Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses
Developing accurate predictive models of sensory neurons is vital to understanding sensory processing and brain computations. The current standard approach to modeling neurons is to start with simple models and to incrementally add interpretable features. An alternative approach is to start with a more complex model that captures responses accurately, and then probe the fitted model structure to understand the neural computations. Here, we show that a multitask recurrent neural network (RNN) framework provides the flexibility necessary to model complex computations of neurons that cannot be captured by previous methods. Specifically, multilayer recurrent neural networks that share features across neurons outperform generalized linear models (GLMs) in predicting the spiking responses of parasol ganglion cells in the primate retina to natural images. The networks achieve good predictive performance given surprisingly small amounts of experimental training data. Additionally, we present a novel GLM-RNN hybrid model with separate spatial and temporal processing components which provides insights into the aspects of retinal processing better captured by the recurrent neural networks.
This is a clearly written paper with a nice, if straightforward, result: RNNs can be good predictive models of neuron firing rates in the retina. On the one hand, the primary scientific contribution seems to just be to confirm that this approach works. On this particular stimulus locked task the gains from using the RNN seemed relatively modest, and it hasn*t yet taught us anything new about the biology. On the other hand, this (along with the concurrent work of McIntosh et al.) is introducing neural network modeling to a field that isn*t currently using it, and where it should prove very effective. I think it would be very interesting to see the results of applying a framework like this one with LFP and other neurons as input and on a shorter discretization time scale. I suspect followup work building on this proof of concept will be increasingly exciting. Minor comments: Sec 3.2: I didn*t understand the role of the 0.833 ms bins. Use *epoch* throughout, rather than alternating between *epoch* and *pass through data*. Fig. 4 would be better with the x-axis on a log scale.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses
Developing accurate predictive models of sensory neurons is vital to understanding sensory processing and brain computations. The current standard approach to modeling neurons is to start with simple models and to incrementally add interpretable features. An alternative approach is to start with a more complex model that captures responses accurately, and then probe the fitted model structure to understand the neural computations. Here, we show that a multitask recurrent neural network (RNN) framework provides the flexibility necessary to model complex computations of neurons that cannot be captured by previous methods. Specifically, multilayer recurrent neural networks that share features across neurons outperform generalized linear models (GLMs) in predicting the spiking responses of parasol ganglion cells in the primate retina to natural images. The networks achieve good predictive performance given surprisingly small amounts of experimental training data. Additionally, we present a novel GLM-RNN hybrid model with separate spatial and temporal processing components which provides insights into the aspects of retinal processing better captured by the recurrent neural networks.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This is a clearly written paper with a nice, if straightforward, result: RNNs can be good predictive models of neuron firing rates in the retina. On the one hand, the primary scientific contribution seems to just be to confirm that this approach works. On this particular stimulus locked task the gains from using the RNN seemed relatively modest, and it hasn*t yet taught us anything new about the biology. On the other hand, this (along with the concurrent work of McIntosh et al.) is introducing neural network modeling to a field that isn*t currently using it, and where it should prove very effective. I think it would be very interesting to see the results of applying a framework like this one with LFP and other neurons as input and on a shorter discretization time scale. I suspect followup work building on this proof of concept will be increasingly exciting. Minor comments: Sec 3.2: I didn*t understand the role of the 0.833 ms bins. Use *epoch* throughout, rather than alternating between *epoch* and *pass through data*. Fig. 4 would be better with the x-axis on a log scale.
assistant
This is a clearly written paper with a nice, if straightforward, result: RNNs can be good predictive models of neuron firing rates in the retina. On the one hand, the primary scientific contribution seems to just be to confirm that this approach works. On this particular stimulus locked task the gains from using the RNN seemed relatively modest, and it hasn*t yet taught us anything new about the biology. On the other hand, this (along with the concurrent work of McIntosh et al.) is introducing neural network modeling to a field that isn*t currently using it, and where it should prove very effective. I think it would be very interesting to see the results of applying a framework like this one with LFP and other neurons as input and on a shorter discretization time scale. I suspect followup work building on this proof of concept will be increasingly exciting. Minor comments: Sec 3.2: I didn*t understand the role of the 0.833 ms bins. Use *epoch* throughout, rather than alternating between *epoch* and *pass through data*. Fig. 4 would be better with the x-axis on a log scale.