Markov Chain Hebbian Learning Algorithm With Ternary Synaptic Units

Markov chain Hebbian learning algorithm with ternary synaptic units

Markov chain Hebbian learning algorithm with ternary synaptic units Guhyun Kim, Vladimir Kornijcuk, Dohun Kim, Inho Kim, Jaewook Kim, Hyo Cheon Woo, Ji Hun Kim, Cheol Seong Hwang*, and Doo Seok Jeong* Center for Electronic Materials, Korea Institute of Science and Technology, Hwarangno 14-gil 5, Seongbuk-gu, 02792 Seoul, Republic of Korea Department of Materials Science and Engineering and Inte...

Beyond Hebbian plasticity: Effective learning with ineffective Hebbian learning rules

In this paper we revisit the classical neuroscience paradigm of Hebbian learning. We find that a necessary requirement for effective associative memory learning is that the efficacies of the incoming synapses should be uncorrelated. This is difficult to achieve in a robust manner by Hebbian synaptic learning, since it depends on network level information. Effective learning can yet be achieved ...

A Complex Valued Hebbian Learning Algorithm

We present a new training rule for a single-layered linear network with complex valued weights and activation levels. This novel network can be used to extract the principal components of a complex valued data set. We also introduce a new training method that reduces the training time of the complex valued as well as of the real valued network. The use of the new network and training algorithm ...

Learning Bayesian Network Structure using Markov Blanket in K2 Algorithm

‎A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG)‎. ‎There are basically two methods used for learning Bayesian network‎: ‎parameter-learning and structure-learning‎. ‎One of the most effective structure-learning methods is K2 algorithm‎. ‎Because the performance of the K2 algorithm depends on node...

Title: Generalized Synaptic Updating in Temporally Asymmetric Hebbian Learning Authors: