Journal articles on the topic 'Neurocomputational models'
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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 (January 14, 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 learning, attention to linguistically interpretable models, and openly shared data sets that allow researchers to compare and contrast a variety of models. We describe one such data set in detail in the Supplemental Appendix .
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Durstewitz, Daniel, Jeremy K. Seamans, and Terrence J. Sejnowski. "Neurocomputational models of working memory." Nature Neuroscience 3, S11 (November 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 (August 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 (March 1, 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 real-valued DNN (RV-DNN) with five hidden layers, a real-valued CNN (RV-CNN) with seven convolutional layers, and a real-valued combined model (RV-MWINet) consisting of CNN and U-Net sub-models were built and trained to generate the radar-based microwave images. While the proposed RV-DNN, RV-CNN, and RV-MWINet models are real-valued, the MWINet model is restructured with complex-valued layers (CV-MWINet), resulting in a total of four models. For the RV-DNN model, the training and test errors in terms of mean squared error (MSE) are found to be 103.400 and 96.395, respectively, whereas for the RV-CNN model, the training and test errors are obtained to be 45.283 and 153.818. Due to the fact that the RV-MWINet model is a combined U-Net model, the accuracy metric is analyzed. The proposed RV-MWINet model has training and testing accuracy of 0.9135 and 0.8635, whereas the CV-MWINet model has training and testing accuracy of 0.991 and 1.000, respectively. The peak signal-to-noise ratio (PSNR), universal quality index (UQI), and structural similarity index (SSIM) metrics were also evaluated for the images generated by the proposed neurocomputational models. The generated images demonstrate that the proposed neurocomputational models can be successfully utilized for radar-based microwave imaging, especially for breast imaging.
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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 (April 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 simulator is used since it is easy to understand and code.
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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 (October 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. Furthermore, the existence of a blocking effect for cocaine is predicted by our model.
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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 (November 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 (April 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 (July 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 cortical–cortical connections within the premotor areas that are responsible for automatic sequence production. The new model is used to simulate four different data sets from human and nonhuman animals, including (1) behavioral data (e.g., RTs), (2) electrophysiology data (e.g., single-neuron recordings), (3) macrostructure data (e.g., TMS), and (4) neurological circuit data (e.g., inactivation studies). We conclude with a comparison of the new model with existing models of automatic sequence production and discuss a possible new role for the BG in automaticity and its implication for Parkinson's disease.
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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 (June 29, 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 when participants read words with irregular spellings. This area is implicated in semantic processing but has not previously been considered part of the reading network. We also found meaningful individual differences in the activation of this region: Activity was predicted by an independent measure of the degree to which participants use semantic knowledge to read. These characteristics are predicted by the connectionist Triangle Model of reading and indicate a key role for semantic knowledge in reading aloud. Premotor regions associated with phonological processing displayed the reverse characteristics. Changes in the functional connectivity of the reading network during irregular word reading also were consistent with semantic recruitment. These data support the view that reading aloud is underpinned by the joint operation of two neural pathways. They reveal that (i) the ATL is an important element of the ventral semantic pathway and (ii) the division of labor between the two routes varies according to both the properties of the words being read and individual differences in the degree to which participants rely on each route.
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Hass, Joachim, and J. Michael Herrmann. "The Neural Representation of Time: An Information-Theoretic Perspective." Neural Computation 24, no. 6 (June 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 frequently observed deviations. Under the assumption that the brain is able to compute optimal estimates of time, we find that Weber's law only holds exactly if the estimate is based on temporal changes in the variance of the process. In contrast, the timing errors scale sublinearly with time if the systematic changes in the mean of a process are used for estimation, as is the case in the majority of time perception models, while estimates based on temporal correlations result in a superlinear scaling. This hierarchy of temporal information is preserved if several sources of temporal information are available. Furthermore, we consider the case of multiple stochastic processes and study the examples of a covariance-based model and a model based on synfire chains. This approach reveals that existing neurocomputational models of time perception can be classified as mean-, variance- and correlation-based processes and allows predictions about the scaling of the resulting timing errors.
