Functional connectomics reveals general wiring rule in mouse visual cortex

Abstract

To understand how the neocortex underlies our ability to perceive, think, and act, it is important to study the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.

NEURD offers automated proofreading and feature extraction for connectomics

“…We use this capability to show that the ratio of synapses to proximities (synaptic conversion rate) varies as expected across cell-type-specific connections, and we show that our automatic proofreading enables us to identify a set of extremely rare multi-synaptic connections with four or more synapses between pairs of excitatory cells. We find that these pairs have more similar functional properties in vivo, as predicted by a principle of "like-to-like" connectivity in the mouse visual cortex (Lee et al, 2016;Ko et al, 2011;Ding et al, 2023).…”

“…We then investigated how the mean functional response correlation varies as a function of the four different multisynaptic groups. The response correlation was calculated as detailed in (Ding et al, 2023;Wang and Tolias, 2023) through the in silico response correlation of their model. Software.…”

“…A key advantage of the MI-CrONS dataset is the additional functional characterization of matched neurons in vivo prior to EM data collection. The relationship between function and synaptic connectivity is covered in detail in a separate paper (Ding et al, 2023), but here we wanted to provide an illustration of how automated proofreading can enable functional connectomics analyses that would be impossible otherwise. We identified pairs of excitatory neurons connected by one, two, three, or four or more synapses.…”

Towards a Foundation Model of the Mouse Visual Cortex

“…For instance, the architecture of the foundation core allows each modeled neuron's predicted responses to be represented by a tuning function, which can be separated into two components: a spatial component (indicating the position of the neuron's receptive field) and a feature component (describing what the neuron responds to). This factorization of the tuning function was utilized by another study-derived from a version of our model-to analyze the relationship between the functional properties of neurons and synaptic connectivity (Ding et al, 2023a). The researchers discovered that the feature component, but not the spatial component, predicted which neurons were connected at a fine synaptic scale.…”

“…Finally, using the foundation core, we produced accurate functional models for the MICrONS study (MICrONS Consortium et al, 2021): a publicly available dataset containing >70,000 neurons within a ∼1mm 3 cortical volume spanning multiple visual areas. In addition to neuronal function, the MICrONS dataset contains anatomical information about the morphology and connectivity of these neurons on the nanoscale-resolution, providing a com-prehensive dataset for relating structure and function (functional connectomics, Ding et al 2023a).…”