Frontiers in Computational Neuroscience最新文献

{"title":"Sex differences in early brain development in a multi-site ASD likelihood cohort.","authors":"Gabriel Blanco-Gomez, Julie Scorah, Christian O'Reilly, Stefon van Noordt, Virginia Carter Leno, Sara Jane Webb, Mayada Elsabbagh","doi":"10.3389/fncom.2026.1868455","DOIUrl":"https://doi.org/10.3389/fncom.2026.1868455","url":null,"abstract":"<p><p>Biological sex is increasingly recognized as a fundamental dimension of brain organization, yet how it influences early neurodevelopment, particularly autism spectrum disorder (ASD), remains understudied. Using data from the International Infant EEG Data Integration Platform (EEG-IP), a multi-site longitudinal cohort of 179 infants (91 males, 88 females) at elevated (ELA) and typical (TLA) likelihood of ASD, we conducted a series of re-analyses of previously published EEG studies to examine whether key EEG metrics differ by biological sex and whether biological sex can moderate the relationship between early brain profiles and language development from 6 to 36 months. Across all EEG metrics, we found that females displayed significantly higher left-hemisphere functional connectivity within speech-related regions at 6 months of age compared to males. Females also demonstrated increased posterior theta consistency during face processing as well as steeper expressive language trajectories from 6 to 36 months. Speech connectivity at 6 months also moderated receptive language growth, but not expressive language growth in females, suggesting sex-specific relationships between brain dynamics and language. These findings show the importance of including biological sex as a primary variable in infant developmental studies and provide support for the study of sex-differentiated biomarkers in prospective ASD studies.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1868455"},"PeriodicalIF":3.3,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13486256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148790222","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":"Interpretable machine-learning for depression classification based on a three-component EEG marker.","authors":"Haisheng Zhang, Jing Kan, Wei Tong, Bicheng Wu, Ye Zhao, Yongchun Ma","doi":"10.3389/fncom.2026.1869159","DOIUrl":"https://doi.org/10.3389/fncom.2026.1869159","url":null,"abstract":"<p><p>Electroencephalography (EEG)-based depression classification requires interpretable machine-learning approaches and validation strategies that avoid subject-level information leakage. This single-center pilot study developed and internally evaluated a marker-based interpretable machine-learning framework using eight-channel resting-state EEG. After quality control, 48 participants were included, comprising 23 clinician-diagnosed major depressive disorder (MDD) patients recruited at Zhejiang Provincial Tongde Hospital and 25 healthy controls (HCs). Subject-level spectral, entropy/complexity, asymmetry, and coherence-based connectivity features were extracted from pre-processed EEG epochs. Exploratory feature analysis was used to define a three-component EEG marker comprising fronto-posterior beta- and gamma-band coherence heterogeneity and F8-F7 beta-band asymmetry variability. The resulting marker was evaluated using classical classifiers under strict subject-wise leave-one-subject-out validation and compared with EEGNet and 1D-CNN baselines under the same subject-wise protocol. Among the evaluated classical models, RBF-SVM achieved the highest subject-level discrimination in the primary native-reference 2-s analysis, with an AUC of 0.910, accuracy of 85.42%, sensitivity of 82.61%, and specificity of 88.00%. The primary result used the native A1/A2 acquisition reference and non-overlapping 2-s epochs, consistent with segmentation used in previous resting-state EEG depression studies. SHAP analysis indicated that fronto-posterior gamma-band coherence heterogeneity and F8-F7 beta-band asymmetry variability were the dominant contributors to the RBF-SVM decision function. These findings support the exploratory value of compact EEG markers for interpretable internal discrimination in data-limited eight-channel settings and motivate validation in independent external cohorts.