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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 (January 18, 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 responses of ON and OFF cells responses in the lateral geniculate nucleus of the macaque monkey [3,4] and partly from human quantitative psychophysical measurements. The model’s computations and architecture are consistent with the properties of human visual neurophysiology as they are currently understood. The neural model's predictions and behavior are contrasted though the simulations with those of other lightness models, including Retinex theory [5] and the lightness filling-in models of Grossberg and his colleagues [6].
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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 (May 2017): 91–99. http://dx.doi.org/10.1177/026119291704500203.
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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 (July 20, 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 (June 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 (May 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 good fits to the data both by increasing nondecision time and by raising decision thresholds as a function of conflict and by allowing this threshold to collapse with time. This profile captures the role and dynamics of the subthalamic nucleus in BG circuitry, and as such, parametric modulations of projection strengths from this nucleus were associated with parametric increases in decision boundary and its modulation by conflict. We then present data from a human reinforcement learning experiment involving decisions with low- and high-reinforcement conflict. Again, the standard model failed to fit the data, but we found that two variants similar to those that fit the BG model data fit the experimental data, thereby providing a convergence of theoretical accounts of complex interactive decision-making mechanisms consistent with available data. This work also demonstrates how to make modest modifications to diffusion models to summarize core computations of the BG model. The result is a better fit and understanding of reinforcement-based choice data than that which would have occurred with either model alone.
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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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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 (June 16, 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 (June 23, 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 neurocomputational model with physiologically based laryngeal motor control. We combined the DIVA model (an established neural network model of speech motor control) with the extended body-cover model (a physics-based vocal fold model). The resulting integrated model, LaDIVA, was validated by comparing its model simulations with behavioral responses to perturbations of auditory vocal fundamental frequency (fo) feedback in adults with typical speech. LaDIVA demonstrated capability to simulate different modes of laryngeal motor control, ranging from short-term (i.e., reflexive) and long-term (i.e., adaptive) auditory feedback paradigms, to generating prosodic contours in speech. Simulations showed that LaDIVA’s laryngeal motor control displays properties of motor equivalence, i.e., LaDIVA could robustly generate compensatory responses to reflexive vocal fo perturbations with varying initial laryngeal muscle activation levels leading to the same output. The model can also generate prosodic contours for studying laryngeal motor control in running speech. LaDIVA can expand the understanding of the physiology of human phonation to enable, for the first time, the investigation of causal effects of neural motor control in the fine structure of the vocal signal.
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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 (April 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 experience grows. Using the Cambridge Face Memory Test and the Vanderbilt Expertise Test, they showed that the shared variance between Cambridge Face Memory Test and Vanderbilt Expertise Test performance increases monotonically as experience increases. Here, we address why a shared resource across different visual domains does not lead to competition and to an inverse correlation in abilities? We explain this conundrum using our neurocomputational model of face and object processing [“The Model”, TM, Cottrell, G. W., & Hsiao, J. H. Neurocomputational models of face processing. In A. J. Calder, G. Rhodes, M. Johnson, & J. Haxby (Eds.), The Oxford handbook of face perception. Oxford, UK: Oxford University Press, 2011]. We model the domain general ability v as the available computational resources (number of hidden units) in the mapping from input to label and experience as the frequency of individual exemplars in an object category appearing during network training. Our results show that, as in the behavioral data, the correlation between subordinate level face and object recognition accuracy increases as experience grows. We suggest that different domains do not compete for resources because the relevant features are shared between faces and objects. The essential power of experience is to generate a “spreading transform” for faces (separating them in representational space) that generalizes to objects that must be individuated. Interestingly, when the task of the network is basic level categorization, no increase in the correlation between domains is observed. Hence, our model predicts that it is the type of experience that matters and that the source of the correlation is in the fusiform face area, rather than in cortical areas that subserve basic level categorization. This result is consistent with our previous modeling elucidating why the FFA is recruited for novel domains of expertise [Tong, M. H., Joyce, C. A., & Cottrell, G. W. Why is the fusiform face area recruited for novel categories of expertise? A neurocomputational investigation. Brain Research, 1202, 14–24, 2008].
