Coupling differences in LIP and FEF can be explained by the strength of recurrent connectivity in attractor networks

To test popular mechanistic models of persistent activity with multi-region in vivo neural data (LIP & FEF), we fit a population GLM to spiking data from a classical ‘bump’ attractor network used to model spatial working memory. Our main goal was to infer the recurrent connectivity of the attractor network using the GLM and to provide a benchmark to relate the data to the mechanistic models. We parametrically varied the recurrent connection strength of the excitatory population in the attractor network and generated synthetic datasets that mimicked the real experiment. Both LIP and FEF appear to be independently capable of stable attractor dynamics, whereas the between-area interactions fell below the threshold for independently generating persistent activity, despite significant coupling.