Throughout history we have used technological systems as metaphors to describe how the body and brain might work. Early on, Greek water technology led to the four humors, and that they must be kept in balance. By the eighteenth century both clock mechanisms and flows of fluids were used as metaphors for what happened in the brain, and by the first half of the twentieth century a common metaphor for the brain was a telephone switching network. Indeed, the mathematics that had been developed for signal propagation in telegraph and telephone wires were used to model action potentials in axons. By the sixties, cyberneticians were using models of negative feedback originally developed for the steam engine, and greatly expanded upon during the war of the forties for controlling the aiming of guns, to try develop models for the brain. But these soon ran out of steam, so to speak, and were supplanted in the general consciousness by metaphors of the brain as a digital computer. One started to hear claims of the brain as the hardware, and the mind as the software, a model that really did not end up helping our understanding of either the brain or the mind very much at all. Throughout the later parts of the twentieth century the brain became a massively parallel digital supercomputer, and now one can find claims that the brain and the world wide web are similar in how they work with webpages and neurons playing similar roles, while hyperlinks and synapses map to each other.
Stepping back from this one might suspect that metaphors for the brain will continue to evolve as technology evolves, with the brain always corresponding to the most complex technology we currently possess. One should therefore expect the metaphors for the brain to continue to evolve along with our technology.
But does the metaphor of the day have impact on the science of the day? I claim that it does, and that the computational metaphor leads researchers to ask questions today that will one day seem quaint, at best.
The power of computation, and computational thinking is immense, and its import for science is still in its infancy. But it is not always helpful to confuse computational approximations with computational theories of a natural phenomenon. For instance, consider a classical model of a single planet orbiting a sun. There is a gravitational model, and the behavior of the two bodies can easily be explained as the solution to a simple differential equation, describing forces and accelerations, and their relationships. The equations can be extended for relativity and for multiple planets and instantaneously those equations describe what a physicist would say is happening in the system. Unfortunately the equations become insoluble by this point, and the best we can do to understand the long-term behavior of the system is to use computation, where time is cut into slices and a digital approximation to the continuous description of the local behaviors is used to run a long term simulation. However, only the most diehard of computationalists (and they do exist) would claim that the planets themselves are "computing" what do at each instant. We know that it is more fruitful to continue to think of the planets as moving under the influence of gravity.
When it comes to explaining the brain, and simpler neural systems, the computational metaphors have taken over, and it is easy to find both language and claims about computation. As one example, we see people talk about neural coding—what is it that is coded in the spike train running along an axon over time? But early neurons evolved to synchronize muscle activity better. For instance jellyfish swim much better if all their swimming muscle activates at once so that they go straight, rather than wobble, and evolution found multiple solutions in different species for this problem. The solutions range from really fast spike propagation to carefully tuned attenuation of signals along the triggering axon and local delays at the muscle fibers dependent on spike strength. Furthermore, in many jellyfish there are multiple neural systems based on different propagation chemistries for different behaviors, and even for different modes of swimming. Just as describing planets as computational systems is not the best way to understand what is going on, thinking of neurons in these simple systems as computational systems sending "messages" to each other, is not the best way for describing the behavior of the system in its environment.
The computational model of neurons of the last sixty plus years excluded the need to understand the role of glial cells in the behavior of the brain, or the diffusion of small molecules effecting nearby neurons, or hormones as ways that different parts of neural systems effect each other, or the continuous generation of new neurons, or countless other things we have not yet thought of. They did not fit within the computational metaphor, so for many they might as well not exist. The new mechanisms that we do discover outside of straight computational metaphors get pasted on to computational models but it is becoming unwieldy, and worse, that unwieldiness is hard to see for those steeped in its traditions, racing along to make new publishable increments to our understanding. I suspect that we will be freer to make new discoveries when the computational metaphor is replaced by metaphors that help us understand the role of the brain as part of a behaving system in the world. I have no clue what those metaphors will look like, but the history of science tells us that they will eventually come along.