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Model-based detection of putative synaptic connections from spike recordings with latency and type constraints

View ORCID ProfileNaixin Ren, View ORCID ProfileShinya Ito, Hadi Hafizi, John M. Beggs, View ORCID ProfileIan H. Stevenson
doi: https://doi.org/10.1101/2020.02.12.944496
Naixin Ren
1Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut, United States
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  • For correspondence: naixin.ren@uconn.edu
Shinya Ito
2Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, Santa Cruz, California, United States
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Hadi Hafizi
3Department of Physics, Indiana University, Bloomington, Indiana, United States
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John M. Beggs
3Department of Physics, Indiana University, Bloomington, Indiana, United States
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Ian H. Stevenson
1Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut, United States
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Abstract

Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. While previous methods often treat the detection of each putative connection as a separate hypothesis test, here we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned from the whole network. We use an extension of the Generalized Linear Model framework to describe the cross-correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the presynaptic neuron’s type, synaptic latencies, and time constants improves synapse detection. In data from simulated networks, this model outperforms two previously developed synapse detection methods, especially on the weak connections. We also apply our model to in vitro multielectrode array recordings from mouse somatosensory cortex. Here our model automatically recovers plausible connections from hundreds of neurons, and the properties of the putative connections are largely consistent with previous research.

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Posted February 13, 2020.
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Model-based detection of putative synaptic connections from spike recordings with latency and type constraints
Naixin Ren, Shinya Ito, Hadi Hafizi, John M. Beggs, Ian H. Stevenson
bioRxiv 2020.02.12.944496; doi: https://doi.org/10.1101/2020.02.12.944496
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Model-based detection of putative synaptic connections from spike recordings with latency and type constraints
Naixin Ren, Shinya Ito, Hadi Hafizi, John M. Beggs, Ian H. Stevenson
bioRxiv 2020.02.12.944496; doi: https://doi.org/10.1101/2020.02.12.944496

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