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Sunday, July 19 • 4:20pm - 4:40pm
O4: Towards multipurpose bio-realistic models of cortical circuits

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One of the central questions in neuroscience is how structure of brain circuits determines their activity and function. To explore such structure-function relations systematically, we integrate information from large-scale experimental surveys into data-driven, bio-realistic models of brain circuits, with the current focus on the mouse cortex.

Our 230,000-neuron models of the mouse cortical area V1 [1] were constructed at two levels of granularity – using either biophysically-detailed or point-neurons. These models systematically integrated a broad array of experimental data [1–3]: the information about distribution and morpho-electric properties of different neuron types in V1; connection probabilities, synaptic weights, axonal delays, and dendritic targeting rules inferred from a thorough survey of the literature; and a sophisticated representation of visual inputs into V1 from the Lateral Geniculate Nucleus, fit to in vivo recordings. The model activity has been tested against large-scale in vivo recordings of neural activity [4]. We found a good agreement between these experimental data and the V1 models for a variety of metrics, such as direction selectivity, as well as less good agreement for other metrics, suggesting avenues for future improvements. In the process of building and testing models, we also made predictions about the logic of recurrent connectivity with respect to functional properties of the neurons, some of which have been verified experimentally [1].

In this presentation, we will focus on the model’s successes in quantitative matching of multiple experimental measures, as well as failures in matching other metrics. Both successes and failures shed light on the potential structure-function relations in cortical circuits, leading to experimentally testable hypotheses. Our models are shared freely with the community: https://portal.brain-map.org/explore/models/mv1-all-layers. We also freely share our software tools – the Brain Modeling ToolKit (BMTK; alleninstitute.github.io/bmtk/), which is a software suite for model building/simulation [5], and the SONATA file format [6] (github.com/allenInstitute/sonata).

1. Billeh, Y. N. et al. Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex. Neuron 106, 388-403.e18 (2020).
2. Gouwens, N. W. et al. Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nat. Neurosci. 22, 1182–1195 (2019).
3. Gouwens, N. W. et al. Systematic generation of biophysically detailed models for diverse cortical neuron types. Nat. Commun. 9, 710 (2018).
4. Siegle, J. H. et al. A survey of spiking activity reveals a functional hierarchy of mouse corticothalamic visual areas. bioRxiv 805010 (2019) doi:10.1101/805010.
5. Gratiy, S. L. et al. BioNet: A Python interface to NEURON for modeling large-scale networks. PLoS One 13, e0201630 (2018).
6. Dai, K. et al. The SONATA data format for efficient description of large-scale network models. PLOS Comput. Biol. 16, e1007696 (2020).

avatar for Anton Arkhipov

Anton Arkhipov

Mindscope Program at the Allen Institute, Seattle, USA

Sunday July 19, 2020 4:20pm - 4:40pm CEST
  Oral, Sensory Systems
  • Moderator Christoph Metzner; Soledad Gonzalo Cogno