Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 2: Strong Reject: For instance, a paper with major technical flaws, and/or poor evaluation, limited impact, poor reproducibility and mostly unaddressed ethical considerations.
review_content: summary: As I think the authors do not respect the NeurIPS reviewers, I just copied what they write in the abstract. 

"we find a biologically plausible spiking model realizing efficient coding in the case of a generalized leaky integrate-and-fire network with excitatory and inhibitory units, equipped with fast and slow synaptic currents, local homeostatic currents such as spike-triggered adaptation, hyperpolarization-activated rebound current, heterogeneous firing thresholds and resets, heterogeneous postsynaptic potentials, and structured, low-rank connectivity."
strengths_and_weaknesses: Weaknesses:

-1. The authors should reformat the manuscript to NeurIPS format, i.e., introduction, related work, method, results, discussion & conclusion. If the manuscript was for nature, science, etc., please reformat it! It is not respectful for NeurIPS reviewers! 

-2. The authors set the objective of the inhibitory population to minimize the distance between E and I. But, no explanation about the objective. Why is it? Then, we can set the objective of the excitatory network to minimize the distance between the desired signal and I? 

-3. lines 109-111: it is not accurate. Not only $Na^+$ and $K^+$ are responsible for the potential changes. Which biological model are the authors talking about?

-4. lines 126-128: It is unclear how Eq. (8) can be re-arranged to the LIF model. 

-5. I did not see any experimental evidence on the application level to support the effectiveness of the proposed approach.

In summary, the manuscript lacks insights. The authors only describe the mathematical models without providing any insights and background knowledge. The writing style is poor. 
questions: Typos: 

1. line 12: remove the first "and"
2. line 340: remove the first "how"
3. line 79: "...of the cell type Y and M ...", missing a "," before "and"



limitations: No insights and no background. The authors even did not change the format from the previous submission. 

No experimental evidence on the application level supports the proposed method. In addition, I did not see the bio-plausible support either.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The authors derive a neural network model that optimizes the information transfer. The optimization leads to a number of biologically relevant features such as generalized leaky integrate-and-fire neuronal dynamics, low-rank connectivity and heterogeneous decoding weights and currents across neurons in the network. 
strengths_and_weaknesses: In terms of originality, this work continues a line of research that builds biologically plausible neural networks whose objective is to maximize information coding. In that line of research, this works presents a leap forward in the right direction because it adds a number of biologically realistic features. It is my understanding that this works follows previous work closely so I would argue that the novelty is limited. The paper itself seems solid and there is no major errors I can see in the derivations. The paper is written in a clear enough manner although a section that explains the structure of the paper and/or a section that clearly outlines the contributions of this work would make it easier to read.  In terms of significance, I would argue that it is hard to judge because it is generally hard or impossible to validate how biologically-plausible these features are. It is without a doubt that features such as heterogeneity are biologically plausible, but I miss a way to validate this claims more quantitively. Also it is not completely clear to me from the paper how these different features are contributing to make the coding more efficient.         
questions: I would like to ask the authors to at least propose experiments that could validate their results. 
limitations: Yes.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The authors present a spiking network model with E/I neurons along with two loss functions which include a squared loss to some stimulus and a quadratic regularizer. From here they show how this model matches a generalized leaky-integrate-and-fire (LIF) network under certain approximations. In this sense the LIF network is ‘optimal’ in the given framework. The authors finally show different network dynamics, first for a simplified network without E-E connections, and later for the full network with varying ranks of the connectivity matrix. The authors highlight their ability to interpret and link different model components to biological processes. 
strengths_and_weaknesses: ### Strengths
The authors give an interesting link of generalized LIF networks to the optimization of specific loss functions. The derivations are extensive and technical sound. The presented model has rich properties and can be used for further analysis. It has the potential to link existing spiking network models with more biological interpretable models and model components. 

However, there are several weaknesses: 

### Weaknesses
1. While ‘efficient coding’ is one of the main selling points of the authors, it is in the end an optimization of quadratic error term with a quadratic regularizer. As the exact formulation of efficient coding and the made assumption play a major role in efficient coding, the authors should give more context and state clearly the made assumptions.
2. The main sections 2.1 and 2.2 are written quite technical and miss some clarity. A clear road map could help to guide through the technicalities. 
3. The made assumptions and approximations as well as the derivation from (6) to (9) are hidden and it is hard to grasp their correspondence. 
4. The analysis of the presented network is limited and an interpretation of the results is missing. What is for example the purpose of investigating the simplified network? Why is this interesting? What are the properties of the network? What influence have the different model parameters? What influence has the noise level? 
5. As the whole setup is formulated as an ‘efficient coding problem’, it would be interesting, if not essential, to see the performance of the network for different configurations and/or stimuli. 

### Minor comments:
- Figures are rather small, and could be at least shown with full text width.
questions: - Why are the loss functions meaningful? While 3a makes sense (if the readout should replicate the signal), 3b is less intuitive. The authors could give some more explanations and context. 
limitations: The authors mention their limited evaluation and list further directions for analysis and investigations. They do not comment on negative societal impacts.
review_title: null

Meta-review: metareview: This paper proposes a derivation of spiking networks from an efficient signal reconstruction cost function. The derivation leads to more biological features than previous ones, including fast and slow synaptic currents, and rebound currents. This is a nice addition to the growing literature in this field. 

The negative review below mostly focused on formatting of the paper. The AC did not see a violation of NeurIPS formatting policy, however agrees that the presentation could be clearer.
confidence: Certain
Decision: Accept