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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 (September 28, 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 supported by theoretically driven neurocomputational models of speech production. Updated views on possible mechanisms of stuttering onset and persistence, and perspectives on promising areas for future research into the mechanisms of stuttering, are discussed.
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Patil, Anuya, and Katherine Duncan. "Lingering Cognitive States Shape Fundamental Mnemonic Abilities." Psychological Science 29, no. 1 (November 8, 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, which can be elicited by exposure to familiarity and novelty. We investigated these models’ behavioral implications by assessing how recent familiarity influences different memory-retrieval processes. We found that recent familiarity selectively benefitted associative memory (Experiment 1) and that this effect decayed over seconds (Experiment 2), consistent with the timescale of hippocampal neuromodulation. Thus, we show that basic memory computations can be shaped by a subtle, biologically motivated manipulation.
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Graf, M., D. Kaping, and H. H. Bülthoff. "Orientation Congruency Effects for Familiar Objects." Psychological Science 16, no. 3 (March 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 either congruent or incongruent picture-plane orientations. Results showed that naming accuracy was higher for congruent than for incongruent orientations. The congruency effect was independent of superordinate category membership (Experiment 1) and was found for objects with different main axes of elongation (Experiment 2). The results indicate congruency effects for common familiar objects even when they have dissimilar shapes. These findings are compatible with models in which object recognition is achieved by an adjustment of a perceptual coordinate system.
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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 (March 2007): 51–71. http://dx.doi.org/10.1177/1059712306076250.
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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 do. To address this issue, I motivate and defend a picture of conscious experience as flowing from “generative entanglements” that mix predictions about the world, the body, and (crucially) our own reactive dispositions.
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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 (January 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 attentional learning, whereas basal ganglia dopamine, consistent with other models, is key for reinforcement and motor learning. The model assumes that competitive dynamics among PFC neurons is the neural mechanism underlying stimulus selection with limited attentional resources, whereas competitive dynamics among striatal neurons is the neural mechanism underlying action selection. According to our model, PD is associated with decreased phasic and tonic dopamine levels in both PFC and basal ganglia. We assume that dopamine medications increase dopamine levels in both the basal ganglia and PFC, which, in turn, increase tonic dopamine levels but decrease the magnitude of phasic dopamine signaling in these brain structures. Increase of tonic dopamine levels in the simulated PFC enhances attentional shifting performance. The model provides a mechanistic account for several phenomena, including (a) medicated PD patients are more impaired at multicue probabilistic category learning than unmedicated patients and (b) medicated PD patients opt out of reversal when there are alternative and redundant cue dimensions.
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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 spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people’s facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism.
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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 (November 5, 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, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions.
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Sirois, Sylvain, and Denis Mareschal. "An Interacting Systems Model of Infant Habituation." Journal of Cognitive Neuroscience 16, no. 8 (October 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. Simulations of a seminal experiment by Fantz [Visual experience in infants: Decreased attention familiar patterns relative to novel ones. Science, 146, 668–670, 1964] are reported. The ability of the model to capture the fine detail of infant data (especially age-related changes in performance) underlines the useful contribution of neurocomputational models to our understanding of behavior in general, and of early cognition in particular.
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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 (September 24, 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, Neuropsychology, Artificial Intelligence (mainly embodied), and even Philosophy, so that each field provides useful cues for the common goal of understanding social interactions (including the interactions with machines).The aim of this paper is to analyze and asses the current trends in psychology and neuroscience for studying the mechanisms of the neurocomputational cognitive-affective architecture related to the conceptualization and use of language.
