Rapid synaptic depression explains nonlinear modulation of spectro-temporal tuning in primary auditory cortex by natural stimuli

By Stephen V. David, Nima Mesgarani, Jonathan B. Fritz, Shihab A. Shamma. 2009

165 citations recorded by the source corpus.

In this study, we explored ways to account more accurately for responses of neurons in primary auditory cortex (A1) to natural sounds. The auditory cortex has evolved to extract behaviorally relevant information from complex natural sounds, but most of our understanding of its function is derived from experiments using simple synthetic stimuli. Previous neurophysiological studies have found that existing models, such as the linear spectro-temporal receptive field (STRF), fail to capture the entire functional relationship between natural stimuli and neural responses. To study this problem, we compared STRFs for A1 neurons estimated using a natural stimulus, continuous speech, with STRFs estimated using synthetic ripple noise. For about one-third of the neurons, we found significant differences between STRFs, usually in the temporal dynamics of inhibition and/or overall gain. This shift in tuning resulted primarily from differences in the coarse temporal structure of the speech and noise stimuli. Using simulations, we found that the stimulus dependence of spectro-temporal tuning can be explained by a model in which synaptic inputs to A1 neurons are susceptible to rapid nonlinear depression. This dynamic reshaping of spectro-temporal tuning suggests that synaptic depression may enable efficient encoding of natural auditory stimuli.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Elia Formisano
2. David J. Heeger
3. Matteo Carandini
4. Sara Hooker

In Cognitive Neuroscience

1. David J. Heeger
2. Matteo Carandini
3. Federico De Martino
4. Michelle Moerel
5. Elia Formisano
6. John-Dylan Haynes
7. Xiao-Jing Wang
8. Geoffrey E. Hinton

In Signal Processing

1. Michelle Moerel
2. Federico De Martino
3. Elia Formisano
4. David J. Heeger
5. Matteo Carandini

Citations in the corpus

• Normalization as a canonical neural computation (2011, background): “There is also evidence for normalization in cortical areas that are devoted to other sensory modalities, and in particular in primary auditory corte”
• Contrast Gain Control in Auditory Cortex (2011, unknown): “For instance, normalizing by global stimulus contrast, without taking into account spectrally local contrast, does not result in an improvement in the predictive power of STRF models (David et al., 2009).”
• Processing of Natural Sounds in Human Auditory Cortex: Tonotopy, Spectral Tuning, and Relation to Voice Sensitivity (2012, background): “…that neural processing of natural sounds involves nonlinear computational mechanisms (Bitterman et al., 2008; Pasley et al., 2012), such as tuning to the statistical regularities of conspecific vocalizations (Theunissen et al., 2000) and context dependence of receptive fields (David et al., 2009).”
• A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy. (2018, unknown): “Neural responses are known to be nonlinear functions of the spectrogram (Christianson et al., 2008; David et al., 2009), and state-of-the-art machine hearing systems are highly nonlinear (Hershey et al.”