Alternating Positive and Negative Feedback Control Model Based on Catastrophe Theories

In automatic control systems, negative feedback control has the advantage of maintaining a steady state, while positive feedback control can enhance some activities of the control system. How to design a controller with both control modes is an interesting and challenging problem. Motivated by it, on the basis idea of catastrophe theories, taking positive feedback and negative feedback as two different states of the system, an adaptive alternating positive and negative feedback (APNF) control model with the advantages of two states is proposed. By adaptively adjusting the relevant parameters of the constructed symmetric catastrophe function and the learning rule based on error and forward weight, the two states can be switched in the form of catastrophe. Through the Lyapunov stability theory, the convergence of the proposed adaptive APNF control model is proven, which indicates that system convergence can be guaranteed by selecting appropriate parameters. Moreover, we present theoretical proof that the negative feedback system with negative parameters can be equivalent to the positive feedback system with positive parameters. Finally, the results of the simulation example show that APNF control has satisfactory performance in response speed and overshoot.

Neutrophils self-limit swarming to contain bacterial growth in vivo

Neutrophils communicate with each other to form swarms in infected organs. Coordination of this population response is critical for the elimination of bacteria and fungi. Using transgenic mice, we found that neutrophils have evolved an intrinsic mechanism to self-limit swarming and avoid uncontrolled aggregation during inflammation. G protein–coupled receptor (GPCR) desensitization acts as a negative feedback control to stop migration of neutrophils when they sense high concentrations of self-secreted attractants that initially amplify swarming. Interference with this process allows neutrophils to scan larger tissue areas for microbes. Unexpectedly, this does not benefit bacterial clearance as containment of proliferating bacteria by neutrophil clusters becomes impeded. Our data reveal how autosignaling stops self-organized swarming behavior and how the finely tuned balance of neutrophil chemotaxis and arrest counteracts bacterial escape.

Achieving natural behavior in a robot using neurally inspired hierarchical control

Terrestrial locomotion presents tremendous computational challenges on account of the enormous degrees of freedom in legged animals, and the complex and unpredictable properties of the natural environment and the effectors. Yet the nervous system can achieve locomotion with ease. Here we introduce a quadrupedal robot capable of goal-directed posture control and locomotion over rough terrain. The underlying control architecture is a hierarchical network of simple negative feedback control systems inspired by the organization of the vertebrate nervous system. Without using an internal model or feedforward planning, and without any training, our robot shows robust posture control and locomotor behavior in novel environments with unpredictable disturbances.

Sensorimotor delays in tracking may be compensated by negative feedback control of motion-extrapolated position

Sensorimotor delays dictate that humans act on outdated perceptual information. As a result, continuous manual tracking of an unpredictable target incurs significant response delays. However, no such delays are observed for repeating targets such as the sinusoids. Findings of this kind have led researchers to claim that the nervous system constructs predictive, probabilistic models of the world. However, a more parsimonious explanation is that visual perception of a moving target position is systematically biased by its velocity. The resultant extrapolated position could be compared with the cursor position and the difference canceled by negative feedback control, compensating sensorimotor delays. The current study tested whether a position extrapolation model fit human tracking of sinusoid (predictable) and pseudorandom (less predictable) targets better than the non-biased position control model, Twenty-eight participants tracked these targets and the two computational models were fit to the data at 60 fixed loop delay values (simulating sensorimotor delays). We observed that pseudorandom targets were tracked with a significantly greater phase delay than sinusoid targets. For sinusoid targets, the position extrapolation model simulated tracking results more accurately for loop delays longer than 120 ms, thereby confirming its ability to compensate for sensorimotor delays. However, for pseudorandom targets, this advantage arose only after 300 ms, indicating that velocity information is unlikely to be exploited in this way during the tracking of less predictable targets. We conclude that negative feedback control of position is a parsimonious model for tracking pseudorandom targets and that negative feedback control of extrapolated position is a parsimonious model for tracking sinusoidal targets.

Gene autoregulation by 3’ UTR-derived bacterial small RNAs

Negative feedback regulation, that is the ability of a gene to repress its own synthesis, is the most abundant regulatory motif known to biology. Frequently reported for transcriptional regulators, negative feedback control relies on binding of a transcription factor to its own promoter. Here, we report a novel mechanism for gene autoregulation in bacteria relying on small regulatory RNA (sRNA) and the major endoribonuclease, RNase E. TIER-seq analysis (transiently-inactivating-an-endoribonuclease-followed-by-RNA-seq) revealed ~25,000 RNase E-dependent cleavage sites in Vibrio cholerae, several of which resulted in the accumulation of stable sRNAs. Focusing on two examples, OppZ and CarZ, we discovered that these sRNAs are processed from the 3’ untranslated region (3’ UTR) of the oppABCDF and carAB operons, respectively, and base-pair with their own transcripts to inhibit translation. For OppZ, this process also triggers Rho-dependent transcription termination. Our data show that sRNAs from 3’ UTRs serve as autoregulatory elements allowing negative feedback control at the post-transcriptional level.