scholarly journals A general spectral decomposition of causal influences applied to integrated information

2019 ◽  
Author(s):  
Dror Cohen ◽  
Shuntaro Sasai ◽  
Naotsugu Tsuchiya ◽  
Masafumi Oizumi
Keyword(s):  
Time Domain ◽  
Neural Activity ◽  
Spectral Decomposition ◽  
Conscious Experience ◽  
Autoregressive Processes ◽  
Spectral Signatures ◽  
Spectral Decompositions ◽  
The Time Domain ◽  
The Brain ◽  
Integrated Information

AbstractQuantifying causal influences between elements of a system remains a central topic in many fields of research. In neuroscience, causal influences among neurons, quantified as integrated information, have been suggested to play a critical role in supporting subjective conscious experience. Recent empirical work has shown that the spectral decomposition of causal influences can reveal frequency-specific influences that are not observed in the time-domain. To date however, a spectral decomposition of integrated information has not been put forward. In this paper, we propose a spectral decomposition of integrated information in linear autoregressive processes. Our proposal is based on a general and flexible framework for deriving the spectral decompositions of causal influences in autoregressive processes. We show that the framework can retrieve the spectral decompositions of other well-known measures such as Granger causality. In simulation, we demonstrate a complex interplay between the spectral decomposition of integrated information and other measures that is not observed in the time-domain. We propose that the spectral decomposition of integrated information will be particularly useful when the underlying frequency-specific causal influences are masked in the time-domain. The proposed method opens the door for empirically investigating the relevance of integrated information to subjective conscious experience in a frequency-specific manner.Author summaryUnderstanding how different parts of the brain influence each other is fundamental to neuroscience. Integrated information measures overall causal influences in the brain and has been theorized to directly relate to subjective consciousness experience. For example, integrated information is predicted to be high during wakefulness and low during sleep or general anesthesia. At the same time, neural activity is characterized by well-known spectral signatures. For example, there is a prominent increase in low frequency power of neural activity during sleep and general anesthesia. Taking account of the spectral characteristics of neural activity, it is important to separately quantify integrated information at each frequency. In this paper, we propose a method for decomposing integrated information in the frequency domain. The proposed framework is general and can be used to derive the spectral decomposition of other well-known measures such as Granger causality. The spectral decomposition of integrated information we propose will allow empirically investigating the relationship between neural spectral signatures, integrated information and subjective consciousness experience.

scholarly journals Harmonic Amplitude Summation for Frequency-tagging Analysis

2021 ◽  
pp. 1-22
Author(s):  
Talia L. Retter ◽  
Bruno Rossion ◽  
Christine Schiltz
Keyword(s):  
Time Domain ◽  
Brain Function ◽  
Harmonic Amplitude ◽  
Harmonic Frequency ◽  
Frequency Responses ◽  
Higher Harmonic ◽  
The Time Domain ◽  
Outstanding Issue ◽  
Frequency Of Stimulation ◽  
The Brain

Abstract In the approach of frequency tagging, stimuli that are presented periodically generate periodic responses of the brain. Following a transformation into the frequency domain, the brain's response is often evident at the frequency of stimulation, F, and its higher harmonics (2F, 3F, etc.). This approach is increasingly used in neuroscience, as it affords objective measures to characterize brain function. However, whether these specific harmonic frequency responses should be combined for analysis—and if so, how—remains an outstanding issue. In most studies, higher harmonic responses have not been described or were described only individually; in other studies, harmonics have been combined with various approaches, for example, averaging and root-mean-square summation. A rationale for these approaches in the context of frequency-based analysis principles and an understanding of how they relate to the brain's response amplitudes in the time domain have been missing. Here, with these elements addressed, the summation of (baseline-corrected) harmonic amplitude is recommended.

scholarly journals A Synergistic Workspace for Human Consciousness Revealed by Integrated Information Decomposition

2020 ◽  
Author(s):  
Andrea I. Luppi ◽  
Pedro A.M. Mediano ◽  
Fernando E. Rosas ◽  
Judith Allanson ◽  
John D. Pickard ◽  
...  
Keyword(s):  
Information Processing ◽  
Human Brain ◽  
Information Integration ◽  
Conscious Experience ◽  
Human Information Processing ◽  
Loss Of Consciousness ◽  
Mathematical Framework ◽  
Human Consciousness ◽  
The Brain ◽  
Integrated Information

