scholarly journals Why the Quantum Brain?

2021 ◽  
Vol 05 (03) ◽  
pp. 1-1
Author(s):  
Sergey B. Yurchenko ◽  
Keyword(s):  
Neural Networks ◽  
Free Will ◽  
Computer Science ◽  
Brain Evolution ◽  
Brain Regions ◽  
Evolutionary Perspective ◽  
Broad Perspective ◽  
Starting Point ◽  
The Brain

This article reviews the modern approaches to the quantum brain hypothesis. The aim is to consider the hypothesis and its classical brain-machine alternative from a broad perspective, including physics, biology, computer science, cosmology, and metaphysics. My starting point is that asking whether consciousness can or cannot have free will is fundamentally incorrect. This aspect is challenged by both physics and neuroscience. The paper argues that the search for conscious free will, as it is typically tested in Libet-type experiments, implies putting the cart before the horse. From the evolutionary perspective, a more correct question is this. Might primitive neural networks of simple organisms have possessed free volitional mechanisms (quantum in origin) as an extremely valuable acquisition for the flourishing of life? Might then the mechanisms have evolved from primary (rapid and random) reflexes in the oldest brain regions such as the brainstem to give rise to conscious cortex-centered properties in later stages of the brain evolution?

scholarly journals Brain as agent and conscious mind as action guide: from Libet-style experiments to necessary conditions for free will

2021 ◽  
Vol 22 (1) ◽  
pp. 78-83
Author(s):  
Jonas Gonçalves Coelho
Keyword(s):  
Free Will ◽  
Necessary Conditions ◽  
Brain Activity ◽  
The Body ◽  
General View ◽  
Conscious Mind ◽  
Sociocultural Environment ◽  
Starting Point ◽  
Conscious Intention ◽  
The Brain

Many neuroscientific experiments, based on monitoring brain activity, suggest that it is possible to predict the conscious intention/choice/decision of an agent before he himself knows that. Some neuroscientists and philosophers interpret the results of these experiments as showing that free will is an illusion, since it is the brain and not the conscious mind that intends/chooses/decides. Assuming that the methods and results of these experiments are reliable the question is if they really show that free will is an illusion. To address this problem, I argue that first it is needed to answer three questions related to the relationship between conscious mind and brain: 1. Do brain events cause conscious events? 2. Do conscious events cause brain events? 3. Who is the agent, that is, who consciously intends/chooses/ decides, the conscious mind, the brain, or both? I answer these questions by arguing that the conscious mind is a property of the brain due to which the brain has the causal capacity to interact adaptively with its body, and trough the body, with the physical and sociocultural environment. In other words, the brain is the agent and the conscious mind, in its various forms - cognitive, volitional and emotional - and contents, is its guide of action. Based on this general view I argue that the experiments aforementioned do not show that free will is an illusion, and as a starting point for examining this problem I point out, from some exemplary situations, what I believe to be some of the necessary conditions for free will.Key-words: Agent brain, conscious mind, free will, Libet-style experiments.

scholarly journals Situating allostasis and interoception at the core of human brain function

2021 ◽  
Author(s):  
Yuta Katsumi ◽  
Karen Quigley ◽  
Lisa Feldman Barrett
Keyword(s):  
Brain Function ◽  
Brain Evolution ◽  
Human Mind ◽  
Biological Mechanisms ◽  
The Core ◽  
Starting Point ◽  
Basic Functions ◽  
Mental Events ◽  
Physical Phenomena ◽  
The Brain

It is now well known that brain evolution, development, and structure do not respect Western folk categories of mind – that is, the boundaries of those folk categories have never been identified in nature, despite decades of search. Categories for cognitions, emotions, perceptions, and so on, may be useful for describing the mental phenomena that constitute a human mind, but they make a poor starting point for understanding the interplay of mechanisms that create those mental events in the first place. In this paper, we integrate evolutionary, developmental, anatomical, and functional evidence and propose that predictive regulation of the body’s internal systems (allostasis) and modeling the sensory consequences of this regulation (interoception) may be basic functions of the brain that are embedded in coordinated structural and functional gradients. Our approach offers the basis for a coherent, neurobiologically-inspired research program that attempts to explain how a variety of psychological and physical phenomena may emerge from the same biological mechanisms, thus providing an opportunity to unify them under a common explanatory framework that can be used to develop shared vocabulary for theory building and knowledge accumulation.

