28 citations · 33 across the 11 of their papers we have counts for
11 papers
Scalable Training of Continuous-Time Spiking Neural Networks with Differentiable Spike-Time Discretization
Yusuke Sakemi, Tomoya Takeuchi, Takeo Hosomi +1
Continuous-time spiking neural networks (SNNs) provide an event-driven framework for temporal computation, computational neuroscience, and neuromorphic hardware. However, training…
Enhancing Time-Series Anomaly Detection by Integrating Spectral-Residual Bottom-Up Attention with Reservoir Computing
Hayato Nihei, Sou Nobukawa, Yusuke Sakemi +1
Reservoir computing (RC) establishes the basis for the processing of time-series data by exploiting the high-dimensional spatiotemporal response of a recurrent neural network to an…
Learning the Simplest Neural ODE
Yuji Okamoto, Tomoya Takeuchi, Yusuke Sakemi
Since the advent of the ``Neural Ordinary Differential Equation (Neural ODE)'' paper, learning ODEs with deep learning has been applied to system identification, time-series foreca…
Harnessing Nonidealities in Analog In-Memory Computing Circuits: A Physical Modeling Approach for Neuromorphic Systems
Yusuke Sakemi, Yuji Okamoto, Takashi Morie +3
Large-scale deep learning models are increasingly constrained by their immense energy consumption, limiting their scalability and applicability for edge intelligence. In-memory com…
Chaos-based reinforcement learning with TD3
Toshitaka Matsuki, Yusuke Sakemi, Kazuyuki Aihara
Chaos-based reinforcement learning (CBRL) is a method in which the agent's internal chaotic dynamics drives exploration. However, the learning algorithms in CBRL have not been thor…
Sparse-firing regularization methods for spiking neural networks with time-to-first spike coding
Yusuke Sakemi, Kakei Yamamoto, Takeo Hosomi +1
The training of multilayer spiking neural networks (SNNs) using the error backpropagation algorithm has made significant progress in recent years. Among the various training scheme…
Learning Reservoir Dynamics with Temporal Self-Modulation
Yusuke Sakemi, Sou Nobukawa, Toshitaka Matsuki +2
Reservoir computing (RC) can efficiently process time-series data by transferring the input signal to randomly connected recurrent neural networks (RNNs), which are referred to as…
Timing-Based Backpropagation in Spiking Neural Networks Without Single-Spike Restrictions
Kakei Yamamoto, Yusuke Sakemi, Kazuyuki Aihara
We propose a novel backpropagation algorithm for training spiking neural networks (SNNs) that encodes information in the relative multiple spike timing of individual neurons withou…
Effects of VLSI Circuit Constraints on Temporal-Coding Multilayer Spiking Neural Networks
Yusuke Sakemi, Takashi Morie, Takeo Hosomi +1
The spiking neural network (SNN) has been attracting considerable attention not only as a mathematical model for the brain, but also as an energy-efficient information processing m…
Model-Size Reduction for Reservoir Computing by Concatenating Internal States Through Time
Yusuke Sakemi, Kai Morino, Timothée Leleu +1
Reservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as r…
A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design
Yusuke Sakemi, Kai Morino, Takashi Morie +1
Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machi…