Masafumi Oizumi 교수

This paper presents Integrated Information Theory (IIT) of consciousness 3.0, which incorporates several advances over previous formulations. IIT starts from phenomenological axioms: information says that each experience is specific--it is what it is by how it differs from alternative experiences; integration says that it is unified--irreducible to non-interdependent components; exclusion says that it has unique borders and a particular spatio-temporal grain. These axioms are formalized into pos

Accumulating evidence indicates that the capacity to integrate information in the brain is a prerequisite for consciousness. Integrated Information Theory (IIT) of consciousness provides a mathematical approach to quantifying the information integrated in a system, called integrated information, Φ. Integrated information is defined theoretically as the amount of information a system generates as a whole, above and beyond the amount of information its parts independently generate. IIT predicts th

Assessment of causal influences is a ubiquitous and important subject across diverse research fields. Drawn from consciousness studies, integrated information is a measure that defines integration as the degree of causal influences among elements. Whereas pairwise causal influences between elements can be quantified with existing methods, quantifying multiple influences among many elements poses two major mathematical difficulties. First, overestimation occurs due to interdependence among influe

The purpose of this study was to compare the microbicidal effect of gaseous ozone with that of ozonated water in order to determine its usefulness as a method for disinfecting dentures. Although a large number of research studies have been done on the bactericidal effect of ozone, little is known about its microbicidal effects on oral microorganisms. Therefore, we tested the effect of ozone on three standard strains of oral microorganisms: Streptococcus mutans (strain IID 973), Staphylococcus au

We propose a new divergence on the manifold of probability distributions, building on the entropic regularization of optimal transportation problems. As Cuturi ( 2013 ) showed, regularizing the optimal transport problem with an entropic term is known to bring several computational benefits. However, because of that regularization, the resulting approximation of the optimal transport cost does not define a proper distance or divergence between probability distributions. We recently tried to intro

Large Language Models (LLMs), such as the General Pre-trained Transformer (GPT), have shown remarkable performance in various cognitive tasks. However, it remains unclear whether these models have the ability to accurately infer human perceptual representations. Previous research has addressed this question by quantifying correlations between similarity response patterns of humans and LLMs. Correlation provides a measure of similarity, but it relies pre-defined item labels and does not distingui

Our novel framework can be used to derive the spectral decomposition of uni- and multi-directional measures of causal influences. We use this framework to derive a spectral decomposition of integrated information, paving the way for better understanding how frequency-specific causal influences in the brain relate to cognition.

We consider two types of causes leading to information loss when neural activities are passed and processed in the brain. One is responses of upstream neurons to stimuli being imperfectly observed by downstream neurons. The other is upstream neurons non-optimally decoding stimuli information contained in the activities of the downstream neurons. To investigate the importance of neural correlation in information processing in the brain, we specifically consider two situations. One is when neural

Modality-invariant categorical representations, i.e., shared representation, is thought to play a key role in learning to categorize multi-modal information. We have investigated how a bimodal autoencoder can form a shared representation in an unsupervised manner with multi-modal data. We explored whether altering the depth of the network and mixing the multi-modal inputs at the input layer affect the development of the shared representations. Based on the activation of units in the hidden layer

Abstract Background Understanding how sensory stimuli are represented across different brains, species, and artificial neural networks is a critical topic in neuroscience. Traditional methods for comparing these representations typically rely on supervised alignment, which assumes direct correspondence between stimuli representations across brains or models. However, it has limitations when this assumption is not valid, or when validating the assumption itself is the goal of the research. New me

The Drosophila antennal lobe is subdivided into multiple glomeruli, each of which represents a unique olfactory information processing channel. In each glomerulus, feedforward input from olfactory receptor neurons (ORNs) is transformed into activity of projection neurons (PNs), which represent the output. Recent investigations have indicated that lateral presynaptic inhibitory input from other glomeruli controls the gain of this transformation. Here, we address why this gain control acts "pre"-s

How is information decoded in the brain? is one of the most difficult and important questions in neuroscience. Whether neural correlation is important or not in decoding neural activities is of special interest. We have developed a general framework for investigating how far the decoding process in the brain can be simplified. First, we hierarchically construct simplified probabilistic models of neural responses that ignore more than Kth-order correlations by using a maximum entropy principle. T

We propose a systematic method of rate reduction for a Hodgkin–Huxley type neural network model. In this context, Shriki et al. assumed that the threshold of the f – I curve for the reduced rate model depends linearly on the leak conductance of the Hodgkin–Huxley equation, while its gain remains constant. First, we show that the threshold and gain have second order dependence on the leak conductance. Second, we show that the Hodgkin–Huxley type network with second order interaction can be natura

Abstract Neuroscience research has extensively explored the commonality of neural representations of sensory stimuli across individuals to uncover universal neural mechanisms in the encoding of sensory information. To compare neural representations across different brains, Representational Similarity Analysis (RSA) has been used, which focuses on the similarity structures of neural representations for different stimuli. Despite the broad applicability and utility of RSA, one limitation is that i

Abstract The common neural mechanisms underlying the reduction of consciousness during sleep and anesthesia remain unclear. Previous studies have examined changes in network structure only using recordings with limited spatial resolution, which has hindered the investigation of the critical spatial scales from the micro (single neurons) to the meso (groups of neurons) for the reduction of consciousness. To address this issue, by leveraging fast, single-cell resolution, and wide-field two-photon