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Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved with a recently developed filter-based template matching procedure. Using tetrodes with a three-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of nonoverlapping spikes and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates, and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons., (Copyright © 2015 the American Physiological Society.)

Neuronal processing of sensory information requires that rapidly changing synaptic inputs are continuously transformed into action potentials. Variability of spike firing is generally considered as noise and might therefore interfere with the reliability of synaptic transmission in sensory systems. In a system in which the number of spikes is a variable that determines the quality of neuronal transmission, variability of spike counts is a paradoxical attribute. In contrast, in a system in which precisely correlated spike firing can influence synaptic integration, response variability might be used as an additional mechanism for coding information. As acetylcholine has been shown to reduce spike-frequency adaptation and enhance gamma frequency (21-70 Hz) oscillations, we set out to study the influence of cholinergic modulation on the variability of spike counts and gamma oscillations. Iontophoretic application of carbachol, a cholinergic agonist, in cat primary visual cortex or electrical stimulation of the mesencephalic reticular formation reduced the spike count variability and stabilized gamma frequency oscillations of visually induced responses. Response stabilization was correlated with enhancement of gamma-frequency oscillations but not with averaged firing rates. Lowering variability of sensory responses might be a mechanism to stabilize and improve reliability of neuronal transmission. Cholinergic activation may therefore influence the efficacy of neuronal transmission by modulating the precise timing of neuronal responses., (Copyright 2010 IBRO. Published by Elsevier Ltd. All rights reserved.)

Background: Many systems in nature are characterized by complex behaviour where large cascades of events, or avalanches, unpredictably alternate with periods of little activity. Snow avalanches are an example. Often the size distribution f(s) of a system's avalanches follows a power law, and the branching parameter sigma, the average number of events triggered by a single preceding event, is unity. A power law for f(s), and sigma = 1, are hallmark features of self-organized critical (SOC) systems, and both have been found for neuronal activity in vitro. Therefore, and since SOC systems and neuronal activity both show large variability, long-term stability and memory capabilities, SOC has been proposed to govern neuronal dynamics in vivo. Testing this hypothesis is difficult because neuronal activity is spatially or temporally subsampled, while theories of SOC systems assume full sampling. To close this gap, we investigated how subsampling affects f(s) and sigma by imposing subsampling on three different SOC models. We then compared f(s) and sigma of the subsampled models with those of multielectrode local field potential (LFP) activity recorded in three macaque monkeys performing a short term memory task., Results: Neither the LFP nor the subsampled SOC models showed a power law for f(s). Both, f(s) and sigma, depended sensitively on the subsampling geometry and the dynamics of the model. Only one of the SOC models, the Abelian Sandpile Model, exhibited f(s) and sigma similar to those calculated from LFP activity., Conclusion: Since subsampling can prevent the observation of the characteristic power law and sigma in SOC systems, misclassifications of critical systems as sub- or supercritical are possible. Nevertheless, the system specific scaling of f(s) and sigma under subsampling conditions may prove useful to select physiologically motivated models of brain function. Models that better reproduce f(s) and sigma calculated from the physiological recordings may be selected over alternatives.

To investigate neuronal processing during short-term memory, we analyzed behavior-related modulations of coupling between signals on two spatial scales: first, very local multiunit activity and second, local field potentials. Coupling was assessed by spike field coherence using a new approach to overcome limitations in cases of low firing rates. We demonstrate the reliability of our approach with simulated data. Application to recordings in prefrontal cortex of two monkeys revealed that locking of spikes was differentially modulated with two different frequency bands depending on behavioral performance.

Neuronal response synchronization with millisecond precision has been proposed to serve feature binding in vision and should therefore, like visual experience, depend on central states. Here we test this hypothesis by examining the occurrence and strength of response synchronization in areas 17 and 18 of anesthetized cats as a function of central states. These were assessed from the frequency content of the electroencephalogram, low power in the delta and high power in the gamma frequency ranges (here 20-70 Hz) being considered as a signature of activated states. We evaluated both spontaneous state changes and transitions induced by electrical stimulation of the mesencephalic reticular formation. During states of low central activation, visual responses were robust but lacked signs of precise synchronization. At intermediate levels of activation, responses became synchronized and exhibited an oscillatory patterning in the range of 70-105 Hz. At higher levels of activation, a different pattern of response synchronization and oscillatory modulation appeared, oscillation frequency now being in the range of 20-65 Hz. The strength of response synchronization and oscillatory modulation in the 20-65 Hz range increased with further activation and was associated with a decrease in oscillation frequency. We propose that the oscillatory patterning in the 70-105 Hz range is attributable to oscillatory retinothalamic input and that a minimal level of activation is necessary for cortical neurons to follow this oscillatory pattern. In contrast, the synchronization of responses at oscillation frequencies in the 20-65 Hz range appears to result from intracortical synchronizing mechanisms, which become progressively more effective as central activation increases. Surprisingly, enhanced synchronization and oscillatory modulation in the gamma frequency range were not associated with consistent increases in response amplitude, excluding a simple relation between central activation and neuronal discharge rate. The fact that intracortical synchronizing mechanisms are particularly effective during states of central activation supports the hypothesis that precise synchronization of responses plays a role in sensory processing.