Abstract
We present the design of an adaptive neural spike detector that dynamically adjusts the spike detection threshold based on the signal to noise ratio of the neural data sets. We propose a self-learning architecture, with a threshold-lock loop that feeds back a spike sorting performance index to the FSM inside the adaptive spike detector. The FSM references this performance index and dynamically determines an optimum threshold level for the incoming neural data sets. The architecture enables an autonomous operation without any manual adjustment from users. The simulation results demonstrate that the adaptive spike detector successfully locks to a threshold level, which is optimum from a spike-sorting standpoint.
Published Version
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