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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. Evidence has also provided that Leabra is able to generalize the subsequences of motor skills, when doing so is appropriate. In this paper, we provide a detailed analysis of the extent of generalization possible with Leabra during sequential learning of multiple tasks. For comparison, we measure the generalization exhibited by the backpropagation of error learning algorithm. Furthermore, we demonstrate the applicability of sequential learning to a pair of movement tasks using a simulated robotic arm.
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Wixted, John T., Stephen D. Goldinger, Larry R. Squire, Joel R. Kuhn, Megan H. Papesh, Kris A. Smith, David M. Treiman, and Peter N. Steinmetz. "Coding of episodic memory in the human hippocampus." Proceedings of the National Academy of Sciences 115, no. 5 (January 16, 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-neuron activity indicated that only a small fraction of neurons exhibited strong responding to a given repeated word and that each repeated word elicited strong responding in a different small fraction of neurons. This finding reflects sparse distributed coding. The remaining large fraction of neurons exhibited a concurrent reduction in firing rates relative to novel words. The observed pattern accords with longstanding predictions that have previously received scant support from single-cell recordings from human hippocampus.
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Cartling, Bo. "Response Characteristics of a Low-Dimensional Model Neuron." Neural Computation 8, no. 8 (November 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 damped oscillatory response to a constant input are derived, and it is shown that a limit-cycle response cannot occur. The capability of the low-dimensional model to approximate higher-dimensional models accurately makes it useful for describing complex dynamics of nets of interconnected neurons. The simplicity of the model facilitates analytic studies, elucidation of neurocomputational mechanisms, and applications to large-scale systems.
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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 (April 25, 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 of vigilance, like wakefulness, sleep, general anaesthesia, etc.; (ii) the capacity for global integration of several sensory and cognitive functions, together with behavioural flexibility resulting in what is referred to as exploratory behaviour, and possibly a minimal form of intentionality. In addition, the contribution of defined neuronal nicotinic receptors species to some of these processes is demonstrated and the data discussed within the framework of recent neurocomputational models for access to consciousness.
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Zhong, Shan, Jeong Woo Choi, Nadia G. Hashoush, Diana Babayan, Mahsa Malekmohammadi, Nader Pouratian, and Vassilios Christopoulos. "A neurocomputational theory of action regulation predicts motor behavior in neurotypical individuals and patients with Parkinson’s disease." PLOS Computational Biology 18, no. 11 (November 17, 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 current study aims to advance our understanding by developing a neurodynamical computational theory that models the mechanism of action regulation that involves suppressing responses, and predicts how disruption of this mechanism can lead to motor deficits in Parkinson’s disease (PD) patients. We tested the model predictions in neurotypical individuals and PD patients in three behavioral tasks that involve free action selection between two opposed directions, action selection in the presence of conflicting information and abandoning an ongoing action when a stop signal is presented. Our results and theory suggest an integrated mechanism of action regulation that affects both action initiation and inhibition. When this mechanism is disrupted, motor behavior is affected, leading to longer reaction times and higher error rates in action inhibition.
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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 (July 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 modality to the visual modality: The connection structure between columns is more distal in the language areas of the left hemisphere and more local in the homotopic regions in the right hemisphere. We show that both processing differences and differential frequency filtering can arise naturally in this neurocomputational model with neuroanatomically inspired differences in connection structures within the two model hemispheres, suggesting that hemispheric asymmetry in the processing of local and global features may be due to hemispheric asymmetry in connection structure rather than in frequency tuning.
