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

Authors:
E. Biffi;D. Ghezzi;A. Pedrocchi;G. Ferrigno
Affiliations:
Neuroengineering and Medical Robotics Laboratory, Bioengineering Department, Politecnico di Milano, Milano, Italy;Neuroscience and Brain Technologies Department, Italian Institute of Technology, Genova, Italy;Neuroengineering and Medical Robotics Laboratory, Bioengineering Department, Politecnico di Milano, Milano, Italy;Neuroengineering and Medical Robotics Laboratory, Bioengineering Department, Politecnico di Milano, Milano, Italy
Venue:
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Year:
2010

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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.