Front Comput Neurosci
· 2025 · PMID 41049355
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Hippocampal neurons generate membrane potential resonance due to specific voltage-gated ion channels, known as resonating conductances, which play crucial physiological roles. However, it is not known whether this phenom...Hippocampal neurons generate membrane potential resonance due to specific voltage-gated ion channels, known as resonating conductances, which play crucial physiological roles. However, it is not known whether this phenomenon of resonance is limited to membrane voltage or whether it propagates through molecular signaling components such as calcium dynamics. To test this, we first utilized a single-compartment model neuron to study the oscillatory intrinsic calcium response dynamics of hippocampal model neurons, and the effects of T-type calcium channel kinetics on the voltage and calcium resonance. We found that in the presence of T-type calcium channels, our model neuron sustained a strong calcium resonance compared to voltage resonance. Unlike voltage resonance, calcium resonance frequency was largely independent of conductance magnitude, and the two types of resonance were dissociated, meaning independent of each other. In addition, we studied the effects of A-type K-channels and h-channels in conjunction with T-type calcium channels on calcium resonance, and showed that these two types of channels differentially affect calcium resonance. Finally, using a multi-compartmental morphologically realistic neuron model, we studied calcium resonance along the somato-apical dendritic axis. Using this model, we found that calcium resonance frequency remains almost constant along the somato-apical trunk for the most part, and only toward its terminal end, the calcium resonance frequency was increased. Nonetheless, this increase was lesser compared to the increase in voltage resonance frequency. Our study opens new horizons in the field of molecular resonance, and deepen our understanding concerning the effects of frequency-based neurostimulation therapies, such as transcranial alternating current stimulation (tACS).
Burghi TB, Schapiro K, Ivanova M
… +3 more, Wang H, Marder E, O'Leary T
Front Comput Neurosci
· 2025 · PMID 40989108
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Fitting models to experimental intracellular data is challenging. While detailed conductance-based models are difficult to train, phenomenological statistical models often fail to capture the rich intrinsic dynamics of c...Fitting models to experimental intracellular data is challenging. While detailed conductance-based models are difficult to train, phenomenological statistical models often fail to capture the rich intrinsic dynamics of circuits such as central pattern generators (CPGs). A recent trend has been to employ tools from deep learning to obtain data-driven models that can quantitatively learn intracellular dynamics from experimental data. This paper addresses the general questions of modeling, training, and interpreting a large class of such models in the context of estimating the dynamics of a neural circuit. In particular, we use recently introduced Recurrent Mechanistic Models to predict the dynamics of a Half-Center Oscillator (HCO), a type of CPG. We construct the HCO by interconnecting two neurons in the Stomatogastric Ganglion using the dynamic clamp experimental protocol. This allows us to gather ground truth synaptic currents, which the model is able to predict-even though these currents are not used during training. We empirically assess the speed and performance of the training methods of teacher forcing, multiple shooting, and generalized teacher forcing, which we present in a unified fashion tailored to data-driven models with explicit membrane voltage variables. From a theoretical perspective, we show that a key contraction condition in data-driven dynamics guarantees the applicability of these training methods. We also show that this condition enables the derivation of data-driven frequency-dependent conductances, making it possible to infer the excitability profile of a real neuronal circuit using a trained model.
