When and where do feed-forward neural networks learn localist representations?
According to parallel distributed processing (PDP) theory in psychology, neural networks (NN) learn distributed rather than interpretable localist representations. This view has been held so strongly that few researchers have analysed single units to determine if this assumption is correct. However, recent results from psychology, neuroscience and computer science have shown the occasional existence of local codes emerging in artificial and biological neural networks. In this paper, we undertake the first systematic survey of when local codes emerge in a feed-forward neural network, using generated input and output data with known qualities. We find that the number of local codes that emerge from a NN follows a well-defined distribution across the number of hidden layer neurons, with a peak determined by the size of input data, number of examples presented and the sparsity of input data. Using a 1-hot output code drastically decreases the number of local codes on the hidden layer. The number of emergent local codes increases with the percentage of dropout applied to the hidden layer, suggesting that the localist encoding may offer a resilience to noisy networks. This data suggests that localist coding can emerge from feed-forward PDP networks and suggests some of the conditions that may lead to interpretable localist representations in the cortex. The findings highlight how local codes should not be dismissed out of hand.
 Quality and Clarity The neural networks and neural codes are studied in a concise way, most of the paper is clear. The section on data design, p3, could use some additional clarification wrt to how the data input is encoded (right now, it is hard to understand exactly what happens). Originality I am not aware of other studies on this topic, the proposed approach seems original. Significance The biggest problem I have is with the significance: I don*t see at all how finding somewhat localized responses in the hidden layer of an MLP with just one hidden layer has any bearing on deeper networks structured as CNNs: compared to MLPs, neurons in CNNs have much smaller receptive fields, and are known to be sensitive to selective and distinct features. Overall the results seem rather trivial without greater implications for modern deep neural networks: ie, of course adding dropout improves the degree of localist coding (sec 3.4). Similarly, for a larger network, you will find fewer localist codes (though this is hard to judge, as an exact definition of selectivity is missing). Minor issues: the "selectivity" p3 is not properly defined. On p3, a figure is undefined. Typo: p2: "could as be". Many of the references are ugly : p3, "in kerasChollet (2015)", this needs fixing.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
When and where do feed-forward neural networks learn localist representations?
According to parallel distributed processing (PDP) theory in psychology, neural networks (NN) learn distributed rather than interpretable localist representations. This view has been held so strongly that few researchers have analysed single units to determine if this assumption is correct. However, recent results from psychology, neuroscience and computer science have shown the occasional existence of local codes emerging in artificial and biological neural networks. In this paper, we undertake the first systematic survey of when local codes emerge in a feed-forward neural network, using generated input and output data with known qualities. We find that the number of local codes that emerge from a NN follows a well-defined distribution across the number of hidden layer neurons, with a peak determined by the size of input data, number of examples presented and the sparsity of input data. Using a 1-hot output code drastically decreases the number of local codes on the hidden layer. The number of emergent local codes increases with the percentage of dropout applied to the hidden layer, suggesting that the localist encoding may offer a resilience to noisy networks. This data suggests that localist coding can emerge from feed-forward PDP networks and suggests some of the conditions that may lead to interpretable localist representations in the cortex. The findings highlight how local codes should not be dismissed out of hand.
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>



 Quality and Clarity The neural networks and neural codes are studied in a concise way, most of the paper is clear. The section on data design, p3, could use some additional clarification wrt to how the data input is encoded (right now, it is hard to understand exactly what happens). Originality I am not aware of other studies on this topic, the proposed approach seems original. Significance The biggest problem I have is with the significance: I don*t see at all how finding somewhat localized responses in the hidden layer of an MLP with just one hidden layer has any bearing on deeper networks structured as CNNs: compared to MLPs, neurons in CNNs have much smaller receptive fields, and are known to be sensitive to selective and distinct features. Overall the results seem rather trivial without greater implications for modern deep neural networks: ie, of course adding dropout improves the degree of localist coding (sec 3.4). Similarly, for a larger network, you will find fewer localist codes (though this is hard to judge, as an exact definition of selectivity is missing). Minor issues: the "selectivity" p3 is not properly defined. On p3, a figure is undefined. Typo: p2: "could as be". Many of the references are ugly : p3, "in kerasChollet (2015)", this needs fixing.
assistant
 Quality and Clarity The neural networks and neural codes are studied in a concise way, most of the paper is clear. The section on data design, p3, could use some additional clarification wrt to how the data input is encoded (right now, it is hard to understand exactly what happens). Originality I am not aware of other studies on this topic, the proposed approach seems original. Significance The biggest problem I have is with the significance: I don*t see at all how finding somewhat localized responses in the hidden layer of an MLP with just one hidden layer has any bearing on deeper networks structured as CNNs: compared to MLPs, neurons in CNNs have much smaller receptive fields, and are known to be sensitive to selective and distinct features. Overall the results seem rather trivial without greater implications for modern deep neural networks: ie, of course adding dropout improves the degree of localist coding (sec 3.4). Similarly, for a larger network, you will find fewer localist codes (though this is hard to judge, as an exact definition of selectivity is missing). Minor issues: the "selectivity" p3 is not properly defined. On p3, a figure is undefined. Typo: p2: "could as be". Many of the references are ugly : p3, "in kerasChollet (2015)", this needs fixing.