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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

PSTHs of the evoked responses in a cortical neuronalnetwork. (a) The post stimulus time histograms obtained in all the respondingchannels are reported over an 8 × 8 grid (i.e.,reproducing the layout of the MEA) after the stimulation from site 46—fourth column, sixth row. The small number reported in each boxrepresents the area of the histogram. Not responding channels are excluded. (b)Responses evoked in the network by stimulation from site 62. As it can beclearly notices the channels responding to site 46 respond also to channel 62,denoting an absence of strong selection with respect to the stimulatingelectrode. X-scale [ 0, 1 ]; Y-scale [ 0, 400 ] milliseconds.
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fig5: PSTHs of the evoked responses in a cortical neuronalnetwork. (a) The post stimulus time histograms obtained in all the respondingchannels are reported over an 8 × 8 grid (i.e.,reproducing the layout of the MEA) after the stimulation from site 46—fourth column, sixth row. The small number reported in each boxrepresents the area of the histogram. Not responding channels are excluded. (b)Responses evoked in the network by stimulation from site 62. As it can beclearly notices the channels responding to site 46 respond also to channel 62,denoting an absence of strong selection with respect to the stimulatingelectrode. X-scale [ 0, 1 ]; Y-scale [ 0, 400 ] milliseconds.

Mentions: Figure 5 shows the PSTHs obtained during the characterizationphase of one example experiment. The responses evoked from differentstimulation sites are similar (i.e., Figures 5(a) and 5(b)), thus revealing alow degree of selectivity and a high degree of connectivity. In a case like theone presented in Figure 5, the preparation can hardly be used to control therobot and it is discarded.


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)

PSTHs of the evoked responses in a cortical neuronalnetwork. (a) The post stimulus time histograms obtained in all the respondingchannels are reported over an 8 × 8 grid (i.e.,reproducing the layout of the MEA) after the stimulation from site 46—fourth column, sixth row. The small number reported in each boxrepresents the area of the histogram. Not responding channels are excluded. (b)Responses evoked in the network by stimulation from site 62. As it can beclearly notices the channels responding to site 46 respond also to channel 62,denoting an absence of strong selection with respect to the stimulatingelectrode. X-scale [ 0, 1 ]; Y-scale [ 0, 400 ] milliseconds.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: PSTHs of the evoked responses in a cortical neuronalnetwork. (a) The post stimulus time histograms obtained in all the respondingchannels are reported over an 8 × 8 grid (i.e.,reproducing the layout of the MEA) after the stimulation from site 46—fourth column, sixth row. The small number reported in each boxrepresents the area of the histogram. Not responding channels are excluded. (b)Responses evoked in the network by stimulation from site 62. As it can beclearly notices the channels responding to site 46 respond also to channel 62,denoting an absence of strong selection with respect to the stimulatingelectrode. X-scale [ 0, 1 ]; Y-scale [ 0, 400 ] milliseconds.
Mentions: Figure 5 shows the PSTHs obtained during the characterizationphase of one example experiment. The responses evoked from differentstimulation sites are similar (i.e., Figures 5(a) and 5(b)), thus revealing alow degree of selectivity and a high degree of connectivity. In a case like theone presented in Figure 5, the preparation can hardly be used to control therobot and it is discarded.

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