A 10 MSampleSec Digital Neural Spike Detection for a 1024 Pixels Multi Transistor Array Sensor 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 Signaltonoise ratio (SNR) of samples recorded by a 1024 pixels Multi Transistor Array (MTA) at 9375 SamplesSec per pixel of  sim 6 boldsymbolmumathbfm  pitch. The implementation integrates in the same system a Time Division Multiplexing (TDM) filter and a spatiotemporal correlation algorithm, to increase the SNR up to identify spikes as low as  215 boldsymbolmumathbfV_0mathbfPEAK . The digital filter is a 2nd order high pass Infinite input response (IIR) Chebyshev filter. The spatiotemporal correlation exploits the MTA smaller pixels size and the high samplerate 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 multichannel 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.

DigitalSignalProcessor Realization of Izhikevich Neural Network for RealTime Interaction with Electrophysiology Experiments The paper presents a realization on a digital signal processor (DSP) of an Izhikevich Neural Network operating with biologically plausible realtime constants. The paper demonstrates the realtime realization of different neuron behavioral modes, i.e., regularspiking, chattering, bursting, and fastspiking under proper parametrization. Realtime spiketimingdependentplasticity has also been embedded in the neural network realization. The paper studies the maximum array size that can be implemented on a TMS320C6455 microprocessor to be able to reproduce correctly the realtime dynamics of the different behaviors. The TMS320C6454, from the same DSP family as TMS320C6455, is embedded in a commercial microelectrode array system for realtime interaction with biological neural cell cultures. As demonstrator, a simple classification of two binary patterns has been implemented. Upon learning activation, the system robustly unsupervisely learns to differentiate the two patterns.