title: Use of adaptive network burst detection methods for multielectrode array data and the generation of artificial spike patterns for method evaluation.
abstract: OBJECTIVE
  Multielectrode arrays are an informative extracellular recording technology that enables the analysis of cultured neuronal networks and network bursts (NBs) are a dominant feature observed in these recordings. This paper focuses on the validation of NB detection methods on different network activity patterns and developing a detection method that performs robustly across a wide variety of activity patterns.
  
  
  METHODS
  A firing rate based approach was used to generate artificial spike timestamps where NBs were introduced as episodes where the probability of spiking increases. Variations in firing and bursting characteristics were also included. In addition, an improved methodology of detecting NBs is proposed, based on time-binned average firing rates and time overlaps of single channel bursts. The robustness of the proposed method was compared against three existing algorithms using simulated, publicly available and newly acquired data.
  
  
  RESULTS
  A range of activity patterns were generated by changing simulation variables that correspond to NB duration (40-2200 ms), intervals (0.3-16 s), firing rates (0.1-1 spikes s(-1)), local burst percentage (0%-90%), number of channels in local bursts (20-40) as well as the number of tonic and frequently-bursting channels. By extracting simulation parameters directly from real data, we generated synthetic data that closely resemble activity of mouse and rat cortical cultures at native and chemically perturbed states. In 50 simulated data sets with randomly selected parameter values, the improved NB detection method performed better (ascertained by the f-measure) than three existing methods (p < 0.005). The improved method was also able to detect clustered, long-tailed and short-frequent NBs on real data.
  
  
  CONCLUSIONS
  This work presents an objective method of assessing the applicability of NB detection methods for different neuronal activity patterns. Furthermore, it proposes an improved NB detection method that can be used robustly across a range of data types.
authors: Department of Mechanical Engineering, University of Melbourne, Parkville, VIC 3010, Australia.
authors: G D C
authors: GD
authors: Mendis
authors: E
authors: E
authors: Morrisroe
authors: S
authors: S
authors: Petrou
authors: S K
authors: SK
authors: Halgamuge
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000222:Adaptation, Physiological / Q000502:physiology*; D000818:Animals; D002478:Cells, Cultured; D002540:Cerebral Cortex / Q000166:cytology / Q000502:physiology*; D019295:Computational Biology / Q000295:instrumentation / Q000379:methods; D051379:Mice; D008810:Mice, Inbred C57BL; D008839:Microelectrodes; D009415:Nerve Net / Q000166:cytology / Q000502:physiology*; D051381:Rats