Relevant bibliographies by topics / Neurocomputational modelling

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Neurocomputational modelling'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Neurocomputational modelling"

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Rai, Ankush, and Jagadeesh Kannan R. "NEUROCOMPUTATIONAL MODELLING OF DISTRIBUTED LEARNING FROM VISUAL STIMULI." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (April 1, 2017): 225. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19645.

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Neurocomputational modeling of visual stimuli can lead not only to identify the neural substrates of attention but also to test cognitive theories ofattention with applications on several visual media, robotics, etc. However, there are many research works done in cognitive model for linguistics,but the studies regarding cognitive modeling of learning mechanisms for visual stimuli are falling back. Based on principles of operation cognitivefunctionalities in human vision processing, the study presents the development of a computational neurocomputational cognitive model for visualperception with detailed algorithmic descriptions. Here, four essential questions of cognition and visual attention is considered for logicallycompressing into one unified neurocomputational model: (i) Segregation of special classes of stimuli and attention modulation, (ii) relation betweengaze movements and visual perception, (iii) mechanism of selective stimulus processing and its encoding in neuronal cells, and (iv) mechanism ofvisual perception through autonomous relation proofing.
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Wang, Di, Ah-Hwee Tan, Chunyan Miao, and Ahmed A. Moustafa. "Modelling Autobiographical Memory Loss across Life Span." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1368–75. http://dx.doi.org/10.1609/aaai.v33i01.33011368.

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Neurocomputational modelling of long-term memory is a core topic in computational cognitive neuroscience, which is essential towards self-regulating brain-like AI systems. In this paper, we study how people generally lose their memories and emulate various memory loss phenomena using a neurocomputational autobiographical memory model. Specifically, based on prior neurocognitive and neuropsychology studies, we identify three neural processes, namely overload, decay and inhibition, which lead to memory loss in memory formation, storage and retrieval, respectively. For model validation, we collect a memory dataset comprising more than one thousand life events and emulate the three key memory loss processes with model parameters learnt from memory recall behavioural patterns found in human subjects of different age groups. The emulation results show high correlation with human memory recall performance across their life span, even with another population not being used for learning. To the best of our knowledge, this paper is the first research work on quantitative evaluations of autobiographical memory loss using a neurocomputational model.
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Ursino, Mauro, Cristiano Cuppini, and Elisa Magosso. "Neurocomputational approaches to modelling multisensory integration in the brain: A review." Neural Networks 60 (December 2014): 141–65. http://dx.doi.org/10.1016/j.neunet.2014.08.003.

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Lieder, Falk, Klaas E. Stephan, Jean Daunizeau, Marta I. Garrido, and Karl J. Friston. "A Neurocomputational Model of the Mismatch Negativity." PLoS Computational Biology 9, no. 11 (November 7, 2013): e1003288. http://dx.doi.org/10.1371/journal.pcbi.1003288.

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Kröger, Bernd J., Jim Kannampuzha, and Christiane Neuschaefer-Rube. "Towards a neurocomputational model of speech production and perception." Speech Communication 51, no. 9 (September 2009): 793–809. http://dx.doi.org/10.1016/j.specom.2008.08.002.

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Smith, Ryan, Rayus Kuplicki, Justin Feinstein, Katherine L. Forthman, Jennifer L. Stewart, Martin P. Paulus, and Sahib S. Khalsa. "A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders." PLOS Computational Biology 16, no. 12 (December 14, 2020): e1008484. http://dx.doi.org/10.1371/journal.pcbi.1008484.

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Recent neurocomputational theories have hypothesized that abnormalities in prior beliefs and/or the precision-weighting of afferent interoceptive signals may facilitate the transdiagnostic emergence of psychopathology. Specifically, it has been suggested that, in certain psychiatric disorders, interoceptive processing mechanisms either over-weight prior beliefs or under-weight signals from the viscera (or both), leading to a failure to accurately update beliefs about the body. However, this has not been directly tested empirically. To evaluate the potential roles of prior beliefs and interoceptive precision in this context, we fit a Bayesian computational model to behavior in a transdiagnostic patient sample during an interoceptive awareness (heartbeat tapping) task. Modelling revealed that, during an interoceptive perturbation condition (inspiratory breath-holding during heartbeat tapping), healthy individuals (N = 52) assigned greater precision to ascending cardiac signals than individuals with symptoms of anxiety (N = 15), depression (N = 69), co-morbid depression/anxiety (N = 153), substance use disorders (N = 131), and eating disorders (N = 14)–who failed to increase their precision estimates from resting levels. In contrast, we did not find strong evidence for differences in prior beliefs. These results provide the first empirical computational modeling evidence of a selective dysfunction in adaptive interoceptive processing in psychiatric conditions, and lay the groundwork for future studies examining how reduced interoceptive precision influences visceral regulation and interoceptively-guided decision-making.
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Miller, Hilary E., and Frank H. Guenther. "Modelling speech motor programming and apraxia of speech in the DIVA/GODIVA neurocomputational framework." Aphasiology, May 18, 2020, 1–18. http://dx.doi.org/10.1080/02687038.2020.1765307.

