Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice

Gillespie, Anna K.; Maya, Daniela A. Astudillo; Denovellis, Eric L.; Liu, Daniel F.; Kastner, David B.; Coulter, Michael; Roumis, Demetris; Eden, Uri T.; Frank, Loren M.

doi:10.1101/2021.03.09.434621

Cited by 22 publications

(37 citation statements)

References 66 publications

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“…Power was quantified as the squared absolute value of the analytic signal, and averaged over all (non-artifactual) MEG sensors. We defined a discrete ripple ‘event’ as any sample exhibiting ripple power exceeding the median + 2 standard deviations of the subject- and session-specific distribution (we obtain similar results when using event-defining thresholds from 2 – 6 SD) ( Gillespie et al., 2021 ; McNamara et al., 2014 ; van de Ven et al., 2016 ). Event rate, interval times, and lifetimes were then calculated for each subject and rest session, in a manner that takes into account the presence of bad samples ( Higgins et al., 2021 ).…”

Section: Methodsmentioning

confidence: 72%

Impaired neural replay of inferred relationships in schizophrenia

Nour

Liu

Arumuham

et al. 2021

Cell

107

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Summary An ability to build structured mental maps of the world underpins our capacity to imagine relationships between objects that extend beyond experience. In rodents, such representations are supported by sequential place cell reactivations during rest, known as replay. Schizophrenia is proposed to reflect a compromise in structured mental representations, with animal models reporting abnormalities in hippocampal replay and associated ripple activity during rest. Here, utilizing magnetoencephalography (MEG), we tasked patients with schizophrenia and control participants to infer unobserved relationships between objects by reorganizing visual experiences containing these objects. During a post-task rest session, controls exhibited fast spontaneous neural reactivation of presented objects that replayed inferred relationships. Replay was coincident with increased ripple power in hippocampus. Patients showed both reduced replay and augmented ripple power relative to controls, convergent with findings in animal models. These abnormalities are linked to impairments in behavioral acquisition and subsequent neural representation of task structure.

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Section: Methodsmentioning

confidence: 72%

Impaired neural replay of inferred relationships in schizophrenia

Nour

Liu

Arumuham

et al. 2021

Cell

107

View full text Add to dashboard Cite

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“…In humans, the non-invasive, non-surgical experimental methods usually required for recording neural activity offer lower spatial and temporal resolution, making replay detection more difficult. Nevertheless, classifiers trained on human neural activity during a task show hippocampal reactivation of task representations during subsequent rest, with a bias towards replaying items that are highly rewarded and subsequently better remembered [109,110]. Replay has also been shown to selectively strengthen weaker memories [111] and re-evaluate state-action values for reinforcement learning [13], and with tentative evidence of hippocampal-tocortical transfer of task-relevant information [112].…”

Section: Box 2 Hippocampal Replaymentioning

confidence: 99%

Learning offline: memory replay in biological and artificial reinforcement learning

Roscow

Chua

Costa

et al. 2021

Trends in Neurosciences

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Learning to act in an environment to maximise rewards is among the brain's key functions. This process has often been conceptualised within the framework of reinforcement learning, which has also gained prominence in machine learning and artificial intelligence (AI) as a way to optimise decision-making. A common aspect of both biological and machine reinforcement learning is the reactivation of previously experienced episodes, referred to as replay. Replay is important for memory consolidation in biological neural networks, and is key to stabilising learning in deep neural networks. Here, we review recent developments concerning the functional roles of replay in the fields of neuroscience and AI. Complementary progress suggests how replay might support learning processes, including generalisation and continual learning, affording opportunities to transfer knowledge across the two fields to advance the understanding of biological and artificial learning and memory. Replay in biological and artificial reinforcement learning Highlights• Reinforcement learning in deep neural networks often relies on the interleaving of new and old episodes, a technique which mimics the replay of neuronal activity in the brain.

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“…Moreover, such SWR-associated replay has been implicated in memory retrieval in humans 19,20 and rodents 21 (but see Gillespie et al 2021 22 ). Similarly, theta oscillations occurring during online locomotion in rodents are associated with the ordered firing of place cells that represent the spatial organization of visited locations, albeit in a temporally compressed manner 23 .…”

Section: The Shallow Cognitive Map Hypothesis Of Thought Disordermentioning

confidence: 99%

The Shallow Cognitive Map Hypothesis: A Hippocampal framework for Thought Disorder in Schizophrenia

Musa¹,

Khan²,

Mujahid³

et al. 2021

Preprint

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Memories are not formed in isolation. They are associated and organized into relational knowledge structures that allow coherent thought. Failure to express such coherent thought is a key hallmark of Schizophrenia. Here we explore the hypothesis that thought disorder arises from disorganized Hippocampal cognitive maps. In doing so, we combine insights from two key lines of investigation, one concerning the neural signatures of cognitive mapping, and another that seeks to understand lower-level cellular mechanisms of cognition within a dynamical systems framework. Specifically, we propose that multiple distinct pathological pathways converge on the shallowing of Hippocampal attractors, giving rise to disorganized Hippocampal cognitive maps and driving thought disorder. We discuss the available evidence at the computational, behavioural, network and cellular levels. We also outline testable predictions from this framework including how it could unify major chemical and psychological theories of schizophrenia and how it can provide a rationale for understanding the aetiology and treatment of the disease.

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Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice

Cited by 22 publications

References 66 publications

Impaired neural replay of inferred relationships in schizophrenia

Impaired neural replay of inferred relationships in schizophrenia

Learning offline: memory replay in biological and artificial reinforcement learning

The Shallow Cognitive Map Hypothesis: A Hippocampal framework for Thought Disorder in Schizophrenia

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