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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

Results with and without delayed NOI. (A) Raster plot with the output of the model with the delay of the NOI set to 0 s. (B) Trajectory of w in the model with a delay of 100 ms in the NOI (solid line) and with no delay (dashed line). The horizontal green dotted line marks the level above which the model does not trigger any CRs. Blocks of 10 trials.
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Figure 6: Results with and without delayed NOI. (A) Raster plot with the output of the model with the delay of the NOI set to 0 s. (B) Trajectory of w in the model with a delay of 100 ms in the NOI (solid line) and with no delay (dashed line). The horizontal green dotted line marks the level above which the model does not trigger any CRs. Blocks of 10 trials.

Mentions: The effects of the NOI latency on the timing of the CRs have already been discussed in the literature at the theoretical level (Hesslow and Ivarsson, 1994; Hofstotter et al., 2002), and demonstrated in experimental set-ups (Herreros Alonso and Verschure, 2013). Here, and because of the noisy input conditions, we see that if we do not apply any delay to the NOI, the triggered CR eventually anticipates the US, but by too short a latency too be considered effective (Figure 6A). Therefore, even though the model triggers CRs, they are maladaptively timed. Indeed, the synaptic efficacy w fails to reach a level sufficiently low to initiate well-timed CRs, as it does when the latency of the NOI is properly set (Figure 6B). Note, however, that the jitter of the trace of the synaptic efficacy w occasionally brings the CR triggers close to the criterion of correct timing. Given that, if such a jitter will be increased it would be possible for occasional CRs to anticipate the US sufficiently to be characterized as well-timed. This occurs if, for instance, the signal to noise ratio of the IO signal decreases (Figure 7B, with TDs in the IO lowered from 70 to 50%) or if we force the learning to be faster (Figure 7A). This by no means indicates that the model works better if the signal conditions are worse, it only indicates that as the dynamics of the model become more noisy (Figure 7C), some well-timed CRs may incidentally be triggered, even if the delay of the NOI is not correctly set.


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)

Results with and without delayed NOI. (A) Raster plot with the output of the model with the delay of the NOI set to 0 s. (B) Trajectory of w in the model with a delay of 100 ms in the NOI (solid line) and with no delay (dashed line). The horizontal green dotted line marks the level above which the model does not trigger any CRs. Blocks of 10 trials.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Results with and without delayed NOI. (A) Raster plot with the output of the model with the delay of the NOI set to 0 s. (B) Trajectory of w in the model with a delay of 100 ms in the NOI (solid line) and with no delay (dashed line). The horizontal green dotted line marks the level above which the model does not trigger any CRs. Blocks of 10 trials.
Mentions: The effects of the NOI latency on the timing of the CRs have already been discussed in the literature at the theoretical level (Hesslow and Ivarsson, 1994; Hofstotter et al., 2002), and demonstrated in experimental set-ups (Herreros Alonso and Verschure, 2013). Here, and because of the noisy input conditions, we see that if we do not apply any delay to the NOI, the triggered CR eventually anticipates the US, but by too short a latency too be considered effective (Figure 6A). Therefore, even though the model triggers CRs, they are maladaptively timed. Indeed, the synaptic efficacy w fails to reach a level sufficiently low to initiate well-timed CRs, as it does when the latency of the NOI is properly set (Figure 6B). Note, however, that the jitter of the trace of the synaptic efficacy w occasionally brings the CR triggers close to the criterion of correct timing. Given that, if such a jitter will be increased it would be possible for occasional CRs to anticipate the US sufficiently to be characterized as well-timed. This occurs if, for instance, the signal to noise ratio of the IO signal decreases (Figure 7B, with TDs in the IO lowered from 70 to 50%) or if we force the learning to be faster (Figure 7A). This by no means indicates that the model works better if the signal conditions are worse, it only indicates that as the dynamics of the model become more noisy (Figure 7C), some well-timed CRs may incidentally be triggered, even if the delay of the NOI is not correctly set.

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