Topic: attractor states

Previous work investigating the neural dynamics underlying context-dependent decision making typically analyses a single brain region or recurrent neural network (RNN) [1,2]. However, evidence suggests that the information required to solve tasks is distributed across multiple regions [3]. Here, we investigate the neural dynamics across seven brain regions of the non-human primate brain where such distributed information has been observed[3]. By examining within-region geometry and dynamics, we identified significant differences not captured by classical decoding analyses. Using multi-regional

The Potts associative network, a neurally-informed generalization of the Hopfield model, can serve as a simplified representation of global cortical dynamics. Considering memories to be sparsely distributed patterns of local cortical activity, we have studied latching dynamics – the largely random hopping through global attractor states driven by adaptation – as a regime potentially underlying complex cognition such as language production and mind wandering. In a recent study, we have differentiated a Potts associative network into two parts, representing frontal and posterior cortices, to cru