  Including local automatic gain control (AGC) circuitry into a silicon cochlea
design has been challenging because of transistor mismatch and model
complexity. To address this, we present an alternative system-level algorithm
that implements channel-specific AGC in a silicon spiking cochlea by measuring
the output spike activity of individual channels. The bandpass filter gain of a
channel is adapted dynamically to the input amplitude so that the average
output spike rate stays within a defined range. Because this AGC mechanism only
needs counting and adding operations, it can be implemented at low hardware
cost in a future design. We evaluate the impact of the local AGC algorithm on a
classification task where the input signal varies over 32 dB input range. Two
classifier types receiving cochlea spike features were tested on a speech
versus noise classification task. The logistic regression classifier achieves
an average of 6% improvement and 40.8% relative improvement in accuracy when
the AGC is enabled. The deep neural network classifier shows a similar
improvement for the AGC case and achieves a higher mean accuracy of 96%
compared to the best accuracy of 91% from the logistic regression classifier.
