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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

Intrinsic latencies of the eye-blink conditioning preparation. (A) ISI, inter-stimulus interval; ωCS, latency between the peripheral CS stimulation and the detection of its associated neuronal response in the PN; tCR, internal response timing learned by the model between the CS detection and the CR triggering; ωCR, latency between the neuronal triggering of the CR and the effective eyelid closure, Λnoi, delay between the CR trigger and the onset of the negative feedback loop inhibition; ωUS, latency between the US-trigger and the detection of its associated neural response in the IO. (B) Same latencies as in (A) for the minimum learnable ISI.
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Figure 2: Intrinsic latencies of the eye-blink conditioning preparation. (A) ISI, inter-stimulus interval; ωCS, latency between the peripheral CS stimulation and the detection of its associated neuronal response in the PN; tCR, internal response timing learned by the model between the CS detection and the CR triggering; ωCR, latency between the neuronal triggering of the CR and the effective eyelid closure, Λnoi, delay between the CR trigger and the onset of the negative feedback loop inhibition; ωUS, latency between the US-trigger and the detection of its associated neural response in the IO. (B) Same latencies as in (A) for the minimum learnable ISI.

Mentions: It is well-known in the domain of control theory that the latencies and delays inherent in a system to be controlled play an important role in the design of the controller. Here, our controller is based on the cerebellar microcircuit involved in eye-blink conditioning. In nature, such a microcircuit must have internalized the latencies to the eye-blink system in several ways, one of them arguably being through the unusually long latency of the NOI (Hesslow, 1986) that we had previously interpreted as allowing for the matching of the system delays (Hofstotter et al., 2002). Informally, once an error signal reaches the IO, such a delay indicates how far ahead of it the cerebellum should have taken a protective action for it to be effective. Consistently with this view, in the computational model that we employ the latency between the activation of the deep nucleus (DN) and the onset of the inhibition of the IO (the NOI delay, Λnoi) sets the anticipation of the CR execution relative to the expected US arrival (Hofstotter et al., 2002) (see Figure 2). Therefore, we will first discuss the latencies associated with the task of classical conditioning, since their specific properties underlie the cerebellar computational model.


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)

Intrinsic latencies of the eye-blink conditioning preparation. (A) ISI, inter-stimulus interval; ωCS, latency between the peripheral CS stimulation and the detection of its associated neuronal response in the PN; tCR, internal response timing learned by the model between the CS detection and the CR triggering; ωCR, latency between the neuronal triggering of the CR and the effective eyelid closure, Λnoi, delay between the CR trigger and the onset of the negative feedback loop inhibition; ωUS, latency between the US-trigger and the detection of its associated neural response in the IO. (B) Same latencies as in (A) for the minimum learnable ISI.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Intrinsic latencies of the eye-blink conditioning preparation. (A) ISI, inter-stimulus interval; ωCS, latency between the peripheral CS stimulation and the detection of its associated neuronal response in the PN; tCR, internal response timing learned by the model between the CS detection and the CR triggering; ωCR, latency between the neuronal triggering of the CR and the effective eyelid closure, Λnoi, delay between the CR trigger and the onset of the negative feedback loop inhibition; ωUS, latency between the US-trigger and the detection of its associated neural response in the IO. (B) Same latencies as in (A) for the minimum learnable ISI.
Mentions: It is well-known in the domain of control theory that the latencies and delays inherent in a system to be controlled play an important role in the design of the controller. Here, our controller is based on the cerebellar microcircuit involved in eye-blink conditioning. In nature, such a microcircuit must have internalized the latencies to the eye-blink system in several ways, one of them arguably being through the unusually long latency of the NOI (Hesslow, 1986) that we had previously interpreted as allowing for the matching of the system delays (Hofstotter et al., 2002). Informally, once an error signal reaches the IO, such a delay indicates how far ahead of it the cerebellum should have taken a protective action for it to be effective. Consistently with this view, in the computational model that we employ the latency between the activation of the deep nucleus (DN) and the onset of the inhibition of the IO (the NOI delay, Λnoi) sets the anticipation of the CR execution relative to the expected US arrival (Hofstotter et al., 2002) (see Figure 2). Therefore, we will first discuss the latencies associated with the task of classical conditioning, since their specific properties underlie the cerebellar computational model.

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