.png){kind=icon}
返回
Sparse ensemble neural code for a complete vocal repertoire
.png){kind=icon}
OA
AI
DOI：10.1016/j.celrep.2023.112034.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The categorization of animal vocalizations into distinct behaviorally relevant groups for communication is an essential operation that must be performed by the auditory system. This auditory object recognition is a diffi-cult task that requires selectivity to the group identifying acoustic features and invariance to renditions within each group. We find that small ensembles of auditory neurons in the forebrain of a social songbird can code the bird's entire vocal repertoire (-10 call types). Ensemble neural discrimination is not, however, correlated with single unit selectivity, but instead with how well the joint single unit tunings to characteristic spectro-temporal modulations span the acoustic subspace optimized for the discrimination of call types. Thus, akin to face recognition in the visual system, call type recognition in the auditory system is based on a sparse code representing a small number of high-level features and not on highly selective grandmother neurons.
Keyword:
ZEBRA FINCH
AUDITORY-CORTEX
SINGLE NEURONS
NATURAL SOUNDS
ACOUSTIC FEATURES
REPRESENTATION
VOCALIZATIONS
SELECTIVITY
EMERGENCE
SONG
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.9
论文数:
1.7W
被引数:
10.2W
机构
引用论文
Mitochondria as a critical target of the chemotheraputic agent cisplatin in head and neck cancer
.png){kind=icon}
PREAI
分享收藏
分享收藏
查看更多内容