scholarly journals Consistency in macroscopic human brain responses to noisy time-varying visual inputs

2019 ◽  
Author(s):  
Keiichi Kitajo ◽  
Takumi Sase ◽  
Yoko Mizuno ◽  
Hiromichi Suetani
Keyword(s):  
Information Processing ◽  
Human Brain ◽  
Brain Dynamics ◽  
Time Varying ◽  
Ongoing Activity ◽  
Visual Inputs ◽  
Brain Responses ◽  
Individual Variations ◽  
Open Question ◽  
The Brain

AbstractIt is an open question as to whether macroscopic human brain responses to repeatedly presented external inputs show consistent patterns across trials. We here provide experimental evidence that human brain responses to noisy time-varying visual inputs, as measured by scalp electroencephalography (EEG), show a signature of consistency. The results indicate that the EEG-recorded responses are robust against fluctuating ongoing activity, and that they respond to visual stimuli in a repeatable manner. This consistency presumably mediates robust information processing in the brain. Moreover, the EEG response waveforms were discriminable between individuals, and were invariant over a number of days within individuals. We reveal that time-varying noisy visual inputs can harness macroscopic brain dynamics and can manifest hidden individual variations.

Introduction

2017 ◽  
pp. 3-12
Author(s):  
Riitta Hari ◽  
Aina Puce
Keyword(s):  
Magnetic Fields ◽  
Human Brain ◽  
Brain Dynamics ◽  
Electric Currents ◽  
Electrical Currents ◽  
Activation Patterns ◽  
Neuronal Communication ◽  
Electrical Potentials ◽  
Electric And Magnetic Fields ◽  
The Brain

Neuronal communication in the brain is associated with minute electrical currents that give rise to both electrical potentials on the scalp (measurable by means of electroencephalography [EEG]) and magnetic fields outside the head (measurable by magnetoencephalography [MEG]). Both MEG and EEG are noninvasive neurophysiological methods used to study brain dynamics, that is temporal changes in the activation patterns, and sequences in signal progression. Differences between MEG and EEG mainly reflect differences in the spread of electric and magnetic fields generated by the same electric currents in the human brain. This chapter provides an overall description of the main principles of MEG and EEG and provides background for the following chapters in this and subsequent sections.

scholarly journals Speech perception at birth: The brain encodes fast and slow temporal information

2020 ◽  
Vol 6 (30) ◽  
pp. eaba7830
Author(s):  
Laurianne Cabrera ◽  
Judit Gervain
Keyword(s):  
Infrared Spectroscopy ◽  
Speech Perception ◽  
Human Brain ◽  
Language Learning ◽  
Auditory Processing ◽  
Near Infrared ◽  
Acoustic Cues ◽  
Language Experience ◽  
Brain Responses ◽  
The Brain

Speech perception is constrained by auditory processing. Although at birth infants have an immature auditory system and limited language experience, they show remarkable speech perception skills. To assess neonates’ ability to process the complex acoustic cues of speech, we combined near-infrared spectroscopy (NIRS) and electroencephalography (EEG) to measure brain responses to syllables differing in consonants. The syllables were presented in three conditions preserving (i) original temporal modulations of speech [both amplitude modulation (AM) and frequency modulation (FM)], (ii) both fast and slow AM, but not FM, or (iii) only the slowest AM (<8 Hz). EEG responses indicate that neonates can encode consonants in all conditions, even without the fast temporal modulations, similarly to adults. Yet, the fast and slow AM activate different neural areas, as shown by NIRS. Thus, the immature human brain is already able to decompose the acoustic components of speech, laying the foundations of language learning.

