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A Bayesian model for identifying hierarchically organised states in neural population activity
Patrick Putzky · Florian Franzen · Giacomo Bassetto · Jakob H Macke

Tue Dec 09 12:30 PM -- 12:50 PM (PST) @ Level 2, room 210
in Spotlight Session 3: Neuroscience and Neural Coding spotlights »

Neural population activity in cortical circuits is not solely driven by external inputs, but is also modulated by endogenous states which vary on multiple time-scales. To understand information processing in cortical circuits, we need to understand the statistical structure of internal states and their interaction with sensory inputs. Here, we present a statistical model for extracting hierarchically organised neural population states from multi-channel recordings of neural spiking activity. Population states are modelled using a hidden Markov decision tree with state-dependent tuning parameters and a generalised linear observation model. We present a variational Bayesian inference algorithm for estimating the posterior distribution over parameters from neural population recordings. On simulated data, we show that we can identify the underlying sequence of population states and reconstruct the ground truth parameters. Using population recordings from visual cortex, we find that a model with two levels of population states outperforms both a one-state and a two-state generalised linear model. Finally, we find that modelling of state-dependence also improves the accuracy with which sensory stimuli can be decoded from the population response.

Author Information

Patrick Putzky (Max Planck Institute for Biological Cybernetics)
Florian Franzen
Giacomo Bassetto (Max Planck Institute for Biological Cybernetics)
Jakob H Macke (University of Tübingen & MPI IS Tübingen)

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