Intrinsic neural diversity quenches the dynamic volatility of neural networks

Abstract

Heterogeneity is the norm in biology. The brain is no different: neuronal cell-types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs and ion channel distributions. While this biophysical diversity enriches neural systems' dynamical repertoire, it remains challenging to reconcile with the robustness and persistence of brain function over time. To better understand the relationship between heterogeneity and resilience, we analyzed both analytically and numerically a non-linear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. We examined how neural diversity expressed as excitability heterogeneity in this network influences its dynamic volatility (i.e., its susceptibility to critical transitions). We exposed this network to slowly-varying modulatory fluctuations, continuously interrogating its stability and resilience. Our results show that excitability heterogeneity implements a homeostatic control mechanism tuning network stability in a context-dependent way. Such diversity was also found to enhance network resilience, quenching the volatility of its dynamics, effectively making the system independent of changes in many control parameters, such as population size, connection probability, strength and variability of synaptic weights as well as modulatory drive. Taken together, these results highlight the fundamental role played by cell-type heterogeneity in the robustness of brain function in the face of change.

Controlling morpho-electrophysiological variability of neurons with detailed biophysical models

“…Indeed, cells often form a 'continuum of types', as visible from the often blurry limits where some cells cannot be consistently classified [Gouwens et al, 2019, Scala et al, 2021. In particular, firing variability has been shown to be important for a range of biological mechanisms of the brain, such as for network properties [Aradi and Soltesz, 2002], resilience to changes in synchrony with epilepsy [Santhakumar and Soltesz, 2004, Hutt et al, 2022, increased information content for efficient population coding [Padmanabhan and Urban, 2010, Tripathy et al, 2013, Chelaru and Dragoi, 2008, Mejias and Longtin, 2012 or energy efficiency [Deistler et al, 2022].…”