Journal of Computational Neuroscience最新文献

{"title":"An astro-neural-field model with application to cortical spreading depolarization.","authors":"E Baspinar, D Avitabile, C Nouveau, M Desroches, F Campillo, M Mantegazza","doi":"10.1007/s10827-026-00948-3","DOIUrl":"https://doi.org/10.1007/s10827-026-00948-3","url":null,"abstract":"<p><p>We present a novel astro-neural-field population model with application to migraine-related cortical spreading depolarization. The model is composed of four spatio-temporal state variables: excitatory and inhibitory membrane potentials, astrocytic potassium uptake recruitment, and extracellular potassium concentration. Extending a previous neural field model, we incorporate activity-dependent astrocytic potassium clearance via a nonlinear term coupled to astrocyte dynamics. The astrocyte transfer function, like its neural counterpart, exhibits three regimes governed by extracellular potassium, capturing its effect on clearance. This yields a more comprehensive framework, better fits experimental data, and provides new insights into the mechanisms of cortical spreading depolarization.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148610070","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":"Computational modeling of prefrontal-amygdala circuits links functional connectivity alterations to circuit-level mechanisms in major depression.","authors":"Licong Li, Fukuan Zhang, Shuaiyang Zhang, Jinzhao Wei, Kun Wang, Bote Zheng, Jianli Yang, Xiuling Liu","doi":"10.1007/s10827-026-00946-5","DOIUrl":"https://doi.org/10.1007/s10827-026-00946-5","url":null,"abstract":"<p><p>Altered connectivity in the prefrontal cortex-amygdala circuit in Major Depressive Disorder is associated with abnormal circuit states. Although substantial evidence from neuroimaging studies has demonstrated that changes in functional connectivity within this circuit during depressive states are commonly reported as a key feature, little is known about how these changes may relate to circuit functioning. In this study, we propose a biophysical computational model that incorporates an improved Jansen-Rit neural mass model to construct the circuit. Resting-state functional magnetic resonance imaging is used to calculate the functional connectivity of the circuit and simulate brain signals. The aim is to investigate how abnormalities in functional connectivity may contribute to or be associated with the underlying mechanisms of circuit dysfunction. The numerical simulation results indicate that under normal conditions, the amygdala plays a key regulatory role in this circuit. When the amygdala is strongly activated in the model, it suppresses the ventromedial prefrontal cortex and activates the rostral anterior cingulate cortex, leading to an abnormal circuit state. In a healthy state, the amygdala maintains activation through bottom-up regulation, supporting emotional regulation and network stability. The amygdala plays a key regulatory role in this circuit, and changes in functional connectivity are a potential factor that may contribute to the imbalance between cognitive control and emotional regulation. We also show within the modeling framework that alterations in functional connectivity are associated with an abnormal circuit state, which may be an important mechanism underlying the expression of depressive symptoms. This study combines data and theoretical modeling techniques, providing a computational perspective on the potential pathological mechanisms of depression and suggests potential therapeutic targets.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563759","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":"Stress-associated alterations in amygdala-striatal activity: a multi-level analysis of distributional, dynamical, and computational signatures.","authors":"Feng Lin","doi":"10.1007/s10827-026-00941-w","DOIUrl":"https://doi.org/10.1007/s10827-026-00941-w","url":null,"abstract":"<p><p>Chronic stress is associated with persistent alterations in neural circuit function, yet how these changes are expressed across multiple descriptive levels remains unclear. Here, we re-analyzed in vivo GCaMP8s recordings from BLA-DMS and CeA-DMS projection pathways using complementary distributional, dynamical, and computational approaches. Distributional analyses based on Kullback-Leibler (KL) divergence revealed stress-associated changes in the temporal organization of neural activity patterns, particularly following acute aversive perturbations where stressed animals exhibited prolonged deviations from baseline distributions. Dynamical analyses using phenomenological second-order regression models revealed corresponding alterations in recovery-related properties. Following footshock, stress was associated with shifts in principal eigenvalue distributions and reconstructed quasi-potential profiles, whereas learned lever press-reward behaviors exhibited broadly similar local stability despite differences in coefficient structure. Notably, stress-related differences in reconstructed dynamics were detectable during task acquisition even when overt behavioral performance remained comparable between groups. To examine whether these qualitative signatures could arise from simple computational principles, we implemented a minimal artificial neural network (ANN) framework as a hypothesis-generating sufficiency test. Models incorporating asymmetric optimization objectives reproduced selected experimental signatures, whereas symmetric objectives did not. Together, these findings suggest that chronic stress is associated with a reorganization of neural activity patterns across multiple descriptive levels, altering responses to perturbations and producing detectable changes in neural activity organization before overt behavioral divergence emerges.