Bayesian estimation of network connectivity and motifs

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PI: Ringach, Dario L


Institution: University of California Los Angeles

Title: Bayesian estimation of network connectivity and motifs

Active Learning of Cortical Connectivity: Applications to Two-Photon Imaging

(Martin Bertran, Natalia Martinez, Ye Wang, David Dunson, Guillermo Sapiro, Dario Ringach)

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this “active learning” method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real two-photon data to infer cortical connectivity in the visual system. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model. The data and code are available at

Grant #: EB022915 

Status: Completed


BRAIN Math Project - Ringach.pptx

The cerebral cortex is the seat of our cognitive function, subserving our ability to sense the environment (vision, audition), to program and execute movements, to make decisions, to store and retrieve memories. How groups of neurons interact within a network to perform such complex functions remains unknown and many developmental disorders consist of a mis-wiring of the cortical circuitry. The goal of this project was to examine common circuit motifs that underlie cortical function and to investigate how the cortex gets wired during development.

These are some of our main findings:

• The thalamocortical projection is sparse, with only a few neurons being integrated by cortical cells. Yet, such low convergence is sufficient to endow cortical cells with new properties, such as their selectivity for the orientation of a visual stimulus

• ON/OFF domains, where cells respond preferentially to the onset or offset of light, provide a scaffold used by the cortex to wire cortical cells.

• We tracked the development of thousands of neurons during the critical period and found that the wiring of binocular cells involves the active turnover the binocular pool in a way that improves the matching of tuning properties of left and right eyes.

• We found that the wiring of binocular cells requires normal visual exposure early on. When this fails, binocular cells fail to wire normally.

• We described a new geometric approach to study how populations of neurons code for a visual stimulus under different contexts (masking).

• We developed a new tool to investigate population of neurons via active learning.

• We discovered a new circuit motif – an inhibitory push-pull network.

• We produced state-of-the-art algorithms for the deconvolution of calcium signals in a community-based effort.


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