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Found 12 papers, 1 papers with code
Learning Reservoir Dynamics with Temporal Self-Modulation
In this paper, to improve the learning ability of RC, we propose self-modulated RC (SM-RC), which extends RC by adding a self-modulation mechanism.
Timing-Based Backpropagation in Spiking Neural Networks Without Single-Spike Restrictions
That was not seen in conventional SNNs with single-spike restrictions on time-to-fast-spike (TTFS) coding.
Entropic Herding
Using the proposed entropic herding algorithm as a framework, we discuss a closer connection between herding and the maximum entropy principle.
Reservoir Computing with Diverse Timescales for Prediction of Multiscale Dynamics
To develop an efficient machine learning method dedicated to modeling and prediction of multiscale dynamics, we propose a reservoir computing (RC) model with diverse timescales by using a recurrent network of heterogeneous leaky integrator (LI) neurons.
Effects of VLSI Circuit Constraints on Temporal-Coding Multilayer Spiking Neural Networks
As SNNs are continuous-state and continuous-time models, it is favorable to implement them with analog VLSI circuits.
Analytical estimation of maximum fraction of infected individuals with one-shot non-pharmaceutical intervention in a hybrid epidemic model
This suggests the importance of the strength and time of NPIs, although detailed studies are necessary for the implementation of NPIs in complicated real-world environments as the model used in this study is based on various simplifications.
Model-Size Reduction for Reservoir Computing by Concatenating Internal States Through Time
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 random networks of neurons, called "reservoirs."
A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design
We also propose several techniques to improve the performance on a recognition task, and show that the classification accuracy of the proposed algorithm is as high as that of the state-of-the-art temporal coding SNN algorithms on the MNIST dataset.
Quantum Expectation-Maximization Algorithm
Clustering algorithms are a cornerstone of machine learning applications.
Forecasting high-dimensional dynamics exploiting suboptimal embeddings
We show that the framework is applicable to a wide range of data lengths and dimensions.
Fully Neural Network based Model for General Temporal Point Processes
We herein propose a novel RNN based model in which the time course of the intensity function is represented in a general manner.
Approximated Infomax Early Stopping: Revisiting Gaussian RBMs on Natural Images
We pursue an early stopping technique that helps Gaussian Restricted Boltzmann Machines (GRBMs) to gain good natural image representations in terms of overcompleteness and data fitting.
