With the help of special neuromorphic hardware, spiking neural networks (SNNs) are
expected to realize artificial intelligence (AI) with less energy consumption. It provides a …

Humans and animals can learn new skills after practicing for a few hours, while current
reinforcement learning algorithms require a large amount of data to achieve good …

We have developed a new neural network model that simulates how the central nervous
system (CNS) governs neural motor sensors. Our model uses reinforcement learning and …

At present, implementation of learning mechanisms in spiking neural networks (SNN) cannot
be considered as a solved scientific problem despite plenty of SNN learning algorithms …

In this article, we propose a circuit to imitate the behavior of a Reward-Modulated spike-
timing-dependent plasticity synapse. When two neurons in adjacent layers produce spikes …

Reinforcement Learning (RL) provides a powerful framework for decision-making in
complex environments. However, implementing RL in hardware-efficient and bio-inspired …

Reinforcement learning (RL) faces substantial challenges when applied to real-life
problems, primarily stemming from the scarcity of available data due to limited interactions …

Achieving energy efficiency in learning is a key challenge for artificial intelligence (AI)
computing platforms. Biological systems demonstrate remarkable abilities to learn complex …

This study investigates a novel approach to robotic arm control through integrating spiking
neural networks with the twin delayed deep deterministic policy gradient reinforcement …

Reinforcement learning (RL) faces substantial challenges when applied to real-life
problems, primarily stemming from the scarcity of available data due to limited interactions …