The Geometry of Abstraction in the Hippocampus and Prefrontal Cortex

Cell, Год журнала: 2020, Номер 183(4), С. 954 - 967.e21

Опубликована: Окт. 14, 2020

Язык: Английский

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Spatial goal coding in the hippocampal formation

Neuron, Год журнала: 2022, Номер 110(3), С. 394 - 422

Опубликована: Янв. 14, 2022

Язык: Английский

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Top-down control of hippocampal signal-to-noise by prefrontal long-range inhibition

Cell, Год журнала: 2022, Номер 185(9), С. 1602 - 1617.e17

Опубликована: Апрель 1, 2022

Prefrontal cortex (PFC) is postulated to exert "top-down control" on information processing throughout the brain promote specific behaviors. However, pathways mediating top-down control remain poorly understood. In particular, knowledge about direct prefrontal connections that might facilitate of hippocampal remains sparse. Here we describe monosynaptic long-range GABAergic projections from PFC hippocampus. These preferentially inhibit vasoactive intestinal polypeptide-expressing interneurons, which are known disinhibit microcircuits. Indeed, stimulating prefrontal–hippocampal increases feedforward inhibition and reduces activity in vivo. The net effect these actions specifically enhance signal-to-noise ratio for encoding object locations augment object-induced spatial information. Correspondingly, activating or inhibiting promotes suppresses exploration, respectively. Together, results elucidate a pathway target disinhibitory microcircuits, thereby enhancing signals network dynamics underlying exploratory behavior.

Язык: Английский

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Assessments of dentate gyrus function: discoveries and debates

Nature reviews. Neuroscience, Год журнала: 2023, Номер 24(8), С. 502 - 517

Опубликована: Июнь 14, 2023

Язык: Английский

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The geometry of cortical representations of touch in rodents

Nature Neuroscience, Год журнала: 2023, Номер 26(2), С. 239 - 250

Опубликована: Янв. 9, 2023

Язык: Английский

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Neural dynamics underlying associative learning in the dorsal and ventral hippocampus

Nature Neuroscience, Год журнала: 2023, Номер 26(5), С. 798 - 809

Опубликована: Апрель 3, 2023

Язык: Английский

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Control of working memory by phase–amplitude coupling of human hippocampal neurons

Nature, Год журнала: 2024, Номер 629(8011), С. 393 - 401

Опубликована: Апрель 17, 2024

Retaining information in working memory is a demanding process that relies on cognitive control to protect memoranda-specific persistent activity from interference

Язык: Английский

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Tuned geometries of hippocampal representations meet the computational demands of social memory

Neuron, Год журнала: 2024, Номер 112(8), С. 1358 - 1371.e9

Опубликована: Фев. 20, 2024

Язык: Английский

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Mixed selectivity: Cellular computations for complexity

Neuron, Год журнала: 2024, Номер 112(14), С. 2289 - 2303

Опубликована: Май 9, 2024

The property of mixed selectivity has been discussed at a computational level and offers strategy to maximize power by adding versatility the functional role each neuron. Here, we offer biologically grounded implementational-level mechanistic explanation for in neural circuits. We define pure, linear, nonlinear discuss how these response properties can be obtained simple Neurons that respond multiple, statistically independent variables display selectivity. If their activity expressed as weighted sum, then they exhibit linear selectivity; otherwise, Neural representations based on diverse are high dimensional; hence, confer enormous flexibility downstream readout circuit. However, circuit cannot possibly encode all possible mixtures simultaneously, this would require combinatorially large number neurons. Gating mechanisms like oscillations neuromodulation solve problem dynamically selecting which transmitted readout.

Язык: Английский

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Low-dimensional dynamics for working memory and time encoding

Proceedings of the National Academy of Sciences, Год журнала: 2020, Номер 117(37), С. 23021 - 23032

Опубликована: Авг. 28, 2020

Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these and, simultaneously, estimate the timing between events. To understand mechanisms underlying working memory and encoding, we analyze neural activity recorded during delays in four experiments nonhuman primates. disambiguate potential mechanisms, propose two analyses, namely, decoding passage of from data computing cumulative dimensionality trajectory over time. Time be decoded with high precision tasks where information is relevant lower when irrelevant for performing task. Neural trajectories are always observed to low-dimensional. In addition, our results further constrain encoding as find that linear “ramping” component each neuron’s firing rate strongly contributes slow timescale variations make possible. These constraints rule out models rely constant, sustained networks high-dimensional trajectories, like reservoir networks. Instead, recurrent trained backpropagation capture time-encoding properties data.

Язык: Английский

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