Relevant bibliographies by topics / Cortex

Academic literature on the topic 'Cortex'

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Published: 4 June 2021
Last updated: 6 July 2024

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Journal articles on the topic "Cortex"

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SELEMON, LYNN D., PATRICIA S. GOLDMAN-RAKIC, and CAROL A. TAMMINGA. "Corex, III; Prefrontal Cortex." American Journal of Psychiatry 152, no. 1 (January 1995): 5. http://dx.doi.org/10.1176/ajp.152.1.5.

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Kaufman, K. J. "The Cerebral Cortex: Visual Cortex." Archives of Ophthalmology 104, no. 8 (August 1, 1986): 1141. http://dx.doi.org/10.1001/archopht.1986.01050200047040.

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Bohn Stafleu van Loghum. "Cortex." Sociaal Bestek 79, no. 5 (October 2017): 2. http://dx.doi.org/10.1007/s41196-017-0100-5.

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Cowey, A. "Cerebral Cortex, Vol. 3, Visual Cortex." Neuroscience 19, no. 3 (November 1986): 1023. http://dx.doi.org/10.1016/0306-4522(86)90314-3.

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Hughes, John R. "Cerebral cortex. Vol. 3. Visual cortex." Electroencephalography and Clinical Neurophysiology 63, no. 4 (April 1986): 392. http://dx.doi.org/10.1016/0013-4694(86)90029-5.

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Kennedy, Philip R. "Cursor Cortex." Science News 156, no. 15 (October 9, 1999): 227. http://dx.doi.org/10.2307/4011902.

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Sasano, Hironobu, and Yuto Yamazaki. "Adrenal Cortex." AJSP: Reviews and Reports 22, no. 4 (July 2017): 217–24. http://dx.doi.org/10.1097/pcr.0000000000000181.

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Cui, Hongchang, and Philip N. Benfey. "Cortex proliferation." Plant Signaling & Behavior 4, no. 6 (June 2009): 551–53. http://dx.doi.org/10.4161/psb.4.6.8731.

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LeBrasseur, Nicole. "Cortex construction." Journal of Cell Biology 175, no. 3 (November 6, 2006): 365b. http://dx.doi.org/10.1083/jcb.1753iti2.

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Dluhy, Robert G. "Adrenal cortex." Current Opinion in Endocrinology, Diabetes and Obesity 14, no. 3 (June 2007): 209. http://dx.doi.org/10.1097/med.0b013e328169eeee.

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Dissertations / Theses on the topic "Cortex"

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Esmaeili, Vahid. "Neuronal correlates of tactile working memory in rat barrel cortex and prefrontal cortex." Doctoral thesis, SISSA, 2014. http://hdl.handle.net/20.500.11767/3869.