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1869159"},"PeriodicalIF":3.3,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13486288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148790208","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":"A feasible probability and graph model for memory engrams.","authors":"Hui Wei, Surun Yang, Yangwang Li","doi":"10.3389/fncom.2026.1822722","DOIUrl":"https://doi.org/10.3389/fncom.2026.1822722","url":null,"abstract":"<p><p>The capacity of long-term memory seems to be extremely large, capable of storing information spanning almost a lifetime. Why does it have such a vast capacity? Why are some memories so enduring? What is the actual physical form of long-term memory? In the movie Inside Out, it is depicted as individual orbs containing information. Is that really the case? Simply explaining this by saying that the cortex has many neurons, numerous neural connections, and complex electrochemical activity between them is not sufficient to answer these fundamental questions. We need to uncover the theory hidden behind these phenomena. In essence, a neural network is equivalent to a very large directed graph, with a massive number of nodes and directed connections. This paper posits that the physical form of long-term memory is a connected subgraph within this complex directed graph. This subgraph is capable of linking together the disparate fragments of the same event, spread across different sensory cortices, to form associations. This provides a physical realization of the engram theory. The robustness of the connected subgraph and the resources it consumes can explain various memory behaviors. Based on anatomical, brain imaging, and electrophysiological evidence, this paper constructs a probabilistic connectivity model and uses theorems from graph theory to prove the ease of constructing connected subgraphs. Finally, it explains why the potential capacity for memory is immense.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1822722"},"PeriodicalIF":3.3,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13478142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148790213","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":"Dopamine modulation of spike-timing-dependent plasticity for spatio-temporal spike pattern detection in single neurons.","authors":"Ayaka Kotajima, Shunta Furuichi, Takashi Kohno","doi":"10.3389/fncom.2026.1777273","DOIUrl":"https://doi.org/10.3389/fncom.2026.1777273","url":null,"abstract":"<p><p>Dopamine (DA) is an important neuromodulator that has been suggested to play key roles in a range of neuropsychiatric disorders. However, its computational impact at a general single-neuron level has not been elucidated. Here, we extended a spike-timing-dependent plasticity (STDP)-based single-neuron spatio-temporal pattern detection model by incorporating a DA input and DA-type STDP modulation. We analyzed the direct effect of the DA-type STDP curve and the effect of DA release. We found that the DA-type STDP curve accelerates learning but may promote coarse potentiation of the synaptic efficacy, which leads to false detections. It was also shown that DA can improve the pattern-detection performance up to a 44% success rate (2.44 × control) with no false detection, but an excessive DA release or a DA release with too short a delay induces false detection. These results suggest that DA can maintain the pattern detection ability only when released within a limited concentration and a sufficient delay.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1777273"},"PeriodicalIF":3.3,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13473435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148764616","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":"Conductance-based reversal potentials in spiking recurrent neural networks enhance energy efficiency and task performance.","authors":"Miguel Rodrigues, Carmen Gasco-Galvez, Martin Vinck","doi":"10.3389/fncom.2026.1870593","DOIUrl":"https://doi.org/10.3389/fncom.2026.1870593","url":null,"abstract":"<p><p>Spiking recurrent neural networks (SRNNs) rival gated recurrent neural networks (RNNs) on various tasks, yet they still lack several hallmarks of biological neural networks. We introduce a biologically grounded SRNN that implements Dale's law with conductance-based stands for a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and gamma-aminobutyric acid (GABA) reversal potentials. These reversal potentials modulate synaptic gain as a function of the postsynaptic membrane potential, and we derive theoretically how they make each neuron's effective dynamics and subthreshold resonance input-dependent. We trained SRNNs on the Spiking Heidelberg Digits (SHD) dataset and show that SRNNs with reversal potentials reduce spike energy by up to 3 × , while maintaining, or increasing, task accuracy. This leads to high-performing Dalean SRNNs that substantially improve on Dalean networks without reversal potentials. SRNNs with reversal potentials exhibited spike-train statistics closer to Poisson statistics, similar to biological neurons, and showed a substantial reduction in oscillatory activity, leading to increased heterogeneity in response properties. Thus, Dale's law with reversal potentials, a core feature of biological neural networks, can render SRNNs more accurate and energy-efficient.