scholarly journals Brain signatures of surprise in EEG and MEG data

2020 ◽  
Author(s):  
Zahra Mousavi ◽  
Mohammad Mahdi Kiani ◽  
Hamid Aghajan
Keyword(s):  
Sensory Modality ◽  
Ideal Observer ◽  
Neural Signals ◽  
Sensory Data ◽  
Temporal Features ◽  
Recorded Data ◽  
Recent Trends ◽  
Temporal Sample ◽  
Past Experiences ◽  
The Brain

AbstractThe brain is constantly anticipating the future of sensory inputs based on past experiences. When new sensory data is different from predictions shaped by recent trends, neural signals are generated to report this surprise. Existing models for quantifying surprise are based on an ideal observer assumption operating under one of the three definitions of surprise set forth as the Shannon, Bayesian, and Confidence-corrected surprise. In this paper, we analyze both visual and auditory EEG and auditory MEG signals recorded during oddball tasks to examine which temporal components in these signals are sufficient to decode the brain’s surprise based on each of these three definitions. We found that for both recording systems the Shannon surprise is always significantly better decoded than the Bayesian surprise regardless of the sensory modality and the selected temporal features used for decoding.Author summaryA regression model is proposed for decoding the level of the brain’s surprise in response to sensory sequences using selected temporal components of recorded EEG and MEG data. Three surprise quantification definitions (Shannon, Bayesian, and Confidence-corrected surprise) are compared in offering decoding power. Four different regimes for selecting temporal samples of EEG and MEG data are used to evaluate which part of the recorded data may contain signatures that represent the brain’s surprise in terms of offering a high decoding power. We found that both the middle and late components of the EEG response offer strong decoding power for surprise while the early components are significantly weaker in decoding surprise. In the MEG response, we found that the middle components have the highest decoding power while the late components offer moderate decoding powers. When using a single temporal sample for decoding surprise, samples of the middle segment possess the highest decoding power. Shannon surprise is always better decoded than the other definitions of surprise for all the four temporal feature selection regimes. Similar superiority for Shannon surprise is observed for the EEG and MEG data across the entire range of temporal sample regimes used in our analysis.

Multi-organ coordination of lipoprotein secretion by hormones, nutrients and neural networks

2021 ◽  
Author(s):  
Priska Stahel ◽  
Changing Xiao ◽  
Avital Nahmias ◽  
Lili Tian ◽  
Gary Franklin Lewis
Keyword(s):  
Neural Networks ◽  
Adipose Tissue ◽  
Peptide Yy ◽  
Atherosclerotic Cardiovascular Disease ◽  
Neural Signals ◽  
Nutritional Factors ◽  
Lipoprotein Secretion ◽  
The Central Nervous System ◽  
Regulatory Effects ◽  
The Brain

Abstract Plasma triglyceride-rich lipoproteins (TRL), particularly atherogenic remnant lipoproteins, contribute to atherosclerotic cardiovascular disease (ASCVD). Hypertriglyceridemia may arise in part from hypersecretion of TRLs by the liver and intestine. Here we focus on the complex network of hormonal, nutritional, and neuronal interorgan communication that regulates secretion of TRLs, and provide our perspective on the relative importance of these factors. Hormones and peptides originating from the pancreas (insulin, glucagon), gut (GLP-1, GLP-2, ghrelin, CCK, peptide YY), adipose tissue (leptin, adiponectin) and brain (GLP-1) modulate TRL secretion by receptor-mediated responses and indirectly via neural networks. In addition, the gut microbiome and bile acids influence lipoprotein secretion in humans and animal models. Several nutritional factors modulate hepatic lipoprotein secretion through effects on the central nervous system. Vagal afferent signalling from the gut to the brain and efferent signals from the brain to the liver and gut are modulated by hormonal and nutritional factors to influence TRL secretion. Some of these factors have been extensively studied and shown to have robust regulatory effects whereas others are ‘emerging’ regulators, whose significance remains to be determined. The quantitative importance of these factors relative to one another and relative to the key regulatory role of lipid availability remains largely unknown. Our understanding of the complex interorgan regulation of TRL secretion is rapidly evolving to appreciate the extensive hormonal, nutritional and neural signals emanating not only from gut and liver but also from the brain, pancreas, and adipose tissue.

