期刊
JOURNAL OF NEUROSCIENCE METHODS
卷 236, 期 -, 页码 92-106出版社
ELSEVIER
DOI: 10.1016/j.jneumeth.2014.08.011
关键词
Biophysically inspired and directed spike-train association metric; Spike-train communities; Community detection; Clustering
资金
- William H. Pickering Fellowship
- Studienstiftung des deutschen Volkes
- Engineering and Physical Sciences Research Council (EPSRC) of the UK [EP/I017267/1]
- Swiss National Science Foundation (SNSF) [PA00P3-131470]
- Human Frontier Sciences Program (HFSP) [RGP0032/2011]
- National Institute of Neurological Disorders and Stroke (NINDS) [R01 NS074015]
- Santander Mobility Award
- EPSRC [EP/I017267/1, EP/I032223/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/I032223/1, EP/I017267/1] Funding Source: researchfish
- Swiss National Science Foundation (SNF) [PA00P3_131470] Funding Source: Swiss National Science Foundation (SNF)
Background: Current neuronal monitoring techniques, such as calcium imaging and multi-electrode arrays, enable recordings of spiking activity from hundreds of neurons simultaneously. Of primary importance in systems neuroscience is the identification of cell assemblies: groups of neurons that cooperate in some form within the recorded population. New method: We introduce a simple, integrated framework for the detection of cell-assemblies from spiking data without a priori assumptions about the size or number of groups present. We define a biophysically-inspired measure to extract a directed functional connectivity matrix between both excitatory and inhibitory neurons based on their spiking history. The resulting network representation is analyzed using the Markov Stability framework, a graph theoretical method for community detection across scales, to reveal groups of neurons that are significantly related in the recorded time-series at different levels of granularity. Results and comparison with existing methods: Using synthetic spike-trains, including simulated data from leaky-integrate-and-fire networks, our method is able to identify important patterns in the data such as hierarchical structure that are missed by other standard methods. We further apply the method to experimental data from retinal ganglion cells of mouse and salamander, in which we identify cell-groups that correspond to known functional types, and to hippocampal recordings from rats exploring a linear track, where we detect place cells with high fidelity. Conclusions: We present a versatile method to detect neural assemblies in spiking data applicable across a spectrum of relevant scales that contributes to understanding spatio-temporal information gathered from systems neuroscience experiments. (C) 2014 The Authors. Published by Elsevier B.V.
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