Proceedings of the Annual Meeting of the Cognitive Science Society

Shared structure in neural responses across people can be obscured because these neural responses sit on different ”co-ordinate systems”; hyperalignment can recover this shared structure by placing different people’s brain responses intoa common functional space (Chen et al., 2015; Haxby et al., 2011). Here, we apply this framework to understand thehidden representations of neural networks. Different neural networks can represent the same input-output mapping usingvery different weights. We show that hyperalignment can construct a shared representational space that recovers sharedrepresentation structure across neural networks. We formally connect representational similarity analysis and hyperalign-ment and use simulations to demonstrate the robustness of hyperalignment against several types of transformations thatpreserve the representation geometry of the network. We also empirically tested our method on some supervised learningbenchmarks (CIFAR10, MNIST) for both standard and convolutional networks.