Neuroinformatics最新文献

{"title":"Correction to: Towards Multi-Brain Decoding in Autism: A Self-Supervised Learning Approach.","authors":"Ghazaleh Ranjbaran, Quentin Moreau, Adrien Dubois, Guillaume Dumas","doi":"10.1007/s12021-026-09805-1","DOIUrl":"10.1007/s12021-026-09805-1","url":null,"abstract":"","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13545141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892772","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":"Application of Machine Learning Models to Identify Differences in Neural Electrophysiological Properties Across Estrous Cycle Phases.","authors":"Armaan Raina, John Meitzen","doi":"10.1007/s12021-026-09814-0","DOIUrl":"10.1007/s12021-026-09814-0","url":null,"abstract":"<p><p>Machine learning models (MLMs) have been used to classify neuron subtypes based upon electrophysiological attributes. What has been less explored is using MLMs to assess differences within a neuron type, especially in the context of subtle changes induced by neuromodulation such as the rodent estrous cycle. Previous research found that the estrous cycle shifts rat nucleus accumbens (NAc) medium spiny neuron (MSN) electrophysiology, including action potential (AP), passive, and miniature excitatory post-synaptic current (mEPSC) properties. This plasticity provides a model system to investigate this question. We hypothesized that MLM classification accuracy would differ when trained across these data types, reflecting the information each encodes regarding estrous phase identity. To test this hypothesis, we extracted electrophysiological features across four estrous cycle phases from a publicly available dataset, and employed this data to train MLMs to classify estrous cycle phase origin. We found: MLMs identified estrous phase origin with up to 94% accuracy (Random Forest); MLM accuracy differed by model and feature set; passive, AP and mEPSC features yielded sequential performances with feature importance analysis revealing input resistance, AP burst length, and mEPSC baseline current as most discriminative for their respective feature sets; the late proestrus phase demonstrated the most distinct electrophysiological profile. These findings demonstrate that MLMs can detect changes associated with the estrous cycle and are useful for assessing electrophysiological differences within a neuron type. They further represent a broader application of MLMs as computational tools useful for understanding the sensitivity of neural properties to the influences of neuromodulatory cycles.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13534189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148876510","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":"Cognitive Sovereignty: An AI-Aware Governance Framework for Neural Data Threats, Autonomous Cyber Defense, and Identity-Aware Security in Neurotechnology Systems.","authors":"Ms Kritika","doi":"10.1007/s12021-026-09813-1","DOIUrl":"10.1007/s12021-026-09813-1","url":null,"abstract":"<p><p>Neural data collected using brain-computer interfaces, neural implants, and emotion detection systems is analyzed by AI classifiers and agentic architectures to serve purposes such as authentication, access control, and behavioral inference, however, there exists no comprehensive, binding cybersecurity or data protection regime to regulate such neural data. The regulations that currently exist i.e., GDPR, HIPAA, the Budapest Convention, the 2025 UNESCO Recommendation on Neurotechnology Ethics, and a small number of state laws (e.g., Colorado 2024, California SB 1223, Montana, Connecticut) create a fragmented and incomplete emerging framework rather than no framework at all. In this paper, the author propose Cognitive Sovereignty architecture, an approach of governance through the combination of a legally recognized definition and technical parameters defining neural data as a new class of data which necessitates specific regulatory, adversarially sound processing frameworks, and jurisdictionally agnostic enforcement mechanisms. By conducting comparative law research, threat modeling based on STRIDE model and governance modeling, this paper highlights structural issues with the existing regimes and suggests a framework composed of Declaration on Cognitive Sovereignty, neuro-cybercrime protocol of the Budapest Convention, and AI layer-specific compliance requirements based on NIST AI RMF and the EU AI Act.