scholarly journals Neuronal Causes and Behavioural Effects: a Review on Logical, Methodological, and Technical Issues With Respect to Causal Explanations of Behaviour in Neuroscience

2019 ◽  
Author(s):  
Kayson Fakhar ◽  
Dominic Gonschorek ◽  
Lisa Schmors ◽  
Natalia Z Bielczyk
Keyword(s):  
Computational Models ◽  
Causal Structure ◽  
Human Environment ◽  
Causal Explanations ◽  
Behavioural Effects ◽  
Formidable Challenge ◽  
Artificial Neuronal Networks ◽  
Modelling Studies ◽  
Technical Issues ◽  
The Brain

Elucidating causal, neurobiological underpinnings of behaviour is an ultimate goal of every neuroscientific study. However, due to the complexity of the brain as well as the complexity of the human environment, finding a~causal architecture that underlies behaviour remains a~formidable challenge. In this manuscript, we review the logical and conceptual issues with respect to causal research in neuroscience.First, we review the state of the art interventional and computational approaches to infer causal brain-behaviour relationships. We provide an~overview of potential issues, flaws, and confounds in these studies. We conclude that studies on the causal structure underlying behaviour should be performed by accumulating evidence coming from several lines of experimental and modelling studies. Lastly, we also propose computational models including artificial neuronal networks and simulated animats as a~potential breakthrough to causal brain-behaviour investigations.

Dissecting the Therapeutic Relevance of Gene Therapy in NeuroAIDS: An Evolving Epidemic

2020 ◽  
Vol 20 (3) ◽  
pp. 174-183
Author(s):  
Bushra Nabi ◽  
Saleha Rehman ◽  
Faheem Hyder Pottoo ◽  
Sanjula Baboota ◽  
Javed Ali
Keyword(s):  
Viral Entry ◽  
Due Process ◽  
Intrinsic Property ◽  
Antiretroviral Drugs ◽  
Viral Reservoir ◽  
Primary Concern ◽  
Cellular Delivery ◽  
Formidable Challenge ◽  
Neurological Conditions ◽  
The Brain

: NeuroAIDS, a disease incorporating both infectious and neurodegenerative pathways, is still a formidable challenge for the researchers to deal with. The primary concern for the treatment of neuroAIDS still remains the inaccessibility of the viral reservoir, making it indispensable for novel techniques to be continuously innovated. Since the brain serves as a reservoir for viral replication, it is pragmatic and a prerequisite to overcome the related barriers in order to improve the drug delivery to the brain. The current treatment ideology is based on the combinatorial approach of a mocktail of antiretroviral drugs. However, complete eradication of the disease could not be achieved. Thereby the arena of gene-based cellular delivery is trending and has created a niche for itself in the present scenario. To establish the supremacy of gene delivery, it is advisable to have a better understanding of the molecular mechanism involved in the due process. The mechanism associated with the activity of the anti-HIV gene lies in their intrinsic property to impart resistance to the HIV infection by targeting the viral entry channels. This review principally emphasizes on different types of gene therapies explored so far for the management of AIDS and its associated neurological conditions. Therefore it could rightly be said that we are at the crossroad where the need of the hour is to develop novel strategies for curbing AIDS and its associated neurological conditions.

scholarly journals Power Failure of Mitochondria and Oxidative Stress in Neurodegeneration and Its Computational Models

Antioxidants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 229
Author(s):  
JunHyuk Woo ◽  
Hyesun Cho ◽  
YunHee Seol ◽  
Soon Ho Kim ◽  
Chanhyeok Park ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Mitochondrial Dysfunction ◽  
Computational Models ◽  
Neuronal Damage ◽  
Living Organism ◽  
The Body ◽  
Mitochondrial Functions ◽  
A Cell ◽  
The Brain ◽  
And Oxidative Stress

The brain needs more energy than other organs in the body. Mitochondria are the generator of vital power in the living organism. Not only do mitochondria sense signals from the outside of a cell, but they also orchestrate the cascade of subcellular events by supplying adenosine-5′-triphosphate (ATP), the biochemical energy. It is known that impaired mitochondrial function and oxidative stress contribute or lead to neuronal damage and degeneration of the brain. This mini-review focuses on addressing how mitochondrial dysfunction and oxidative stress are associated with the pathogenesis of neurodegenerative disorders including Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease. In addition, we discuss state-of-the-art computational models of mitochondrial functions in relation to oxidative stress and neurodegeneration. Together, a better understanding of brain disease-specific mitochondrial dysfunction and oxidative stress can pave the way to developing antioxidant therapeutic strategies to ameliorate neuronal activity and prevent neurodegeneration.

scholarly journals Cyclic quantum causal models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jonathan Barrett ◽  
Robin Lorenz ◽  
Ognyan Oreshkov
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Bell Inequalities ◽  
Quantum Correlations ◽  
Quantum Systems ◽  
Causal Models ◽  
Causal Order ◽  
Quantum Processes ◽  
Causal Explanations ◽  
Causal Structures

AbstractCausal reasoning is essential to science, yet quantum theory challenges it. Quantum correlations violating Bell inequalities defy satisfactory causal explanations within the framework of classical causal models. What is more, a theory encompassing quantum systems and gravity is expected to allow causally nonseparable processes featuring operations in indefinite causal order, defying that events be causally ordered at all. The first challenge has been addressed through the recent development of intrinsically quantum causal models, allowing causal explanations of quantum processes – provided they admit a definite causal order, i.e. have an acyclic causal structure. This work addresses causally nonseparable processes and offers a causal perspective on them through extending quantum causal models to cyclic causal structures. Among other applications of the approach, it is shown that all unitarily extendible bipartite processes are causally separable and that for unitary processes, causal nonseparability and cyclicity of their causal structure are equivalent.

An embedded-atom method interatomic potential for Pd–H alloys

2008 ◽  
Vol 23 (3) ◽  
pp. 704-718 ◽  
Author(s):  
X.W. Zhou ◽  
J.A. Zimmerman ◽  
B.M. Wong ◽  
J.J. Hoyt
Keyword(s):  
Computational Models ◽  
Mechanical Response ◽  
Hydrogen Diffusion ◽  
Interatomic Potential ◽  
Diffusion Mechanism ◽  
Miscibility Gap ◽  
Embedded Atom Method ◽  
Embedded Atom ◽  
Formidable Challenge ◽  
Embedded Atom Method Potential

Palladium hydrides have important applications. However, the complex Pd–H alloy system presents a formidable challenge to developing accurate computational models. In particular, the separation of a Pd–H system to dilute (α) and concentrated (β) phases is a central phenomenon, but the capability of interatomic potentials to display this phase miscibility gap has been lacking. We have extended an existing palladium embedded-atom method potential to construct a new Pd–H embedded-atom method potential by normalizing the elemental embedding energy and electron density functions. The developed Pd–H potential reasonably well predicts the lattice constants, cohesive energies, and elastic constants for palladium, hydrogen, and PdHx phases with a variety of compositions. It ensures the correct hydrogen interstitial sites within the hydrides and predicts the phase miscibility gap. Preliminary molecular dynamics simulations using this potential show the correct phase stability, hydrogen diffusion mechanism, and mechanical response of the Pd–H system.

scholarly journals Inferring brain-computational mechanisms with models of activity measurements

2016 ◽  
Vol 371 (1705) ◽  
pp. 20160278 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Summary Statistics ◽  
Activity Data ◽  
Individual Measurement ◽  
Activity Measurements ◽  
The Brain ◽  
The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

2021 ◽  
Vol 376 (1821) ◽  
pp. 20190765 ◽  
Author(s):  
Giovanni Pezzulo ◽  
Joshua LaPalme ◽  
Fallon Durant ◽  
Michael Levin
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
State Of The Art ◽  
Memory Representation ◽  
Theme Issue ◽  
Evolutionary Innovation ◽  
New Interpretation ◽  
The Brain

Nervous systems’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states—in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo . This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

scholarly journals Attention controls multisensory perception via 2 distinct mechanisms at different levels of the cortical hierarchy

PLoS Biology ◽  
2021 ◽  
Vol 19 (11) ◽  
pp. e3001465
Author(s):  
Ambra Ferrari ◽  
Uta Noppeney
Keyword(s):  
Causal Inference ◽  
Causal Structure ◽  
Multisensory Perception ◽  
Spatial Representations ◽  
Perceptual Inference ◽  
Sensory Signals ◽  
Cortical Hierarchy ◽  
Parietal Cortices ◽  
The Brain ◽  
Different Levels

To form a percept of the multisensory world, the brain needs to integrate signals from common sources weighted by their reliabilities and segregate those from independent sources. Previously, we have shown that anterior parietal cortices combine sensory signals into representations that take into account the signals’ causal structure (i.e., common versus independent sources) and their sensory reliabilities as predicted by Bayesian causal inference. The current study asks to what extent and how attentional mechanisms can actively control how sensory signals are combined for perceptual inference. In a pre- and postcueing paradigm, we presented observers with audiovisual signals at variable spatial disparities. Observers were precued to attend to auditory or visual modalities prior to stimulus presentation and postcued to report their perceived auditory or visual location. Combining psychophysics, functional magnetic resonance imaging (fMRI), and Bayesian modelling, we demonstrate that the brain moulds multisensory inference via 2 distinct mechanisms. Prestimulus attention to vision enhances the reliability and influence of visual inputs on spatial representations in visual and posterior parietal cortices. Poststimulus report determines how parietal cortices flexibly combine sensory estimates into spatial representations consistent with Bayesian causal inference. Our results show that distinct neural mechanisms control how signals are combined for perceptual inference at different levels of the cortical hierarchy.

scholarly journals Distributed Code for Semantic Relations Predicts Neural Similarity

2019 ◽  
Author(s):  
Jeffrey N. Chiang ◽  
Yujia Peng ◽  
Hongjing Lu ◽  
Keith J. Holyoak ◽  
Martin M. Monti
Keyword(s):  
Computational Models ◽  
Semantic Network ◽  
Building Blocks ◽  
Semantic Relations ◽  
Human Cognition ◽  
Sequential Presentation ◽  
Specific Content ◽  
Distributed Representations ◽  
Relational Similarity ◽  
The Brain

AbstractThe ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coded as atomistic links in a semantic network, or as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations remains to be empirically established. The present study combined computational modeling and neuroimaging to investigate the representation and comparison of abstract semantic relations in the brain. By using sequential presentation of verbal analogies, we decoupled the neural activity associated with encoding the representation of the first-order semantic relation between words in a pair from that associated with the second-order comparison of two relations. We tested alternative computational models of relational similarity in order to distinguish between rival accounts of how semantic relations are coded and compared in the brain. Analyses of neural similarity patterns supported the hypothesis that semantic relations are coded, in the parietal cortex, as distributed representations over a pool of abstract relations specified in a theory-based taxonomy. These representations, in turn, provide the immediate inputs to the process of analogical comparison, which draws on a broad frontoparietal network. This study sheds light not only on the form of relation representations but also on their specific content.SignificanceRelations provide basic building blocks for language and thought. For the past half century, cognitive scientists exploring human semantic memory have sought to identify the code for relations. In a neuroimaging paradigm, we tested alternative computational models of relation processing that predict patterns of neural similarity during distinct phases of analogical reasoning. The findings allowed us to draw inferences not only about the form of relation representations, but also about their specific content. The core of these distributed representations is based on a relatively small number of abstract relation types specified in a theory-based taxonomy. This study helps to resolve a longstanding debate concerning the nature of the conceptual and neural code for semantic relations in the mind and brain.

Peri-personal space as an interface for self-environment interaction

2021 ◽  
pp. 17-46
Author(s):  
Jean-Paul Noel ◽  
Tommaso Bertoni ◽  
Andrea Serino
Keyword(s):  
Motor System ◽  
Computational Models ◽  
Multisensory Processing ◽  
Critical Role ◽  
Personal Space ◽  
Environment Interaction ◽  
Specific System ◽  
Physical Interactions ◽  
Psychophysical Studies ◽  
The Brain

The brain has developed a specific system to encode the space closely surrounding our body, our peri-personal space (PPS). This space is the theatre where all physical interactions with objects in the environment occur, and thus is postulated to play a critical role in both approaching and defensive behaviour. Here, we first describe the classic neurophysiological findings that have led researchers to conceive of PPS as a multisensory-motor interface. This historical perspective is given to clarify what properties are strictly related to PPS encoding, and what characteristics bear out or are related to PPS. Then, in an effort to uncover gaps in knowledge that often go unnoticed, we critically examine the association between PPS and i) multisensory processing, and ii) the motor system—its strongest allies. We do not mean to say that PPS isn’t multisensory-motor, simply to pinpoint current research shortcomings. Subsequently, we detail more recent psychophysical studies, highlighting the extreme plasticity of PPS, and its putative role in bodily self-consciousness and social cognition. Lastly, we briefly discuss computational models of PPS. Throughout the chapter, we particularly attempt to emphasize open areas of investigation. By critically evaluating past findings, many of them our own, we hope to provide a forward-looking perspective on the study of PPS.

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