AbstractA central goal of neuroscience is to understand how the brain synthesises information from multiple inputs to give rise to a unified conscious experience. This process is widely believed to require integration of information. Here, we combine information theory and network science to address two fundamental questions: how is the human information-processing architecture functionally organised? And how does this organisation support human consciousness? To address these questions, we leverage the mathematical framework of Integrated Information Decomposition to delineate a cognitive architecture wherein specialised modules interact with a “synergistic global workspace,” comprising functionally distinct gateways and broadcasters. Gateway regions gather information from the specialised modules for processing in the synergistic workspace, whose contents are then further integrated to later be made widely available by broadcasters. Through data-driven analysis of resting-state functional MRI, we reveal that gateway regions correspond to the brain’s well-known default mode network, whereas broadcasters of information coincide with the executive control network. Demonstrating that this synergistic workspace supports human consciousness, we further apply Integrated Information Decomposition to BOLD signals to compute integrated information across the brain. By comparing changes due to propofol anaesthesia and severe brain injury, we demonstrate that most changes in integrated information happen within the synergistic workspace. Furthermore, it was found that loss of consciousness corresponds to reduced integrated information between gateway, but not broadcaster, regions of the synergistic workspace. Thus, loss of consciousness may coincide with breakdown of information integration by this synergistic workspace of the human brain. Together, these findings demonstrate that refining our understanding of information-processing in the human brain through Integrated Information Decomposition can provide powerful insights into the human neurocognitive architecture, and its role in supporting consciousness.

scholarly journals The Mathematical Structure of Integrated Information Theory

2021 ◽  
Vol 6 ◽  
Author(s):  
Johannes Kleiner ◽  
Sean Tull
Keyword(s):  
Information Theory ◽  
Conscious Experience ◽  
Mathematical Structure ◽  
Starting Point ◽  
Special Cases ◽  
Axiomatic Definition ◽  
Definition Of ◽  
The Brain ◽  
Integrated Information Theory ◽  
Integrated Information

Integrated Information Theory is one of the leading models of consciousness. It aims to describe both the quality and quantity of the conscious experience of a physical system, such as the brain, in a particular state. In this contribution, we propound the mathematical structure of the theory, separating the essentials from auxiliary formal tools. We provide a definition of a generalized IIT which has IIT 3.0 of Tononi et al., as well as the Quantum IIT introduced by Zanardi et al. as special cases. This provides an axiomatic definition of the theory which may serve as the starting point for future formal investigations and as an introduction suitable for researchers with a formal background.

scholarly journals Integrated information structure collapses with anesthetic loss of conscious arousal in Drosophila melanogaster

2021 ◽  
Vol 17 (2) ◽  
pp. e1008722
Author(s):  
Angus Leung ◽  
Dror Cohen ◽  
Bruno van Swinderen ◽  
Naotsugu Tsuchiya
Keyword(s):  
Information Structure ◽  
Conscious Experience ◽  
The Novel ◽  
Information Structures ◽  
Specific Content ◽  
Level Of Consciousness ◽  
Rich Information ◽  
Novel Concept ◽  
The Brain ◽  
Integrated Information

The physical basis of consciousness remains one of the most elusive concepts in current science. One influential conjecture is that consciousness is to do with some form of causality, measurable through information. The integrated information theory of consciousness (IIT) proposes that conscious experience, filled with rich and specific content, corresponds directly to a hierarchically organised, irreducible pattern of causal interactions; i.e. an integrated informational structure among elements of a system. Here, we tested this conjecture in a simple biological system (fruit flies), estimating the information structure of the system during wakefulness and general anesthesia. Consistent with this conjecture, we found that integrated interactions among populations of neurons during wakefulness collapsed to isolated clusters of interactions during anesthesia. We used classification analysis to quantify the accuracy of discrimination between wakeful and anesthetised states, and found that informational structures inferred conscious states with greater accuracy than a scalar summary of the structure, a measure which is generally championed as the main measure of IIT. In stark contrast to a view which assumes feedforward architecture for insect brains, especially fly visual systems, we found rich information structures, which cannot arise from purely feedforward systems, occurred across the fly brain. Further, these information structures collapsed uniformly across the brain during anesthesia. Our results speak to the potential utility of the novel concept of an “informational structure” as a measure for level of consciousness, above and beyond simple scalar values.

