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Littérature scientifique sur le sujet « Neurocomputational models »

Auteur : Grafiati

Publié le 10 mars 2023

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Neurocomputational models"

Hale, John T., Luca Campanelli, Jixing Li, Shohini Bhattasali, Christophe Pallier, and Jonathan R. Brennan. "Neurocomputational Models of Language Processing." Annual Review of Linguistics 8, no. 1 (2022): 427–46. http://dx.doi.org/10.1146/annurev-linguistics-051421-020803.

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Efforts to understand the brain bases of language face the Mapping Problem: At what level do linguistic computations and representations connect to human neurobiology? We review one approach to this problem that relies on rigorously defined computational models to specify the links between linguistic features and neural signals. Such tools can be used to estimate linguistic predictions, model linguistic features, and specify a sequence of processing steps that may be quantitatively fit to neural signals collected while participants use language. Progress has been helped by advances in machine

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Durstewitz, Daniel, Jeremy K. Seamans, and Terrence J. Sejnowski. "Neurocomputational models of working memory." Nature Neuroscience 3, S11 (2000): 1184–91. http://dx.doi.org/10.1038/81460.

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Cutsuridis, Vassilis, Tjitske Heida, Wlodek Duch, and Kenji Doya. "Neurocomputational models of brain disorders." Neural Networks 24, no. 6 (2011): 513–14. http://dx.doi.org/10.1016/j.neunet.2011.03.016.

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Hardy, Nicholas F., and Dean V. Buonomano. "Neurocomputational models of interval and pattern timing." Current Opinion in Behavioral Sciences 8 (April 2016): 250–57. http://dx.doi.org/10.1016/j.cobeha.2016.01.012.

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Bicer, Mustafa Berkan. "Radar-Based Microwave Breast Imaging Using Neurocomputational Models." Diagnostics 13, no. 5 (2023): 930. http://dx.doi.org/10.3390/diagnostics13050930.

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In this study, neurocomputational models are proposed for the acquisition of radar-based microwave images of breast tumors using deep neural networks (DNNs) and convolutional neural networks (CNNs). The circular synthetic aperture radar (CSAR) technique for radar-based microwave imaging (MWI) was utilized to generate 1000 numerical simulations for randomly generated scenarios. The scenarios contain information such as the number, size, and location of tumors for each simulation. Then, a dataset of 1000 distinct simulations with complex values based on the scenarios was built. Consequently, a r

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Holker, Ruchi, and Seba Susan. "Neuroscience-Inspired Parameter Selection of Spiking Neuron Using Hodgkin Huxley Model." International Journal of Software Science and Computational Intelligence 13, no. 2 (2021): 89–106. http://dx.doi.org/10.4018/ijssci.2021040105.

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Spiking neural networks (SNN) are currently being researched to design an artificial brain to teach it how to think, perform, and learn like a human brain. This paper focuses on exploring optimal values of parameters of biological spiking neurons for the Hodgkin Huxley (HH) model. The HH model exhibits maximum number of neurocomputational properties as compared to other spiking models, as per previous research. This paper investigates the HH model parameters of Class 1, Class 2, phasic spiking, and integrator neurocomputational properties. For the simulation of spiking neurons, the NEURON simu

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Dezfouli, Amir, Payam Piray, Mohammad Mahdi Keramati, Hamed Ekhtiari, Caro Lucas, and Azarakhsh Mokri. "A Neurocomputational Model for Cocaine Addiction." Neural Computation 21, no. 10 (2009): 2869–93. http://dx.doi.org/10.1162/neco.2009.10-08-882.

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Based on the dopamine hypotheses of cocaine addiction and the assumption of decrement of brain reward system sensitivity after long-term drug exposure, we propose a computational model for cocaine addiction. Utilizing average reward temporal difference reinforcement learning, we incorporate the elevation of basal reward threshold after long-term drug exposure into the model of drug addiction proposed by Redish. Our model is consistent with the animal models of drug seeking under punishment. In the case of nondrug reward, the model explains increased impulsivity after long-term drug exposure. F

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Spitzer, M. "Neurocomputational models of cognitive dysfunctions in schizophrenia and therapeutic implications." European Neuropsychopharmacology 8 (November 1998): S63—S64. http://dx.doi.org/10.1016/s0924-977x(98)80018-7.

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Sivia, Jagtar Singh, Amar Partap Singh Pharwaha, and Tara Singh Kamal. "Neurocomputational Models for Parameter Estimation of Circular Microstrip Patch Antennas." Procedia Computer Science 85 (2016): 393–400. http://dx.doi.org/10.1016/j.procs.2016.05.178.

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Reggia, James A. "Neurocomputational models of the remote effects of focal brain damage." Medical Engineering & Physics 26, no. 9 (2004): 711–22. http://dx.doi.org/10.1016/j.medengphy.2004.06.010.

