A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design.

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 machine-learning algorithms but also energy-efficient computational models when implemented in very-large-scale integration (VLSI) circuits. In this article, we propose a novel supervised learning algorithm for SNNs based on temporal coding.

Development of a highly sensitive method for the quantitative analysis of modified nucleosides using UHPLC-UniSpray-MS/MS.

There are more than 150 types of naturally occurring modified nucleosides, which are believed to be involved in various biological processes. Recently, an ultrahigh performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UHPLC-ESI-MS/MS) technique has been developed to measure low levels of modified nucleosides. A comprehensive analysis of modified nucleosides will lead to a better understanding of intracellular ribonucleic acid modification, but this analysis requires high-sensitivity measurements.

Reconstructing bifurcation diagrams only from time-series data generated by electronic circuits in discrete-time dynamical systems.

Bifurcation-diagram reconstruction estimates various attractors of a system without observing all of them but only from observing several attractors with different parameter values. Therefore, the bifurcation-diagram reconstruction can be used to investigate how attractors change with the parameter values, especially for real-world engineering and physical systems for which only a limited number of attractors can be observed. Although bifurcation diagrams of various systems have been reconstructed from time-series data generated in numerical experiments, the systems that have been targeted for reconstructing bifurcation diagrams from time series measured from physical phenomena so far have only been continuous-time dynamical systems.

A shared synapse architecture for efficient FPGA implementation of autoencoders.

This paper proposes a shared synapse architecture for autoencoders (AEs), and implements an AE with the proposed architecture as a digital circuit on a field-programmable gate array (FPGA). In the proposed architecture, the values of the synapse weights are shared between the synapses of an input and a hidden layer, and between the synapses of a hidden and an output layer. This architecture utilizes less of the limited resources of an FPGA than an architecture which does not share the synapse weights, and reduces the amount of synapse modules used by half.

Three drowsiness categories assessment by electroencephalogram in driving simulator environment.

Traffic accidents remain one of the most critical issues in many countries. One of the major causes of traffic accidents is drowsiness while driving. Since drowsiness is related to human physiological conditions, drowsiness is hard to prevent.