Biology subjects

Norman, K. A.

Publications and source records attributed to Norman, K. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Reactivation during sleep segregates the neural representations of episodic memories

Sleep involves the reactivation of recently acquired memories, thereby shaping the neural representations supporting them. An essential feature of episodic memory (i.e., memory for events) is the link between specific elements (e.g., a cake) and the context in which they were embedded (e.g., a birthday party). We investigated how reactivation during sleep impacts item-context binding. Participants (N = 22) formed stories linking together objects in unique contexts. Using functional MRI, we measured the overlap between neural representations for contextually linked objects. During a 90-min nap, some object memories were reactivated by unobtrusively presenting object-specific sounds. Across multiple brain regions, reactivation reduced the overlap between representations of contextually linked memories, promoting object specificity. Furthermore, reactivation reduced the representational overlap between contexts, thereby promoting context specificity. Taken together, data suggest that reactivation during sleep decontextualizes memories. These results inform our understanding of how sleep contributes to interlinked memories embedded in naturalistic contexts and demonstrate that memory reactivation during sleep changes representations of memories within their distinctive contexts.

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bioRxiv2026

A neural mechanism for online discovery of latent contexts

Experience unfolds as a stream shaped by hidden causes that change over time. Adaptive behavior requires inferring the underlying states and adjusting when they change. Yet, how neural circuits discover and track latent states remains unclear. Here we introduce NeuraGEM, a neural architecture that combines fast transient activity with slow synaptic plasticity to implement an online analogue of Expectation-Maximization. By separating timescales, NeuraGEM clusters sequential experiences, detects context changes, and stabilizes task-specific computations. The model generalizes beyond conventional recurrent networks and reproduces key features of human contextual learning, including curriculum-dependent effects. It also gives rise to population dynamics resembling those observed in brain circuits, including line-attractor structure and transient error responses at change points. Together, these findings provide a mechanistic account of how neural circuits organize experience into latent states that support rapid inference and adaptive behavior.

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bioRxiv2025

Synthesizing images to map neural networks to the human brain

Computational models can be used to generate hypotheses about the brain. In the visual system, this approach has revealed similarities between how natural images are represented in convolutional neural networks and in cortical regions. However, natural images generate highly correlated representations across the hierarchy of model layers, meaning that each brain region will not correspond selectively to a single layer. Because each model layer performs additional image transformations, gaining a more selective mapping between individual layers and regions could reveal how specific algorithmic stages of processing are instantiated in the brain. To enable this mapping, we developed a generative framework for image synthesis that minimizes the similarity of image representational similarity matrices across model layers, aiming to orthogonalize the distances between the patterns of unit activations evoked by the same set of images. With the patterns orthogonalized, the resulting similarity matrix for each layer provides a fingerprint of the unique computational role of that layer. To test this approach, we synthesized 16 artificial images from the Inception-V1/GoogLeNet model and scanned participants with fMRI while they viewed these images repeatedly in random order. Image-specific patterns of voxel activity were used to compute image-by-image similarity matrices across the whole brain with searchlights. Most layers could be mapped to circumscribed cortical regions, and these mappings overlapped less than the mappings obtained with natural images. Given the prevalence of existing fMRI datasets with natural images, we used the synthesis method as a benchmark to develop an alternative residual method that can achieve comparable performance for natural image datasets. These approaches could be extended to other neural network architectures and stimulus modalities for targeted mappings of model computations to the brain and behavior.

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bioRxiv2025

Music-evoked reactivation during continuous perception is associated with enhanced subsequent recall of naturalistic events

Music is a potent cue for recalling personal experiences, yet the neural basis of music-evoked memory remains elusive. We address this question by using the full-length film Eternal Sunshine of the Spotless Mind to examine how repeated musical themes reactivate previously encoded events in cortex and shape next-day recall. Participants in an fMRI study viewed either the original film (with repeated musical themes) or a no-music version. By comparing neural activity patterns between these groups, we found that music-evoked reactivation of neural patterns linked to earlier scenes in the default mode network was associated with improved subsequent recall. This relationship was specific to the music condition and persisted when we controlled for a proxy measure of initial encoding strength (spatial intersubject correlation), suggesting that music-evoked reactivation may play a role in making event memories stick that is distinct from what happens at initial encoding.

