The mammalian visual system, from retina to neocortex, has been extensively studied at both anatomical and functional levels. Anatomy indicates the cortico-thalamic system is hierarchical, but characterization of cellular-level functional interactions across multiple levels of this hierarchy is lacking, partially due to the challenge of simultaneously recording activity across numerous regions. Here, we describe a large, open dataset (part of the Allen Brain Observatory) that surveys spiking from units in six cortical and two thalamic regions responding to a battery of visual stimuli. Using spike cross-correlation analysis, we find that inter-area functional connectivity mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas. Classical functional measures of hierarchy, including visual response latency, receptive field size, phase-locking to a drifting grating stimulus, and autocorrelation timescale are all correlated with the anatomical hierarchy. Moreover, recordings during a visual task support the behavioral relevance of hierarchical processing. Overall, this dataset and the hierarchy we describe provide a foundation for understanding coding and dynamics in the mouse cortico-thalamic visual system..
19Despite advances in experimental techniques and accumulation of large datasets concerning the 20 composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like 21 conditions remains challenging. Here we report and publicly release a biophysically detailed circuit 22 109 future, more sophisticated studies of all cortical layers. To enable this, we make the software code, the 110 model, and simulation results publicly available (see SI). 111 112 113 RESULTS 114 Construction and optimization of the model 115The network ( Fig. 1a, b) consisted (see Methods) of models of individual neurons [30] from an early 116 version of the Allen Cell Types Database [31], employing compartmental representation of somato-117 dendritic morphologies (~100-200 compartments per cell) and 10 active conductances at the soma that 118 enabled spiking and spike adaptation. Although recent additions to the Allen Cell Types Database 119include models of neurons with active conductances in the dendrites as well, those models are very 120 computationally expensive, which was prohibitive for the breadth of our study (see below). In addition, 121in terms of somatic spike output, the current versions of such models do not exhibit much better 122 performance than the models with active conductances restricted to the soma [31], and, thus, we used 123 these latter, cheaper models. The cells were distributed uniformly in a cylinder ~400 m in radius, 124representing the central portion of V1, and 100 m height with density of 200,000 mm -3 [32]. Five single 125 neuron models represent five "types" of neurons -three major excitatory groups as determined by Cre-126 lines (Scnn1a, Rorb, Nr5a1, 85% of all cells) and two groups of parvalbumin-positive fast-spiking 127 5 interneurons (15%, denoted as PV1 and PV2), which form the majority of interneurons in L4 [33]. All 128 10,000 biophysical cells were exact copies of these five models. The cell models correspond to regular-129 spiking excitatory cells and fast-spiking interneurons (PV+); whereas non-PV+ interneurons do exist in 130 L4, they are a relative minority [33], and therefore were neglected for simplicity. Furthermore, 35,000 131 much simpler leaky-integrate-and-fire (LIF) neurons, with only two groups -excitatory and inhibitory -132were placed around the biophysically detailed "core" to prevent boundary artefacts (see SI). The 133 complete model accounted for over half of V1 L4 cells (45,000 out of ~70,000). Below, we primarily 134 focus on the properties of the biophysical core circuit. 135Three independent instantiations were generated using different random seeds. The connectivity and 136inputs into these three model instantiations were distinct, but all followed the rules described below. 137All simulations were performed using the python 2.7 code (an early version of the BioNet package [35]) 138 employing NEURON 7.4 [34]. 139
12Experimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting 13 opportunities and formidable challenges to existing theoretical and modeling approaches. To turn 14 massive datasets into predictive quantitative frameworks, the field needs software solutions for 15 systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling 16ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of 17 resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical 18 approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and 19 others, BMTK offers consistent user experience across multiple levels of resolution. It permits highly 20 sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new 21 users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is 22an open-source package provided as a resource supporting modeling-based discovery in the community. 23 24
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