P109 From point neurons to biophysically detailed networks: A data-driven framework for multi-scale modeling of brain circuits
Beatriz Herrera*1, Xiao-Ping Liu2, Shinya Ito1, Darrell Haufler1Kael Dai1, Brian Kalmbach2, Anton Arkhipov1
1Allen Institute, Seattle WA, 98109, USA
2Allen Institute for Brain Science, Seattle WA, 98109, USA
*Email: beatriz.herrera@alleninstitute.org
Introduction
Patch-seq technique links transcriptomics, neuron morphology and electrophysiology in individual neurons. Recent work at the Allen Institute resulted in a comprehensive mouse and human neuron database with Patch-seq data for diverse cell types. In this study, we carry out a large-scale optimization of generalized-leaky integrate-and-fire (GLIF) models on these Patch-seq data for thousands of cells. Furthermore, anticipating applications of models of diverse cell types for network simulations at multiple levels of resolution, we propose a strategy to convert point-neuron network models into detailed biophysical models.Methods
GLIF modelswere obtained from the Patch-seq electrophysiology recordings, and we quantified the optimization performance for different types of current injection stimulus, as well as comparing with an earlier approach[1].
GLIF biophysical conversion strategyinvolved (1) mapping each GLIF neuron to a biophysical neuron model; (2) replacing GLIF network parameters with corresponding biophysical parameters[2]; (3) estimating conversion factors to translate current-based synaptic weights to conductance-based for each source-target pair; (4) constructing the biophysical network model using the scaling factors from (3).Results
We find that optimizing GLIF models using long-square step current stimuli generalizes better to noise stimuli (than vice versa). With this approach, we obtained GLIF models for a total of 6,460 cells from diverse types of both mouse and human glutamatergic and GABAergic interneurons[3–6].
We tested our GLIF-to-biophysical network conversion on our V1 point-neuron model[2]. We simulated responses to pre-synaptic populations and calculated synaptic weight factors for matching GLIF firing rates. We built the V1 biophysical model, fine-tuning weights to align with recordings and validating againstin vivoNeuropixels data.Discussion
Our work establishes the foundation for more comprehensive simulations of brain networks. We shed light on the relationships between genes and morpho-electrophysiological features by developing models for various cell types with available transcriptomic data from Patch-Seq experiments. Furthermore, our method for transforming point-neuron network models into detailed biophysical models will aid in developing and optimizing such complex models, as point-neuron networks are less computationally intensive and simpler to optimize for reproducing experimental data.
Acknowledgements
We thank the founder of the Allen Institute, Paul G. Allen, for his vision, encouragement, and support. This work was supported by the National Institutes of Health (NIH) under the following award nos.: NIBIB R01EB029813, NINDS R01NS122742 and U24NS124001, and NIMH U01MH130907. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
References
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