Towards spike-based machine intelligence with neuromorphic computing
Guided by brain-like 'spiking'computational frameworks, neuromorphic computing—brain-
inspired computing for machine intelligence—promises to realize artificial intelligence while …
inspired computing for machine intelligence—promises to realize artificial intelligence while …
[HTML][HTML] Eligibility traces and plasticity on behavioral time scales: experimental support of neohebbian three-factor learning rules
Most elementary behaviors such as moving the arm to grasp an object or walking into the
next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal …
next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal …
[HTML][HTML] Robust spike-based continual meta-learning improved by restricted minimum error entropy criterion
S Yang, J Tan, B Chen - Entropy, 2022 - mdpi.com
The spiking neural network (SNN) is regarded as a promising candidate to deal with the
great challenges presented by current machine learning techniques, including the high …
great challenges presented by current machine learning techniques, including the high …
Long short-term memory and learning-to-learn in networks of spiking neurons
Recurrent networks of spiking neurons (RSNNs) underlie the astounding computing and
learning capabilities of the brain. But computing and learning capabilities of RSNN models …
learning capabilities of the brain. But computing and learning capabilities of RSNN models …
[HTML][HTML] SAM: a unified self-adaptive multicompartmental spiking neuron model for learning with working memory
S Yang, T Gao, J Wang, B Deng, MR Azghadi… - Frontiers in …, 2022 - frontiersin.org
Working memory is a fundamental feature of biological brains for perception, cognition, and
learning. In addition, learning with working memory, which has been show in conventional …
learning. In addition, learning with working memory, which has been show in conventional …
A large-scale model of the functioning brain
A central challenge for cognitive and systems neuroscience is to relate the incredibly
complex behavior of animals to the equally complex activity of their brains. Recently …
complex behavior of animals to the equally complex activity of their brains. Recently …
[HTML][HTML] Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules
N Frémaux, W Gerstner - Frontiers in neural circuits, 2016 - frontiersin.org
Classical Hebbian learning puts the emphasis on joint pre-and postsynaptic activity, but
neglects the potential role of neuromodulators. Since neuromodulators convey information …
neglects the potential role of neuromodulators. Since neuromodulators convey information …
[图书][B] Reinforcement learning: An introduction
The significantly expanded and updated new edition of a widely used text on reinforcement
learning, one of the most active research areas in artificial intelligence. Reinforcement …
learning, one of the most active research areas in artificial intelligence. Reinforcement …
Liaf-net: Leaky integrate and analog fire network for lightweight and efficient spatiotemporal information processing
Spiking neural networks (SNNs) based on the leaky integrate and fire (LIF) model have
been applied to energy-efficient temporal and spatiotemporal processing tasks. Due to the …
been applied to energy-efficient temporal and spatiotemporal processing tasks. Due to the …
[HTML][HTML] A survey of robotics control based on learning-inspired spiking neural networks
Biological intelligence processes information using impulses or spikes, which makes those
living creatures able to perceive and act in the real world exceptionally well and outperform …
living creatures able to perceive and act in the real world exceptionally well and outperform …