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Olasagasti, Itsaso, and Anne-Lise Giraud. "Integrating prediction errors at two time scales permits rapid recalibration of speech sound categories." eLife 9 (March 30, 2020). http://dx.doi.org/10.7554/elife.44516.

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Speech perception presumably arises from internal models of how specific sensory features are associated with speech sounds. These features change constantly (e.g. different speakers, articulation modes etc.), and listeners need to recalibrate their internal models by appropriately weighing new versus old evidence. Models of speech recalibration classically ignore this volatility. The effect of volatility in tasks where sensory cues were associated with arbitrary experimenter-defined categories were well described by models that continuously adapt the learning rate while keeping a single representation of the category. Using neurocomputational modelling we show that recalibration of natural speech sound categories is better described by representing the latter at different time scales. We illustrate our proposal by modeling fast recalibration of speech sounds after experiencing the McGurk effect. We propose that working representations of speech categories are driven both by their current environment and their long-term memory representations.
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Susi, Gianluca, Pilar Garcés, Emanuele Paracone, Alessandro Cristini, Mario Salerno, Fernando Maestú, and Ernesto Pereda. "FNS allows efficient event-driven spiking neural network simulations based on a neuron model supporting spike latency." Scientific Reports 11, no. 1 (June 9, 2021). http://dx.doi.org/10.1038/s41598-021-91513-8.

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AbstractNeural modelling tools are increasingly employed to describe, explain, and predict the human brain’s behavior. Among them, spiking neural networks (SNNs) make possible the simulation of neural activity at the level of single neurons, but their use is often threatened by the resources needed in terms of processing capabilities and memory. Emerging applications where a low energy burden is required (e.g. implanted neuroprostheses) motivate the exploration of new strategies able to capture the relevant principles of neuronal dynamics in reduced and efficient models. The recent Leaky Integrate-and-Fire with Latency (LIFL) spiking neuron model shows some realistic neuronal features and efficiency at the same time, a combination of characteristics that may result appealing for SNN-based brain modelling. In this paper we introduce FNS, the first LIFL-based SNN framework, which combines spiking/synaptic modelling with the event-driven approach, allowing us to define heterogeneous neuron groups and multi-scale connectivity, with delayed connections and plastic synapses. FNS allows multi-thread, precise simulations, integrating a novel parallelization strategy and a mechanism of periodic dumping. We evaluate the performance of FNS in terms of simulation time and used memory, and compare it with those obtained with neuronal models having a similar neurocomputational profile, implemented in NEST, showing that FNS performs better in both scenarios. FNS can be advantageously used to explore the interaction within and between populations of spiking neurons, even for long time-scales and with a limited hardware configuration.
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Dissertations / Theses on the topic "Neurocomputational modelling"

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Colombo, Matteo. "Complying with norms : a neurocomputational exploration." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6462.

