Varieties of representation in evolved and embodied neural networks

In this paper I discuss one of the key issues in the philosophy of neuroscience: neurosemantics. The project of neurosemantics involves explaining what it means for states of neurons and neural systems to have representational contents. Neurosemantics thus involves issues of common concern between the philosophy of neuroscience and philosophy of mind. I discuss a problem that arises for accounts of representational content that I call "the economy problem": the problem of showing that a candidate theory of mental representation can bear the work required within in the causal economy of a mind and an organism. My approach in the current paper is to explore this and other key themes in neurosemantics through the use of computer models of neural networks embodied and evolved in virtual organisms. The models allow for the laying bare of the causal economies of entire yet simple artificial organisms so that the relations between the neural bases of, for instance, representation in perception and memory can be regarded in the context of an entire organism. On the basis of these simulations, I argue for an account of neurosemantics adequate for the solution of the economy problem.