A guide towards optimal detection of transient oscillatory bursts with unknown parameters

Journal article

Responding to threats in the real world demands a sophisticated orchestration of freeze and flight behaviors dynamically modulated by the neural activity. While the medial prefrontal cortex-basolateral amygdala (mPFC-BLA) network is known to play a pivotal role in coordinating these responses, the mechanisms underlying its population dynamics remain vague. As traditional Pavlovian fear conditioning models fall short in encapsulating the breadth of natural escape behaviors, we introduce a novel dataset to bridge this gap, capturing the defensive strategies of mice against a spider robot in a natural-like environment. The adaptive escape behaviors and concurrent mPFC-BLA activity in eight mice were monitored using wireless local field potential (LFP) and video recordings, both individually and in groups. Our data offers a unique avenue to explore the neural dynamics that govern fear- and vigilance-induced threat responses in isolated and social contexts. Supplemented by detailed methodologies and validation, the dataset allows for the analysis of the transient neural oscillatory dynamics, with prospective implications for the fields of neuroscience, robotics, and artificial intelligence

Authors: Cho, S; Choi, JH

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IOP Publishing
• Journal: Journal of Neural Engineering
• Volume: 20
• Issue: 4
• Pages: 046007-046007
• Publication date: 2023-06-20
• DOI: 10.1088/1741-2552/acdffd
• EISSN: 1741-2552
• ISSN: 1741-2560
• Language: English
• Keywords: Image (mathematics); Detection theory; Machine learning; Noise (video); Data mining; Event (particle physics); Statistical hypothesis testing; Robustness (evolution); Computer science; Mathematics; Algorithm; Transient (computer programming); Metric (unit); Artificial intelligence; Detector; Statistics; Pattern recognition (psychology)