Title: Seemingly Redundant Modules Enhance Robust Odor Learning in Fruit Flies
Venue: NeurIPS 2025
Accepted: yes
Keywords: olfactory representations; lateral inhibition；spike frequency adaptation;pattern classification；spiking neural networks
Abstract: Biological circuits have evolved to incorporate multiple modules that perform similar functions. In the fly olfactory circuit, both lateral inhibition (LI) and neuronal spike frequency adaptation (SFA) are thought to enhance pattern separation for odor learning. However, it remains unclear whether these mechanisms play redundant or distinct roles in this process. In this study, we present a computational model of the fly olfactory circuit to investigate odor discrimination under varying noise conditions that simulate complex environments. Our results show that LI primarily enhances odor discrimination in low‑ and medium‑noise scenarios, but this benefit diminishes and may reverse under higher‑noise conditions. In contrast, SFA consistently improves discrimination across all noise levels. LI is preferentially engaged in low‑ and medium‑noise environments, whereas SFA dominates in high‑noise settings. When combined, these two sparsification mechanisms enable optimal discrimination performance.  This work demonstrates that seemingly redundant modules in biological circuits can, in fact, be essential for achieving optimal learning in complex contexts.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies the seemingly redundant computational mechanisms of lateral inhibition (LI) and spike frequency adaptation (SFA) in olfactory identification. The authors utilize models that do or do not include LI and SFA, and find that LI enables better learning of odor identity than SFA, when the amount of noise present in the olfactory input is low. In contrast, the authors find that SFA leads to better learning when the noise present is high. The authors argue that this demonstrates that the seeming redundancy enables complementary benefits in learning across different noise scenarios.\n\nSTRENGTHS AND WEAKNESSES:\n**STRENGTHS**\n\n1. This paper is well motivated and I found the introduction well written. In addition, I believe the introduction provides a good coverage of past work and situates the authors' work nicely in the existing literature. \n\n2. The results are interesting and provide a nice demonstration of the growing understanding that seeming redundancy in neural computations can actually be playing distinct roles. The results are also demonstrated across a number of noise conditions and odor sizes, which was helpful for understanding the extent to which these conclusions hold. \n\n3. The results are clearly explained and Figure 1 was very helpful at understanding the authors' model.\n\n**WEAKNESSES**\n\n1. I think the biggest weakness of this work is that clarity could be improved. In particular: \n    a. I found the description of the model somewhat confusing, in part because of the notation. For instance, $X$ is used both to denote layer and input. Also, what $t_{jk}^X$ and $t{kl}^{LN}$ are (Eqs. 2 and 3) were not ever defined. I additionally felt like a few of the modeling choices were also not explained in great detail. For instance, why was the spiking threshold of the readout layer neurons \"slightly higher\"? What does slightly mean? And why were only one set of weights plastic? \n   b. The author's mention that Fig. 3 shows that the LI mechanism leads to larger differences in population firing rates and patters. But that was not clear to me from the figure. The authors should provide more commentary on this and some quantitative analysis to show that this is clear, or they should remove this point from the text. \n    c. Fig. 4 seems to be a very interesting one, but it was not clear to me what \"base, low, medium, and high\" were (each of the lines). I assume this was achieved by changing the relative strength of the different mechanisms, but how was that actually implemented? \n\n2. The paper ends on a slightly lower note, as the conclusion and limitations are very short and do not leave the reader feeling the significance of the work. The authors' have the space (almost a whole additional page) and they should use it to provide more discussion on what the implications of their work are, what this work inspires next (e.g., looking at the learning dynamics associated with having LI and SFA) and how their work could be improved upon. The author's wrote a really great introduction, and having a similar quality discussion would elevate the quality and impact of this work. \n\n3. I really liked the authors' approach in studying the different mechanisms by including only SFA or only LI. However, both exist in the fly. I think it would be good to include some results from a \"full\" model, which includes LI ***and*** SFA. This could help illustrate that the combination of both computational elements allows for good learning in any noise condition. I don't think the authors have to do a full analysis of this, but even one table or figure would be good. \n\n**MINOR POINTS**\n\n1. The authors mention LI improves discrimination in their abstract. But their results seem to point to LI improving the ***learning*** for discriminating odors (which the authors say throughout their paper). I think making it clear in the abstract that the results are about learning would be good. \n\n2. It was not clear to why assuming a normal distribution in odor noise was reasonable. Is there any evidence of this? Or is this just for keeping it simple and in-line with previous work? \n\n3. \\tau_{trace_ln} and \\tau_{trace_LN}are used. \n\n4.  