Biological Cybernetics最新文献

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.

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.

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.

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.

Molecular dynamics (MD) simulations have emerged as a powerful and extensively employed tool in biomedical research, offering insights into intricate biomolecular processes such as structural flexibility and molecular interactions, and playing a pivotal role in the development of therapeutic approaches. Although MD techniques are applied to a variety of biomolecules including DNA, RNA, proteins, and their assemblies, this review focuses specifically on the role of MD in elucidating protein behavior and their interactions with inhibitors across different disease contexts. The selection of an appropriate force field is essential, as it greatly influences the reliability of simulation outcomes. Widely adopted MD software packages such as GROMACS, DESMOND, and AMBER leverage rigorously tested force fields and have shown consistent performance across diverse biological applications. Despite current successes, challenges remain in narrowing the gap between computational models and actual cellular conditions. The integration of machine learning and deep learning technologies is expected to accelerate progress in this evolving field.

Correlated spiking has been widely found in large population of neurons and been linked to neural coding. Transcranial alternating current stimulation (tACS) is a promising non-invasive brain stimulation technique that can modulate the spiking activity of neurons. Despite its growing application, the tACS effects on the temporal correlation between spike trains are still not fully understood. In this study, we use a pair of unconnected two-compartment model neurons of the integrate-and-fire (IF) type to simulate the correlated spike trains driven by shared fluctuating dendritic inputs and exposed to weak alternating electric fields. Our results show that the output correlation increases with field intensity, but increases and then decreases with field frequency, displaying thus a frequency resonance. Through varying somatic and dendritic morphologies, we demonstrate that morphological differences between the soma and dendrites fundamentally shape the correlation-frequency resonance, with more pronounced differences yielding stronger resonance effects. Moreover, the anti-phase sinusoidal modulations induced by tACS at the soma and dendrite promote this correlation-frequency resonance, particularly when dendritic fluctuations exhibit a large mean value. We further examine the tACS effects on output correlation in biophysically and morphologically realistic pyramidal model neurons, revealing similar patterns to those observed in the two-compartment models. Our findings provide new insights into how tACS modulates the correlated spike trains and highlight the critical role of morphological differences between the soma and dendrites in determining the frequency-dependent output correlation. These predictions should be taken into consideration when understanding the tACS effects on population correlation and population coding.

In this article, a biophysically realistic model of a soft octopus arm with internal musculature is presented. The modeling is motivated by experimental observations of sensorimotor control where an arm localizes and reaches a target. Major contributions of this article are: (i) development of models to capture the mechanical properties of arm musculature, the electrical properties of the arm peripheral nervous system (PNS), and the coupling of PNS with muscular contractions; (ii) modeling the arm sensory system, including chemosensing and proprioception; and (iii) algorithms for sensorimotor control, which include a novel feedback neural motor control law for mimicking target-oriented arm reaching motions, and a novel consensus algorithm for solving sensing problems such as locating a food source from local chemical sensory information (exogenous) and arm deformation information (endogenous). Several analytical results, including rest-state characterization and stability properties of the proposed sensing and motor control algorithms, are provided. Numerical simulations demonstrate the efficacy of our approach. Qualitative comparisons against observed arm rest shapes and target-oriented reaching motions are also reported.

The dexterity of the human hand is largely due to its multiple degrees of freedom. However, coordinating the movements of the ring and little fingers independently can be challenging because of the biomechanical and neurological interdependencies between them. This research presents a cascade control system based on fuzzy logic to manage the dynamic movements of these fingers within a simulated biomechanical model of a human hand. A mathematical model that incorporates transfer functions and state-space representations has been developed for the fingers. The fuzzy logic controller is designed to address the nonlinearity of the biomechanical model, optimizing both the transient and steady-state response parameters. The simulation results indicate that the system achieves a rise time of 0.6 s and a peak time of 0.3 s for the ring finger, with an overshoot of 5%. The little finger, on the other hand, exhibits an overshoot of less than 0.6% and a settling time ranging from 1 to 2.6 s across various joints. Overall, the proposed control system successfully coordinates finger movements, achieving a stable response within 3.5 s and minimal disturbances. These findings represent significant advancements in precision and robustness for prosthetic and robotic hand systems, providing a promising foundation for assistive technologies aimed at fine motor control rehabilitation.

Previous studies on the computational principle for solving the movement selection problem for the human arm have primarily focused on hand trajectories associated with the two-joint movements of the shoulder and elbow joints. Further, only a few computational models, that consider the musculoskeletal system, have been investigated. From this perspective, a minimum muscle-stress-change model was evaluated for the fingertip trajectories and arm postures during three-joint movements in the horizontal plane, including wrist joint rotation. A musculoskeletal model of a three-joint arm with eight muscles was used to perform the optimization calculations that determine the optimal arm movements. Results show that the computational model can reproduce the measured fingertip trajectories and arm postures to an equal or greater extent compared with the minimum angular-jerk model and the minimum torque-change model. Furthermore, the errors of the minimum muscle-stress-change model remained small for different values of joint viscosity, physiological cross-sectional areas, and moment arms, resulting in a small dependency of these parameters. In contrast, the minimum torque-change model resulted in considerable errors under low-viscosity conditions. Consequently, the minimum muscle-stress-change model has emerged as a promising candidate for elucidating the computational principle.

This study examines self-motion perception incorporated into motion sickness models. Research on modeling self-motion perception and motion sickness has advanced independently, though both are thought to share neural mechanisms, making the construction of a unified model opportune. Models based on the Subjective Vertical Conflict (SVC) theory, a refinement of the neural mismatch theory, have primarily focused on motion sickness, with limited validation for self-motion perception. Emerging studies have begun evaluating the perceptual validity of these models, suggesting that some models can reproduce perception in specific paradigms, while they often struggle to jointly capture motion perception and sickness. One prior study demonstrated that one of the SVC models could replicate illusory tilt during centrifugation, while others produced unrealistic responses, such as persistent tilt after motion cessation. In reality, under steady-state conditions such as being motionless, perceived motion is expected to settle to an appropriate state regardless of prior states. Based on the idea that this behavior is closely related to the equilibrium points and stability of the model dynamics, this study theoretically analyzed 6DoF-SVC models with a focus on them. Results confirmed that only one model ensures convergence from any state to a unique equilibrium point corresponding to plausible perception. In contrast, other SVC models and a conventional self-motion perception model converged to values dependent on earlier states. Further analysis showed that only this model captured both the somatogravic and Ferris wheel illusion. In conclusion, this 6DoF-SVC model unifies motion perception and sickness modeling, with theoretical convergence of the perceptual state.