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 In silico exploration of mouse brain dynamics by focal stimulation reflects the organization of functional networks and sensory processing

2020 ◽  
Vol 4 (3) ◽  
pp. 807-851
Author(s):  
Andreas Spiegler ◽  
Javad Karimi Abadchi ◽  
Majid Mohajerani ◽  
Viktor K. Jirsa
Keyword(s):  
Information Processing ◽  
Sensory Processing ◽  
Resting State ◽  
Functional Organization ◽  
Brain Dynamics ◽  
Functional Networks ◽  
Brain Responses ◽  
Sensory Cortices ◽  
Stimulation Paradigm

Resting-state functional networks such as the default mode network (DMN) dominate spontaneous brain dynamics. To date, the mechanisms linking brain structure and brain dynamics and functions in cognition, perception, and action remain unknown, mainly due to the uncontrolled and erratic nature of the resting state. Here we used a stimulation paradigm to probe the brain’s resting behavior, providing insights on state-space stability and multiplicity of network trajectories after stimulation. We performed explorations on a mouse model to map spatiotemporal brain dynamics as a function of the stimulation site. We demonstrated the emergence of known functional networks in brain responses. Several responses heavily relied on the DMN and were suggestive of the DMN playing a mechanistic role between functional networks. We probed the simulated brain responses to the stimulation of regions along the information processing chains of sensory systems from periphery up to primary sensory cortices. Moreover, we compared simulated dynamics against in vivo brain responses to optogenetic stimulation. Our results underwrite the importance of anatomical connectivity in the functional organization of brain networks and demonstrate how functionally differentiated information processing chains arise from the same system.

Cannabis and the Brain

1996 ◽  
Vol 30 (2) ◽  
pp. 179-183 ◽  
Author(s):  
David J. Castle ◽  
Frances R. Ames
Keyword(s):  
Information Processing ◽  
Literature Review ◽  
Human Brain ◽  
Cannabis Sativa ◽  
Cerebral Damage ◽  
Functional Psychoses ◽  
Precise Relationship ◽  
Relationship Of ◽  
The Brain

Objective: The aim of the paper is to review the effects of Cannabis sativa on the human brain. Method: A selective literature review was undertaken. Results/Conclusions: Cannabis sativa causes an acute and, with regular heavy ingestion, a subacute encephalopathy. There is no evidence of irreversible cerebral damage resulting from its use, although impairment of information processing might be a long-term consequence of heavy prolonged use. The precise relationship of cannabis to the functional psychoses such as schizophrenia has yet to be clarified.

scholarly journals Fine-scale computations for adaptive processing in the human brain

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Elisa Zamboni ◽  
Valentin G Kemper ◽  
Nuno Reis Goncalves ◽  
Ke Jia ◽  
Vasilis M Karlaftis ◽  
...  
Keyword(s):  
Information Processing ◽  
Human Brain ◽  
Brain Plasticity ◽  
Sensory Information ◽  
Adaptive Processing ◽  
Fine Scale ◽  
Brain Responses ◽  
Efficient Information ◽  
High Field Imaging ◽  
Higher Visual Areas

Adapting to the environment statistics by reducing brain responses to repetitive sensory information is key for efficient information processing. Yet, the fine-scale computations that support this adaptive processing in the human brain remain largely unknown. Here, we capitalise on the sub-millimetre resolution of ultra-high field imaging to examine functional magnetic resonance imaging signals across cortical depth and discern competing hypotheses about the brain mechanisms (feedforward vs. feedback) that mediate adaptive processing. We demonstrate layer-specific suppressive processing within visual cortex, as indicated by stronger BOLD decrease in superficial and middle than deeper layers for gratings that were repeatedly presented at the same orientation. Further, we show altered functional connectivity for adaptation: enhanced feedforward connectivity from V1 to higher visual areas, short-range feedback connectivity between V1 and V2, and long-range feedback occipito-parietal connectivity. Our findings provide evidence for a circuit of local recurrent and feedback interactions that mediate rapid brain plasticity for adaptive information processing.

scholarly journals The effects of alcohol on the emotional stimuli processing through the investigation of neurophysiological signals

2012 ◽  
Author(s):  
Χρυσούλα Λιθαρή
Keyword(s):  
Human Brain ◽  
Functional Organization ◽  
Emotional States ◽  
Experimental Procedure ◽  
Additional Information ◽  
Work Related ◽  
Brain Responses ◽  
Social Drinking ◽  
Neural Underpinnings ◽  
The Brain