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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 (June 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 facilitation (observational learning) effects based on the SCP. SCP2 extends SCP1 by adding action-recognition elements and (vicarious) reward-processing elements to facilitate performance following observation of a demonstrator. Our simulations suggest prior experience is important for the observed facilitation and serves to bridge the (separately collected) neurophysiological and behavioral data. Crucially, the inner workings of SCP1, as distinct from its successful performance on the SCP dataset, are irrelevant. What is crucial is the “wrapping” of the “do it alone” model to support social facilitation. This study provides an example of dyadic brain modeling, simulating brain models of interacting agents, in the case in which the behavior of only one member of the dyad is affected by the behavior of the other.
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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 patterns of differences in the responses of people who stutter to feedback perturbations. The implications for hypotheses on the nature of the disruption to sensorimotor control of speech in the disorder are discussed, with reference to neurocomputational models of speech control (predominantly, the DIVA model; Guenther et al., 2006; Tourville et al., 2008). While some consistent patterns are emerging from this evidence, it is clear that more work in this area is needed with developmental samples in particular, in order to tease apart differences related to symptom onset from those related to compensatory strategies that develop with experience of stuttering.
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CARTLING, BO. "A LOW-DIMENSIONAL, TIME-RESOLVED AND ADAPTING MODEL NEURON." International Journal of Neural Systems 07, no. 03 (July 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 model closely reproduces the firing pattern and excitability behaviour of a detailed multicompartment conductance-based model of a neocortical pyramidal cell. The inclusion of adaptation in a model neuron is important for its capability to generate complex dynamics of networks of interconnected neurons. The time-resolution is required for studies of systems in which the temporal aspects of neural coding are important. The simplicity of the model facilitates analytical studies, insight into neurocomputational mechanisms and simulations of large-scale systems. The capability to generate complex network computations may also make the model useful in practical applications of artificial neural networks.
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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 (April 1, 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 cells driven by task variables of interest and another of no selectivity for those variables. The single cluster of task-sensitive cells argues against robust categorical tuning in these areas. The no-selectivity cluster was unanticipated and raises important questions about what distinguishes these neurons and what role they play. Moreover, the ability to formally identify these nonselective cells allows for more accurate measurement of ensemble effects by excluding or appropriately down-weighting them in analysis. Our findings provide a valuable tool for analysis of neural data, challenge simple categorization schemes previously proposed for these regions, and place useful constraints on neurocomputational models of economic choice and control. NEW & NOTEWORTHY We present a Bayesian method for formally detecting whether a population of neurons can be naturally classified into clusters based on their response tuning properties. We then examine several data sets of reward system neurons for variables and find in all cases that neurons can be classified into only two categories: a functional class and a non-task-driven class. These results provide important constraints for neural models of the reward system.
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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 (July 1, 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 emotional faces and pictures as S1s and S2s, respectively. Contextual uncertainty was manipulated across three blocks, each with 100%, 75%, or 50% S1-S2 emotional congruency. ERPs, brain sources and their relationship with IU scores were analyzed for each stage. IU did not affect prediction generation. During prediction implementation, higher IU predicted larger Contingent Negative Variation in the 75% block, and lower left anterior cingulate cortex and supplementary motor area activations. During prediction updating, as IU increased P2 to positive S2s decreased, along with P2 and Late Positive Potential in the 75% block, and right orbito-frontal cortex activity to emotional S2s. IU was therefore associated with altered uncertainty assessment and heightened attention deployment during implementation, and to uncertainty avoidance, reduced attention to safety cues and disrupted access to emotion regulation strategies during prediction updating.
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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 (December 27, 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 acquisition supported by medial temporal and hippocampal learning, (ii) slower neocortical learning achieved by offline consolidation of previously acquired information. We review behavioural and neuroscientific evidence consistent with this account, including a meta-analysis of PET and functional Magnetic Resonance Imaging (fMRI) studies that contrast responses to spoken words and pseudowords. From this meta-analysis we derive predictions for the location and direction of cortical response changes following familiarization with pseudowords. This allows us to assess evidence for learning-induced changes that convert pseudoword responses into real word responses. Results provide unique support for the CLS account since hippocampal responses change during initial learning, whereas cortical responses to pseudowords only become word-like if overnight consolidation follows initial learning.