Neuroendocrine modulation of the “menopause”: insights into the aging brain

1999 ◽  
Vol 277 (6) ◽  
pp. E965-E970 ◽  
Author(s):  
Phyllis M. Wise
Keyword(s):  
Animal Models ◽  
Ovarian Follicles ◽  
Physiological Change ◽  
Menopausal Transition ◽  
Aging Brain ◽  
Neural Signals ◽  
Present Evidence ◽  
The Brain

The menopause marks the permanent end of fertility in women. It was once thought that this dramatic physiological change could be explained simply by the exhaustion of the reservoir of ovarian follicles. New data from studies performed in women and animal models make us reassess this assumption. An increasing body of evidence suggests that there are multiple pacemakers that contribute to the transition to irregular cycles, decreasing fertility, and the timing of the menopause. We will present evidence that lends credence to the possibility that a dampening and desynchronization of the precisely orchestrated neural signals lead to miscommunication between the brain and the pituitary-ovarian axis, and that this constellation of hypothalamic-pituitary-ovarian events leads to the deterioration of regular cyclicity and heralds menopausal transition.

scholarly journals Using the past to estimate sensory uncertainty

2019 ◽  
Author(s):  
Ulrik Beierholm ◽  
Tim Rohe ◽  
Ambra Ferrari ◽  
Oliver Stegle ◽  
Uta Noppeney
Keyword(s):  
Temporal Dynamics ◽  
Visual Signals ◽  
Perceptual Inference ◽  
Reliability Estimates ◽  
Uncertainty Estimates ◽  
Classical Models ◽  
Past Experiences ◽  
Combine Information ◽  
Sensory Uncertainty ◽  
The Brain

AbstractTo form the most reliable percept of the environment, the brain needs to represent sensory uncertainty. Current theories of perceptual inference assume that the brain computes sensory uncertainty instantaneously and independently for each stimulus.In a series of psychophysics experiments human observers localized auditory signals that were presented in synchrony with spatially disparate visual signals. Critically, the visual noise changed dynamically over time with or without intermittent jumps. Our results show that observers integrate audiovisual inputs weighted by sensory reliability estimates that combine information from past and current signals as predicted by an optimal Bayesian learner or approximate strategies of exponential discountingOur results challenge classical models of perceptual inference where sensory uncertainty estimates depend only on the current stimulus. They demonstrate that the brain capitalizes on the temporal dynamics of the external world and estimates sensory uncertainty by combining past experiences with new incoming sensory signals.

Neuroscience Applications on the Assessments of TV Ads

2017 ◽  
pp. 231-256
Author(s):  
Tuna Çakar ◽  
Kaan Gez
Keyword(s):  
Individual Preference ◽  
Emotional Engagement ◽  
Medical Applications ◽  
Specific Interest ◽  
Neural Signals ◽  
Self Reports ◽  
Related Application ◽  
The Given ◽  
The Brain ◽  
Objective Tool

The progress in neurotechnologies has enabled a potentially better and cheaper analysis for the neural signals not limited to medical applications but influencing several fields from marketing to economics and law to ethics. Since the main targets have been to understand the brain mechanisms better as well as providing useful applications specifically regarding the sector-specific interest, one related application has been about the assessments of TV ads as a complementary and more objective tool than traditional methods that rely on the verbal self-reports and interviews that could be speculative and misleading depending on the given context. For assessing several TV ads within a shorter duration, the use of neuroscientific methods has attracted much interest. This chapter will focus on the current practices with the given constructs for the TV ad research specifically in relation to the practices such as attention, emotional engagement, individual preference, and market success.