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The subject matter of this thesis can be summarized by a triplet of questions and answers. Showing what these questions and answers mean is, in essence, the goal of my project. The triplet goes like this: Q: How can we make progress in our understanding of social norms and norm compliance? A: Adopting a neurocomputational framework is one effective way to make progress in our understanding of social norms and norm compliance. Q: What could the neurocomputational mechanism of social norm compliance be? A: The mechanism of norm compliance probably consists of Bayesian - Reinforcement Learning algorithms implemented by activity in certain neural populations. Q: What could information about this mechanism tell us about social norms and social norm compliance? A: Information about this mechanism tells us that: a1: Social norms are uncertainty-minimizing devices. a2: Social norm compliance is one trick that agents employ to interact coadaptively and smoothly in their social environment. Most of the existing treatments of norms and norm compliance (e.g. Bicchieri 2006; Binmore 1993; Elster 1989; Gintis 2010; Lewis 1969; Pettit 1990; Sugden 1986; Ullmann‐Margalit 1977) consist in what Cristina Bicchieri (2006) refers to as “rational reconstructions.” A rational reconstruction of the concept of social norm “specifies in which sense one may say that norms are rational, or compliance with a norm is rational” (Ibid., pp. 10-11). What sets my project apart from these types of treatments is that it aims, first and foremost, at providing a description of some core aspects of the mechanism of norm compliance. The single most original idea put forth in my project is to bring an alternative explanatory framework to bear on social norm compliance. This is the framework of computational cognitive neuroscience. The chapters of this thesis describe some ways in which central issues concerning social norms can be fruitfully addressed within a neurocomputational framework. In order to qualify and articulate the triplet above, my strategy consists firstly in laying down the beginnings of a model of the mechanism of norm compliance behaviour, and then zooming in on specific aspects of the model. Such a model, the chapters of this thesis argue, explains apparently important features of the psychology and neuroscience of norm compliance, and helps us to understand the nature of the social norms we live by.
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Habtegiorgis, Selam Wondimu [Verfasser], and Felix A. [Akademischer Betreuer] Wichmann. "Evaluation and neurocomputational modelling of visual adaptation to optically induced distortions / Selam Wondimu Habtegiorgis ; Betreuer: Felix A. Wichmann." Tübingen : Universitätsbibliothek Tübingen, 2019. http://d-nb.info/1204930260/34.

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Habtegiorgis, Selam W. [Verfasser], and Felix A. [Akademischer Betreuer] Wichmann. "Evaluation and neurocomputational modelling of visual adaptation to optically induced distortions / Selam Wondimu Habtegiorgis ; Betreuer: Felix A. Wichmann." Tübingen : Universitätsbibliothek Tübingen, 2019. http://d-nb.info/1204930260/34.

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Vissani, Matteo. "Multisensory features of peripersonal space representation: an analysis via neural network modelling." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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The peripersonal space (PPS) is the space immediately surrounding the body. It is coded in the brain in a multisensory, body part-centered (e.g. hand-centered, trunk-centered), modular fashion. This is supported by the existence of multisensory neurons (in fronto-parietal areas) with tactile receptive field on a specific body part (hand, arm, trunk, etc.) and visual/auditory receptive field surrounding the same body part. Recent behavioural results (Serino et al. Sci Rep 2015), obtained by using an audio-tactile paradigm, have further supported the existence of distinct PPS representations, each specific of a single body part (hand, trunk, face) and characterized by specific properties. That study has also evidenced that the PPS representations– although distinct – are not independent. In particular, the hand-PPS loses its properties and assumes those of the trunk-PPS when the hand is close to the trunk, as the hand-PPS was encapsulated within the trunk-PPS. Similarly, the face-PPS appears to be englobed into the trunk-PPS. It remains unclear how this interaction, which manifests behaviourally, can be implemented at a neural level by the modular organization of PPS representations. The aim of this Thesis is to propose a neural network model to help the comprehension of the underlying neurocomputational mechanisms. The model includes three subnetworks devoted to the single PPS representations around the hand, face and the trunk. Furthermore, interaction mechanisms– controlled by proprioceptive neurons – have been postulated among the subnetworks. The network is able to reproduce the behavioural data, explaining them in terms of neural properties and response. Moreover, the network provides some novel predictions, that can be tested in vivo. One of this prediction has been tested in this work, by performing an ad-hoc behavioural experiment at the Laboratory of Cognitive Neuroscience (Campus Biotech, Geneva) under the supervision of the neuropsychologist Dr Serino.
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Kolbeck, Carter. "A neurocomputational model of the mammalian fear conditioning circuit." Thesis, 2013. http://hdl.handle.net/10012/7897.

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In this thesis, I present a computational neural model that reproduces the high-level behavioural results of well-known fear conditioning experiments: first-order conditioning, second-order conditioning, sensory preconditioning, context conditioning, blocking, first-order extinction and renewal (AAB, ABC, ABA), and extinction and renewal after second-order conditioning and sensory preconditioning. The simulated neural populations used to account for the behaviour observed in these experiments correspond to known anatomical regions of the mammalian brain. Parts of the amygdala, periaqueductal gray, cortex and thalamus, and hippocampus are included and are connected to each other in a biologically plausible manner. The model was built using the principles of the Neural Engineering Framework (NEF): a mathematical framework that allows information to be encoded and manipulated in populations of neurons. Each population represents information via the spiking activity of simulated neurons, and is connected to one or more other populations; these connections allow computations to be performed on the information being represented. By specifying which populations are connected to which, and what functions these connections perform, I developed an information processing system that behaves analogously to the fear conditioning circuit in the brain.
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Conference papers on the topic "Neurocomputational modelling"

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

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