Feedback Connections Research Articles

Dissipativity reinforcement in interconnected systems

This paper focuses on the reinforcement of the quantitative performance in interconnected dynamical systems. The following problem is addressed that concerns dissipativity reinforcement via interconnection: Find a class of subsystems and their interconnection rule such that the L2 gain bound of the entire interconnected system is reduced compared with that of each individual subsystem. We assume that each subsystem has a special passivity property that is characterized by two parameters, and has a bounded L2 gain. Then, the feedback connection and the more general interconnection of the subsystems are expressed by the transition of the two parameters inheriting the same passivity property. In addition, the L2 gain bound of the entire interconnected system, estimated with the parameters, is strictly reduced and becomes less than that of each subsystem. Finally, special interconnection rules are considered to show that the scale-expansion of the interconnected system, i.e., increasing the number of subsystems, gradually reduces the L2 gain bound.

Recurrent fuzzy wavelet neural networks based on robust adaptive sliding mode control for industrial robot manipulators

A robust adaptive control method is proposed in this paper based on recurrent fuzzy wavelet neural networks (RFWNNs) system for industrial robot manipulators (IRMs) to improve high accuracy of the tracking control. The RFWNNs consist of four layers, and second layer has the feedback connections. Wavelet basis function is used as fuzzy membership function. In general, it is not easy to adopt a model-based method to achieve this control object due to the uncertainties of the IRM, such as unknown dynamic, disturbances and parameter variations. To solve this problem, all the parameters of the RFWNNs system are tuned online by an adaptive learning algorithm, and online adaptive control laws are determined by Lyapunov stability theorem. In addition, the robust controller is designed to deal with the approximation error, optimal parameter vectors and higher-order terms in Taylor series. Therefore, with the proposed control, the desired tracking performance, stability and robustness of the closed-loop manipulators system are guaranteed. The simulations and experimental performed on a three-link IRMs are provided in comparison with fuzzy wavelet neural network and robust neural fuzzy network to demonstrate the effectiveness and robustness of the proposed RFWNNs methodology.

Task-dependent modulation of the visual sensory thalamus assists visual-speech recognition

The cerebral cortex modulates early sensory processing via feed-back connections to sensory pathway nuclei. The functions of this top-down modulation for human behavior are poorly understood. Here, we show that top-down modulation of the visual sensory thalamus (the lateral geniculate body, LGN) is involved in visual-speech recognition. In two independent functional magnetic resonance imaging (fMRI) studies, LGN response increased when participants processed fast-varying features of articulatory movements required for visual-speech recognition, as compared to temporally more stable features required for face identification with the same stimulus material. The LGN response during the visual-speech task correlated positively with the visual-speech recognition scores across participants. In addition, the task-dependent modulation was present for speech movements and did not occur for control conditions involving non-speech biological movements. In face-to-face communication, visual speech recognition is used to enhance or even enable understanding what is said. Speech recognition is commonly explained in frameworks focusing on cerebral cortex areas. Our findings suggest that task-dependent modulation at subcortical sensory stages has an important role for communication: Together with similar findings in the auditory modality the findings imply that task-dependent modulation of the sensory thalami is a general mechanism to optimize speech recognition.

Robust control and data‐driven tuning of a hybrid integrator‐gain system with applications to wafer scanners

SummaryA hybrid integrator‐gain system is discussed that aims for improved low‐frequency disturbance rejection, while, at the same time, does not deteriorate overshoot and settling times when compared with a linear integrator. The hybrid integrator has similar phase advantages as the well‐known Clegg integrator but without inducing the discontinuous behavior resulting from resetting system state values. Optimal tuning of the controller parameters of the hybrid integrator is strongly influenced by machine‐specific properties and therefore favors a data‐driven optimization approach. However, as a time‐domain optimization algorithm can easily lead to nonrobust solutions in the sense of large peaking of the closed‐loop frequency response functions, frequency‐domain robustness constraints will be imposed. By means of an adaptive weighting filter design, the parameter updates are penalized upon violation of said robustness constraints. Posed in an unconstrained problem formulation, this is subsequently solved by applying a Gauss‐Newton–based parameter update scheme. Closed‐loop stability of the linear time‐invariant plant and controller in feedback connection with a hybrid integrator‐gain system element follows from a circle‐criterion‐like analysis, which is based on evaluating (measured) frequency response data. Measurement results obtained from an industrial wafer scanner demonstrate the effectiveness of the approach.

