Abstract
Neuroprostheses could potentially recover functions lost due to neural damage. Typical neuroprostheses connect an intact brain with the external environment, thus replacing damaged sensory or motor pathways. Recently, closed-loop neuroprostheses, bidirectionally interfaced with the brain, have begun to emerge, offering an opportunity to substitute malfunctioning brain structures. In this proof-of-concept study, we demonstrate a neuro-inspired model-based approach to neuroprostheses. A VLSI chip was designed to implement essential cerebellar synaptic plasticity rules, and was interfaced with cerebellar input and output nuclei in real time, thus reproducing cerebellum-dependent learning in anesthetized rats. Such a model-based approach does not require prior system identification, allowing for de novo experience-based learning in the brain-chip hybrid, with potential clinical advantages and limitations when compared to existing parametric “black box” models.
Highlights
A neuro-inspired model-based closed-loop neuroprosthesis for the substitution of a cerebellar learning function in anesthetized rats
This function is often tested by employing the eyeblink conditioning paradigm, consisting of repeated trials comprised of a conditioned stimulus (CS) paired with an unconditioned stimulus (US) - typically an auditory CS preceding a periorbital-airpuff US by several hundred ms (Fig. 2a), and by monitoring the acquisition of eyeblink-conditioned motor responses (CRs) triggered by the CS9,16
Conditioned motor responses are not expressed under general anesthesia[18], and the observed motor responses of the brain-chip hybrids would depend on deep brain stimulation controlled by the on-chip synthetic cerebellar circuit, rather than on biological compensation mechanisms or spared cerebellar function that would be expected in surgical or chemical lesion studies
Summary
A neuro-inspired model-based closed-loop neuroprosthesis for the substitution of a cerebellar learning function in anesthetized rats. The best attempt so far has targeted the hippocampus[7], which lends its extensively studied connectivity and physiology to selective substitution of its substructures Achieving this and extending the approach to even more complex brain circuits (like the prefrontal cortex of non-human primates8) was possible by adopting powerful general-purpose non-linear system-identification techniques like Volterra series, which effectively lump possibly very complex dynamics into a limited set of kernels. A neuroinspired closed-loop system must rely on real, dynamic neuronal sensory representation, and could serve as an additional means to evaluate the performance of a model – which is by definition an incomplete representation of a neural circuit In this respect, the current study provides additional support for the functionality of a www.nature.com/scientificreports cerebellar model previously embedded in a robotic device that successfully acquired conditioned motor responses (CRs)[10]
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