title: How many neurons can we see with current spike sorting algorithms?
abstract: Recent studies highlighted the disagreement between the typical number of neurons observed with extracellular recordings and the ones to be expected based on anatomical and physiological considerations. This disagreement has been mainly attributed to the presence of sparsely firing neurons. However, it is also possible that this is due to limitations of the spike sorting algorithms used to process the data. To address this issue, we used realistic simulations of extracellular recordings and found a relatively poor spike sorting performance for simulations containing a large number of neurons. In fact, the number of correctly identified neurons for single-channel recordings showed an asymptotic behavior saturating at about 8-10 units, when up to 20 units were present in the data. This performance was significantly poorer for neurons with low firing rates, as these units were twice more likely to be missed than the ones with high firing rates in simulations containing many neurons. These results uncover one of the main reasons for the relatively low number of neurons found in extracellular recording and also stress the importance of further developments of spike sorting algorithms.
authors: Department of Engineering, University of Leicester, UK. cp155@le.ac.uk
authors: Carlos
authors: C
authors: Pedreira
authors: Juan
authors: J
authors: Martinez
authors: Matias J
authors: MJ
authors: Ison
authors: Rodrigo
authors: R
authors: Quian Quiroga
mesh_terms: D000465:Algorithms; D002452:Cell Count / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D055724:Electrophysiological Phenomena; D005110:Extracellular Space / Q000502:physiology; D005189:False Positive Reactions; D008839:Microelectrodes; D009474:Neurons / Q000502:physiology*