Network anatomy and in vivo physiology of visual cortical neurons

By Davi D. Bock, W. Lee, Aaron Kerlin, M. Andermann, Greg Hood, A. Wetzel, S. Yurgenson, E. Soucy, Hyon-Suk Kim, R. Reid. Nature, 2011

887 citations recorded by the source corpus.

In the cerebral cortex, local circuits consist of tens of thousands of neurons, each of which makes thousands of synaptic connections. Perhaps the biggest impediment to understanding these networks is that we have no wiring diagrams of their interconnections. Even if we had a partial or complete wiring diagram, however, understanding the network would also require information about each neuron's function. Here we show that the relationship between structure and function can be studied in the cortex with a combination of in vivo physiology and network anatomy. We used two-photon calcium imaging to characterize a functional property—the preferred stimulus orientation—of a group of neurons in the mouse primary visual cortex. Large-scale electron microscopy of serial thin sections was then used to trace a portion of these neurons’ local network. Consistent with a prediction from recent physiological experiments, inhibitory interneurons received convergent anatomical input from nearby excitatory neurons with a broad range of preferred orientations, although weak biases could not be rejected.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/124dabf0da002ec1aa44eabd3885cd80873d304c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Molecular Biology

Citations in the corpus

The human connectome: Origins and challenges (2013, background): “, 2011) and in mouse visual cortex (Bock et al., 2011).”
On simplicity and complexity in the brave new world of large-scale neuroscience. (2015, background): “…to extract a simple conceptual understanding from complex data is increasingly hampered by the lack of theoretically principled data analytic procedures, as well as theoretical frameworks for how circuit connectivity and dynamics can conspire to generate emergent behavioral and cognitive functions.”
Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images (2012, background): “ingly urgent, as they enable acquisition of huge datasets [6, 20], whose manual analysis is simply unfeasible.”
Advanced CLARITY for rapid and high-resolution imaging of intact tissues (2014, methodology): “Over the last few decades, electron microscopy (EM) has emerged as a foundational method for deciphering details of neuronal circuit structure (Bock et al., 2011; Briggman et al., 2011; Kim et al., 2014).”
How Inhibition Shapes Cortical Activity (2011, neutral): “One possibility that would account for the differences in interneuron tuning properties observed in different systems is that they receive convergent excitatory inputs from surrounding principal cells irrespective of their tuning properties (Bock et al., 2011).”
From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks (2013, background): “…the first and (to date) only available in-vivo single neuron connectome (obtained by means of in vivo two-photon calcium imaging in combination with large-scale electron microscopy) that reports mouse primary visual cortex (layers 1, 2/3 and upper 4) synaptic connections between neurons 19 .”
A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster (2017, methodology): “Eighty-three percent of imaged sections were acquired with a TEMCA2 system (4.3 million Fast Stage moves), while the Autoloader was still in development, and 17% of imaged sections were acquired by the Autoloader (3.5 million GPS moves; ~6,800 machine vision-guided steps to pick, pre-align, and re-s”
Saturated Reconstruction of a Volume of Neocortex. (2015, background): “This work has provided new insights into the visual system (Anderson et al., 2011; Helmstaedter et al., 2011; Kim et al., 2014; Briggman et al., 2011; Bock et al., 2011; see also Takemura et al., 2013; Mishchenko et al., 2010).”
Functional selectivity and specific connectivity of inhibitory neurons in primary visual cortex (2018, methodology): “The limit on the prediction of single cells’ tuning, imposed by trial-totrial variability of their responses, was estimated as the mean Pearson correlation coefficient of single trial responses ri, j and r̃i, j: RC−C = 1 N N ∑ j=1 cov(ri, j, ři, j) σri, jσři, j (11)”
Cellular-resolution connectomics: challenges of dense neural circuit reconstruction (2013, methodology): “All high-resolution imaging techniques generate large volumes of images, which totaled several hundred gigabytes per data set for published projects 11,16,21 , are currently around a dozen terabytes per data set and will likely be several petabytes per data set in the foreseeable future.”