Abstract
Deep neural networks with rate-based neurons have exhibited tremendous progress in the last decade. However, the same level of progress has not been observed in research on spiking neural networks (SNN), despite their capability to handle temporal data, energy-efficiency and low latency. This could be because the benchmarking techniques for SNNs are based on the methods used for evaluating deep neural networks, which do not provide a clear evaluation of the capabilities of SNNs. Particularly, the benchmarking of SNN approaches with regards to energy efficiency and latency requires realization in suitable hardware, which imposes additional temporal and resource constraints upon ongoing projects. This review aims to provide an overview of the current real-world applications of SNNs and identifies steps to accelerate research involving SNNs in the future.
Highlights
Similar to artificial neural networks (ANN), spiking neural networks (SNN) are inspired by the neural networks observed in biology
Reinforcement learning (RL) involves adapting the parameters of an SNN based on external feedback that depends on the predictions generated by the network
One aspect of brain-inspired SNN (BI-SNN) architectures is the use of a brain template to structure a 3D SNN structure that is trained on spike sequence data [15]
Summary
Similar to artificial neural networks (ANN), spiking neural networks (SNN) are inspired by the neural networks observed in biology. Biological neurons process and transmit information using action potentials, known as spikes, which underlie the incredible energy efficiency exhibited by the brain. These similarities between spiking neurons and biological neurons imply that SNNs with similar power requirements as the human brain could potentially be developed. This has motivated several studies to compare ANNs and SNNs from different perspectives [1,2,3].
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