A Cerebellar Neuroprosthetic System: Computational Architecture and in vivo Test

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.

Brain and cognitive reserve: Translation via network control theory

“…The application of control theory to neuroscience has already provided critical insights and innovations under an emerging discipline known as neural control engineering (Schiff, 2012). This integration between neuroscience and control theory has been developing since the early 2000's (see Voss et al, 2004, for an initial intersection between these disciplines), and has afforded applications for brain-computer interfaces and decoding strategies (Lagang & Srinivasan, 2013;Srinivasan & Brown, 2007) that support adaptive and robust neuroprosthetics (Berger et al, 2011;Gorzelic et al, 2013;Herreros et al, 2014;Hsiao et al, 2013;Taylor et al, 2002). These applications have begun to provide powerful translational opportunities in subcortical systems based on local micro-architectural control models.…”

Neural Substrate Expansion for the Restoration of Brain Function

“…This interface appears to improve memory encoding in an intact animal as well as recover of memory function when the native hippocampus has been compromised by pharmacological inhibition of synaptic transmission. Similar prosthetics have been constructed to simulate prefrontal cortex (Hampson et al, 2012 ) and cerebellar activity (Herreros et al, 2014 ). While these studies are promising proofs of concept, they are based upon experimental tasks that may not generalize to natural animal behavior and often do not have a direct human behavioral correlate.…”

A neuro-inspired model-based closed-loop neuroprosthesis for the substitution of a cerebellar learning function in anesthetized rats

“…An autonomous adaptive parameter setting strategy (e.g., ref. 43 ) would be a desirable development for the future. On the stimulation side, the closed-loop system is prone to lose periods of input data due to contamination by stimulation-induced artifacts, especially when prolonged, high-frequency electrical trains are used 5 ; a solution will require either enhanced signal-processing to clean the input data or stimulation modes that do not introduce electrical artifacts, such as optogenetic stimulation 6 .…”