Modeling the structure-function relation in a reconstructed cortical tissue

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PI: Mihalas, Stefan (contact); Arkhipov, Anton


Institution: Allen Institute

Title: Modeling the structure-function relation in a reconstructed cortical tissue  


How is connectivity between neurons related to patterns of activity exhibited by these neurons in vivo? This question of structure-function relations in brain circuits is of fundamental importance. However, our current understanding of structure-function relations is relatively poor, in large part because the fine structure of neuronal connectivity has remained largely unknown.
Fortunately, recent experimental work by our collaborators at the Allen Institute is now resulting in transformational new datasets that characterize connectivity in the mouse cortical area V1 at the level of Cell Types using multi-patch synaptic physiology and at the level of individual neurons using electron microscopy (EM). For the first time in history of neuroscience, we will have connectome of individual neurons coupled with dense recordings of activity in ~1 mm^3 of V1, plus systematic characterization of synaptic properties.
We are leveraging these unique datasets to build and share with the community new models of V1 and use them to study the relationships between cortical connectivity and in vivo activity and computations. We are analyzing how multiple features of neuronal code depend on individual cell properties and on higher-order connectivity motifs, which are present in the EM connectome, but not in the statistics-based connectivity inferred from sparse measurements at the Cell Types level or from existing literature. The resulting models and simulations are freely shared with the community as a resource for guiding future experiment designs, improve biological realism in models, and assist in generating and testing theories.


Grant #: EB029813 




Our main model of the mouse V1 is shared with the community (at two levels of resolution - using biophysically detailed and point-neuron models): 


As of 8/2023, we are aware of the following papers and preprints where these models and/or its components were used by external groups:

1.Keller et al., Neuron 108:1181-1193.e8 (2020).
2.Giacopelli et al. Sci. Rep. 11, 4345 (2021).  
3.Stöckl et al. bioRxiv 2021.05.18.444689 (2021).
4.Scherr, F. & Maass, W. bioRxiv 2021.11.17.469025 (2021).
5.Chen et al. Science Adv. 8, eabq7592 (2022).
6.Riihimäki. arXiv:2202.07307 (2022).
7.Jabri & MacLean. Neural Computation 34, 2347–2373 (2022).
8.Dura-Bernal et al. bioRxiv 2022.02.03.4790366 (2022).
9.Olah et al. eLife 11:e79535 (2022).
10. Wang et al. Artificial Intelligence. CICAI 2022. vol. 13606 (2022).
11. Shi et al. PLOS Computational Biology 18(9): e1010427 (2022).
12. Unger et al. Computational Geometry, 109: 101941 (2023).
13. Schneider et al. Cell Reports 42, 112318 (2023).
14. Schneider et al., Cell Reports 42, 112492 (2023).

Furthermore, we have recently updated the biophysically detailed V1 model to enable accurate simulations of the local field potential (LFP). This work is published (Rimehaug et al., eLife, 2023; and the updated model is shared publicly along with the simulation results:
Slide for the Brain Awardee Welcome Meeting

2021 Brain PI Meeting


Link to Poster:


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