Matthias H. J. Munk

Coherent oscillations: A mechanism of feature linking in the visual cortex?

Primary visual coding can be characterized by the receptive field (RF) properties of single neurons. Subject of this paper is our search for a global,second coding step beyond the RF-concept that links related features in a visual scene. In recent models of visual coding, oscillatory activities have been proposed to constitute such linking signals. We tested the neurophysiological relevance of this hypothesis for the visual system. Single and multiple spikes as well as local field potentials were recorded simultaneously from several locations in the primary visual cortex (A17 and A18) using 7 or 19 individually advanceable fibermicroelectrodes (250 or 330 μm apart).Stimulusevoked (SE)-resonances of 35–85 Hz were found in these three types of signals throughout the visual cortex when the primary coding channels were activated by their specific stimuli. Stimulus position, orientation, movement direction and velocity, ocularity and stationary flicker caused specific SE-resonances.Coherent SE-resonances were found at distant cortical positions when at least one of the primary coding properties was similar. Coherence was found1) within a vertical cortex column,2) between neighbouring hypercolumns, and3) between two different cortical areas. We assume that the coherence of SE-resonances is mediated by recurrent excitatory intra- and inter-areal connections via phase locking between assemblies that represent the linking features of the actual visual scene. Visually related activities are, thus, transiently labelled by a temporal code that signalizes their momentary association.

Cognitive functions of gamma-band activity: memory match and utilization

Oscillatory neural activity in the gamma frequency range (>30Hz) has been shown to accompany a wide variety of cognitive processes. So far, there has been limited success in assigning a unitary basic function to these oscillations, and critics have raised the argument that they could just be an epiphenomenon of neural processing. We propose a new framework that relates gamma oscillations observed in human, as well as in animal, experiments to two underlying processes: the comparison of memory contents with stimulus-related information and the utilization of signals derived from this comparison. This model attempts to explain early gamma-band responses in terms of the match between bottom-up and top-down information. Furthermore, it assumes that late gamma-band activity reflects the readout and utilization of the information resulting from this match.

Role of reticular activation in the modulation of intracortical synchronization

During aroused states of the brain, electroencephalographic activity is characterized by fast, irregular fluctuations of low amplitude, which are thought to reflect desynchronization of neuronal activity. This phenomenon seems at odds with the proposal that synchronization of cortical responses may play an important role in the processing of sensory signals. Here, activation of the mesencephalic reticular formation (MRF), an effective way to “desynchronize the electroencephalogram,” was shown to facilitate oscillatory activity in the gamma frequency range and to enhance the stimulus-specific synchronization of neuronal spike responses in the visual cortex of cats.

Visual latencies in areas V1 and V2 of the macaque monkey

Latencies to small flashing spots of light were measured in different layers of areas V1 and V2 in anesthetized and paralyzed macaque monkeys. The shortest latencies were found in layers 4C alpha and 4B of area V1. Latencies in layer 4C beta were on average 20 ms longer than those in 4C alpha and 4B. The shortest latencies in area V2 were observed in the infragranular layers and they did not differ significantly from those found in the infragranular layers in V1. Similarly, latencies in the supragranular layers of V2 were not significantly different from those measured in the supragranular layers of V1. These results show that, in area V1, neurons of the magnocellular pathway are activated on average 20 ms earlier than those of the parvocellular pathway. Our data also suggest that much processing begins simultaneously in areas V1 and V2.

Cortical capacity constraints for visual working memory: dissociation of fMRI load effects in a fronto-parietal network

Working memory (WM) capacity limitations and their neurophysiological correlates are of special relevance for the understanding of higher cognitive functions. Evidence from behavioral studies suggests that restricted attentional resources contribute to these capacity limitations. In an event-related functional magnetic resonance imaging (fMRI) study, we probed the capacity of the human visual WM system for up to four complex nonnatural objects using a delayed discrimination task. A number of prefrontal and parietal areas bilaterally showed increased blood oxygen level-dependent activity, relative to baseline, throughout the task when more than one object had to be held in memory. Monotonic increases in response to memory load were observed bilaterally in the dorsolateral prefrontal cortex (DLPFC) and the presupplementary motor area (pre-SMA). Conversely, activity in the frontal eye fields (FEFs) and in areas along the intraparietal sulcus (IPS) peaked when subjects had to maintain only two or three objects and decreased in the highest load condition. This dissociation of memory load effects on cortical activity suggests that the cognitive operations subserved by the IPS and FEF, which are most likely related to attention, fail to support visual WM when the capacity limit is approached. The correlation of brain activity with performance implies that only the operations performed by the DLPFC and pre-SMA, which support an integrated representation of visual information, helped subjects to maintain a reasonable level of performance in the highest load condition. These results indicate that at least two distinct cortical subsystems are recruited for visual WM, and that their interplay changes when the capacity limit is reached.

Neuronal assemblies: necessity, signature and detectability.

