Skip to main content

Online neural connectivity estimation with noisy group testing

Publication ,  Conference
Draelos, A; Pearson, JM
Published in: Advances in Neural Information Processing Systems
January 1, 2020

One of the primary goals of systems neuroscience is to relate the structure of neural circuits to their function, yet patterns of connectivity are difficult to establish when recording from large populations in behaving organisms. Many previous approaches have attempted to estimate functional connectivity between neurons using statistical modeling of observational data, but these approaches rely heavily on parametric assumptions and are purely correlational. Recently, however, holographic photostimulation techniques have made it possible to precisely target selected ensembles of neurons, offering the possibility of establishing direct causal links. A naive method for inferring functional connections is to stimulate each individual neuron multiple times and observe the responses of cells in the local network, but this approach scales poorly with the number of neurons. Here, we propose a method based on noisy group testing that drastically increases the efficiency of this process in sparse networks. By stimulating small ensembles of neurons, we show that it is possible to recover binarized network connectivity with a number of tests that grows only logarithmically with population size under minimal statistical assumptions. Moreover, we prove that our approach, which reduces to an efficiently solvable convex optimization problem, can be related to Variational Bayesian inference on the binary connection weights, and we derive rigorous bounds on the posterior marginals. This allows us to extend our method to the streaming setting, where continuously updated posteriors allow for optional stopping, and we demonstrate the feasibility of inferring connectivity for networks of up to tens of thousands of neurons online.

Duke Scholars

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2020

Volume

2020-December

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Draelos, A., & Pearson, J. M. (2020). Online neural connectivity estimation with noisy group testing. In Advances in Neural Information Processing Systems (Vol. 2020-December).
Draelos, A., and J. M. Pearson. “Online neural connectivity estimation with noisy group testing.” In Advances in Neural Information Processing Systems, Vol. 2020-December, 2020.
Draelos A, Pearson JM. Online neural connectivity estimation with noisy group testing. In: Advances in Neural Information Processing Systems. 2020.
Draelos, A., and J. M. Pearson. “Online neural connectivity estimation with noisy group testing.” Advances in Neural Information Processing Systems, vol. 2020-December, 2020.
Draelos A, Pearson JM. Online neural connectivity estimation with noisy group testing. Advances in Neural Information Processing Systems. 2020.

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2020

Volume

2020-December

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology