Mereologisch, homunkulogisch oder pseudo-objektivierend? Über einige neurophilosophische Fehlschlüsse und Kategorienfehler

Conceptus ◽  
2008 ◽  
Vol 37 (91) ◽  
Author(s):  
Hans Lenk
Keyword(s):  
Ordinary Language ◽  
Human Action ◽  
Higher Order ◽  
Sense Perception ◽  
Philosophical Foundations ◽  
Theoretical Perspectives ◽  
Language Analysis ◽  
Mereological Fallacy ◽  
The Brain

SummaryThe paper discusses Bennett’s and Hacker’s critical work on the philosophical foundations of neuroscience and their so-called ” mereological fallacy“. It argues that Wittgensteinian arguments of mere ordinary language analysis are not enough to cover activations of patterns in the brain and especially of sense perception and meaningful human action. The approach offered by the author’s methodological scheme-interpretationism may solve these problems by using and differentiating higher-order concepts and metatheoretical and methodological as well as schema-theoretical perspectives.

scholarly journals PSYCHOLOGICAL CONCEPTS IN COGNITIVE NEUROSCIENCE

2020 ◽  
Author(s):  
Marcelo Carvalho
Keyword(s):  
Cognitive Neuroscience ◽  
Scientific Research ◽  
Philosophical Foundations ◽  
Critical Revision ◽  
Mereological Fallacy ◽  
The Brain ◽  
Psychological Concepts

The use of psychological concepts in cognitive neuroscience is heavily criticized by Bennett & Hacker's Philosophical Foundations of Neuroscience. The central objection points to neuroscience's attribution to the brain of psychological concepts that are meaningful only when applied to the entire being. That is supposedly the case of “seeing,” “communicating,” and “reading.” Bennett & Hacker identify in such attributions what they call a mereological fallacy. The critical revision of Bennett & Hacker's argument is an opportunity to present the debate about philosophy and psychological neuroscience and outline a Wittgensteinian perspective about the meaning of psychological concepts, its interest, and its relevance to scientific research.

The novel guanylyl cyclase MsGC-I is strongly expressed in higher-order neuropils in the brain of Manduca sexta

2001 ◽  
Vol 204 (2) ◽  
pp. 305-314 ◽  
Author(s):  
A. Nighorn ◽  
P.J. Simpson ◽  
D.B. Morton
Keyword(s):  
Nervous System ◽  
Manduca Sexta ◽  
Guanylyl Cyclase ◽  
Higher Order ◽  
Adult Brain ◽  
Central Complex ◽  
Amino Acid Residues ◽  
Order Processing ◽  
Guanylyl Cyclases ◽  
The Brain

Guanylyl cyclases are usually characterized as being either soluble (sGCs) or receptor (rGCs). We have recently cloned a novel guanylyl cyclase, MsGC-I, from the developing nervous system of the hawkmoth Manduca sexta that cannot be classified as either an sGC or an rGC. MsGC-I shows highest sequence identity with receptor guanylyl cyclases throughout its catalytic and dimerization domains, but does not contain the ligand-binding, transmembrane or kinase-like domains characteristic of receptor guanylyl cyclases. In addition, MsGC-I contains a C-terminal extension of 149 amino acid residues. In this paper, we report the expression of MsGC-I in the adult. Northern blots show that it is expressed preferentially in the nervous system, with high levels in the pharate adult brain and antennae. In the antennae, immunohistochemical analyses show that it is expressed in the cell bodies and dendrites, but not axons, of olfactory receptor neurons. In the brain, it is expressed in a variety of sensory neuropils including the antennal and optic lobes. It is also expressed in structures involved in higher-order processing including the mushroom bodies and central complex. This complicated expression pattern suggests that this novel guanylyl cyclase plays an important role in mediating cyclic GMP levels in the nervous system of Manduca sexta.

scholarly journals Evolutionary modifications in human brain connectivity associated with schizophrenia

Brain ◽  
2019 ◽  
Vol 142 (12) ◽  
pp. 3991-4002 ◽  
Author(s):  
Martijn P van den Heuvel ◽  
Lianne H Scholtens ◽  
Siemon C de Lange ◽  
Rory Pijnenburg ◽  
Wiepke Cahn ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Connectivity ◽  
Brain Evolution ◽  
Higher Order ◽  
Brain Functions ◽  
Human Brain Evolution ◽  
The Brain

See Vértes and Seidlitz (doi:10.1093/brain/awz353) for a scientific commentary on this article. Is schizophrenia a by-product of human brain evolution? By comparing the human and chimpanzee connectomes, van den Heuvel et al. demonstrate that connections unique to the human brain show greater involvement in schizophrenia pathology. Modifications in service of higher-order brain functions may have rendered the brain more vulnerable to dysfunction.

scholarly journals Artificial intelligence, dynamic efficiency and Economics

2021 ◽  
pp. 337-350
Author(s):  
Vincent Wolters
Keyword(s):  
Artificial Intelligence ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Human Action ◽  
Medical Doctor ◽  
Dynamic Efficiency ◽  
Demand And Supply ◽  
New Information ◽  
Back Ground ◽  
The Brain

In this work I will lend support to the theory of «dynamic efficien - cy», as outlined by Prof. Huerta de Soto in The Theory of Dynamic Efficiency (2010a). Whereas Huerta de Soto connects economics with ethics, I will take a different approach. Since I have a back-ground in Artificial Intelligence (A.I.), I will show that this and related fields have yielded insights that, when applied to the study of economics, may call for a different way of looking at the eco-nomy and its processes. At first glance, A.I. and economics do not seem to have a lot in common. The former is thought to attempt to build a human being; the latter is supposed to deal with depressions, growth, inflation, etc. That view is too simplistic; in fact there are strong similarities. First, economics is based on (inter-)acting individuals, i.e. on human action. A.I. tries to understand and simulate human (and animal) behavior. Second, economics deals with information pro-cessing, such as how the allocation of resources can best be orga-nized. A.I. also investigates information processing. This can be in specific systems, such as the brain, or the evolutionary process, or purely in an abstract form. Finally, A.I. tries to answer more philosophical questions like: what is intelligence? What is a mind? What is consciousness? Is there free will? These topics play a less prominent role in economics, but are sometimes touched upon, together with the related topic of the «entrepreneurial function». The paradigm that was dominant in the early days of A.I. is static in nature. Reaching a solution is done in different steps. First: gathering all necessary information. Second: processing this in - formation. Finally: the outcome of this process, a clear conclusion. Each step in the process is entirely separate. During information gathering no processing is done, and during processing, no new information is added. The conclusion reached is final and cannot change later on. Logical problems are what is mostly dealt with, finding ways in which a computer can perform deductions based on the information that is represented as logical statements. Other applications are optimization problems, and so-called «Expert Systems», developed to perform the work of a judge reaching a verdict, or a medical doctor making a diagnosis based on the symptoms of the patient. This paradigm is also called «top-down», because information flows to a central point where it is processed, or «symbolic processing», referring to deduction in formal logic.1 In economics there is a similar paradigm, and it is still the do-minant one. This is the part of economics that deals with opti - mization of resources: given costs and given prices, what is the allocation that will lead to the highest profit? Also belonging to this paradigm are the equilibrium models. Demand and supply curves are supposed to be knowable and unchangeable, and the price is a necessary outcome. The culmination is central planning that supposes all necessary information, such as demand and supply curves and available resources to be known. Based on this, the central planner determines prices.

scholarly journals The role of thickness inhomogeneities in hierarchical cortical folding

2020 ◽  
Author(s):  
Lucas da Costa Campos ◽  
Raphael Hornung ◽  
Gerhard Gompper ◽  
Jens Elgeti ◽  
Svenja Caspers
Keyword(s):  
Fetal Brain ◽  
Epileptic Seizures ◽  
Quantitative Model ◽  
Higher Order ◽  
Mammalian Brain ◽  
Cortical Folding ◽  
Differential Growth ◽  
The Brain ◽  
Short Life Expectancy

AbstractThe morphology of the mammalian brain cortex is highly folded. For long it has been known that specific patterns of folding are necessary for an optimally functioning brain. On the extremes, lissencephaly, a lack of folds in humans, and polymicrogyria, an overly folded brain, can lead to severe mental retardation, short life expectancy, epileptic seizures, and tetraplegia. The construction of a quantitative model on how and why these folds appear during the development of the brain is the first step in understanding the cause of these conditions. In recent years, there have been various attempts to understand and model the mechanisms of brain folding. Previous works have shown that mechanical instabilities play a crucial role in the formation of brain folds, and that the geometry of the fetal brain is one of the main factors in dictating the folding characteristics. However, modeling higher-order folding, one of the main characteristics of the highly gyrencephalic brain, has not been fully tackled. The effects of thickness inhomogeneity in the gyrogenesis of the mammalian brain are studied in silico. Finite-element simulations of rectangular slabs are performed. The slabs are divided into two distinct regions, where the outer layer mimics the gray matter, and the inner layer the underlying white matter. Differential growth is introduced by growing the top layer tangentially, while keeping the underlying layer untouched. The brain tissue is modeled as a neo-Hookean hyperelastic material. Simulations are performed with both, homogeneous and inhomogeneous cortical thickness. The homogeneous cortex is shown to fold into a single wavelength, as is common for bilayered materials, while the inhomogeneous cortex folds into more complex conformations. In the early stages of development of the inhomogeneous cortex, structures reminiscent of the deep sulci in the brain are obtained. As the cortex continues to develop, secondary undulations, which are shallower and more variable than the structures obtained in earlier gyrification stage emerge, reproducing well-known characteristics of higher-order folding in the mammalian, and particularly the human, brain.

scholarly journals Neuropeptides in modulation of Drosophila behavior: how to get a grip on their pleiotropic actions

2019 ◽  
Author(s):  
Dick R Nässel ◽  
Dennis Pauls ◽  
Wolf Huetteroth
Keyword(s):  
Expression Pattern ◽  
Internal State ◽  
Neurosecretory Cells ◽  
Higher Order ◽  
Specific Expression ◽  
Specific Expression Pattern ◽  
Pleiotropic Functions ◽  
Pleiotropic Actions ◽  
The Brain ◽  
Different Levels

Neuropeptides constitute a large and diverse class of signaling molecules that are produced by many types of neurons, neurosecretory cells, endocrines and other cells. Many neuropeptides display pleiotropic actions either as neuromodulators, co-transmitters or circulating hormones, while some play these roles concurrently. Here, we highlight pleiotropic functions of neuropeptides and different levels of neuropeptide signaling in the brain, from context-dependent orchestrating signaling by higher order neurons, to local executive modulation in specific circuits. Additionally, orchestrating neurons receive peptidergic signals from neurons conveying organismal internal state cues and relay these to executive circuits. We exemplify these levels of signaling with four neuropeptides, SIFamide, short neuropeptide F, allatostatin-A and leucokinin, each with a specific expression pattern and level of complexity in signaling.

scholarly journals Ask and You Shall Receive: A closer look on unsolved consciousness issue

2021 ◽  
Author(s):  
Ali Motavalli ◽  
◽  
Javad Mahmoudi ◽  
Alireza Majdi ◽  
Saeed Sadigh-Eteghad ◽  
...  
Keyword(s):  
Middle Ages ◽  
Brain Function ◽  
Neural Correlates ◽  
Higher Order ◽  
Narrative Review ◽  
Historical Aspects ◽  
Neural Correlates Of Consciousness ◽  
States Of Consciousness ◽  
Mind And Body ◽  
The Brain

Although there are numerous views about the concept of consciousness, no consensus exists regarding the meaning. However, with the aid of the latest neuroscientific developments, the misleading obstacles related to consciousness have been removed. Over the last few decades, neuroscientific efforts in determining the function of the brain and merging these findings with philosophical theories, have brought a more comprehensive perception of the notion of consciousness. In addition to metaphysical/ontological views of consciousness e.g., higher-order theories, reflexive theories, and representationalist theories, there are some brain directed topics in this matter which include but not are limited to neural correlates of consciousness (NCC), brain loop connectivity, and lateralization. This narrative review sheds light on cultural and historical aspects of consciousness in old and middle ages and introduces some of the prominent philosophical discussions related to mind and body. Also, it illustrates the correlation of brain function with states of consciousness with a focus on the roles of function and connectivity.

1. How we smell

2020 ◽  
pp. 1-20
Author(s):  
Matthew Cobb
Keyword(s):  
Higher Order ◽  
Receptor Type ◽  
Ancient Greek ◽  
Wiring Diagram ◽  
Olfactory Neurons ◽  
Olfactory Information ◽  
The Brain

‘How we smell’ explains the processes, mechanisms, and anatomy behind smell or olfaction. What is the dimensionality of smell? Ancient Greek and Roman philosophers argued that pleasant smells were made up of round atoms and unpleasant smells of pointed ones; while the detail is incorrect, the theory is accurate. When we smell something, olfactory neurons send a response into the brain where they converge with cells with the same receptor type to form a ball-shaped structure, the glomerulus. Higher order neurons then combine signals across glomeruli to extract olfactory information from the environment. All animals with a brain share this basic wiring diagram for detecting smells.

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