The ease with which highly developed brains can generate representations of a virtually unlimited diversity of perceptual objects indicates that they have developed very efficient mechanisms to analyse and represent relations among incoming signals. Here, we propose that two complementary strategies are applied to cope with these combinatorial problems. First, elementary relations are represented by the tuned responses of individual neurons that acquire their specific response properties (feature selectivity) through appropriate convergence of input connections in hierarchically structured feed-forward architectures. Second, complex relations that cannot be represented economically by the responses of individual neurons are represented by assemblies of cells that are generated by dynamic association of individual, featureselective cells. The signature identifying the responses of an assembly as components of a coherent code is thought to be the synchronicity of the respective discharges. The compatibility of this hypothesis is examined in the context of recent data on the dynamics of synchronization phenomena, the dependence of synchronization on central states and the relations between the synchronization behaviour of neurons and perception.

Short- and long-term effects of cholinergic modulation on gamma oscillations and response synchronization in the visual cortex.

Neurons can engage in synchronized oscillatory activity in the gamma-frequency range when responding to sensory stimuli. Both the oscillatory patterning and the synchronization of responses are enhanced with arousal and attention or when the electroencephalogram is activated by electrical stimulation of the mesencephalic reticular formation. Here we show with intracortical application of cholinergic antagonists that the enhancement of gamma oscillations and response synchronization is mediated by acetylcholine and muscarinic receptors. We demonstrate further that coapplication of cholinergic agonists with synchrony-inducing light stimuli causes a lasting increase in the probability that the stimulated cells engage in gamma oscillations and response synchronization. These changes develop slowly over tens of minutes and then persist for many hours. Thus, cholinergic modulation plays a crucial role both in the fast, state-dependent facilitation of gamma oscillations and response synchronization and in use-dependent long-term modifications of cortical dynamics that favor synchronization of responses in the gamma-frequency range.

Spike avalanches in vivo suggest a driven, slightly subcritical brain state

In self-organized critical (SOC) systems avalanche size distributions follow power-laws. Power-laws have also been observed for neural activity, and so it has been proposed that SOC underlies brain organization as well. Surprisingly, for spiking activity in vivo, evidence for SOC is still lacking. Therefore, we analyzed highly parallel spike recordings from awake rats and monkeys, anesthetized cats, and also local field potentials from humans. We compared these to spiking activity from two established critical models: the Bak-Tang-Wiesenfeld model, and a stochastic branching model. We found fundamental differences between the neural and the model activity. These differences could be overcome for both models through a combination of three modifications: (1) subsampling, (2) increasing the input to the model (this way eliminating the separation of time scales, which is fundamental to SOC and its avalanche definition), and (3) making the model slightly sub-critical. The match between the neural activity and the modified models held not only for the classical avalanche size distributions and estimated branching parameters, but also for two novel measures (mean avalanche size, and frequency of single spikes), and for the dependence of all these measures on the temporal bin size. Our results suggest that neural activity in vivo shows a mélange of avalanches, and not temporally separated ones, and that their global activity propagation can be approximated by the principle that one spike on average triggers a little less than one spike in the next step. This implies that neural activity does not reflect a SOC state but a slightly sub-critical regime without a separation of time scales. Potential advantages of this regime may be faster information processing, and a safety margin from super-criticality, which has been linked to epilepsy.

An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes.

Analyzing Short-Term Noise Dependencies of Spike-Counts in Macaque Prefrontal Cortex Using Copulas and the Flashlight Transformation

Simultaneous spike-counts of neural populations are typically modeled by a Gaussian distribution. On short time scales, however, this distribution is too restrictive to describe and analyze multivariate distributions of discrete spike-counts. We present an alternative that is based on copulas and can account for arbitrary marginal distributions, including Poisson and negative binomial distributions as well as second and higher-order interactions. We describe maximum likelihood-based procedures for fitting copula-based models to spike-count data, and we derive a so-called flashlight transformation which makes it possible to move the tail dependence of an arbitrary copula into an arbitrary orthant of the multivariate probability distribution. Mixtures of copulas that combine different dependence structures and thereby model different driving processes simultaneously are also introduced. First, we apply copula-based models to populations of integrate-and-fire neurons receiving partially correlated input and show that the best fitting copulas provide information about the functional connectivity of coupled neurons which can be extracted using the flashlight transformation. We then apply the new method to data which were recorded from macaque prefrontal cortex using a multi-tetrode array. We find that copula-based distributions with negative binomial marginals provide an appropriate stochastic model for the multivariate spike-count distributions rather than the multivariate Poisson latent variables distribution and the often used multivariate normal distribution. The dependence structure of these distributions provides evidence for common inhibitory input to all recorded stimulus encoding neurons. Finally, we show that copula-based models can be successfully used to evaluate neural codes, e.g., to characterize stimulus-dependent spike-count distributions with information measures. This demonstrates that copula-based models are not only a versatile class of models for multivariate distributions of spike-counts, but that those models can be exploited to understand functional dependencies.