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The Surprising Relativism of the Brain’s GPS
- IshKebab 8y agoSurely our current knowledge of artificial neural networks shows that a single cell is unlikely to encode for a simple disentangled concept like absolute position, or even predicted future position? I know artificial and real neural networks are only loosely similar, but I would be surprised if real neural networks did not also have distributed representations.
- RootGenerated 8y agoThere are many areas of the brain that have very different properties from each other. This makes sense when you consider that they likely solve different (sub) problems. While a distributed representation is very useful for things like semantics clustering, it actually hurts the ability to discriminate between similar things when you need to remember a specific relationship between things. This is the case in the hippocampus where we store memories of events, as well as the structure of the world (e.g. a map of sorts as mentioned in this article). So, an artificial neural network probably isn't the best model for this.
- IshKebab 8y agoArtificial neural networks also have different regions that have very different properties from each other. They have convolutional layers and fully connected layers, and some architectures have separate semantic labelling and visual processing parts. Take a look at Mask R-CNN for example: https://medium.com/@jonathan_hui/image-segmentation-with-mask-r-cnn-ebe6d793272 https://medium.com/@jonathan_hui/image-segmentation-with-mas... I don't think that is evidence that the brain doesn't use distributed representations.
- heavenlyblue 8y agoTriangulate a volume of space. Let neurons be vertices of that graph. Every time you are at a certain point of space three or more (or four, for 3D space) neurons will be spiking depending on how close you are to them. That gives you absolute positioning. Also the ability to increase resolution for certain volumes of space.