Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems

Abstract

Understanding the activity of large populations of neurons is difficult due to the combinatorial complexity of possible cell-cell interactions. To reduce the complexity, coarse-graining had been previously applied to experimental neural recordings, which showed over two decades of scaling in free energy, activity variance, eigenvalue spectra, and correlation time, hinting that the mouse hippocampus operates in a critical regime. We model the experiment by simulating conditionally independent binary neurons coupled to a small number of long-timescale stochastic fields and then replicating the coarse-graining procedure and analysis. This reproduces the experimentally-observed scalings, suggesting that they may arise from coupling the neural population activity to latent dynamic stimuli. Further, parameter sweeps for our model suggest that emergence of scaling requires most of the cells in a population to couple to the latent stimuli, predicting that even the celebrated place cells must also respond to non-place stimuli.

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Cited by in corpus (10)

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• Does the brain behave like a (complex) network? I. Dynamics
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• Statistical properties of large data sets with linear latent features
• Inferring couplings in networks across order-disorder phase transitions
• Information propagation in Gaussian processes on multilayer networks
• Memory-induced long-range order in dynamical systems
• Extrinsic vs Intrinsic Criticality in Systems with Many Components
• Generative random latent features models and statistics of natural images
• Mutual information in changing environments: non-linear interactions, out-of-equilibrium systems, and continuously-varying diffusivities