Neural networks : the official journal of the International Neural Network Society
Oct 16, 2019
Learning in neural networks inspired by brain tissue has been studied for machine learning applications. However, existing works primarily focused on the concept of synaptic weight modulation, and other aspects of neuronal interactions, such as non-s...
Though succeeding in solving various learning tasks, most existing reinforcement learning (RL) models have failed to take into account the complexity of synaptic plasticity in the neural system. Models implementing reinforcement learning with spiking...
Neural networks : the official journal of the International Neural Network Society
Oct 11, 2019
Artificial neural networks have been used as a powerful processing tool in various areas such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged researchers to improve artificial neural networks by ...
Neural networks : the official journal of the International Neural Network Society
Sep 27, 2019
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally cat...
Neural networks : the official journal of the International Neural Network Society
Sep 26, 2019
Machine learning is yielding unprecedented interest in research and industry, due to recent success in many applied contexts such as image classification and object recognition. However, the deployment of these systems requires huge computing capabil...
Neural networks : the official journal of the International Neural Network Society
Sep 25, 2019
We present a framework based on iterative free-energy optimization with spiking neural networks for modeling the fronto-striatal system (PFC-BG) for the generation and recall of audio memory sequences. In line with neuroimaging studies carried out in...
Neural networks : the official journal of the International Neural Network Society
Sep 24, 2019
Neural networks have enabled great advances in recent times due mainly to improved parallel computing capabilities in accordance to Moore's Law, which allowed reducing the time needed for the parameter learning of complex, multi-layered neural archit...
Analytical tools that estimate the directed information flow between simultaneously recorded neural populations, such as directed information or Granger causality, typically focus on measuring how much information is exchanged between such population...
Neural networks : the official journal of the International Neural Network Society
Sep 20, 2019
OBJECTIVE: This paper argues that Brain-Inspired Spiking Neural Network (BI-SNN) architectures can learn and reveal deep in time-space functional and structural patterns from spatio-temporal data. These patterns can be represented as deep knowledge, ...
Neural networks : the official journal of the International Neural Network Society
Sep 19, 2019
Artificial neural networks (ANNs), a popular path towards artificial intelligence, have experienced remarkable success via mature models, various benchmarks, open-source datasets, and powerful computing platforms. Spiking neural networks (SNNs), a ca...