Neuronal Computation Underlying Inferential Reasoning in Humans and Mice

Barron, Helen C.; Reeve, Hayley M.; Koolschijn, Renée S.; Perestenko, Pavel V.; Shpektor, Anna; Nili, Hamed; Rothaermel, Roman; Campo-Urriza, Natalia; O’Reilly, Jill X.; Bannerman, David M.; Behrens, Timothy E.J.; Dupret, David

doi:10.1016/j.cell.2020.08.035

Cited by 102 publications

(179 citation statements)

References 99 publications

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“…9a) that were interleaved with the inference test trials during the MRI scan and shared the same temporal structure. Importantly, previous studies suggest performance on conditioning trials is not hippocampal-dependent 33 . During the conditioning trials, we observed no change in glu/GABA ratio during presentation of the visual cue or outcome, relative to the ITI period ( Supplementary Fig.…”

Section: Dynamic Increase In the Ratio Between Glutamate And Gaba In mentioning

confidence: 82%

“…To validate this interpretation of event-related fMRS it is important to leverage animal studies where more sensitive methods can be employed to relate fMRS measures to physiological parameters. Here, by implementing an inference task in VR, we operationalize memory recall using the exact same paradigm previously employed in rodents 33 . Therefore, in addition to engaging attention and memory-dependent inference, "opening the box" to find a reward in the VR environment approximated the process of rodents finding a reward from a dispenser in a 3D environment.…”

Section: Discussionmentioning

confidence: 99%

“…1a) that has previously been shown to involve associative memory recall in humans and mice 33 . Moreover, we chose to implement an inference task because, unlike some forms of first-order associative recall, previous lesion and optogenetic studies in rodents demonstrate that second-order associative recall required for inference is a hippocampal dependent process [33][34][35] . Thus, the inference task provided an opportunity to investigate how the hippocampus mediates neocortical excitation and inhibition during memory recall.…”

Section: Task Design and Behaviourmentioning

confidence: 99%

“…All stages of the task, including the day 3 inference test were performed in virtual reality (VR) ( Fig. 1b), an immersive and highly controlled 3D environment that has the potential to benefit from cross-species comparisons in the future 33 .…”

Section: Task Design and Behaviourmentioning

confidence: 99%

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A mechanism for hippocampal memory recall based on excitatory-inhibitory fluctuations in neocortex

Koolschijn

Shpektor

et al. 2020

Preprint

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The brain has a remarkable capacity to acquire and store memories that can later be selectively recalled. These processes are supported by the hippocampus which is thought to index memory recall by reinstating information stored across distributed neocortical circuits. However, the mechanism that supports this interaction remains unclear. Here, in humans, we show that recall of a visual cue from a paired associate is accompanied by a transient increase in the ratio between glutamate and GABA in visual cortex. Moreover, these excitatory-inhibitory fluctuations are predicted by activity in the hippocampus. These data suggest the hippocampus gates memory recall by indexing information stored across neocortical circuits using a disinhibitory mechanism.

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Section: Dynamic Increase In the Ratio Between Glutamate And Gaba In mentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Task Design and Behaviourmentioning

confidence: 99%

Section: Task Design and Behaviourmentioning

confidence: 99%

See 2 more Smart Citations

A mechanism for hippocampal memory recall based on excitatory-inhibitory fluctuations in neocortex

Koolschijn

Shpektor

et al. 2020

Preprint

Self Cite

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“…R. Soc. B 376: 20190633 rodents reveals equivalent representational structure in the hippocampus on an inference task [210].…”

Section: Cross-species Neural Analyses: a Common Spacementioning

confidence: 99%

Cross-species neuroscience: closing the explanatory gap

Barron

Mars

Dupret

et al. 2020

Phil. Trans. R. Soc. B

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Neuroscience has seen substantial development in non-invasive methods available for investigating the living human brain. However, these tools are limited to coarse macroscopic measures of neural activity that aggregate the diverse responses of thousands of cells. To access neural activity at the cellular and circuit level, researchers instead rely on invasive recordings in animals. Recent advances in invasive methods now permit large-scale recording and circuit-level manipulations with exquisite spatio-temporal precision. Yet, there has been limited progress in relating these microcircuit measures to complex cognition and behaviour observed in humans. Contemporary neuroscience thus faces an explanatory gap between macroscopic descriptions of the human brain and microscopic descriptions in animal models. To close the explanatory gap, we propose adopting a cross-species approach. Despite dramatic differences in the size of mammalian brains, this approach is broadly justified by preserved homology. Here, we outline a three-armed approach for effective cross-species investigation that highlights the need to translate different measures of neural activity into a common space. We discuss how a cross-species approach has the potential to transform basic neuroscience while also benefiting neuropsychiatric drug development where clinical translation has, to date, seen minimal success. This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity’.

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Microinstrumentation for Brain Organoids

Patel,

Shetty,

Acha

et al. 2024

Adv Healthcare Materials

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Brain organoids are three‐dimensional aggregates of self‐organized differentiated stem cells that mimic the structure and function of human brain regions. Organoids bridge the gaps between conventional drug screening models such as planar mammalian cell culture, animal studies, and clinical trials. They can revolutionize the fields of developmental biology, neuroscience, toxicology, and computer engineering. Conventional microinstrumentation for conventional cellular engineering, such as planar microfluidic chips; microelectrode arrays (MEAs), and optical, magnetic, and acoustic techniques, have limitations when applied to 3D organoids, primarily due to their limits with inherently 2D geometry and interfacing. Hence, there is an urgent need to develop new instrumentation that is compatible with live cell culture techniques and with scalable 3D formats of relevance to organoids. This review discusses conventional planar approaches and emerging 3D microinstrumentation necessary for advanced organoid‐machine interfaces. Specifically, the article surveys recently developed microinstrumentation, including 3D printed and curved microfluidics, 3D and fast‐scan optical techniques, buckling and self‐folding microelectrode arrays, 3D interfaces for electrochemical measurements, and 3D spatially controllable magnetic and acoustic technologies relevant to two‐way information transfer with brain organoids. The article highlights key challenges that must be addressed for robust organoid culture and reliable 3D spatiotemporal information transfer.This article is protected by copyright. All rights reserved

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Neuronal Computation Underlying Inferential Reasoning in Humans and Mice

Cited by 102 publications

References 99 publications

A mechanism for hippocampal memory recall based on excitatory-inhibitory fluctuations in neocortex

A mechanism for hippocampal memory recall based on excitatory-inhibitory fluctuations in neocortex

Cross-species neuroscience: closing the explanatory gap

Microinstrumentation for Brain Organoids

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