Biological Cybernetics最新文献

{"title":"Fluctuation-response relations for a two-stage population of spiking neurons stimulated by common noise.","authors":"Leander Dittrich, Benjamin Lindner","doi":"10.1007/s00422-026-01043-7","DOIUrl":"10.1007/s00422-026-01043-7","url":null,"abstract":"<p><p>Recently a method has been put forward to connect the measures of spontaneous neuronal activity and the measures of the average single-neuron response to stimuli via fluctuation-response relations (FRRs) for some integrate-and-fire (IF) type neuron models. In this work we expand this method to populations of neurons, relating their spontaneous correlation and linear-response statistics. To this end, we analyze the simple case of uncoupled cells modeled by IF neurons (first stage of processing) which receive common stochastic input and project their output spike trains onto a readout neuron (second stage of processing). We derive and verify FRRs connecting the single neuron response to cross-correlations among neurons and the response of the full system to cross-stage correlations. Furthermore, we utilize these FRRs to derive approximations of all cross-stage cross-spectra for a relevant model of a second-stage cell, the partial synchronous output (PSO). We conclude with a discussion of how our results can be expanded to more involved network settings and neuron models.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 3-4","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13124920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometric Learning Dynamics.","authors":"Vitaly Vanchurin","doi":"10.1007/s00422-026-01041-9","DOIUrl":"https://doi.org/10.1007/s00422-026-01041-9","url":null,"abstract":"<p><p>We present a unified geometric framework for modeling learning dynamics in physical, biological, and machine learning systems. The theory reveals three fundamental regimes, each emerging from the power-law relationship <math><mrow><mi>g</mi> <mo>∝</mo> <msup><mi>κ</mi> <mi>α</mi></msup> </mrow> </math> between the metric tensor <math><mi>g</mi></math> in the space of trainable variables and the noise covariance matrix <math><mi>κ</mi></math> . The quantum regime corresponds to <math><mrow><mi>α</mi> <mo>=</mo> <mn>1</mn></mrow> </math> and describes Schrödinger-like dynamics that emerges from a discrete shift symmetry. The efficient learning regime corresponds to <math><mrow><mi>α</mi> <mo>=</mo> <mstyle><mfrac><mn>1</mn> <mn>2</mn></mfrac> </mstyle> </mrow> </math> and describes very fast machine learning algorithms. The equilibration regime corresponds to <math><mrow><mi>α</mi> <mo>=</mo> <mn>0</mn></mrow> </math> and describes classical models of biological evolution. We argue that the emergence of the intermediate regime <math><mrow><mi>α</mi> <mo>=</mo> <mstyle><mfrac><mn>1</mn> <mn>2</mn></mfrac> </mstyle> </mrow> </math> is a key mechanism underlying the emergence of biological complexity.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 3-4","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147719032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contribution of spike timing to the neural code: from fast to slow timescales.","authors":"Stefano Panzeri, Nicola Marie Engel, Marco Celotto","doi":"10.1007/s00422-026-01042-8","DOIUrl":"10.1007/s00422-026-01042-8","url":null,"abstract":"<p><p>The publication of Mainen and Sejnowski's 1995 seminal paper strongly renewed interest in how spike timing contributes to the neural code. In the 3 decades since then, considerable experimental and theoretical research has investigated the timescales at which spike timing contributes to the neural code. Here we review theoretical and experimental research of the last 30 years aimed at defining conceptually and measuring operationally these timescales. By a critical review of the literature, we individuate six broad classes of timescales that have been conceptualized and operationalized: the maximal temporal precision of spiking that a neuron can achieve, the encoding time window (the time window containing the information-bearing spike times), the encoding timescale (the coarsest time resolution for measuring spikes without losing information), the maximal discrimination precision timescale (the smallest spike time difference that can be discriminated behaviorally), the encoding-readout intersection timescale (the maximal timing precision at which stimulus information encoded in neural activity is also actually read out to inform behavior), and the information consistency timescale (measuring the stability of information encoding over time). Together, this work has revealed short and long timescales that influence information coding and affect behavior. Short encoding timescales, from milliseconds to tens of milliseconds, are useful for sensory information encoding and perception. Long consistency timescales, ranging from hundreds of milliseconds to seconds, are useful for accumulating evidence and stabilizing decisions.