Danko Nikolić

The gamma cycle

Activated neuronal groups typically engage in rhythmic synchronization in the gamma-frequency range (30-100 Hz). Experimental and modeling studies demonstrate that each gamma cycle is framed by synchronized spiking of inhibitory interneurons. Here, we review evidence suggesting that the resulting rhythmic network inhibition interacts with excitatory input to pyramidal cells such that the more excited cells fire earlier in the gamma cycle. Thus, the amplitude of excitatory drive is recoded into phase values of discharges relative to the gamma cycle. This recoding enables transmission and read out of amplitude information within a single gamma cycle without requiring rate integration. Furthermore, variation of phase relations can be exploited to facilitate or inhibit exchange of information between oscillating cell assemblies. The gamma cycle could thus serve as a fundamental computational mechanism for the implementation of a temporal coding scheme that enables fast processing and flexible routing of activity, supporting fast selection and binding of distributed responses. This review is part of the INMED/TINS special issue Physiogenic and pathogenic oscillations: the beauty and the beast, based on presentations at the annual INMED/TINS symposium (http://inmednet.com).

Neural synchrony in cortical networks: history, concept and current status

Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies.

The Role of Oscillations and Synchrony in Cortical Networks and Their Putative Relevance for the Pathophysiology of Schizophrenia

Neural oscillations and their synchronization may represent a versatile signal to realize flexible communication within and between cortical areas. By now, there is extensive evidence to suggest that cognitive functions depending on coordination of distributed neural responses, such as perceptual grouping, attention-dependent stimulus selection, subsystem integration, working memory, and consciousness, are associated with synchronized oscillatory activity in the theta-, alpha-, beta-, and gamma-band, suggesting a functional mechanism of neural oscillations in cortical networks. In addition to their role in normal brain functioning, there is increasing evidence that altered oscillatory activity may be associated with certain neuropsychiatric disorders, such as schizophrenia, that involve dysfunctional cognition and behavior. In the following article, we aim to summarize the evidence on the role of neural oscillations during normal brain functioning and their relationship to cognitive processes. In the second part, we review research that has examined oscillatory activity during cognitive and behavioral tasks in schizophrenia. These studies suggest that schizophrenia involves abnormal oscillations and synchrony that are related to cognitive dysfunctions and some of the symptoms of the disorder. Perspectives for future research will be discussed in relationship to methodological issues, the utility of neural oscillations as a biomarker, and the neurodevelopmental hypothesis of schizophrenia.

A Small World of Neuronal Synchrony

A small-world network has been suggested to be an efficient solution for achieving both modular and global processing—a property highly desirable for brain computations. Here, we investigated functional networks of cortical neurons using correlation analysis to identify functional connectivity. To reconstruct the interaction network, we applied the Ising model based on the principle of maximum entropy. This allowed us to assess the interactions by measuring pairwise correlations and to assess the strength of coupling from the degree of synchrony. Visual responses were recorded in visual cortex of anesthetized cats, simultaneously from up to 24 neurons. First, pairwise correlations captured most of the patterns in the populations activity and, therefore, provided a reliable basis for the reconstruction of the interaction networks. Second, and most importantly, the resulting networks had small-world properties; the average path lengths were as short as in simulated random networks, but the clustering coefficients were larger. Neurons differed considerably with respect to the number and strength of interactions, suggesting the existence of “hubs” in the network. Notably, there was no evidence for scale-free properties. These results suggest that cortical networks are optimized for the coexistence of local and global computations: feature detection and feature integration or binding.

Common neural substrates for visual working memory and attention

Humans are severely limited in their ability to memorize visual information over short periods of time. Selective attention has been implicated as a limiting factor. Here we used functional magnetic resonance imaging to test the hypothesis that this limitation is due to common neural resources shared by visual working memory (WM) and selective attention. We combined visual search and delayed discrimination of complex objects and independently modulated the demands on selective attention and WM encoding. Participants were presented with a search array and performed easy or difficult visual search in order to encode one or three complex objects into visual WM. Overlapping activation for attention-demanding visual search and WM encoding was observed in distributed posterior and frontal regions. In the right prefrontal cortex and bilateral insula blood oxygen-level-dependent activation additively increased with increased WM load and attentional demand. Conversely, several visual, parietal and premotor areas showed overlapping activation for the two task components and were severely reduced in their WM load response under the condition with high attentional demand. Regions in the left prefrontal cortex were selectively responsive to WM load. Areas selectively responsive to high attentional demand were found within the right prefrontal and bilateral occipital cortex. These results indicate that encoding into visual WM and visual selective attention require to a high degree access to common neural resources. We propose that competition for resources shared by visual attention and WM encoding can limit processing capabilities in distributed posterior brain regions.

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.

Higher-Order Interactions Characterized in Cortical Activity

In the cortex, the interactions among neurons give rise to transient coherent activity patterns that underlie perception, cognition, and action. Recently, it was actively debated whether the most basic interactions, i.e., the pairwise correlations between neurons or groups of neurons, suffice to explain those observed activity patterns. So far, the evidence reported is controversial. Importantly, the overall organization of neuronal interactions and the mechanisms underlying their generation, especially those of high-order interactions, have remained elusive. Here we show that higher-order interactions are required to properly account for cortical dynamics such as ongoing neuronal avalanches in the alert monkey and evoked visual responses in the anesthetized cat. A Gaussian interaction model that utilizes the observed pairwise correlations and event rates and that applies intrinsic thresholding identifies those higher-order interactions correctly, both in cortical local field potentials and spiking activities. This allows for accurate prediction of large neuronal population activities as required, e.g., in brain–machine interface paradigms. Our results demonstrate that higher-order interactions are inherent properties of cortical dynamics and suggest a simple solution to overcome the apparent formidable complexity previously thought to be intrinsic to those interactions.

Distributed Fading Memory for Stimulus Properties in the Primary Visual Cortex

The brain has a one-back memory for visual stimuli. Neural responses to an image contain as much information about the current image as it does about another image presented immediately before.

Brightness Induction: Rate Enhancement and Neuronal Synchronization as Complementary Codes

In cat visual cortex, we investigated with parallel recordings from multiple units the neuronal correlates of perceived brightness. The perceived brightness of a center grating was changed by varying the orientation or the relative spatial phase of a surrounding grating. Brightness enhancement by orientation contrast is associated with an increase of discharge rates of responses to the center grating but not with changes in spike synchronization. In contrast, if brightness enhancement is induced by phase offset, discharge rates are unchanged but synchronization increases between neurons responding to the center grating. The changes in synchronization correlate well with changes in perceived brightness that were assessed in parallel in human subjects using the same stimuli. These results indicate that in cerebral cortex the modulation of synchronicity of responses is used as a mechanism complementary to rate changes to enhance the saliency of neuronal responses.

Scaled correlation analysis: a better way to compute a cross-correlogram

When computing a cross‐correlation histogram, slower signal components can hinder the detection of faster components, which are often in the research focus. For example, precise neuronal synchronization often co‐occurs with slow co‐variation in neuronal rate responses. Here we present a method – dubbed scaled correlation analysis – that enables the isolation of the cross‐correlation histogram of fast signal components. The method computes correlations only on small temporal scales (i.e. on short segments of signals such as 25 ms), resulting in the removal of correlation components slower than those defined by the scale. Scaled correlation analysis has several advantages over traditional filtering approaches based on computations in the frequency domain. Among its other applications, as we show on data from cat visual cortex, the method can assist the studies of precise neuronal synchronization.