Artificial Neural Networks for Prediction

2011 ◽  
pp. 54-58
Author(s):  
Rafael Marti
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Human Capabilities ◽  
Design And Implementation ◽  
Starting Point ◽  
Input Signals ◽  
Artificial Neural ◽  
The Brain

The design and implementation of intelligent systems with human capabilities is the starting point to design Artificial Neural Networks (ANNs). The original idea takes after neuroscience theory on how neurons in the human brain cooperate to learn from a set of input signals to produce an answer. Because the power of the brain comes from the number of neurons and the multiple connections between them, the basic idea is that connecting a large number of simple elements in a specific way can form an intelligent system.

scholarly journals Predicting Cortical Signatures of Consciousness using Dynamic Functional Connectivity Graph-Convolutional Neural Networks

2020 ◽  
Author(s):  
A. Grigis ◽  
J. Tasserie ◽  
V. Frouin ◽  
B. Jarraya ◽  
L. Uhrig
Keyword(s):  
Neural Networks ◽  
Functional Connectivity ◽  
Clustering Algorithms ◽  
Brain Regions ◽  
Supervised Machine Learning ◽  
Brain State ◽  
Level Of Consciousness ◽  
Clinical Consequences ◽  
Brain States ◽  
The Brain

AbstractDecoding the levels of consciousness from cortical activity recording is a major challenge in neuroscience. Using clustering algorithms, we previously demonstrated that resting-state functional MRI (rsfMRI) data can be split into several clusters also called “brain states” corresponding to “functional configurations” of the brain. Here, we propose to use a supervised machine learning method based on artificial neural networks to predict functional brain states across levels of consciousness from rsfMRI. Because it is key to consider the topology of brain regions used to build the dynamical functional connectivity matrices describing the brain state at a given time, we applied BrainNetCNN, a graph-convolutional neural network (CNN), to predict the brain states in awake and anesthetized non-human primate rsfMRI data. BrainNetCNN achieved a high prediction accuracy that lies in [0.674, 0.765] depending on the experimental settings. We propose to derive the set of connections found to be important for predicting a brain state, reflecting the level of consciousness. The results demonstrate that deep learning methods can be used not only to predict brain states but also to provide additional insight on cortical signatures of consciousness with potential clinical consequences for the monitoring of anesthesia and the diagnosis of disorders of consciousness.

scholarly journals Pediatric cancer, posttraumatic stress and fear-related neural circuitry

2019 ◽  
Vol 8 (2) ◽  
pp. IJH17 ◽  
Author(s):  
Hilary A Marusak ◽  
Felicity W Harper ◽  
Jeffrey W Taub ◽  
Christine A Rabinak
Keyword(s):  
Childhood Cancer ◽  
Posttraumatic Stress ◽  
Pediatric Cancer ◽  
Neural Correlates ◽  
Brain Regions ◽  
Future Research ◽  
Typically Developing ◽  
Stress Symptoms ◽  
Starting Point ◽  
The Brain

This review examines the neurobiological effects of pediatric cancer-related posttraumatic stress symptoms (PTSS). We first consider studies on prevalence and predictors of childhood cancer-related PTSS and compare these studies to those in typically developing (i.e., noncancer) populations. Then, we briefly introduce the brain regions implicated in PTSS and review neuroimaging studies examining the neural correlates of PTSS in noncancer populations. Next, we present a framework and recommendations for future research. In particular, concurrent evaluation of PTSS and neuroimaging, as well as sociodemographic, medical, family factors, and other life events, are needed to uncover mechanisms leading to cancer-related PTSS. We review findings from neuroimaging studies on childhood cancer and one recent study on cancer-related PTSS as a starting point in this line of research.

scholarly journals Investigating the Discrimination Ability of 3D Convolutional Neural Networks Applied to Altered Brain MRI Parametric Maps

2021 ◽  
Author(s):  
Giulia Maria Mattia ◽  
Federico Nemmi ◽  
Edouard Villain ◽  
Marie-Véronique Le Lann ◽  
Xavier Franceries ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Performance ◽  
Brain Mri ◽  
Brain Regions ◽  
Target Region ◽  
Discrimination Ability ◽  
Weighted Magnetic Resonance Imaging ◽  
The Brain ◽  
Parametric Maps

