Poster E10, Monday, March 27, 2:30 – 4:30 pm, Pacific Concourse
Internal consistency of spatial information in a cognitive map
Yuri Dabaghian1; 1Baylor College of Medicine, Houston, TX 77019 USA
Learning and memory emerge from the activity of groups of neurons, yet there are few models that can meaningfully connect data acquired at the level of individual cells with whole-animal behavior beyond mere correlation. We have begun to tackle this problem by computationally modeling spatial learning. The foundation of our approach is the hypothesis that the hippocampus provides a rough-and-ready topological framework of an environment rather than a precise metrical map. This model, which is supported by animal studies, allows us to employ algebraic topology to ascertain the effects of specific parameters (e.g., firing rate) on the ability of an ensemble of virtual neurons to correctly “learn” an experimental environment. We have recently studied the effects of two brain rhythms, θ- and γ-waves, on spatial learning. Both have been correlated with learning but it has been difficult to explain precisely why. We found that θ-phase precession parcellates place cell coactivity at the network scale (~150-200 msec), as recorded in animal experiments, and show how this enhances spatial learning. We also found that γ-rhythm synchronizes spiking in dynamical place cell assemblies to enable encoding and retrieval of spatial memories at the synaptic timescale (~50 msec). Topological theory thus provides a conceptually elegant description of the spatial learning process and enables us to explain a wide range of phenomena.
Topic Area: ATTENTION: Spatial