Front Comput Neurosci
· 2025 · PMID 40980209
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INTRODUCTION: People with post-stroke aphasia (PSA) exhibit significant interindividual variability attributed to distinctive network disruption patterns across individuals. This complexity limits the effectiveness of co...INTRODUCTION: People with post-stroke aphasia (PSA) exhibit significant interindividual variability attributed to distinctive network disruption patterns across individuals. This complexity limits the effectiveness of conventional one-size-fits-all brain stimulation approaches, but to date no individualized tACS targeting on functional network was studied in PSA. This two-phase study aimed to investigate the immediate network-modulation and language-facilitation effects of dual-site in-phase tACS utilizing a novel individualized targeting method based on individual's EEG dysfunctome. METHODS: In the first phase, network-based linear regression was used to identify aphasia-severity-predictive dysfunctome from the speech-production EEG data of 15 Cantonese-speaking people with aphasia (PWA). Individualized stimulation targets were determined using two targeting principles. Restoration-based targeting aims to restore a target edge which is centralized within the target dysfunctome but weakly-connected in the individual, whereas enhancement-based targeting selects a strongly-connected target edge. The second phase involved a single-session double-blinded sham-controlled trial with the same group to evaluate the immediate effects of dual-site 7-Hz 1-mA tACS under four conditions: Restoration In-phase (RI), Enhancement In-phase (EI), Enhancement Anti-phase (EA), and Sham (SH). RESULTS: In the first phase, we explored a range of frequency bands and EEG tasks and identified a left frontal-temporal theta network under divergent naming task that significantly predicted aphasia severity. The single-session clinical trial in the second phase demonstrated that RI condition produced increases in the target node strength, global network properties, and divergent naming performance, which were absent in sham and the other two real stimulation conditions. DISCUSSION: This was the first-of-its-kind dysfunctome-based data-driven individualized tACS demonstrated immediate neuromodulatory effects in PSA. The findings suggest that EEG dysfunctome can help pinpointing effective individualized targets for tACS to promote clinically-beneficial functional reorganization. Despite limited generalizability due to the small sample, this methodology holds significant potential for application in longer-term treatment and other network-based disorders.
Chacon PFS, Wochner I, Hammer M
… +3 more, Eppler JM, Kunkel S, Schmitt S
Front Comput Neurosci
· 2025 · PMID 40933538
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The development of new studies that consider different structures of the hierarchical sensorimotor control system is essential to enable a more holistic understanding about movement. The incorporation of more biological...The development of new studies that consider different structures of the hierarchical sensorimotor control system is essential to enable a more holistic understanding about movement. The incorporation of more biological proprioceptive and neuronal circuit models to muscles can turn neuromusculoskeletal systems more appropriate to investigate and elucidate motor control. Specifically, further studies that consider the closed-loop between proprioception and central nervous system may allow to better understand the yet open question about the importance of afferent feedback for sensorimotor learning and execution in the intact biological system. Therefore, this study aims to investigate the processing of spindle afferent firings by spiking neuronal network and their relevance for sensorimotor control. We integrated our previously published physiological model of the muscle spindle in a biological arm model, corresponding to a musculoskeletal system able to reproduce biological motion inside of the demoa multi-body simulation framework. We coupled this musculoskeletal system to physiologically-motivated neuronal spinal pathways, which were implemented based on literature in the NEST spiking neural network simulator, intended to perform human center-out reaching arising from spinal synaptic learning. As result, the spindle connections to the spinal neurons were strengthened for the more difficult targets (i.e. higher above placed targets) under perturbation, highlighting the importance of spindle proprioception to succeed in more difficult scenarios. Furthermore, an additionally-implemented simpler spinal network (that does not include the pathways with spindle proprioception) presented an inferior performance in the task by not being able to reach all the evaluated targets.
Front Comput Neurosci
· 2025 · PMID 40933537
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The brain's faculty to assimilate and retain information, continually updating its memory while limiting the loss of valuable past knowledge, remains largely a mystery. We address this challenge related to continuous lea...The brain's faculty to assimilate and retain information, continually updating its memory while limiting the loss of valuable past knowledge, remains largely a mystery. We address this challenge related to continuous learning in the context of associative memory networks, where the sequential storage of correlated patterns typically requires non-local learning rules or external memory systems. Our work demonstrates how incorporating biologically inspired inhibitory plasticity enables networks to autonomously explore their attractor landscape. The algorithm presented here allows for the autonomous retrieval of stored patterns, enabling the progressive incorporation of correlated memories. This mechanism is reminiscent of memory consolidation during sleep-like states in the mammalian central nervous system. The resulting framework provides insights into how neural circuits might maintain memories through purely local interactions and takes a step forward toward a more biologically plausible mechanism for memory rehearsal and continuous learning.
