 Before we leave the topic of proposed neurophilosophical applications of this theoretical development from “neural networks”-style cognitive/computational neuroscience, one final point of actual scientific detail bears mention. This approach did not remain state-of-the-art computational neuroscience for long. Many neural modelers quickly gave up this approach to modeling the brain. Compartmental modeling enabled computational neuroscientists to mimic activity in and interactions between patches of neuronal membrane (Bower and Beeman 1995). This approach permitted modelers to control and manipulate a variety of subcellular factors that determine action potentials per time unit, including the topology of membrane structure in individual neurons, variations in ion channels across membrane patches, and field properties of post-synaptic potentials depending on the location of the synapse on the dendrite or soma. By the mid-1990s modelers quickly began to “custom build” the neurons in their target circuitry. Increasingly powerful computer hardware still allowed them to study circuit properties of modeled networks. For these reasons, many serious computational neuroscientists switched to working at a level of analysis that treats neurons as structured rather than simple computational devices. With compartmental modeling, vector-to-vector transformations came to be far less useful in serious neurobiological models, replaced by differential equations representing ion currents across patches of neural membrane. Far more biological detail came to be captured in the resulting models than “connectionist” models permitted. This methodological change across computational neuroscience meant that a neurophilosophy guided by “connectionist” resources no longer drew from the state of the art of the scientific field.