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148851230","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":"A Perovskite Memdiode-Based Neuromorphic in Silico Surrogate Model for Emulating Predictive Coding Failure in Diabetic Neuropathy.","authors":"Deiva Kumar K, Mathivanan Ponnambalam","doi":"10.1007/s12021-026-09809-x","DOIUrl":"10.1007/s12021-026-09809-x","url":null,"abstract":"<p><p>Halide perovskite memdiodes have coupled ionic-electronic dynamics and are promising candidates for artificial synapses in neuromorphic computing. We provide an in silico neuromorphic circuit that includes a comprehensive perovskite memdiode model and confirm its synaptic plasticity repertoire through simulation-based validation. The proposed model recapitulates analog long-term potentiation/depression (LTP/LTD), spike-timing-dependent plasticity (STDP), spike-rate-dependent plasticity (SRDP), and paired-pulse facilitation/depression (PPF/PPD). We also describe pulse-amplitude- and pulse-width-dependent conductance modulation, pinched hysteresis I-V curves, and statistical robustness to device-to-device and cycle-to-cycle fluctuations through Monte Carlo simulation. This memdiode is implemented into a continuous-time neuromorphic predictive coding framework to minimize Variational Free Energy (VFE). In this architecture, we directly map four clinical hallmarks of diabetic peripheral neuropathy (DPN)-plasticity impairment, homeostatic failure, afferent attenuation, and conduction delay-to localized circuit parameters ([Formula: see text]). One severity parameter α is used to continuously tune the network between healthy predictive coding and computational collapse. Healthy baseline parameters minimize VFE quickly. Moderate DPN (α = 0.5) results in permanently elevated oscillatory VFE, a hypothetical counterpart of allodynia. Severe DPN (α ≥ 0.8) paralyzes the homeostatic plasticity, and the system collapses to a frozen maladaptive state that is similar to sensory ataxia. The noise resilience analysis and hyperparameter sensitivity analysis indicate that the architecture is highly resilient to stochasticity within the simulated parameter space and structurally robust to hyperparameter perturbations in all dynamical regimes. The primary contribution of this work is a computational framework demonstrating how second-order, BCM-capable memristive device dynamics can be used to model systems-level predictive-coding failure; the behaviorally validated perovskite memdiode model and the DPN severity mapping serve as a concrete, illustrative instantiation of this framework.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800581","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":"Range-Limited Scale Stability of Dynamic Direction Representations in a Connectome-Constrained Fly Visual Model.","authors":"Nalin Dhiman, Siddhrath Panwar","doi":"10.1007/s12021-026-09811-3","DOIUrl":"10.1007/s12021-026-09811-3","url":null,"abstract":"<p><p>We test whether motion direction in a frozen, independently pretrained FLYVIS checkpoint remains linearly accessible when retinal apparent scale is held out. The downstream decoder is supervised on direction labels from the other scales, so the experiment measures transfer of an existing representation rather than autonomous six-way classification by FLYVIS. The analysis unit is a unique rendered condition; moving bars and translating targets form the 192-condition scale-varying set, while geometrically scale-neutral moving edges provide a direction control. Across eight scales, the frozen representation gives leave-one-scale-out accuracy 0.885 (Wilson 95% interval 0.833-0.923). Transfer is nearly perfect across the six interior held-out scales (0.986) but reaches chance at the upper boundary [Formula: see text] (0.167), revealing strong but range-limited scale stability. Capacity and sampling-matched direction-trained temporal models reach 0.983-0.986 with hexagonal, collision-free square-projected, or self-only neighbourhood operators. This establishes raw task solvability and shows that sampling geometry alone does not explain success; unlike FLYVIS, these controls are optimized directly for direction labels. Direct interventions further show that T4/T5 activity contributes to preservation of the intact linear code: after T4/T5 silencing, a refitted decoder retains 0.891 accuracy while the fixed intact decoder falls to 0.193, indicating distributed information in reorganized coordinates. Learned-parameter shuffles reveal realization-dependent code disruption and dynamical fragility. Together, the results establish a reusable cross-scale benchmark, strong interior-scale transfer in a frozen connectome-constrained model, a sharp extrapolation boundary, and specific perturbational sensitivities. Optical-flow pretraining, connectome-constrained dynamics, and learned parameters define complementary mechanistic factors for controlled factorial study.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800632","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":"Meta-Analysis of Face-Validity Indicators in Automatically Detected Dog Sleep Spindles.","authors":"Ivaylo Borislavov Iotchev, Anna Kis","doi":"10.1007/s12021-026-09810-4","DOIUrl":"10.1007/s12021-026-09810-4","url":null,"abstract":"<p><p>Visual inspection as the golden standard for sleep spindle detection has received mixed support and may not be equally viable across species and experimental conditions. Here we propose a novel intermediate strategy for approximating the quality of a detector and subject our own algorithm for spindle detection in dogs to the procedure. Automatic detections across the full range of our data (excluding only data sets