Biological Cybernetics最新文献

{"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":2.0,"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":"<p><p>We propose Waves as Space-Time (WST) as a conceptual framework for understanding how the brain may utilize subthreshold travelling waves of the membrane potential to flexibly link spatial and temporal dimensions of neural activity. Rather than presenting a complete biological model, WST is intended to serve as a simplified lens for investigating how wave dynamics can enrich neural coding. The core proposition is that subthreshold membrane potential oscillations constitute a substrate capable of transforming as well as transmitting neural activity, enabling temporal spike patterns to be recast as spatially differentiated signatures and spatial patterns to be unfolded into temporally structured sequences. This perspective highlights how such bidirectional transformations offer the brain a versatile encoding strategy that can support functions such as memory, propagation, and dynamic reconfiguration of information. By considering WST travelling waves as a powerful mechanism for neural computation, it encourages further theoretical, computational and experimental work on their potential role in complex cognitive processes.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 2","pages":""},"PeriodicalIF":2.0,"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":2.0,"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":"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":2.0,"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":"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":2.0,"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":"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":2.0,"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}

{"title":"Stochastic dynamics of postural sway modeled by double Ornstein Uhlenbeck process.","authors":"Naci Barış Yaradanakul, Maryam Hassanpour, Senih Gürses","doi":"10.1007/s00422-025-01028-y","DOIUrl":"10.1007/s00422-025-01028-y","url":null,"abstract":"<p><p>The study examines center-of-pressure dynamics in the anteroposterior direction (CoPx). It is assumed that CoPx dynamics involve two dynamical processes during quiet stance. The first process describes fast postural corrections around the given equilibrium. The second process describes slowly changing equilibrium point which is assumed to be controlled by higher nervous system. We proposed a novel system of coupled stochastic differential equations, double Ornstein-Uhlenbeck process (dOU), where two processes are described in terms of two Ornstein-Uhlenbeck processes (OU). Specifically, the equilibrium point of the fast postural correction OU process is controlled by the slowly evolving equilibrium point OU process. We derived closed forms of correlation and the power spectral density (PSD) functions of the processes. We conducted experiments with three repetitions from eight healthy subjects at four different sensory conditions on rigid and compliant surfaces. We optimized four model parameters in frequency domain by comparing averaged PSD estimates of experimental data and analytical PSD functions at each sensory combination. We found that mean reversion rate λ of the first OU governing postural reflexes around a given equilibrium, was significantly higher on the rigid surface. Consequently, the dynamics of postural sway on rigid surface were predominantly captured by a single OU. Contrarily, on compliant surface, λ approached the second OU's mean reversion rate, κ, and we observed a significant increase in its volatility, [Formula: see text]. Findings suggest that two-level CoPx dynamics become more pronounced under the compliant surface. We showed that dOU is capable of capturing bounded diffusive characteristics of CoPx dynamics.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 1","pages":"4"},"PeriodicalIF":2.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146013466","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":"Encoding of movement primitives and body posture through distributed proprioception in walking and climbing insects.","authors":"Thomas van der Veen, Volker Dürr, Elisabetta Chicca","doi":"10.1007/s00422-025-01029-x","DOIUrl":"10.1007/s00422-025-01029-x","url":null,"abstract":"<p><p>Targeted reaching movements and spatial coordination of footfall patterns are prime examples of spatial coordination of animal limbs. To explain this, both physiological and computational studies have suggested the use of movement primitives or the existence of an internal body representation. Since insects lack a dedicated posture-sensing organ or vestibular system (which vertebrates possess), it has been hypothesized that they derive high-level postural information from distributed low-level proprioceptive cues, integrated across their limbs. To test this possibility, we use a multi-layer spiking neural network to extract high-level information about limb movement and whole-body posture from information provided by distributed local proprioceptors. The preceding companion paper introduced the phasic-tonic encoding of joint angles by strictly local proprioceptive afferents, and high-accuracy encoding of joint angles and angular velocities in first-order interneurons. Here, we