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bioRxiv2025

Fast-timescale hippocampal processes bridge between slowly unfurling neocortical states during memory search

Prior behavioral work showed that event structure supports our ability to search through continuous naturalistic memories. We hypothesized that, neurally, this memory search process involves a division of labor between slowly unfurling neocortical states representing event knowledge and fast hippocampal-neocortical communication supporting retrieval of new information at transitions between events. In a sample of ten intracranial EEG patients we tracked slow neural state-patterns as patients viewed a movie and then searched their memories in a structured naturalistic interview. As patients answered questions ("after A, when does B happen next?"), state-patterns from movie-viewing were reinstated in neocortex. During the critical memory-search period, states unfurled in a forward direction, realizing a trajectory from A to B in memory. Punctate moments of state-transition during memory-search were marked by low-frequency power decreases in cortex and preceded by power decreases in hippocampus that correlated with reinstatement. Connectivity-analysis revealed information-flow from hippocampus to cortex underpinning state-transitions.

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bioRxiv2024

Binding items to contexts through conjunctive neural representations with the Method of Loci

Schematic prior knowledge can provide a powerful scaffold for episodic memories, yet the neural mechanisms underlying this scaffolding process are still poorly understood. A crucial step of the scaffolding process is the way in which details of a new episode are connected to an existing schema, forming a robust memory representation that can be easily accessed in the future. A unique testbed for studying this binding process is a mnemonic technique called the Method of Loci (MoL), in which people meaningfully connect items to be remembered with a well-learned list of imagined loci. We collected fMRI data from participants in 3 longitudinal sessions while they were enrolled in a month-long MoL training course, all of whom showed dramatic improvements in their ability to remember lists of 20 or 40 words. We obtained neural patterns when the loci and objects are presented by themselves, when they are combined into an integrated representation at encoding, and when the integrated representation was subsequently retrieved, as well as verbal descriptions from the participants about the way they associated each item to each locus. We found that in default mode network regions, including medial prefrontal cortex (mPFC), the combined representations of loci and items are highly conjunctive: the unified locus-item representation was substantially different from a linear combination of the isolated locus and item representation, reflecting the addition of new integrative details specific to each combined pair. The conjunctive component of the representation reflected the particular creative details generated by individual participants and increased over time as participants gained expertise in MoL. Our findings reveal a critical role for the default mode network in creating meaningful connections between new information and well-learned schematic contexts.

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bioRxiv2024

How sturdy is your memory palace? Reliable room representations predict subsequent reinstatement of placed objects

What are the neural properties that make spatial contexts effective scaffolds for storing and accessing memories? We hypothesized that spatial locations with stable and distinctive (i.e., reliable) neural representations would best support memory for new experiences. To test this, participants learned the layout of a custom-built 23-room virtual reality (VR) "memory palace" that they explored using a head-mounted display. The next day, participants underwent whole-brain fMRI while watching videos of the rooms, allowing us to measure the reliability of the neural activity pattern associated with each room. Participants then returned to VR to encode 23 objects placed in each of the 23 rooms and later recalled the rooms and objects during fMRI. We found that our room reliability measure (computed prior to encoding) predicted object reinstatement during recall across cortex; this was driven not only by group-level reliability across participants, but also idiosyncratic reliability within participants. Moreover, this effect did not arise through enhanced retrieval of reliable rooms during recall, since the relationship between reliability and object reinstatement remained significant when controlling for room reinstatement during retrieval; this suggests that, instead, room reliability promotes improved binding of rooms to objects at encoding. Together, these results showcase how the quality of the neural repre-sentation of a spatial context can be quantified and used to audit its utility as a memory scaffold for future experiences.

