About ac-6

Summary of the data.

The accompanying neural data, sounds, and models are outlined in the publication:

Fatemeh Khatami and Monty A. Escabí, Spiking network optimized for word recognition in noise predicts auditory system hierarchy. PLOS Comp. Bio (in press).

The archive includes a MATLAB implementation of the auditory model from the above citation. The auditory model consists of a front end cochlear model that is connected to a hierarchical spiking neural network (HSNN). The HSNN contains inhibitory and excitatory connections between consecutive layers as outlined in the above manuscript. The original sounds used to test the network in a speech recognition task were derived from clean speech from the TIMIT Acoustic-Phonetic Continuous Speech Corpus (https://catalog.ldc.upenn.edu/LDC93S1). Here, edited speech sounds consisting of digits (“zero” to “nine”) that have added background noise and that were used in the study to test the network are included.

The archive also includes neural data that was used to compare results from the auditory system to the auditory HSNN model. Neural data consists of recordings from auditory nerve (AN), inferior colliculus (IC), auditory thalamus (MGB) and cortex (A1) from the following previously published manuscripts:

Auditory Nerve (AN):
Kim, P. J. & Young, E. D. Comparative analysis of spectro-temporal receptive fields, reverse correlation functions, and frequency tuning curves of auditory-nerve fibers. J Acoust Soc Am 95, 410-422 (1994).

Inferior Colliculus (IC):
Chen, C., Read, H. L. & Escabi, M. A. Precise feature based time-scales and frequency decorrelation lead to a sparse auditory code. J Neurosis 32, 8454-8468 (2012).

Auditory Thalamus (MGB ) and Cortex (A1):
Miller, L. M., Escabi, M. A., Read, H. L. & Schreiner, C. E. Spectrotemporal receptive fields in the lemniscal auditory thalamus and cortex. J Neurophysiol 87, 516-527 (2002).

Link to data and documentation

Getting help using the data

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