Triadic body representations in the human cerebral cortex and peripheral nerves

2021 ◽  
pp. 133-151 ◽  
Author(s):  
Noriaki Kanayama ◽  
Kentaro Hiromitsu
Keyword(s):  
Peripheral Nerves ◽  
Sensory Modality ◽  
Body Schema ◽  
Body Representation ◽  
Neural System ◽  
The Body ◽  
Neural Representation ◽  
Structural Description ◽  
Research Fields ◽  
The Brain

Is the body reducible to neural representation in the brain? There is some evidence that the brain contributes to the functioning of the body from neuroimaging, neurophysiological, and lesion studies. Well-known dyadic taxonomy of the body schema and the body image (hereafter BSBI) is based primarily on the evidence in brain-damaged patients. Although there is a growing consensus that the BSBI exists, there is little agreement on the dyadic taxonomy because it is not a concrete and common concept across various research fields. This chapter tries to investigate the body representation in the cortex and nervous system in terms of sensory modality and psychological function using two different approaches. The first approach is to review the neurological evidence and cortical area which is related to body representation, regardless of the BSBI, and then to reconsider how we postulate the BSBI in our brain. It can be considered that our body representation could be constructed by the whole of the neural system, including the cortex and peripheral nerves. The second approach is to revisit the BSBI conception from the viewpoint of recent neuropsychology and propose three types of body representation: body schema, body structural description, and body semantics. This triadic taxonomy is considered consistent with the cortical networks based on the evidence of bodily disorders due to brain lesions. These two approaches allow to reconsider the BSBI more carefully and deeply and to give us the possibility that the body representation could be underpinned with the network in the brain.

Introduction

1998 ◽  
Author(s):  
Christof Koch
Keyword(s):  
Membrane Potential ◽  
Nerve Cells ◽  
Neuronal Firing ◽  
Western World ◽  
Long Term Memory ◽  
Sensory Data ◽  
Relevant Variables ◽  
Elementary Circuit ◽  
Circuit Designer ◽  
The Brain

The brain computes! This is accepted as a truism by the majority of neuroscientists engaged in discovering the principles employed in the design and operation of nervous systems. What is meant here is that any brain takes the incoming sensory data, encodes them into various biophysical variables, such as the membrane potential or neuronal firing rates, and subsequently performs a very large number of ill-specified operations, frequently termed computations, on these variables to extract relevant features from the input. The outcome of some of these computations can be stored for later access and will, ultimately, control the motor output of the animal in appropriate ways. The present book is dedicated to understanding in detail the biophysical mechanisms responsible for these computations. Its scope is the type of information processing underlying perception and motor control, occurring at the millisecond to fraction of a second time scale. When you look at a pair of stereo images trying to fuse them into a binocular percept, your brain is busily computing away trying to find the “best” solution. What are the computational primitives at the neuronal and subneuronal levels underlying this impressive performance, unmatched by any machine? Naively put and using the language of the electronic circuit designer, the book asks: “What are the diodes and the transistors of the brain?” and “What sort of operations do these elementary circuit elements implement?” Contrary to received opinion, nerve cells are considerably more complex than suggested by work in the neural network community. Like morons, they are reduced to computing nothing but a thresholded sum of their inputs. We know, for instance, that individual nerve cells in the locust perform an operation akin to a multiplication. Given synapses, ionic channels, and membranes, how is this actually carried out? How do neurons integrate, delay, or change their output gain? What are the relevant variables that carry information? The membrane potential? The concentration of intracellular Ca2+ ions? What is their temporal resolution? And how large is the variability of these signals that determines how accurately they can encode information? And what variables are used to store the intermediate results of these computations? And where does long-term memory reside? Natural philosophers and scientists in the western world have always compared the brain to the most advanced technology of the day.