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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 (October 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 dynamical system consisting of a set of fixed input maps. We argue that contractive fixed-input maps are likely to produce Markovian organizations of receptive fields on the RecSOM map. We derive bounds on parameter β (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed-input maps is guaranteed. Some generalizations of SOM contain a dynamic module responsible for processing temporal contexts as an integral part of the model. We show that Markovian topographic maps of sequential data can be produced using a simple fixed (nonadaptable) dynamic module externally feeding a standard topographic model designed to process static vectorial data of fixed dimensionality (e.g., SOM). However, by allowing trainable feedback connections, one can obtain Markovian maps with superior memory depth and topography preservation. We elaborate on the importance of non-Markovian organizations in topographic maps of sequential data.
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Grent-’t-Jong, Tineke, Robert Oostenveld, Ole Jensen, W. Pieter Medendorp, and Peter Praamstra. "Competitive interactions in sensorimotor cortex: oscillations express separation between alternative movement targets." Journal of Neurophysiology 112, no. 2 (July 15, 2014): 224–32. http://dx.doi.org/10.1152/jn.00127.2014.
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Choice behavior is influenced by factors such as reward and number of alternatives but also by physical context, for instance, the relative position of alternative movement targets. At small separation, speeded eye or hand movements are more likely to land between targets (spatial averaging) than at larger separation. Neurocomputational models explain such behavior in terms of cortical activity being preshaped by the movement environment. Here, we manipulate target separation, as a determinant of motor cortical activity in choice behavior, to address neural mechanisms of response selection. Specifically, we investigate whether context-induced changes in the balance of cooperative and competitive interactions between competing groups of neurons are expressed in the power spectrum of sensorimotor rhythms. We recorded magnetoencephalography while participants were precued to two possible movement target locations at different angles of separation (30, 60, or 90°). After a delay, one of the locations was cued as the target for a joystick pointing movement. We found that late delay-period movement-preparatory activity increased more strongly for alternative targets at 30 than at 60 or 90° of separation. This nonlinear pattern was evident in slow event-related fields as well as in beta- and low-gamma-band suppression. A comparable pattern was found within an earlier window for theta-band synchronization. We interpret the late delay effects in terms of increased movement-preparatory activity when there is greater overlap and hence less competition between groups of neurons encoding two response alternatives. Early delay-period theta-band synchronization may reflect covert response activation relevant to behavioral spatial averaging effects.
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Servant, Mathieu, Gabriel Tillman, Jeffrey D. Schall, Gordon D. Logan, and Thomas J. Palmeri. "Neurally constrained modeling of speed-accuracy tradeoff during visual search: gated accumulation of modulated evidence." Journal of Neurophysiology 121, no. 4 (April 1, 2019): 1300–1314. http://dx.doi.org/10.1152/jn.00507.2018.
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Stochastic accumulator models account for response times and errors in perceptual decision making by assuming a noisy accumulation of perceptual evidence to a threshold. Previously, we explained saccade visual search decision making by macaque monkeys with a stochastic multiaccumulator model in which accumulation was driven by a gated feed-forward integration to threshold of spike trains from visually responsive neurons in frontal eye field that signal stimulus salience. This neurally constrained model quantitatively accounted for response times and errors in visual search for a target among varying numbers of distractors and replicated the dynamics of presaccadic movement neurons hypothesized to instantiate evidence accumulation. This modeling framework suggested strategic control over gate or over threshold as two potential mechanisms to accomplish speed-accuracy tradeoff (SAT). Here, we show that our gated accumulator model framework can account for visual search performance under SAT instructions observed in a milestone neurophysiological study of frontal eye field. This framework captured key elements of saccade search performance, through observed modulations of neural input, as well as flexible combinations of gate and threshold parameters necessary to explain differences in SAT strategy across monkeys. However, the trajectories of the model accumulators deviated from the dynamics of most presaccadic movement neurons. These findings demonstrate that traditional theoretical accounts of SAT are incomplete descriptions of the underlying neural adjustments that accomplish SAT, offer a novel mechanistic account of decision-making mechanisms during speed-accuracy tradeoff, and highlight questions regarding the identity of model and neural accumulators. NEW & NOTEWORTHY A gated accumulator model is used to elucidate neurocomputational mechanisms of speed-accuracy tradeoff. Whereas canonical stochastic accumulators adjust strategy only through variation of an accumulation threshold, we demonstrate that strategic adjustments are accomplished by flexible combinations of both modulation of the evidence representation and adaptation of accumulator gate and threshold. The results indicate how model-based cognitive neuroscience can translate between abstract cognitive models of performance and neural mechanisms of speed-accuracy tradeoff.
