Journal of Computational Neuroscience最新文献

{"title":"Cell to network computational model of the epileptic human hippocampus suggests specific roles of network and channel dysfunctions in the ictal and interictal oscillations.","authors":"Amélie Aussel,&nbsp;Radu Ranta,&nbsp;Olivier Aron,&nbsp;Sophie Colnat-Coulbois,&nbsp;Louise Maillard,&nbsp;Laure Buhry","doi":"10.1007/s10827-022-00829-5","DOIUrl":"https://doi.org/10.1007/s10827-022-00829-5","url":null,"abstract":"<p><p>The mechanisms underlying the generation of hippocampal epileptic seizures and interictal events and their interactions with the sleep-wake cycle are not yet fully understood. Indeed, medial temporal lobe epilepsy is associated with hippocampal abnormalities both at the neuronal (channelopathies, impaired potassium and chloride dynamics) and network level (neuronal and axonal loss, mossy fiber sprouting), with more frequent seizures during wakefulness compared with slow-wave sleep. In this article, starting from our previous computational modeling work of the hippocampal formation based on realistic topology and synaptic connectivity, we study the role of micro- and mesoscale pathological conditions of the epileptic hippocampus in the generation and maintenance of seizure-like theta and interictal oscillations. We show, through the simulations of hippocampal activity during slow-wave sleep and wakefulness that: (i) both mossy fiber sprouting and sclerosis account for seizure-like theta activity, (ii) but they have antagonist effects (seizure-like activity occurrence increases with sprouting but decreases with sclerosis), (iii) though impaired potassium and chloride dynamics have little influence on the generation of seizure-like activity, they do play a role on the generation of interictal patterns, and (iv) seizure-like activity and fast ripples are more likely to occur during wakefulness and interictal spikes during sleep.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"50 4","pages":"519-535"},"PeriodicalIF":1.2,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9781203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of astrocytes in place cell formation: A computational modeling study.","authors":"Ioannis Polykretis,&nbsp;Konstantinos P Michmizos","doi":"10.1007/s10827-022-00828-6","DOIUrl":"10.1007/s10827-022-00828-6","url":null,"abstract":"<p><p>Place cells develop spatially-tuned receptive fields during the early stages of novel environment exploration. The generative mechanism underlying these spatially-selective responses remains largely elusive, but has been associated with theta rhythmicity. An important factor implicating the transformation of silent cells to place cells is a spatially-uniform depolarization that is mediated by a persistent sodium current. This neuronal current is modulated by extracellular calcium concentration, which, in turn, is actively controlled by astrocytes. However, there is no established relationship between the neuronal depolarization and astrocytic activity. To consider this link, we designed a bioplausible computational model of a neuronal-astrocytic network, where astrocytes induced the transient emergence of place fields in silent cells, and accelerated the plasticity-induced consolidation of place cells. Interestingly, theta oscillations emerged naturally at the network level, resulting from the astrocytic modulation of subcellular neuronal properties. Our results suggest that astrocytes participate in spatial mapping and exploration, and further highlight the computational roles of these cells in the brain.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"50 4","pages":"505-518"},"PeriodicalIF":1.2,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9671849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10138735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic branching in a neural network model for probabilistic prediction of sequences.","authors":"Elif Köksal Ersöz,&nbsp;Pascal Chossat,&nbsp;Martin Krupa,&nbsp;Frédéric Lavigne","doi":"10.1007/s10827-022-00830-y","DOIUrl":"https://doi.org/10.1007/s10827-022-00830-y","url":null,"abstract":"<p><p>An important function of the brain is to predict which stimulus is likely to occur based on the perceived cues. The present research studied the branching behavior of a computational network model of populations of excitatory and inhibitory neurons, both analytically and through simulations. Results show how synaptic efficacy, retroactive inhibition and short-term synaptic depression determine the dynamics of selection between different branches predicting sequences of stimuli of different probabilities. Further results show that changes in the probability of the different predictions depend on variations of neuronal gain. Such variations allow the network to optimize the probability of its predictions to changing probabilities of the sequences without changing synaptic efficacy.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"50 4","pages":"537-557"},"PeriodicalIF":1.2,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9836067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal filters in response to presynaptic spike trains: interplay of cellular, synaptic and short-term plasticity time scales.","authors":"Yugarshi Mondal,&nbsp;Rodrigo F O Pena,&nbsp;Horacio G Rotstein","doi":"10.1007/s10827-022-00822-y","DOIUrl":"https://doi.org/10.1007/s10827-022-00822-y","url":null,"abstract":"<p><p>Temporal filters, the ability of postsynaptic neurons to preferentially select certain presynaptic input patterns over others, have been shown to be associated with the notion of information filtering and coding of sensory inputs. Short-term plasticity (depression and facilitation; STP) has been proposed to be an important player in the generation of temporal filters. We carry out a systematic modeling, analysis and computational study to understand how characteristic postsynaptic (low-, high- and band-pass) temporal filters are generated in response to periodic presynaptic spike trains in the presence STP. We investigate how the dynamic properties of these filters depend on the interplay of a hierarchy of processes, including the arrival of the presynaptic spikes, the activation of STP, its effect on the excitatory synaptic connection efficacy, and the response of the postsynaptic cell. These mechanisms involve the interplay of a collection of time scales that operate at