P222 Computational framework for analyzing oscillatory patterns in neural circuit models
Nikita Novikov1*, Chelsea Ekwughalu1,2, Samuel Neymotin1,3, Salvador Dura-Bernal1,4
1Center for Biomedical Imaging & Neuromodulation, The Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
2Department of Physics, Barnard College, New York, NY, USA
3Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
4Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
*nikknovikov@gmail.com
Introduction
Neural oscillations coordinate brain activity, with abnormal patterns linked to neurological disorders. Understanding their emergence from biological parameters is crucial for effective intervention. While biophysically detailed models provide mechanistic insight, their complexity makes direct analysis computationally expensive and mathematically intractable. To address this, we present a computational framework for the systematic exploration of key parameters governing oscillatory dynamics in large-scale neural networks.
Methods
Our approach relies on the eigenmode decomposition of frequency-dependent transfer matrices, originally proposed in [1] for LIF neurons. In contrast to [1], we do not derive transfer matrices analytically, but instead, we developed a toolbox for their numerical estimation, extending the method to arbitrary models. The estimation is done by automatic construction and simulation of surrogate models, where a single population remains intact, others are replaced by equivalent spike generators, and a sinusoidal signal is added to the probed input. The toolbox is built on top of the NetPyNE framework [2] and supports high-performance parallel simulations.
Results
We validated our approach on a simplified model of cortical layers 2/3 and 4, demonstrating that it accurately decomposes network activity into oscillatory modes and predicts the amplitudes and phases of the oscillations (Fig. 1A-C). Using the computed transfer matrices, we estimated the effects of synaptic weight perturbations by modifying the relevant transfer coefficients and analyzing the resulting eigenmodes, without needing full-model simulations. These predictions closely matched direct simulations of the perturbed model (Fig. 1D, E), confirming that our method reliably identifies key connections that shape oscillatory activity.
Discussion
We propose a framework for systematically exploring the relationship between biological parameters and emergent oscillations. Our tool estimates inter-population transfer coefficients through multiple independent simulations of simple surrogate models, a process well-suited for efficient parallelization. Once computed, these coefficients provide insight into the full model’s oscillatory modes and their sensitivity to parameter perturbations. Our results validate the approach, demonstrating its potential for analyzing neural circuits and informing future neurostimulation and pharmacological interventions.
Figure 1. Figure 1. A – power spectral densities (PSDs. B – eigenmode amplitudes. C – complex relations between L2e and other populations at 60 Hz; arrows – projections of the 1st mode onto populations; black – distribution of simulated instantaneous relations. D, E – effects of L2e->L2i weight perturbation on the 1st mode amplitude (D) and L2i PSD (E).
Acknowledgements
The work is supported by the grants: R01 MH134118-01, RF1NS133972-01, R01DC012947-06A1, R01DC019979, ARL Cooperative Agreement W911NF-22-2-0139, P50 MH109429
References
1. Bos, H., Diesmann, M., & Helias, M. (2016). Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit. PLOS Computational Biology, 12(10), e1005132. https://doi.org/10.1371/journal.pcbi.1005132
2. Dura-Bernal, S., Suter, B. A., Gleeson, P., Cantarelli, M., Quintana, A., Rodriguez, F., Kedziora, D. J., Chadderdon, G. L., Kerr, C. C., Neymotin, S. A., McDougal, R. A., Hines, M., Shepherd, G. M., & Lytton, W. W. (2019). NetPyNE, a tool for data-driven multiscale modeling of brain circuits. eLife, 8, e44494. https://doi.org/10.7554/eLife.44494
