ArticleFrontiers in neuroanatomy2016
Real-World-Time Simulation of Memory Consolidation in a Large-Scale Cerebellar Model.
Article in Frontiers in neuroanatomy, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A theory of cerebellar learning as spike-based reinforcement learning in continuous time and space.PNAS nexus · 2025Article
- Real-Time Simulation of a Cerebellar Scaffold Model on Graphics Processing Units.Frontiers in cellular neuroscience · 2021Article
- The Concept of Transmission Coefficient Among Different Cerebellar Layers: A Computational Tool for Analyzing Motor Learning.Frontiers in neural circuits · 2019Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We report development of a large-scale spiking network model of the cerebellum composed of more than 1 million neurons. The model is implemented on graphics processing units (GPUs), which are dedicated hardware for parallel computing. Using 4 GPUs simultaneously, we achieve realtime simulation, in which computer simulation of cerebellar activity for 1 s completes within 1 s in the real-world time, with temporal resolution of 1 ms. This allows us to carry out a very long-term computer simulation of cerebellar activity in a practical time with millisecond temporal resolution. Using the model, we carry out computer simulation of long-term gain adaptation of optokinetic response (OKR) eye movements for 5 days aimed to study the neural mechanisms of posttraining memory consolidation. The simulation results are consistent with animal experiments and our theory of posttraining memory consolidation. These results suggest that realtime computing provides a useful means to study a very slow neural process such as memory consolidation in the brain.
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Registered trials
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