 One of the attractions of distributed representations in connectionist models is that they suggest a solution to the problem of providing a theory of how brain states could have meaning. The idea is that the similarities and differences between activation patterns along different dimensions of neural activity record semantical information. So the similarity properties of neural activations provide intrinsic properties that determine meaning. However, when it comes to compositional linguistic representations, Fodor and Lepore (1992: Ch. 6) challenge similarity based accounts, on two fronts. The first problem is that human brains presumably vary significantly in the number of and connections between their neurons. Although it is straightforward to define similarity measures on two nets that contain the same number of units, it is harder to see how this can be done when the basic architectures of two nets differ. The second problem Fodor and Lepore cite is that even if similarity measures for meanings can be successfully crafted, they are inadequate to the task of meeting the desiderata which a theory of meaning must satisfy.