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A Cerebellar Neuroprosthetic System: Computational Architecture and in vivo Test.

Herreros I, Giovannucci A, Taub AH, Hogri R, Magal A, Bamford S, Prueckl R, Verschure PF - Front Bioeng Biotechnol (2014)

Bottom Line: As a result, we show that the anesthetized rat, equipped with our neuroprosthetic system, can be classically conditioned to the acquisition of an eye-blink response.The resulting system represents an important step toward replacing lost functions of the central nervous system via neuroprosthetics, obtained by integrating a synthetic circuit with the afferent and efferent pathways of a damaged brain region.These results also embody an early example of science-based medicine, where on the one hand the neuroprosthetic system directly validates a theory of cerebellar learning that informed the design of the system, and on the other one it takes a step toward the development of neuro-prostheses that could recover lost learning functions in animals and, in the longer term, humans.

View Article: PubMed Central - PubMed

Affiliation: Synthetic Perceptive, Emotive and Cognitive Systems group (SPECS), Universitat Pompeu Fabra , Barcelona , Spain.

ABSTRACT
Emulating the input-output functions performed by a brain structure opens the possibility for developing neuroprosthetic systems that replace damaged neuronal circuits. Here, we demonstrate the feasibility of this approach by replacing the cerebellar circuit responsible for the acquisition and extinction of motor memories. Specifically, we show that a rat can undergo acquisition, retention, and extinction of the eye-blink reflex even though the biological circuit responsible for this task has been chemically inactivated via anesthesia. This is achieved by first developing a computational model of the cerebellar microcircuit involved in the acquisition of conditioned reflexes and training it with synthetic data generated based on physiological recordings. Secondly, the cerebellar model is interfaced with the brain of an anesthetized rat, connecting the model's inputs and outputs to afferent and efferent cerebellar structures. As a result, we show that the anesthetized rat, equipped with our neuroprosthetic system, can be classically conditioned to the acquisition of an eye-blink response. However, non-stationarities in the recorded biological signals limit the performance of the cerebellar model. Thus, we introduce an updated cerebellar model and validate it with physiological recordings showing that learning becomes stable and reliable. The resulting system represents an important step toward replacing lost functions of the central nervous system via neuroprosthetics, obtained by integrating a synthetic circuit with the afferent and efferent pathways of a damaged brain region. These results also embody an early example of science-based medicine, where on the one hand the neuroprosthetic system directly validates a theory of cerebellar learning that informed the design of the system, and on the other one it takes a step toward the development of neuro-prostheses that could recover lost learning functions in animals and, in the longer term, humans.

No MeSH data available.


Related in: MedlinePlus

Performance of the experiment predicted by the training data. (A) Trajectory of the memory parameter after 2500 simulations plotted in blocks of 10 trials. The simulated experiment contained 120 trials of acquisition and 180 trials of extinction. Distribution of the block-by-block values of w (grayscale) with mean (blue) and output of a sample simulation (red) are shown. We indicate the levels of the weight that result in late (upper green line) and well-timed CRs (lower green line). The transition from acquisition to extinction training is marked by a vertical line. (B) Predicted behavioral performance after 2500 simulations. Percentage of well-timed CRs. Distribution of the block-by-block performance (grayscale) with mean (blue) and result (red) of a sample simulation [same as in(A)].
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Figure 10: Performance of the experiment predicted by the training data. (A) Trajectory of the memory parameter after 2500 simulations plotted in blocks of 10 trials. The simulated experiment contained 120 trials of acquisition and 180 trials of extinction. Distribution of the block-by-block values of w (grayscale) with mean (blue) and output of a sample simulation (red) are shown. We indicate the levels of the weight that result in late (upper green line) and well-timed CRs (lower green line). The transition from acquisition to extinction training is marked by a vertical line. (B) Predicted behavioral performance after 2500 simulations. Percentage of well-timed CRs. Distribution of the block-by-block performance (grayscale) with mean (blue) and result (red) of a sample simulation [same as in(A)].

Mentions: The offline simulation parameterized with the previous values is shown in Figure 10. Firstly, on average acquisition occurs in 40 trials with an asymptotic performance of 40% well-timed CRs. Secondly, there is low chance of obtaining total extinction after 120 trials of CS-alone stimulation. Thus, the simulations predict that a low detection quality in the IO channel may hinder extinction.


