A biological neural network drives a robotic actuator (original) (raw)
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Learning in human neural networks on microelectrode arrays
Biosystems, 2007
This paper describes experiments involving the growth of human neural networks of stem cells on a MEA (microelectrode array) support. The microelectrode arrays (MEAs) are constituted by a glass support in which a set of tungsten electrodes are inserted. The artificial neural network (ANN) paradigm was used by stimulating the neurons in parallel with digital patterns distributed on eight channels, then by analyzing a parallel multichannel output. In particular, the microelectrodes were connected following two different architectures, one inspired by the Kohonen's SOM, the other by the Hopfield network. The output signals have been analyzed in order to evaluate the possibility of organized reactions by the natural neurons. The results show that the network of human neurons reacts selectively to the subministered digital signals, i.e., it produces similar output signals referred to identical or similar patterns, and clearly differentiates the outputs coming from different stimulations. Analyses performed with a special artificial neural network called ITSOM show the possibility to codify the neural responses to different patterns, thus to interpret the signals coming from the network of biological neurons, assigning a code to each output. It is straightforward to verify that identical codes are generated by the neural reactions to similar patterns. Further experiments are to be designed that improve the hybrid neural networks' capabilities and to test the possibility of utilizing the organized answers of the neurons in several ways.
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.
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.
Behavior of living human neural networks on microelectrode array support
Proceedings Nanotechnology Conference and Trade Show, Boston, 2004
Researchers of the Department of Information Technologies of the University of Milano and of the Stem Cells Research Institute of the DIBIT-S. Raffaele Milano are experimenting the growth of human neural networks of stem cells on a MEA (Microelectrode Array) support. The Microelectrode arrays (MEAs) are constituted by a glass support where a set of tungsten electrodes is inserted. We connected the microelectrodes following the architecture of classical artificial neural networks, in particular Kohonen and Hopfield ...
A cultured human neural network operates a robotic actuator
Biosystems, 2009
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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.
14 Neuro-Engineering: from neural interfaces to biological computers
2001
Abstract. In this chapter, we report on the state of the art in the technology of neural interfaces, and describe in some detail a number of on-going projects, in which they are used to explore the neurobiology of learning and memory. In particular, it is proposed to use artificial multi-sensory information coming from a roving robot that interacts with the environment, under the perspective of feeding an in vitro network of real neurons with a set of time and spacedependent signal resembling those processed by the nervous system.
A hybrid creature learns to move
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 [1]. Analyses performed with a novel Artificial Neural Network called ITSOM showed the possibility of decoding 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, which drives a minirobot. Key-Words: Neurons, MicroElectrode Arrays, Stem Cells, Bionics, Artificial Neural Networks
Interaction and Intelligence in Living Neuronal Networks Connected to Moving Robot
2006
The temporal patterns of spontaneous action potentials are analyzed, using the multi-site recording system for extracellular potentials of neurons and the living neuronal networks cultured on a 2-dimensional electrode arrays. We carried out the system integration for Khepera II robot and living neuronal network. We call the system as "biomodeling system". Our goal is reconstruction of the neuronal network, which can process "thinking" in the dissociated culture system.
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.