Convolutional neural networks are gradually being recognized in the neuroimaging community as a powerful tool for image analysis. In the present study, we tested the ability of 3D convolutional neural networks to discriminate between whole-brain parametric maps obtained from diffusion-weighted magnetic resonance imaging. Original parametric maps were subjected to intensity-based region-specific alterations, to create altered maps. To analyze how position, size and intensity of altered regions affected the networks’ learning process, we generated monoregion and biregion maps by systematically modifying the size and intensity of one or two brain regions in each image. We assessed network performance over a range of intensity increases and combinations of maps, carrying out 10-fold cross-validation and using a hold-out set for testing. We then tested the networks trained with monoregion images on the corresponding biregion images and vice versa. Results showed an inversely proportional link between size and intensity for the monoregion networks, in that the larger the region, the smaller the increase in intensity needed to achieve good performances. Accuracy was better for biregion networks than for their monoregion counterparts, showing that altering more than one region in the brain can improve discrimination. Monoregion networks correctly detected their target region in biregion maps, whereas biregion networks could only detect one of the two target regions at most. Biregion networks therefore learned a more complex pattern that was absent from the monoregion images. This deep learning approach could be tailored to explore the behavior of other convolutional neural networks for other regions of interest. <br>

scholarly journals Deep Neural Networks Carve the Brain at its Joints

2020 ◽  
Author(s):  
Maxwell A. Bertolero ◽  
Danielle S. Bassett
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Linear Models ◽  
Brain Connectivity ◽  
Network Models ◽  
Brain Regions ◽  
Linear Functions ◽  
Complex Relationships ◽  
And Behavior ◽  
The Brain

AbstractHow an individual’s unique brain connectivity determines that individual’s cognition, behavior, and risk for pathology is a fundamental question in basic and clinical neuroscience. In seeking answers, many have turned to machine learning, with some noting the particular promise of deep neural networks in modelling complex non-linear functions. However, it is not clear that complex functions actually exist between brain connectivity and behavior, and thus if deep neural networks necessarily outperform simpler linear models, or if their results would be interpretable. Here we show that, across 52 subject measures of cognition and behavior, deep neural networks fit to each brain region’s connectivity outperform linear regression, particularly for the brain’s connector hubs—regions with diverse brain connectivity—whereas the two approaches perform similarly when fit to brain systems. Critically, averaging deep neural network predictions across brain regions results in the most accurate predictions, demonstrating the ability of deep neural networks to easily model the various functions that exists between regional brain connectivity and behavior, carving the brain at its joints. Finally, we shine light into the black box of deep neural networks using multislice network models. We determined that the relationship between connector hubs and behavior is best captured by modular deep neural networks. Our results demonstrate that both simple and complex relationships exist between brain connectivity and behavior, and that deep neural networks can fit both. Moreover, deep neural networks are particularly powerful when they are first fit to the various functions of a system independently and then combined. Finally, deep neural networks are interpretable when their architectures are structurally characterized using multislice network models.

scholarly journals Evidence for Concerted and Mosaic Brain Evolution in Dragon Lizards

2017 ◽  
Vol 90 (3) ◽  
pp. 211-223 ◽  
Author(s):  
Daniel Hoops ◽  
Marta Vidal-García ◽  
Jeremy F.P. Ullmann ◽  
Andrew L. Janke ◽  
Timothy Stait-Gardner ◽  
...  
Keyword(s):  
Body Size ◽  
Critical Role ◽  
Brain Evolution ◽  
Brain Regions ◽  
Evolution Model ◽  
Brain Morphology ◽  
Evolutionary Patterns ◽  
Mosaic Brain Evolution ◽  
The Brain ◽  
Regional Brain Volume

