Predictive coding: a theoretical and experimental review
Predictive coding offers a potentially unifying account of cortical function--postulating that the
core function of the brain is to minimize prediction errors with respect to a generative model …
core function of the brain is to minimize prediction errors with respect to a generative model …
Backpropagation-based learning techniques for deep spiking neural networks: A survey
M Dampfhoffer, T Mesquida… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the adoption of smart systems, artificial neural networks (ANNs) have become
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …
Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …
chips with high energy efficiency by introducing neural dynamics and spike properties. As …
Training spiking neural networks using lessons from deep learning
The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
Advancing neuromorphic computing with loihi: A survey of results and outlook
Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
Incorporating learnable membrane time constant to enhance learning of spiking neural networks
Abstract Spiking Neural Networks (SNNs) have attracted enormous research interest due to
temporal information processing capability, low power consumption, and high biological …
temporal information processing capability, low power consumption, and high biological …
Deep residual learning in spiking neural networks
Abstract Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-
based approaches due to discrete binary activation and complex spatial-temporal dynamics …
based approaches due to discrete binary activation and complex spatial-temporal dynamics …
Temporal effective batch normalization in spiking neural networks
Abstract Spiking Neural Networks (SNNs) are promising in neuromorphic hardware owing to
utilizing spatio-temporal information and sparse event-driven signal processing. However, it …
utilizing spatio-temporal information and sparse event-driven signal processing. However, it …
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks
Inspired by detailed modelling of biological neurons, spiking neural networks (SNNs) are
investigated as biologically plausible and high-performance models of neural computation …
investigated as biologically plausible and high-performance models of neural computation …
Temporal-wise attention spiking neural networks for event streams classification
How to effectively and efficiently deal with spatio-temporal event streams, where the events
are generally sparse and non-uniform and have the us temporal resolution, is of great value …
are generally sparse and non-uniform and have the us temporal resolution, is of great value …