2018 Fiscal Year Annual Research Report
Using recurrent neural networks to study neural computations in cortical networks
Publicly Offered Research
| Project Area | Correspondence and Fusion of Artificial Intelligence and Brain Science |
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| Project/Area Number |
17H06037
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| Research Institution | Institute of Physical and Chemical Research |
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Principal Investigator |
ベヌッチ アンドレア 国立研究開発法人理化学研究所, 脳神経科学研究センター, チームリーダー (50722352)
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| Project Period (FY) |
2017-04-01 – 2019-03-31
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| Keywords | Decision-making / sensory processing / visual cortex / neural computation / optogenetics / cognition |
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| Outline of Annual Research Achievements |
The research carried over during this fiscal year has led to the refinement and optimization of computational tools for the analysis of large-scale neural recordings and to advance our understanding on fundamental coding questions on sensory-based decision making.
We have progressed on three computational frameworks for the modeling of widefield data from the mouse occipital cortex during goal-directed behaviors. Recordings consisted on GCaMP signals across 10-12 visual cortical areas as the mouse performed in a two-alternative forced choice orientation discrimination task (Abdolrahmani et al., bioRxiv 2019). The first framework is a recurrent neural network using Hessian-free (FORCE) optimization for back-to-back learning of behavioral and neural data. The RNN has been developed in Matlab (Mathworks) and aimed to closely mimic basic functional principles of cortical connectivity (including a 4:1 ratio of excitatory and inhibitory units). The second framework is an adaptation to our data of a published variational autoencoder (LFADS, Pandarinath et al., 2018), a potent machine-learning tool aiming to uncover the latent dynamics of possibly highly non-linear dynamical systems. Development was done under the TensorFlow programming environment. Finally, under the PyTorch environment we have developed an agile, vanilla RNN currently used to tackle computational questions on decision-making under uncertain input-evidence conditions. The latter framework is also being developed for correlative analysis of widefield GCaMP data as described in the previous report.
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| Research Progress Status |
平成30年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
平成30年度が最終年度であるため、記入しない。
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