  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 32- channel polytrode data recorded from cat visual cortex, the higher-order
mod- els discover functional connectivity preferentially linking groups of
cells within a cortical layer. Both RBM and sRBM models outperform Ising models
in log- likelihood. 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 in- teractions across space and time when characterizing activity
in cortical networks.