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The neuronal mechanisms of parametric working memory – the short-term storage of graded stimuli to guide behavior – are not fully elucidated. We have designed a working memory task where rats compare two sequential vibrations, S1 and S2, delivered to their whiskers (Fassihi et al, 2014). Vibrations are a series of velocities sampled from a zero-mean normal distribution. Rats must judge which stimulus had greater velocity standard deviation, σ (e.g. σ1 > σ2 turn left, σ1 < σ2 turn right). A critical operation in this task is to hold S1 information in working memory for subsequent comparison. In an earlier work we uncovered this cognitive capacity in rats (Fassihi et al, 2014), an ability previously ascribed only to primates. Where in the brain is such a memory kept and what is the nature of its representation? To address these questions, we performed simultaneous multi-electrode recordings from barrel cortex – the entryway of whisker sensory information into neocortex – and prelimbic area of medial prefrontal cortex (mPFC) which is involved in higher order cognitive functioning in rodents. During the presentation of S1 and S2, a majority of neurons in barrel cortex encoded the ongoing stimulus by monotonically modulating their firing rate as a function of σ; i.e. 42% increased and 11% decreased their firing rate for progressively larger σ values. During the 2 second delay interval between the two stimuli, neuronal populations in barrel cortex kept a graded representation of S1 in their firing rate; 30% at early delay and 15% at the end. In mPFC, neurons expressed divers coding characteristics yet more than one-fourth of them varied their discharge rate according to the ongoing stimulus. Interestingly, a similar proportion carried the stimulus signal up to early parts of delay period. A smaller but considerable proportion (10%) kept the memory until the end of delay interval. We implemented novel information theoretic measures to quantify the stimulus and decision signals in neuronal responses in different stages of the task. By these measures, a decision signal was present in barrel cortex neurons during the S2 period and during the post stimulus delay, when the animal needed to postpone its action. Medial PFC units also represented animal choice, but later in the trial in comparison to barrel cortex. Decision signals started to build up in this area after the termination of S2. We implemented a regularized linear discriminant algorithm (RDA) to decode stimulus and decision signals in the population activity of barrel cortex and mPFC neurons. The RDA outperformed individual clusters and the standard linear discriminant analysis (LDA). The stimulus and animal’s decision could be extracted from population activity simply by linearly weighting the responses of neuronal clusters. The population signal was present even in epochs of trial where no single cluster was informative. We predicted that coherent oscillations between brain areas might optimize the flow of information within the networks engaged by this task. Therefore, we quantified the phase synchronization of local field potentials in barrel cortex and mPFC. The two signals were coherent at theta range during S1 and S2 and, interestingly, prior to S1. We interpret the pre-stimulus coherence as reflecting top-down preparatory and expectation mechanisms. We showed, for the first time to our knowledge, the neuronal correlates of parametric working memory in rodents. The existence of both positive and negative codes in barrel cortex, besides the representation of stimulus memory and decision signals suggests that multiple functions might be folded into single modules. The mPFC also appears to be part of parametric working memory and decision making network in rats.
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Ribenis, Aksels. "Epilepsy surgery around language cortex." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-98318.

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Rönnqvist, Oskar. "Pekskärmsanvändargränssnittsmodul till ARM Cortex-M3." Thesis, KTH, Maskinkonstruktion (Inst.), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-40281.

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SammanfattningDenna rapport utreder hur ett pekskärmsanvändargränssnitt kan implementeras på Syntronic AB’s hård- och mjukvaruplattform baserad på ARM Cortex-M3.Ett pekskärmsanvändargränssnitt kräver två system, ett tryckkänsligtsystem för att detektera och positionera när användaren ger input till systemet och en skärm för att presentera ett grafiskt användargränssnitt.Rapporten börjar med att beskriva de tillgängliga teknikerna för pekpaneler och skärmar. Detta syftar till att ge djupare kunskap om de olika teknologierna och deras för- och nackdelar. Denna kunskap används sedan för att i samarbete med Syntronic AB utvärdera de olika teknologierna utifrån Syntronic AB’s krav. Utvärderingen leder till val av teknik för både pekpanel och skärm som köps in och implementeras på plattformen.Implementationen består av både hårdvarunära programmering för kommunikationen med pekpanel och skärm och API mjukvaror för att hantera hårdvaran på ett enkelt sätt.Både hård- och mjukvara är dokumenterad för att visa hur systemet är uppbyggt. Denna dokumentation skall också kunna användas som en manual till mjukvaran vid vidareutveckling eller tillämpning.Resultatet av implementationen är en pekpanel och en skärm med ett förenklat användargränssnitt. Implementationen visar tydligt hur ett pekkänsligt användargränssnitt kan se ut på plattformen och tillhandahåller mjukvara som är återanvändbar.
AbstractThis report investigates how a touch sensitive user interface can be implemented on Syntronic AB’s hardware and software platform based on an ARM Cortex-M3 microcontroller.A touch sensitive user interface consists of two systems, one touch sensitive system to detect and position user input and one display to present a graphical user interface.The first part of the report describes available touch and display technologies. The purpose is to deepen the knowledge about the different technologies and their advantages and disadvantages. This knowledge is then used to evaluate the technologies in collaboration with Syntronic AB based on their needs. The evaluation leads to a choice of technology both for the touch sensitive system and display technology. Hardware corresponding to the technology choices is ordered and implemented.The implementation consists of both software that is strongly connected to the hardware to handle the communication with the touch panel and display and API software to enable easier interfacing to the hardware.Both hard- and software are documented to describe the system structure. This documentation can also be used as a software manual to ease further development or implementation.The result of the implementation is a touch panel and a display with a simple GUI. The implementation clearly shows how a touch sensitive interface can be implemented on the platform and provides software that is reusable.
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Kleppe, Ingo Christian. "Synaptic variability in the cortex." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619941.

