CRCNS_Khatami_Escabi_v1.0_Repository/README.md

Spiking auditory network model and spectro-temporal receptive fields from auditory nerve, midbrain, thalamus and cortex.

Fatemeh Khatami and Monty A. Escabí

Summary

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).

All of the above data is from anesthetized cats although the experimental procedures and sounds for each of these studies differed. The experimental details for each can be found in the respective publications. The AN data from Kim and Young uses white noise to drive and record from auditory nerve fibers. The AN data was provided by E.D. Young and is included here with his permission. Studies in the IC, MGB and A1 all used dynamic moving ripple sounds as described in Escabi and Schreiner (2002) and Miller et al. (2002), although the parameters for IC were different from the MBG/A1 studies (to allow for faster modulations). For all of these studies, spectro-temporal receptive fields (STRF) were computed using the delivered sounds and correlation based estimation methods (as described in the respective publications).

The archive also includes MATLAB code used to analyze STRFs from neural and model data. The analysis procedures used were developed and are described in the following studies:

M.A. Escabí and C.E. Schreiner (2002). Nonlinear spectrotemporal sound analysis by neurons in the auditory midbrain. J Neurosci 22(10): 4114-31.

A. Qiu, C.E. Schreiner, and M.A. Escabí. (2003) Gabor analysis of auditory midbrain receptive fields: Spectro-temporal and binaural composition. J Neurophysiol. 90 (1): 456-476.

F.A. Rodríguez, H.L. Read, M.A. Escabí (2010) Spectrotemporal Modulation Tradeoff Along the Tonotopic Axis of the Inferior Colliculus. Journal of Neurophysiol. 103: 887-903.

Conditions for using the data

If you use this dataset or any of the accompanying analysis or modeling code please cite the above manuscript along with a citation to the CRCNS dataset:

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

http://dx.doi.org/xxxxxx/xxxxxx

If neural data or a specific analysis code is used a citation should be provided to the respective manuscript for the original data or analysis source (e.g., Kim and Young 1994 for AN data; e.g., Qiu et al. for Gabor analysis code; etc.).

Methods

The methods for implementing the auditory HSNN model can be found in the above citation (Khatami & Escabí 2020). Details of the neural recordings, data acquisition and sound delivery for each of the auditory structures (AN, IC, MGB and A1) can be found in the respective manuscripts noted above.

Data files organization

The data is organized in a directory tree with the following subdirectories. Below, unix convention (using '/' instead of '\') is used for the directory structure.

[Documentation]{.underline}

./Documentation/

• This directory contains the documentation (this file) for the CRCNS database.

[Manuscripts]{.underline}

This directory contains all of the manuscripts noted above.

[MATLAB Code]{.underline}

This directory contains the relevant MATLAB code used for the auditory network model as well as the code used to analyze neural data. The directory structure is as follows:

./MatlabCode/audnetwork/ - Auditory network code

./MatlabCode/cochleogram/ - Cochlear model (cochleogram)

./MatlabCode/examples/ - Example code for both the neural data and sound analysis

./MatlabCode/ripplesounds/ - Code used to generate ripple sounds

./MatlabCode/strfanalysis/ - Code used to analyze neural and model STRFs

./MatlabCode/strfgabor/ - Code used for Gabor STRF model fitting procedure

[Model Data]{.underline}

../ModelData/DigitsInNoise/

• Contains the digits in speech babble noise sounds at multiple SNRs that are used in this study. The original clean sounds are available from TIMIT Acoustic-Phonetic Continuous Speech Corpus (https://catalog.ldc.upenn.edu/LDC93S1).

./ModelData/ModelSTRF/

• Contains the measured and Gabor fitted model STRFs for the optimal and high-resolution auditory networks

[Neural Data]{.underline}

./NeuralData/AuditoryNerve/

• Auditory nerve data. This is the original data directory provided by E.D. Young. This directory contains all of the auditory nerve data along with code for reading and analyzing the data. Details are available in the file README.doc

[Inferior Colliculus Data]{.underline}

./NeuralData/InferiorColliculus/DMREnvelope/

• Dynamic moving ripple (DMR) envelopes used for sound generation and used to estimate STRFs (see Escabi & Schreiner 2002)