The brain plays a critical role in a wide variety of functions including behaviour, perception, motor control, and homeostatic maintenance. Each function can undergo different selective pressures over the course of evolution, and as selection acts on the outputs of brain function, it necessarily alters the structure of the brain. Two models have been proposed to explain the evolutionary patterns observed in brain morphology. The concerted brain evolution model posits that the brain evolves as a single unit and the evolution of different brain regions are coordinated. The mosaic brain evolution model posits that brain regions evolve independently of each other. It is now understood that both models are responsible for driving changes in brain morphology; however, which factors favour concerted or mosaic brain evolution is unclear. Here, we examined the volumes of the 6 major neural subdivisions across 14 species of the agamid lizard genus Ctenophorus (dragons). These species have diverged multiple times in behaviour, ecology, and body morphology, affording a unique opportunity to test neuroevolutionary models across species. We assigned each species to an ecomorph based on habitat use and refuge type, then used MRI to measure total and regional brain volume. We found evidence for both mosaic and concerted brain evolution in dragons: concerted brain evolution with respect to body size, and mosaic brain evolution with respect to ecomorph. Specifically, all brain subdivisions increase in volume relative to body size, yet the tectum and rhombencephalon also show opposite patterns of evolution with respect to ecomorph. Therefore, we find that both models of evolution are occurring simultaneously in the same structures in dragons, but are only detectable when examining particular drivers of selection. We show that the answer to the question of whether concerted or mosaic brain evolution is detected in a system can depend more on the type of selection measured than on the clade of animals studied.

scholarly journals Investigating the Discrimination Ability of 3D Convolutional Neural Networks Applied to Altered Brain MRI Parametric Maps

2021 ◽  
Author(s):  
Giulia Maria Mattia ◽  
Federico Nemmi ◽  
Edouard Villain ◽  
Marie-Véronique Le Lann ◽  
Xavier Franceries ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Performance ◽  
Brain Mri ◽  
Brain Regions ◽  
Target Region ◽  
Discrimination Ability ◽  
Weighted Magnetic Resonance Imaging ◽  
The Brain ◽  
Parametric Maps

Convolutional neural networks are gradually being recognized in the neuroimaging community as a powerful tool for image analysis. In the present study, we tested the ability of 3D convolutional neural networks to discriminate between whole-brain parametric maps obtained from diffusion-weighted magnetic resonance imaging. Original parametric maps were subjected to intensity-based region-specific alterations, to create altered maps. To analyze how position, size and intensity of altered regions affected the networks’ learning process, we generated monoregion and biregion maps by systematically modifying the size and intensity of one or two brain regions in each image. We assessed network performance over a range of intensity increases and combinations of maps, carrying out 10-fold cross-validation and using a hold-out set for testing. We then tested the networks trained with monoregion images on the corresponding biregion images and vice versa. Results showed an inversely proportional link between size and intensity for the monoregion networks, in that the larger the region, the smaller the increase in intensity needed to achieve good performances. Accuracy was better for biregion networks than for their monoregion counterparts, showing that altering more than one region in the brain can improve discrimination. Monoregion networks correctly detected their target region in biregion maps, whereas biregion networks could only detect one of the two target regions at most. Biregion networks therefore learned a more complex pattern that was absent from the monoregion images. This deep learning approach could be tailored to explore the behavior of other convolutional neural networks for other regions of interest. <br>

scholarly journals Modern human alleles differentially regulate gene expression across brain regions: implications for brain evolution

2019 ◽  
Author(s):  
Alejandro Andirkó ◽  
Cedric Boeckx
Keyword(s):  
Gene Expression ◽  
Homo Sapiens ◽  
Regulation Of Gene Expression ◽  
Brain Evolution ◽  
Brain Regions ◽  
Modern Human ◽  
High Coverage ◽  
Evolutionary Trajectory ◽  
Regulate Gene Expression ◽  
The Brain

AbstractThe availability of high-coverage genomes of our extinct relatives, the Neanderthals and Denisovans, and the emergence of large, tissue-specific databases of modern human genetic variation, offer the possibility of probing the evolutionary trajectory of heterogenous structures of great interest, such as the brain. Using the GTEx cis-eQTL dataset and an extended catalog of Homo sapiens-specific alleles relative to Neanderthals and Denisovans, we generated a dataset of nearly fixed, Homo sapiens-derived alleles that affect the regulation of gene expression across 15 brain (and brain related) structures. The list of variants obtained reveals enrichments in regions of the modern human genome showing putative signals of positive selection relative to archaic humans, and bring out the highly derived status of the cerebellum. Additionally, we complement previous literature on the expression effects of ancestral alleles in the Homo sapiens brain by pointing at a downregulation bias caused by linkage disequilibrium.

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