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148474160","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 learning creates invariant schema within plastic neural networks.","authors":"James R Elder, Jie Zheng, Lydia B Shimelis, Ueli Rutishauser, Milo M Lin","doi":"10.1007/s10827-026-00940-x","DOIUrl":"https://doi.org/10.1007/s10827-026-00940-x","url":null,"abstract":"<p><p>Cognitive neural circuits must balance the plasticity needed for continual learning with the stability needed to preserve an underlying reasoning framework, called a schema. How circuit learning rules form and protect such schemata from being continually overwritten during learning remains unknown. On a visual boundary detection task, we show that a hierarchical learning algorithm creates an invariant schema circuit whose weights remain fixed following sparse initial training, with additional data serving to refine the upstream representation. In contrast, the end-to-end backpropagation algorithm used to train nearly all current artificial neural networks comprehensively changes its weights throughout training. We show that the hierarchical schema makes an independent mechanistic hypothesis about circuit computation that is consistent with experimental ordering observed in single-neuron measurements in humans. These results suggest that hierarchical learning is sufficient to encode biologically consistent persistent cognitive models within otherwise malleable neural networks.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148354767","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":"Intrinsic chaos control in cortical circuits: A minimal E-I-M rate model for primary visual cortex.","authors":"Mehdi Borjkhani, Morteza A Sharif, Hadi Borjkhani","doi":"10.1007/s10827-026-00938-5","DOIUrl":"https://doi.org/10.1007/s10827-026-00938-5","url":null,"abstract":"<p><p>Cortical circuits exhibit variable yet bounded activity patterns, suggesting operation near-but not within-fully chaotic regimes. Here we develop a minimal three-variable rate model for primary visual cortex (V1) that reveals how biologically motivated feedback mechanisms can function as intrinsic chaos controllers. We adopt the simplest known chaotic Lotka-Volterra system as a phenomenological scaffold and introduce three biologically motivated modifications: excitatory-to-inhibitory (E→I) feedback coupling, homeostatic regulation of modulatory drive, and orientation-tuned sensory input. These modifications transform the excitatory (E), inhibitory (I), and modulatory (M) population dynamics from chaotic strange attractors into controlled limit cycles-a 93% reduction in dynamical variance. The model reproduces key V1 phenomena: orientation selectivity matching experimental distributions (OSI [Formula: see text]), stimulus-induced variability quenching, and realistic spiking irregularity when coupled to Hodgkin-Huxley neurons (CV[Formula: see text], within in vitro range). Parameter space analysis reveals that feedback mechanisms robustly stabilize activity across most of the tested chaotic regime. We further demonstrate that, within this minimal structure, the specific [Formula: see text] disinhibition nonlinearity enables chaos-bounded alternatives tested do not support chaotic dynamics. Our findings suggest that cortical circuits possess an intrinsic capacity for chaos that is actively suppressed by canonical feedback motifs, positioning the brain at the edge of instability where computational flexibility meets reliable signal processing.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148297416","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":"Modeling developmental spiking behavior driven by ionic current dynamics of mouse and human inner hair cells using a calcium-enhanced Izhikevich framework.","authors":"Sneha Singh, Biswajit Das, Soumik Roy","doi":"10.1007/s10827-026-00939-4","DOIUrl":"https://doi.org/10.1007/s10827-026-00939-4","url":null,"abstract":"<p><p>Hearing loss (HL) is the third leading cause of years lived with disability worldwide, underscoring the critical need for comprehensive research to unravel the developmental mechanisms that shape auditory function. This work presents a biologically grounded yet computationally efficient model of the inner hair cell (IHC) maturation in the mammalian (Mouse and Human) cochlea replicating the temporal dynamics of spiking suppression along with membrane behaviour accurately. A hybrid modeling framework combining the spiking dynamics of the modified Izhikevich model with bio-physically inspired ionic currents is analysed in this study. The proposed model particularly emphasizes on the significance of calcium-induced potassium currents ([Formula: see text]) which are critical for early developmental excitability. The current study focuses on neonatal mice, reproducing key features of IHC electrophysiology which includes spontaneous action potential (AP) firing at postnatal day 7 (P7) and generation of graded action potentials by day 14(P14) and day 21(P21) stages. The model is further extended to human fetal IHCs at neonatal, mid-mature and adult stages. Unlike in rodents, human IHCs undergo significant maturation inside utero. Furthermore, the results from the current work aligns closely with available in vitro electro-physiological data, membrane potential profiles, intracellular calcium transients and delayed rectifier potassium currents. The proposed framework enables the study of auditory encoding development which may be used to mimic congenital hearing loss(HL) and guide the creation of auditory prostheses.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2026-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148297392","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":"Inhibitory-stabilization is sufficient for history-dependent computation in a randomly connected attractor network.","authors":"Caelen J Hilty, Paul Miller","doi":"10.1007/s10827-026-00929-6","DOIUrl":"10.1007/s10827-026-00929-6","url":null,"abstract":"<p><p>For effective information processing, the response to a sensory stimulus should depend on both the incoming stimulus and the history of prior stimuli. We demonstrate how a network of randomly connected inhibition-stabilized pairs of units can produce a network with multiple attractor states, whose number increases exponentially with network size. The resulting network can preserve the computational abilities of recurrent excitatory networks, while activity of active units can stabilize at arbitrarily low firing rates. Inhibitory-stabilization also plays a functional role in history-dependent computation: transient oscillations made possible by inhibitory feedback are sufficient for state-dependent responses to stimulation. We formalize the computational processing of such networks given a set of stimuli as finite state machines and demonstrate a role for a small amount of heterogeneity in boosting their performance. Such networks may underlie many cognitive tasks, suggesting a functional role for inhibition-stabilized dynamics in cortical computation.