Development and validation of a spike detection and classification algorithm aimed at implementation on hardware devices

E. Biffi, D. Ghezzi, A. Pedrocchi, G. Ferrigno

Research output: Contribution to journalArticlepeer-review

Abstract

Neurons cultured in vitro on MicroElectrode Array (MEA) devices connect to each other, forming a network. To study electrophysiological activity and long term plasticity effects, long period recording and spike sorter methods are needed. Therefore, on-line and real time analysis, optimization of memory use and data transmission rate improvement become necessary. We developed an algorithm for amplitude-threshold spikes detection, whose performances were verified with (a) statistical analysis on both simulated and real signal and (b) Big O Notation. Moreover, we developed a PCA-hierarchical classifier, evaluated on simulated and real signal. Finally we proposed a spike detection hardware design on FPGA, whose feasibility was verified in terms of CLBs number, memory occupation and temporal requirements; once realized, it will be able to execute on-line detection and real time waveform analysis, reducing data storage problems.

Original languageEnglish
Article number659050
JournalComputational Intelligence and Neuroscience
Volume2010
DOIs
Publication statusPublished - 2010

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)
  • Neuroscience(all)

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