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.

Fluctuating landscapes and heavy tails in animal behavior

“…Indeed, slow modes that evolve on time scales comparable to the observation time are challenging to infer from data, and can give rise to best-fit models that appear "critical". While some of the arguments we have put forward have also been proposed to explain neural "criticality" [101][102][103], we here generalize to a wider range of model classes, using the framework of out-of-equilibrium statistical mechanics to explicitly connect the long time scale emergent behavior with the underlying effective fluctuations. In addition, unlike other approaches [102,104], our framework does not require explicit external drives, but simply collective modes that evolve in a weakly non-ergodic fashion.…”

Sleep restores an optimal computational regime in cortical networks

“…Recent modeling links changes in power law exponents and DCC to the dimensionality and timescales of population dynamics. Specifically, nearness to criticality is influenced by latent variables arising from local activity and external drive, suggesting a locus of control (Morrell et al, 2023). At the cellular/molecular level, plasticity of inhibitory circuits may be foundational to this process (Ma et al, 2019; Stepp et al, 2015; Zeraati et al, 2021), and key molecules that regulate the interaction of interneuron input to pyramidal cells in a sleep/wake-dependent fashion are promising avenues for future work (Pelkey et al, 2015; Severin et al, 2021).…”

Self-Organization Toward Criticality by Synaptic Plasticity

“…the renormalization group approach [197] indicates a closeness to criticality in the sense of thermodynamic phase-transitions, and 4) estimating the branching parameter directly became feasible even from a small set of neurons; this estimate returns a quantification of the distance to criticality [17,39]. It was recently pointed out that the results from fitting the maximal entropy models [198,199] and coarse-graining [200,201] based on empirical correlations should be interpreted with caution. Finding the best way to unite these definitions, and select the most suitable ones for a given experiment remains largely an open problem.…”