The increasing need for compact and low-power computing solutions for machine learning applications has triggered significant interest in energy-efficient neuromorphic systems. However, most of these architectures rely on spiking neural networks, which typically perform poorly compared to their non-spiking counterparts in terms of accuracy. In this paper, we propose a new adaptive spiking neuron model that can be abstracted as a low-pass filter. This abstraction enables faster and better training of spiking networks using back-propagation, without simulating spikes. We show that this model dramatically improves the inference performance of a recurrent neural network and validate it with three complex spatio-temporal learning tasks: the temporal addition task, the temporal copying task, and a spoken-phrase recognition task. We estimate at least 500× higher energy-efficiency using our models on compatible neuromorphic chips in comparison to Cortex-M4, a popular embedded microprocessor.
The paper promises efficient training of spiking neuron models using back-propagation. The authors say that this is important because spiking networks offer significant energy savings, yet they typically perform poorly compared to prevalent artificial neural networks (ANNs). They aim to support this with several synthetic and one real-world task.
I selected to reject the paper based on the support for the main claim of the paper being weak and inadequately demonstrated. No comparisons are to baselines are presented in the text. Reproducibility is hampered by a great deal of the experimental details missing.
On the experimental results:
* In section 4.1, some justification is needed for why these experiments are relevant. 
* In sections 4.4 and 4.5, experiments are discussed, but results are not presented.
* If space is a limitation, I would strongly prefer a table of actual results for sections 4.4 and 4.5 rather than Figure 2.
* Since the encoding of the task in the spiking and non-spiking cases is so drastically different, making that crystal clear would be great.
Other comments:
* The goal of energy savings is very appealing, so it would help to give *some* quantitative measure of savings promised.
* Similarly, the claim about the poor performance of spiking neurons would benefit from some quantitative measures.
* A figure showing the operation of the novel SigmaDelta neuron, compared to the standard aI&F neuron, would be welcome.
Overall I think the paper would benefit from focussing much more on the results, making the claims as quantitatively apparent as possible.