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13079490/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147678661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"challenging synchronization-based network reconstruction from time series: a critical evaluation using Fitzhugh-Nagumo neural networks.","authors":"Bahareh Karimi Rahjerdi, Shivakumar Rajagopal, Fahimeh Nazarimehr, Karthikeyan Rajagopal","doi":"10.1007/s00422-025-01033-1","DOIUrl":"10.1007/s00422-025-01033-1","url":null,"abstract":"<p><p>The reconstruction of complex networks from time series data has become a common practice in neuroscience and dynamical systems, particularly using synchronization-based measures such as phase locking value (PLV) or correlation. However, the validity of such reconstructions-especially the assumption that synchronization implies direct coupling-remains questionable. In this work, we critically investigate the relationship between structural connectivity and functional synchronization in networks of coupled FitzHugh-Nagumo (FHN) neurons. We generate synthetic networks with known topologies (regular, small-world, and scale-free) and compute pairwise synchronization from the resulting time series. A functional network is then reconstructed based on synchronization strength, and its adjacency matrix is compared with the original structural network using the Root Mean Square Error (RMSE). Our results demonstrate that high synchronization between nodes does not necessarily indicate a direct structural link, and conversely, structurally coupled nodes may remain desynchronized. These findings challenge the reliability of synchronization-based network inference methods and call for caution in interpreting functional connectivity as structural connectivity, particularly in brain network studies.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147505622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The active role of non-synaptic electromagnetic induction in modulating absence epilepsy: a kinetic modeling study.","authors":"Tianying Yang, Peiyao Zheng, Xuyue Fang, Jia Song, Bing Hu","doi":"10.1007/s00422-026-01036-6","DOIUrl":"10.1007/s00422-026-01036-6","url":null,"abstract":"<p><p>We propose a novel computational model incorporating simplified representations of the basal ganglia, cortex, and thalamus (SGGCT network), and systematically investigate the regulatory and control mechanisms underlying typical absence seizures in the cortex under memristive electromagnetic induction. Spike-and-wave discharges (SWDs, 2-4 Hz), a hallmark of absence epilepsy, are successfully reproduced in the SGGCT model by modulating the coupling strengths of two excitatory output projections to the thalamic specific relay nuclei (SRN). Our findings highlight the critical role of both the direct glutamatergic cortico-pallidal and cortico-nigral pathways in seizure regulation, acting through distinct inhibitory routes: the globus pallidus interna (GPi)-SRN pathway and the GPi-thalamic reticular nucleus (TRN) pathway, respectively. The cortex emerges as a promising target for electrical stimulation to suppress SWDs. We observe that applying memristive electromagnetic induction to the cortex significantly reduces the parameter space conducive to SWDs generation. Furthermore, electromagnetic induction enhances the ability of basal ganglia pathways to inhibit SWDs. Specifically, electromagnetic induction can transform previously unsuppressible SWDs regimes into suppressible ones. It also alters the operational mode of basal ganglia pathways in controlling seizure dynamics. Notably, the suppression efficacy can be optimized by tuning the memristor's control parameters. These results provide computational evidence supporting the potential of electromagnetic induction as a neuromodulatory strategy, which might offer testable hypotheses for future clinical interventions in epilepsy treatment.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147476653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The wave-coded brain: transforming spike patterns through space-time dynamics.","authors":"Hubert Löffler, Andreas Bahmer","doi":"10.1007/s00422-026-01034-8","DOIUrl":"10.1007/s00422-026-01034-8","url":null,"abstract":"","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147470310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bio-inspired minimal model for non-stationary K-armed bandits.","authors":"Krubeal Danieli, Mikkel Elle Lepperød","doi":"10.1007/s00422-026-01037-5","DOIUrl":"10.1007/s00422-026-01037-5","url":null,"abstract":"<p><p>While reinforcement learning algorithms have made significant progress in solving multi-armed bandit problems, they often lack biological plausibility in architecture and dynamics. Here, we propose a bio-inspired neural model based on interacting populations of rate neurons, drawing inspiration from the orbitofrontal cortex and anterior cingulate cortex. Our model reports robust performance across various stochastic bandit problems, matching the effectiveness of standard algorithms such as Thompson Sampling and UCB. Notably, the model exhibits adaptive behavior: employing greedy strategies in low-uncertainty situations while increasing exploratory behavior as uncertainty rises. Through evolutionary optimization, the model's hyperparameters converged to values that align with the principles of synaptic mechanisms, particularly in terms of synapse-dependent neural activity and learning rate adaptation. These findings suggest that biologically-inspired computational architectures can achieve competitive performance while providing insights into neural mechanisms of decision-making under uncertainty.