I assume the bold dot in Eqs. 3 and 8 are just inner product, but this should be noted. \n\n5. Figure 2 was never referenced in the main text. \n\n6. In Table 1, the first row, left column, the Base model is incorrectly labeled as having the second highest accuracy. Table 1 should also have a note on what the colors mean.\n\nQUESTIONS:\nAs noted in the weakness section, I have a few questions about the results: \n\n1. What $t_{jk}^X$ and $t{kl}^{LN}$ are (Eqs. 2 and 3)? \n\n2. Why was the spiking threshold of the readout layer neurons \"slightly higher\"? What does slightly mean? \n\n3. And why are only one set of weights plastic? \n\n4. How does Fig. 3 shows that the LI mechanism leads to larger differences in population firing rates and patterns? l\n \n5. What are the different lines in Fig. 4?\n\nLIMITATIONS:\nThe authors need to add more discussion on the limitations as the two sentences do not adequately provide context. The authors should discuss the limitations of having only 1 layer of weights being plastic, using the assumption of Gaussian noise, and training with a gradient based optimizer.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this work, the Authors investigate two parts of the fruit fly's olfactory circuit. These parts, performing lateral inhibition and spike frequency adaptation, were both previously attributed to pattern separation in odor learning, although their mechanisms are different. To see if there are differences in the function of these parts of the olfactory circuit, the Authors have built a spiking neural network model grounded in the architecture of the fruit fly's olfactory circuit. Through the modifications of the model and ablation studies, they found the differences in the pattern separation performance of lateral inhibition vs. spike frequency adaptation under different levels of noise, suggesting different roles of these subcircuits in the fruit fly's olfaction.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nI really liked this work. It deals with the fruit fly's olfactory system, which is a system of interest to the olfactory community and has made notable appearances in top machine-learning conferences due to its fine neuronal mapping that has led to the emergence of local-sensitive hashing algorithms.\n\nAs the Authors have pointed out, this system has further potential to inform the field of machine learning and help the development of noise-tolerant classifiers based on an architecture successfully deployed in the real world.\n\nThe results on the different functions of LI and SFA are, to my knowledge, novel, and useful for the fields of neuroscience and machine learning alike.\n\nThe text is well-written and easy to follow: the steps are explained and the concepts / variables are introduced. Overall, it's a nice, principled, well-documented work.\n\nWeaknesses:\n\nThe PN-to-KC connections are fixed and not trained in the model here. Meanwhile, there's growing empirical and theoretical evidence suggesting that there may be structure: Modelling work suggests that training that sparse matrix via backpropagation improves the model's performance (works by Dmitry Krotov and colleagues), while experimental work suggests that there may be a shared low-dimensional structure in that matrix (works by Alex Koulakov and colleagues). Theoretical works (the models of olfaction) also suggest that there may be a coupling between the statistics of olfactory inputs and the statistics of the PN-to-KC connections. Whether that turns out to be true or not, it would be interesting to test these ideas in the modeling work here and see how these factors would affect the obtained results.\n\nCould you please discuss the need for the use of spiking neural networks here as opposed to the usual, non-spiking models? Spikes seem to require significant computational resources, so it is important to know what additional benefits they provide compared to simpler models.\n\nIt would also be interesting to hear the Authors' thoughts / further Discussion on possible differences in the ethological roles of the two investigated subcircuits based on the new results of this paper.\n\nQUESTIONS:\nPlease see the Weaknesses section above.\n\nLIMITATIONS:\nPlease see the Weaknesses section above.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper did spiking RNN simulations of fruit fly olfactory circuits. The main scientific question is whether inhibition through self-adaptation (LI model) or another group of inhibitory neurons (SFA model) helps random pattern classification performance and robustness. The authors claim that LI is more effective when the input noise is low and SFA is more effective when the input noise is high.\n\nSTRENGTHS AND WEAKNESSES:\n### Strength:\n\nThe question is framed clearly. The view of the role of self-adaptation and lateral inhibition regarding different levels of input noise is novel.\n\n### Weakness:\n\nSee my questions on the simulation, which makes me suspect the experiments are not sufficient to support conclusions. The manuscript does not include any theoretical or more rigorous analysis. The combined effect of both LI and SFA is not shown. \n\nSince the simulation results could be due to the choice of the parameters, and most of the curves in the result figures look noisy and sometimes complex non-monotonic trends, I do not think the experiments conducted are sufficient to fully understand the behavior of this RNN model. The analysis of the representation geometry is somehow simple. Thus the conclusions are weakly supported.