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Journal articles on the topic 'Neurocomputational models'

Author: Grafiati

Published: 10 March 2023

Last updated: 31 July 2025

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1

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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Cohen, Michael X., and Michael J. Frank. "Neurocomputational models of basal ganglia function in learning, memory and choice." Behavioural Brain Research 199, no. 1 (2009): 141–56. http://dx.doi.org/10.1016/j.bbr.2008.09.029.

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Helie, Sebastien, Jessica L. Roeder, Lauren Vucovich, Dennis Rünger, and F. Gregory Ashby. "A Neurocomputational Model of Automatic Sequence Production." Journal of Cognitive Neuroscience 27, no. 7 (2015): 1456–69. http://dx.doi.org/10.1162/jocn_a_00794.

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Most behaviors unfold in time and include a sequence of submovements or cognitive activities. In addition, most behaviors are automatic and repeated daily throughout life. Yet, relatively little is known about the neurobiology of automatic sequence production. Past research suggests a gradual transfer from the associative striatum to the sensorimotor striatum, but a number of more recent studies challenge this role of the BG in automatic sequence production. In this article, we propose a new neurocomputational model of automatic sequence production in which the main role of the BG is to train

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Hoffman, Paul, Matthew A. Lambon Ralph, and Anna M. Woollams. "Triangulation of the neurocomputational architecture underpinning reading aloud." Proceedings of the National Academy of Sciences 112, no. 28 (2015): E3719—E3728. http://dx.doi.org/10.1073/pnas.1502032112.

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The goal of cognitive neuroscience is to integrate cognitive models with knowledge about underlying neural machinery. This significant challenge was explored in relation to word reading, where sophisticated computational-cognitive models exist but have made limited contact with neural data. Using distortion-corrected functional MRI and dynamic causal modeling, we investigated the interactions between brain regions dedicated to orthographic, semantic, and phonological processing while participants read words aloud. We found that the lateral anterior temporal lobe exhibited increased activation

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Hass, Joachim, and J. Michael Herrmann. "The Neural Representation of Time: An Information-Theoretic Perspective." Neural Computation 24, no. 6 (2012): 1519–52. http://dx.doi.org/10.1162/neco_a_00280.

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A prominent finding in psychophysical experiments on time perception is Weber's law, the linear scaling of timing errors with duration. The ability to reproduce this scaling has been taken as a criterion for the validity of neurocomputational models of time perception. However, the origin of Weber's law remains unknown, and currently only a few models generi- cally reproduce it. Here, we use an information-theoretical framework that considers the neuronal mechanisms of time perception as stochastic processes to investigate the statistical origin of Weber's law in time perception and also its f

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Schlossmacher, Insa, Felix Lucka, Antje Peters, Maximilian Bruchmann, and Thomas Straube. "Effects of awareness and task relevance on neurocomputational models of mismatch negativity generation." NeuroImage 262 (November 2022): 119530. http://dx.doi.org/10.1016/j.neuroimage.2022.119530.

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Rudd, Michael E. "Neurocomputational model explains spatial variations in perceived lightness induced by luminance edges in the image." Electronic Imaging 2021, no. 11 (2021): 151–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.11.hvei-151.

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Computer simulations of an extended version of a neural model of lightness perception [1,2] are presented. The model provides a unitary account of several key aspects of spatial lightness phenomenology, including contrast and assimilation, and asymmetries in the strengths of lightness and darkness induction. It does this by invoking mechanisms that have also been shown to account for the overall magnitude of dynamic range compression in experiments involving lightness matches made to real-world surfaces [2]. The model assumptions are derived partly from parametric measurements of visual respon

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Cheung, Vincent K. M., and Shu Sakamoto. "Separating Uncertainty from Surprise in Auditory Processing with Neurocomputational Models: Implications for Music Perception." Journal of Neuroscience 42, no. 29 (2022): 5657–59. http://dx.doi.org/10.1523/jneurosci.0594-22.2022.

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Capelier-Mourguy, Arthur, Katherine E. Twomey, and Gert Westermann. "Neurocomputational Models Capture the Effect of Learned Labels on Infants’ Object and Category Representations." IEEE Transactions on Cognitive and Developmental Systems 12, no. 2 (2020): 160–68. http://dx.doi.org/10.1109/tcds.2018.2882920.