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Thèses sur le sujet "Neurocomputational models"

Ragonetti, Gianmarco. "A neurocomputational model of reward-based motor learning." Doctoral thesis, Universita degli studi di Salerno, 2017. http://hdl.handle.net/10556/3028.

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2015 - 2016<br>The following thesis deals with computational models of nervous system employed in motor reinforcement learning. The novel contribution of this work is that it includes a methodology of experiments for evaluating learning rates for human which we compared with the results coming from a computational model we derived from a deep analysis of literature. Rewards or punishments are particular stimuli able to drive for good or for worse the performance of the action to learn. This happens because they can strengthen or weaken the connections among a combination of sensory input stim

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Parziale, Antonio. "A neurocomputational model of reaching movements." Doctoral thesis, Universita degli studi di Salerno, 2016. http://hdl.handle.net/10556/2341.

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2013 - 2014<br>How the brain controls movement is a question that has fascinated researchers from different areas as neuroscience, robotics and psychology. To understand how we move is not only an intellectual challenge, but it is important for finding new strategies for nursing people with movement diseases, for rehabilitation and to develop new robotic technology. While there is an agreement about the role of the primary motor cortex (M1) in the execution of voluntary movements, it is still debated what (and how) is encoded by the neural activity of the motor cortex. To unveil the "c

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Marsh, Steven Joseph Thomas. "Efficient programming models for neurocomputation." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709268.

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Dupuy, Nathalie. "Neurocomputational model for learning, memory consolidation and schemas." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33144.

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This thesis investigates how through experience the brain acquires and stores memories, and uses these to extract and modify knowledge. This question is being studied by both computational and experimental neuroscientists as it is of relevance for neuroscience, but also for artificial systems that need to develop knowledge about the world from limited, sequential data. It is widely assumed that new memories are initially stored in the hippocampus, and later are slowly reorganised into distributed cortical networks that represent knowledge. This memory reorganisation is called systems consolida

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Chadderdon, George L. "A neurocomputational model of the functional role of dopamine in stimulus-response task learning and performance." [Bloomington, Ind.] : Indiana University, 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3355003.

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Thesis (Ph.D.)--Indiana University, Dept. of Psychological and Brain Sciences and Cognitive Science, 2009.<br>Title from PDF t.p. (viewed on Feb. 5, 2010). Source: Dissertation Abstracts International, Volume: 70-04, Section: B, page: 2609. Adviser: Olaf Sporns.

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Kolbeck, Carter. "A neurocomputational model of the mammalian fear conditioning circuit." Thesis, 2013. http://hdl.handle.net/10012/7897.

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In this thesis, I present a computational neural model that reproduces the high-level behavioural results of well-known fear conditioning experiments: first-order conditioning, second-order conditioning, sensory preconditioning, context conditioning, blocking, first-order extinction and renewal (AAB, ABC, ABA), and extinction and renewal after second-order conditioning and sensory preconditioning. The simulated neural populations used to account for the behaviour observed in these experiments correspond to known anatomical regions of the mammalian brain. Parts of the amygdala, periaqueductal g

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Barwiński, Marek [Verfasser]. "A neurocomputational model of memory acquisition for novel faces / by Marek Barwi`nski." 2008. http://d-nb.info/997248939/34.

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Sadat, Rezai Seyed Omid. "A Neurocomputational Model of Smooth Pursuit Control to Interact with the Real World." Thesis, 2014. http://hdl.handle.net/10012/8224.

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Whether we want to drive a car, play a ball game, or even enjoy watching a flying bird, we need to track moving objects. This is possible via smooth pursuit eye movements (SPEMs), which maintain the image of the moving object on the fovea (i.e., a very small portion of the retina with high visual resolution). At first glance, performing an accurate SPEM by the brain may seem trivial. However, imperfect visual coding, processing and transmission delays, wide variety of object sizes, and background textures make the task challenging. Furthermore, the existence of distractors in the environmen

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"A neurocomputational model of the functional role of dopamine in stimulus-response task learning and performance." INDIANA UNIVERSITY, 2009. http://pqdtopen.proquest.com/#viewpdf?dispub=3355003.

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Livres sur le sujet "Neurocomputational models"

Cottrell, Garrison W., and Janet H. Hsiao. Neurocomputational Models of Face Processing. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199559053.013.0021.

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Eliasmith, Chris. Neurocomputational Models: Theory, Application, Philosophical Consequences. Edited by John Bickle. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780195304787.003.0014.

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This article describes the neural engineering framework (NEF), a systematic approach to studying neural systems that has collected and extended a set of consistent methods that are highly general. The NEF draws heavily on past work in theoretical neuroscience, integrating work on neural coding, population representation, and neural dynamics to enable the construction of large-scale biologically plausible neural simulations. It is based on the principles that neural representations defined by a combination of nonlinear encoding and optimal linear decoding and that neural dynamics are characteri

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Chapitres de livres sur le sujet "Neurocomputational models"

Moustafa, Ahmed A., Błażej Misiak, and Dorota Frydecka. "Neurocomputational Models of Schizophrenia." In Computational Models of Brain and Behavior. John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119159193.ch6.

