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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

PSTH and STS in a neurorobotic experiment. (a) PSTHsfor two electrodes (chosen as recording —motor electrodes) with respectto two stimulating sites. Electrode 15 responds well to stimulation from electrode16 and bad to stimulation from electrode 48; on the contrary electrode 45responds well to 48 and bad to 16. This tendency is maintained and evenimproved after the robotic experiment (b). The STS graphs before (c) and after(d) the robotic experiment prove again the selectivity of the chosen electrodesand the improvement in the performances (increased STS area).
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fig7: PSTH and STS in a neurorobotic experiment. (a) PSTHsfor two electrodes (chosen as recording —motor electrodes) with respectto two stimulating sites. Electrode 15 responds well to stimulation from electrode16 and bad to stimulation from electrode 48; on the contrary electrode 45responds well to 48 and bad to 16. This tendency is maintained and evenimproved after the robotic experiment (b). The STS graphs before (c) and after(d) the robotic experiment prove again the selectivity of the chosen electrodesand the improvement in the performances (increased STS area).

Mentions: Figures Figure7(a) and Figure7(b) show the PSTHs corresponding tothe inputs/outputs chosen after the characterization phase; during anexperimental session with the robot (i.e., experiment is different from theprevious one). In this example, the recording electrode 15 is very sensitive tothe stimulation delivered from electrode 16 (top left) while it is quiteunaffected by stimuli delivered form electrode 48 (top right). At the sametime, the recording site 45 is not sensitive to stimuli coming from electrode16 (bottom left) while it is very affected by stimulation from electrode 48. Inthis case, different stimulation sites evoke very different response, thusrevealing a high degree of selectivity that is also confirmed by theconnectivity maps presented in Figure 6.


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)

PSTH and STS in a neurorobotic experiment. (a) PSTHsfor two electrodes (chosen as recording —motor electrodes) with respectto two stimulating sites. Electrode 15 responds well to stimulation from electrode16 and bad to stimulation from electrode 48; on the contrary electrode 45responds well to 48 and bad to 16. This tendency is maintained and evenimproved after the robotic experiment (b). The STS graphs before (c) and after(d) the robotic experiment prove again the selectivity of the chosen electrodesand the improvement in the performances (increased STS area).
© Copyright Policy
Related In: Results  -  Collection

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

fig7: PSTH and STS in a neurorobotic experiment. (a) PSTHsfor two electrodes (chosen as recording —motor electrodes) with respectto two stimulating sites. Electrode 15 responds well to stimulation from electrode16 and bad to stimulation from electrode 48; on the contrary electrode 45responds well to 48 and bad to 16. This tendency is maintained and evenimproved after the robotic experiment (b). The STS graphs before (c) and after(d) the robotic experiment prove again the selectivity of the chosen electrodesand the improvement in the performances (increased STS area).
Mentions: Figures Figure7(a) and Figure7(b) show the PSTHs corresponding tothe inputs/outputs chosen after the characterization phase; during anexperimental session with the robot (i.e., experiment is different from theprevious one). In this example, the recording electrode 15 is very sensitive tothe stimulation delivered from electrode 16 (top left) while it is quiteunaffected by stimuli delivered form electrode 48 (top right). At the sametime, the recording site 45 is not sensitive to stimuli coming from electrode16 (bottom left) while it is very affected by stimulation from electrode 48. Inthis case, different stimulation sites evoke very different response, thusrevealing a high degree of selectivity that is also confirmed by theconnectivity maps presented in Figure 6.

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