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Thompson, P. D. "Motor cortex stimulation in man." Thesis, King's College London (University of London), 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261197.

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Jánossy, Andrea. "Régulation cholinergique du cortex surrénal." Lyon 1, 2001. http://www.theses.fr/2001LYO1T014.

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Duret, Margaux. "Organisation spatiale et temporelle de l'activité neuronale du cortex moteur chez le singe macaque dans une tâche d'atteinte et de saisie manuelle." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0408/document.

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Il est classiquement admis que le cortex moteur des primates est organisé topographiquement en lien avec le contrôle des différentes parties du corps. Il a également été suggéré que différentes zones de cette aires corticales pourraient être impliquées dans différents processus de préparation motrice. Suivant cette dernière hypothèse, cette thèse a pour objectif d’étudier les modulations spatiales et temporelles de l’activité neuronale du cortex moteur au cours de la préparation et de l’exécution de mouvements de saisie manuelle. Trois singes ont été entraînés à réaliser une tâche pré-indicée de saisie manuelle. Chez chaque animal, une matrice d’électrodes a été implantée chroniquement dans le cortex moteur. Dans une première étude, nous avons démontré que les modulations d’activité associées à différents processus préparatoires sont localisées dans différentes zones du cortex moteur. Ces zones seraient activées séquentiellement au cours de la préparation motrice suivant une alternance de phases de traitement stationnaire et de propagation dynamique. Dans une seconde étude, nous avons exploré les interactions neuronales par l’utilisation de la mesure de corrélation de variabilité (rsc) entre paires de neurones. Cette deuxième étude a fait ressortir 3 résultats principaux. Les valeurs de rsc sont plus élevées au cours de la préparation du mouvement que lors de son exécution. Elles diminuent avec la distance qui sépare les neurones. Elles sont plus importantes entre interneurones qu’entre neurones supposés pyramidaux. L’ensemble de ces observations ont été discutées en lien avec différentes modèles d’organisation spatiale des aires motrices corticales
The motor cortex follows a somatotopic organization in which the different body parts are controlled by distinct cortical zones. It has also been proposed that different spatial zones of this cortical area could be involed in distinct processes of motor preparation. Following this latter hypothesis, the objective of this thesis is to study the spatio-temporal modulations of motor cortex activity during movement preparation and execution. Three monkeys have been trained in an instructed delayed reach-to-grasp task. In each animal, a multielectrode Utah array was chronically implanted in the motor cortex to explore the dynamic modulations of neural activity during task performance. In a first study, we demonstrated that the modulations of neural activity related to distinct processes of motor preparation occur at different cortical locations. These locations are activated sequentially during motor preparation through alternating phases of stationary processing and dynamic propagation. In a second study, we analysed the neural interactions using a measure of spike count correlation (rsc) between pair of neurons. We reported 3 main results. Correlations are higher during movement preparation than during execution. They decrease with the distance between neurons. Finally, they are higher bewteen putative interneurons than bewteen putative pyramidal neurones. All these observations are discussed in relation to several models of the spatial organization the motor cortex
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Andres, Michael. "Number and finger interactions : from the parietal to the motor cortex / Interactions entre les nombres et les doigts : du cortex pariétal au cortex moteur." Université catholique de Louvain, 2006. http://edoc.bib.ucl.ac.be:81/ETD-db/collection/available/BelnUcetd-03192006-125748/.

