Limits...
Connecting neurons to a mobile robot: an in vitro bidirectional neural interface.

Novellino A, D'Angelo P, Cozzi L, Chiappalone M, Sanguineti V, Martinoia S - Comput Intell Neurosci (2007)

Bottom Line: These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body.In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested.This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

View Article: PubMed Central - PubMed

Affiliation: Neuroengineering and Bio-nanotechnology Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genova, Via Opera Pia 11a, 16145 Genova, Italy. antonio.novellino@ettsolutions.com

ABSTRACT
One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason "embodiment" represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

No MeSH data available.


Related in: MedlinePlus

Computationalarchitecture of the closed-loop system. The signals coming from the infraredsensors (IR) of the robot are translated into patterns of stimuli that aredelivered to the neural preparation through a set of selected stimulatingelectrodes. Then the activity recorded by two groups of electrodes is evaluatedin terms of firing rate (i.e., mean number of detected spikes/s) and used asdriving speed for each of the robot's wheel.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2266971&req=5

fig2: Computationalarchitecture of the closed-loop system. The signals coming from the infraredsensors (IR) of the robot are translated into patterns of stimuli that aredelivered to the neural preparation through a set of selected stimulatingelectrodes. Then the activity recorded by two groups of electrodes is evaluatedin terms of firing rate (i.e., mean number of detected spikes/s) and used asdriving speed for each of the robot's wheel.

Mentions: To establish a bidirectional communication between theneuronal preparation and a mobile robot, the electrophysiological signals needto be translated into motor commands for the robot (decoding of neuralactivity), and at the same time the sensory signal from the robot need to betranslated into a pattern of electrical stimulation (coding of sensoryinformation). Figure 2 presents the general computational architecture of theproposed closed-loop system that can be summarized in the following three mainparts (i.e., from left to right in Figure 2).


Connecting neurons to a mobile robot: an in vitro bidirectional neural interface.

Novellino A, D'Angelo P, Cozzi L, Chiappalone M, Sanguineti V, Martinoia S - Comput Intell Neurosci (2007)

Computationalarchitecture of the closed-loop system. The signals coming from the infraredsensors (IR) of the robot are translated into patterns of stimuli that aredelivered to the neural preparation through a set of selected stimulatingelectrodes. Then the activity recorded by two groups of electrodes is evaluatedin terms of firing rate (i.e., mean number of detected spikes/s) and used asdriving speed for each of the robot's wheel.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2266971&req=5

fig2: Computationalarchitecture of the closed-loop system. The signals coming from the infraredsensors (IR) of the robot are translated into patterns of stimuli that aredelivered to the neural preparation through a set of selected stimulatingelectrodes. Then the activity recorded by two groups of electrodes is evaluatedin terms of firing rate (i.e., mean number of detected spikes/s) and used asdriving speed for each of the robot's wheel.
Mentions: To establish a bidirectional communication between theneuronal preparation and a mobile robot, the electrophysiological signals needto be translated into motor commands for the robot (decoding of neuralactivity), and at the same time the sensory signal from the robot need to betranslated into a pattern of electrical stimulation (coding of sensoryinformation). Figure 2 presents the general computational architecture of theproposed closed-loop system that can be summarized in the following three mainparts (i.e., from left to right in Figure 2).

Bottom Line: These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body.In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested.This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

View Article: PubMed Central - PubMed

Affiliation: Neuroengineering and Bio-nanotechnology Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genova, Via Opera Pia 11a, 16145 Genova, Italy. antonio.novellino@ettsolutions.com

ABSTRACT
One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason "embodiment" represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

No MeSH data available.


Related in: MedlinePlus