Connectivity of neuronal populations within and between areas of primate somatosensory cortex

Functions of the cerebral cortex emerge via interactions of horizontally distributed neuronal populations within and across areas. However, the connectional underpinning of these interactions is not well understood. The present study explores the circuitry of column-size cortical domains within the hierarchically organized somatosensory cortical areas 3b and 1 using tract tracing and optical intrinsic signal imaging (OIS). The anatomical findings reveal that feedforward connections exhibit high topographic specificity, while intrinsic and feedback connections have a more widespread distribution. Both intrinsic and inter-areal connections are topographically oriented across the finger representations. Compared to area 3b, the low clustering of connections and small cortical magnification factor supports that the circuitry of area 1 scaffolds a sparse functional representation that integrates peripheral information from a large area that is fed back to area 3b. Fast information exchange between areas is ensured by thick axons forming a topographically organized, reciprocal pathway. Moreover, the highest density of projecting neurons and groups of axon arborization patches corresponds well with the size and locations of the functional population response reported by OIS. The findings establish connectional motifs at the mesoscopic level that underpin the functional organization of the cerebral cortex.

Amygdala Corticofugal Input Shapes Mitral Cell Responses in the Accessory Olfactory Bulb.

Interconnections between the olfactory bulb and the amygdala are a major pathway for triggering strong behavioral responses to a variety of odorants. However, while this broad mapping has been established, the patterns of amygdala feedback connectivity and the influence on olfactory circuitry remain unknown. Here, using a combination of neuronal tracing approaches, we dissect the connectivity of a cortical amygdala [posteromedial cortical nucleus (PmCo)] feedback circuit innervating the mouse accessory olfactory bulb. Optogenetic activation of PmCo feedback mainly results in feedforward mitral cell (MC) inhibition through direct excitation of GABAergic granule cells. In addition, LED-driven activity of corticofugal afferents increases the gain of MC responses to olfactory nerve stimulation. Thus, through corticofugal pathways, the PmCo likely regulates primary olfactory and social odor processing.

Contextual modulation of sensitivity to naturalistic image structure in macaque V2.

The stimulus selectivity of neurons in V1 is well known, as is the finding that their responses can be affected by visual input to areas outside of the classical receptive field. Less well understood are the ways selectivity is modified as signals propagate to visual areas beyond V1, such as V2. We recently proposed a role for V2 neurons in representing the higher order statistical dependencies found in images of naturally occurring visual texture. V2 neurons, but not V1 neurons, respond more vigorously to “naturalistic” images that contain these dependencies than to “noise” images that lack them. In this work, we examine the dependency of these effects on stimulus size. For most V2 neurons, the preference for naturalistic over noise stimuli was modest when presented in small patches and gradually strengthened with increasing size, suggesting that the mechanisms responsible for this enhanced sensitivity operate over regions of the visual field that are larger than the classical receptive field. Indeed, we found that surround suppression was stronger for noise than for naturalistic stimuli and that the preference for large naturalistic stimuli developed over a delayed time course consistent with lateral or feedback connections. These findings are compatible with a spatially broad facilitatory mechanism that is absent in V1 and suggest that a distinct role for the receptive field surround emerges in V2 along with sensitivity for more complex image structure.NEW & NOTEWORTHY The responses of neurons in visual cortex are often affected by visual input delivered to regions of the visual field outside of the conventionally defined receptive field, but the significance of such contextual modulations are not well understood outside of area V1. We studied the importance of regions beyond the receptive field in establishing a novel form of selectivity for the statistical dependencies contained in natural visual textures that first emerges in area V2.

Inter-areal Balanced Amplification Enhances Signal Propagation in a Large-Scale Circuit Model of the Primate Cortex

Understanding reliable signal transmission represents a notable challenge for cortical systems, which display a wide range of weights of feedforward and feedback connections among heterogeneous areas. We re-examine the question of signal transmission across the cortex in a network model based on mesoscopic directed and weighted inter-areal connectivity data of the macaque cortex. Our findings reveal that, in contrast to purely feedforward propagation models, the presence of long-range excitatory feedback projections could compromise stable signal propagation. Using population rate models as well as a spiking network model, we find that effective signal propagation can be accomplished by balanced amplification across cortical areas while ensuring dynamical stability. Moreover, the activation of prefrontal cortex in our model requires the input strength to exceed a threshold, which is consistent with the ignition model of conscious processing. These findings demonstrate our model as an anatomically realistic platform for investigations of global primate cortex dynamics.