Front Comput Neurosci
· 2025 · PMID 40927644
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Artificial neural networks are limited in the number of patterns that they can store and accurately recall, with capacity constraints arising from factors such as network size, architectural structure, pattern sparsity,...Artificial neural networks are limited in the number of patterns that they can store and accurately recall, with capacity constraints arising from factors such as network size, architectural structure, pattern sparsity, and pattern dissimilarity. Exceeding these limits leads to recall errors, eventually leading to catastrophic forgetting, which is a major challenge in continual learning. In this study, we characterize the theoretical maximum memory capacity of single-layer feedforward networks as a function of these parameters. We derive analytical expressions for maximum theoretical memory capacity and introduce a grid-based construction and sub-sampling method for pattern generation that takes advantage of the full storage potential of the network. Our findings indicate that maximum capacity scales as (/) , where N is the number of input/output units and S the pattern sparsity, under threshold constraints related to minimum pattern differentiability. Simulation results validate these theoretical predictions and show that the optimal pattern set can be constructed deterministically for any given network size and pattern sparsity, systematically outperforming random pattern generation in terms of storage capacity. This work offers a foundational framework for maximizing storage efficiency in neural network systems and supports the development of data-efficient, sustainable AI.
Xinli M, Jie Z, Ming Y
… +4 more, Yanping Z, Fan L, Jing J, Lu D
Front Comput Neurosci
· 2025 · PMID 40901301
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Diaphragm dysfunction represents a significant complication in elderly patients undergoing mechanical ventilation, often resulting in extended intensive care stays, unsuccessful weaning attempts, and increased healthcare...Diaphragm dysfunction represents a significant complication in elderly patients undergoing mechanical ventilation, often resulting in extended intensive care stays, unsuccessful weaning attempts, and increased healthcare expenditures. To address the deficiency of precise, real-time decision support in this context, a novel artificial intelligence framework is proposed, integrating imaging, physiological signals, and ventilator parameters. Initially, a hierarchical Transformer encoder is employed to extract modality-specific embeddings, followed by an attention-guided cross-modal fusion module and a temporal network for dynamic trend prediction. The framework was assessed using three public datasets, which are, the MIMIC-IV, eICU, and Chest X-ray. The proposed model achieved the highest accuracy (92.3% on MIMIC-IV, 91.8% on eICU, 92.0% on Chest X-ray) and surpassed all baselines in precision, recall, F1-score, and Matthews correlation coefficient. Additionally, the model's probability estimates were well-calibrated, and its SHAP-based explainability analysis identified ventilator volume and key imaging features as primary predictors. The clinical implications of this study are significant. By providing precise and interpretable predictions, the proposed model has the potential to transform critical care practices by offering a pathway to more effective and personalized interventions for high-risk patients.
Umair M, Khan MS, Hanif M
… +4 more, Ghaban W, Nafea I, Qasem SN, Saeed F
Front Comput Neurosci
· 2025 · PMID 40901300
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As global life expectancy rises, a growing proportion of the population is affected by dementia, particularly Alzheimer's disease (AD) and Frontotemporal dementia (FTD). Electroencephalography (EEG) based diagnosis prese...As global life expectancy rises, a growing proportion of the population is affected by dementia, particularly Alzheimer's disease (AD) and Frontotemporal dementia (FTD). Electroencephalography (EEG) based diagnosis presents a non-invasive, cost effective alternative for early detection, yet existing methods are challenged by data scarcity, inter-subject variability, and privacy concerns. This study proposes lightweight and privacy-preserving EEG classification framework combining deep learning and Federated Learning (FL). Five convolutional neural networks (EEGNetv1, EEGNetv4, EEGITNet, EEGInception, EEGInceptionERP) have been evaluated on resting-state EEG dataset comprising 88 subjects. EEG signals are preprocessed using band-pass (1-45 Hz) and notch filtering, followed by exponential standardization and 4-second windowing. EEGNetv4 outperformed among other EEG tailored models, and upon utilizing the hybrid fusion techniques it achieves 97.1% accuracy using only 1,609 parameters and less than 1 MB of memory, demonstrating high efficiency. Moreover, FL using FedAvg is implemented across five stratified clients, achieving 96.9% accuracy on the hybrid fused EEGNetV4 model while preserving data privacy. This work establishes a scalable, resource-efficient, and privacy-compliant framework for EEG-based dementia diagnosis, suitable for deployment in real-world clinical and edge-device settings.