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bioRxiv2024

The role of REM sleep in neural differentiation of memories in the hippocampus

When faced with a familiar situation, we can use memory to make predictions about what will happen next. If such predictions turn out to be erroneous, the brain can adapt by differentiating the representations of the cues that generated the prediction from the mispredicted item itself, reducing the likelihood of future prediction errors. Prior work by Kim et al. (2017) found that violating a sequential association in a statistical learning paradigm triggered differentiation of the neural representations of the associated items in the hippocampus. Here, we used fMRI to test the preregistered hypothesis that this hippocampal differentiation occurs only when violations are followed by rapid eye movement (REM) sleep. In the morning, participants first learned that some items predict others (e.g., A predicts B) then encountered a violation in which a predicted item (B) failed to appear when expected after its associated item (A); the predicted item later appeared on its own after an unrelated item. Participants were then randomly assigned to one of three conditions: remain awake, take a nap containing non-REM sleep only, or take a nap with both non-REM and REM sleep. While the predicted results were not observed in the preregistered left CA2/3/DG ROI, we did observe evidence for our hypothesis in closely related hippocampal ROIs, uncorrected for multiple comparisons: In right CA2/3/DG, differentiation in the group with REM sleep was greater than in the groups without REM sleep (wake and non-REM nap); this differentiation was item-specific and concentrated in right DG. Differentiation effects were also greater in bilateral DG when the predicted item was more strongly reactivated during the violation. Overall, the results presented here provide initial evidence linking REM sleep to changes in the hippocampal representations of memories in humans.

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bioRxiv2024

Episodic memory supports the acquisition of structured task representations

Generalization to new tasks requires learning of task representations that accurately reflect the similarity structure of the task space. Here, we argue that episodic memory (EM) plays an essential role in this process by stabilizing task representations, thereby supporting the accumulation of structured knowledge. We demonstrate this using a neural network model that infers task representations that minimize the current tasks objective function; crucially, the model can retrieve previously encoded task representations from EM and use these to initialize the task inference process. With EM, the model succeeds in learning the underlying task structure; without EM, task representations drift and the network fails to learn the structure. We further show that EM errors can support structure learning by promoting the activation of similar task representations in tasks with similar sensory inputs. Overall, this model provides a novel account of how EM supports the acquisition of structured task representations.

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bioRxiv2024

Neural codes track prior events in a narrative and predict subsequent memory for details

AbstractThroughout our lives, we learn schemas that specify what types of events to expect in particular contexts and the temporal order in which these events usu- ally occur. Here, our first goal was to investigate how such context-dependent temporal structures are represented in the brain during processing of temporally- extended events. To accomplish this, we ran a 2-day fMRI study in which we exposed participants to many unique animated videos of weddings composed of sequences of rituals; each sequence originated from one of two fictional cultures (North and South), where rituals were shared across cultures, but the transition structure between these rituals differed across cultures. The results, obtained using representational similarity analysis, revealed that context-dependent tem- poral structure is represented in multiple ways in parallel, including distinct neural representations for the culture, for particular sequences, and for past and current events within the sequence. Our second goal was to test the hypothe- sis that neural schema representations scaffold memory for specific details. In keeping with this hypothesis, we found that the strength of the neural represen- tation of the North/South schema for a particular wedding predicted subsequent episodic memory for the details of that wedding.

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bioRxiv2023

Inducing representational change in the hippocampus through real-time neurofeedback

AO_SCPLOWBSTRACTC_SCPLOWWhen you perceive or remember one thing, other related things come to mind. This competition has consequences for how these items are later perceived, attended, or remembered. Such behavioral consequences result from changes in how much the neural representations of the items overlap, especially in the hippocampus. These changes can reflect increased (integration) or decreased (differentiation) overlap; previous studies have posited that the amount of coactivation between competing representations in cortex determines which will occur: high coactivation leads to hippocampal integration, medium coactivation leads to differentiation, and low coactivation is inert. However, those studies used indirect proxies for coactivation, by manipulating stimulus similarity or task demands. Here we induce coactivation of competing memories in visual cortex more directly using closed-loop neurofeedback from real-time fMRI. While viewing one object, participants were rewarded for implicitly activating the representation of another object as strongly as possible. Across multiple real-time fMRI training sessions, they succeeded in using the neurofeedback to induce coactivation. Compared with untrained objects, this coactivation led to behavioral and neural integration: The trained objects became harder for participants to discriminate in a categorical perception task and harder to decode from patterns of fMRI activity in the hippocampus.