[…] Social drinking, for most people, is an inseparable part of every-day life. Alcohol is used and abused for its ability to modify emotional states, and more precisely, to reduce anxiety [1], [2]. It is therefore essential to study the effects of inebriation in healthy, non-dependent individuals, given the frequency of abuse and binge drinking. A better understanding of the neural underpinnings of alcohol consumption could have a number of social implications, including the origin of the inebriation-induced aggressiveness, the tendency to abuse and the driving or work-related hazards [3]. More precisely, this study aimed to answer to the following questions: - How acute alcohol intake affects the human brain responses to affective pictures? - Is the effect of alcohol emotion-specific or is it the same for all kinds of emotion-eliciting images? - Is the brain functional organization at rest modulated by inebriation? - What are the similarities and the differences between the EEG and MEG studies conducted? In a second level, there has been an effort to design the optimal experimental procedure to examine as accurately as possible the multi-factorial issue of inebriation effects on the human brain. Regarding the analysis of the recordings, the standard analysis techniques on sensor levels were first applied, and then, more advanced techniques, such as cortical source estimation and functional connectivity were used to examine whether any additional information is provided. […]

scholarly journals Limits to visual representational correspondence between convolutional neural networks and the human brain

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yaoda Xu ◽  
Maryam Vaziri-Pashkam
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Convolutional Neural Networks ◽  
Real World ◽  
Visual Information ◽  
Visual Representations ◽  
Human Vision ◽  
Brain Responses ◽  
General Correspondence ◽  
The Brain

AbstractConvolutional neural networks (CNNs) are increasingly used to model human vision due to their high object categorization capabilities and general correspondence with human brain responses. Here we evaluate the performance of 14 different CNNs compared with human fMRI responses to natural and artificial images using representational similarity analysis. Despite the presence of some CNN-brain correspondence and CNNs’ impressive ability to fully capture lower level visual representation of real-world objects, we show that CNNs do not fully capture higher level visual representations of real-world objects, nor those of artificial objects, either at lower or higher levels of visual representations. The latter is particularly critical, as the processing of both real-world and artificial visual stimuli engages the same neural circuits. We report similar results regardless of differences in CNN architecture, training, or the presence of recurrent processing. This indicates some fundamental differences exist in how the brain and CNNs represent visual information.

scholarly journals What Is Consciousness, and Could Machines Have It?

2021 ◽  
pp. 43-56
Author(s):  
Stanislas Dehaene ◽  
Hakwan Lau ◽  
Sid Kouider
Keyword(s):  
Information Processing ◽  
Human Brain ◽  
Careful Consideration ◽  
The Self ◽  
Self Monitoring ◽  
Unconscious Processing ◽  
Word Consciousness ◽  
Types Of Information ◽  
The Brain ◽  
Selection Of

AbstractThe controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the human brain. We suggest that the word “consciousness” conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense). We argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. We review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures.

scholarly journals Inductive biases, pretraining and fine-tuning jointly account for brain responses to speech

2021 ◽  
Author(s):  
Juliette MILLET ◽  
Jean-Remi KING
Keyword(s):  
Human Brain ◽  
Deep Neural Networks ◽  
Fine Tuning ◽  
Functional Magnetic Resonance ◽  
Scene Classification ◽  
Linear Projection ◽  
Brain Responses ◽  
Optimal Linear ◽  
The Brain ◽  
Fine Tune

Our ability to comprehend speech remains, to date, unrivaled by deep learning models. This feat could result from the brain’s ability to fine-tune generic sound representations for speech-specific processes. To test this hypothesis, we compare i) five types of deep neural networks to ii) human brain responses elicited by spoken sentences and recorded in 102 Dutch subjects using functional Magnetic Resonance Imaging (fMRI). Each network was either trained on an acoustics scene classification, a speech-to-text task (based on Bengali, English, or Dutch), or not trained. The similarity between each model and the brain is assessed by correlating their respective activations after an optimal linear projection. The differences in brain-similarity across networks revealed three main results. First, speech representations in the brain can be accounted for by random deep networks. Second, learning to classify acoustic scenes leads deep nets to increase their brain similarity. Third, learning to process phonetically-related speech inputs (i.e., Dutch vs English) leads deep nets to reach higher levels of brain-similarity than learning to process phonetically-distant speech inputs (i.e. Dutch vs Bengali). Together, these results suggest that the human brain fine-tunes its heavily-trained auditory hierarchy to learn to process speech.

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