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When movements become dysmetric, the resultant motor error induces a plastic change in the cerebellum to correct the movement, i.e., motor adaptation. Current evidence suggests that the error signal to the cerebellum is delivered by complex spikes originating in the inferior olive (IO). To prove a causal link between the IO error signal and motor adaptation, several studies blocked the IO, which, unfortunately, affected not only the adaptation but also the movement itself. We avoided this confound by inactivating the source of an error signal to the IO. Several studies implicate the superior colliculus (SC) as the source of the error signal to the IO for saccade adaptation. When we inactivated the SC, the metrics of the saccade to be adapted were unchanged, but saccade adaptation was impaired. Thus, an intact rostral SC is necessary for saccade adaptation. Our data provide experimental evidence for the cerebellar learning theory that requires an error signal to drive motor adaptation.

Binary neural networks (BNNs) significantly reduce computational complexity and memory usage in machine and deep learning by representing weights and activations with just one bit. However, most existing training algorithms for BNNs rely on quantization-aware floating-point stochastic gradient descent (SGD), limiting the full exploitation of binary operations to the inference phase only. In this work, we propose, for the first time, a fully binary and gradient-free training algorithm for multi-layer BNNs, eliminating the need for back-propagated floating-point gradients. Specifically, the proposed algorithm relies on local binary error signals and binary weight updates, employing integer-valued hidden weights that serve as a synaptic metaplasticity mechanism, thereby enhancing its neurobiological plausibility. Our proposed solution enables the training of binary multi-layer perceptrons by using exclusively XNOR, Popcount, and increment/decrement operations. Experimental results on multi-class classification benchmarks show test accuracy improvements of up to +35.47% over the only existing fully binary single-layer state-of-the-art solution. Compared to full-precision SGD, our solution improves test accuracy by up to +35.30% under the same total memory demand, while also reducing computational cost by two to three orders of magnitude in terms of the total number of Boolean gates. The proposed algorithm is made available to the scientific community as a public repository.

In order to improve the speech quality in communication systems, acoustic echo cancellation techniques are commonly used to mitigate the deleterious effect of acoustic feedback. In fact, double-talk situations hinder the performance of acoustic echo cancellation when the two speakers in the two ends talk simultaneously. For this reason, double-talk detection is included to control the echo canceler system. In this paper we proposed a new method of double-talk detection based on the error signal variance. Opposed to the previous works where the most of the existing methods are based on a comparison between the received far-end and the microphone observation signals, we accurately account for the variation of the error signal. To evaluate the proposed method, we used acoustic echo cancellation based on the normalized least mean square algorithm. Simulation results indicate the good performance of the proposed double-talk detector.

This study deepens on the transient response analysis of the so-called vector proportional–integral (VPI) controllers and compares them with the popular proportional–resonant (PR) controllers for grid-connected applications. The employed methodology is based on the study of the error signal roots: both reference tracking and disturbance rejection abilities are considered for proper gain tuning. This study proves that PR controllers lead to shorter settling times than VPI controllers. A three-phase voltage source converter prototype has been implemented. Experimental results comparing the transient behaviour of VPI and PR controllers in different conditions are provided: a + 90° phase-angle jump in the current reference and a ‘type C’ voltage sag at the point of common coupling.

One of the basic requirements of today's sophisticated world is the availability of electrical energy, and neglect of this matter may have irreparable damages such as an extensive blackout. The problems which were introduced about the traditional power grid, and also, the growing advances in smart technologies make the traditional power grid go towards smart power grid. Although widespread utilisation of telecommunication networks in smart power grid enhances the efficiency of the system, it will create a critical platform for cyber attacks and penetration into the system. Automatic generation control (AGC) is a fundamental control system in the power grid, and it is responsible for controlling the frequency of the grid. An attack on the data transmitted through the telecommunications link from the sensors to the AGC will cause frequency deviation, resulting in disconnection of the load, generators and ultimately global blackout. In this study, by using a Kalman filter and a proposed detector, a solution has been presented to detect the attack before it can affect the system. Contrary to existing methods, this method is able to detect attacks that are stealthy from the area control error signal and χ2 ‐detector. Simulations confirm the effectiveness of this method.

Regret helps to optimize decision behaviour. It can be defined as a rational emotion. Several recent neurobiological studies have confirmed the interface between emotion and cognition at which regret is located and documented its role in decision behaviour. These data give credibility to the incorporation of regret in decision theory that had been proposed by economists in the 1980s. However, finer distinctions are required in order to get a better grasp of how regret and behaviour influence each other. Regret can be defined as a predictive error signal but this signal does not necessarily transpose into a decision-weight influencing behaviour. Clinical studies on several types of patients show that the processing of an error signal and its influence on subsequent behaviour can be dissociated. We propose a general understanding of how regret and decision-making are connected in terms of regret being modulated by rational antecedents of choice. Regret and the modification of behaviour on its basis will depend on the criteria of rationality involved in decision-making. We indicate current and prospective lines of research in order to refine our views on how regret contributes to optimal decision-making.

