Proceedings of the Annual Meeting of the Cognitive Science Society

In this paper, we present a spiking neural model of life spaninference. Through this model, we explore the biologicalplausibility of performing Bayesian computations in the brain.Specifically, we address the issue of representing probabil-ity distributions using neural circuits and combining them inmeaningful ways to perform inference. We show that applyingthese methods to the life span inference task matches humanperformance on this task better than an ideal Bayesian modeldue to the use of neuron tuning curves. We also describe po-tential ways in which humans might be generating the priorsneeded for this inference. This provides an initial step towardsbetter understanding how Bayesian computations may be im-plemented in a biologically plausible neural network.