Transcortical descending pathways through granular insular cortex conveying orofacial proprioception

Our motor behavior can be affected by proprioceptive information. However, little is known about which brain circuits contribute to this process. We have recently revealed that the proprioceptive information arising from jaw-closing muscle spindles (JCMSs) is conveyed to the supratrigeminal nucleus (Su5) by neurons in the trigeminal mesencephalic nucleus (Me5), then to the caudo-ventromedial edge of ventral posteromedial thalamic nucleus (VPMcvm), and finally to the dorsal part of granular insular cortex rostroventrally adjacent to the rostralmost part of secondary somatosensory cortex (dGIrvs2). Our next question is which brain areas receive the information from the dGIrvs2 for the jaw-movements. To test this issue, we injected an anterograde tracer, biotinylated dextranamine, into the dGIrvs2, and analyzed the resultant distribution profiles of the labeled axon terminals. Anterogradely labeled axons were distributed in the pontomedullary areas (including the Su5) which are known to receive JCMS proprioceptive inputs conveyed directly by the Me5 neurons and to contain premotoneurons projecting to the jaw-closing motoneurons in the trigeminal motor nucleus (Mo5). They were also found in and around the VPMcvm. In contrast, no labeled axonal terminals were detected on the cell bodies of Me5 neurons and motoneurons in the Mo5. These data suggest that jaw-movements, which are evoked by the classically defined jaw-reflex arc originating from the peripheral JCMS proprioceptive information, could also be modulated by the transcortical feedback connections from the dGIrvs2 to the VPMcvm and Su5.

Control of synaptic plasticity in deep cortical networks.

Humans and many other animals have an enormous capacity to learn about sensory stimuli and to master new skills. However, many of the mechanisms that enable us to learn remain to be understood. One of the greatest challenges of systems neuroscience is to explain how synaptic connections change to support maximally adaptive behaviour. Here, we provide an overview of factors that determine the change in the strength of synapses, with a focus on synaptic plasticity in sensory cortices. We review the influence of neuromodulators and feedback connections in synaptic plasticity and suggest a specific framework in which these factors can interact to improve the functioning of the entire network.

Laminar Organization of Working Memory Signals in Human Visual Cortex

The primary visual cortex (V1) not only represents incoming visual information but is also engaged by higher-level cognitive processes such as visual working memory. Given the laminar terminations of feedback connections between brain areas, such internally generated signals are thought to be present selectively in the deep and superficial layers of V1, and avoid the middle layers. Here, we provide empirical support for this notion, using lamina-resolved fMRI in humans. We observed item-specific working memory signals in early visual cortex. In V1, this item-specific activity was selectively present in deep and superficial layers, avoiding the middle layers, while working memory related activity was present in all layers of V2 and V3. These results show for the first time the laminar organization of internally generated signals during visual working memory in the human visual system, and provide new insights into how bottom-up and top-down signals in visual cortex are separated.

Prediction of dynamic responses of floating structures using NARX with mirroring technique

Displacements, velocities and accelerations of Six Degree of freedom of a single floating structure was predicted using Time Series NARX feedback neural Networks. The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network is based on the linear ARX model, which is commonly used in time-series modelling is used in this study. Time series data of displacements of a single floating structure was used for training and testing the ANN model. In the training stage, this time series data of environment parameters was used as input and dynamic responses was used as target. Benchmarking result and error prediction was compared between two techniques of Neural Network training. The prediction result of the model responses can be concluded that NARX with mirroring technique increase the accuracy and can be used to predict time series of dynamic responses of floating structures.

Dynamic Switching of Two Degree-of-Freedom Control for Belt-Driven Servomechanism

This paper proposes a new two degree-of-freedom control structure such that the robustness control of a belt-driven servomechanism is adequately addressed. In conventional design approaches, a trade-off between robust stability and robust tracking performance is unavoidable because the control engineer must take different frequency regions into consideration. In this paper, a frequency-dependent switching control structure is proposed, where the feedback connection to the external-loop controller is dynamically switched between the outputs of the controlled plant and its nominal model. The disturbance attenuation at lower frequencies by the double-loop control, as well as the robust stability at higher frequencies through the reference feedforward control, can be achieved at one fixed two degree-of-freedom control structure. The feasibility of the proposed approach is verified by theoretical analysis and experimental results.

