In vitro validation of in silico identified inhibitory interactions

Honglei Liu; Daniel Bridges; Connor Randall; Sara A Solla; Bian Wu; Paul Hansma; Xifeng Yan; Kenneth S Kosik; Kristofer Bouchard

doi:10.1016/j.jneumeth.2019.04.002

In vitro validation of in silico identified inhibitory interactions

J Neurosci Methods. 2019 Jun 1:321:39-48. doi: 10.1016/j.jneumeth.2019.04.002. Epub 2019 Apr 6.

Authors

Honglei Liu¹, Daniel Bridges², Connor Randall², Sara A Solla³, Bian Wu⁴, Paul Hansma², Xifeng Yan¹, Kenneth S Kosik⁵, Kristofer Bouchard⁶

Affiliations

¹ Department of Computer Science, University of California, Santa Barbara, CA, USA.
² Department of Physics, University of California, Santa Barbara, CA, USA.
³ Department of Physiology, Northwestern University, Chicago, IL, USA.
⁴ Neuroscience Research Institute, University of California, Santa Barbara, CA, USA; Department of Molecular Cellular and Developmental Biology, University of California, Santa Barbara, CA, USA.
⁵ Neuroscience Research Institute, University of California, Santa Barbara, CA, USA. Electronic address: kenneth.kosik@lifesci.ucsb.edu.
⁶ Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience, University of California, Berkeley, CA, USA. Electronic address: kebouchard@lbl.gov.

PMID: 30965073
DOI: 10.1016/j.jneumeth.2019.04.002

Abstract

Background: Understanding how neuronal signals propagate in local network is an important step in understanding information processing. As a result, spike trains recorded with multi-electrode arrays (MEAs) have been widely used to study the function of neural networks. Studying the dynamics of neuronal networks requires the identification of both excitatory and inhibitory connections. The detection of excitatory relationships can robustly be inferred by characterizing the statistical relationships of neural spike trains. However, the identification of inhibitory relationships is more difficult: distinguishing endogenous low firing rates from active inhibition is not obvious.

New method: In this paper, we propose an in silico interventional procedure that makes predictions about the effect of stimulating or inhibiting single neurons on other neurons, and thereby gives the ability to accurately identify inhibitory effects.

Comparison: To experimentally test these predictions, we have developed a Neural Circuit Probe (NCP) that delivers drugs transiently and reversibly on individually identified neurons to assess their contributions to the neural circuit behavior.

Results: Using the NCP, putative inhibitory connections identified by the in silico procedure were validated through in vitro interventional experiments.

Conclusions: Together, these results demonstrate how detailed microcircuitry can be inferred from statistical models derived from neurophysiology data.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Action Potentials*
Algorithms
Animals
Cells, Cultured
Computer Simulation
Drug Delivery Systems
Hippocampus / drug effects
Hippocampus / physiology
Male
Mice, Inbred C57BL
Models, Neurological*
Neural Inhibition / drug effects
Neural Inhibition / physiology*
Neurons / drug effects
Neurons / physiology*
Signal Processing, Computer-Assisted
Sodium Channel Blockers / administration & dosage
Tetrodotoxin / administration & dosage

Substances

Sodium Channel Blockers
Tetrodotoxin