Locally embedded presages of global network bursts

Spontaneous, synchronous bursting of neural population is a widely observed phenomenon in nervous networks, which is considered important for functions and dysfunctions of the brain. However, how the global synchrony across a large number of neurons emerges from an initially non-bursting network state is not fully understood. In this study, we develop a new state-space reconstruction method combined with high-resolution recordings of cultured neurons. This method extracts deterministic signatures of upcoming global bursts in "local" dynamics of individual neurons during non-bursting periods. We find that local information within a single-cell time series can compare with or even outperform the global mean field activity for predicting future global bursts. Moreover, the inter-cell variability in the burst predictability is found to reflect the network structure realized in the non-bursting periods. These findings demonstrate the deterministic mechanisms underlying the locally concentrated early-warnings of the global state transition in self-organized networks

Loss of consciousness is related to hyper-1 correlated gamma-band activity in anesthetized macaques and sleeping humans

Loss of consciousness can result from a wide range of causes, including natural sleep and pharmacologically induced anesthesia. Important insights might thus come from identifying neuronal mechanisms of loss and re-emergence of consciousness independent of a specific manipulation. Therefore, to seek neuronal signatures of loss of consciousness common to sleep and anesthesia we analyzed spontaneous electrophysiological activity recorded in two experiments. First, electrocorticography (ECoG) acquired from 4 macaque monkeys anesthetized with different anesthetic agents (ketamine, medetomidine, propofol) and, second, stereo-electroencephalography (sEEG) from 10 epilepsy patients in different wake-sleep stages (wakefulness, NREM, REM). Specifically, we investigated co-activation patterns among brain areas, defined as correlations between local amplitudes of gamma-band activity. We found that resting wakefulness was associated with intermediate levels of gamma-band coupling, indicating neither complete dependence, nor full independence among brain regions. In contrast, loss of consciousness during NREM sleep and propofol anesthesia was associated with excessively correlated brain activity, as indicated by a robust increase of number and strength of positive correlations. However, such excessively correlated brain signals were not observed during REM sleep, and were present only to a limited extent during ketamine anesthesia. This might be related to the fact that, despite suppression of behavioral responsiveness, REM sleep and ketamine anesthesia often involve presence of dream-like conscious experiences. We conclude that hyper-correlated gamma-band activity might be a signature of loss of consciousness common across various manipulations and independent of behavioral responsiveness

The role of previous experience in conscious perception

Which factors determine whether a stimulus is consciously perceived or unconsciously processed? Here, I investigate how previous experience on two different time scales – long term experience over the course of several days, and short term experience based on the previous trial – impact conscious perception. Regarding long term experience, I investigate how perceptual learning does not only change the capacity to process stimuli, but also the capacity to consciously perceive them. To this end, subjects are trained extensively to discriminate between masked stimuli, and concurrently rate their subjective experience. Both the ability to discriminate the stimuli as well as subjective awareness of the stimuli increase as a function of training. However, these two effects are not simple byproducts of each other. On the contrary, they display different time courses, with above chance discrimination performance emerging before subjective experience; importantly, the two learning effects also rely on different circuits in the brain: Moving the stimuli outside the trained receptive field size abolishes the learning effects on discrimination ability, but preserves the learning effects on subjective awareness. This indicates that the receptive fields serving subjective experience are larger than the ones serving objective performance, and that the channels through which they receive their information are arranged in parallel. Regarding short term experience, I investigate how memory based predictions arising from information acquired on the trial before affect visibility and the neural correlates of consciousness. To this end, I vary stimulus evidence as well as predictability and acquire electroencephalographic data. A comparison of the neural processes distinguishing consciously perceived from unperceived trials with and without predictions reveals that predictions speed up processing, thus shifting the neural correlates forward in time. Thus, the neural correlates of consciousness display a previously unappreciated flexibility in time and do not arise invariably late as had been predicted by some theorists. Admittedly, however, previous experience does not always stabilize perception. Instead, previous experience can have the reverse effect: Seeing the opposite of what was there, as in so-called repulsive aftereffects. Here, I investigate what determines the direction of previous experience using multistable stimuli. In a functional magnetic resonance imaging experiment, I find that a widespread network of frontal, parietal, and ventral occipital brain areas is involved in perceptual stabilization, whereas the reverse effect is only evident in extrastriate cortex. This areal separation possibly endows the brain with the flexibility to switch between exploiting already available information and emphasizing the new. Taken together, my data show that conscious perception and its neuronal correlates display a remarkable degree of flexibility and plasticity, which should be taken into account in future theories of consciousness

Complete Inference of Causal Relations between Dynamical Systems

From philosophers of ancient times to modern economists, biologists and other researchers are engaged in revealing causal relations. The most challenging problem is inferring the type of the causal relationship: whether it is uni- or bi-directional or only apparent - implied by a hidden common cause only. Modern technology provides us tools to record data from complex systems such as the ecosystem of our planet or the human brain, but understanding their functioning needs detection and distinction of causal relationships of the system components without interventions. Here we present a new method, which distinguishes and assigns probabilities to the presence of all the possible causal relations between two or more time series from dynamical systems. The new method is validated on synthetic datasets and applied to EEG (electroencephalographic) data recorded in epileptic patients. Given the universality of our method, it may find application in many fields of science

Distance preservation in state-space methods for detecting causal interactions in dynamical systems

We analyze the popular ``state-space'' class of algorithms for detecting casual interaction in coupled dynamical systems. These algorithms are often justified by Takens' embedding theorem, which provides conditions under which relationships involving attractors and their delay embeddings are continuous. In practice, however, state-space methods often do not directly test continuity, but rather the stronger property of how these relationships preserve inter-point distances. This paper theoretically and empirically explores state-space algorithms explicitly from the perspective of distance preservation. We first derive basic theoretical guarantees applicable to simple coupled systems, providing conditions under which the distance preservation of a certain map reveals underlying causal structure. Second, we demonstrate empirically that typical coupled systems do not satisfy distance preservation assumptions. Taken together, our results underline the dependence of state-space algorithms on intrinsic system properties and the relationship between the system and the function used to measure it -- properties that are not directly associated with causal interaction.Comment: 24 pages, 8 figures, 1 tabl