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1870593"},"PeriodicalIF":3.3,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13468798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148763957","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":"Organizational closure through regenerative signaling as a possible basis for conscious awareness.","authors":"Peter Cariani, Janet M Baker","doi":"10.3389/fncom.2026.1806695","DOIUrl":"10.3389/fncom.2026.1806695","url":null,"abstract":"<p><p>Along the lines of neuronal global workspace theories, the paper hypothesizes that active neural signal regeneration in recurrent, re-entrant circuits could constitute an organizational basis for states of conscious awareness. In this view, brains are self-production systems that regenerate their own informational, signal states (\"neural autopoiesis\"). States of awareness themselves depend critically on sustained regeneration of sets of neural signals in local and global circuits. The set of regenerated signals at each moment determines the specific contents of consciousness. Two hypotheses are proposed. H1: Organizational closure and with it, awareness, is achieved when regenerative, self-production loops are completed, such that a stable set of signal productions is sustained. The organization of informational processes stabilizes and closes on itself. H2: Neural coding is critical for signal regeneration. Neural signals must be properly encoded in order for them to be regenerated and thereby affect the contents of awareness. In addition to simple signal suppressions at their points of origin, one means by which general anesthetic agents may abolish awareness is by scrambling neural signals. Rendering internal control signals incoherent by altering within- and across-neuron rate profiles and/or temporal patternings may disrupt signal regeneration in local and global circuits. The two hypotheses are agnostic with respect to neural coding. Our own signal-centric time-domain (TD) brain theory framework is presented to illustrate how signal regeneration could be mediated by local and global temporal codes and neural temporal processing networks. Similarities and differences between TD, global neuronal workspaces (GNW), recurrent processing (RP), integrated information (IIT) and predictive processing (PP) are discussed. Empirical testing will necessitate solving the neural coding problem at multiple system levels. This will involve investigating correlations and causal linkages between regeneration of tracked neural signals and states of awareness as well as between alternative candidate neural codes and the contents of awareness. Rhythm-tagged stimuli and high temporal resolution neural recordings are proposed to enable tracking of specific neural signals throughout the brain under conditions of waking vs. sleep vs. anesthesia, with masking and unmasking signal/noise levels. Some philosophical comments regarding organization as a potential basis for consciousness are made.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1806695"},"PeriodicalIF":3.3,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13447300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148688799","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":"A nullcline-guided discrete-time map for neurons with subcritical Hopf bifurcation dynamics.","authors":"Mustafa Zeki, Sinan Kapçak, Hunseok Kang","doi":"10.3389/fncom.2026.1857116","DOIUrl":"10.3389/fncom.2026.1857116","url":null,"abstract":"<p><p>Neurons near a subcritical Hopf bifurcation are distinguished by their ability to produce subthreshold oscillations, maintain bistability between rest and repetitive spiking, and fire preferentially in response to inputs near their intrinsic resonant frequency. Simulating these properties in large neural networks using continuous-time models such as the Hodgkin-Huxley formalism is computationally prohibitive, motivating the search for reduced representations that retain the essential dynamics. Here we derive a two-dimensional discrete-time map from the continuous <i>I</i> _{<i>Na, p</i>} +<i>I</i> _<i>K</i> model by exploiting the time-scale separation between the membrane potential and the potassium gating variable, and using nullcline geometry to approximate the slow drift along each branch of the cubic <i>v</i>-nullcline. The resulting map consists of an outer piecewise-linear loop that produces realistic action potential waveforms and an inner switching region that reproduces focus-like subthreshold oscillations, with a slow recovery variable governing the transition between the two. The injected current modulates the size of the inner region, naturally encoding the bifurcation structure of the original system. Numerical simulations confirm that the discrete model reproduces the main dynamical signatures of bistability, hysteresis under a slowly varying current ramp, and frequency-selective firing in response to periodic burst stimulation-all without solving a single differential equation. Crucially, the map retains selected physically interpretable parameters of the continuous model while replacing continuous-time integration with a lightweight algebraic update. Benchmarking against explicit Euler and fourth-order Runge-Kutta integration shows that the optimized map has a per-update cost comparable to Euler and substantially lower than Runge-Kutta, supporting its use as a compact, interpretable discrete representation of subcritical-Hopf dynamics rather than as a direct numerical integrator of the continuous model.