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How Can I Know What You Think?: Assessing Representational Similarity in Neural Systems
Abstract
How do my mental states compare to yours? We suggest that, while we may not be able to compare experiences, we can compare neural representations, and that the correct way to compare neural representations is through analysis of the distances between them. In this paper, we present a technique for measuring the similarities between representations at various layers of neural networks. We then use the measure to demonstrate empirically that different artificial neural networks trained by backpropagation on the same categorization task, even with different representational encodings of the input patterns and different numbers of hidden units, reach states in which representations at the hidden units are similar.
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