extend this model by second-order interneurons that encode movement primitives of single legs by coincidence detection from two or three leg-local inputs. By validation against annotated experimental data on whole-body kinematics of unrestrained stick insect locomotion, we show that modelled interneurons can signal particular step cycle phases, but also step cycle transitions such as leg lift-off. To indicate climbing behaviour, third-order interneurons encode body pitch relative to the substrate from position and motion of [Formula: see text] local leg joints. Our results demonstrate that simple combinations of two or three position/velocity inputs from disjunct proprioceptor arrays are sufficient to encode high-order movement information about step cycle phases. The resulting movement primitive encoding may converge to represent particular locomotor states and whole-body posture.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 1","pages":"3"},"PeriodicalIF":2.0,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12783276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936548","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":"Task success in trained spiking neural network models coincides with emergence of cross-stimulus-modulated inhibition.","authors":"Yuqing Zhu, Chadbourne M B Smith, Tarek Jabri, Mufeng Tang, Franz Scherr, Jason N MacLean","doi":"10.1007/s00422-025-01030-4","DOIUrl":"10.1007/s00422-025-01030-4","url":null,"abstract":"<p><p>The neocortex is composed of spiking neurons interconnected in a sparse, recurrent network. Spiking activity within these networks underlies the computations that transform sensory inputs into appropriate behavioral responses. In this study, we train recurrent spiking neural network (SNN) models constrained by neocortical connectivity statistics and investigate the architectural changes that enable task-relevant, spike-based computations. We employ a binary state change detection task-an experimental paradigm used in animal behavioral studies. Our SNNs consist of interconnected excitatory and inhibitory units with connection probabilities and strengths modeled after the mouse neocortex and maintained throughout training and evaluation. Following training, we find that SNNs selectively modulate firing rates based on the binary input state, and that excitatory and inhibitory connectivity within and between input and recurrent layers adjusts accordingly. Notably, inhibitory neurons in the recurrent layer that positively modulate firing rates in response to one input state strengthen their connections to recurrent units with the opposite modulation. This push-pull connectivity-where excitation and inhibition are dynamically balanced in an opponent fashion-emerges as a key computational strategy and is reminiscent of connectivity observed in primary visual cortex. Using a one-hot output encoding yields identical firing rates to both input states, yet the push-pull inhibitory motif still arises. Importantly, this motif fails to emerge when Dale's principle is not enforced during training, and task performance also declines.Furthermore, disrupting spike timing by a few milliseconds significantly impairs task performance, highlighting the importance of precise spike time coordination for computation in sparse networks like neocortex. The emergence of push-pull inhibition through task training in spiking models underscores the crucial role of interneurons and structured inhibition in shaping neural dynamics and spike-based information processing.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 1","pages":"2"},"PeriodicalIF":2.0,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12779710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145914053","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 control engineering perspective on the advantages of efference copies.","authors":"Benjamin P Campbell, Huai-Ti Lin, Holger G Krapp","doi":"10.1007/s00422-025-01027-z","DOIUrl":"10.1007/s00422-025-01027-z","url":null,"abstract":"<p><p>Biological systems have evolved to perform high-speed voluntary movements whilst maintaining robustness and stability. This paper examines a control architecture based on the principles of efference copies found in insect sensorimotor control which we call the fully-separable-degrees-of-freedom (FSDoF) controller. Within a control engineering framework, we benchmark the advantages of this control architecture against two common engineering control schemes: a pure feedback (PFB) controller and a Smith predictor (SP). Our study identifies three advantages of the FSDoF for biology. It is advantageous in controlling systems with sensor delays, and it can effectively handle noise. Thirdly, it allows biological sensors to increase their operating range. We evaluate the robustness of the FSDoF controller and show that it achieves improved performance with equal stability margins and robustness. Finally, we discuss variations of the FSDoF which theoretically provide the same performance.</p>","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"120 1","pages":"1"},"PeriodicalIF":2.0,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12698751/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145726842","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}