ArticleIBM journal of research and development
Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis.
Article in IBM journal of research and development. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 45 citations in OpenAlex.
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- Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics.Cell reports · 2023Article
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- NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML.PLoS computational biology · 2023Article
- Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning.Biological cybernetics · 2022Article
- EDEN: A High-Performance, General-Purpose, NeuroML-Based Neural Simulator.Frontiers in neuroinformatics · 2022Article
- Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning.Frontiers in computational neuroscience · 2022Article
- Multiscale modeling meets machine learning: What can we learn?Archives of computational methods in engineering : state of the art reviews · 2021Article
- Article
- Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform.PEARC20 : Practice and Experience in Advanced Research Computing 2020 : Catch the wave : July 27-31, 2020, Portland, Or Virtual Conference. Practice and Experience in Advanced Research Computing (Conference) (2020 : Online) · 2020Article
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- Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences.NPJ digital medicine · 2019Review
- Optimization by Adaptive Stochastic Descent.PloS one · 2018Article
- Optimizing computer models of corticospinal neurons to replicate in vitro dynamics.Journal of neurophysiology · 2017Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics.
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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.