From Biological Synapses to “Intelligent” Robots

Dresp-Langley, Birgitta

doi:10.3390/electronics11050707

Cited by 4 publications

(8 citation statements)

References 161 publications

(409 reference statements)

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“…To avoid single observer bias [66], objective quantitative performance criteria need to be worked out for defining gold standards of true expert performance in this emerging realm of assistive technology, pushing optimal training programs for novices. Cogently designed and parsimoniously deployed Artificial Intelligence [20,67] can help move things forward in this direction. Finally, the control of the human hand by the brain has evolved as a function of environmental constraints in interaction with the other sensory systems, and grip force profiles are a direct reflection of the complex cognitive and behavioral synergies these interactions have produced.…”

Section: Discussionmentioning

confidence: 99%

“…Successful grip force deployment involves central processes of neural control [32,33], and grip force is currently explored as a marker of brain health [70] in clinical studies of cognitive disorders such as major chronic depression [71], Parkinson's disease [72], or the non-pathological cognitive decline in ageing [73][74][75][76]. As a directly measurable behavioral correlate of self-organizing control mechanisms in brain learning [77], grip force patterns and their evolution are suited for feeding theoretical approaches and hypotheses that exploit neural network architectures driven by unsupervised biological learning [20].…”

Section: Discussionmentioning

confidence: 99%

“…Sensorial and cognitive processes underlying hand-specific grip force variations (dominant versus non-dominant hand) in manual tasks have been studied in a variety of contexts by selectively probing multiple measurement locations in the fingers and palm of the human hand [12][13][14][15][16][17]. Grip force modulation is governed by neuronal connections that are potentiated on the basis of self-organized learning [18,19,20], which drives the development of functionally specific neural networks in the continuously learning brain. The neural activities in these networks are modulated by sensory signals processed in the somatosensory cortex, the so-called S1 map [21].…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Modeling of Grip Forces Captures Proficiency in Manual Robot Control

et al. 2023

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New technologies for monitoring grip forces during hand and finger movements in non-standard task contexts have provided unprecedented functional insights into somatosensory cognition. Somatosensory cognition is the basis of our ability to manipulate and transform objects of the physical world and to grasp them with the right amount of force. In previous work, the wireless tracking of grip-force signals recorded from biosensors in the palm of the human hand has permitted us to unravel some of the functional synergies that underlie perceptual and motor learning under conditions of non-standard and essentially unreliable sensory input. This paper builds on this previous work and discusses further, functionally motivated, analyses of individual grip-force data in manual robot control. Grip forces were recorded from various loci in the dominant and non-dominant hands of individuals with wearable wireless sensor technology. Statistical analyses bring to the fore skill-specific temporal variations in thousands of grip forces of a complete novice and a highly proficient expert in manual robot control. A brain-inspired neural network model that uses the output metric of a self-organizing pap with unsupervised winner-take-all learning was run on the sensor output from both hands of each user. The neural network metric expresses the difference between an input representation and its model representation at any given moment in time and reliably captures the differences between novice and expert performance in terms of grip-force variability.Functionally motivated spatiotemporal analysis of individual average grip forces, computed for time windows of constant size in the output of a restricted amount of task-relevant sensors in the dominant (preferred) hand, reveal finger-specific synergies reflecting robotic task skill. The analyses lead the way towards grip-force monitoring in real time. This will permit tracking task skill evolution in trainees, or identify individual proficiency levels in human robot-interaction, which represents unprecedented challenges for perceptual and motor adaptation in environmental contexts of high sensory uncertainty. Cross-disciplinary insights from systems neuroscience and cognitive behavioral science, and the predictive modeling of operator skills using parsimonious Artificial Intelligence (AI), will contribute towards improving the outcome of new types of surgery, in particular the single-port approaches such as NOTES (Natural Orifice Transluminal Endoscopic Surgery) and SILS (Single-Incision Laparoscopic Surgery).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Modeling of Grip Forces Captures Proficiency in Manual Robot Control

et al. 2023

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“…In his latest book [1], Grossberg discusses empirical findings and his own neural network models to illustrate, and forecast, how autonomous adaptive intelligence [2] is or may be implemented in artificial systems at unprecedentedly high levels of brain function [3,4,5]. His account of how the brain generates conscious cognition and, ultimately, individual minds provides mechanistic insights into complex phenomena such as mental disorders, or the biological basis of morality and religion.…”

