Piecewise quadratic neuron model: A tool for close-to-biology spiking neuronal network simulation on dedicated hardware [PDF]
Frontiers in Neuroscience, 2023 Spiking neuron models simulate neuronal activities and allow us to analyze and reproduce the information processing of the nervous system. However, ionic-conductance models, which can faithfully reproduce neuronal activities, require a huge computational
Takuya Nanami, Takashi Kohno
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A lightweight data-driven spiking neuronal network model of Drosophila olfactory nervous system with dedicated hardware support [PDF]
Frontiers in NeuroscienceData-driven spiking neuronal network (SNN) models enable in-silico analysis of the nervous system at the cellular and synaptic level. Therefore, they are a key tool for elucidating the information processing principles of the brain.
Takuya Nanami +7 more
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Integrated workflows for spiking neuronal network simulations [PDF]
Frontiers in Neuroinformatics, 2013 The increasing availability of computational resources is enabling more detailed, realistic modelling in computational neuroscience, resulting in a shift towards more heterogeneous models of neuronal circuits, and employment of complex experimental ...
Ján eAntolík, Andrew P Davison
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Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning. [PDF]
PLoS ONE, 2022 Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism.
Haroon Anwar +12 more
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Macroscopic Description for Networks of Spiking Neurons [PDF]
Physical Review X, 2015 A major goal of neuroscience, statistical physics, and nonlinear dynamics is to understand how brain function arises from the collective dynamics of networks of spiking neurons.
Ernest Montbrió +2 more
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Effects of internal noise on the spiking regularity of a clustered Hodgkin-Huxley neuronal network
Theoretical and Applied Mechanics Letters, 2014 Spiking regularity in a clustered Hodgkin–Huxley (HH) neuronal network has been studied in this letter. A stochastic HH neuronal model with channel blocks has been applied as local neuronal model.
Xiaojuan Sun
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Frontiers in Computational Neuroscience, 2015 In this paper, we investigate how clustering factors influent spiking regularity of the neuronal network of subnetworks. In order to do so, we fix the averaged coupling probability and the averaged coupling strength, and take the cluster number M, the ...
Huiyan eLi, Xiaojuan eSun, Jinghua eXiao
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Autapses enable temporal pattern recognition in spiking neural networks. [PDF]
PLoS ONEMost sensory stimuli are temporal in structure. How action potentials encode the information incoming from sensory stimuli remains one of the central research questions in neuroscience.
Muhammad Yaqoob +2 more
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Inverse stochastic resonance in networks of spiking neurons. [PDF]
PLoS Computational Biology, 2017 Inverse Stochastic Resonance (ISR) is a phenomenon in which the average spiking rate of a neuron exhibits a minimum with respect to noise. ISR has been studied in individual neurons, but here, we investigate ISR in scale-free networks, where the average ...
Muhammet Uzuntarla +2 more
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Conditions for replay of neuronal assemblies. [PDF]
PLoS Computational BiologyFrom cortical synfire chains to hippocampal replay, the idea that neural populations can be activated sequentially with precise spike timing is thought to be essential for several brain functions.
Gaspar Cano, Richard Kempter
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