CRCNS Dataset: Spiking auditory network model and spectro-temporal receptive fields from auditory nerve, midbrain, thalamus and cortex
- 1University of the Pacific
- 2University of Connecticut
Published 09 May. 2020 | License Creative Commons CC BY-NC-SA 4.0
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).
Keywords| Neuroscience | Auditory | Auditory Model | Hearing | Electrophysiology | Spiking Neural Network |
- Fatemeh Khatami and Monty A. Escabí (2018) Spiking network optimized for noise robust word recognition approaches human-level performance and predicts auditory system hierarchy. BioRxiv https://doi.org/10.1101/243915
- Fatemeh Khatami and Monty A. Escabí, Spiking network optimized for word recognition in noise predicts auditory system hierarchy. PLOS Comp. Bio (in press).
- NIH, NIDCD R01DC01513