Unified selective sorting approach to analyse multi-electrode extracellular data

Abstract

Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators.

Rational design of silicon structures for optically controlled multiscale biointerfaces

“…The detected spike-like events using criteria of a high pass filter of 800 Hz and a threshold of 5 times the noise level standard deviation (SD) exhibit waveforms typical of natural extracellular electrophysiological recordings (Fig. 4e) [44][45][46] . Peristimulus time histograms (PSTH) from 16 channels ( Fig.…”

Compatibility Evaluation of Clustering Algorithms for Contemporary Extracellular Neural Spike Sorting

“…The results of clustering are extremely important for deriving statistical analyses; inter-spike intervals (Li et al, 2018 ), correlogram analysis (Harris et al, 2000 ), spike rates (Pillow et al, 2013 ; Ekanadham et al, 2014 ; Veerabhadrappa et al, 2016 ), and detection of bursting neurons (Lewicki, 1998 ; Rey et al, 2015 ). Value of knowledge disseminated from many experimental studies depends on the accuracy of results obtained by spike sorting algorithms.…”

“…Value of knowledge disseminated from many experimental studies depends on the accuracy of results obtained by spike sorting algorithms. For example, probabilities derived from spike rates are employed in the identification of spike classes contributing to overlapping spike events (Pillow et al, 2013 ; Ekanadham et al, 2014 ; Veerabhadrappa et al, 2016 ). The spike sorting procedures strongly depend on clustering algorithms as a primary approach to distinguish spikes with minimal or no human intervention (Pachitariu et al, 2016 ; Rossant et al, 2016 ).…”

Electrophysiological evidence of RML12 mosquito cell line towards neuronal differentiation by 20-hydroxyecdysdone

“…For raster plots, 3 dimension (3D) electrode maps and network burst analysis the self-built software NeuroSigX was used. NeuroSigX software uses novel spike sorting and data analysis algorithms as described in 52 , 53 , to explore the neural spike activity and spatio-temporal behaviour of the neuronal network (http://www.deakin.edu.au/~asimbh). A threshold of 22 µV is employed to maintain the analytical consistency between the preliminary analysis by MC_Rack software and analysis by NeuroSigX.…”