CNS*2026 Halifax

Introduction


Throughout biology, diversity plays an important role in maintaining robustness and stability [1]. The same is true of the brain [2], where recent datasets [3,4] have shown widespread heterogeneity, marking it as an unavoidable component of neuronal composition. While heterogeneity has been linked to stability and increased computational potential [2], recent experiments have shown its loss accompanies pathological states [5], suggesting an important functional role. Despite this, how changes in heterogeneity arise remains unknown. Oftentimes considered to be a static metaparameter resulting from solely genetic disposition, heterogeneity is, in fact, a highly dynamic property of biological networks [3] arising from various sources.

Methods


We endowed a simple network model of excitatory neurons with a candidate mechanism for homeostatic adaptation of neural excitability [6]. Through combined analytical and numerical approaches, we measured the effect of input statistics on the excitability of individual cells and how this translated into changes in network heterogeneity at the population scale.

Results


Our results indicated that, through adaptation, diversity in synaptic inputs promotes heterogeneity in cell-to-cell excitability due to changes in the statistics of presynaptic firing rates and network topology. In contrast, whenever the statistics of synaptic inputs between cells were too similar, the same adaptation mechanism promoted the decline in heterogeneity. Further, we demonstrate that these changes in heterogeneity can coexist with degeneracy in firing rates between neurons, reminiscent of what is observed in cortical neurons [3].

Discussion


We have demonstrated that a degenerate adaptation rule is a viable mechanism for dynamically regulating heterogeneity in an excitatory network. Specifically, we showed that this adaptation can sustain, increase, or decrease diversity. Such “dynamic diversity” is dependent on the input statistics to each neuron, which are manipulated by external stimuli, and the amount of cell-to-cell diversity in the network itself. These results thus form the framework for future investigation into how the statistics that arise in more complex networks may influence the heterogeneity and hence functional capacity and resilience of neuronal networks.

References


[1] Landi, P, et al (2018). Complexity and stability of ecological networks: a review of the theory. Popul. Ecol., 60(4).
[2] Hutt, A, et al (2023). Intrinsic neural diversity quenches the dynamic volatility of neural networks. PNAS, 120(28).
[3] Lee, B R, et al (2023). Signature morphoelectric properties of diverse GABAergic interneurons in the human neocortex. Science, 382(6667).
[4] Braun, E, et al (2023). Comprehensive cell atlas of the first-trimester developing human brain. Science, 382(6667).
[5] Rich, S, et al (2022). Loss of neuronal heterogeneity in epileptogenic human tissue impairs network resilience to sudden changes in synchrony. Cell Rep., 39(8).
[6] Trotter, D, et al (2026). Intrinsic Plasticity Underlies the Malleability of Neural Network Heterogeneity. PRX Life, 4(1).

Acknowledgement
The authors thank Andre Longtin for helpful discussions.