the single-event level (roughly, during each presynaptic interspike-interval) and control the long-term development of the temporal filters over multiple presynaptic events. These time scales are generated at the levels of the presynaptic cell (captured by the presynaptic interspike-intervals), short-term depression and facilitation, synaptic dynamics and the post-synaptic cellular currents. We develop mathematical tools to link the single-event time scales with the time scales governing the long-term dynamics of the resulting temporal filters for a relatively simple model where depression and facilitation interact at the level of the synaptic efficacy change. We extend our results and tools to account for more complex models. These include multiple STP time scales and non-periodic presynaptic inputs. The results and ideas we develop have implications for the understanding of the generation of temporal filters in complex networks for which the simple feedforward network we investigate here is a building block.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"50 4","pages":"395-429"},"PeriodicalIF":1.2,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10138737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weight dependence in BCM leads to adjustable synaptic competition.","authors":"Albert Albesa-González,&nbsp;Maxime Froc,&nbsp;Oliver Williamson,&nbsp;Mark C W van Rossum","doi":"10.1007/s10827-022-00824-w","DOIUrl":"https://doi.org/10.1007/s10827-022-00824-w","url":null,"abstract":"<p><p>Models of synaptic plasticity have been used to better understand neural development as well as learning and memory. One prominent classic model is the Bienenstock-Cooper-Munro (BCM) model that has been particularly successful in explaining plasticity of the visual cortex. Here, in an effort to include more biophysical detail in the BCM model, we incorporate 1) feedforward inhibition, and 2) the experimental observation that large synapses are relatively harder to potentiate than weak ones, while synaptic depression is proportional to the synaptic strength. These modifications change the outcome of unsupervised plasticity under the BCM model. The amount of feed-forward inhibition adds a parameter to BCM that turns out to determine the strength of competition. In the limit of strong inhibition the learning outcome is identical to standard BCM and the neuron becomes selective to one stimulus only (winner-take-all). For smaller values of inhibition, competition is weaker and the receptive fields are less selective. However, both BCM variants can yield realistic receptive fields.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"50 4","pages":"431-444"},"PeriodicalIF":1.2,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9666303/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10156523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thalamocortical bistable switch as a theoretical model of fibromyalgia pathogenesis inferred from a literature survey.","authors":"Ilaria Demori,&nbsp;Giulia Giordano,&nbsp;Viviana Mucci,&nbsp;Serena Losacco,&nbsp;Lucio Marinelli,&nbsp;Paolo Massobrio,&nbsp;Franco Blanchini,&nbsp;Bruno Burlando","doi":"10.1007/s10827-022-00826-8","DOIUrl":"https://doi.org/10.1007/s10827-022-00826-8","url":null,"abstract":"<p><p>Fibromyalgia (FM) is an unsolved central pain processing disturbance. We aim to provide a unifying model for FM pathogenesis based on a loop network involving thalamocortical regions, i.e., the ventroposterior lateral thalamus (VPL), the somatosensory cortex (SC), and the thalamic reticular nucleus (TRN). The dynamics of the loop have been described by three differential equations having neuron mean firing rates as variables and containing Hill functions to model mutual interactions among the loop elements. A computational analysis conducted with MATLAB has shown a transition from monostability to bistability of the loop behavior for a weakening of GABAergic transmission between TRN and VPL. This involves the appearance of a high-firing-rate steady state, which becomes dominant and is assumed to represent pathogenic pain processing giving rise to chronic pain. Our model is consistent with a bulk of literature evidence, such as neuroimaging and pharmacological data collected on FM patients, and with correlations between FM and immunoendocrine conditions, such as stress, perimenopause, chronic inflammation, obesity, and chronic dizziness. The model suggests that critical targets for FM treatment are to be found among immunoendocrine pathways leading to GABA/glutamate imbalance having an impact on the thalamocortical system.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"50 4","pages":"471-484"},"PeriodicalIF":1.2,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9666334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10156529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exact mean-field models for spiking neural networks with adaptation.","authors":"Liang Chen,&nbsp;Sue Ann Campbell","doi":"10.1007/s10827-022-00825-9","DOIUrl":"https://doi.org/10.1007/s10827-022-00825-9","url":null,"abstract":"<p><p>Networks of spiking neurons with adaption have been shown to be able to reproduce a wide range of neural activities, including the emergent population bursting and spike synchrony that underpin brain disorders and normal function. Exact mean-field models derived from spiking neural networks are extremely valuable, as such models can be used to determine how individual neurons and the network they reside within interact to produce macroscopic network behaviours. In the paper, we derive and analyze a set of exact mean-field equations for the neural network with spike frequency adaptation. Specifically, our model is a network of Izhikevich neurons, where each neuron is modeled by a two dimensional system consisting of a quadratic integrate and fire equation plus an equation which implements spike frequency adaptation. Previous work deriving a mean-field model for this type of network, relied on the assumption of sufficiently slow dynamics of the adaptation variable. However, this approximation did not succeed in establishing an exact correspondence between the macroscopic description and the realistic neural network, especially when the adaptation time constant was not large. The challenge lies in how to achieve a closed set of mean-field equations with the inclusion of the mean-field dynamics