Biological Cybernetics最新文献

{"title":"A three-component dynamical index of consciousness-related neural organisation.","authors":"Hassan Ugail, Newton Howard","doi":"10.1007/s00422-026-01049-1","DOIUrl":"10.1007/s00422-026-01049-1","url":null,"abstract":"<p><p>Quantifying consciousness from brain activity remains a major challenge in neuroscience and clinical practice. Many existing EEG measures focus on a single feature of neural activity, such as complexity, synchrony, or spectral structure, but no single feature appears sufficient across different brain states. We introduce a composite dynamical framework that combines three complementary properties of brain activity, i.e., scale-free temporal organisation, cross-frequency organisation, and metastable flexibility in large-scale synchronisation. These components are normalised and combined into a single index designed to capture organised dynamical complexity rather than raw signal complexity alone. We test the framework in both synthetic and empirical settings. In a generative model of nine EEG-like brain states, including wakefulness, dreaming, anaesthesia, non-conscious states, and seizure states, the index separates the synthetic conscious and non-conscious classes without overlap and remains stable across ablation, sensitivity, and Monte Carlo analyses. We then apply the framework to two-channel Sleep-EDF recordings from 30 healthy adults, where it provides a proof-of-principle subject-level separation of wakefulness from N2 and REM sleep. The framework is dynamical-systems-inspired and is not committed to any single theory of consciousness, making it compatible with a range of theoretical perspectives. With further validation, the framework may be applicable across multichannel brain recordings, including anaesthesia, disorders of consciousness, and basic consciousness-research settings.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 3-4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13364888/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148431572","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":"Harmonic memory in phasor neural networks.","authors":"F Hoppensteadt","doi":"10.1007/s00422-026-01046-4","DOIUrl":"10.1007/s00422-026-01046-4","url":null,"abstract":"<p><p>An array of inertial phasor units coupled through a common substrate can exhibit stable collective phase patterns. This paper explores this phenomenon by investigating a closed array-interface-substrate feedback structure. Each array unit has a device-level angular coordinate <math><msub><mi>θ</mi> <mi>n</mi></msub> </math> and produces a phasor output <math><msup><mi>e</mi> <mrow><mi>i</mi> <msub><mi>θ</mi> <mi>n</mi></msub> </mrow> </msup> </math> . The interface forms products and combinations of these phasor outputs, producing harmonics indexed by integer mode vectors. The substrate processes these harmonic components and returns a modulatory signal to the array. In the case studied here, the returned feedback has gradient form and is generated by a harmonic potential <math><mrow><mi>V</mi> <mo>(</mo> <mi>θ</mi> <mo>)</mo></mrow> </math> . This structure leads to a geometric theory of harmonic memory. Memories appear as stable phase-locked periodic solutions, or memory loops, selected by the resonant harmonic structure of <math><mi>V</mi></math> . Memory recall occurs when the drive parameters, interpreted as attentional control variables, tune the system toward a harmonic channel: resonance selects a latent coherent loop, and the closed dynamics relax toward recurrent activity in the array.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 3-4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148297373","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":"Correction: Decreased spinal inhibition leads to undiversified locomotor patterns.","authors":"Myriam Lauren de Graaf, Heiko Wagner, Luis Mochizuki, Charlotte Le Mouel","doi":"10.1007/s00422-026-01040-w","DOIUrl":"10.1007/s00422-026-01040-w","url":null,"abstract":"","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 3-4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13272256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266422","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":"Foundational issues of network models in biology.","authors":"Danilo Bondi, Damiano Bondi","doi":"10.1007/s00422-026-01044-6","DOIUrl":"10.1007/s00422-026-01044-6","url":null,"abstract":"<p><p>Despite the growing prevalence of network models in biological and medical research, the philosophical foundations of these constructs remain elusive and insufficiently examined. Building on data-driven insights in systems biology and teleological models of integrative physiology, we have criticised agentless theory, relational ontologies, and cybernetic perspectives in biological contexts. We have posited the necessity for a philosophical advocacy of a holistic approach to biology alongside a relational epistemology. A foundational issue in network models in biology and physiology is recognising the network as the predominant meme of contemporary society, which has permeated all aspects of human life and has been cultivated within biology and physiology through modern theoretical constructs and practical applications. By discussing minimal cognition, tinkering, and stigmergy, we argued for a philosophical advocacy of the person as a relational cybernetic organism. Philosophical arguments concerning graph and relational models in biology can be resolved by embracing epistemic humility.