Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: The current paper presented an algorithm using sparse feedback signals at spikes and gradient-based updating rule to train the intrinsic parameter of spiking neuron models. The authors laid out the parameter updating rules for leaky-integrate-and-fire neuron and resonant-and-fire neuron models, and demonstrated the algorithm in training the two neuron types to recover a teacher signal.  
main_review: In general, the current work presented a learning algorithm for intrinsic properties of spiking neurons. Following the current learning rule, the parameter update rate was both modulated by the sign of an event (a miss or false positive spike), and also regulated by the proximity of the event. The authors showed that the current parameter updating rule allows various parameters to converge robustly. In contrast, a ‘vanilla’ updating rule (without temporal dependency) or a surrogate rule (instead of temporal dependency, it use voltage dependency rule) both were failed to converge in multiple simulations. The paper was well written and messages were clearly delivered. However, the learning algorithm converges extremely slow on neurons with relatively low firing rate (<10 Hz, >10 h). Notice that this range of firing rate (< 10 Hz) is biologically meaningful. The slow convergence might limited the application of the current algorithm. 
limitations_and_societal_impact: The authors acknowledged that the current parameter updating rule lacks theoretical support. In addition, the current work demonstrated the learning algorithm in a simple teacher-student training paradigm. It is unclear how it would behavior in a more complicated training environment.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: This work demonstrates that the intrinsic parameters of a leaky integrate-and-fire neuron and a resonate-and-fire neuron can be learned in a student-teacher paradigm. The student neuron learns the intrinsic parameters of the teacher neuron using only the teacher neuron's spike times and gradient based updates.
main_review: Strengths:

1. This work demonstrates that the internal parameters of a neuron can be learned using only spike-time feedback. This is important since the neural computations are sensitive to changes in intrinsic parameters. While most ANN models focus on synaptic updates, this work suggests that adapting the internal parameters of a neuron can play an important rule in learning.

Weaknesses:

1. The work seems like an extension of [29] to include internal parameters of a neuron and therefore the results are perhaps not surprising.

2. If the reader is to be convinced that the internal parameters of the neuron are important for learning, then there should be a comparison of the expressivity of a neuron with fixed internal parameters (but adaptive synapses) and a neuron with adaptive internal parameters (and adaptive synapses). 

3. The EDS rule is presented without a theoretical justification.

Overall: This work is an interesting proof-of-concept demonstration that the internal parameters of a neuron can be learned and suggests that these parameters should be given more attention.
limitations_and_societal_impact: The authors address the fact that the EDS rule lacks a theoretical justification; however, the other weaknesses listed above are not addressed.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: This paper introduces the event-dependent scaling (EDS) rule to use in neural network training that allows neural computations to be learned online - i.e. without error propagation. It presents a teacher-student paradigm for problem formulation, then characterizes the error signal and EDS rule. It then describes its architectures (leaky integrate-and-fire or leaky resonate-and-fire neurons, Poisson-distributed input spike trains, and other standard parameters), and metrics (of convergence and robustness). Next, it discusses resulting learning dynamics and analyses the effectiveness of EDS empirically, and concludes by acknowledging concerns behind lack of theoretical backing and a rarefied testing paradigm.  

main_review: *Originality:* The primary contribution of this paper is an event-dependent scaling online learning rule. To my knowledge, this is a novel and original contribution to the field, building on top of existing work in online learning rules, spiking architectures, and methods of training spiking architectures given that they are nondifferentiable. The introduction cites helpful relevant work to set up the problem, and the related work subsection does both careful and thorough review of existing work in related fields and differentiation from similar work.

*Quality:* I believe this paper is technically sound. The empirical analysis is convincing and the figures are helpful - I don’t feel that the text overstates the effectiveness of the EDS rule. The ablation and gradient analysis in section 3.4 is useful, and reasonable and accurate to the best of my understanding. The dynamics in Fig 3 and various descriptive statistics shown in Fig 4 are effective and say a lot on their own. 

The methods are also detailed and make a lot of sense. I think section 2 is the best section of the paper - it lays out each section of the experimental setup well, highlights the contribution in section 2.3, and keeps all non-novel parts standard and familiar. My only concern with methods is the teacher-student paradigm. It feels very toy because the solution is both single-dimensional and an exact imitation of the teacher. This could easily be a limitation of my own knowledge, but the uses of teacher-student I’ve seen before (and the ones discussed in this paper) are from a very long time ago and generally used for theoretical work where the simplicity of the problem is at least mitigated by the fact that the solution is provably reachable/entailed by the initialization. For an empirical work, it feels like a good but not independent contribution, but again I recognize that this could be a limitation of my knowledge. I will say that the authors discuss this limitation and others with honesty and detail, and the paper seems to warrant its claims.

*Clarity:* The clarity of this paper fluctuates. Section 1 is clearly written, and section 2 is not only clearly written but well-organized, with each subsection answering the question I have or the next thing I want to know at the end of the previous section. I think this is also true of the subsection structure in section 3. 

The writing in Section 3 is less clear, and sounds more like a process statement than results and analysis. There is a lot of “we designed X”, “we trained Y”, etc. This is mainly a stylistic issue, but it does make the organization feel temporal/chronological rather than claims-driven and this leads to a results section that is hard to understand despite holding valuable information. It would help to have some framework at the beginning that outlines the flow of the argument in the section 3 subsections and makes all main claims, let each subsection derive from that framework.

*Significance:* I find this method to be important and unique. The results seem to open a new direction in online learning without backprop that people could in theory expand or extend upon. Insofar as spiking neurons are a good model of the brain, this paper seems to advance their modeling capacity. However, I am not confident that the expansion or extension is actually viable. The problem seems very toy and while it could be a limitation of my own knowledge, I don't know it to be one that has a history of transferring well to more complex domains. 

limitations_and_societal_impact: The limitations are discussed in detail and hit upon all concerns I could think of. My only disagreements with the authors would be on the magnitude of the limitations, not the limitations themselves. The authors don’t really discuss societal impact, and there may be some from accurate models of biological intelligence that can’t be dismissed by saying the work is theoretical, but we are nowhere close so I don’t think it warrants a flag.
review_title: null

Meta-review: This paper presents an approach for optimizing the intrinsic parameters of spiking neurons, and tests it within a teacher-student paradigm. The initial reviews were generally positive, and reviewers agreed the paper was technically sound and interesting, though there was some concern about the paradigm used for evaluation. However, after the authors' rebuttals, and some discussion amongst reviewers, a consensus was reached that this paper merits acceptance at NeurIPS.
Decision: Accept (Poster)