Acquisition of logicality in living neuronal networks and its operation to fuzzy bio-robot system

Hayashi, Isao; Kiyotoki, Megumi; Kiyohara, Ai; Tokuda, Minori; Kudoh, Suguru N.

doi:10.1109/fuzzy.2010.5584887

Cited by 4 publications

(3 citation statements)

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“…to Proceedings of International Joint Conference on Neural Networks, San Jose, California, USA, July 31 -August 5, 2011 978-1-4244-9637-2/11/$26.00 ©2011 IEEE determine the action of) robot based on the responses from the living neuronal network. We estimated the learning of living neuronal networks with an example of straight running with neuro-robot hybrid [9]. We show the usefulness of a fuzzy bio-interface through some examples.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy bio-interface: Indicating logicality from living neuronal network and learning control of bio-robot

Hayashi

Kiyotoki

Kiyohara

et al. 2011

The 2011 International Joint Conference on Neural Networks

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Recently, many attractive brain-computer interface and brain-machine interface have been proposed. The outer computer and machine are controlled by brain action potentials detected through a device such as near-infrared spectroscopy (NIRS) and electroencephalograph (EEG), and some discriminant model determines a control process. In this paper, we introduce a fuzzy bio-interface between a culture dish of rat hippocampal neurons and the khepera robot. We propose a model to analyze logic of signals and connectivity of electrodes in a culture dish, and show the bio-robot hybrid we developed. We believe that the framework of fuzzy system is essential for BCI and BMI, thus name this technology "fuzzy bio-interface". We show the usefulness of a fuzzy bio-interface through some examples.

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Section: Introductionmentioning

confidence: 99%

“…We propose a model to analyze logic of signals and connectivity of electrodes in a culture dish, and show the bio-robot hybrid we developed [9], [10]. First, we discuss how to indicate the logicality and connectivity from living neuronal network in vitro [11].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy bio-interface: Indicating logicality from living neuronal network and learning control of bio-robot

Hayashi

Kiyotoki

Kiyohara

et al. 2011

The 2011 International Joint Conference on Neural Networks

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show abstract

“…In this paper, we propose a model to analyse logicality of signals and connectivity of electrodes in a culture dish of rat hippocampal neurons [14,15]. In addition, we introduce a "fuzzy bioindicator" to show activity in a culture dish.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Bio-Indicator: Evaluation of Logicality and Connectivity for Living Neuronal Network

Hayashi

Mitsumoto

Kudoh

2013

Proceedings of the 8th International Conference on Body Area Networks

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Recently, many attractive brain-computer interface (BCI) and brainmachine interface (BMI) have been proposed. It is necessary to predict activity of a neuronal network in the brain to control an outer equipment by BCI and BMI exactly. However, it is difficult for the action potential to identify from the input-output data because a neuronal network in the brain is multilayered structure, and the multilayer structure is too complex. Therefore we should discuss an analysis model of action potentials using a simple structure of neuronal network more like a culture dish. In this paper, we propose a model to analyse logicality of signals and connectivity of electrodes in a culture dish. In addition, we introduce a "fuzzy bio-indicator" to show activity of the culture dish of rat hippocampal neurons. We show here the usefulness of fuzzy bio-indicator through numerical examples and action potential detected from the culture neuronal network.

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Description of activity of living neuronal network by fuzzy bio-indicator

Hayashi

Kudoh

2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Acquisition of logicality in living neuronal networks and its operation to fuzzy bio-robot system

Cited by 4 publications

References 13 publications

Fuzzy bio-interface: Indicating logicality from living neuronal network and learning control of bio-robot

Fuzzy bio-interface: Indicating logicality from living neuronal network and learning control of bio-robot

Fuzzy Bio-Indicator: Evaluation of Logicality and Connectivity for Living Neuronal Network

Description of activity of living neuronal network by fuzzy bio-indicator

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