26 research outputs found
Temporal dynamics and mechanisms of oscillatory pattern reinstatement in human episodic memory
A fundamental question in the investigation of episodic memory is how the human brain represents information from the past.
This thesis introduces a new method that tracks content specific representations in rhythmic fluctuations of brain activity (i.e. brain oscillations). It is demonstrated that a frequency band centred at 8 Hz carries information about remembered stimulus content. This is shown in human electrophysiological recordings during
episodic memory formation and retrieval.
Strong and sustained power decreases consistently mark this 8 Hz frequency band; successful memory encoding and retrieval are associated with power decreases in low frequencies (<30 Hz) throughout this thesis and in numerous former studies. The presented results link power decreases to the reinstatement of oscillatory patterns in sensory specific areas for the first time and therefore implicate them in the representation of information.
Finally, the temporal dynamics of recollection are investigated by tracking information from distinct sub-events in continuous episodic memories. In behavioural and neural data, memory replay is faster than perception and takes place in a forward direction. Herein, fragments of fine-grained temporal patterns are reinstated; yet, subjects can skip flexibly between sub-events. Leveraging oscillatory mechanisms to track information can
therefore identify episodic memory replay as a dynamic process
Large language models can segment narrative events similarly to humans
Humans perceive discrete events such as "restaurant visits" and "train rides"
in their continuous experience. One important prerequisite for studying human
event perception is the ability of researchers to quantify when one event ends
and another begins. Typically, this information is derived by aggregating
behavioral annotations from several observers. Here we present an alternative
computational approach where event boundaries are derived using a large
language model, GPT-3, instead of using human annotations. We demonstrate that
GPT-3 can segment continuous narrative text into events. GPT-3-annotated events
are significantly correlated with human event annotations. Furthermore, these
GPT-derived annotations achieve a good approximation of the "consensus"
solution (obtained by averaging across human annotations); the boundaries
identified by GPT-3 are closer to the consensus, on average, than boundaries
identified by individual human annotators. This finding suggests that GPT-3
provides a feasible solution for automated event annotations, and it
demonstrates a further parallel between human cognition and prediction in large
language models. In the future, GPT-3 may thereby help to elucidate the
principles underlying human event perception
Replay of Stimulus-specific Temporal Patterns during Associative Memory Formation
Forming a memory often entails the association of recent experience with present events. This recent experience is usually an information rich and dynamic representation of the world around us. We here show that associating a static cue with a previously shown dynamic stimulus, yields a detectable, dynamic representation of this stimulus. We further implicate this representation in the decrease of low-frequency power (~4-30 Hz) in the ongoing electroencephalogram (EEG), which is a well-known correlate of successful memory formation. The reappearance of content specific patterns in desynchronizing brain oscillations was observed in two sensory domains, i.e. in a visual and in an auditory condition. Together with previous results, these data suggest a mechanism that generalizes across domains and processes, in which the decrease in oscillatory power allows for the dynamic representation of information in ongoing brain oscillations
Hippocampal pattern completion is linked to gamma power increases and alpha power decreases during recollection
Contains fulltext :
161812.pdf (publisher's version ) (Open Access
The hippocampus as the switchboard between perception and memory.
Adaptive memory recall requires a rapid and flexible switch
from external perceptual reminders to internal mnemonic representations.
However, owing to the limited temporal or spatial
resolution of brain imaging modalities used in isolation, the
hippocampal–cortical dynamics supporting this process remain
unknown. We thus employed an object-scene cued recall paradigm
across two studies, including intracranial electroencephalography
(iEEG) and high-density scalp EEG. First, a sustained increase in hippocampal
high gamma power (55 to 110 Hz) emerged 500 ms after
cue onset and distinguished successful vs. unsuccessful recall. This
increase in gamma power for successful recall was followed by a
decrease in hippocampal alpha power (8 to 12 Hz). Intriguingly,
the hippocampal gamma power increase marked the moment at
which extrahippocampal activation patterns shifted from perceptual
cue toward mnemonic target representations. In parallel,
source-localized EEG alpha power revealed that the recall signal
progresses from hippocampus to posterior parietal cortex and
then to medial prefrontal cortex. Together, these results identify
the hippocampus as the switchboard between perception and
memory and elucidate the ensuing hippocampal–cortical dynamics
supporting the recall process.post-print1844 K
Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)
Background: Intracranial recordings from patients implanted with depth electrodes are a valuable source of information in neuroscience. They allow for the unique opportunity to record brain activity with high spatial and temporal resolution. A common pre-processing choice in stereotactic EEG (S-EEG) is to re-reference the data with a bipolar montage. In this, each channel is subtracted from its neighbor, to reduce commonalities between channels and isolate activity that is spatially confined.
New Method: We challenge the assumption that bipolar reference effectively performs this task. To extract local activity, the distribution of the signal source of interest, interfering distant signals, and noise need to be considered. Referencing schemes with fixed coefficients can decrease the signal to noise ratio (SNR) of the data, they can lead to mislocalization of activity and consequently to misinterpretation of results. We propose to use Independent Component Analysis (ICA), to derive filter coefficients that reflect the statistical dependencies of the data at hand.
Results: We describe and demonstrate this on human S-EEG recordings. In a simulation with real data, we quantitatively show that ICA outperforms the bipolar referencing operation in sensitivity and importantly in specificity when revealing local time series from the superposition of neighboring channels.
Comparison with Existing Method: We argue that ICA already performs the same task that bipolar referencing pursues, namely undoing the linear superposition of activity and will identify activity that is local.
Conclusions: When investigating local sources in human S-EEG, ICA should be preferred over re-referencing the data with a bipolar montage
The Temporal Signature of Memories: Identification of a General Mechanism for Dynamic Memory Replay in Humans
Reinstatement of dynamic memories requires the replay of neural patterns that unfold over
time in a similar manner as during perception. However, little is known about the mechanisms
that guide such a temporally structured replay in humans, because previous studies
used either unsuitable methods or paradigms to address this question. Here, we overcome
these limitations by developing a new analysis method to detect the replay of temporal patterns
in a paradigm that requires participants to mentally replay short sound or video clips.
We show that memory reinstatement is accompanied by a decrease of low-frequency (8
Hz) power, which carries a temporal phase signature of the replayed stimulus. These replay
effects were evident in the visual as well as in the auditory domain and were localized to
sensory-specific regions. These results suggest low-frequency phase to be a domain-general
mechanism that orchestrates dynamic memory replay in humans
Speed of time-compressed forward replay flexibly changes in human episodic memory
Data and analysis script
Data and code from: Large language models can segment narrative events similarly to humans.
<p>Data and code supporting the paper: Large language models can segment narrative events similarly to humans.</p>
Analysis scripts and statistic data files
In this repository you will find the analysis scripts as well as the group statistic data that were used to generate the figures in the manuscript. All files are Matlab based script (*.m) or data (*.mat) files