SIMONE: A Realistic Neural Network Simulator to Reproduce MEA-Based Recordings

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2008-04, Vol.16 (2), p.149-160
• Main Authors: Escola, Ricardo, Pouzat, Christophe, Chaffiol, Antoine, Yvert, Blaise, Magnin, Isabelle E., Guillemaud, RÉgis
• Format: Article
• Language: English
• Subjects: Action Potentials; Action Potentials - physiology; Algorithms; American cockroach; Application specific integrated circuits; Bioengineering; Bioinformatics; Biological neural networks; Circuit simulation; Computer Science; Computer Simulation; Data compression; Engineering Sciences; Engines; Extracellular; Image Processing; integrate-and-fire; Life Sciences; Microelectrodes; Modeling and Simulation; Models, Neurological; multielectrode array (MEA); Nerve Net; Nerve Net - physiology; Neural networks; Neurons; Neurons - physiology; noise; Quantitative Methods; Robustness; Signal and Image processing; Signal processing algorithms; SIMONE; simulation; spike detection; spike sorting; SpikeOMatic; Studies; Synaptic Transmission; Synaptic Transmission - physiology; Testing
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2007.914467

Contemporary multielectrode arrays (MEAs) used to record extracellular activity from neural tissues can deliver data at rates on the order of 100 Mbps. Such rates require efficient data compression and/or preprocessing algorithms implemented on an application specific integrated circuit (ASIC) close to the MEA. We present SIMONE (Statistical sIMulation Of Neuronal networks Engine), a versatile simulation tool whose parameters can be either fixed or defined by a probability distribution. We validated our tool by simulating data recorded from the first olfactory relay of an insect. Different key aspects make this tool suitable for testing the robustness and accuracy of neural signal processing algorithms (such as the detection, alignment, and classification of spikes). For instance, most of the parameters can be defined by a probabilistic distribution, then tens of simulations may be obtained from the same scenario. This is especially useful when validating the robustness of the processing algorithm. Moreover, the number of active cells and the exact firing activity of each one of them is perfectly known, which provides an easy way to test accuracy.