title: Revealing cell assemblies at multiple levels of granularity.
abstract: 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.
  
  
  METHODS
  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
  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.
authors.affiliation: Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA 91125, USA. Electronic address: ybilleh@caltech.edu.
authors.forename: Yazan N
authors.initials: YN
authors.lastname: Billeh
authors.affiliation: Department of Mathematics, Imperial College London, London SW7 2AZ, UK. Electronic address: michael.schaub09@imperial.ac.uk.
authors.forename: Michael T
authors.initials: MT
authors.lastname: Schaub
authors.affiliation: Allen Institute for Brain Science, Seattle, WA 98103, USA.
authors.forename: Costas A
authors.initials: CA
authors.lastname: Anastassiou
authors.affiliation: Department of Mathematics, Imperial College London, London SW7 2AZ, UK.
authors.forename: Mauricio
authors.initials: M
authors.lastname: Barahona
authors.affiliation: Allen Institute for Brain Science, Seattle, WA 98103, USA.
authors.forename: Christof
authors.initials: C
authors.lastname: Koch
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D000557:Ambystoma; D000818:Animals; D003198:Computer Simulation; D005106:Exploratory Behavior / Q000502:physiology; D006624:Hippocampus / Q000502:physiology; D008390:Markov Chains; D008810:Mice, Inbred C57BL; D008959:Models, Neurological; D009433:Neural Inhibition / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D051381:Rats; D012165:Retinal Ganglion Cells / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D013028:Space Perception / Q000502:physiology; D046509:Tissue Culture Techniques
keywords: Biophysically inspired and directed spike-train association metric; Clustering; Community detection; Spike-train communities