The Brain-Like Sensorimotor Control System

Cognitive Systems Monographs, 2009

In this Chapter the application of dynamical systems to model reactive and precognitive behaviours is discussed. We present an approach to navigation based on the control of a chaotic system that is enslaved, on the basis of sensory stimuli, into low order dynamics that are used as percepts of the environmental situations. Another aspect taken into consideration, is the introduction of correlation mechanisms, important for the emergence of anticipation. In this case a spiking network is used to control a simulated robot learning to anticipate sensory events. Finally the proposed approach has been applied to solve a landmark navigation problem.

International Journal of Artificial Intelligence & Applications, 2011

This article presents a model of robotic control system inspired by the human neuroregulatory system. This model allows the application of functional and organizational principles of biological systems to robotic systems. It also proposes appropriate technologies to implement this proposal, in particular the services. To illustrate the proposal, we implemented a control system for mobile robots in dynamic open environments, demonstrating the viability of both the model and the technologies chosen for implementation.

Proceedings of the IEEE, 2000

In this paper, the authors address recent advances and ongoing challenges in reference to cognitive dynamic systems, embodying cognitive perception, and cognitive control.

Kalpa Publications in Engineering

This research paper presents to develop a bio-signal acquisition system and rehabilitation technique based on “Cognitive Science application of robot controlled by brain signal”. We are trying to Developing a data acquisition system for acquiring EEG signals from Brain sense head band and also designing new algorithm for detecting attention and meditation wave and implementing on Robotics platform By using Embedded core.

Frontiers in human neuroscience, 2016

The goal of this research is to test the potential for neuroadaptive automation to improve response speed to a hazardous event by using a brain-computer interface (BCI) to decode perceptual-motor intention. Seven participants underwent four experimental sessions while measuring brain activity with magnetoencephalograpy. The first three sessions were of a simple constrained task in which the participant was to pull back on the control stick to recover from a perturbation in attitude in one condition and to passively observe the perturbation in the other condition. The fourth session consisted of having to recover from a perturbation in attitude while piloting the plane through the Grand Canyon constantly maneuvering to track over the river below. Independent component analysis was used on the first two sessions to extract artifacts and find an event related component associated with the onset of the perturbation. These two sessions were used to train a decoder to classify trials in w...

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

Animal Behaviour, 1998

We evaluate the ability of artificial neural network models (multi-layer perceptrons) to predict stimulus-response relationships. A variety of empirical results are considered, such as generalization, peak-shift (supernormality) and stimulus intensity effects. The networks were trained on the same tasks as the animals in the considered experiments. The subsequent generalization tests on the networks showed that the model replicates correctly the empirical results. It is concluded that these models are valuable tools in the study of animal behaviour.

IEEE Access

Brain-Controlled Vehicle (BCV) is an already established technology usually designed for disabled patients. This review focuses on the most relevant topics on brain-controlled vehicles, with a special reference to the terrestrial BCV (e.g., the mobile car, car simulator, real car, graphical and gaming car) and the aerial BCV, also called BCAV (e.g., real quadcopters, drones, fixed wings, graphical helicopter, and aircraft) controlled by using bio-signals, such as electroencephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG). For instance, EEG-based algorithms detect patterns from the motor imaginary cortex area of the brain for intention detection, patterns like event-related desynchronization/event-related synchronization, steady-state visually evoked potentials, P300, and generated local evoked potential patterns. We have identified that the reported best-performing approaches employ machine learning and artificial intelligence optimization methods, namely support vector machine, neural network, linear discriminant analysis, k-nearest neighbor, k-means, water drop optimization, and chaotic tug of war. We considered the following metrics to analyze the efficiency of the different methods: type and combination of bio-signals, time response, and accuracy values with statistical analysis. The present work provides an extensive literature review of the key findings of the past ten years, indicating future perspectives in the field.

Frontiers in Neurorobotics, 2018

Decision-making is a crucial cognitive function for various animal species surviving in nature, and it is also a fundamental ability for intelligent agents. To make a step forward in the understanding of the computational mechanism of human-like decision-making, this paper proposes a brain-inspired decision-making spiking neural network (BDM-SNN) and applies it to decision-making tasks on intelligent agents. This paper makes the following contributions: (1) A spiking neural network (SNN) is used to model human decision-making neural circuit from both connectome and functional perspectives. (2) The proposed model combines dopamine and spike-timing-dependent plasticity (STDP) mechanisms to modulate the network learning process, which indicates more biological inspiration. (3) The model considers the effects of interactions among sub-areas in PFC on accelerating the learning process. (4) The proposed model can be easily applied to decision-making tasks in intelligent agents, such as an...

Lecture Notes in Computer Science, 2013

As part of the Robobee project, we have modified a coaxial helicopter to operate using a discrete time map-based neuronal network for the control of heading, altitude, yaw, and odometry. Two concepts are presented: 1. A model for the integration of sensory data into the neural network. 2. A function for transferring the instantaneous spike frequency of motor neurons to a pulse width modulated signal required to drive motors and other types of actuators. The helicopter is provided with a flight vector and distance to emulate the information conveyed by the honeybee's waggle dance. This platform allows for the testing of proposed networks for adaptive navigation in an effort to simulate honeybee foraging on a flying robot.