Formal Models and Cognitive Mechanisms of the Human Sensory System

2013 ◽  
Vol 5 (3) ◽  
pp. 55-75 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Sensory System ◽  
Cognitive Mechanisms ◽  
Neural Signals ◽  
Sensory Neural ◽  
Comprehensive Framework ◽  
Strategic Project ◽  
Cognitive Robots ◽  
Physical And Chemical ◽  
The Brain ◽  
Theoretical Foundations

The human sensory system is a perfect natural real-time distributed system. It transforms physical and chemical stimuli of the external environment into electronic neural signals by specialized sensory receptors. This paper presents a comprehensive framework of the human sensory system as well as its cognitive and theoretical foundations. A set of primary and perceptual sensory and neural receptors is formally modeled and analyzed. Sensory neural interfaces and interactions to the central and peripheral nervous systems of the brain and associated memories are systematically described. This work is a part of a strategic project towards the development of cognitive computers and cognitive robots.

scholarly journals Hand size underestimation grows during childhood

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Lucilla Cardinali ◽  
Andrea Serino ◽  
Monica Gori
Keyword(s):  
Motor Behavior ◽  
Sensory Modality ◽  
The Body ◽  
Typically Developing ◽  
Cognitive Representations ◽  
Hand Size ◽  
Infancy And Childhood ◽  
Sensory Modalities ◽  
Sensory Maps ◽  
The Brain

Abstract Cortical body size representations are distorted in the adult, from low-level motor and sensory maps to higher levels multisensory and cognitive representations. Little is known about how such representations are built and evolve during infancy and childhood. Here we investigated how hand size is represented in typically developing children aged 6 to 10. Participants were asked to estimate their hand size using two different sensory modalities (visual or haptic). We found a distortion (underestimation) already present in the youngest children. Crucially, such distortion increases with age and regardless of the sensory modality used to access the representation. Finally, underestimation is specific for the body as no bias was found for object estimation. This study suggests that the brain does not keep up with the natural body growth. However, since motor behavior nor perception were impaired, the distortion seems functional and/or compensated for, for proper interaction with the external environment.

Pavlov, Penfield, and the physiology of the mind

Neurology ◽  
2019 ◽  
Vol 92 (12) ◽  
pp. 575-578 ◽  
Author(s):  
Richard Leblanc
Keyword(s):  
Conditioned Reflex ◽  
Functional Integration ◽  
Temporal Cortex ◽  
Spoken Language ◽  
Conditioned Reflexes ◽  
Temporal Lobes ◽  
Past Experiences ◽  
The Mind ◽  
The Brain ◽  
Stimulation Of

Wilder Penfield's contributions to the structure–function relationships of the brain are well-known. Less well-known is the influence that Ivan Pavlov and the conditioned reflex had on Penfield's understanding of epileptogenesis, and on his concept of the acquisition of memories, language, and perception—what Penfield referred to as the physiology of the mind. Penfield invoked conditioned reflexes to explain responses to electrocortical stimulation of the temporal lobes that encompass memory, perception, and affect. Penfield referred to these responses as experiential phenomena since he considered that they constituted a record of past experiences. Penfield also invoked the conditioned reflex to explain the acquisition of the interpretive aspects of written and spoken language in the dominant temporal cortex. This article describes and discusses these neglected aspects of Penfield's work, and how they contributed to a broader understanding of the functional integration of the temporal cortex and the limbic system.

scholarly journals Neural Control of Immunity in Hypertension: Council on Hypertension Mid Career Award for Research Excellence, 2019

Hypertension ◽  
2020 ◽  
Vol 76 (3) ◽  
pp. 622-628
Author(s):  
Daniela Carnevale
Keyword(s):  
Immune Response ◽  
Nervous System ◽  
Neural Control ◽  
Neural Signals ◽  
The Past ◽  
Target Organs ◽  
Long Time ◽  
The Common ◽  
The Brain

The nervous system and the immune system share the common ability to exert gatekeeper roles at the interfaces between internal and external environment. Although interaction between these 2 evolutionarily highly conserved systems has been recognized for long time, the investigation into the pathophysiological mechanisms underlying their crosstalk has been tackled only in recent decades. Recent work of the past years elucidated how the autonomic nervous system controls the splenic immunity recruited by hypertensive challenges. This review will focus on the neural mechanisms regulating the immune response and the role of this neuroimmune crosstalk in hypertension. In this context, the review highlights the components of the brain-spleen axis with a focus on the neuroimmune interface established in the spleen, where neural signals shape the immune response recruited to target organs of high blood pressure.

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