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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

Quantitative results. (A) Events detected in the PN. Histogram of PN detections relative to the CS-trigger: TDs (black bars) and FAs (gray bars); in this case all FAs are late CS-detections. CS period (yellow area) and US period (pink area). (B) Events detected in the IO. Detections in the IO sorted relative to the US-trigger. Data plotted as in (A). (C) Behavioral performance of the bio-hybrid. Percentage of well-timed CRs during acquisition and extinction (solid line) are shown. CRs that were not triggered at least 20 ms ahead of the US-trigger appear as late CRs (dashed line). Each block contains 10 trials. (D) Timing of CRs. Histogram of the CRs: well-timed (black bars) and late ones (gray bars). CS period (yellow area) and US period (pink area) are indicated. The information is extracted from trials 118 to 190.
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Figure 12: Quantitative results. (A) Events detected in the PN. Histogram of PN detections relative to the CS-trigger: TDs (black bars) and FAs (gray bars); in this case all FAs are late CS-detections. CS period (yellow area) and US period (pink area). (B) Events detected in the IO. Detections in the IO sorted relative to the US-trigger. Data plotted as in (A). (C) Behavioral performance of the bio-hybrid. Percentage of well-timed CRs during acquisition and extinction (solid line) are shown. CRs that were not triggered at least 20 ms ahead of the US-trigger appear as late CRs (dashed line). Each block contains 10 trials. (D) Timing of CRs. Histogram of the CRs: well-timed (black bars) and late ones (gray bars). CS period (yellow area) and US period (pink area) are indicated. The information is extracted from trials 118 to 190.

Mentions: Detections in both channels were delayed by tens of milliseconds with respect to the stimulus trigger. The mean latency of the TDs in the PN (ωCS) was of 96.2 ms after the CS-trigger (Figure 12A) whereas the mean latency in the IO channel (ωUS) was of 68.5 ms (Figure 12B).


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)

Quantitative results. (A) Events detected in the PN. Histogram of PN detections relative to the CS-trigger: TDs (black bars) and FAs (gray bars); in this case all FAs are late CS-detections. CS period (yellow area) and US period (pink area). (B) Events detected in the IO. Detections in the IO sorted relative to the US-trigger. Data plotted as in (A). (C) Behavioral performance of the bio-hybrid. Percentage of well-timed CRs during acquisition and extinction (solid line) are shown. CRs that were not triggered at least 20 ms ahead of the US-trigger appear as late CRs (dashed line). Each block contains 10 trials. (D) Timing of CRs. Histogram of the CRs: well-timed (black bars) and late ones (gray bars). CS period (yellow area) and US period (pink area) are indicated. The information is extracted from trials 118 to 190.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 12: Quantitative results. (A) Events detected in the PN. Histogram of PN detections relative to the CS-trigger: TDs (black bars) and FAs (gray bars); in this case all FAs are late CS-detections. CS period (yellow area) and US period (pink area). (B) Events detected in the IO. Detections in the IO sorted relative to the US-trigger. Data plotted as in (A). (C) Behavioral performance of the bio-hybrid. Percentage of well-timed CRs during acquisition and extinction (solid line) are shown. CRs that were not triggered at least 20 ms ahead of the US-trigger appear as late CRs (dashed line). Each block contains 10 trials. (D) Timing of CRs. Histogram of the CRs: well-timed (black bars) and late ones (gray bars). CS period (yellow area) and US period (pink area) are indicated. The information is extracted from trials 118 to 190.
Mentions: Detections in both channels were delayed by tens of milliseconds with respect to the stimulus trigger. The mean latency of the TDs in the PN (ωCS) was of 96.2 ms after the CS-trigger (Figure 12A) whereas the mean latency in the IO channel (ωUS) was of 68.5 ms (Figure 12B).

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