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.


Connectivity maps (same data reported in Figure 4).The connectivity map represents a plot of the PSTH areas evoked by a couple ofstimulating electrodes on a specific electrode. In this way we are able torepresent the network response to a specific choice of stimulating sites. Theideal case should be to have two recording electrodes placed on the axis, farfrom the origin (i.e., maximum response to one stimulating electrode and zeroto the other). (a) Before the robotic experiment. (b) After the roboticexperiment.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2266971&req=5

fig6: Connectivity maps (same data reported in Figure 4).The connectivity map represents a plot of the PSTH areas evoked by a couple ofstimulating electrodes on a specific electrode. In this way we are able torepresent the network response to a specific choice of stimulating sites. Theideal case should be to have two recording electrodes placed on the axis, farfrom the origin (i.e., maximum response to one stimulating electrode and zeroto the other). (a) Before the robotic experiment. (b) After the roboticexperiment.

Mentions: Figure 6 presents an example of good connectivity mapsobtained during the characterization phase (Figure 6(a)) and after the roboticexperiment (Figure 6(b)): the electrodes 15 and 45, that will be further chosenas recording electrodes, are positioned close to the axis, indicating thattheir responses to the stimulating channel are selective (see Section 3.2 forfurther details).


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)

Connectivity maps (same data reported in Figure 4).The connectivity map represents a plot of the PSTH areas evoked by a couple ofstimulating electrodes on a specific electrode. In this way we are able torepresent the network response to a specific choice of stimulating sites. Theideal case should be to have two recording electrodes placed on the axis, farfrom the origin (i.e., maximum response to one stimulating electrode and zeroto the other). (a) Before the robotic experiment. (b) After the roboticexperiment.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: Connectivity maps (same data reported in Figure 4).The connectivity map represents a plot of the PSTH areas evoked by a couple ofstimulating electrodes on a specific electrode. In this way we are able torepresent the network response to a specific choice of stimulating sites. Theideal case should be to have two recording electrodes placed on the axis, farfrom the origin (i.e., maximum response to one stimulating electrode and zeroto the other). (a) Before the robotic experiment. (b) After the roboticexperiment.
Mentions: Figure 6 presents an example of good connectivity mapsobtained during the characterization phase (Figure 6(a)) and after the roboticexperiment (Figure 6(b)): the electrodes 15 and 45, that will be further chosenas recording electrodes, are positioned close to the axis, indicating thattheir responses to the stimulating channel are selective (see Section 3.2 forfurther details).

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.