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Ratcliff, Roger, and Michael J. Frank. "Reinforcement-Based Decision Making in Corticostriatal Circuits: Mutual Constraints by Neurocomputational and Diffusion Models." Neural Computation 24, no. 5 (2012): 1186–229. http://dx.doi.org/10.1162/neco_a_00270.

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In this letter, we examine the computational mechanisms of reinforce-ment-based decision making. We bridge the gap across multiple levels of analysis, from neural models of corticostriatal circuits—the basal ganglia (BG) model (Frank, 2005 , 2006 ) to simpler but mathematically tractable diffusion models of two-choice decision making. Specifically, we generated simulated data from the BG model and fit the diffusion model (Ratcliff, 1978 ) to it. The standard diffusion model fits underestimated response times under conditions of high response and reinforcement conflict. Follow-up fits showed go

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Ruff, Christian. "Know your targets: Informing NIBS applications in psychiatry by neurocomputational models of behavioral control." L'Encéphale 45 (June 2019): S63—S64. http://dx.doi.org/10.1016/j.encep.2019.04.065.

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Beuter, Anne. "The Use of Neurocomputational Models as Alternatives to Animal Models in the Development of Electrical Brain Stimulation Treatments." Alternatives to Laboratory Animals 45, no. 2 (2017): 91–99. http://dx.doi.org/10.1177/026119291704500203.

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Ortega-Zamorano, Francisco, José M. Jerez, Gustavo E. Juárez, and Leonardo Franco. "FPGA Implementation of Neurocomputational Models: Comparison Between Standard Back-Propagation and C-Mantec Constructive Algorithm." Neural Processing Letters 46, no. 3 (2017): 899–914. http://dx.doi.org/10.1007/s11063-017-9655-x.

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Weerathunge, Hasini R., Gabriel A. Alzamendi, Gabriel J. Cler, Frank H. Guenther, Cara E. Stepp, and Matías Zañartu. "LaDIVA: A neurocomputational model providing laryngeal motor control for speech acquisition and production." PLOS Computational Biology 18, no. 6 (2022): e1010159. http://dx.doi.org/10.1371/journal.pcbi.1010159.

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Many voice disorders are the result of intricate neural and/or biomechanical impairments that are poorly understood. The limited knowledge of their etiological and pathophysiological mechanisms hampers effective clinical management. Behavioral studies have been used concurrently with computational models to better understand typical and pathological laryngeal motor control. Thus far, however, a unified computational framework that quantitatively integrates physiologically relevant models of phonation with the neural control of speech has not been developed. Here, we introduce LaDIVA, a novel n

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Wang, Panqu, Isabel Gauthier, and Garrison Cottrell. "Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration." Journal of Cognitive Neuroscience 28, no. 4 (2016): 558–74. http://dx.doi.org/10.1162/jocn_a_00919.

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Are face and object recognition abilities independent? Although it is commonly believed that they are, Gauthier et al. [Gauthier, I., McGugin, R. W., Richler, J. J., Herzmann, G., Speegle, M., & VanGulick, A. E. Experience moderates overlap between object and face recognition, suggesting a common ability. Journal of Vision, 14, 7, 2014] recently showed that these abilities become more correlated as experience with nonface categories increases. They argued that there is a single underlying visual ability, v, that is expressed in performance with both face and nonface categories as experienc

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Chang, Soo-Eun, Emily O. Garnett, Andrew Etchell, and Ho Ming Chow. "Functional and Neuroanatomical Bases of Developmental Stuttering: Current Insights." Neuroscientist 25, no. 6 (2018): 566–82. http://dx.doi.org/10.1177/1073858418803594.

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Affecting 5% of all preschool-aged children and 1% of the general population, developmental stuttering—also called childhood-onset fluency disorder—is a complex, multifactorial neurodevelopmental disorder characterized by frequent disruption of the fluent flow of speech. Over the past two decades, neuroimaging studies of both children and adults who stutter have begun to provide significant insights into the neurobiological bases of stuttering. This review highlights convergent findings from this body of literature with a focus on functional and structural neuroimaging results that are support

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Patil, Anuya, and Katherine Duncan. "Lingering Cognitive States Shape Fundamental Mnemonic Abilities." Psychological Science 29, no. 1 (2017): 45–55. http://dx.doi.org/10.1177/0956797617728592.