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bioRxiv2023

Differentiation and Integration of Competing Memories: A Neural Network Model

What determines when neural representations of memories move together (integrate) or apart (differentiate)? Classic supervised learning models posit that, when two stimuli predict similar outcomes, their representations should integrate. However, these models have recently been challenged by studies showing that pairing two stimuli with a shared associate can sometimes cause differentiation, depending on the parameters of the study and the brain region being examined. Here, we provide a purely unsupervised neural network model that can explain these and other related findings. The model can exhibit integration or differentiation depending on the amount of activity allowed to spread to competitors -- inactive memories are not modified, connections to moderately active competitors are weakened (leading to differentiation), and connections to highly active competitors are strengthened (leading to integration). The model also makes several novel predictions -- most importantly, that when differentiation occurs as a result of this unsupervised learning mechanism, it will be rapid and asymmetric, and it will give rise to anticorrelated representations in the region of the brain that is the source of the differentiation. Overall, these modeling results provide a computational explanation for a diverse set of seemingly contradictory empirical findings in the memory literature, as well as new insights into the dynamics at play during learning.

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bioRxiv2022

Test-Retest Reliability of the Human Connectome: An OPM-MEG study

Magnetoencephalography with optically pumped magnetometers (OPM-MEG) offers a new way to record electrophysiological brain function, with significant advantages over conventional MEG including adaptability to head shape/size, free movement during scanning, better spatial resolution, increased signal, and no reliance on cryogenics. However, OPM-MEG remains in its infancy, with significant questions to be answered regarding optimal system design and robustness. Here, we present an open-source dataset acquired using a newly constructed OPM-MEG system with a triaxial sensor design averaging 168 channels. Using OPM-optimised magnetic shielding and active background-field control, we measure the test-retest reliability of the human connectome. We employ amplitude envelope correlation to measure whole-brain functional connectivity in 10 individuals whilst they watch a 600 s move clip. Our results show high repeatability between experimental runs at the group level, with a correlation coefficient of 0.81 in the theta, 0.93 in alpha and 0.94 in beta frequency ranges. At the individual subject level, we found marked differences between individuals, but high within-subject robustness (correlations of 0.56 {+/-} 0.25, 0.72 {+/-} 0.15 and 0.78 {+/-} 0.13 in theta, alpha and beta respectively). These results compare well to previously reported findings using conventional MEG; they show that OPM-MEG is a viable way to characterise whole brain connectivity and add significant weight to a growing argument that OPMs can overtake cryogenic sensors as the fundamental building block of MEG systems.

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bioRxiv2022

Reconstructing the cascade of language processing in the brain using the internal computations of a transformer-based language model

Humans use complex linguistic structures to transmit ideas to one another. The brain is thought to deploy specialized computations to process these structures. Recently, a new class of artificial neural networks based on the Transformer architecture has revolutionized the field of language modeling, attracting attention from neuroscientists seeking to understand the neurobiology of language in silico. Transformers integrate information across words via multiple layers of structured circuit computations, forming increasingly contextualized representations of linguistic content. Prior work has focused on the internal representations (the "embeddings") generated by these circuits. In this paper, we instead analyze the circuit computations directly: we deconstruct these computations into functionally-specialized "transformations" to provide a complementary window onto linguistic computations in the human brain. Using functional MRI data acquired while participants listened to naturalistic spoken stories, we first verify that the transformations account for considerable variance in brain activity across the cortical language network. We then demonstrate that the emergent syntactic computations performed by individual, functionally-specialized "attention heads" differentially predict brain activity in specific cortical regions. These heads fall along gradients corresponding to different layers, contextual distances, and syntactic dependencies in a low-dimensional cortical space. Our findings indicate that large language models and the cortical language network may converge on similar trends of functional specialization for processing natural language.