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1857116"},"PeriodicalIF":3.3,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13442640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148683701","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":"Neural-network-inspired federated coordination for noise-tolerant distributed optimization in cyber-physical demand-side management.","authors":"Atef Gharbi, Ahmad Alshammari, Nasser Albalawi, Nadhir Ben Halima","doi":"10.3389/fncom.2026.1895688","DOIUrl":"10.3389/fncom.2026.1895688","url":null,"abstract":"<p><strong>Introduction: </strong>This study proposes a neural-network-inspired computational framework for hierarchical, noise-tolerant coordination in distributed agent networks. The framework is evaluated in a cyber-physical demand-side management testbed in which autonomous home energy management systems perform local scheduling under a shared global price signal.</p><p><strong>Methods: </strong>Privacy-Preserving Federated Congestion-Signal Coordination separates local appliance scheduling from global adaptive coordination. Each home energy management system uses a genetic algorithm with a population of 100 over 50 generations. A federated coordinator aggregates clipped congestion-gradient updates and applies Gaussian differential privacy with a noise multiplier of 1.1 and a clipping norm of 1.0. The main evaluation was conducted over 30 federated rounds using 50 home energy management systems, EirGrid demand traces, SEM-O market prices, and 10 independent simulation runs. A complementary noise-location analysis used 20 home energy management systems and five independent seeds.</p><p><strong>Results: </strong>The proposed method reduced the peak-to-average ratio from 1.468 to 1.276, corresponding to a statistically significant 13.1% improvement. Its normalized energy cost remained within 0.04% of centralized coordination. Energy cost varied by only 0.016% across eight evaluated privacy budgets, and the framework transmitted 200 times fewer numerical values than centralized coordination at 50 agents. In the complementary noise-location experiment, aggregation-stage noise was better tolerated than local-encoding noise at four of eight evaluated noise levels.</p><p><strong>Discussion: </strong>The results show that hierarchical feedback, bounded unit influence, stochastic aggregation, and compressed message passing can support stable privacy-preserving distributed coordination. The framework is not a biological neural-circuit model but provides a reproducible testbed for studying neural-network-inspired principles of noise-tolerant collective computation.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1895688"},"PeriodicalIF":3.3,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13442819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148683749","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":"A proposal for realizing cognitive functions using traveling waves, phase-locked and synchronized patterns, holography, neural mixing, and single sideband communications.","authors":"Janet M Baker, Peter Cariani","doi":"10.3389/fncom.2026.1833027","DOIUrl":"10.3389/fncom.2026.1833027","url":null,"abstract":"<p><p>Waves are fundamental. In our view, waves in the brain may constitute and drive organized neural activity patterns on individual neural and population levels. Their interactions follow basic physical principles. Taking a comprehensive, temporal and spatiotemporal perspective, we endeavor to explain multiple brain functions and behaviors with a unified mechanistic approach. Starting with neural architectures, traveling waves, and spikes as the basic signals of the system, our multidisciplinary theory proposes that precise temporal phase-locking codes, spike coincidences, phase relationships, recurrent networks, temporal and spatiotemporal population patterns, pattern correlations, their interactions, synchronizations and couplings play an essential role in determining brain dynamics at multiple processing scales. Analogously to optical holography, it posits that traveling brain waves convey spike timing information, interact to form distinctive time/phase interference patterns and spatially distribute these widely. These in turn can interact with other traveling waves producing yet new spike patterns. Traveling waves also serve to selectively reactivate/refresh existing patterns. Spatially distributed temporal spike patterns and representations derived from these, are used to code, process, synchronize, integrate, and decode objects (e.g., sensorimotor events, concepts, etc.). We