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

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

Contrast of hits and correct rejections.

<p>Successful memory reinstatement was associated with a cluster of strong power decreases in the lower frequencies (<30 Hz). (a,b). The sum of t-values across all electrodes in the cluster of significant differences is plotted in the visual condition (a) and in the auditory condition (b). (c) The t-statistic of power decreases that were averaged over electrodes and time, showing a peak at 8 Hz. (d,e) Topography of power decreases in the visual condition (d) and in the auditory condition (e). Power decreases are plotted as t-values of average difference at 8 Hz between 0 and 4 s during retrieval. (f,g) Reconstruction of 8 Hz power difference in source space using an “lcmv” beamforming-algorithm in the visual (f) and in the auditory (g) condition.</p