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The observations made in brain-lesioned patients and the result of functional brain imaging studies converge to the hypothesis that the posterior parietal cortex (PPC) is involved in calculation and number processing. However, if numerical disorders generally result from a left parietal lesion, the results of some brain imaging studies suggest that the right PPC could also play a role in number magnitude processing. In order to clarify this question, we used transcranial magnetic stimulation to induce a virtual lesion of the left or right PPC in healthy subjects while they performed number comparison. Our results show that the integrity of the left PPC is a necessary condition for the precise discrimination required during close number comparison; whereas the comparison of far numbers can be performed by either hemisphere as suggested by the fact that this task is affected only by the simultaneous virtual lesion of both hemispheres. In order to better identify which processes underlie the numerical competence of the PPC, we then studied the possible interactions between number processing and visuo-motor functions. Indeed, a meta-analysis performed on functional imaging data revealed that number processing depends on parietal regions, but also on certain premotor areas, which are very close to those involved in the control of finger movements. In a first series of experiments, we thus observed an excitability increase in motor circuits during the enumeration of dots presented on a computer screen. Given that the counting task was performed with both hands at rest, this increase was interpreted as reflecting the mental simulation of pointing movements or sequential finger rising as counting goes on. In a second series of experiments, we showed that information related to number magnitude could interfere with the aperture of the finger grip required to grasp an object. These results suggest that the conformation of the hand to object size shares, with the representation of numbers, common processes for magnitude estimate. In conclusion, our thesis supports the hypothesis that our numerical capacities rely, at least partially, on visuo-motor functions involving the PPC; this could explain why the numerical capacities of the left hemisphere, which is dominant for motor activities, are more precise. / Les observations réalisées chez les patients cérébrolésés ainsi que le résultat des études d'imagerie cérébrale fonctionnelle convergent vers l'hypothèse selon laquelle le cortex pariétal postérieur (CPP) est impliqué dans le traitement des nombres et le calcul. Cependant, si les troubles du calcul résultent le plus souvent d'une lésion pariétale gauche, les résultats de certaines études d'imagerie fonctionnelle suggèrent que le CPP droit pourrait également jouer un rôle dans le traitement de la magnitude des nombres. Afin de clarifier cette question, nous avons utilisé la stimulation magnétique transcrânienne pour induire une lésion virtuelle du CPP gauche ou droit chez des sujets sains réalisant une tâche de comparaison de nombres. Nos résultats montrent que l'intégrité du CPP gauche est une condition nécessaire à la discrimination précise requise lors de la comparaison de nombres proches; la comparaison de nombres éloignés peut, quant à elle, être réalisée par l'un ou l'autre hémisphère comme le suggère le fait que cette tâche n'est affectée que par lésion virtuelle simultanée des deux hémisphères. Afin de mieux appréhender les processus sur lesquels s'appuient les compétences numériques du CPP, nous avons ensuite étudié les interactions possibles entre le traitement des nombres et les fonctions visuo-motrices. En effet, une méta-analyse réalisée sur des données d'imagerie fonctionelle a révélé que le traitement des nombres dépend de régions pariétales, mais également de certaines aires prémotrices, proches de celles impliquées dans le contrôle des mouvements des doigts. Dans une première série d'expériences, nous avons ainsi observé une augmentation de l'excitabilité des circuits moteurs lors du comptage de points présentés sur l'écran d'un ordinateur. Etant donné que la tâche de comptage était réalisée avec les mains au repos, cette augmentation a été interprétée comme le reflet d'une simulation mentale de mouvements de pointage ou d'extension séquentielle des doigts pendant le comptage. Dans une deuxième série d'expériences, nous avons montré que l'information relative à la magnitude des nombres pouvait interférer avec l'ouverture de la pince bidigitale requise pour saisir un objet. Ces résultats suggèrent que la conformation de la main adaptée à la taille des objets partage, avec la représentation des nombres, des processus communs d'estimation de la magnitude. En conclusion, notre travail supporte l'hypothèse selon laquelle nos capacités numériques pourraient, en partie du moins, reposer sur des fonctions visuo-motrices impliquant le CPP ; ceci pourrait expliquer pourquoi les capacités numériques de l'hémisphère gauche, dominant pour les activités motrices, sont plus précises.
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Nadji, Al-Husein, and Hgi Haval Sarbast. "Bearbetningstid och CPU-användning i Snort IPS : En jämförelse mellan ARM Cortex-A53 och Cortex-A7." Thesis, Tekniska Högskolan, Jönköping University, JTH, Datateknik och informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-50899.