\n\nQUESTIONS:\nThe authors did not include line numbers, which makes commenting on all the issues difficult. Below, I pointed out some of them.\n\n### Formulation and typos:\n\nSome formulations and notations are not consistent\n\nPage 2, Input Data, $X_{i_j}$, double subscription is confusing, and not consistent with later usage of commas to separate i and j.\n\nPage 2, Input Data, Xki = max(0, Xki). This is a confusing formulation. It looks like a self-consistent equation, but it is not.\n\nm in subscription of $\\bar{V}$ in eq (4) is not explained. Does it mean MBONs?\n\nPage 3, 2nd paragraph, “…reduced by the value of the value of the threshold.”\n\nPage 3, bottom, “the synaptic weights wLN→PN, representing the connection from LNs to PNs, are often negative” Why it is not “always negative”?\n\n### Questions on simulations:\n\nThe output neuron, MBON, is time-averaged (Learning Algorithm, page 4, middle), so why the temporal difference/separate is important (5.1 page 5 bottom, 3 page 4 bottom)? \n\nPage 5 bottom “The LI mechanism results in larger spatio-temporal differences in spike trains and firing rates, facilitating better separation of odor input patterns.” It is not obvious. The blue and red look always overlap with some fluctuation noise.\n\nQuestions on 5.2, table 1, and figure 4. In Figure 4, the authors show the base model performance as gray curves. However, the curve trend doesn’t match the number in Table 1 (0.3 noise level, 53.82% in the table, but ~ 52% in the plot). In the table, the LI model is always better than the base models, but in the plot when the noise level is high, LI is worse than the base model. Another question on the “inhibition level”, it looks like in both LI and SFA the best performance is always the largest inhibition level. Does this mean an even higher inhibition can even lead to better performance?\n\nFigure 5 (B) is redundant as the curves already appear in Figure 4. \n\nThe metrics $S_\\phi$ and $S_\\theta$ are confusing. They are the product of the intra-class dissimilarity and inter-class dissimilarity. However, intra-class dissimilarity and inter-class dissimilarity have opposite effects on the embedding quality as the authors said, why the product of the two is a meaningful measure?\n\nThe authors claim LI is more effective in low-noise situations, and SFA is more effective in high-noise situations. A straightforward expectation is that combining the two will lead to better performance in both cases. However, the author did not explore it.\n\n### Sparsity\n\nThe current analysis of the representation is simple and not sufficient to reveal the underlying mechanism. The authors talked about sparsity but did not analyze it. A simple hypothesis is that the effectiveness of LI and SFA can be fully explained by the coding sparsity they induced. Such analysis will give a better picture of the underlying mechanics.\n\nLIMITATIONS:\nAs I noted in the weaknesses and questions section, I believe the simulations in the manuscript are not thorough or sufficient to support the conclusions.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper explores the role of lateral inhibition (LI) and spike frequency adaptation (SFA) in the olfactory circuitry of fruit flies for various levels of input noise. For this, they simulated a layered network featuring receptor, projection and local neurons, Kenyon cells, and mushroom body output neurons. The neurons were modeled using the leaky-integrate-and-fire model. All synapses were initialized randomly kept fixed except for the Kenyon cells which were trained using Backpropagation through time. Experiments were conducted using a generated dataset of odor signals. The base model was compared to incorporating LI and SFA respectively. The results suggest that LI increases classification accuracy for low noise levels. Moreover, SFA increases accuracy especially for higher noise levels.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n1. An interesting premise was explored in the paper: the use of two different inhibition mechanisms in the olfactory circuitry for enhancing learning under different input noise levels.\n2. The paper text ist well written and easy to follow.\n3. Reproducibility is facilitated by including all necessary information in the main manuscript such as all hyperparameter settings, optimizer, and loss function used.\n\n**Weaknesses**:\n1. Overall the scope of the investigation is very limited. More experiments would improve the paper. e.g.:\n- How does the architecture used compare to regular unstructured neural networks?\n- What is the combined effect of LI an SFA?\n- What about different types of noise?\n3. The learning algorithm makes use of BPTT which is not deemed biologically plausible. It would add a lot to the paper if biologically plausible alternatives to BPTT (i.e. STDP) were used.\n\nQUESTIONS:\n1. How would the switching between the two modes of inhibition work? How can the circuitry distinguish between low- and high-noise settings?\n2. You say that \"These findings suggest that the fly olfactory circuit is inherently efficient in pattern recognition due to its structural properties, even in the absence of sparsening activity in Kenyon cells.\" How does an unstructured network (LSTM or even MLP) of similar size compare to the architecture used here?\n3. Can minibatch optimization have an influence on the reported results? If so, how could it be implemented in-vivo?\n\nLIMITATIONS:\nThe discussion of limitations is very limited. Please also focus on differences between BPTT and more biologically plausible alternatives.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}