This paper presents an FPGA implementation of a DSP performing real time spike detection on the electrical activity of an in vitro neuronal culture of rat hippocampi. The DSP enhances the Signal-to-noise ratio (SNR) of samples recorded by a 1024 pixels Multi Transistor Array (MTA) at 9375 Samples/Sec per pixel of ~6 μm pitch. The implementation integrates in the same system a Time Division Multiplexing (TDM) filter and a spatio-temporal correlation algorithm, to increase the SNR up to identify spikes as low as 215 μ V0-PEAK. The digital filter is a 2nd order high pass Infinite input response (IIR) Chebyshev filter. The spatio-temporal correlation exploits the MTA smaller pixels size and the high sample-rate to compute an equivalent pixel from a group of 7 pixels and 3 consecutive frames for a total of 21 samples and it is supported by a multi-channel noise power estimation. Finally, this paper shows the results achieved on the performed experiments and compares the system with others experiments using different sensors and algorithms.
Tambaro, M., Vallicelli, E., Tomasella, D., Baschirotto, A., Vassanelli, S., Maschietto, M., et al. (2019). A 10 MSample/Sec digital neural spike detection for a 1024 pixels multi transistor array sensor. In 2019 26th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2019 (pp.711-714). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICECS46596.2019.8965141].
A 10 MSample/Sec digital neural spike detection for a 1024 pixels multi transistor array sensor
Tambaro M.;Vallicelli E. A.;Baschirotto A.;De Matteis M.
2019
Abstract
This paper presents an FPGA implementation of a DSP performing real time spike detection on the electrical activity of an in vitro neuronal culture of rat hippocampi. The DSP enhances the Signal-to-noise ratio (SNR) of samples recorded by a 1024 pixels Multi Transistor Array (MTA) at 9375 Samples/Sec per pixel of ~6 μm pitch. The implementation integrates in the same system a Time Division Multiplexing (TDM) filter and a spatio-temporal correlation algorithm, to increase the SNR up to identify spikes as low as 215 μ V0-PEAK. The digital filter is a 2nd order high pass Infinite input response (IIR) Chebyshev filter. The spatio-temporal correlation exploits the MTA smaller pixels size and the high sample-rate to compute an equivalent pixel from a group of 7 pixels and 3 consecutive frames for a total of 21 samples and it is supported by a multi-channel noise power estimation. Finally, this paper shows the results achieved on the performed experiments and compares the system with others experiments using different sensors and algorithms.
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