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Syftet med denna studie är att undersöka hur bearbetningstiden hos Snort intrångsskyddssystem varierar mellan två olika processorer; ARM Cortex-A53 och Cortex-A7. CPU-användningen undersöktes även för att kontrollera om bearbetningstid är beroende av hur mycket CPU Snort använder. Denna studie ska ge kunskap om hur viktig en processor är för att Snort ska kunna prestera bra när det gäller bearbetningstid och CPU användning samt visa det uppenbara valet mellan Cortex-A53 och Cortex-A7 när man ska implementera Snort IPS. Med hjälp av litteratursökning konstruerades en experimentmiljö för att kunna ge svar på studiens frågeställningar. Snort kan klassificeras som CPU-bunden vilket innebär att systemet är beroende av en snabb processor. I detta sammanhang innebär en snabb processor gör att Snort hinner bearbeta den mängd nätverkstrafik den får, annars kan trafiken passera utan att den inspekteras vilket kan skada enheten som är skyddat av Snort. Studiens resultat visar att bearbetningstiden i Snort på Cortex-A53 och Cortex-A7 skiljer sig åt och en tydlig skillnad i CPU-användning mellan processorerna observerades. Studien visar även kopplingen mellan bearbetningstiden och CPUanvändning hos Snort. Studiens slutsats är att ARM Cortex-A53 har bättre prestanda vid användning av Snort IPS avseende bearbetningstid och CPU-användning, där Cortex-A53 har 10 sekunder kortare bearbetningstid och använder 2,87 gånger mindre CPU.
The purpose of this study is to examine how the processing time of the Snort intrusion prevention system varies on two different processors; ARM Cortex-A53 and CortexA7. CPU usage was also examined to check if processing time depends on how much CPU Snort uses. This study will provide knowledge about how important a processor is for Snort to be able to perform well in terms of processing time and CPU usage. This knowledge will help choosing between Cortex-A53 and Cortex-A7 when implementing Snort IPS. To achieve the purpose of the study a literature search has been done to design an experimental environment. Snort can be classified as CPU-bound, which means that the system is dependent on a fast processor. In this context, a fast processor means that Snort is given enough time to process the amount of traffic it receives, otherwise the traffic can pass through without it being inspected, which can be harmful to the device that is protected by Snort. The results of the study show that the processing time in Snort on Cortex-A53 and Cortex-A7 differs and an obvious difference in CPU usage between the processors is shown. The study also presents the connection between processing time and CPU usage for Snort. In conclusion, ARM Cortex-A53 has better performance when using Snort IPS in terms of processing time and CPU usage, Cortex-A53 has 10 seconds less processing time and uses 2,87 times less CPU.
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Chavez, Candice Monique. "Top-down modulation by medial prefrontal cortex of basal forebrain activation of auditory cortex during learning." CSUSB ScholarWorks, 2006. https://scholarworks.lib.csusb.edu/etd-project/3053.

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The experiment tested the hypothesis that the acetylcholine (ACh) release in the rat auditory cortex is greater in rats undergoing auditory classical conditioning compared to rats in a truly random control paradigm where no associative learning takes place and that this is mediated by prefrontal afferent projections influencing the nucleus basalis magnocellularis (NBM), which in turn modulates ACh release in neocortex. Rats with bilateral ibotenic acid lesions of medial prefrontal and agranular insular cortices were tested in an auditory classical conditioning task while ACh was collected from the primary auditory cortex. It was hypothesized that lesions of these prefrontal areas would prevent learning-related increases of ACh release in the primary auditory cortex. The hypothesized results were supported. Results from this experiment provide unique evidence that medial prefrontal cortex projections to the NBM are important for mediating cortical ACh release during associative learning.
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Books on the topic "Cortex"

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Jones, Edward G., and Alan Peters, eds. Cerebral Cortex. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4615-6616-8.

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Peters, Alan, and Edward G. Jones, eds. Cerebral Cortex. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4615-6619-9.

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Jones, Edward G., and Alan Peters, eds. Cerebral Cortex. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4615-3824-0.

