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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 with the adaptive calibration method. (A) Trajectory of the memory parameter w plotted in blocks of 10 trials. Distribution of the performance of the simulations of unpaired CS–US stimulation (grayscale), with mean (dotted blue) and lower 10% (dotted red), for a total of 2500 simulations of 36 blocks. Trajectory of the simulated classical conditioning experiment (solid black), with 18 blocks of acquisition and 18 blocks of extinction. The transition from acquisition to extinction training is marked by a vertical line. (B) Behavioral performance of the same simulations. Percentage of well-timed CRs per block is shown. Results plotted with the same convention as in (A).
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Figure 16: Performance with the adaptive calibration method. (A) Trajectory of the memory parameter w plotted in blocks of 10 trials. Distribution of the performance of the simulations of unpaired CS–US stimulation (grayscale), with mean (dotted blue) and lower 10% (dotted red), for a total of 2500 simulations of 36 blocks. Trajectory of the simulated classical conditioning experiment (solid black), with 18 blocks of acquisition and 18 blocks of extinction. The transition from acquisition to extinction training is marked by a vertical line. (B) Behavioral performance of the same simulations. Percentage of well-timed CRs per block is shown. Results plotted with the same convention as in (A).

Mentions: The results (Figure 16) now clearly separate the performance between the unpaired- and paired-stimulation experiments. Most importantly, there is no acquisition of CRs during unpaired stimulation. In this case, the fluctuations of the baseline IO rate do not push w further than ±0.1 from the starting value, both during acquisition and extinction. Regarding the performance, in average there are no well-timed CRs with unpaired CS–US training. On the contrary, in the simulated acquisition and extinction experiment, the overall behavior of acquisition followed by extinction is preserved. In this experiment, the CR performance decreases relative to the result with the bio-hybrid, especially by the end of acquisition, when the recorded IO baseline rate was higher. The reason is that now a high rate of spontaneous detections in the IO diminishes the relative saliency of the US-evoked events, because such a high rate harms rather than helps acquisition. Thus, this simulation confirms that if the same conditions of signal instability of the bio-hybrid experiment are repeated, with the adaptive calibration method we can ensure that any CRs observed will specifically be due to the CS–US association.


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 with the adaptive calibration method. (A) Trajectory of the memory parameter w plotted in blocks of 10 trials. Distribution of the performance of the simulations of unpaired CS–US stimulation (grayscale), with mean (dotted blue) and lower 10% (dotted red), for a total of 2500 simulations of 36 blocks. Trajectory of the simulated classical conditioning experiment (solid black), with 18 blocks of acquisition and 18 blocks of extinction. The transition from acquisition to extinction training is marked by a vertical line. (B) Behavioral performance of the same simulations. Percentage of well-timed CRs per block is shown. Results plotted with the same convention as in (A).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 16: Performance with the adaptive calibration method. (A) Trajectory of the memory parameter w plotted in blocks of 10 trials. Distribution of the performance of the simulations of unpaired CS–US stimulation (grayscale), with mean (dotted blue) and lower 10% (dotted red), for a total of 2500 simulations of 36 blocks. Trajectory of the simulated classical conditioning experiment (solid black), with 18 blocks of acquisition and 18 blocks of extinction. The transition from acquisition to extinction training is marked by a vertical line. (B) Behavioral performance of the same simulations. Percentage of well-timed CRs per block is shown. Results plotted with the same convention as in (A).
Mentions: The results (Figure 16) now clearly separate the performance between the unpaired- and paired-stimulation experiments. Most importantly, there is no acquisition of CRs during unpaired stimulation. In this case, the fluctuations of the baseline IO rate do not push w further than ±0.1 from the starting value, both during acquisition and extinction. Regarding the performance, in average there are no well-timed CRs with unpaired CS–US training. On the contrary, in the simulated acquisition and extinction experiment, the overall behavior of acquisition followed by extinction is preserved. In this experiment, the CR performance decreases relative to the result with the bio-hybrid, especially by the end of acquisition, when the recorded IO baseline rate was higher. The reason is that now a high rate of spontaneous detections in the IO diminishes the relative saliency of the US-evoked events, because such a high rate harms rather than helps acquisition. Thus, this simulation confirms that if the same conditions of signal instability of the bio-hybrid experiment are repeated, with the adaptive calibration method we can ensure that any CRs observed will specifically be due to the CS–US association.

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