P246 A Computational Pipeline for Simulating Mouse Visual Cortex Microcircuits with Spiking Neural Networks
Margherita Premi*1, Carlo Andrea Sartori1, Giancarlo Ferrigno1, Alessandra Pedrocchi1, Fiorenzo Artoni1, Alberto Antonietti1 1NeuroEngineering and Medical Robotics Laboratory - Department of Electronics, Information and Bioengineering - Politecnico di Milano, Milan, Italy *E-mail: margherita.premi@polimi.it
Introduction To integrate in vitro methodologies with in silico techniques, to investigate brain development and neural circuit interactions, we are preparing a computational pipeline to recreate Brain-on-Chip [1] systems with spiking neural networks. We leveraged the MICrONs dataset [2], which provides detailed reconstructions of neurons and astrocytes, with their connections, in a cubic millimeter of mouse visual cortex. The dataset presents significant challenges for computational modeling, particularly regarding quality and quantity of the automatically identified synapses. In this work, we establish a pipeline for transforming raw data into functional spiking neural networks that accurately represent cortical microcircuits.
Methods The MICrONs dataset showed critical limitations: insufficient synapses and incorrect morphological attributions. Two solutions were implemented:
● Synapse enhancement through cloning, generating a cluster of synapses placed in a sphere centered on the original synapse. The new synapses are validated through layer densities analyses [3]. ● Improve synapse attribution using proofread astrocytes to establish connectivity patterns for non-proofread cells. For neurons, templates from proofread synapses will serve as models for non-proofread neurons.
The framework incorporates layer-specific connectivity with bidirectional astrocyte-neuron interactions. Comparisons were made with networks having the same neurons but different connectivity [4].
Results Our synapse enhancement method generated clusters of 10 synapses placed in spheres with 10 μm radius, centered on original synapses. This successfully increased the overall synapses count while maintaining layer-specific patterns. A geometric approach was developed that defines minimum ellipsoidal domains containing all synapses belonging to each proofread astrocyte [5]. These ellipsoid representations served as spatial patterns for non-proofread astrocytes. For neurons, template-based attribution from proofread synapses increased the accuracy of connection identification. Layer-specific connectivity analysis demonstrated that our reconstructed network successfully preserved the characteristic connection patterns across cortical layers (Fig1). Discussion This work addresses the identified limitations in using the MICrONs dataset. The developed methods correct connectivity data, enabling more accurate modeling of cortical microcircuits. The approach preserves connections and layer-specific organization unique to the MICrONs dataset. This network is then imported and simulated as a spiking neural model to generate biologically realistic activity. This framework also allows testing alternative network architectures (e.g., random, small-world, etc) compared to the accurate structural connectivity. Future work will refine astrocyte-neuron interaction models. These methodologies could then be applied to BoC experimental data, further validating the computational approaches.
Figure 1. Fig. 1: A. Enhanced synaptic density distribution across cortical depth. B. Astrocyte influence zones represented as ellipsoidal regions, each containing associated synapses. C. Functional connectivity diagram of the reconstructed microcircuit showing layer-specific connections and bidirectional signaling with astrocytes. Acknowledgements This work is part of the Extended Partnership "A multiscale integrated approach to the nervous system in health and disease" (MNESYS), funded by the European Union - Next Generation EU under the National Recovery and Resilience Plan, Mission 4, Component 2, Investment 1.4, Project PE00000006, CUP E63C22002170007, Spoke 3 "Neuronal Homeostasis and brain-environment interaction". References 1. https://doi.org/10.1063/5.0121476 2. https://doi.org/10.1101/2021.07.28.454025 3. https://doi.org/10.1523/JNEUROSCI.0090-23.2023 4. https://doi.org/10.1101/2024.11.18.624135 5. https://github.com/rmsandu/Ellipsoid-Fit
