Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis.
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Your vote was cast
Thank you for your feedback
Thank you for your feedback
Journal titleIBM journal of research and development
Publication Begin page6.1
Publication End page6.14
MetadataShow full item record
AbstractBiomimetic 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.
CitationDura-Bernal S, Neymotin SA, Kerr CC, Sivagnanam S, Majumdar A, Francis JT, Lytton WW. Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis. IBM J Res Dev. 2017 Mar-May;61(2-3):6.1-6.14. doi: 10.1147/JRD.2017.2656758. Epub 2017 May 23. PMID: 29200477; PMCID: PMC5708558.
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International
- Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm.
- Authors: Dura-Bernal S, Zhou X, Neymotin SA, Przekwas A, Francis JT, Lytton WW
- Issue date: 2015
- Towards a real-time interface between a biomimetic model of sensorimotor cortex and a robotic arm.
- Authors: Dura-Bernal S, Chadderdon GL, Neymotin SA, Francis JT, Lytton WW
- Issue date: 2014 Jan 15
- Model-Driven Analysis of Eyeblink Classical Conditioning Reveals the Underlying Structure of Cerebellar Plasticity and Neuronal Activity.
- Authors: Antonietti A, Casellato C, D'Angelo E, Pedrocchi A
- Issue date: 2017 Nov
- Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm.
- Authors: Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW
- Issue date: 2016
- A Scalable Weight-Free Learning Algorithm for Regulatory Control of Cell Activity in Spiking Neuronal Networks.
- Authors: Zhang X, Foderaro G, Henriquez C, Ferrari S
- Issue date: 2018 Mar