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":"207-225"},"PeriodicalIF":1.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13233633/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147488507","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":"When can neuronal activity-dependent homeostatic plasticity maintain circuit-level properties?","authors":"Lindsay J Stolting, Randall D Beer","doi":"10.1007/s10827-026-00936-7","DOIUrl":"10.1007/s10827-026-00936-7","url":null,"abstract":"<p><p>Neural circuits are remarkably robust to perturbations that threaten their function. Activity-dependent homeostatic plasticity (ADHP) is a stabilizing mechanism that supports robustness by tuning neuronal ion conductances to combat chronic over- or under-activity. Its restorative capacity has been demonstrated in the pyloric circuit of the crustacean stomatogastric ganglion, whose neurons must burst in a specific order to coordinate digestive muscles. After disruption by physical and pharmacological manipulations, this circuit reliably recovers not only the activity levels of constituent neurons, but also the proper burst order. But how could ADHP, operating only on local information about each neuron's average activity, maintain higher-order circuit properties? We explored this question in a computational model of the pyloric pattern generator. We first optimized a set of pyloric-like networks, then optimized ADHP mechanisms for each network to restore its pyloric character after parametric perturbations. This was possible for some networks and impossible for others, so we aimed to explain this disparity. We found that successful homeostatic regulators target average neural activity levels which happen to occur only among pyloric circuits and not among non-pyloric ones, within the set of reachable circuit configurations. Therefore, in subsets of parameter space where such dissociation is possible, activity carries indirect information about burst order, which ADHP can exploit to maintain pyloricness. Other subsets, whose pyloric averages are inseparable from non-pyloric ones, cannot be perfectly regulated. This separability property may explain differences in recovery capacity across perturbations and across individuals.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":"365-391"},"PeriodicalIF":1.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13233920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147935558","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":"Astrocyte-induced dynamics of a pyramidal cell with a dendrite-connected astrocyte.","authors":"Lenka Přibylová, Jan Ševčík, Anastasia Egorova, Štěpán Husa, Lucia Kajanová, Eva Kopřivová, Lucie Alexandra Mega, Veronika Eclerová","doi":"10.1007/s10827-026-00924-x","DOIUrl":"10.1007/s10827-026-00924-x","url":null,"abstract":"<p><p>Previous multi-parameter bifurcation analyses of the Pinsky–Rinzel neuron model have elucidated a mechanistic explanation for the complex interplay between the membrane potentials of CA3 pyramidal cells and their intracellular dendritic calcium levels. By coupling this neuron model with the Li–Rinzel-type model of astrocytic Ca[Formula: see text] dynamics, we demonstrate how astrocytic calcium signaling dynamically modulates neuronal activity. We present a classification of potential dynamical transients, including transitions to epileptiform activity. Furthermore, we identify a bidirectional role of astrocytes where they may not only facilitate the emergence of high-frequency oscillations associated with epileptiform activity but may also contribute to their attenuation. Additionally, we propose a mechanism that prolongs the bursting duration of pyramidal cells, which may be associated with synaptic plasticity. Finally, we validate our modeling framework by replicating experimental paradigms that link astrocytic Ca[Formula: see text] dynamics with neuronal hyperexcitability, demonstrating that astrocytes may drive neurons toward the seizure threshold. These findings enhance our understanding of integrated neural circuit dynamics, particularly the role of neuron–astrocyte interactions in modulating bursting behavior, neural signaling, and their potential contribution to both the generation and suppression of epileptiform ripples.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":"153-176"},"PeriodicalIF":1.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13233653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147437735","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":"Spike train entropy and information transmission for a mathematical model of a bursting neuron.","authors":"Peter K D Hovland, Alexandria Kissas, Elisa H Welch, John T Birmingham","doi":"10.1007/s10827-026-00931-y","DOIUrl":"10.1007/s10827-026-00931-y","url":null,"abstract":"<p><p>In this paper we present calculations of the entropy and information transmission associated with spike trains produced by the circle/circle bursting model neuron in response to filtered white-noise stimuli. For most computations, we treated the bursts as unitary objects and estimated the entropy from the time intervals between the first spikes in consecutive bursts. In one case, we considered the intervals associated with all the spikes in the burst train or only the first and last spikes of each burst. We found that the entropy per burst was maximized when the stimulus was well matched to the neuron’s natural burst frequency and that the entropy/spike increased considerably when the duration of the burst was considered. Moreover, for a noisy stimulus for which the deterministic part of the stimulus was close to the natural burst frequency but most of the noise was at much higher frequencies, the bursting neuron transmitted considerably more information in bits/spike than a spiking model neuron constructed using similar ionic conductances.</p>","PeriodicalId":54857,"journal":{"name":"Journal of Computational Neuroscience","volume":" ","pages":"311-327"},"PeriodicalIF":1.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13234074/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147718520","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}