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Why are people sometimes able to recall associations in exquisite detail while at other times left frustrated by the deficiencies of memory? Although this apparent fickleness of memory has been extensively studied by investigating factors that build strong memory traces, researchers know less about whether memory success also depends on cognitive states that are in place when a cue is encountered. Motivating this possibility, neurocomputational models propose that the hippocampus’s capacity to support associative recollection (pattern completion) is biased by persistent neurochemical states, w

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Graf, M., D. Kaping, and H. H. Bülthoff. "Orientation Congruency Effects for Familiar Objects." Psychological Science 16, no. 3 (2005): 214–21. http://dx.doi.org/10.1111/j.0956-7976.2005.00806.x.

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How do observers recognize objects after spatial transformations? Recent neurocomputational models have proposed that object recognition is based on coordinate transformations that align memory and stimulus representations. If the recognition of a misoriented object is achieved by adjusting a coordinate system (or reference frame), then recognition should be facilitated when the object is preceded by a different object in the same orientation. In the two experiments reported here, two objects were presented in brief masked displays that were in close temporal contiguity; the objects were in ei

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Clark, Andy. "Consciousness as Generative Entanglement." Journal of Philosophy 116, no. 12 (2019): 645–62. http://dx.doi.org/10.5840/jphil20191161241.

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Recent work in cognitive and computational neuroscience depicts the human brain as a complex, multi-layer prediction engine. This family of models has had great success in accounting for a wide variety of phenomena involving perception, action, and attention. But despite their clear promise as accounts of the neurocomputational origins of perceptual experience, they have not yet been leveraged so as to shed light on the so-called “hard problem” of consciousness—the problem of explaining why and how the world is subjectively experienced at all, and why those experiences seem just the way they d

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Moustafa, Ahmed A., and Mark A. Gluck. "A Neurocomputational Model of Dopamine and Prefrontal–Striatal Interactions during Multicue Category Learning by Parkinson Patients." Journal of Cognitive Neuroscience 23, no. 1 (2011): 151–67. http://dx.doi.org/10.1162/jocn.2010.21420.

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Most existing models of dopamine and learning in Parkinson disease (PD) focus on simulating the role of basal ganglia dopamine in reinforcement learning. Much data argue, however, for a critical role for prefrontal cortex (PFC) dopamine in stimulus selection in attentional learning. Here, we present a new computational model that simulates performance in multicue category learning, such as the “weather prediction” task. The model addresses how PD and dopamine medications affect stimulus selection processes, which mediate reinforcement learning. In this model, PFC dopamine is key for attentiona

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Davelaar, Eddy J. "Sequential Retrieval and Inhibition of Parallel (Re)Activated Representations: A Neurocomputational Comparison of Competitive Queuing and Resampling Models." Adaptive Behavior 15, no. 1 (2007): 51–71. http://dx.doi.org/10.1177/1059712306076250.

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Fu, Si-Yao, Guo-Sheng Yang, and Xin-Kai Kuai. "A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition." Computational Intelligence and Neuroscience 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/946589.

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In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the s

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Kachergis, George, Dean Wyatte, Randall C. O'Reilly, Roy de Kleijn, and Bernhard Hommel. "A continuous-time neural model for sequential action." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1655 (2014): 20130623. http://dx.doi.org/10.1098/rstb.2013.0623.

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Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, aff

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Sirois, Sylvain, and Denis Mareschal. "An Interacting Systems Model of Infant Habituation." Journal of Cognitive Neuroscience 16, no. 8 (2004): 1352–62. http://dx.doi.org/10.1162/0898929042304778.

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Habituation and related procedures are the primary behavioral tools used to assess perceptual and cognitive competence in early infancy. This article introduces a neurally constrained computational model of infant habituation. The model combines the two leading process theories of infant habituation into a single functional system that is grounded in functional brain circuitry. The HAB model (for Habituation, Autoassociation, and Brain) proposes that habituation behaviors emerge from the opponent, complementary processes of hippocampal selective inhibition and cortical long-term potentiation.

