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.

How do humans find their way in new environments, so quickly and efficiently? Humans reuse old knowledge to build new concepts in many non-spatial domains, such as language and drawing. Could people learn maps by a similar process, that extracts common structure to speed up learning, and generalize across maps? Understanding the computational cognitive mechanisms that support this efficiency can advance the study of the human mind and enable more efficient exploration algorithms. We hypothesize that human map learning relies on inferences over the structure of unobserved spaces, based on spatial priors informed by previous experience. We model this by combining Program Induction with a Hierarchical Bayesian framework that explicitly reasons about uncertainty through strong spatial priors. Using a new behavioral Map Induction Task, we demonstrate that this computational framework explains human exploration behavior better than non-inductive models and outperforms state-of-the-art planning algorithms in a realistic spatial navigation domain.

In this paper, we present a spiking neural model of contextdependent decision making. Prefrontal cortex (PFC) plays afundamental role in context dependent behaviour. We modelthe PFC at the level of single spiking neurons, to explore theunderlying computations which determine its contextual re-sponses. The model is built using the Neural EngineeringFramework and performs input selection and integration as anonlinear recurrent dynamical process. The results obtainedfrom the model closely match behavioural and neural experi-mental data obtained from macaque monkeys that are trainedto perform a context sensitive perceptual decision task. Theclose match suggests that the low-dimensional, nonlinear dy-namical model we suggest captures central aspects of contextdependent decision making in primates.