We present a computational model performing the n-back task. This task requires a number of cognitive processes including rapid binding, updating, and retrieval of items in working memory. The model is implemented in spiking leakyintegrate- and-fire neurons with physiologically constrained parameters, and anatomically constrained organization. The methods of the Semantic Pointer Architecture (SPA) are used to construct the model. Accuracies and reaction times produced by the model are shown to match human data. Namely, characteristic decline in accuracy and response speed with increase of n is reproduced. Furthermore, the model provides evidence, contrary to some past proposals, that an active removal process of items in working memory is not necessary for an accurate performance on the n-back task

Winner-take-all (WTA) mechanisms are an important compo-nent of many cognitive models. For example, they are oftenused to decide between multiple choices or to selectively di-rect attention. Here we compare two biologically plausible,spiking neural WTA mechanisms. We first provide a novelspiking implementation of the well-known leaky, competingaccumulator (LCA) model, by mapping the dynamics onto apopulation-level representation. We then propose a two-layerspiking independent accumulator (IA) model, and compare itsperformance against the LCA network on a variety of WTAbenchmarks. Our findings suggest that while the LCA net-work can rapidly adapt to new winners, the IA network is bet-ter suited for stable decision making in the presence of noise.

The ability to associate words is an important cognitive skill.In this study we investigate different methods for representingword associations in the brain, using the Remote AssociatesTest (RAT) as a task. We explore representations derived fromfree association norms and statistical n-gram data. Althoughn-gram representations yield better performance on the test, acloser match with the human performance is obtained with rep-resentations derived from free associations. We propose thatword association strengths derived from free associations playan important role in the process of RAT solving. Furthermore,we show that this model can be implemented in spiking neu-rons, and estimate the number of biologically realistic neuronsthat would suffice for an accurate representation.

The semantic fluency task has been used to understand the ef-fects of semantic relationships on human memory search. Avariety of computational models have been proposed that ex-plain human behavioral data, yet it remains unclear how mil-lions of spiking neurons work in unison to realize the cogni-tive processes involved in memory search. In this paper, wepresent a biologically constrained neural network model thatperforms the task in a fashion similar to humans. The modelreproduces experimentally observed response timing effects,as well as similarity trends within and across semantic cate-gories derived from responses. Three different sources of theassociation data have been tested by embedding associationsin neural connections, with free association norms providingthe best match.