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- The University of TokyoJP10 papers
- Yokohama National UniversityJP8 papers
- Kobe City College of TechnologyJP5 papers
- Yamaguchi UniversityJP4 papers
- Hiroshima City UniversityJP3 papers
- Japan Science and Technology AgencyJP3 papers
- Kobe UniversityJP2 papers
- Kwansei Gakuin UniversityJP2 papers
- RIKENJP2 papers
- Bernstein Center for Computational Neuroscience FreiburgDE1 paper
- Hasselt UniversityBE1 paper
- Hitachi (Japan)JP1 paper
7 papers · 1 filter
Decomposition of neuronal assembly activity via empirical de-Poissonization
Werner Ehm, Benjamin Staude, Stefan Rotter
Consider a compound Poisson process with jump measure supported by finitely many positive integers. We propose a method for estimating from a single, equidistantly sampled…
A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance
Jun Namikawa, Jun Tani
This paper proposes a novel learning method for a mixture of recurrent neural network (RNN) experts model, which can acquire the ability to generate desired sequences by dynamicall…
Sparse and Dense Encoding in Layered Associative Network of Spiking Neurons
Kazuya Ishibashi, Kosuke Hamaguchi, Masato Okada
A synfire chain is a simple neural network model which can propagate stable synchronous spikes called a pulse packet and widely researched. However how synfire chains coexist in on…
Statistical Mechanics of Nonlinear On-line Learning for Ensemble Teachers
Hideto Utsumi, Seiji Miyoshi, Masato Okada
We analyze the generalization performance of a student in a model composed of nonlinear perceptrons: a true teacher, ensemble teachers, and the student. We calculate the generaliza…
Synchronization of Excitatory Neurons with Strongly Heterogeneous Phase Responses
Yasuhiro Tsubo, Jun-nosuke Teramae, Tomoki Fukai
In many real-world oscillator systems, the phase response curves are highly heterogeneous. However, dynamics of heterogeneous oscillator networks has not been seriously addressed.…
Retrieval of branching sequences in associative memory model with common external input and bias input
Kentaro Katahira, Masaki Kawamura, Kazuo Okanoya +1
We investigate a recurrent neural network model with common external and bias inputs that can retrieve branching sequences. Retrieval of memory sequences is one of the most importa…