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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

Computing performances inthe Simulink implementation of the neurorobotic interface. Different colorscorrespond to different sampling rates (red = 10 kHz, green = 250 Hz, blue 5 Hz, yellow = mixed values),whereas the numbers indicate signal dimensions (i.e., in this case, we had 2inputs and 16 outputs). The percentages in each block indicate the relativesimulation time for each of the modules of the neurorobotic interface (2 inputsand 16 outputs). (b) Library of the modules that can be used in theneurorobotic interface. Each subset encloses Simulink blocks implementingdifferent algorithms for that purpose.
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fig3: Computing performances inthe Simulink implementation of the neurorobotic interface. Different colorscorrespond to different sampling rates (red = 10 kHz, green = 250 Hz, blue 5 Hz, yellow = mixed values),whereas the numbers indicate signal dimensions (i.e., in this case, we had 2inputs and 16 outputs). The percentages in each block indicate the relativesimulation time for each of the modules of the neurorobotic interface (2 inputsand 16 outputs). (b) Library of the modules that can be used in theneurorobotic interface. Each subset encloses Simulink blocks implementingdifferent algorithms for that purpose.

Mentions: The feedback loop computation time reached by ourfinal neurorobotic architecture is under 1 millisecond; therefore, thereal-time performance in the closed-loop system is compatible with the responsetime (4 ms) of our neuronal model. This value includes the time needed for (I)the electrophysiological signals acquisition, (II) the spike detection and theartifact suppression, (III) decoding of neural activity, (IV) computing of thespeeds of the robot's wheels, and (V) coding of sensory feedback. The relativecomputational loads for each block are displayed in Figure 3(a): the mosttime-consuming parts are those running at 10 kHz, for technical reasons, andthe blocks including sampling rate transitions, such as the interface with therobot, with the CCD camera system and with the stimulator. In theseexperiments, we used a robot with a standard RS232 interface that supports abaud rate of 9600 bit/s. We expect that by including a more recent protocol(e.g., USB2 or Firewire), the block would be less time consuming and wouldassure better performance. Spike detection and artifact blanking are alsotime-consuming due to the high dimension of the signals being processed. Theperformances were evaluated by means of Simulink Profiler, reportedschematically in Figure 3(b).


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)

Computing performances inthe Simulink implementation of the neurorobotic interface. Different colorscorrespond to different sampling rates (red = 10 kHz, green = 250 Hz, blue 5 Hz, yellow = mixed values),whereas the numbers indicate signal dimensions (i.e., in this case, we had 2inputs and 16 outputs). The percentages in each block indicate the relativesimulation time for each of the modules of the neurorobotic interface (2 inputsand 16 outputs). (b) Library of the modules that can be used in theneurorobotic interface. Each subset encloses Simulink blocks implementingdifferent algorithms for that purpose.
© Copyright Policy
Related In: Results  -  Collection

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

fig3: Computing performances inthe Simulink implementation of the neurorobotic interface. Different colorscorrespond to different sampling rates (red = 10 kHz, green = 250 Hz, blue 5 Hz, yellow = mixed values),whereas the numbers indicate signal dimensions (i.e., in this case, we had 2inputs and 16 outputs). The percentages in each block indicate the relativesimulation time for each of the modules of the neurorobotic interface (2 inputsand 16 outputs). (b) Library of the modules that can be used in theneurorobotic interface. Each subset encloses Simulink blocks implementingdifferent algorithms for that purpose.
Mentions: The feedback loop computation time reached by ourfinal neurorobotic architecture is under 1 millisecond; therefore, thereal-time performance in the closed-loop system is compatible with the responsetime (4 ms) of our neuronal model. This value includes the time needed for (I)the electrophysiological signals acquisition, (II) the spike detection and theartifact suppression, (III) decoding of neural activity, (IV) computing of thespeeds of the robot's wheels, and (V) coding of sensory feedback. The relativecomputational loads for each block are displayed in Figure 3(a): the mosttime-consuming parts are those running at 10 kHz, for technical reasons, andthe blocks including sampling rate transitions, such as the interface with therobot, with the CCD camera system and with the stimulator. In theseexperiments, we used a robot with a standard RS232 interface that supports abaud rate of 9600 bit/s. We expect that by including a more recent protocol(e.g., USB2 or Firewire), the block would be less time consuming and wouldassure better performance. Spike detection and artifact blanking are alsotime-consuming due to the high dimension of the signals being processed. Theperformances were evaluated by means of Simulink Profiler, reportedschematically in Figure 3(b).

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