Regimes and mechanisms of transient amplification in abstract and biological networks
Abstract
We use upper triangular matrices as abstract representations of neuronal networks and directly manipulate their eigenspectra and non-normality to explore different regimes of transient amplification. Counter-intuitively, manipulating the imaginary distribution can lead to highly amplifying regimes. This is noteworthy, because biological networks are constrained by Dales law and the non-existence of neuronal self-loops, limiting the range of manipulations in the real dimension. Within these constraints we can further manipulate transient amplification by controlling global inhibition.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 14
- FWCI
- 1.55
- Field percentile
- top 19%
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Nonlinear transient amplification in recurrent neural networks with short-term plasticity
Yue Kris Wu, Friedemann Zenke·eLifePeer-reviewed·Dec 13, 2021
- Imbalanced amplification: A mechanism of amplification and suppression from local imbalance of excitation and inhibition in cortical circuits
Christopher Ebsch, Robert Rosenbaum·PLoS Computational BiologyPeer-reviewed·Mar 15, 2018
- Dale's law, stability and nonlinearity are sufficient constraints to ignite transient and self-sustained neural dynamics
Nicoleta Condruz, Ivan Bulygin, … Tim P. Vogels·bioRxiv·Sep 21, 2026
- Stability of synchronous states in sparse neuronal networks
Afifurrahman Afifurrahman, Ekkehard Ullner, Antonio Politi·Nonlinear DynamicsPeer-reviewed·Aug 23, 2020
- Discrete synaptic events induce global oscillations in balanced neural networks
Denis Sergeevich Goldobin, Matteo di Volo, Alessandro Torcini·Physical Review LettersPeer-reviewed·Dec 6, 2024
- Controlling Neural Synchrony Through Variance-Driven Coupling in Complex Network Topologies
Nilanjan Panda·bioRxiv·Oct 1, 2025