of the adaptation variable. We address this problem by using a Lorentzian ansatz combined with the moment closure approach to arrive at a mean-field system in the thermodynamic limit. The resulting macroscopic description is capable of qualitatively and quantitatively describing the collective dynamics of the neural network, including transition between states where the individual neurons exhibit asynchronous tonic firing and synchronous bursting. We extend the approach to a network of two populations of neurons and discuss the accuracy and efficacy of our mean-field approximations by examining all assumptions that are imposed during the derivation. Numerical bifurcation analysis of our mean-field models reveals bifurcations not previously observed in the models, including a novel mechanism for emergence of bursting in the network. We anticipate our results will provide a tractable and reliable tool to investigate the underlying mechanism of brain function and dysfunction from the perspective of computational neuroscience.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":"445-469"},"PeriodicalIF":1.2,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40521415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models.","authors":"Jonathan Oesterle,&nbsp;Nicholas Krämer,&nbsp;Philipp Hennig,&nbsp;Philipp Berens","doi":"10.1007/s10827-022-00827-7","DOIUrl":"https://doi.org/10.1007/s10827-022-00827-7","url":null,"abstract":"<p><p>Understanding neural computation on the mechanistic level requires models of neurons and neuronal networks. To analyze such models one typically has to solve coupled ordinary differential equations (ODEs), which describe the dynamics of the underlying neural system. These ODEs are solved numerically with deterministic ODE solvers that yield single solutions with either no, or only a global scalar error indicator on precision. It can therefore be challenging to estimate the effect of numerical uncertainty on quantities of interest, such as spike-times and the number of spikes. To overcome this problem, we propose to use recently developed sampling-based probabilistic solvers, which are able to quantify such numerical uncertainties. They neither require detailed insights into the kinetics of the models, nor are they difficult to implement. We show that numerical uncertainty can affect the outcome of typical neuroscience simulations, e.g. jittering spikes by milliseconds or even adding or removing individual spikes from simulations altogether, and demonstrate that probabilistic solvers reveal these numerical uncertainties with only moderate computational overhead.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"50 4","pages":"485-503"},"PeriodicalIF":1.2,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9666333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9836065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Homogeneous inhibition is optimal for the phase precession of place cells in the CA1 field.","authors":"Georgy Vandyshev,&nbsp;Ivan Mysin","doi":"10.1007/s10827-023-00855-x","DOIUrl":"10.1007/s10827-023-00855-x","url":null,"abstract":"<p><p>Place cells are hippocampal neurons encoding the position of an animal in space. Studies of place cells are essential to understanding the processing of information by neural networks of the brain. An important characteristic of place cell spike trains is phase precession. When an animal is running through the place field, the discharges of the place cells shift from the ascending phase of the theta rhythm through the minimum to the descending phase. The role of excitatory inputs to pyramidal neurons along the Schaffer collaterals and the perforant pathway in phase precession is described, but the role of local interneurons is poorly understood. Our goal is estimating of the contribution of field CA1 interneurons to the phase precession of place cells using mathematical methods. The CA1 field is chosen because it provides the largest set of experimental data required to build and verify the model. Our simulations discover optimal parameters of the excitatory and inhibitory inputs to the pyramidal neuron so that it generates a spike train with the effect of phase precession. The uniform inhibition of pyramidal neurons best explains the effect of phase precession. Among interneurons, axo-axonal neurons make the greatest contribution to the inhibition of pyramidal cells.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"51 3","pages":"389-403"},"PeriodicalIF":1.2,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9950436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical processing underpins competition in tactile perceptual bistability.","authors":"Farzaneh Darki, Andrea Ferrario, James Rankin","doi":"10.1007/s10827-023-00852-0","DOIUrl":"10.1007/s10827-023-00852-0","url":null,"abstract":"<p><p>Ambiguous sensory information can lead to spontaneous alternations between perceptual states, recently shown to extend to tactile perception. The authors recently proposed a simplified form of tactile rivalry which evokes two competing percepts for a fixed difference in input amplitudes across antiphase, pulsatile stimulation of the left and right fingers. This study addresses the need for a tactile rivalry model that captures the dynamics of perceptual alternations and that incorporates the structure of the somatosensory system. The model features hierarchical processing with two stages. The first and the second stages of model could be located at the secondary somatosensory cortex (area S2), or in higher areas driven by S2. The model captures dynamical features specific to the tactile rivalry percepts and produces general characteristics of perceptual rivalry: input strength dependence of dominance times (Levelt's proposition II), short-tailed skewness of dominance time distributions and the ratio of distribution moments. The presented modelling work leads to experimentally testable predictions. The same hierarchical model could generalise to account for percept formation, competition and alternations for bistable stimuli that involve pulsatile inputs from the visual and auditory domains.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":"51 3","pages":"343-360"},"PeriodicalIF":1.2,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10404575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9956697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}