with high subject overlap) were analyzed for their compliance with three face validity criteria, originally discovered by expert scorers in human EEG. These criteria are the shortness of the events (mostly below 2 s, maximum 6), the distribution of faster (> 13 Hz) spindles along the midline (higher in central and posterior derivations) and an association between amplitude and negative chirp (positive for sleep spindles and mostly unrelated for other oscillations). All three criteria were observed in the majority of data sets and sampling events (subsets of data defined by condition and/or recording channel). Replication was more consistent for data sets than for sampling events, but combined probabilities (Fisher's method) always favoured face validity compliance. Our results indicate that automatic detections in the dog, which were shown in the past to display similar to humans associations with age and cognition, also share face validity criteria with human spindles. This strengthens the dog as a model in sleep spindle research and offers additional arguments for the quality of our detection method.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13477484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148760109","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":"Optimal Size of Electrocorticography Grids for Classification of Hand Movements.","authors":"Elena C Offenberg, Dirk Keller, Siamak Mehrkanoon, Julia Berezutskaya, Mariska J Vansteensel, Mariana P Branco","doi":"10.1007/s12021-026-09807-z","DOIUrl":"10.1007/s12021-026-09807-z","url":null,"abstract":"<p><p>Implanted Brain-Computer Interfaces (BCIs) hold significant promise as a replacement for conventional assistive technologies for people with extensive motor impairments. In recent years, significant progress has been made in enhancing BCI performance, bringing clinically viable systems within reach. Some of these developments rely on increasingly large numbers of high spatial-density electrocorticography (ECoG) electrodes, which enable the extraction of spatially detailed information from extensive brain areas, but may also elevate surgical burden, posing a challenge for the clinical adoption of implanted BCIs. To mitigate this risk, we conducted an exhaustive investigation of hand movement classification performance involving 4, 5, and 8 classes in nine individuals with epilepsy, exploring all possible rectangular ECoG subgrids within the 32-, 64-, and 128-channel grids that were implanted in these individuals. Our findings reveal that the surface area of ECoG grids can be substantially reduced by 75-94% compared to the original grids, without a meaningful decline in classification performance, if electrodes are placed over informative areas. Classification performance across datasets was stable for progressively smaller subgrids until a critical threshold of approximately 60mm² was reached, below which performance declined substantially. We show that smallest subgrids with an area above the threshold achieved equally high classification F1 scores (range 81.64-99.71%) as the full grids with an area > 230mm² (range 82.85-96.75%). We conclude that ECoG-based BCI can be used to accurately decode up to seven different hand movements from well-located grids with a small number of electrodes, paving the way for smaller and safer BCI implants.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13447543/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148686428","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":"Mouse to Human Cross-Species Transfer Learning for Electrophysiology-to-Transcriptomics Mapping in Cortical GABAergic Interneurons.","authors":"Theo Schwider, Ramin Ramezani","doi":"10.1007/s12021-026-09806-0","DOIUrl":"10.1007/s12021-026-09806-0","url":null,"abstract":"<p><p>Single-cell electrophysiological recordings provide a powerful window into neuronal functional diversity and offer an interpretable route for linking intrinsic physiology to transcriptomic identity. Here, we adapt the IPFX/Gouwens electrophysiological feature-family organization introduced by Gouwens et al. (2020) using publicly available Allen Institute Patch-seq datasets from mouse and human cortex. We focused on GABAergic inhibitory interneurons in an aligned subclass structure (Lamp5, Pvalb, Sst, Vip). After quality control, we analyzed 3,699 mouse visual cortex neurons and 506 human neocortical neurons from neurosurgical resections. Using standardized electrophysiological features and a Gouwens-style sparse-PCA random-forest baseline, we recovered major class-level separations consistent with the original mouse study. For supervised prediction, a class-balanced random forest provided a strong feature-engineered baseline in mouse data and a reduced but still informative baseline in human data. We then developed an attention-based BiLSTM that deliberately diverges from the sparse-PCA baseline by operating directly on the pre-sPCA family-vector tensor rather than on the 44 sparse-PC representation, providing feature-family-level interpretability via learned attention weights. Finally, we evaluated cross-species transfer by pretraining on mouse data and fine-tuning on human data for an aligned 4-class task, improving human macro-F1 versus human-only training. Together, these results support the utility of the Gouwens feature-family organization while distinguishing the sPCA-compressed baseline from our family-structured neural sequence model.