AIMR Math Group Hybrid Seminar
#11 FY2023
Date :December. 5th (Tue) 2023 15:15~18:15
Venue: AIMR, common space in 3C/Zoom(Hybrid)
1.
Speaker: Masato Hara (Kyoto University)
Title: Learning dynamics by reservoir computing
abstract:
Reservoir computing is a kind of machine learning method proposed in early 2000s. It can be formulated as a dynamical system naturally so can deal with time series data well. For example, after tuning parameters of a reservoir by using a given time series, the reservoir autonomously generates a time series which is essentially the same with the original one. In this talk, I will present a scenario to explain the mechanism of time series forecasting in the sense described above and show some supporting numerical results. This is a joint work with Hiroshi Kokubu (Kyoto University).
2.
Speaker: Takuma Sumi (WPI-AIMR, Tohoku University)
Title: Speech Recognition in Reservoir Computing Using Cultured Neuronal Networks
abstract:
Reservoir computing is a machine learning paradigm that transforms the transient dynamics of high-dimensional nonlinear systems for processing time-series data.
Although the paradigm was initially proposed to model information processing in the mammalian cortex, it remains unclear how the nonrandom network architecture, such as the modular architecture, in the cortex integrates with the biophysics of living neurons to characterize the function of biological neuronal networks (BNNs). Here, we used optogenetics and calcium imaging to record the multicellular responses of cultured BNNs and employed the reservoir computing framework to decode their computational capabilities. We used a timer task to verify that BNNs possess a short-term memory of several 100 ms and show that this property can be exploited for speech recognition. We also show that the modularity of the BNNs positively correlates with a spoken digit classification accuracy. Interestingly, BNN-based reservoirs allow categorical learning, wherein a network trained on one dataset can be used to classify separate datasets of the same category. Such classification was not possible when the inputs were directly decoded by a linear decoder, suggesting that BNNs act as a generalization filter to improve reservoir computing performance.
(This presentation is based on https://www.pnas.org/doi/10.1073/pnas.2217008120)
3.
Speaker: Koichi Taniguchi (WPI-AIMR, Tohoku University)
Title:On edge of bifurcation for reservoir computing with the Kuramoto model
abstract:
The reservoir computing aims to achieve high-performance and low-cost
machine learning by using dynamical systems as reservoirs, but in
general, there is almost no theoretical guideline for high-performance
or optimality. In this talk, we discuss the reservoir computing with
the Kuramoto model and the "edge of bifurcation" conjecture which
means that its best performance is achieved by taking the model
parameters just below the bifurcation point of the dynamical system.
This talk is based on a joint work with Prof. Hayato Chiba (Tohoku
Univ.).
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