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Iza, Mauricio, and Jesús Ezquerro. "Recent developments in the study of cognitive processing of emotionally arousing words." Cognitive Linguistic Studies 2, no. 1 (2015): 129–49. http://dx.doi.org/10.1075/cogls.2.1.07iza.

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Research on the interaction between emotion, cognition and language in the field of Artificial Intelligence has become particularly active along the last years. Lots of computational models of emotion have been developed. There are accounts stressing the role of canonical and mirror neurons as underlying the use of nouns and verbs. At the same time, neuropsychology is developing new approaches for modeling language, emotion and cognition inspired on the insights gained from robotics. The current landscape is thus a promising collaboration between several approaches: Social Psychology, Neuropsy

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Dutta, Anirban. "Neurocomputational Mechanisms of Sense of Agency: Literature Review for Integrating Predictive Coding and Adaptive Control in Human–Machine Interfaces." Brain Sciences 15, no. 4 (2025): 396. https://doi.org/10.3390/brainsci15040396.

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Background: The sense of agency (SoA)—the subjective experience of controlling one’s own actions and their consequences—is a fundamental aspect of human cognition, volition, and motor control. Understanding how the SoA arises and is disrupted in neuropsychiatric disorders has significant implications for human–machine interface (HMI) design for neurorehabilitation. Traditional cognitive models of agency often fail to capture its full complexity, especially in dynamic and uncertain environments. Objective: This review synthesizes computational models—particularly predictive coding, Bayesian inf

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Gupta, Ashish, Lovekesh Vig, and David C. Noelle. "A Cognitive Model for Generalization during Sequential Learning." Journal of Robotics 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/617613.

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Traditional artificial neural network models of learning suffer fromcatastrophic interference. They are commonly trained to perform only one specific task, and, when trained on a new task, they forget the original task completely. It has been shown that the foundational neurocomputational principles embodied by the Leabra cognitive modeling framework, specifically fast lateral inhibition and a local synaptic plasticity model that incorporates both correlational and error-based components, are sufficient to largely overcome this limitation during the sequential learning of multiple motor skills

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Wixted, John T., Stephen D. Goldinger, Larry R. Squire, et al. "Coding of episodic memory in the human hippocampus." Proceedings of the National Academy of Sciences 115, no. 5 (2018): 1093–98. http://dx.doi.org/10.1073/pnas.1716443115.

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Neurocomputational models have long posited that episodic memories in the human hippocampus are represented by sparse, stimulus-specific neural codes. A concomitant proposal is that when sparse-distributed neural assemblies become active, they suppress the activity of competing neurons (neural sharpening). We investigated episodic memory coding in the hippocampus and amygdala by measuring single-neuron responses from 20 epilepsy patients (12 female) undergoing intracranial monitoring while they completed a continuous recognition memory task. In the left hippocampus, the distribution of single-

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Cartling, Bo. "Response Characteristics of a Low-Dimensional Model Neuron." Neural Computation 8, no. 8 (1996): 1643–52. http://dx.doi.org/10.1162/neco.1996.8.8.1643.

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It is shown that a low-dimensional model neuron with a response time constant smaller than the membrane time constant closely reproduces the activity and excitability behavior of a detailed conductance-based model of Hodgkin-Huxley type. The fast response of the activity variable also makes it possible to reduce the model to a one-dimensional model, in particular for typical conditions. As an example, the reduction to a single-variable model from a multivariable conductance-based model of a neocortical pyramidal cell with somatic input is demonstrated. The conditions for avoiding a spurious da

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Changeux, Jean-Pierre. "The Ferrier Lecture 1998 The molecular biology of consciousness investigated with genetically modified mice." Philosophical Transactions of the Royal Society B: Biological Sciences 361, no. 1476 (2006): 2239–59. http://dx.doi.org/10.1098/rstb.2006.1832.

