Cognitive control persistently enhances hippocampal information processing

Chung, Ain; Jou, Claudia; Grau‐Perales, Alejandro; Levy, Eliott R. J.; Dvořák, Dino; Hussain, Nida; Fenton, André A.

doi:10.1038/s41586-021-04070-5

Cited by 31 publications

(41 citation statements)

References 52 publications

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“…This is in line with the original conceptualization of remapping as a reorganization of the temporal discharge properties within an ensemble (Kubie et al, 2020;Kubie and Muller, 1991). Such changes in coactivity are consistent with synaptic plasticity studies that demonstrate balance (Okun and Lampl, 2008), bidirectionality (Milstein et al, 2021), and involvement of both excitatory and inhibitory cells (Basu et al, 2016;Caroni, 2015;Chung et al, 2021;Mongillo et al, 2018;Ruediger et al, 2011). We find that the strongly coactive and anti-coactive cell pairs are particularly discriminative at the 1-s timescale and, remarkably, anticoactive cells are both more discriminative and more important for organizing the activity on a manifold (Fig.…”

Section: Reregistration Instead Of Remapping To Represent Spaces and ...supporting

confidence: 88%

“…1E), reproducing observations after blocking NMDA receptor-dependent LTP (Kao et al, 2017;Kentros et al, 1998;Lamsa et al, 2005), which dominates at E®E connections but not at E®I or I®E connections in hippocampus (Lamsa et al, 2005). This illustrates that tunable excitationinhibition coordination can be important for network and memory function, as observed (Caroni, 2015;Chung et al, 2021;Dvorak et al, 2021;Fenton, 2015b;Lamsa et al, 2010;Mongillo et al, 2018;van Dijk and Fenton, 2018); see papers dedicated to the topic (Kullmann and Lamsa, 2011). To simulate remapping after learning a second environment, we remapped the positionto-input relationships and allowed the recurrent weights to change according to the same STDP rules during another 30 laps.…”

Section: Distinguishing Place Tuning and Cofiring Contributions To Re...mentioning

confidence: 80%

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A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields

Levy

Carrillo-Segura²,

Redman

et al. 2021

Preprint

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Hippocampus CA1 place cells express a spatial neural code by discharging action potentials in cell-specific locations (′place fields′), but their discharge timing is also coordinated by multiple mechanisms, suggesting an alternative ′ensemble cofiring′ neural code, potentially distinct from place fields. We compare the importance of these distinct information representation schemes for encoding environments. Using miniature microscopes, we recorded the ensemble activity of mouse CA1 principal neurons expressing GCaMP6f across a multi-week experience of two distinct environments. We find that both place fields and ensemble coactivity relationships are similarly reliable within environments and distinctive between environments. Decoding the environment from cell-pair coactivity relationships is effective and improves after removing cell-specific place tuning. Ensemble decoding relies most crucially on anti-coactive cell pairs distributed across CA1 and is independent of place cell firing fields. We conclude that ensemble cofiring relationships constitute an advantageous neural code for environmental space, independent of place fields.

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Section: Reregistration Instead Of Remapping To Represent Spaces and ...supporting

confidence: 88%

Section: Distinguishing Place Tuning and Cofiring Contributions To Re...mentioning

confidence: 80%

A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields

Levy

Carrillo-Segura²,

Redman

et al. 2021

Preprint

Self Cite

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“…Specifically, DG PV-INs powerfully regulate seizures and circuit excitability in Scn1a +/- mice and may thus be an attractive candidate population for targeted therapeutic intervention. As sparse GC firing is also thought to be crucial for cognitive functions subserved by the hippocampus, such as pattern separation and memory encoding ( Chung et al, 2021 ; Leutgeb et al, 2007 ; McHugh et al, 2007 ; Rolls, 2010 ; Treves et al, 2008 ), correction of this circuit dysfunction may also potentially modulate the severe and chronic intellectual disability characteristic of DS and even more difficult to approach than epilepsy.…”

