ArticleScientific reports2015
A neuro-inspired model-based closed-loop neuroprosthesis for the substitution of a cerebellar learning function in anesthetized rats.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 26 citations in OpenAlex.
- Advancing neuroengineering with Neuromorphic Twins.Nature communications · 2026Review
- Brain-inspired classical conditioning model.iScience · 2021Article
- Plasticity and Adaptation in Neuromorphic Biohybrid Systems.iScience · 2020Review
- Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics.Frontiers in neuroscience · 2020Article
- Article
- A Bidirectional Brain-Machine Interface Featuring a Neuromorphic Hardware Decoder.Frontiers in neuroscience · 2016Article
- A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems.Frontiers in neuroscience · 2016Review
- Qualitative-Modeling-Based Silicon Neurons and Their Networks.Frontiers in neuroscience · 2016Review
- Future think: cautiously optimistic about brain augmentation using tissue engineering and machine interface.Frontiers in systems neuroscience · 2015Article
- Toward Building Hybrid Biological/in silico Neural Networks for Motor Neuroprosthetic Control.Frontiers in neurorobotics · 2015Article
- Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis.IBM journal of research and developmentArticle
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.