Sensory context for coding of natural sounds in auditory cortex
Lopez Espejo, M.; David, S. V.
10.1101/2023.06.14.544866 bioRxivShow abstract
Accurate sound perception can require integrating information over hundreds of milliseconds or even seconds. Spectro-temporal models of sound coding by single neurons in auditory cortex indicate that the majority of sound-evoked activity can be attributed to stimuli with a few tens of milliseconds. It remains uncertain how the auditory system integrates information about sensory context on a longer timescale. Here we characterized long-lasting contextual effects in auditory cortex (AC) using a diverse set of natural sound stimuli. We measured context effects as the difference in a neurons response to a single probe sound following two different context sounds. Many AC neurons showed context effects lasting longer than the temporal window of a traditional spectro-temporal receptive field. The duration and magnitude of context effects varied substantially across neurons and stimuli. This diversity of context effects formed a sparse code across the neural population that encoded a wider range of contexts than any constituent neuron. Encoding model analysis indicates that context effects can be explained by activity in the local neural population, suggesting that recurrent local circuits support a long-lasting representation of sensory context in auditory cortex.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- An emergent population code in primary auditory cortex supports selective attention to spectral and temporal sound features 98%
- Correlates of auditory decision making in prefrontal, auditory, and basal lateral amygdala cortical areas 97%
- Acoustic context modulates natural sound discrimination in auditory cortex through frequency specific adaptation 97%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.