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Bourdillon, Pierre, Caroline Apra, Marc Lévêque, and Fabien Vinckier. "Neuroplasticity and the brain connectome: what can Jean Talairach’s reflections bring to modern psychosurgery?" Neurosurgical Focus 43, no. 3 (September 2017): E11. http://dx.doi.org/10.3171/2017.6.focus17251.
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Contrary to common psychosurgical practice in the 1950s, Dr. Jean Talairach had the intuition, based on clinical experience, that the brain connectome and neuroplasticity had a role to play in psychosurgery. Due to the remarkable progress of pharmacology at that time and to the technical limits of neurosurgery, these concepts were not put into practice. Currently, these concepts are being confirmed by modern techniques such as neuroimaging and computational neurosciences, and could pave the way for therapeutic innovation in psychiatry.Psychosurgery commonly uses a localizationist approach, based on the idea that a lesion to a specific area is responsible for a deficit opposite to its function. To psychosurgeons such as Walter Freeman, who performed extensive lesions causing apparently inevitable deficit, Talairach answered with clinical data: complex psychic functions cannot be described that simply, because the same lesion does not provoke the same deficit in different patients. Moreover, cognitive impairment did not always follow efficacious psychosurgery. Talairach suggested that selectively destructing part of a network could open the door to a new organization, and that early psychotherapy could encourage this psychoplasticity. Talairach did not have the opportunity to put these concepts into practice in psychiatric diseases because of the sudden availability of neuroleptics, but connectomics and neuroplasticity gave rise to major advances in intraparenchymal neurosurgery, from epilepsy to low-grade glioma. In psychiatry, alongside long-standing theories implicating focal lesions and diffuse pathological processes, neuroimaging techniques are currently being developed. In mentally healthy individuals, combining diffusion tensor imaging with functional MRI, magnetoencephalography, and electroencephalography allows the determination of a comprehensive map of neural connections in the brain on many spatial scales, the so-called connectome. Ultimately, global neurocomputational models could predict physiological activity, behavior, and subjective feeling, and describe neuropsychiatric disorders.Connectomic studies comparing psychiatric patients with controls have already confirmed the early intuitions of Talairach. As a striking example, massive dysconnectivity has been found in schizophrenia, leading some authors to propose a “dysconnection hypothesis.” Alterations of the connectome have also been demonstrated in obsessive-compulsive disorder and depression. Furthermore, normalization of the functional dysconnectivity has been observed following clinical improvement in several therapeutic interventions, from psychotherapy to pharmacological treatments. Provided that mental disorders result from abnormal structural or functional wiring, targeted psychosurgery would require that one be able: 1) to identify the pathological network involved in a given patient; 2) to use neurostimulation to safely create a reversible and durable alteration, mimicking a lesion, in a network compatible with neuroplasticity; and 3) to predict which functional lesion would result in adapted neuronal plasticity and/or to guide neuronal plasticity to promote recovery. All these conditions, already suggested by Talairach, could now be achievable considering modern biomarkers and surgical progress.