%0 Journal Article
%T Development and Validation of a Spike Detection and Classification Algorithm Aimed at Implementation on Hardware Devices
%A E. Biffi
%A D. Ghezzi
%A A. Pedrocchi
%A G. Ferrigno
%J Computational Intelligence and Neuroscience
%D 2010
%I Hindawi Publishing Corporation
%R 10.1155/2010/659050
%X 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. 1. Introduction Neuronal cells communicate by means of electric pulses, called Action Potentials (APs) or, briefly, spikes [1, 2]. These voltage changes have been traditionally recorded with conventional electrodes (e.g., glass pipettes), therefore the number of neurons simultaneously recorded and the time needed for electrodes placement are well known limits [3]. To overcome these experimental difficulties, the use of MicroElectrode Array biochips (MEAs) guarantees the possibility to record extracellular activity of neuronal preparations from tens of electrodes at the same time [4]. Because of the inherent nature of the extracellular recording, each electrode records the neuronal activity from a region, where generally tens of neurons are present thus providing the acquisition of a Multi Unit Activity (MUA). To extract the activity of every single firing unit influencing that electrode from the MUA, we need a process called ˇ°spike sortingˇ± which includes AP detection and classification. There are two different ways to acquire and analyze electrophysiological data: store the raw trace observed on all electrodes and perform spike detecting and sorting later (offline sorting) or detect and sort spikes immediately (during acquisition) and only store the sorted spikes (real-time online sorting) [5]. A compromise between these approaches is to detect spikes online and only store the detected spikes for later offline sorting. Spike detection and classification of neuronal action potentials can be performed using supervised methods [6, 7] or using unsupervised ones [8, 9]. As the particular knowledge of the APs is not available before we
%U http://www.hindawi.com/journals/cin/2010/659050/