scholarly journals TCDre C

2020 ◽  
Author(s):  
Timothy C Durbridge
Keyword(s):  
Information Theory ◽  
Network Analysis ◽  
Neural Activity ◽  
Conscious Experience ◽  
The Brain

Theories about consciousness applying information theory and network analysis have assisted our understanding of brain processes that lead to conscious experience: this approach may be reaching its most productive limits. In these theories nothing more than some neural activity is required for consciousness. However assembling the information required to specify a conscious experience may not be sufficient for it to occur. The brain might have to use particular machinery to convert the assembled information into conscious representations. Such machinery theories have an advantage: machinery can be discovered and experimentally explored to reveal how it works. I explore the characteristics this stuff would have and suggest a candidate

Information processing with spectral decomposition of ultrafast pulses in the time domain

2006 ◽  
Author(s):  
Robert E. Saperstein ◽  
Nikola Alic ◽  
Dmitriy Panasenko ◽  
Xiaobo Xie ◽  
Paul K. L. Yu ◽  
...  
Keyword(s):  
Information Processing ◽  
Time Domain ◽  
Spectral Decomposition ◽  
Ultrafast Pulses ◽  
The Time Domain

Apodisation in the time domain

1992 ◽  
Vol 2 (4) ◽  
pp. 615-620
Author(s):  
G. W. Series
Keyword(s):  
Time Domain ◽  
The Time Domain

JOINT INTERPRETATION OF AIRBORNE ELECTROMAGNETIC DATA IN THE TIME DOMAIN AND FREQUENCY DOMAIN

2018 ◽  
Vol 12 (7-8) ◽  
pp. 76-83
Author(s):  
E. V. KARSHAKOV ◽  
J. MOILANEN
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Interval ◽  
Single Spectrum ◽  
Simultaneous Inversion ◽  
Homogeneous Half Space ◽  
Time Domain Data ◽  
The Time Domain

Тhe advantage of combine processing of frequency domain and time domain data provided by the EQUATOR system is discussed. The heliborne complex has a towed transmitter, and, raised above it on the same cable a towed receiver. The excitation signal contains both pulsed and harmonic components. In fact, there are two independent transmitters operate in the system: one of them is a normal pulsed domain transmitter, with a half-sinusoidal pulse and a small "cut" on the falling edge, and the other one is a classical frequency domain transmitter at several specially selected frequencies. The received signal is first processed to a direct Fourier transform with high Q-factor detection at all significant frequencies. After that, in the spectral region, operations of converting the spectra of two sounding signals to a single spectrum of an ideal transmitter are performed. Than we do an inverse Fourier transform and return to the time domain. The detection of spectral components is done at a frequency band of several Hz, the receiver has the ability to perfectly suppress all sorts of extra-band noise. The detection bandwidth is several dozen times less the frequency interval between the harmonics, it turns out thatto achieve the same measurement quality of ground response without using out-of-band suppression you need several dozen times higher moment of airborne transmitting system. The data obtained from the model of a homogeneous half-space, a two-layered model, and a model of a horizontally layered medium is considered. A time-domain data makes it easier to detect a conductor in a relative insulator at greater depths. The data in the frequency domain gives more detailed information about subsurface. These conclusions are illustrated by the example of processing the survey data of the Republic of Rwanda in 2017. The simultaneous inversion of data in frequency domain and time domain can significantly improve the quality of interpretation.

Consciousness

2018 ◽  
Author(s):  
Anil K. Seth
Keyword(s):  
Conscious Experience ◽  
Real Problem ◽  
Active Inference ◽  
Biological Mechanisms ◽  
Specific Content ◽  
Brain Responses ◽  
Biomimetic Approach ◽  
Multiple Levels ◽  
In Virtue Of ◽  
The Brain

Consciousness is perhaps the most familiar aspect of our existence, yet we still do not know its biological basis. This chapter outlines a biomimetic approach to consciousness science, identifying three principles linking properties of conscious experience to potential biological mechanisms. First, conscious experiences generate large quantities of information in virtue of being simultaneously integrated and differentiated. Second, the brain continuously generates predictions about the world and self, which account for the specific content of conscious scenes. Third, the conscious self depends on active inference of self-related signals at multiple levels. Research following these principles helps move from establishing correlations between brain responses and consciousness towards explanations which account for phenomenological properties—addressing what can be called the “real problem” of consciousness. The picture that emerges is one in which consciousness, mind, and life, are tightly bound together—with implications for any possible future “conscious machines.”

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