Computational models of neural auditory processing

Explicit neuron firing models are investigated for use in computational modeling of auditory processing. Models for primary auditory neurons are driven by the receptor signal from a hair cell model, which is driven in turn by a filtering model of basilar membrane motion. The output of the primary auditory neuron model is a times-of-firing representation of neural signals. Certain types of processing, such as auto-correlation and cross-correlation, are very simple with this representation, not requiring multiplication. The neuron model used is a leaky-integrate-to-threshold model with a refractory period. Several neurons are modeled at each hair cell, or filter channel. It is found in experiments with these models that the detailed time-of-firing information contains most of the cues of speech formants, pitch, direction, etc. The more conventionally studied firing rate vs. place representation misses important aspects of these cues. Models of pitch perception, binaural directional perception, and sound separation are being based on the cochlear and neural models. The models are all implemented as computational algorithms, and are used in support of related speech recognition and hearing research.