2014 Fiscal Year Annual Research Report
下オリーブ核神経細胞群におけるギャップ結合および抑制性結合のベイズ推定
| Project/Area Number |
14J09356
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| Research Institution | Ritsumeikan University |
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Principal Investigator |
HOANG HUUTHIEN 立命館大学, 理工学研究科, 特別研究員(DC2)
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| Project Period (FY) |
2014-04-25 – 2016-03-31
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| Keywords | Bayes inference / spike trains / Inferior olive neurons |
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| Outline of Annual Research Achievements |
From the beginning of this project, we planned to construct a framework to estimate two model parameters from spike trains of Inferior olive (IO) neurons, which play a crucial role in cerebellar learning. Those two parameters have been known to determine the spatiotemporal dynamics of IO firing. Typically, the main challenge of parameter estimation is the presence of a huge mismatch in the system complexity between the brain and the model. Apparently, the model cannot precisely reproduce the dynamics of the brain. We therefore introduced a stochastic approach based on a hierarchical Bayesian framework, which allows fluctuations of parameter estimates in the neuronal constraint base, in order to compensate the modeling errors. The proposed approach was shown to significantly reduce the estimate errors. In a relatively recent study, we also validated the minimum error method developed in our previous study (Onizuka et al., 2013) using simulated spike data as the test data. Our evaluation on the test data confirmed that the minimum error method was quite effective even when the model is imperfect.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
Last year, we presented those results in two international conferences and received positive feedback from audiences. Therefore, we submitted two manuscripts for consideration of publication on the two journals, Frontiers in Computational Neuroscience and Journal of Signal Processing, independently. Up to date, both two paper have been accepted for publication.
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| Strategy for Future Research Activity |
In the following year, we are considering an extension of our project. This comes from the fact that the experimental data of IO neurons used in our previous studies possess two severe limits: the number of recorded neurons was quite small and its neural network structure was unknown. Those limitations intensely cause restrictions of exploring neuronal interactions among IO cells within or across cerebellar micro-zones. Here, different micro-zones correspond to various functional modules of the cerebellum. To tackle this issue, we aimed to utilize other experimental data sets which were collected by a state-of-the-art recording technique, namely, two-photon imaging. The two-photon imaging can track, in vivo, Calcium concentration activities of hundreds of Purkinje cells in different cerebellar micro-zones, and thus provides plentiful neural information for our analysis of IO firing. Please note that complex spikes of Purkinje cells are generated in a one-to-one manner by IO spikes and can substitute for directly measuring IO spike activity. The task of our next project is thus to develop a sophisticated method to detect the complex spikes from Calcium responses of Purkinje cells.
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Research Products
(4 results)
