Limits...
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

Behavior of the model with simulated data. (A) Behavioral performance. Percentage of CRs per block of trials of the model fitted with stability constraint (solid line) and without (dashed line). The vertical dotted line separates acquisition and extinction training. (B) Trajectory of w in the model fitted with stability constraint (dashed line) and without (solid 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
getmorefigures.php?uid=PMC4126458&req=5

Figure 5: Behavior of the model with simulated data. (A) Behavioral performance. Percentage of CRs per block of trials of the model fitted with stability constraint (solid line) and without (dashed line). The vertical dotted line separates acquisition and extinction training. (B) Trajectory of w in the model fitted with stability constraint (dashed line) and without (solid line). The horizontal green dotted line marks the level above which the model does not trigger any CRs. Blocks of 10 trials.

Mentions: As a first step, we test whether our model supports the acquisition and extinction of CRs when the IO displays spontaneous activity (see parameters in Table 3). The outcome of a representative simulation shows that indeed the model adapts well to the case of baseline IO activity (Figure 4A). It acquires well-timed CRs in CS–US paired trials and extinguishes them in CS-alone unpaired trials (Figures 4A and 5A), and, importantly, the parameter w reaches a stable plateau after complete extinction (Figure 5B). We stress that the stabilization occurring at the end of extinction even in the presence of CS-alone stimulations, stems from the stability condition in Equation (5). If we remove this constraint, the overt behavioral results are similar (Figures 4B and 5A) but the underlying memory dynamics differ (Figure 5B). Indeed, behaviorally both models only differ in the extinction phase, which is slower for the model with stability. However, in regard to the model’s state, without stability, the synaptic efficacy w grows also after extinction of the CRs has been accomplished. Note that, in consequence this can make reacquisition harder than acquisition if the extinction training is maintained, which goes against the experimental evidence (Kehoe and Macrae, 2002). In conclusion, the computational model of the cerebellum is also functional when the IO has baseline activity, requiring only a proper calibration of the plasticity parameters.


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)

Behavior of the model with simulated data. (A) Behavioral performance. Percentage of CRs per block of trials of the model fitted with stability constraint (solid line) and without (dashed line). The vertical dotted line separates acquisition and extinction training. (B) Trajectory of w in the model fitted with stability constraint (dashed line) and without (solid 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 5: Behavior of the model with simulated data. (A) Behavioral performance. Percentage of CRs per block of trials of the model fitted with stability constraint (solid line) and without (dashed line). The vertical dotted line separates acquisition and extinction training. (B) Trajectory of w in the model fitted with stability constraint (dashed line) and without (solid line). The horizontal green dotted line marks the level above which the model does not trigger any CRs. Blocks of 10 trials.
Mentions: As a first step, we test whether our model supports the acquisition and extinction of CRs when the IO displays spontaneous activity (see parameters in Table 3). The outcome of a representative simulation shows that indeed the model adapts well to the case of baseline IO activity (Figure 4A). It acquires well-timed CRs in CS–US paired trials and extinguishes them in CS-alone unpaired trials (Figures 4A and 5A), and, importantly, the parameter w reaches a stable plateau after complete extinction (Figure 5B). We stress that the stabilization occurring at the end of extinction even in the presence of CS-alone stimulations, stems from the stability condition in Equation (5). If we remove this constraint, the overt behavioral results are similar (Figures 4B and 5A) but the underlying memory dynamics differ (Figure 5B). Indeed, behaviorally both models only differ in the extinction phase, which is slower for the model with stability. However, in regard to the model’s state, without stability, the synaptic efficacy w grows also after extinction of the CRs has been accomplished. Note that, in consequence this can make reacquisition harder than acquisition if the extinction training is maintained, which goes against the experimental evidence (Kehoe and Macrae, 2002). In conclusion, the computational model of the cerebellum is also functional when the IO has baseline activity, requiring only a proper calibration of the plasticity parameters.

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