 The philosophy of science is another area where vector space interpretations of neural network activity patterns has impacted philosophy. In the Introduction to his (1989) book, A Neurocomputational Perspective, Paul Churchland asserted, distinctively neurophilosophically, that it will soon be impossible to do serious work in the philosophy of science without drawing on empirical work in the brain and behavioral sciences. To justify this claim, in Part II of the book he suggested neurocomputational reformulations of key concepts from the philosophy of science. At the heart of his reformulations is a neurocomputational account of the structure of scientific theories (1989: chapter 9). Problems with the orthodox “sets-of-sentences” view of scientific theories have been well-known since the 1960s. Churchland advocated replacing the orthodox view with one inspired by the “vectorial” interpretation of neural network activity. Representations implemented in neural networks (as sketched above) compose a system that corresponds to important distinctions in the external environment, are not explicitly represented as such within the input corpus, and allow the trained network to respond to inputs in a fashion that continually reduces error. According to Churchland, these are functions of theories. Churchland was bold in his assertion: an individual’s theory-of-the-world is a specific point in that individual’s error-synaptic weight vector space. It is a configuration of synaptic weights that partitions the individual’s activation vector space into subdivisions that reduce future error messages to both familiar and novel inputs. (Consider again  Figure 2  and  Figure 3.)  This reformulation invites an objection, however. Churchland boasts that his theory of theories is preferable to existing alternatives to the orthodox “sets-of-sentences” account—for example, the semantic view (Suppe 1974; van Fraassen 1980)—because his is closer to the “buzzing brains” that use theories. But as Bickle (1993) noted, neurocomputational models based on the mathematical resources described above are a long way into the realm of mathematical abstraction. They are little more than novel (albeit suggestive) application of the mathematics of quasi-linear dynamical systems to simplified schemata of brain circuitries. Neurophilosophers owe some account of identifications across ontological categories (vector representations and transformation to what?) before the philosophy of science community will treat theories as points in high-dimensional state spaces implemented in biological neural networks. (There is an important methodological assumption lurking in Bickle’s objection, however, which we will discuss toward the end of the next paragraph.)