Section: Discussionmentioning

confidence: 99%

Corticohippocampal circuit dysfunction in a mouse model of Dravet syndrome

Mattis

Somarowthu

Goff

et al. 2022

eLife

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Dravet syndrome (DS) is a neurodevelopmental disorder due to pathogenic variants in SCN1A encoding the Nav1.1 sodium channel subunit, characterized by treatment-resistant epilepsy, temperature-sensitive seizures, developmental delay/intellectual disability with features of autism spectrum disorder, and increased risk of sudden death. Convergent data suggest hippocampal dentate gyrus (DG) pathology in DS (Scn1a+/-) mice. We performed two-photon calcium imaging in brain slice to uncover a profound dysfunction of filtering of perforant path input by DG in young adult Scn1a+/- mice. This was not due to dysfunction of DG parvalbumin inhibitory interneurons (PV-INs), which were only mildly impaired at this timepoint; however, we identified enhanced excitatory input to granule cells, suggesting that circuit dysfunction is due to excessive excitation rather than impaired inhibition. We confirmed that both optogenetic stimulation of entorhinal cortex and selective chemogenetic inhibition of DG PV-INs lowered seizure threshold in vivo in young adult Scn1a+/- mice. Optogenetic activation of PV-INs, on the other hand, normalized evoked responses in granule cells in vitro. These results establish the corticohippocampal circuit as a key locus of pathology in Scn1a+/- mice and suggest that PV-INs retain powerful inhibitory function and may be harnessed as a potential therapeutic approach towards seizure modulation.

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“…The increasing computational abilities of computers have certainly inspired conceptual bridgebuilding between neuronal computations operating in brains and the computations within artificial neural networks that occur within computers. A key conceptual distinction is that unlike typical computers, brains are self-organizing and changing, simply using a brain changes it structurally as well as functionally, so that it will operate differently in the future, as we recently showed in mice (Chung et al, 2021). Among the many other less conceptual differences, a key practical distinction between brains and computers is that we have complete information on the latter's processing, a ground truth of sorts because we know the precise mechanisms that define a microprocessor's structure and function -in short, using the Marr framework for memory (Marr, 1971), we know the implementation, algorithms, and the computations, completely.…”

Section: The Challenge Of Cross-scale Analysis and Understandingmentioning

confidence: 98%

“…4B). We have shown that this apparent loss of spatial tuning is because the neuronal populations collectively signal the animal’s location and direction in an internally-organized, multistable manner such that the ensemble activity patterns switch between representing the current location and direction in either the room frame or the arena frame, but not both (Kelemen and Fenton, 2010;Kelemen and Fenton, 2013;Talbot et al, 2018;van Dijk and Fenton, 2018;Park et al, 2019;Chung et al, 2021). Multistable switching between room and arena representations is rapid (sub-second) and occurs in a purposeful way with a periodicity in the range of 10 seconds (Kelemen and Fenton, 2010;Kelemen and Fenton, 2013;van Dijk and Fenton, 2018;Park et al, 2019).…”

Section: Entorhinal-hippocampal Neuronal Population Dynamics: Phenome...mentioning

confidence: 99%

Do place cells dream of deceptive moves in a signaling game?

Fenton

Hurtado

Broek

et al. 2022

Preprint

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We consider the possibility of applying game theory to analysis and modeling of neurobiological systems. Specifically, the basic properties and features of information asymmetric signal games are considered and discussed as having potential to explain diverse neurobiological phenomena at levels of biological function that include gene regulation, molecular and biochemical signaling, cellular and metabolic function, as well as neuronal action potential discharge to represent cognitive variables that may underlie memory and behavior. We begin by arguing that there is a pressing need for conceptual frameworks that can permit analysis and integration of information and explanations across the many scales of diverse levels of biological function if we are to understand cognitive functions like learning, memory, and perception. The present work focuses on systems level neuroscience organized around the connected brain regions of the entorhinal cortex and hippocampus. These areas are intensely studied in rodent subjects as model neuronal systems that undergo activity-dependent synaptic plasticity to form and represent memories and spatial knowledge used for purposeful navigation. Examples of cognition-related spatial information in the observed neuronal discharge of hippocampal place cell populations and medial entorhinal head-direction cell populations are used to illustrate possible challenges to information maximization concepts that may be natural to explain using the ideas and features of information asymmetric signaling games.

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Cognitive control persistently enhances hippocampal information processing

Cited by 31 publications

References 52 publications

A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields

A manifold neural population code for space in hippocampal coactivity dynamics independent of place fields

Corticohippocampal circuit dysfunction in a mouse model of Dravet syndrome

Do place cells dream of deceptive moves in a signaling game?

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