Higher Order Correlations within Cortical Layers Dominate Functional Connectivity in Microcolumns

We report on simultaneous recordings from cells in all layers of visual cortex and models developed to capture the higher order structure of population spiking activity. Specifically, we use Ising, Restricted Boltzmann Machine (RBM) and semi-Restricted Boltzmann Machine (sRBM) models to reveal laminar patterns of activity. While the Ising model describes only pairwise couplings, the RBM and sRBM capture higher-order dependencies using hidden units. Applied to 32channel polytrode data recorded from cat visual cortex, the higher-order models discover functional connectivity preferentially linking groups of cells within a cortical layer. Both RBM and sRBM models outperform Ising models in loglikelihood. Additionally, we train all three models on spatiotemporal sequences of states, exposing temporal structure and allowing us to predict spiking from network history. This demonstrates the importance of modeling higher order interactions across space and time when characterizing activity in cortical networks.