Feature-Specific Organization of Feedback Pathways in Mouse Visual Cortex

Higher and lower cortical areas in the visual hierarchy are reciprocally connected [1]. Although much is known about how feedforward pathways shape receptive field properties of visual neurons, relatively little is known about the role of feedback pathways in visual processing. Feedback pathways are thought to carry top-down signals, including information about context (e.g., figure-ground segmentation and surround suppression) [2-5], and feedback has been demonstrated to sharpen orientation tuning of neurons in the primary visual cortex (V1) [6, 7]. However, the response characteristics of feedback neurons themselves and how feedback shapes V1 neurons' tuning for other features, such as spatial frequency (SF), remain largely unknown. Here, using a retrograde virus, targeted electrophysiological recordings, and optogenetic manipulations, we show that putatively feedback neurons in layer 5 (hereafter "L5 feedback") in higher visual areas, AL (anterolateral area) and PM (posteromedial area), display distinct visual properties in awake head-fixed mice. AL L5 feedback neurons prefer significantly lower SF (mean: 0.04 cycles per degree [cpd]) compared to PM L5 feedback neurons (0.15 cpd). Importantly, silencing AL L5 feedback reduced visual responses of V1 neurons preferringlow SF (mean change in firing rate: -8.0%), whereas silencing PM L5 feedback suppressed responses of high-SF-preferring V1 neurons (-20.4%). These findings suggest that feedback connections from higher visual areas convey distinctly tuned visual inputs to V1 that serve to boost V1 neurons' responses to SF. Such like-to-like functional organization may represent animportant feature of feedback pathways in sensorysystems and in the nervous system in general.

Recurrent Processing of Contour Integration in the Human Visual Cortex as Revealed By fMRI-Guided TMS.

Contour integration is a critical step in visual perception because it groups discretely local elements into perceptually global contours. Previous investigations have suggested that striate and extrastriate visual areas are involved in this mid-level processing of visual perception. However, the temporal dynamics of these areas in the human brain during contour integration is less understood. The present study used functional magnetic resonance imaging-guided transcranial magnetic stimulation (TMS) to briefly disrupt 1 of 2 visual areas (V1/V2 and V3B) and examined the causal contributions of these areas to contour detection. The results demonstrated that the earliest critical time window at which behavioral detection performance was impaired by TMS pluses differed between V1/V2 and V3B. The first critical window of V3B (90-110 ms after stimulus onset) was earlier than that of V1/V2 (120-140 ms after stimulus onset), thus indicating that feedback connection from higher to lower area was necessary for complete contour integration. These results suggested that the fine processing of contour-related information in V1/V2 follows the generation of a coarse template in the higher visual areas, such as V3B. Our findings provide direct causal evidence that a recurrent mechanism is necessary for the integration of contours from cluttered background in the human brain.

LSD modulates effective connectivity and neural adaptation mechanisms in an auditory oddball paradigm

Under the predictive coding framework, perceptual learning and inference are dependent on the interaction between top-down predictions and bottom-up sensory signals both between and within regions in a network. However, how such feedback and feedforward connections are modulated in the state induced by lysergic acid diethylamide (LSD) is poorly understood. In this study, an auditory oddball paradigm was presented to healthy participants (16 males, 4 female) under LSD and placebo, and brain activity was recorded using magnetoencephalography (MEG). Scalp level Event Related Fields (ERF) revealed reduced neural adaptation to familiar stimuli, and a blunted neural ‘surprise’ response to novel stimuli in the LSD condition. Dynamic causal modelling revealed that both the presentation of novel stimuli and LSD modulate backward extrinsic connectivity within a task-activated fronto-temporal network, as well as intrinsic connectivity in the primary auditory cortex. These findings show consistencies with those of previous studies of schizophrenia and ketamine but also studies of reduced consciousness – suggesting that rather than being a marker of conscious level per se, backward connectivity may index modulations of perceptual learning common to a variety of altered states of consciousness, perhaps united by a shared altered sensitivity to environmental stimuli. Since recent evidence suggests that the psychedelic state may correspond to a heightened ‘level’ of consciousness with respect to the normal waking state, our data warrant a re-examination of the top-down hypotheses of conscious level and suggest that several altered states may feature this specific biophysical effector.This article is part of the Special Issue entitled ‘Psychedelics: New Doors, Altered Perceptions’.