Front Comput Neurosci
· 2025 · PMID 40880621
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This paper proposes a new theoretical framework for understanding time perception centered on information processing in the brain. We introduce the concept of "perceptual time" as distinct from inertial clock time and de...This paper proposes a new theoretical framework for understanding time perception centered on information processing in the brain. We introduce the concept of "perceptual time" as distinct from inertial clock time and develop a model relating perceptual time experience to the brain's computational capacity and information processing rate. This framework explains phenomena like time dilation and compression during intense experiences in terms of neural information processing, bridging perceptual time with physical theories of time.
Front Comput Neurosci
· 2025 · PMID 40880619
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Understanding how functional connectivity between cortical neurons varies with spatial distance is crucial for characterizing large-scale neural dynamics. However, inferring these spatial patterns is challenging when spi...Understanding how functional connectivity between cortical neurons varies with spatial distance is crucial for characterizing large-scale neural dynamics. However, inferring these spatial patterns is challenging when spike trains are collected from large populations of neurons. Here, we present a maximum likelihood estimation (MLE) framework to quantify distance-dependent functional interactions directly from observed spiking activity. We validate this method using both synthetic spike trains generated from a linear Poisson model and biologically realistic simulations performed with Izhikevich neurons. We then apply the approach to large-scale electrophysiological recordings from V1 cortical neurons. Our results show that the proposed MLE approach robustly captures spatial decay in functional connectivity, providing insights into the spatial structure of population-level neural interactions.
Front Comput Neurosci
· 2025 · PMID 40822710
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INTRODUCTION: The effectiveness of neural interfacing devices depends on the anatomical and physiological properties of the target region. Multielectrode arrays, used for neural recording and stimulation, are influenced...INTRODUCTION: The effectiveness of neural interfacing devices depends on the anatomical and physiological properties of the target region. Multielectrode arrays, used for neural recording and stimulation, are influenced by electrode placement and stimulation parameters, which critically impact tissue response. This study presents a multiscale computational model that predicts responses of neurons in the hippocampus-a key brain structure primarily involved in memory formation, especially the conversion of short-term memories into long-term storage-to extracellular electrical stimulation, providing insights into the effects of electrode positioning and stimulation strategies on neuronal response. METHODS: We modeled the rat hippocampus with highly detailed axonal projections, integrating the Admittance Method to model propagation of the electric field in the tissue with the NEURON simulation platform. The resulting model simulates electric fields generated by virtual electrodes in the perforant path of entorhinal cortical (EC) axons projecting to the dentate gyrus (DG) and predicts DG granule cell activation via synaptic inputs. RESULTS: We determined stimulation amplitude thresholds required for granule cell activation at different electrode placements along the perforant path. Membrane potential changes during synaptic activation were validated against experimental recordings. Additionally, we assessed the effects of bipolar electrode placements and stimulation amplitudes on direct and indirect activation. CONCLUSION: Stimulation amplitudes above 750 μA consistently activate DG granule cells. Lower stimulation amplitudes are required for axonal activation and downstream synaptic transmission when electrodes are placed in the molecular layer, infra-pyramidal region, and DG crest. SIGNIFICANCE: The study and underlying methodology provide useful insights to guide the stimulation protocol required to activate DG granule cells following the stimulation of EC axons; the complete realistic 3D model presented constitutes an invaluable tool to strengthen our understanding of hippocampal response to electrical stimulation and guide the development and placement of prospective stimulation devices and strategies.
