While oscillations of the local field potential (LFP) are commonly attributed to the synchronization of neuronal firing rate on the same time scale, their relationship to coincident spiking in the millisecond range is unknown. Here, we present experimental evidence to reconcile the notions of synchrony at the level of spiking and at the mesoscopic scale. We demonstrate that only in time intervals of significant spike synchrony that cannot be explained on the basis of firing rates, coincident spikes are better phase locked to the LFP than predicted by the locking of the individual spikes. This effect is enhanced in periods of large LFP amplitudes. A quantitative model explains the LFP dynamics by the orchestrated spiking activity in neuronal groups that contribute the observed surplus synchrony. From the correlation analysis, we infer that neurons participate in different constellations but contribute only a fraction of their spikes to temporally precise spike configurations. This finding provides direct evidence for the hypothesized relation that precise spike synchrony constitutes a major temporally and spatially organized component of the LFP.

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 crosscorrelations 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.

We publish two electrophysiological datasets recorded in motor cortex of two macaque monkeys during an instructed delayed reach-to-grasp task, using chronically implanted 10-by-10 Utah electrode arrays. We provide a) raw neural signals (sampled at 30 kHz), b) time stamps and spike waveforms of offline sorted single and multi units (93/49 and 156/19 SUA/MUA for the two monkeys, respectively), c) trial events and the monkey’s behavior, and d) extensive metadata hierarchically structured via the odML metadata framework (including quality assessment post-processing steps, such as trial rejections). The dataset of one monkey contains a simultaneously saved record of the local field potential (LFP) sampled at 1 kHz. To load the datasets in Python, we provide code based on the Neo data framework that produces a data structure which is annotated with relevant metadata. We complement this loading routine with an example code demonstrating how to access the data objects (e.g., raw signals) contained in such structures. For Matlab users, we provide the annotated data structures as mat files.

For networks of pulse-coupled oscillators with complex connectivity, we demonstrate that in the presence of coupling heterogeneity precisely timed periodic firing patterns replace the state of global synchrony that exists in homogenous networks only. With increasing disorder, these patterns persist until they reach a critical temporal extent that is of the order of the interaction delay. For stronger disorder these patterns cease to exist and only asynchronous, aperiodic states are observed. We derive self-consistency equations to predict the precise temporal structure of a pattern from the network heterogeneity. Moreover, we show how to design heterogenous coupling architectures to create an arbitrary prescribed pattern. PACS numbers: 05.45.Xt, 89.75.Fb, 89.75.Hc, 87.10.+e Understanding how the structure of a complex network [1] determines its dynamics is currently in the focus of research in physics, biology and technology [2]. Pulse-coupled oscillators provide a paradigmatic class of models to describe a variety of networks that occur in nature, such as populations of fireflies, pacemakers cells of the heart, earthquakes, or neural networks [3,4]. Synchronization is one of the most prevalent kinds of collective dynamics in such networks [5].Recent theoretical studies, which analyze conditions for the existence and stability of synchronous states, have focused on homogenous networks with simple topologies, e.g. global couplings [6] or regular lattices [7]. In nature, however, intricately structured and heterogenous interactions are ubiquitous. Previously, aspects of heterogeneity have been studied mostly in globally coupled networks [8,9,10]. However, for networks with structured connectivity only a few studies exist [11,12] and it is still an open question, how heterogeneity influences the dynamics, in particular synchronization, in such networks.In this Letter we present an exact analysis of the dynamics of complex networks of pulse-coupled oscillators in the presence of coupling heterogeneity. We demonstrate that the synchronous state, that exists in homogenous networks, is replaced by precisely timed periodic firing patterns. The temporal extent of these patterns grows with the degree of disorder. Patterns persist below a critical strength of the disorder beyond which only asynchronous, aperiodic states are observed. We show how this transition is controlled by the interaction delay. Simple criteria for the stability of firing patterns are derived. Furthermore, an approach is presented to predict the relative timings of firing events from self-consistency conditions for the phases in a given network. Conversely, any prescribed pattern can be created by designing a heterogenous coupling architecture in a network of specified connectivity.Consider a system of N pulse-coupled oscillators that interact via directed connections. A matrix C defines the connectivity of the network, where C ij = 1 if a connection from oscillator j to i exists, and C ij = 0 otherwise. The number of inputs k i := j C ij to every ...

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