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Peters, Alan, and John H. Morrison, eds. Cerebral Cortex. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4885-0.

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Ulinski, Philip S., Edward G. Jones, and Alan Peters, eds. Cerebral Cortex. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4903-1.

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1929-, Peters Alan, and Jones Edward G. 1939-, eds. Cerebral cortex. New York: Plenum, 1991.

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1929, Peters Alan, and Jones Edward G. 1939-, eds. Cerebral cortex. London: Plenum, 1988.

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1939-, Jones Edward G., and Peters Alan 1929-, eds. Cerebral cortex. New York: Plenum, 1986.

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1929-, Peters Alan, and Rockland Kathleen S, eds. Cerebral cortex. New York: Plenum Press, 1994.

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1929-, Peters Alan, and Jones Edward G. 1939-, eds. Cerebral cortex. New York: Plenum, 1987.

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Book chapters on the topic "Cortex"

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Bährle-Rapp, Marina. "Cortex." In Springer Lexikon Kosmetik und Körperpflege, 129. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71095-0_2446.

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Mehlhorn, Heinz. "Cortex." In Encyclopedia of Parasitology, 586. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-43978-4_4145.

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Mehlhorn, Heinz. "Cortex." In Encyclopedia of Parasitology, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27769-6_4145-1.

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Gooch, Jan W. "Cortex." In Encyclopedic Dictionary of Polymers, 885. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_13477.

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Labhart, A. "Adrenal Cortex." In Clinical Endocrinology, 349–486. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-70509-0_7.

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Schwabe, Lars. "Cortex: Overview." In Encyclopedia of Computational Neuroscience, 26–31. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_784.

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Malloy, Paul. "Prefrontal Cortex." In Encyclopedia of Clinical Neuropsychology, 2771–74. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_1904.

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Jacobs, Kimberle M. "Cerebral Cortex." In Encyclopedia of Clinical Neuropsychology, 731–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_304.

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Zaydan, Islam. "Striate Cortex." In Encyclopedia of Clinical Neuropsychology, 3311. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_361.

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Felton, Warren L. "Visual Cortex." In Encyclopedia of Clinical Neuropsychology, 3611–12. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_377.

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Conference papers on the topic "Cortex"

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Csapo, Adam B., Andras Roka, and Peter Baranyi. "Visual Cortex Inspired Vertex and Corner Detection." In 2006 IEEE International Conference on Mechatronics. IEEE, 2006. http://dx.doi.org/10.1109/icmech.2006.252586.

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Mayer, Frédéric. "Cortex academy." In ACM SIGGRAPH 2004 Computer animation festival. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1186015.1186027.

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Jakubik, Tomas. "Cortex-M Simulator." In 2020 International Conference on Applied Electronics (AE). IEEE, 2020. http://dx.doi.org/10.23919/ae49394.2020.9232712.

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Osborn, Luke E., David P. McMullen, Breanne P. Christie, Pawel Kudela, Tessy M. Thomas, Margaret C. Thompson, Robert W. Nickl, et al. "Intracortical microstimulation of somatosensory cortex generates evoked responses in motor cortex." In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2021. http://dx.doi.org/10.1109/ner49283.2021.9441123.

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Farokhniaee, AmirAli, and Madeleine M. Lowery. "A Thalamo-Cortex Microcircuit Model of Beta Oscillations in the Parkinsonian Motor Cortex*." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8857790.

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Kuebler, Olaf, Gabor Szekely, Christian Brechbuehler, Robert Ogniewicz, Thomas F. Budinger, and Peter T. Sander. "Charting the human cerebral cortex." In San Diego '92, edited by David C. Wilson and Joseph N. Wilson. SPIE, 1992. http://dx.doi.org/10.1117/12.130903.

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Sheline, Yvette I., Kevin J. Black, Daniel Y. Lin, Joseph Pimmel, Po Wang, John W. Haller, John G. Csernansky, et al. "MRI volumetry of prefrontal cortex." In Medical Imaging 1995, edited by Murray H. Loew. SPIE, 1995. http://dx.doi.org/10.1117/12.208749.

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Stylianou, Georgios. "Cortex registration: a geometric approach." In Medical Imaging, edited by Robert L. Galloway, Jr. and Kevin R. Cleary. SPIE, 2005. http://dx.doi.org/10.1117/12.591444.

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Gilbert, Charles. "Color processing in visual cortex." In Advances in Color Vision. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/acv.1992.fc1.

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Wavelength specific cells in visual cortex are grouped together into compartments that are interdigitated between other compartments specializing in form, movement and depth. The first evidence for a functional organization of color based on cortical area came from the work of Semir Zeki, who described an area prestriate cortex, known as area V4, that was enriched for color specific cells. Other cortical areas also contained wavelength selective cells, but the precise distribution of these cells eluded investigators for a number of years until the discovery by Margaret Wong- Riley that in area VI the enzyme cytochrome oxidase (CO) was distributed in a regular series of patches, as seen in tangential sections through the superficial cortical layers. Using CO histochemistry, David Hubei and Margaret Livingstone found that wavelength selective cells were located within these patches or "blobs", with broadband, orientation selective cells being found predominantly outside them. This lent support to the idea that color, as represented by wavelength selective cells, and form, as represented by orientation selective cells, were analyzed by separate, parallel pathways in the visual system. This left open, however, the question of how information about form and color might be attributed to a particular object, the so called "binding" problem, and also about how boundaries made by different colors could provide information about form. In fact, the existence of cells selective both for wavelength and for orientation had been known from a number of studies, and Ts'o and Gilbert found that such cells tended to lie at the boundaries of the CO blobs. With regard to the color selectivity found within the blobs, they found that cells sharing similar color opponency were grouped into columns, and that individual blobs specialized in either red-green or blue-yellow opponency.
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Maunsell, John H. R. "Motion processing in visual cortex." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.tuj2.

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Many lines of anatomical and physiological evidence have shown that the visual system contains a distinct pathway that is responsible for most motion analysis. In primates this pathway originates in the retinal ganglion cells that send their axons to the magnocellular layers of the lateral geniculate nucleus (LGN). The outputs from the magnocellular LGN layers directly provide the primary excitatory drive to structures like layer 4B in striate cortex and the middle temporal area (MT) in extrastriate cortex. Both of these structures contain a high proportion of neurons that are selective for the direction of stimulus motion. Later stages of motion processing in parietal cortex appear to contribute to analyzing more complex types of movement such as rotation or looming.
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Reports on the topic "Cortex"

1

Shaw, Dan, and Christine Flauta. Cortex Commons. Landscape Architecture Foundation, 2023. http://dx.doi.org/10.31353/cs1920.

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Hammerstorm, Dan. Silicon Association Cortex. Fort Belvoir, VA: Defense Technical Information Center, August 1992. http://dx.doi.org/10.21236/ada257283.

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Hammerstorm, Dan. Silicon Association Cortex. Fort Belvoir, VA: Defense Technical Information Center, June 1991. http://dx.doi.org/10.21236/ada237182.

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Intrator, Nathan, Mark F. Bear, Leon N. Cooper, and Michael A. Paradiso. Theory of Synaptic Plasticity in Visual Cortex. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada260052.

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Intrator, Nathan, Mark F. Bear, Leon N. Cooper, and Michael A. Paradiso. Theory of Synaptic Plasticity in Visual Cortex. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada260322.

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Serre, Thomas, Lior Wolf, and Tomaso Poggio. Object Recognition with Features Inspired by Visual Cortex. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada454604.

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Sajda, Paul, and Leif H. Finkel. Computer Simulations of Object Discrimination by Visual Cortex,. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada253345.

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Riesenhuber, Maximilian, and Tomaso Poggio. Computational Models of Object Recognition in Cortex: A Review. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada458109.

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Chapline, G. Spontaneous origin of topological complexity in the cerebral cortex. Office of Scientific and Technical Information (OSTI), April 1995. http://dx.doi.org/10.2172/82487.

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Masri, Radi. Motor Cortex Stimulation Reverses Maladaptive Plasticity Following Spinal Cord Injury. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada568224.

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