A Cerebellar Neuroprosthetic System: Computational Architecture and in vivo Test.

Herreros I, Giovannucci A, Taub AH, Hogri R, Magal A, Bamford S, Prueckl R, Verschure PF - Front Bioeng Biotechnol (2014)

Performance of the experiment predicted by the training data. (A) Trajectory of the memory parameter after 2500 simulations plotted in blocks of 10 trials. The simulated experiment contained 120 trials of acquisition and 180 trials of extinction. Distribution of the block-by-block values of w (grayscale) with mean (blue) and output of a sample simulation (red) are shown. We indicate the levels of the weight that result in late (upper green line) and well-timed CRs (lower green line). The transition from acquisition to extinction training is marked by a vertical line. (B) Predicted behavioral performance after 2500 simulations. Percentage of well-timed CRs. Distribution of the block-by-block performance (grayscale) with mean (blue) and result (red) of a sample simulation [same as in(A)].
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4126458&req=5

Figure 10: Performance of the experiment predicted by the training data. (A) Trajectory of the memory parameter after 2500 simulations plotted in blocks of 10 trials. The simulated experiment contained 120 trials of acquisition and 180 trials of extinction. Distribution of the block-by-block values of w (grayscale) with mean (blue) and output of a sample simulation (red) are shown. We indicate the levels of the weight that result in late (upper green line) and well-timed CRs (lower green line). The transition from acquisition to extinction training is marked by a vertical line. (B) Predicted behavioral performance after 2500 simulations. Percentage of well-timed CRs. Distribution of the block-by-block performance (grayscale) with mean (blue) and result (red) of a sample simulation [same as in(A)].
Mentions: The offline simulation parameterized with the previous values is shown in Figure 10. Firstly, on average acquisition occurs in 40 trials with an asymptotic performance of 40% well-timed CRs. Secondly, there is low chance of obtaining total extinction after 120 trials of CS-alone stimulation. Thus, the simulations predict that a low detection quality in the IO channel may hinder extinction.

Bottom Line: As a result, we show that the anesthetized rat, equipped with our neuroprosthetic system, can be classically conditioned to the acquisition of an eye-blink response.The resulting system represents an important step toward replacing lost functions of the central nervous system via neuroprosthetics, obtained by integrating a synthetic circuit with the afferent and efferent pathways of a damaged brain region.These results also embody an early example of science-based medicine, where on the one hand the neuroprosthetic system directly validates a theory of cerebellar learning that informed the design of the system, and on the other one it takes a step toward the development of neuro-prostheses that could recover lost learning functions in animals and, in the longer term, humans.

View Article: PubMed Central - PubMed

Affiliation: Synthetic Perceptive, Emotive and Cognitive Systems group (SPECS), Universitat Pompeu Fabra , Barcelona , Spain.

ABSTRACT
Emulating the input-output functions performed by a brain structure opens the possibility for developing neuroprosthetic systems that replace damaged neuronal circuits. Here, we demonstrate the feasibility of this approach by replacing the cerebellar circuit responsible for the acquisition and extinction of motor memories. Specifically, we show that a rat can undergo acquisition, retention, and extinction of the eye-blink reflex even though the biological circuit responsible for this task has been chemically inactivated via anesthesia. This is achieved by first developing a computational model of the cerebellar microcircuit involved in the acquisition of conditioned reflexes and training it with synthetic data generated based on physiological recordings. Secondly, the cerebellar model is interfaced with the brain of an anesthetized rat, connecting the model's inputs and outputs to afferent and efferent cerebellar structures. As a result, we show that the anesthetized rat, equipped with our neuroprosthetic system, can be classically conditioned to the acquisition of an eye-blink response. However, non-stationarities in the recorded biological signals limit the performance of the cerebellar model. Thus, we introduce an updated cerebellar model and validate it with physiological recordings showing that learning becomes stable and reliable. The resulting system represents an important step toward replacing lost functions of the central nervous system via neuroprosthetics, obtained by integrating a synthetic circuit with the afferent and efferent pathways of a damaged brain region. These results also embody an early example of science-based medicine, where on the one hand the neuroprosthetic system directly validates a theory of cerebellar learning that informed the design of the system, and on the other one it takes a step toward the development of neuro-prostheses that could recover lost learning functions in animals and, in the longer term, humans.

No MeSH data available.


Related in: MedlinePlus