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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

Robot trajectories and performances in a neuroroboticexperiment. (a) Robot trajectory during the first free running phase. (b) Robottrajectory during the last free running phase (i.e., after the learning phase).(c) Indicators of the robot's performance. The last two parameters only showthat the two phases are comparable in terms of covered space and trajectorylength during the robot's movement inside the arena. For this reason, thereduction of hits in the second phase (i.e., first parameter) suggests animprovement of performances during the obstacle-avoidance task. The conclusionis that an improvement in the robot's behavior in terms of a decreased numberof hits must depend from the modulation of the neuronal activity, as alsoconfirmed by the graphs of the STS presented in the previous figure.
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fig8: Robot trajectories and performances in a neuroroboticexperiment. (a) Robot trajectory during the first free running phase. (b) Robottrajectory during the last free running phase (i.e., after the learning phase).(c) Indicators of the robot's performance. The last two parameters only showthat the two phases are comparable in terms of covered space and trajectorylength during the robot's movement inside the arena. For this reason, thereduction of hits in the second phase (i.e., first parameter) suggests animprovement of performances during the obstacle-avoidance task. The conclusionis that an improvement in the robot's behavior in terms of a decreased numberof hits must depend from the modulation of the neuronal activity, as alsoconfirmed by the graphs of the STS presented in the previous figure.

Mentions: Examples of the robot trajectories are presented inFigures Figure 8(a) and Figure 8(b). Figure 8(c) shows the indicators generally used forquantifying the robot performance. The first indicator alone, that is, thenumber of hits, is not sufficient for describing the performances of anobstacle avoidance task. In fact, a low number of hits could result fromlimited robot movements or from the repetition of the same trajectory. For thisreason, it is necessary to consider also the fraction of space covered by therobot and the length of its trajectory. Together, these simple indicatorsevaluate the robot performances inside the arena, even if they are not relatedto the sensory feedback coming from the external environment. These parametersdo not allow quantifying any relationship between the motor response and thesensory information, but, considering different phases, if the robot coveredthe same area and the trajectories are in the same order, then the two phasesare comparable, and a reduction of the number of hits should indicate animprovement of the robot's behavior. An improvement in the robot's behaviormust correspond to an improvement in the relationship between the motorresponse and the feedback sensory information (i.e., the STSs). The STS is theonly parameter that permits to understand and demonstrate whether a differentbehavior of the robot actually corresponds to a different dynamics of theneuronal activity, and for this reason it can be considered the best indicatorof the performance of the overall neurorobotic system.


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)

Robot trajectories and performances in a neuroroboticexperiment. (a) Robot trajectory during the first free running phase. (b) Robottrajectory during the last free running phase (i.e., after the learning phase).(c) Indicators of the robot's performance. The last two parameters only showthat the two phases are comparable in terms of covered space and trajectorylength during the robot's movement inside the arena. For this reason, thereduction of hits in the second phase (i.e., first parameter) suggests animprovement of performances during the obstacle-avoidance task. The conclusionis that an improvement in the robot's behavior in terms of a decreased numberof hits must depend from the modulation of the neuronal activity, as alsoconfirmed by the graphs of the STS presented in the previous figure.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2266971&req=5

fig8: Robot trajectories and performances in a neuroroboticexperiment. (a) Robot trajectory during the first free running phase. (b) Robottrajectory during the last free running phase (i.e., after the learning phase).(c) Indicators of the robot's performance. The last two parameters only showthat the two phases are comparable in terms of covered space and trajectorylength during the robot's movement inside the arena. For this reason, thereduction of hits in the second phase (i.e., first parameter) suggests animprovement of performances during the obstacle-avoidance task. The conclusionis that an improvement in the robot's behavior in terms of a decreased numberof hits must depend from the modulation of the neuronal activity, as alsoconfirmed by the graphs of the STS presented in the previous figure.
Mentions: Examples of the robot trajectories are presented inFigures Figure 8(a) and Figure 8(b). Figure 8(c) shows the indicators generally used forquantifying the robot performance. The first indicator alone, that is, thenumber of hits, is not sufficient for describing the performances of anobstacle avoidance task. In fact, a low number of hits could result fromlimited robot movements or from the repetition of the same trajectory. For thisreason, it is necessary to consider also the fraction of space covered by therobot and the length of its trajectory. Together, these simple indicatorsevaluate the robot performances inside the arena, even if they are not relatedto the sensory feedback coming from the external environment. These parametersdo not allow quantifying any relationship between the motor response and thesensory information, but, considering different phases, if the robot coveredthe same area and the trajectories are in the same order, then the two phasesare comparable, and a reduction of the number of hits should indicate animprovement of the robot's behavior. An improvement in the robot's behaviormust correspond to an improvement in the relationship between the motorresponse and the feedback sensory information (i.e., the STSs). The STS is theonly parameter that permits to understand and demonstrate whether a differentbehavior of the robot actually corresponds to a different dynamics of theneuronal activity, and for this reason it can be considered the best indicatorof the performance of the overall neurorobotic system.

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