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The question is raised of the relevance of experimental work with the mouse and some of its genetically modified individuals in the study of consciousness. Even if this species does not go far beyond the level of ‘minimal consciousness’, it may be a useful animal model to examine the elementary building blocks of consciousness using the methods of molecular biology jointly with investigations at the physiological and behavioural levels. These building blocks which are anticipated to be universally shared by higher organisms (from birds to humans) may include: (i) the access to multiple states

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Zhong, Shan, Jeong Woo Choi, Nadia G. Hashoush, et al. "A neurocomputational theory of action regulation predicts motor behavior in neurotypical individuals and patients with Parkinson’s disease." PLOS Computational Biology 18, no. 11 (2022): e1010111. http://dx.doi.org/10.1371/journal.pcbi.1010111.

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Surviving in an uncertain environment requires not only the ability to select the best action, but also the flexibility to withhold inappropriate actions when the environmental conditions change. Although selecting and withholding actions have been extensively studied in both human and animals, there is still lack of consensus on the mechanism underlying these action regulation functions, and more importantly, how they inter-relate. A critical gap impeding progress is the lack of a computational theory that will integrate the mechanisms of action regulation into a unified framework. The curren

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Hsiao, Janet H., Ben Cipollini, and Garrison W. Cottrell. "Hemispheric Asymmetry in Perception: A Differential Encoding Account." Journal of Cognitive Neuroscience 25, no. 7 (2013): 998–1007. http://dx.doi.org/10.1162/jocn_a_00377.

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Hemispheric asymmetry in the processing of local and global features has been argued to originate from differences in frequency filtering in the two hemispheres, with little neurophysiological support. Here we test the hypothesis that this asymmetry takes place at an encoding stage beyond the sensory level, due to asymmetries in anatomical connections within each hemisphere. We use two simple encoding networks with differential connection structures as models of differential encoding in the two hemispheres based on a hypothesized generalization of neuroanatomical evidence from the auditory mod

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Gasser, Brad, and Michael Arbib. "When one brain needs to learn from another: the case of observational facilitation of list learning in macaques." Adaptive Behavior 25, no. 3 (2017): 147–61. http://dx.doi.org/10.1177/1059712317715866.

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Given a neural system that equips an agent to attempt to carry out some learning task based on its own interaction with the “non-social” world, what extra neural machinery is required to enable learning to be facilitated by (repeated) observation of successful completion of that task by another agent? We provide one answer by exploiting an understanding of data and models on mirror neurons to extend a prior neurocomputational model of list learning by macaques, SCP1, which addressed results of the simultaneous chaining paradigm (SCP) to yield a new model, SCP2, that addresses social facilitati

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Bradshaw, Abigail R., Daniel R. Lametti, and Carolyn McGettigan. "The Role of Sensory Feedback in Developmental Stuttering: A Review." Neurobiology of Language 2, no. 2 (2021): 308–34. http://dx.doi.org/10.1162/nol_a_00036.

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Abstract Developmental stuttering is a neurodevelopmental disorder that severely affects speech fluency. Multiple lines of evidence point to a role of sensory feedback in the disorder; this has led to a number of theories proposing different disruptions to the use of sensory feedback during speech motor control in people who stutter. The purpose of this review was to bring together evidence from studies using altered auditory feedback paradigms with people who stutter, in order to evaluate the predictions of these different theories. This review highlights converging evidence for particular pa

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CARTLING, BO. "A LOW-DIMENSIONAL, TIME-RESOLVED AND ADAPTING MODEL NEURON." International Journal of Neural Systems 07, no. 03 (1996): 237–46. http://dx.doi.org/10.1142/s012906579600021x.

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A low-dimensional, time-resolved and adapting model neuron is formulated and evaluated. The model is an extension of the integrate-and-fire type of model with respect to adaptation and of a recent adapting firing-rate model with respect to time-resolution. It is obtained from detailed conductance-based models by a separation of fast and slow ionic processes of action potential generation. The model explicitly includes firing-rate regulation via the slow afterhyperpolarization phase of action potentials, which is controlled by calcium-sensitive potassium channels. It is demonstrated that the mo

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Blanchard, Tommy C., Steven T. Piantadosi, and Benjamin Y. Hayden. "Robust mixture modeling reveals category-free selectivity in reward region neuronal ensembles." Journal of Neurophysiology 119, no. 4 (2018): 1305–18. http://dx.doi.org/10.1152/jn.00808.2017.

