activity
20162021
most citedMarkov chain Hebbian learning algorithm with ternary synaptic units

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2021

CBP: Backpropagation with constraint on weight precision using a pseudo-Lagrange multiplier method

Guhyun Kim, Doo Seok Jeong

Backward propagation of errors (backpropagation) is a method to minimize objective functions (e.g., loss functions) of deep neural networks by identifying optimal sets of weights a…

q-bio.NC2019

Simplified calcium signaling cascade for synaptic plasticity

Vladimir Kornijcuk, Dohun Kim, Guhyun Kim +1

We propose a model for synaptic plasticity based on a calcium signaling cascade. The model simplifies the full signaling pathways from a calcium influx to the phosphorylation (pote…

q-bio.NC2018

Tutorial: Neuromorphic spiking neural networks for temporal learning

Doo Seok Jeong

Spiking neural networks (SNN) as time-dependent hypotheses consisting of spiking nodes (neurons) and directed edges (synapses) are believed to offer unique solutions to reward pred…

cs.NE20171 cited

Markov chain Hebbian learning algorithm with ternary synaptic units

Guhyun Kim, Vladimir Kornijcuk, Dohun Kim +6

In spite of remarkable progress in machine learning techniques, the state-of-the-art machine learning algorithms often keep machines from real-time learning (online learning) due i…

cond-mat.mtrl-sci2016

Non-modified Thermally-derived Onion-like Carbon As Electrocatalyst for [VO]2+/[VO2]+ Redox Flow Battery

Young-Jin Ko, Jung-Min Cho, Doo Seok Jeong +3

We report the nanodiamond-derived onion-like carbon successfully applied as an electrocatalyst for [VO]2+/[VO2]+ redox flow battery, as drop-coated (in the as-synthesized state) on…