./NeuralData/InferiorColliculus/SPET/

• Spike event time (SPET) files

./NeuralData/InferiorColliculus/STRF/

• Computed Inferior Colliculus STRFs

[Auditory Thalamus and Cortex Data]{.underline}

./NeuralData/AuditoryThalamusAndCortex/DMRENvelope/c534/

• Contains the dynamic moving ripple (DMR) envelopes used for experiment c534

./NeuralData/AuditoryThalamusAndCortex/DMRENvelope/ct476/

• Contains the dynamic moving ripple (DMR) envelope used for experiment ct476

./NeuralData/AuditoryThalamusAndCortex/Cortex/SPET/

• Cortical spike event time (SPET) files

./NeuralData/AuditoryThalamusAndCortex/Cortex/STRF/

• Computed cortical STRFs

./NeuralData/AuditoryThalamusAndCortex/Thalamus/SPET/

• Thalamic spike event time (SPET) files

./NeuralData/AuditoryThalamusAndCortex/Thalamus/STRF/

• Computed thalamic STRFs

Data format

[Neural and model data files]{.underline}

All neural data from IC, thalamus and cortex as well as the model data are stored in MATLAB files (.mat). Details of the data format and usage are available in the provided MATLAB examples which are commented for usage details. Documentation for all of the analysis routines also provide details of the data format and their usage. This information can be obtained using 'help filename.m.'

The auditory nerve data is stored in binary formatted files as originally provided by E.D. Young. Details for analyzing and extracting the data is available in the documentation file ./NeuralData/AuditoryNerve/README.doc.

[Speech in babble noise sound files]{.underline}

All sound files are stored in standard WAV format as well as in MATLAB (.mat) format.

[Dynamic moving ripple envelope files]{.underline}

These files contain the sound envelopes that are used to generate the DMR stimuli and which are used to calculate the STRFs for IC, MGB and A1. These envelope files are generated by the main program used to generate the DMR sounds (using 'ripnoise.m') and are subsequently used during the STRF analysis (using 'rtwstrfdb.m').

Each of the respective directories for IC, thalamus and cortex contains the envelope files in the subdirectory 'DMREnvelope'. The envelopes are stored in binary formatted files ('float') with an extension '.spr.' For instance, in the IC envelope directory, the envelope is stored in the file dynamicripple750ic.spr where 750 indicates the maximum temporal modulation rate for this sound. In addition, there is an accompanying file dynamicripple750ic_param.mat which contains parameters that were used to generate the DMR sounds and which are also necessary to analyze neural data and compute STRFs (using 'rtwstrfdb.m').

Code

[Auditory Model]{.underline}

The MATLAB functions that are used to simulate the auditory network model are located in the directory ./MatlabCode/audnetwork/ and ./Matalab/cochleogram/. The primary routines are:

cochleogram.m - Cochlear model output given a sound vector. The output of this file is used as the input to the hierarchical spiking neural network

integratefirenetworkaud.m - Auditory spiking neural network model

glnpaudnetwork - Gabor Linear-Nonlinear-Poisson (LNP) network

[Neural data analysis code]{.underline}

The routines used to analyze neural data are located in the directories: ./MatlabCode/strfanalysis/ and ./MatlabCode/strfgabor/. The primary routines used are:

rtwstrfdb.m - Used to compute STRFs from the dynamic moving ripple sounds (Escabi & Scrheiner 2003)

strfparam.m - Computes a variety of STRF parameters (Rodriguez et al 2010)

strfgaborfit.m - Fits neural STRFs to the Gabor STRF model (Qiu et al 2003)

For all of the above files, a detailed list of input and output parameters along with the function syntax can be obtained by typing "help filename.m" on the MATLAB command line.

[Ripple stimuli generation code]{.underline}

The routines used to generate dynamic moving ripple and ripple noise stimuli are located in the directory: './MatlabCode/ripplesounds/'. The main routine used to generate both sounds is ripnoise.m.

How to get started

MATLAB example files are provided that illustrate the usage of the model and neural data analysis codes. All of the example routines are commented to guide the reader through the various examples as well as to document the parameters used. These files are all located in the directory ./MatlabCode/examples/:

Example1.m - HSNN model simulation

Example2.m - STRF parameter estimation for IC, Thal, CTX

Example3.m - STRF calculation for IC unit example

Example4.m - STRF calculation for cortical unit example

Example5.m - STRF calculation for thalamic unit example

Example6.m - Gabor STRF model fitting (IC, Thal and CTX)

Example7.m - STRF Parameter estimation for HSNN model

Example8.m - Gabor LNP Network simulation

Example9.m - Illustrates how to generate ripple stimuli

Example10.m - Illustrates how to apply a Bayesian Classifier to the outputs of the auditory spiking neural network

How to get help

To get help with the data set post any questions on the forum at CRCNS.org.