Nakhle F, Harfouche AH, Karam H
… +1 more, Tserolas V
Front Comput Neurosci
· 2025 · PMID 40822709
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The energy demands of modern AI systems have reached unprecedented levels, driven by the rapid scaling of deep learning models, including large language models, and the inefficiencies of current computational architectur...The energy demands of modern AI systems have reached unprecedented levels, driven by the rapid scaling of deep learning models, including large language models, and the inefficiencies of current computational architectures. In contrast, biological neural systems operate with remarkable energy efficiency, achieving complex computations while consuming orders of magnitude less power. A key mechanism enabling this efficiency is subthreshold processing, where neurons perform computations through graded, continuous signals below the spiking threshold, reducing energy costs. Despite its significance in biological systems, subthreshold processing remains largely overlooked in AI design. This perspective explores how principles of subthreshold dynamics can inspire the design of novel AI architectures and computational methods as a step toward advancing TinyAI. We propose pathways such as algorithmic analogs of subthreshold integration, including graded activation functions, dendritic-inspired hierarchical processing, and hybrid analog-digital systems to emulate the energy-efficient operations of biological neurons. We further explore neuromorphic and compute-in-memory hardware platforms that could support these operations, and propose a design stack aligned with the efficiency and adaptability of the brain. By integrating subthreshold dynamics into AI architecture, this work provides a roadmap toward sustainable, responsive, and accessible intelligence for resource-constrained environments.
Hoheisel L, Hacker H, Fink GR
… +2 more, Daun S, Kambeitz J
Front Comput Neurosci
· 2025 · PMID 40799942
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Functional connectivity (FC) is a widely used indicator of brain function in health and disease, yet its neurobiological underpinnings still need to be firmly established. Recent advances in computational modelling allow...Functional connectivity (FC) is a widely used indicator of brain function in health and disease, yet its neurobiological underpinnings still need to be firmly established. Recent advances in computational modelling allow us to investigate the relationship of both static FC (sFC) and dynamic FC (dFC) with neurobiology non-invasively. In this study, we modelled the brain activity of 200 healthy individuals based on empirical resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data. Simulations were conducted using a group-averaged structural connectome and four parameters guiding global integration and local excitation-inhibition balance: (i) G, a global coupling scaling parameter; (ii) J , an inhibitory coupling parameter; (iii) J , the excitatory NMDA synaptic coupling parameter; and (iv) w , the excitatory population recurrence weight. For each individual, we optimised the parameters to replicate empirical sFC and temporal correlation (TC). We analysed associations between brain-wide sFC and TC features with optimal model parameters and fits with a univariate correlation approach and multivariate prediction models. In addition, we used a group-average perturbation approach to investigate the effect of coupling in each region on overall network connectivity. Our models could replicate empirical sFC and TC but not the FC variance or node cohesion (NC). Both fits and parameters exhibited strong associations with brain connectivity. G correlated positively and J negatively with a range of static and dynamic FC features (|| > 0.2, p < 0.05). TC fit correlated negatively, and sFC fit positively with static and dynamic FC features. TC features were predictive of TC fit, sFC features of sFC fit ( > 0.5). Perturbation analysis revealed that the sFC fit was most impacted by coupling changes in the left paracentral gyrus (Δr = 0.07), TC fit by alterations in the left pars triangularis (Δr = 0.24). Our findings indicate that neurobiological characteristics are associated with individual variability in sFC and dFC, and that sFC and dFC are shaped by small sets of distinct regions. By modelling both sFC and dFC, we provide new evidence of the role of neurophysiological characteristics in establishing brain network configurations.