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 3-4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13249665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213444","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":"Dynamical mechanisms for coordinating long-term working memory based on the precision of spike-timing in cortical neurons.","authors":"Terrence J Sejnowski","doi":"10.1007/s00422-026-01039-3","DOIUrl":"10.1007/s00422-026-01039-3","url":null,"abstract":"<p><p>In the last century, most sensorimotor studies of cortical neurons relied on average firing rates. Rate coding is efficient for fast sensorimotor processing that occurs within a few seconds. Much less is known about the neural mechanisms underlying long-term working memory with a time scale of hours (Ericsson and Kintsch in Psychol Rev 102(2):211, 1995). Cognitive states may not have sensory or motor correlates. For example, you can sit in a quiet room making plan without moving or sensory processing. You can also make plans while out walking. This suggests that the neural substrate for cognitive states neither depends on nor interferes with ongoing sensorimotor brain activity. In this perspective, I make the case for a possible second tier of neural activity that coexists with the well-established sensorimotor tier. The prominent physiological feature of the second tier is coordinated spike timing activity. The interplay of data supporting this hypothesis involves three puzzling yet highly intriguing experimental observations, without any obvious indication that they might actually represent different aspects of a single functional organization. First, consider the precision of spiking in individual neurons. The discovery of millisecond-precision spike initiation in cortical neurons was unexpected (Mainen and Sejnowski in Science 268:1503-1506, 1995). Even more striking was the precision of spiking in vivo, in response to rapidly fluctuating sensory inputs. Second, high temporal resolution can also mediate spike timing-dependent plasticity (STDP) by controlling the relative timing of presynaptic and postsynaptic spikes at the millisecond scale. Third, we observe waves across many frequency bands traveling across the cortex. Strikingly, their timing is highly precise. Gamma waves, for example, which are triggered by attention, can plausibly trigger STDP that lasts for hours in cortical neurons. This temporary cortical network, ostensibly a second tier of functionality, rides astride the long-term sensorimotor network and could support cognitive processing and long-term working memory.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 3-4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13371168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147935679","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":"Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.","authors":"Baram Sosis, Jonathan E Rubin","doi":"10.1007/s00422-025-01031-3","DOIUrl":"10.1007/s00422-025-01031-3","url":null,"abstract":"<p><p>Various mathematical models have been formulated to describe the changes in synaptic strengths resulting from spike-timing-dependent plasticity (STDP). A subset of these models include a third factor, dopamine, which interacts with spike timing to contribute to plasticity at specific synapses, notably those from cortex to striatum at the input layer of the basal ganglia. Theoretical work to analyze these plasticity models has largely focused on abstract issues, such as the conditions under which they may promote synchronization and the weight distributions induced by inputs with simple correlation structures, rather than on scenarios associated with specific tasks, and has generally not considered dopamine-dependent forms of STDP. In this paper we introduce forms of dopamine-modulated STDP adapted from previously proposed plasticity rules. We then analyze, mathematically and with simulations, their performance in two biologically relevant scenarios. We test the ability of each of the three models to complete simple value estimation and action selection tasks, studying the learned weight distributions and corresponding task performance in each setting. Interestingly, we find that each plasticity rule is well suited to a subset of the scenarios studied but falls short in others. Different tasks may therefore require different forms of synaptic plasticity, yielding the prediction that the precise form of the STDP mechanism present may vary across regions of the striatum, and other brain areas impacted by dopamine, that are involved in distinct computational functions.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 3-4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13215186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147870339","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":"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":2.0,"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":"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":2.0,"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":2.0,"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":2.0,"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}