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bioRxiv2022

A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidation

How do we build up our knowledge of the world over time? Many theories of memory formation and consolidation have posited that the hippocampus stores new information, then "teaches" this information to neocortex over time, especially during sleep. But it is unclear, mechanistically, how this actually works -- how are these systems able to interact during periods with virtually no environmental input to accomplish useful learning and shifts in representation? We provide a framework for thinking about this question, with neural network model simulations serving as demonstrations. The model contains hippocampus and neocortical areas, which replay memories and interact with one another completely autonomously during simulated sleep. Oscillations are leveraged to support error-driven learning that leads to useful changes in memory representation and behavior. The model has a non-Rapid Eye Movement (NREM) sleep stage, where dynamics between hippocampus and neocortex are tightly coupled, with hippocampus helping neocortex to reinstate high-fidelity versions of new attractors, and a REM sleep stage, where neocortex is able to more freely explore existing attractors. We find that alternating between NREM and REM sleep stages, which alternately focuses the models replay on recent and remote information, facilitates graceful continual learning. We thus provide an account of how the hippocampus and neocortex can interact without any external input during sleep to drive useful new cortical learning and to protect old knowledge as new information is integrated.

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bioRxiv2022

Real-time neurofeedback to alter interpretations of a naturalistic narrative

We explored the potential of using real-time fMRI (rt-fMRI) neurofeedback training to bias interpretations of naturalistic narrative stimuli. Participants were randomly assigned to one of two possible conditions, each corresponding to a different interpretation of an ambiguous spoken story. While participants listened to the story in the scanner, neurofeedback was used to reward neural activity corresponding to the assigned interpretation. After scanning, final interpretations were assessed. While neurofeedback did not change story interpretations on average, participants with higher levels of decoding accuracy during the neurofeedback procedure were more likely to adopt the assigned interpretation. Thus, we believe that individualized neurofeedback shaped interpretations successfully when the signal was accurate, although more work is needed to improve this method and validate the result. While naturalistic stimuli introduce a unique set of challenges in providing effective and individualized neurofeedback, we believe that this technique holds promise for individualized cognitive therapy.

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bioRxiv2021

Here's the twist: How the brain updates naturalistic event memories as our understanding of the past changes

The brain actively reshapes our understanding of past events in light of new incoming information. In the current study, we ask how the brain supports this updating process during the encoding and recall of naturalistic stimuli. One group of participants watched a movie ("The Sixth Sense") with a cinematic "twist" at the end that dramatically changed the interpretation of previous events. Next, participants were asked to verbally recall the movie events, taking into account the new "twist" information. Most participants updated their recall to incorporate the twist. Two additional groups recalled the movie without having to update their memories during recall: one group never saw the twist; another group was exposed to the twist prior to the beginning of the movie, and thus the twist information was incorporated both during encoding and recall. We found that providing participants with information about the twist beforehand altered neural response patterns during movie-viewing in the default mode network (DMN). Moreover, presenting participants with the twist at the end of the movie changed the neural representation of the previously-encoded information during recall in a subset of DMN regions. Further evidence for this transformation was obtained by comparing the neural activation patterns during encoding and recall and correlating them with behavioral signatures of memory updating. Our results demonstrate that neural representations of past events encoded in the DMN are dynamically integrated with new information that reshapes our understanding in natural contexts.

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bioRxiv2021

Schema representations in distinct brain networks support narrative memory during encoding and retrieval

Schematic prior knowledge can scaffold the construction of event memories during perception and also provide structured cues to guide memory search during retrieval. We measured the activation of story-specific and schematic representations using fMRI while participants were presented with 16 stories and then recalled each of the narratives, and related these activations to memory for specific story details. We predicted that schema representations in mPFC would be correlated with successful recall of story details. In keeping with this prediction, an anterior mPFC region showed a significant correlation between activation of schema representations at encoding and subsequent behavioral recall performance; however, this mPFC region was not implicated in schema representation during retrieval. More generally, our analyses revealed largely distinct brain networks at encoding and retrieval in which schema activation was related to successful recall. These results provide new insight into when and where event knowledge can support narrative memory.

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