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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

Functional model of the cerebellum. The processes in the top row (white boxes) map PN activity into action; in the case of eye-blink conditioning, tone detections into eye-blinks. Such mapping is controlled by the memory parameter w. The shaded processes adapt the mapping, namely, they are involved in the adjustment of w. The numbers identify specific processes. The latencies affecting each of the recording and stimulating channels as well as the parameters used in each process (see main text for an explanation).
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Figure 3: Functional model of the cerebellum. The processes in the top row (white boxes) map PN activity into action; in the case of eye-blink conditioning, tone detections into eye-blinks. Such mapping is controlled by the memory parameter w. The shaded processes adapt the mapping, namely, they are involved in the adjustment of w. The numbers identify specific processes. The latencies affecting each of the recording and stimulating channels as well as the parameters used in each process (see main text for an explanation).

Mentions: The trace generation, scaling, and thresholding processes (1, 2, and 3 in Figure 3) model the processing of information that enters the cerebellum via the mossy fibers and leaves it through the excitatory axons of deep nuclear cells that projects to red nucleus which, in turn, excites FN (Hesslow and Yeo, 2002; Christian and Thompson, 2003). The trace generation (1) process codes the time since the CS onset with a decaying trace having a fixed initial value (τ0), final value (τ1), and duration (Λτ). This trace defines the memory span of the system; i.e., the maximum temporal gap between CS detection and a CR execution learnable by the system. The Scaling (2) process multiplies the trace with the memory parameter w, which is the only parameter modified by learning. With w, we mimic the changes in synaptic efficacy that occur in the molecular layer, due to LTD in the parallel fiber to Purkinje cell synapse and/or other kinds of associative plasticity (Dean et al., 2010). Lastly, the thresholding (3) process triggers a CR whenever the value of the scaled trace falls below a decision threshold (θCR). Within this process, we collapse all the transductions that occur postsynaptically from the Purkinje cells down to the efferents of the deep nuclei. In short, these three processes map event detections in the PN into stimulation of the rat FN. The parameter w regulates the mapping and, by scaling the trace signal, controls whether a response is triggered or not, and if so, determines its timing in a way analogous to the biological system.


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)

Functional model of the cerebellum. The processes in the top row (white boxes) map PN activity into action; in the case of eye-blink conditioning, tone detections into eye-blinks. Such mapping is controlled by the memory parameter w. The shaded processes adapt the mapping, namely, they are involved in the adjustment of w. The numbers identify specific processes. The latencies affecting each of the recording and stimulating channels as well as the parameters used in each process (see main text for an explanation).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Functional model of the cerebellum. The processes in the top row (white boxes) map PN activity into action; in the case of eye-blink conditioning, tone detections into eye-blinks. Such mapping is controlled by the memory parameter w. The shaded processes adapt the mapping, namely, they are involved in the adjustment of w. The numbers identify specific processes. The latencies affecting each of the recording and stimulating channels as well as the parameters used in each process (see main text for an explanation).
Mentions: The trace generation, scaling, and thresholding processes (1, 2, and 3 in Figure 3) model the processing of information that enters the cerebellum via the mossy fibers and leaves it through the excitatory axons of deep nuclear cells that projects to red nucleus which, in turn, excites FN (Hesslow and Yeo, 2002; Christian and Thompson, 2003). The trace generation (1) process codes the time since the CS onset with a decaying trace having a fixed initial value (τ0), final value (τ1), and duration (Λτ). This trace defines the memory span of the system; i.e., the maximum temporal gap between CS detection and a CR execution learnable by the system. The Scaling (2) process multiplies the trace with the memory parameter w, which is the only parameter modified by learning. With w, we mimic the changes in synaptic efficacy that occur in the molecular layer, due to LTD in the parallel fiber to Purkinje cell synapse and/or other kinds of associative plasticity (Dean et al., 2010). Lastly, the thresholding (3) process triggers a CR whenever the value of the scaled trace falls below a decision threshold (θCR). Within this process, we collapse all the transductions that occur postsynaptically from the Purkinje cells down to the efferents of the deep nuclei. In short, these three processes map event detections in the PN into stimulation of the rat FN. The parameter w regulates the mapping and, by scaling the trace signal, controls whether a response is triggered or not, and if so, determines its timing in a way analogous to the biological system.

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