Section: Introductionmentioning

confidence: 99%

The Grossberg Code: Universal Neural Network Signatures of Perceptual Experience

Dresp-Langley

2023

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Two universal functional principles of Grossberg’s Adaptive Resonance Theory decipher the brain code of all biological learning and adaptive intelligence. Low-level representations of multisensory stimuli in their immediate environmental context are formed on the basis of bottom-up activation and under the control of top-down matching rules that integrate high-level, long-term traces of contextual configuration. These universal coding principles lead to the establishment of lasting brain signatures of perceptual experience in all living species, from aplysiae to primates. They are re-visited in this concept paper on the basis of examples drawn from the original code and from some of the most recent related empirical findings on contextual modulation in the brain, highlighting the potential of Grossberg’s pioneering insights and groundbreaking theoretical work for intelligent solutions in the domain of developmental and cognitive robotics.

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“…e OAM multiplexing technology of vortex beams is introduced into wireless optical communication, which can be organically integrated with MIMO technology. However, according to recent related optical communication surveys, with the development and progress of society, optical ber communication is facing a more and more prominent key problem, that is, the existing optical communication capabilities are gradually unable to meet the increasing demand for communication capacity [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Orbital Angular Momentum Data Model Based on Mechanically Reconfigurable Arrays and Neural Networks

Zhang

Wang

Zhu

et al. 2022

Computational Intelligence and Neuroscience

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Multimodal orbital angular momentum is a research hotspot in the field of electromagnetic wave communication. How to accurately detect and identify multimodal orbital angular momentum data is a current academic problem. Based on the theory of mechanically reconfigurable arrays and neural networks, the purity, detection method, and transmission and reception of orbital angular momentum vortex waves are modeled in this paper. Through the network identification of the dynamic model of the three-degree-of-freedom reconfigurable manipulator, the paper takes the identification result and the control input of the single neuron PID as the input of the system control torque of the manipulator and realizes the reconfigurable manipulator. In the simulation process, the local approximation effect of the nonlinear control system used is very ideal. The single neuron PID controller overcomes the shortcomings of time-consuming and unsatisfactory control accuracy caused by the constant parameter of the traditional PID controller and realizes the circular loop. On the other hand, at the point of interest of the human eye, its resolution value is the largest, and its value gradually decreases as the distance from the pit increases. The experimental results show that the three-transmitting and three-receiving orbital angular momentum vortex wave transceiver system based on the mechanically reconfigurable array and neural network theory is relatively complete, and the transmission coefficient between the same modes reaches 0.827, which is much higher than that between different modes. On this basis, the modal purity, detection method, and reception of orbital angular momentum are studied accordingly. At the same time, the damage to the microscopic particles can be greatly reduced. At the same time, the information delay is reduced to 8.25%, which effectively improves the isolation characteristics of different modal orbital angular momentum channels and promotes the communication transmission of multimodal signals.

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From Biological Synapses to “Intelligent” Robots

Cited by 4 publications

References 161 publications

Spatiotemporal Modeling of Grip Forces Captures Proficiency in Manual Robot Control

Spatiotemporal Modeling of Grip Forces Captures Proficiency in Manual Robot Control

The Grossberg Code: Universal Neural Network Signatures of Perceptual Experience

Multimodal Orbital Angular Momentum Data Model Based on Mechanically Reconfigurable Arrays and Neural Networks

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