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Classification of neurons into clusters based on their response properties is an important tool for gaining insight into neural computations. However, it remains unclear to what extent neurons fall naturally into discrete functional categories. We developed a Bayesian method that models the tuning properties of neural populations as a mixture of multiple types of task-relevant response patterns. We applied this method to data from several cortical and striatal regions in economic choice tasks. In all cases, neurons fell into only two clusters: one multiple-selectivity cluster containing all ce

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Del Popolo Cristaldi, Fiorella, Giovanni Mento, Michela Sarlo, and Giulia Buodo. "Dealing with uncertainty: A high-density EEG investigation on how intolerance of uncertainty affects emotional predictions." PLOS ONE 16, no. 7 (2021): e0254045. http://dx.doi.org/10.1371/journal.pone.0254045.

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Intolerance of uncertainty (IU) can influence emotional predictions, constructed by the brain (generation stage) to prearrange action (implementation stage), and update internal models according to incoming stimuli (updating stage). However, neurocomputational mechanisms by which IU affects emotional predictions are unclear. This high-density EEG study investigated if IU predicted event-related potentials (ERPs) and brain sources activity developing along the stages of emotional predictions, as a function of contextual uncertainty. Thirty-six undergraduates underwent a S1-S2 paradigm, with emo

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Davis, Matthew H., and M. Gareth Gaskell. "A complementary systems account of word learning: neural and behavioural evidence." Philosophical Transactions of the Royal Society B: Biological Sciences 364, no. 1536 (2009): 3773–800. http://dx.doi.org/10.1098/rstb.2009.0111.

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In this paper we present a novel theory of the cognitive and neural processes by which adults learn new spoken words. This proposal builds on neurocomputational accounts of lexical processing and spoken word recognition and complementary learning systems (CLS) models of memory. We review evidence from behavioural studies of word learning that, consistent with the CLS account, show two stages of lexical acquisition: rapid initial familiarization followed by slow lexical consolidation. These stages map broadly onto two systems involved in different aspects of word learning: (i) rapid, initial ac

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Tiňo, Peter, Igor Farkaš, and Jort van Mourik. "Dynamics and Topographic Organization of Recursive Self-Organizing Maps." Neural Computation 18, no. 10 (2006): 2529–67. http://dx.doi.org/10.1162/neco.2006.18.10.2529.

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Recently there has been an outburst of interest in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, there is no general consensus as to how best to process sequences using topographic maps, and this topic remains an active focus of neurocomputational research. The representational capabilities and internal representations of the models are not well understood. Here, we rigorously analyze a generalization of the self-organizing map (SOM) for processing sequential data, recursive SOM(RecSOM) (Voegtlin, 2002), as a nonautonomous dy

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Mr. Jeevan K P and Dr. P Sandhya. "Comprehensive Review of the Cognitive and Therapeutic Effects of Mantras." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 06 (2025): 2950–66. https://doi.org/10.47392/irjaeh.2025.0436.

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Mantra, a spiritual and cognitive practice, has gained significant attention in contemporary scientific discourse due to its potential mental health benefits. This review consolidates existing research on the effects of mantra chanting, particularly its influence on cognitive function, emotional regulation, and physiological well-being. Studies indicate that mantra enhances mindfulness, reduces stress, and improves cognitive flexibility, with neuroimaging data suggesting increased alpha and theta wave activity during practice. The impact of mantra is particularly relevant in academic settings,

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Albaqami, Nasser Nammas. "Using Artificial Neural Network Analysis to Study Jeffrey Nanofluid Flow in Cone–Disk Systems." Mathematical and Computational Applications 29, no. 6 (2024): 98. http://dx.doi.org/10.3390/mca29060098.

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Artificial intelligence (AI) is employed in fluid flow models to enhance the simulation’s accuracy, to more effectively optimize the fluid flow models, and to realize reliable fluid flow systems with improved performance. Jeffery fluid flow through the interstice of a cone-and-disk system is considered in this study. The mathematical description of this flow involves converting a partial differential system into a nonlinear ordinary differential system and solving it using a neurocomputational technique. The fluid streaming through the disk–cone gap is investigated under four contrasting frame

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