apply established physical principles (e.g., wave dynamics, holography) and signal processing principles (e.g., linear additive operations, and nonlinear, multiplicative frequency mixing). This theory proposes that oscillations may serve as signal carriers for communications in transmitting progressively processed signals through an emergent cascade of neuron mixing stages. Such cascades closely correspond to intermediate frequency (IF) processing stages in broadly used radio communications, specifically superheterodyned Single Sideband Suppressed Carrier (SSBSC = SSB) communications technology. This is illustrated with a numerical speech/language hierarchy oscillatory cascade model. This model correlates signal processing stages with both canonical oscillation bands and associated cognitive stages. Plausible biophysical mechanisms are proposed to realize these processes. Many neurophysiological observations consistent with these proposed mechanisms are referenced. This proposal is novel in suggesting conceptual integrations and coordinations of multiple disciplines that mechanistically trace informational neural signals from inception to conception. Some suggestions for empirically testing these hypotheses are presented.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1833027"},"PeriodicalIF":3.3,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148673194","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":"Structural synaptogenesis superior to functional modulation in a pruning-based recurrent network model of OCD.","authors":"Ngo Cheung","doi":"10.3389/fncom.2026.1799705","DOIUrl":"10.3389/fncom.2026.1799705","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Obsessive-compulsive disorder (OCD) is characterized by intrusive thoughts and repetitive behaviors, with incomplete response to current treatments suggesting limitations in prevailing neurotransmitter-focused models. Epidemiological data indicate lifetime DSM-IV OCD in approximately 2.3% and 12-month OCD in approximately 1.2% of US adults, while subthreshold obsessions or compulsions are substantially more common. One mechanistic possibility is that abnormal synaptic pruning contributes to persistent cortico-striato-thalamo-cortical circuit rigidity. This idea remains indirect in OCD, but is supported by convergent synaptic-marker findings in OCD and by complement-linked pruning mechanisms shown most clearly in schizophrenia.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed a modular gated recurrent unit network approximating cortico-striato-thalamo-cortical dynamics, trained on a rule-switching task sensitive to perseveration. The architecture included a recurrent cortical integration layer, a schematic striatal \"habit\" module, and a thalamic confidence-gating scalar. Excessive pruning was implemented as 60% activity-dependent pruning, using a composite of low recent gradient-based usage and low weight magnitude, with partial protection of recurrent and habit-related weights. From identical pruned baselines, three mechanistically distinct interventions were simulated: rapid gradient-guided structural reopening, treated as a ketamine-like synaptogenesis motif, prolonged low-learning-rate adaptation with stress-noise annealing (SSRI-like), and tonic inhibitory scaling (neurosteroid-like). An iso-dose pipeline recorded L1 and L2 weight-change norms, synaptic turnover, and change in sparsity; linear interpolation was used when bracketing sweep points existed, and residual mismatch was reported otherwise. Multi-seed statistics and sensitivity analyses were performed.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;At 60% activity-dependent pruning, the untreated network showed impaired accuracy (0.4972) and elevated perseveration (0.5247). In the fixed-parameter comparison, ketamine-like structural repair reduced perseveration to 0.2582, SSRI-like adaptation to 0.3209, and neurosteroid-like inhibition to 0.2654. Relapse vulnerability differed by mechanism: cumulative relapse increased perseveration by +0.1086 after ketamine-like repair, +0.0259 after SSRI-like adaptation, and -0.0017 after neurosteroid-like inhibition in the representative seed. Across five seeds, best acute perseveration was lowest for ketamine-like repair (0.2330 ± 0.0065), followed by neurosteroid-like inhibition (0.2413 ± 0.0021) and SSRI-like adaptation (0.2831 ± 0.0099). SSRI-like adaptation showed the highest mean efficiency because it produced smaller absolute weight changes. Sensitivity analyses showed that untreated perseveration increased with pruning severity, from 0.2627 at 40% pruning to 0.6218 at 70% pruning, while ketamine-like repair remained relatively ","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1799705"},"PeriodicalIF":3.3,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13429669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148668897","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}