The cerebellum could solve the motor error problem through error increase prediction

We present a cerebellar architecture with two main characteristics. The first one is that complex spikes respond to increases in sensory errors. The second one is that cerebellar modules associate particular contexts where errors have increased in the past with corrective commands that stop the increase in error. We analyze our architecture formally and computationally for the case of reaching in a 3D environment. In the case of motor control, we show that there are synergies of this architecture with the Equilibrium-Point hypothesis, leading to novel ways to solve the motor error problem. In particular, the presence of desired equilibrium lengths for muscles provides a way to know when the error is increasing, and which corrections to apply. In the context of Threshold Control Theory and Perceptual Control Theory we show how to extend our model so it implements anticipative corrections in cascade control systems that span from muscle contractions to cognitive operations.Comment: 34 pages (without bibliography), 13 figure

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Data Preprocessing and Homogeneity: The Influence on Robustness and Modeling by PLS Via NIR of Fish Burgers

Fish burgers as new products require their shelf life investigated. Sensory results usually do not follow a homogeneous profile, as it measures human perception. Once the sensory and physicochemical monitoring of the shelf life takes time and considerable investment, the Near Infrared spectroscopy comes as a fast instrumental technique, which can access multiple parameters from the sample at the same time. In order to replace traditional methods improving mathematical modeling, the objective of this study is the estimation of the data preprocessing and homogeneity (Kolmogorov–Smirnov) influence in the quality parameters of Partial Least Squares modeling. Calibration and validation models were evaluated by means of correlation coefficient, Rank, robustness and Residual Prediction Deviation. All the preprocessing available on the software Opus Lab® were tested and compared. 72 readings/8 samples of refrigerated grass carp burgers originated the data regarding its water activity, rancid taste, pH and reactive substances of thiobarbituric acid results. The preprocessing methods accessible were Standard Normal Variate, Multiplicative Scatter Correction, 2nd derivative, 1st derivative, Straight Line Subtraction and Min/Max. Each chosen preprocessing generated a model with different parameters. The homogeneity of data proved to have a direct influence on the robustness, confirming the challenge to fit sensory results in Partial Least Squares prediction models. New possibilities to investigate meat products were shown. DOI: http://dx.doi.org/10.17807/orbital.v11i6.123

Transfer Effect of Speech-sound Learning on Auditory-motor Processing of Perceived Vocal Pitch Errors

Speech perception and production are intimately linked. There is evidence that speech motor learning results in changes to auditory processing of speech. Whether speech motor control benefits from perceptual learning in speech, however, remains unclear. This event-related potential study investigated whether speech-sound learning can modulate the processing of feedback errors during vocal pitch regulation. Mandarin speakers were trained to perceive five Thai lexical tones while learning to associate pictures with spoken words over 5 days. Before and after training, participants produced sustained vowel sounds while they heard their vocal pitch feedback unexpectedly perturbed. As compared to the pre-training session, the magnitude of vocal compensation significantly decreased for the control group, but remained consistent for the trained group at the post-training session. However, the trained group had smaller and faster N1 responses to pitch perturbations and exhibited enhanced P2 responses that correlated significantly with their learning performance. These findings indicate that the cortical processing of vocal pitch regulation can be shaped by learning new speech-sound associations, suggesting that perceptual learning in speech can produce transfer effects to facilitating the neural mechanisms underlying the online monitoring of auditory feedback regarding vocal production

Towards the development of a smart flying sensor: illustration in the field of precision agriculture

Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology

Low-frequency oscillatory correlates of auditory predictive processing in cortical-subcortical networks: a MEG-study

Emerging evidence supports the role of neural oscillations as a mechanism for predictive information processing across large-scale networks. However, the oscillatory signatures underlying auditory mismatch detection and information flow between brain regions remain unclear. To address this issue, we examined the contribution of oscillatory activity at theta/alpha-bands (4–8/8–13 Hz) and assessed directed connectivity in magnetoencephalographic data while 17 human participants were presented with sound sequences containing predictable repetitions and order manipulations that elicited prediction-error responses. We characterized the spectro-temporal properties of neural generators using a minimum-norm approach and assessed directed connectivity using Granger Causality analysis. Mismatching sequences elicited increased theta power and phase-locking in auditory, hippocampal and prefrontal cortices, suggesting that theta-band oscillations underlie prediction-error generation in cortical-subcortical networks. Furthermore, enhanced feedforward theta/alpha-band connectivity was observed in auditory-prefrontal networks during mismatching sequences, while increased feedback connectivity in the alpha-band was observed between hippocampus and auditory regions during predictable sounds. Our findings highlight the involvement of hippocampal theta/alpha-band oscillations towards auditory prediction-error generation and suggest a spectral dissociation between inter-areal feedforward vs. feedback signalling, thus providing novel insights into the oscillatory mechanisms underlying auditory predictive processing

Neural Dynamics of Delayed Feedback in Robot Teleoperation: Insights from fNIRS Analysis

As robot teleoperation increasingly becomes integral in executing tasks in distant, hazardous, or inaccessible environments, the challenge of operational delays remains a significant obstacle. These delays are inherent in signal transmission and processing and can adversely affect the operators performance, particularly in tasks requiring precision and timeliness. While current research has made strides in mitigating these delays through advanced control strategies and training methods, a crucial gap persists in understanding the neurofunctional impacts of these delays and the efficacy of countermeasures from a cognitive perspective. Our study narrows this gap by leveraging functional Near-Infrared Spectroscopy (fNIRS) to examine the neurofunctional implications of simulated haptic feedback on cognitive activity and motor coordination under delayed conditions. In a human-subject experiment (N=41), we manipulated sensory feedback to observe its influences on various brain regions of interest (ROIs) response during teleoperation tasks. The fNIRS data provided a detailed assessment of cerebral activity, particularly in ROIs implicated in time perception and the execution of precise movements. Our results reveal that certain conditions, which provided immediate simulated haptic feedback, significantly optimized neural functions related to time perception and motor coordination, and improved motor performance. These findings provide empirical evidence about the neurofunctional basis of the enhanced motor performance with simulated synthetic force feedback in the presence of teleoperation delays.Comment: Submitted to Frontiers in Human Neuroscienc