Multi-compartment neuron and population encoding improved spiking neural network for deep distributional reinforcement learning
Inspired by the information processing with binary spikes in the brain, the spiking neural
networks (SNNs) exhibit significant low energy consumption and are more suitable for …
networks (SNNs) exhibit significant low energy consumption and are more suitable for …
Two-compartment neuronal spiking model expressing brain-state specific apical-amplification,-isolation and-drive regimes
There is mounting experimental evidence that brain-state specific neural mechanisms
supported by connectomic architectures serve to combine past and contextual knowledge …
supported by connectomic architectures serve to combine past and contextual knowledge …
Error-based or target-based? A unified framework for learning in recurrent spiking networks
The field of recurrent neural networks is over-populated by a variety of proposed learning
rules and protocols. The scope of this work is to define a generalized framework, to move a …
rules and protocols. The scope of this work is to define a generalized framework, to move a …
NREM and REM: cognitive and energetic gains in thalamo-cortical sleeping and awake spiking model
Sleep is essential for learning and cognition, but the mechanisms by which it stabilizes
learning, supports creativity, and manages the energy consumption of networks engaged in …
learning, supports creativity, and manages the energy consumption of networks engaged in …
Multi-compartment neuron and population encoding powered spiking neural network for deep distributional reinforcement learning
Inspired by the brain's information processing using binary spikes, spiking neural networks
(SNNs) offer significant reductions in energy consumption and are more adept at …
(SNNs) offer significant reductions in energy consumption and are more adept at …