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Knott, Alistair. "Neurocomputational Models of Natural Language." In Springer Handbook of Bio-/Neuroinformatics. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-30574-0_48.

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Hass, Joachim, and Daniel Durstewitz. "Neurocomputational Models of Time Perception." In Advances in Experimental Medicine and Biology. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1782-2_4.

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Freund, Michael C., and Todd S. Braver. "Neurocomputational Models of Task Representation." In The Sage Handbook of Cognitive and Systems Neuroscience. SAGE Publications Ltd, 2024. http://dx.doi.org/10.4135/9781529616651.n29.

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Denham, Susan L., Salvador Dura-Bernal, Martin Coath, and Emili Balaguer-Ballester. "6. Neurocomputational models of perceptual organization." In Unconscious Memory Representations in Perception. John Benjamins Publishing Company, 2010. http://dx.doi.org/10.1075/aicr.78.08den.

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Aleksander, Igor, Barry Dunmall, and Valentina Del Frate. "Neurocomputational models of visualisation: A preliminary report." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0098238.

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Serrano, Miguel Ángel, Francisco Molins, and Adrián Alacreu-Crespo. "Human Decision-Making Evaluation: From Classical Methods to Neurocomputational Models." In Studies in Systems, Decision and Control. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00856-6_9.

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Balcı, Fuat, and Patrick Simen. "Neurocomputational Models of Interval Timing: Seeing the Forest for the Trees." In Advances in Experimental Medicine and Biology. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-60183-5_4.

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Liènard, Jean, Agnès Guillot, and Benoît Girard. "Multi-objective Evolutionary Algorithms to Investigate Neurocomputational Issues: The Case Study of Basal Ganglia Models." In From Animals to Animats 11. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15193-4_56.

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Chen, Eric Y. H. "A Neurocomputational Model of Early Psychosis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45226-3_156.

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Actes de conférences sur le sujet "Neurocomputational models"

Torres-Molina, Richard, Andrés Riofrío-Valdivieso, Carlos Bustamante-Orellana, and Francisco Ortega-Zamorano. "Prediction of Learning Improvement in Mathematics through a Video Game using Neurocomputational Models." In 11th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007348605540559.

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Carvalho, Luís Alfredo Vidal de, Nivea de Carvalho Ferreira, and Adriana Fiszman. "A Neurocomputational Model for Autism." In 4. Congresso Brasileiro de Redes Neurais. CNRN, 2016. http://dx.doi.org/10.21528/cbrn1999-082.

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Rubio, Erismar, and Nagaraju Reddicharla. "Enhanced Back Allocation Process Assisted by Real Time AI-Based Virtual Metering. Field Case Study in a Giant Field Project." In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216936-ms.

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Abstract Artificial Intelligence (AI) is relatively new neurocomputational science employed in oil industry to solve wide spectrum of non-linear problems with high parallelism, fault and noise tolerance. AI seems very attractive for its remarkable capabilities of processing-correlating data and learning attributes. This paper briefs a successful implementation of an ecosystem between AI and physics-based models in a smart field, that support accurate well allocation process during the project start-up when testing facilities were not available. This case study is presented in a smart field dur

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Davis, Gregory P., Garrett E. Katz, Daniel Soranzo, et al. "A Neurocomputational Model of Posttraumatic Stress Disorder." In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2021. http://dx.doi.org/10.1109/ner49283.2021.9441345.

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Rodriguez-Alabarce, Jose, Francisco Ortega-Zamorano, Jose M. Jerez, Kusha Ghoreishi, and Leonardo Franco. "Thermal comfort estimation using a neurocomputational model." In 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI). IEEE, 2016. http://dx.doi.org/10.1109/la-cci.2016.7885703.

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Sivian, Jagtar S., Amarpartap S. Pharwaha, and Tara S. Kamal. "Neurocomputational Model for Analysis Microstrip Antennas for Wireless Communication." In Visualization, Imaging and Image Processing / 783: Modelling and Simulation / 784: Wireless Communications. ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.784-009.

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Yan, Han, Jianwu Dang, Mengxue Cao, and Bernd J. Kroger. "A new framework of neurocomputational model for speech production." In 2014 9th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, 2014. http://dx.doi.org/10.1109/iscslp.2014.6936623.

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Baston, Chiara, and Mauro Ursino. "A neurocomputational model of dopamine dependent finger tapping task." In 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI). IEEE, 2016. http://dx.doi.org/10.1109/rtsi.2016.7740581.

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Socasi, Francisco, Ronny Velastegui, Luis Zhinin-Vera, Rafael Valencia-Ramos, Francisco Ortega-Zamorano, and Oscar Chang. "Digital Cryptography Implementation using Neurocomputational Model with Autoencoder Architecture." In 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009154908650872.

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Helie, Sebastien, and F. Gregory Ashby. "A neurocomputational model of automaticity and maintenance of abstract rules." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178593.

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