Vocalizations are integral to social communication, with acoustic features that may convey both meaning and information about an animal’s identity or emotional state. The neural representation of vocalizations must therefore permit animals to generalize across one set of acoustic features to recognize meaning, and a different set of features to recognize vocalizer identity. To test this idea, we recorded the responses of gerbil core auditory cortex (AC) neurons to a large array (n>1500) of variants drawn from four vocalization categories and produced by 5 different families. Each vocalization category could be decoded from AC population activity with high accuracy, despite the acoustic variance across vocalization renditions or different families-of-origin, and displayed a ‘boundary effect’ (i.e., better classification across, than within, a category). Moreover, the family identity of each vocalization category could be decoded from a larger AC population. Thus, AC activity can be used to simultaneously predict vocalization category and family identity, and is robust to the natural range of acoustic variance.
The Hidden Power of Normalization: Exponential Capacity Control in Deep Neural Networks
arXiv:2511.00958v1 Announce Type: cross Abstract: Normalization methods are fundamental components of modern deep neural networks (DNNs). Empirically, they are known to stabilize optimization dynamics and