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148680614","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":"Optimizing Photogrammetry Parameters for 3D Reconstruction of Fresh Brain Specimens.","authors":"Carlos A Rueda-Pérez, María-Camila Valencia-Loaiza, Paula Vega-Cordoba, Juliana Tobón, Dylan S Anaya, Andres Gonzalez-Leyton, Juan F Mazo, Jesús D Tarazona, Santiago Ruiz, Juan G Martinez, Andrés Echeverri-Garcia, Catherine J Gomez-Moreno, Brian Vicaño-Metaute, Johana Gómez-Ramirez, Andres Villegas-Lanau","doi":"10.1007/s12021-026-09789-y","DOIUrl":"10.1007/s12021-026-09789-y","url":null,"abstract":"<p><p>Photogrammetry has become an essential tool in medical and research fields for generating high-fidelity 3D models from 2D images. However, optimizing the imaging and processing parameters remains a challenge, particularly for fresh brain specimens, where repeated imaging is not feasible. This study systematically evaluates the impact of various Metashape alignment settings on 3D reconstruction outcomes, analyzing 12,600 configuration combinations and 63,000 alignments. Our results suggest that the optimal imaging setup consists of four photo sets: two captured at anatomical position (level with the brain and 30 cm above it at a 30° camera tilt) and two identical sets with the basal side facing upwards. The brain should be rotated 3° between shots, generating 120 images per set. Initial processing should be performed without masks using medium-precision alignment in Metashape. If alignment fails, we recommend generating one mask per image, delineating the brain's borders, and applying masks to key points. Notably, higher image density only improves alignment reliability when masking is selected to detected features and may only increase processing time. We also observed variability in alignment results under identical conditions, suggesting an inherent stochastic component in Metashape. Consequently, unsuccessful alignments should be repeated before modifying imaging parameters. To our knowledge, this is the first study to systematically define an optimal imaging and processing parameters for 3D photogrammetry of fresh brains using a turntable. Future research should focus on determining the minimum number of images required to ensure high-quality reconstructions.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13401559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148594264","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":"SACS: A Reproducible, Configuration-Driven Software Framework for Schematic Multi-Region Circuit Simulation and Integrated Analytics.","authors":"Eyasu Desalegne Beyene, Erkan Atmaca","doi":"10.1007/s12021-026-09799-w","DOIUrl":"10.1007/s12021-026-09799-w","url":null,"abstract":"<p><p>Computational neuroscience projects often combine simulation code, configuration files, analysis scripts, plotting utilities, and provenance records through loosely coupled and difficult-to-replay workflows. SACS is presented as a configuration-driven research-software framework for reproducible schematic multi-region circuit simulation with integrated analytics, validation checks, deterministic execution, deterministic replay, artifact replay, and graphical inspection. Implemented as the Python package brain_sim, the framework converts declarative model and scenario specifications into standardized run directories containing numerical outputs, machine-readable summaries, figure-generation recipes, validation reports, and provenance metadata. The contribution is methodological and neuroinformatics-oriented. SACS is not presented as a biologically validated model of anxiety, brain function, or treatment response, and its built-in circuit materials are used as configurable demonstration components rather than as evidence of clinical or biological validity. Instead, the framework is evaluated as software for reproducible computational experimentation: hypotheses are encoded as explicit configurations, executed under recorded seeds, inspected through analytics and validation layers, and then used to inform subsequent configurations in an iterative workflow. The manuscript describes the software architecture, configuration and execution model, artifact structure, replay mechanisms, and graphical/programmatic interfaces that support this workflow. The central claim is that SACS improves transparency, inspectability, and reuse for schematic circuit experiments by binding configuration, execution, analytics, provenance, and replay within a single software environment.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148580891","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}