Symmetric Predictive Estimator for Biologically Plausible Neural Learning.

In a real brain, the act of perception is a bidirectional process, depending on both feedforward sensory pathways and feedback pathways that carry expectations. We are interested in how such a neural network might emerge from a biologically plausible learning rule. Other neural network learning methods either only apply to feedforward networks, or employ assumptions (such as weight copying) that render them unlikely in a real brain. Predictive estimators (PEs) offer a better solution to this bidirectional learning scenario. However, PEs also depend on weight copying. In this paper, we propose the symmetric PE (SPE), an architecture that can learn both feedforward and feedback connection weights individually using only locally available information. We demonstrate that the SPE can learn complicated mappings without the use of weight copying. The SPE networks also show promise in deeper architectures.

Dissipativity reinforcement in feedback systems and its application to expanding power systems

SummaryThis paper focuses on the performance analysis and improvement of interconnected passive systems. We assume that each subsystem has a special passivity property that is characterized by 2 parameters. The parameters are also utilized for evaluating the dissipation performance as the L2‐gain. Then, the feedback system composed of passive subsystems inherits the parameter‐dependent passivity, and the parameter transition is given. In addition, it is shown that the dissipation performance of the feedback system is strictly improved as compared with that of the subsystems, which is called dissipativity reinforcement in this paper. Furthermore, we expand the feedback system to a larger‐scale system via the iterative feedback connection of the passive subsystems. Then, the performance of the entire system is gradually reinforced with the increase in the number of subsystems. Subsequently, we extend the class of parameter‐dependent passivity to a frequency‐dependent one. Finally, dissipativity reinforcement via an iterative feedback connection is applied to a power system that involves a large number of renewable energy generators. In particular, we propose a strategy for designing the power system, such that the dissipation performance of the entire system is gradually reinforced via scale expansion, ie, with the increase in the amount of energy generators installed.

Disruption of cortical network activity by the general anaesthetic isoflurane

BackgroundActions of general anaesthetics on activity in the cortico-thalamic network likely contribute to loss of consciousness and disconnection from the environment. Previously, we showed that the general anaesthetic isoflurane preferentially suppresses cortically evoked synaptic responses compared with thalamically evoked synaptic responses, but how this differential sensitivity translates into changes in network activity is unclear. MethodsWe investigated isoflurane disruption of spontaneous and stimulus-induced cortical network activity using multichannel recordings in murine auditory thalamo-cortical brain slices. ResultsUnder control conditions, afferent stimulation elicited short latency, presumably monosynaptically driven, spiking responses, as well as long latency network bursts that propagated horizontally through the cortex. Isoflurane (0.05–0.6 mM) suppressed spiking activity overall, but had a far greater effect on network bursts than on early spiking responses. At isoflurane concentrations >0.3 mM, network bursts were almost entirely blocked, even with increased stimulation intensity and in response to paired (thalamo-cortical + cortical layer 1) stimulation, while early spiking responses were <50% blocked. Isoflurane increased the threshold for eliciting bursts, decreased their propagation speed and prevented layer 1 afferents from facilitating burst induction by thalamo-cortical afferents. ConclusionsDisruption of horizontal activity spread and of layer 1 facilitation of thalamo-cortical responses likely contribute to the mechanism by which suppression of cortical feedback connections disrupts sensory awareness under anaesthesia.

Complex scaling circle criteria for Luré systems

ABSTRACTThis study reports alternative frequency-domain interpretation and implementation of the circle criteria for absolute stability in Luré systems by means of complex scaling and the argument principle. By Luré system, a feedback configuration with a nominal LTI model subject to sector nonlinearities is meant as usual. First, the complex scaling stability criterion is proved for asymptotic stability in LTI feedback connections, which dispenses open-loop poles and contour/locus pre-orientation and possesses bounded loci without prior frequency sweeping. Second, a novel frequency-domain interpretation for positive realness of transfer functions is developed and employed for claiming the complex scaling circle criteria, which accommodate various sector nonlinearities with unified conditions. The new circle criteria are conformable in both multivariable and scalar cases, implementable graphically and tractable numerically, besides being a frequency/complex-domain analytical technique. Third, the results also reveal several frequency-domain facts about Luré systems that remain unknown so far. Finally, numerical examples are included to illustrate the main results.