This experimental study investigates the practical benefits and drawbacks of error-cube control for closed-loop PID control structures. The error-cube control approach employs the cube power of the error signal for controllers and this causes variability in control characteristics due to the non-linearity of the cube power operation. The error-cube signal introduces attenuated and magnified error regions. These two characteristic error regions result in a tight control regime and a slack control regime, depending on magnitude of the error signal. The study presents a discussion on non-linear error signals in a practical aspect and demonstrates the effects of non-linear error signals on the step response of closed-loop PID control systems via simulation results and experimental measurements. An enhanced error-cube controller was proposed to improve the control performance of the error-cube control and results are discussed.

Perception is characterized by a reciprocal exchange of predictions and prediction error signals between neural regions. However, the relationship between such sensory mismatch responses and hierarchical predictive processing has not yet been demonstrated at the neuronal level in the auditory pathway. We recorded single-neuron activity from different auditory centers in anaesthetized rats and awake mice while animals were played a sequence of sounds, designed to separate the responses due to prediction error from those due to adaptation effects. Here we report that prediction error is organized hierarchically along the central auditory pathway. These prediction error signals are detectable in subcortical regions and increase as the signals move towards auditory cortex, which in turn demonstrates a large-scale mismatch potential. Finally, the predictive activity of single auditory neurons underlies automatic deviance detection at subcortical levels of processing. These results demonstrate that prediction error is a fundamental component of singly auditory neuron responses. Perception can be explained by predictive coding, but it is unclear how this theory applies at the single-neuron level. Here, authors describe how auditory patterns are encoded and detected by single neurons along the auditory pathway, demonstrating that prediction error exists in single auditory neurons.

The brain supports adaptive behavior by generating predictions, learning from errors, and updating memories to incorporate new information. Prediction error, or surprise, triggers learning when reality contradicts expectations. Prior studies have shown that the hippocampus signals prediction errors, but the hypothesized link to memory updating has not been demonstrated. In a human functional MRI study, we elicited mnemonic prediction errors by interrupting familiar narrative videos immediately before the expected endings. We found that prediction errors reversed the relationship between univariate hippocampal activation and memory: greater hippocampal activation predicted memory preservation after expected endings, but memory updating after surprising endings. In contrast to previous studies, we show that univariate activation was insufficient for understanding hippocampal prediction error signals. We explain this surprising finding by tracking both the evolution of hippocampal activation patterns and the connectivity between the hippocampus and neuromodulatory regions. We found that hippocampal activation patterns stabilized as each narrative episode unfolded, suggesting sustained episodic representations. Prediction errors disrupted these sustained representations and the degree of disruption predicted memory updating. The relationship between hippocampal activation and subsequent memory depended on concurrent basal forebrain activation, supporting the idea that cholinergic modulation regulates attention and memory. We conclude that prediction errors create conditions that favor memory updating, prompting the hippocampus to abandon ongoing predictions and make memories malleable.

It has recently been suggested that predictive processing principles may apply to interoception, defined as the processing of hormonal, autonomic, visceral, and immunological signals. In the current study, we aimed at providing empirical evidence for the role of cardiac interoceptive prediction errors signals on allostatic adjustments, using transcutaneous auricular vagus nerve stimulation (taVNS) as a tool to modulate the processing of interoceptive afferents. In a within‐subject design, participants performed a cardiac‐related interoceptive task (heartbeat counting task) under taVNS and sham stimulation, spaced 1‐week apart. We observed that taVNS, in contrast to sham stimulation, facilitated the maintenance of interoceptive accuracy levels over time (from the initial, stimulation‐free, baseline block to subsequent stimulation blocks), suggesting that vagus nerve stimulation may have helped to maintain engagement to cardiac afferent signals. During the interoceptive task, taVNS compared to sham, produced higher heart‐evoked potentials (HEP) amplitudes, a potential readout measure of cardiac‐related prediction error processing. Further analyses revealed that the positive relation between interoceptive accuracy and allostatic adjustments—as measured by heart rate variability (HRV)—was mediated by HEP amplitudes. Providing initial support for predictive processing accounts of interoception, our results suggest that the stimulation of the vagus nerve may increase the precision with which interoceptive signals are processed, favoring their influence on allostatic adjustments. We aimed at investigating the role of interoceptive prediction errors on allostatic adjustments, using transcutaneous auricular vagus nerve stimulation (taVNS) to increase precision. We found that taVNS facilitated the maintenance of interoceptive accuracy levels, increased heart‐evoked potentials amplitudes (correlates of cardiac‐related prediction error signals) and mediated their influence on allostatic adjustments (measured by heart rate variability).