IJERT-Paper on Recent Development in Artificial Neural Control in Robotics (original) (raw)
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A biological neural network drives a robotic actuator
2006
During the past three years our group experimented the growth of networks of human neural stem cells on a MEA (Microelectrode Array) support. The neurons were stimulated by digital patterns and the output signals were analysed. In previous experiments, the neurons replied selectively to different patterns and showed similar reactions in front of the presentation of identical or similar patterns. Analyses performed with a novel Artificial Neural Network called ITSOM showed the possibility to decode the neural responses to different patterns. In the described experiment, the neurons are connected to a robotic actuator: simulated perceptions stimulate the neurons, that react with organized electric signals. The signals are decoded by the Artificial Neural Network, that drives a minirobot.
Redes neuronales artificiales–un enfoque práctico
2004
Artificial Neural Networks belong to the sub-symbolic branch of Artificial Intelligence since they allow to find the solution of a problem without the need of knowing the algorithm necessary to solve it. This turns them into a tool based on an approach completely different from that used by conventional Computing. Artificial neural networks (ANN) have been inspired in how the brain works and in the way its cells relate to each other. Technological advances provide ever-greater resources to represent really complex structures, perform computations at high speed and in parallel. This has indeed motivated research on this kind of tool. The contents of each chapter of the book are the following: Chapter 1. Introduction to Artificial Neural Networks-This chapter presents the biological basis necessary to understand how ANN works. It also describes the difference in the development of applications when using this approach in contrast with conventional Computing. Finally, it provides a description of the main historical events that have brought the progress in this discipline. Chapter 2. First Computational Models-As usual, most of the basic textbooks present a description of the first Neural Network architectures: Perceptron and Adaline. It finally presents the limitations of these processing elements in solving the XOR-problem.
Journal of Physics: Conference Series, 2023
Migrating from machine learning and deep learning into the next wave of technology will likely require biological replication rather than biological inspiration. An approach to achieving this requires duplicating entire nervous systems, or at least parts thereof. In theory, these artificial nervous systems (ANS) could reproduce everything required for a system to be biologically intelligent even to the point of being self-aware. This would additionally entail that the resultant systems have the ability to acquire information from both their internal and external environments as well as having the ability to act within the external environment using locomotion and manipulators. Robots are a natural answer for the resultant mechanism and if supplied with an artificial nervous system, the robot might be expected to achieve biologically modelled intelligence (BMI) and control. This paper will provide an overview of the tools for creating artificial nervous systems, as well as provide a roadmap for utilizing the tools to develop robots with general-purpose learning skills and biologically modelled intelligence.
Embodied Models and Neurorobotics
2016
Neuroscience has become a very broad field indeed: each year around 30,000 researchers and students from around the ... We trace a path from neuron to cognition via computational neuroscience, but what is computational neuroscience?
Artificial Life, 2000
We have created a hybrid neuro-robotic system that establishes two-way communication between the brain of a lamprey and a small mobile robot. The purpose of this system is to offer a new paradigm for investigating the behavioral, computational, and neurobiological mechanisms of sensory-motor learning in a uni ed context. The mobile robot acts as an arti cial body that delivers sensory information to the neural tissue and receives command signals from it. The sensory information encodes the intensity of light generated by a xed source. The closed-loop interaction between brain and robot generates autonomous behaviors whose features are strictly related to the structure and operation of the neural preparation. We provide a detailed description of the hybrid system, and we present experimental ndings on its performance. In particular, we found (a) that the hybrid system generates stable behaviors, (b) that different preparations display different but systematic responses to the presentation of an optical stimulus, and (c) that alteration of the sensory input leads to short-and long-term adaptive changes in the robot responses. The comparison of the behaviors generated by the lamprey's brain stem with the behaviors generated by network models of the same neural system provides us with a new tool for investigating the computational properties of synaptic plasticity.
A Biological Neural Network for Robotic Control - Towards a Human Neuroprocessor
The main objective of this work is to analyze the computing capabilities of human neuroblastoma cultured cells and to define stimulation patterns able to modulate the neural activity in response to external stimuli for controlling an autonomous robot. Multielectrode Arrays Setups have been designed for direct culturing neural cells over silicon or glass substrates, providing the capability to stimulate and record simultaneously populations of neural cells. This paper tries to modulate the natural physiologic responses of human neural cells by tetanic stimulation of the culture. If we are able to modify the selective responses of some cells with a external pattern stimuli over different time scales, the neuroblastoma-cultured structure could be trained to process pre-programmed spatio-temporal patterns. We show that the large neuroblastoma networks developed in cultured MEAs are capable of learning: stablishing numerous and dynamic connections, with modifiability induced by external stimuli.
Integrating robotics and neuroscience: brains for robots, bodies for brains
Advanced Robotics, 2007
Researchers in robotics and artificial intelligence have often looked at biology as a source of inspiration for solving their problems. From the opposite perspective, neuroscientists have recently turned their attention to the use of robotic systems as a way to quantitatively test and analyze theories that would otherwise remain at a speculative stage. Computational models of neurons and networks of neurons are often activated with simplified artificial patterns that bear little resemblance to natural stimuli. The use of robotic systems has the advantage of introducing phenotypic and environmental constraints similar to those that brains of animals have to face during development and in everyday life. Consideration of these constraints is particularly important in light of modern brain theories, which emphasize the importance of closing the perception/action loop between the agent and the environment. To provide concrete examples of the use of robotic systems in neuroscience, this paper reviews our work in the areas of sensory perception and motor learning. The interdisciplinary approach followed by this research establishes a direct link between natural sciences and engineering. This research can lead to the understanding of basic biological problems while producing robust and flexible systems that operate in the real world.
Controlling a Mobile Robot with a Biological Brain
Defence Science Journal, 2010
The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Some recent research is ongoing in which biological neurons are being cultured and trained to act as the brain of an interactive real world robotthereby either completely replacing, or operating in a cooperative fashion with, a computer system. Studying such hybrid systems can provide distinct insights into the operation of biological neural structures, and therefore, such research has immediate medical implications as well as enormous potential in robotics. The main aim of the research is to assess the computational and learning capacity of dissociated cultured neuronal networks. A hybrid system incorporating closed-loop control of a mobile robot by a dissociated culture of neurons has been created. The system is flexible and allows for closed-loop operation, either with hardware robot or its software simulation. The paper provides an overview of the problem area, gives an idea of the breadth of present ongoing research, establises a new system architecture and, as an example, reports on the results of conducted experiments with real-life robots.
Interaction and intelligence in living neuronal networks interfaced with moving robot
2006
Neurons form complex networks and it seems that the living neuronal network can perform certain type of information processing. We are interested in intelligence autonomously formed in vitro. The most important features of the two-dimensional culture neural network are that it is a system in which the information processing is autonomously carries out. We reported previously that the functional connections were dynamically modified by synaptic potentiation and the process may be required for reorganization of the functional group of neurons. Such neuron assemblies are critical for information processing in brain. Certain types of feedback stimulation caused suppression of spontaneous network electrical activities and drastic re-organization of functional connections between neurons, when these activities are initially almost synchronized. The result suggests that neurons in dissociated culture autonomously re-organized their functional neuronal networks interacted with their environment. The spatio-temporal pattern of activity in the networks may be a reflection of their external environment. We also interfaced the cultured neuronal network with moving robot. The planar microelectrodes can be used for detecting neuronal electrical signals from the living neuronal network cultured on a 2-dimensional electrode array. The speed of actuators of moving robot was determined by these detected signals. Our goal is reconstruction of the neural network, which can process "thinking" in the dissociated culture system.