Intrinsic neural diversity quenches the dynamic volatility of neural networks
authors: Axel Hutt, Scott Rich, Taufik A. Valiante, Jérémie Lefebvre
doi: 10.1073/pnas.2218841120
CITATION
Hutt, A., Rich, S., Valiante, T. A., & Lefebvre, J. (2023). Intrinsic neural diversity quenches the dynamic volatility of neural networks. Proceedings of the National Academy of Sciences, 120(28), e2218841120. https://doi.org/10.1073/pnas.2218841120
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 (resilience). To better understand the relationship between excitability heterogeneity (variability in excitability within a population of neurons) and resilience, we analyzed both analytically and numerically a nonlinear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. Homogeneous networks demonstrated increases in excitability, and strong firing rate correlations—signs of instability—in response to a slowly varying modulatory fluctuation. Excitability heterogeneity tuned network stability in a context-dependent way by restraining responses to modulatory challenges and limiting firing rate correlations, while enriching dynamics during states of low modulatory drive. Excitability heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in population size, connection probability, strength and variability of synaptic weights, by quenching the volatility (i.e., its susceptibility to critical transitions) of its dynamics. Together, these results highlight the fundamental role played by cell-to-cell heterogeneity in the robustness of brain function in the face of change.
fleeting notes
“heterogeneity is the norm in biology” - i like this quote
tons of cell types and properties = lots of dynamical possibilities but a challenge to understand robustness and resilience
analyzed data - structural an non linear neural networks
homogenous networks have increased excitability and correlations - which is instable
excitability heterogeneity tunes network stability
brains change overtime but neural dynamics stay the same in healthy brains
- a sign of resilience
resilience of neural circuits have been studied through experiments in the STG
- highly stable and robust activity despite heterogeneity and environmental changes
complex networks lose stability when population size increases, coupling weights are too strong, connection probability is too dense, or connectivity motifs become too heterogeneous
to model neural dynamics:
- used large nonlinear neural netowkr
- sparse balanced excitatory and inhibitory connections
- looked at many time scales to see persistence of dynamics
- added fluctuating modulatory input
slow time scales - slow modulation mimicks resting state
diversity in excitability restrains population response to perturbations
- it promotes gradual and linear changes to firing rates
defining “insults” as increases in network size, conneciton probability, strength and variability of synaptic weights, modulatory fluctuations