Stemming from neuroscience, Spiking neural networks (SNNs), a brain-inspired neural network that is a versatile solution to fault-tolerant and energy efficient information processing pertains to the "event-driven" characteristic as the analogy of the behavior of biological neurons. However, they are inferior to artificial neural networks (ANNs) in real complicated tasks and only had it been achieved good results in rather simple applications. When ANNs usually being questioned about it expensive processing costs and lack of essential biological plausibility, the temporal characteristic of RNN-based architecture makes it suitable to incorporate SNN inside as imitating the transition of membrane potential through time, and a brain-inspired Recurrent Leaky Integrate-and-Fire (RLIF) model has been put forward to overcome a series of challenges, such as discrete binary output and dynamical trait. The experiment results show that our recurrent architecture has an ultra anti-interference ability and strictly follows the guideline of SNN that spike output through it is discrete. Furthermore, this architecture achieves a good result on neuromorphic datasets and can be extended to tasks like text summarization and video understanding.
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A. Summarize what the paper claims to do/contribute. Be positive and generous.
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The paper translates the Leaky Integrate and Fire model of neural computation via spike trains into a discrete-time RNN core similar to LSTM. The architecture would be readily amenable to the modern deep learning toolkit if not for the non-differentiability of the hard decision to spike or not. The hard decision is made by thresholding. The paper adopts a simple approximation of backpropagating a "gradient" of 1.0 through the operation if the threshold is within a neighbourhood [thresh - a, thresh + a], and otherwise 0.0, so the system can be trained by backpropagation.
The architecture is tested on a few "neuromorphic" video classification datasets including MNIST-DVS and CIFAR-DVS. Experiments are also run on a text summarization task.
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B. Clearly state your decision (accept or reject) with one or two key reasons for this choice.
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The reviewer thinks the paper should be rejected in its current state. 
The proposed architecture is a straightforward change to a standard LSTM core. Thus it should be compared head-to-head to LSTM on standard datasets for these models (e.g. classic synthetic tasks, language modeling, speech recognition, machine translation, etc) with everything else held constant (hidden size, learning rate, sequence length, etc etc).
It also doesn't really carry over any of the benefits of Spiking Neural Nets even though it is inspired by Leaky Integrate and Fire because it operates in discrete time like a normal RNN, just with an extra binary output produced by spiking. It's unclear that a spiking inductive bias is actually useful, even though event-driven computation could in theory allow much less computation, the proposed method does not have that property. 
So the paper doesn't really provide evidence to back up their claim that the proposed model combines the complimentary advantages of Deep Learning and Spiking Neural Nets. 
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C. Provide supporting arguments for the reasons for the decision.
While the proposed method is in-spirit inspired by the leaky integrate and fire model, it is operated/trained in discrete time which does not allow it to achieve the benefits of continuous time integrate-and-fire models which allow for less computation and time-discretization-invariance. 
The conversion of the spiking model to the deep learning framework is rather crude, as the differentiable approximation to the non-differentiable threshold operation is biased and not well-motivated either empirically, intuitively, or theoretically (i.e. there are no comparisons to alternative choices).
There are new techniques for marrying continuous-time models and deep learning which seem more promising to investigate to this end (e.g. Neural ODE).
So in summary, the method doesn't have the computational benefits of a biologically plausible spiking algorithms and is not well-tested against competing deep learning methods, making it hard to verify the motivation of pushing toward a performant yet biologically plausible algorithm.
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D. Provide additional feedback with the aim to improve the paper. Make it clear that these points are here to help, and not necessarily part of your decision assessment.
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There are many grammatical and word-choice mistakes which make the paper hard to read.
Mainly, from a practical perspective, the paper would be much-improved by showing what benefit the spiking inductive bias confers over a standard LSTM on standard tasks in the deep learning community.
The method/landscape should be developed and studied in further detail until claims can be made about combining the strengths of spiking and deep-learning models.