Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex

Abstract

Structural rules underlying functional properties of cortical circuits are poorly understood. To explore these rules systematically, we integrated information from extensive literature curation and large-scale experimental surveys into a data-driven, biologically realistic model of the mouse primary visual cortex. The model was constructed at two levels of granularity, using either biophysically-detailed or pointneurons, with identical network connectivity. Both variants were compared to each other and to experimental recordings of neural activity during presentation of visual stimuli to awake mice. While constructing and tuning these networks to recapitulate experimental data, we identified a set of rules governing cell-class specific connectivity and synaptic strengths. These structural constraints constitute hypotheses that can be tested experimentally. Despite their distinct single cell abstraction, spatially extended or point-models, both perform similarly at the level of firing rate distributions. All data and models are freely available as a resource for the community.

Local Connectivity and Synaptic Dynamics in Mouse and Human Neocortex

“…Microcircuit representations have evolved from experiments in different species, regions, ages, etc that focus on one or a few circuit elements. These efforts offer an excellent depth of insight to isolated regions of the circuit but lack a complete and unified view of the circuit (14) . Furthermore the difficulty of accessing these historical data discourages reuse and reanalysis.…”

“…Transcriptomic delineation of cell types currently offers the most refined description (10)(11)(12)(13) , however, tools that target transcriptomic cell types are not available. Though our knowledge of cell types has advanced, a complete description of the connectivity and synaptic properties among cell subclasses in each cortical layer is still lacking (14) .…”

Anatomical and neurophysiological data on primary visual cortex suffice for reproducing brain-like robust multiplexing of visual function

“…Both use high-dimensional neural codes for images. But the neural representation in the model of (1) is more robust because it employs, like the brain (11), a power law for the explained variance in higher PCA components that is close to a theoretically optimal compromise between the goal to be sensitive to details of visual inputs, and the goal to be robust to noise from the visual input and within the network.…”

Cortical representation of touchin silico

“…Accordingly, a large number of large-scale reconstructed computational models of cortical function (see Supplemental Table 1, the discussion section and this recent review (Fan and Markram, 2019)), including macaque (Chariker et al, 2016; Schmidt et al, 2018a, 2018b; Schuecker et al, 2017; Zhu et al, 2009), cat (Ananthanarayanan et al, 2009) and mouse/rat (Arkhipov et al, 2018; Billeh et al, 2019) visual cortex, rat auditory cortex (Traub et al, 2005), rat hindlimb sensory cortex (Markram et al, 2015), cerebellum (Sudhakar et al, 2017) and “stereotypical” mammalian neocortex (Izhikevich and Edelman, 2008; Markram, 2006; Potjans and Diesmann, 2014; Reimann et al, 2013; Tomsett et al, 2015), have been introduced, where neuronal dynamics are approximated using neuron models that range from integrate-and-fire point neurons (Ananthanarayanan et al 2009, Sharp et al, 2014; Zhu et al, 2009, Potjans & Diesmann, 2014, Chariker et al 2016, Bernardi et al 2020, Schmidt et al, 2018a, Schmidt et a. 2018b, Schuecker et al 2017) to morphologically reconstructed multi-compartment neurons (Traub et al 2005, Markram et al 2006, Izhikevich & Edelman 2008, Reimann et al 2013, Markram et al 2015, Tomsett et al, 2015, Sudhakar et al 2017, Arkhipov et al 2018, Billeh et al 2019). These models have given insights in a range of topics including the nature of the local field potentials (Reimann et al, 2013; Tomsett et al, 2015), mechanisms of state transitions (Markram et al, 2015), frequency selectivity (Zhu et al, 2009), the influence of single-neuron properties on network activity (Arkhipov et al 2018) and the relation between connectivity patterns and single-cell functional properties (i.e.…”

“…These models have given insights in a range of topics including the nature of the local field potentials (Reimann et al, 2013; Tomsett et al, 2015), mechanisms of state transitions (Markram et al, 2015), frequency selectivity (Zhu et al, 2009), the influence of single-neuron properties on network activity (Arkhipov et al 2018) and the relation between connectivity patterns and single-cell functional properties (i.e. receptive fields, Billeh et al 2019).…”