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12975818/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147437972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A spiking neural network model for fractional proprioceptive encoding of limb posture and movement in insects.","authors":"Thomas van der Veen, Yonathan Cohen, Elisabetta Chicca, Volker Dürr","doi":"10.1007/s00422-025-01032-2","DOIUrl":"10.1007/s00422-025-01032-2","url":null,"abstract":"<p><p>Proprioception is key to all behaviours that involve the control of force, posture or movement. Computationally, many proprioceptive afferents share three features: First, their strictly local encoding of stimulus magnitudes causes range fractionation in sensory arrays. As a result, encoding of large joint angle ranges requires convergence of afferent information onto first-order interneurons. Second, their phasic-tonic response properties lead to fractional encoding of the fundamental sensory magnitude and its derivatives (e.g., joint angle and angular velocity). Third, the distribution of disjunct sensory arrays across the body implies that complex movements involve information from multiple joints or limbs. The present study proposes a multi-layer spiking neural network for distributed computation of whole-body posture and movement. The first part of the study models strictly local, phasic-tonic encoding of joint angle by proprioceptive hair field afferents by use of Adaptive Exponential Integrate-and-Fire neurons. Fractionally encoded afferent information about single-joint posture and movement converges on two types of first-order interneurons, tuned to encode either joint angle or velocity across the entire working range with high accuracy. In velocity-encoding interneurons, spike rate increases linearly with angular velocity. The companion paper exploits this distributed position/velocity encoding in second- and third-order interneurons, using combinations of two or three position/velocity inputs from disjunct arrays. The encoding properties of all interneuron layers are evaluated with experimental data on whole-body kinematics of unrestrained stick insect locomotion, comprising concurrent joint angle time courses of [Formula: see text] leg joints. The hierarchical model allows increasingly complex encoding of posture and movement, from angular velocity of a single joint, to movement cycle phases of an entire limb, to parameters of overall body posture.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12950026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147319217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bifurcation of spiking oscillations from a center in resonate-and-fire neurons.","authors":"Oleg Makarenkov, Marianne Bezaire, Michael Hasselmo","doi":"10.1007/s00422-026-01035-7","DOIUrl":"10.1007/s00422-026-01035-7","url":null,"abstract":"<p><p>The theta rhythm is important for many cognitive functions including spatial processing, memory encoding, and memory recall. The information processing underlying these functions is thought to rely on consistent, phase-specific spiking throughout a theta oscillation that may fluctuate significantly in baseline (center of oscillations), frequency, or amplitude. Experimental evidence shows that spikes can occur at specific phases even when the baseline membrane potential varies significantly, such that the integrity of phase-locking persists across a large variability in spike threshold. The mechanism of this precise spike timing during the theta rhythm is not yet known and previous mathematical models have not reflected the large variability in threshold potential seen experimentally. Here we introduce a straightforward mathematical neural model capable of demonstrating a phase-locked spiking in the face of significant baseline membrane potential fluctuation during theta rhythm. This novel approach incorporates a degenerate grazing bifurcation of an asymptotically stable oscillation. This model suggests a potential mechanism for how biological neurons can consistently produce spikes near the peak of a variable membrane potential oscillation.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147319258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain-inspired energy efficient technologies for next-generation artificial intelligence.","authors":"Hillel J Chiel, Jay S Coggan, Gourav Datta, Jean-Marc Fellous, William R P Nourse, Roger D Quinn, Peter J Thomas","doi":"10.1007/s00422-026-01038-4","DOIUrl":"10.1007/s00422-026-01038-4","url":null,"abstract":"<p><p>Since the advent of widely accessible AI tools, AI technology has been in high demand by businesses, academic researchers and individuals. Technology companies are building AI infrastructure at a rapid pace, and these facilities consume vast and growing resources, particularly electricity and water, with significant real and projected climate impacts. There is a need for new research initiatives to support long time horizon efforts to develop energy efficient computing capabilities to support the continued growth of AI infrastructure in a sustainable fashion. Such efficiency is required at both the hardware and software levels. Where can industry turn for examples of ultra-low power, energy efficient computing? We argue here that neurobiological principles offer rich and under-exploited sources of inspiration for energy efficient NeuroAI, and that new partnerships between industry and academia should be developed in this direction.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 2","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12929283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147277720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}