta oscillations to EMs. These results suggest the presence of cross-frequency interactions in the form of phase-amplitude coupling (PAC) between slow (delta-theta) and faster (alpha-beta and low gamma) oscillations. As shown previously, spikes evoked by visual fixations during free viewing are phase-locked to the fast oscillations. Thus, signals of different types and at different temporal scales are nested to each other during natural viewing. Such cross-frequency interaction may provide a general mechanism to coordinate sensory processing on a fast time scale and motor behavior on a slower time scale during active sensing.

Analysis of Parallel Spike Trains

Part 1: Basic spike train statistics: Point process models. Ch 1.- Stochastic models of spike trains-Carl van Vreeswijk. Ch 2.- Estimating the firing rate- Shigeru Shinomoto. Ch 3.- Processing of phase-locked spikes and periodic signals- Go Ashida, Hermann Wagner and Catherine E. Carr. Ch 4.- Analysis and interpretation of interval and count variability in neural spike trains- Martin Paul Nawrot. Part II: Pairwise comparison of spike trains. Ch 5.- Dependence of spike-count correlations on spike-train statistics and observation time-scale- Tom Tetzlaff and Markus Diesmann. Ch 6.- Pair-correlation in the time and frequency domain- Jos J. Eggermont. Ch 7.- Spike metrics- Jonathan D. Victor and Keith P. Purpura. Ch8.- Gravitational clustering- George Gerstein. Part III: Multiple-neuron spike patterns. Ch 9.- Spatio-temporal patterns- Moshe Abeles. Ch 10.- Unitary Events analysis- Sonja Grun, Markus Diesmann and Ad Aertsen. Ch 11.- Information geometry of multiple spike trains- Shun-ichi Amari. Ch 12.- Higher-order correlations and cumulants- Benjamin Staude, Sonja Grun and Stefan Rotter. Part IV: Population-based approaches. Ch13.- Information theory and systems neuroscience- Don H. Johnson, lan N. Goodman and Christopher J. Rozell. Ch 14.- Population coding- Stefano Panzeri, Fernando Montani, Giuseppe Notaro, Cesare Magri and Rasmus S. Petersen. Ch 15. Chastic models for multivariate neural point processes: Collective dynamics and neural decoding- Wilson Truccolo. Part V: Practical issues. Ch 15.- Simulation of stochastic point processes with defined properties-Stefano Cardanobile and Stefan Rotter. Ch 16. Generation and selection of surrogate methods for correlation analysis-Sebastien Louis, Christian Borgelt and Sonja Grun. Ch 17.- Bootstrap tests of hypotheses-Valerie Ventura. Ch18.- Generating random numbers.- Hans Ekkehard Plesser. Ch 19. Practically trivial parallel data processing in a neuroscience laboratory- Michael Denker, Bernd Wiebelt, Denny Fliegner,Markus Diesmann and Abigail Morrison

Synchronization of Neuronal Responses in Primary Visual Cortex of Monkeys Viewing Natural Images

When inspecting visual scenes, primates perform on average four saccadic eye movements per second, which implies that scene segmentation, feature binding, and identification of image components is accomplished in <200 ms. Thus individual neurons can contribute only a small number of discharges for these complex computations, suggesting that information is encoded not only in the discharge rate but also in the timing of action potentials. While monkeys inspected natural scenes we registered, with multielectrodes from primary visual cortex, the discharges of simultaneously recorded neurons. Relating these signals to eye movements revealed that discharge rates peaked around 90 ms after fixation onset and then decreased to near baseline levels within 200 ms. Unitary event analysis revealed that preceding this increase in firing there was an episode of enhanced response synchronization during which discharges of spatially distributed cells coincided within 5-ms windows significantly more often than predicted by the discharge rates. This episode started 30 ms after fixation onset and ended by the time discharge rates had reached their maximum. When the animals scanned a blank screen a small change in firing rate, but no excess synchronization, was observed. The short latency of the stimulation-related synchronization phenomena suggests a fast-acting mechanism for the coordination of spike timing that may contribute to the basic operations of scene segmentation.

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.

Different Subtypes of Striatal Neurons Are Selectively Modulated by Cortical Oscillations

The striatum is the key site for cortical input to the basal ganglia. Cortical input to striatal microcircuits has been previously studied only in the context of one or two types of neurons. Here, we provide the first description of four putative types of striatal neurons (medium spiny, fast spiking, tonically active, and low-threshold spiking) in a single data set by separating extracellular recordings of sorted single spikes recorded under halothane anesthesia using waveform and burst parameters. Under halothane, the electrocorticograms and striatal local field potential displayed spontaneous oscillations at both low (2–9 Hz) and high (35–80 Hz) frequencies. Putative fast spiking interneurons were significantly more likely to phase lock to high-frequency cortical oscillations and displayed significant cross-correlations in this frequency range. These findings suggest that, as in neocortex and hippocampus, the coordinated activity of fast spiking interneurons may specifically be involved in mediating oscillatory synchronization in the striatum.

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.

State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data.

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand.

Detecting Synfire Chain Activity Using Massively Parallel Spike Train Recording

The synfire chain model has been proposed as the substrate that underlies computational processes in the brain and has received extensive theoretical study. In this model cortical tissue is composed of a superposition of feedforward subnetworks (chains) each capable of transmitting packets of synchronized spikes with high reliability. Computations are then carried out by interactions of these chains. Experimental evidence for synfire chains has so far been limited to inference from detection of a few repeating spatiotemporal neuronal firing patterns in multiple single-unit recordings. Demonstration that such patterns actually come from synfire activity would require finding a meta organization among many detected patterns, as yet an untried approach. In contrast we present here a new method that directly visualizes the repetitive occurrence of synfire activity even in very large data sets of multiple single-unit recordings. We achieve reliability and sensitivity by appropriately averaging over neuron space (identities) and time. We test the method with data from a large-scale balanced recurrent network simulation containing 50 randomly activated synfire chains. The sensitivity is high enough to detect synfire chain activity in simultaneous single-unit recordings of 100 to 200 neurons from such data, enabling application to experimental data in the near future.

Whisker barrel cortex delta oscillations and gamma power in the awake mouse are linked to respiration

Current evidence suggests that delta oscillations (0.5–4u2009Hz) in the brain are generated by intrinsic network mechanisms involving cortical and thalamic circuits. Here we report that delta band oscillation in spike and local field potential (LFP) activity in the whisker barrel cortex of awake mice is phase locked to respiration. Furthermore, LFP oscillations in the gamma frequency band (30–80u2009Hz) are amplitude modulated in phase with the respiratory rhythm. Removal of the olfactory bulb eliminates respiration-locked delta oscillations and delta-gamma phase-amplitude coupling. Our findings thus suggest respiration-locked olfactory bulb activity as a main driving force behind delta oscillations and gamma power modulation in the whisker barrel cortex in the awake state.