  Metazoans are capable of gathering information from their environments and
respond in predictable ways. These computational tasks are achieved by means of
more or less complex networks of neurons. Task performance must be reliable
over an individual's lifetime and must deal robustly with the finite lifespan
of cells or with connection failure - rendering aging a relevant feature in
this context. How do computations degrade over an organism's lifespan? How
reliable can computations remain throughout? In order to answer these
questions, here we approach the problem under a multiobjective (Pareto)
optimization approach. We consider a population of digital organisms equipped
with a neural network that must solve a given computational task reliably. We
demand that they remain functional (as reliable as possible) for an extended
lifespan. Neural connections are costly (as an associated metabolism in living
beings) and degrade over time. They can also be regenerated at some expense. We
investigate the simultaneous minimization of the metabolic burden (due to
connections and regeneration costs) and the computational error and the
tradeoffs emerging thereof. We show that Pareto optimal designs display a broad
range of potential solutions: from small networks with high regeneration rate,
to larger and redundant circuits that regenerate slowly. The organism's
lifespan and the external damage rates are found to act as an evolutionary
pressure that improve the exploration of the space of solutions and poses
tighter optimality conditions. We also find that large damage rates to the
circuits can constrain the space of the possible and pose organisms to commit
to unique strategies for neural systems maintenance.
