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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

Observed performance vs. performance during simulated unpaired acquisition. (A) Acquisition during paired CS–US training versus simulated unpaired CS–US. Trajectory of the weight during the acquisition phase of the experiment (black line) plotted against results of 20,000 simulations of unpaired training. Distribution of the simulation results (grayscale), median (blue dotted line), and the 0.05 bottom of the distribution (red line) is shown. Blocks of 10 trials. (B) Behavioral performance during acquisition against performance in the simulations. Percentage of CRs during acquisition in the experiment (black line) plotted against the percentage obtained in the simulations. Distribution of the simulation performances (grayscale), median (blue dotted), and the upper 0.1 of the distribution (red line).
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Figure 15: Observed performance vs. performance during simulated unpaired acquisition. (A) Acquisition during paired CS–US training versus simulated unpaired CS–US. Trajectory of the weight during the acquisition phase of the experiment (black line) plotted against results of 20,000 simulations of unpaired training. Distribution of the simulation results (grayscale), median (blue dotted line), and the 0.05 bottom of the distribution (red line) is shown. Blocks of 10 trials. (B) Behavioral performance during acquisition against performance in the simulations. Percentage of CRs during acquisition in the experiment (black line) plotted against the percentage obtained in the simulations. Distribution of the simulation performances (grayscale), median (blue dotted), and the upper 0.1 of the distribution (red line).

Mentions: To perform an a posteriori control for this, we checked whether the observed oscillations in spontaneous activity may lead to acquisition by themselves even in the presence of temporally unrelated CSs and USs. We tested this by simulating unpaired CS–US presentations. For this, we generate experiments with shuffled IO detections within each trial. After performing 20,000 simulations, we observed that the increased spontaneous activity of the IO causes a decrement in w during the acquisition phase for unpaired stimulation (Figure 15A). Considering the behavior (Figure 15B) in the average simulation, the decrease of w yielded to the triggering of a small number of CRs. These results both at the level of the memory parameter and the behavior were clearly below the performance observed on the bio-hybrid experiment but demonstrated nonetheless that under experimental conditions with big variability in the recorded signals, the model might acquire spurious associations.


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)

Observed performance vs. performance during simulated unpaired acquisition. (A) Acquisition during paired CS–US training versus simulated unpaired CS–US. Trajectory of the weight during the acquisition phase of the experiment (black line) plotted against results of 20,000 simulations of unpaired training. Distribution of the simulation results (grayscale), median (blue dotted line), and the 0.05 bottom of the distribution (red line) is shown. Blocks of 10 trials. (B) Behavioral performance during acquisition against performance in the simulations. Percentage of CRs during acquisition in the experiment (black line) plotted against the percentage obtained in the simulations. Distribution of the simulation performances (grayscale), median (blue dotted), and the upper 0.1 of the distribution (red line).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 15: Observed performance vs. performance during simulated unpaired acquisition. (A) Acquisition during paired CS–US training versus simulated unpaired CS–US. Trajectory of the weight during the acquisition phase of the experiment (black line) plotted against results of 20,000 simulations of unpaired training. Distribution of the simulation results (grayscale), median (blue dotted line), and the 0.05 bottom of the distribution (red line) is shown. Blocks of 10 trials. (B) Behavioral performance during acquisition against performance in the simulations. Percentage of CRs during acquisition in the experiment (black line) plotted against the percentage obtained in the simulations. Distribution of the simulation performances (grayscale), median (blue dotted), and the upper 0.1 of the distribution (red line).
Mentions: To perform an a posteriori control for this, we checked whether the observed oscillations in spontaneous activity may lead to acquisition by themselves even in the presence of temporally unrelated CSs and USs. We tested this by simulating unpaired CS–US presentations. For this, we generate experiments with shuffled IO detections within each trial. After performing 20,000 simulations, we observed that the increased spontaneous activity of the IO causes a decrement in w during the acquisition phase for unpaired stimulation (Figure 15A). Considering the behavior (Figure 15B) in the average simulation, the decrease of w yielded to the triggering of a small number of CRs. These results both at the level of the memory parameter and the behavior were clearly below the performance observed on the bio-hybrid experiment but demonstrated nonetheless that under experimental conditions with big variability in the recorded signals, the model might acquire spurious associations.

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