Biological Cybernetics最新文献

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.

Parkinson's Disease (PD) is a neurodegenerative disorder associated with Basal Ganglia (BG) dysfunction, where abnormal neuronal β-oscillations ([Formula: see text] Hz) have been shown to correlate with motor symptoms. Non-pharmacological therapies are based on Deep Brain Stimulation (DBS), delivering electric current waveform with constant frequency and amplitude to BG regions, commonly single targeting either the Subthalamic Nucleus (STN) or the Globus Palidus (GP). More recently, studies have also employed dual-target stimulation, which may synergistically increase therapeutic benefit. Additionally, novel designs of adaptive DBS (aDBS) with closed-loop feedback aim to further enhance efficiency when compared to open-loop procedures, while enabling it to deal with patient variability and disease progression. In this way, here we propose a dual-target aDBS controller, considering a computational model for STN-GPe circuit. Its goal is to suppress the mentioned oscillations at any stage of illness development and synaptic and connectivity parameters ranges, hence in principle adjustable to distinct patient conditions. The control method generally addresses the STN-GPe circuit as a nonlinear-delayed dynamical system, employing a robust technique of delay compensation. The controller architecture relies on recording and stimulating both STN and GPe, also using a straightforward predictor algorithm to select the external inputs for the STN-GPe circuit. The stimulation inputs consist of initial simple brief pulses that suppress or shift the onset of β-oscillations. Then, weak amplitude signals are enough to sustain the achieved stabilization. The protocol has been fully simulated considering an in silico model. Within such theoretical framework, it was shown to be extremely efficient if the processing time is not too long. The dual-target aDBS put forward here is based on implementable technologies, thus potentially amenable to novel strategies for biomedical close-loop approaches. But concrete challenges for doing so are also discussed.

Visual foraging tasks provide great insights into how organisms orient within their visual environment. These tasks are useful for simultaneously investigating concepts often addressed separately, such as attentional guidance, working memory, and strategy. Foraging tasks enable the study of continuous real-world visual exploration and how information about the environment is gathered. They yield rich and multifaceted datasets and can provide insights into the mechanisms of visual attention, visual search, visual memory, and other cognitive factors in a setting more closely resembling how we employ those factors in the real world. We provide a review of the literature and discuss the pros and cons of different ways of understanding and explaining human foraging. A popular approach has been to test whether foraging performance fits certain mathematical rules, such as the marginal value theorem, or so-called Lévy flights. We question the usefulness of such approaches, in particular in the context human foraging (or the foraging of any organism with a sizeable nervous system). The goals and rewards that determine foraging behavior are multifaceted, and understanding those will bring us closer to understanding how humans interact with the world. The usefulness of assessing whether performance falls in line with a particular mathematical rule is, in our opinion, questionable and resources may have been wasted on trying to answer such questions, instead of focusing on the rich insights that foraging data provides about vision, attentional selection, visual short-term memory and the gathering of information.

A fundamental question in neuroscience is how sensory signals are decoded from noisy cortical activity. We address this question in the olfactory system, decoding the route by which odorants arrive into the nasal cavity: through the nostrils (orthonasal), or through the back of the throat (retronasal). We recently showed with modeling and novel experiments on anesthetized rats that orthonasal versus retronasal modality information is encoded in the olfactory bulb (OB, a pre-cortical region). However, key questions remain: is modality information transmitted from OB to anterior piriform cortex (aPC)? How can this information be extracted from a much noisier cortical population with overall less firing? With simultaneous spike recordings of populations of neurons in OB and aPC, we show that an unsupervised and biologically plausible algorithm, Shared Covariance Decoding (SCD), can indeed linearly encode modality in low dimensional subspaces. Specifically, SCD improves encoding of ortho/retro in aPC compared to Fisher's linear discriminant analysis (LDA). Consistent with our theoretical analysis, when noise correlations between OB and aPC are low and OB well-encodes modality, modality in aPC tends to be encoded optimally with SCD. We observe that with several algorithms (LDA, SCD, optimal) the decoding accuracy distributions are invariant when GABA[Formula: see text] (ant-)agonists (bicuculline and muscimol) are applied to OB, which is consistent with invariance in population firing in aPC. Overall, we show modality information can be encoded efficiently in piriform cortex.