A state-space framework for causal detection of hippocampal ripple-replay events
Hippocampal ripple-replay events are typically identified using a two-step process that at each time point uses past and future data to determine whether an event is occurring. This prevents researchers from identifying these events in real time for closed-loop experiments. It also prevents the identification of periods of nonlocal representation that are not accompanied by large changes in the spectral content of the local field potentials (LFPs). In this work, we present a new state-space model framework that is able to detect concurrent changes in the rhythmic structure of LFPs with nonlocal activity in place cells to identify ripple-replay events in a causal manner. The model combines latent factors related to neural oscillations, represented space, and switches between coding properties to explain simultaneously the spiking activity from multiple units and the rhythmic content of LFPs recorded from multiple sources. The model is temporally causal, meaning that estimates of the switching state can be made at each instant using only past information from the spike and LFP signals, or can be combined with future data to refine those estimates. We applied this model framework to simulated and real hippocampal data to demonstrate its performance in identifying ripple-replay events.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A Unified Phase-native Computational Principle Governs Hippocampal Spike Timing and Neural Coding
Hippocampal neurons exhibit precise phase locking to network oscillations, but the computational principle governing this temporal precision is still unclear. Neural information is conveyed jointly by firing rates and sp…
Hippocampus-DETR: An Explicit Memory Object Detection Framework Based on Hippocampus Modeling
This paper addresses the lack of explicit memory mechanisms in current object detection models and proposes Hippocampus-DETR, a novel detection framework based on biological hippocampal memory modeling. This framework in…
Few-Shot Image ClassificationNovel Object DetectionImage RestorationDiscretization of continuous input spaces in the hippocampal autoencoder
The hippocampus has been associated with both spatial cognition and episodic memory formation, but integrating these functions into a unified framework remains challenging. Here, we demonstrate that forming discrete memo…
HippocampusTranCIT: Transient Causal Interaction Toolbox
Quantifying transient causal interactions from non-stationary neural signals is a fundamental challenge in neuroscience. Traditional methods are often inadequate for brief neural events, and advanced, event-specific tech…
Design Principles of the Hippocampal Cognitive Map
Hippocampal place fields have been shown to reflect behaviorally relevant aspects of space. For instance, place fields tend to be skewed along commonly traveled directions, they cluster around rewarded locations, and the…
Hierarchical Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)