The D-REX model is designed for exploring the computational mechanisms in the brain involved in statistical regularity extraction from dynamic sounds. Utilizing a Bayesian inference framework for performing sequential prediction in the presence of unknown changepoints, this model can be used to test alternative statistics collected by the brain while listening to ongoing sounds. Perceptual parameters can be used to fit the model to individual behavior.

Download includes README, model code, and a helper function for displaying model output --- all code is in MATLAB.

Please enter the information below to download:


NOTE: Contact information will be used solely to track usage of this software.
Our ability to parse our acoustic environment relies on the brain's capacity to extract statistical regularities from surrounding sounds. Previous work in regularity extraction has predominantly focused on the brain's sensitivity to predictable patterns in sound sequences. However, natural sound environments are rarely completely predictable, often containing some level of randomness, yet the brain is able to effectively interpret its surroundings by extracting useful information from stochastic sounds. It has been previously shown that the brain is sensitive to the marginal lower-order statistics of sound sequences (i.e., mean and variance). In this work, we investigate the brain's sensitivity to higher-order statistics describing temporal dependencies between sound events through a series of change detection experiments, where listeners are asked to detect changes in randomness in the pitch of tone sequences. Behavioral data indicate listeners collect statistical estimates to process incoming sounds, and a perceptual model based on Bayesian inference shows a capacity in the brain to track higher-order statistics. Further analysis of individual subjects' behavior indicates an important role of perceptual constraints in listeners' ability to track these sensory statistics with high fidelity. In addition, the inference model facilitates analysis of neural electroencephalography (EEG) responses, anchoring the analysis relative to the statistics of each stochastic stimulus. This reveals both a deviance response and a change-related disruption in phase of the stimulus-locked response that follow the higher-order statistics. These results shed light on the brain's ability to process stochastic sound sequences.

Please enter the information below to download:


NOTE: Contact information will be used solely to track usage of this software.

The stream segregation model leverages the multiplexed and non-linear representation of sounds along an auditory hierarchy and learns local and global statistical structure naturally emergent in natural and complex sounds. The three key components of the architecture are : (1) A stochastic network RBM layer that encodes two-dimensional input spectrogram into localized specto-temporal bases based on short term feature analysis; (2) A dynamic aRBM that captures the long-term temporal dependencies across spectro-temporal bases characterizing the transformation of sound from fast changing details to slower dynamics. (3) A temporal coherence layer that mimics the Hebbian process of binding local and global details together to mediate the mapping from feature space to formation of auditory objects.



DOWNLOAD AVAILABLE SOON.

This research is funded by: