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  • 1
    Online Resource
    Online Resource
    American Association for the Advancement of Science (AAAS) ; 2020
    In:  Science Vol. 368, No. 6498 ( 2020-06-26)
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 368, No. 6498 ( 2020-06-26)
    Abstract: Decision-making in complex environments relies on flexibly using prior experience. This process depends on the medial frontal cortex (MFC) and the medial temporal lobe, but it remains unknown how these structures implement selective memory retrieval. We recorded single neurons in the MFC, amygdala, and hippocampus while human subjects switched between making recognition memory–based and categorization-based decisions. The MFC rapidly implemented changing task demands by using different subspaces of neural activity and by representing the currently relevant task goal. Choices requiring memory retrieval selectively engaged phase-locking of MFC neurons to amygdala and hippocampus field potentials, thereby enabling the routing of memories. These findings reveal a mechanism for flexibly and selectively engaging memory retrieval and show that memory-based choices are preferentially represented in the frontal cortex when required.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
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    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2020
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    detail.hit.zdb_id: 2066996-3
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  • 2
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2013
    In:  Nature Vol. 497, No. 7451 ( 2013-5), p. 585-590
    In: Nature, Springer Science and Business Media LLC, Vol. 497, No. 7451 ( 2013-5), p. 585-590
    Type of Medium: Online Resource
    ISSN: 0028-0836 , 1476-4687
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    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2013
    detail.hit.zdb_id: 120714-3
    detail.hit.zdb_id: 1413423-8
    SSG: 11
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  • 3
    Online Resource
    Online Resource
    Proceedings of the National Academy of Sciences ; 2021
    In:  Proceedings of the National Academy of Sciences Vol. 118, No. 51 ( 2021-12-21)
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 118, No. 51 ( 2021-12-21)
    Abstract: The observation of place cells has suggested that the hippocampus plays a special role in encoding spatial information. However, place cell responses are modulated by several nonspatial variables and reported to be rather unstable. Here, we propose a memory model of the hippocampus that provides an interpretation of place cells consistent with these observations. We hypothesize that the hippocampus is a memory device that takes advantage of the correlations between sensory experiences to generate compressed representations of the episodes that are stored in memory. A simple neural network model that can efficiently compress information naturally produces place cells that are similar to those observed in experiments. It predicts that the activity of these cells is variable and that the fluctuations of the place fields encode information about the recent history of sensory experiences. Place cells may simply be a consequence of a memory compression process implemented in the hippocampus.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
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    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2021
    detail.hit.zdb_id: 209104-5
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  • 4
    Online Resource
    Online Resource
    American Association for the Advancement of Science (AAAS) ; 2008
    In:  Science Vol. 319, No. 5869 ( 2008-03-14), p. 1495-1496
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 319, No. 5869 ( 2008-03-14), p. 1495-1496
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
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    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2008
    detail.hit.zdb_id: 128410-1
    detail.hit.zdb_id: 2066996-3
    detail.hit.zdb_id: 2060783-0
    SSG: 11
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  • 5
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 118, No. 32 ( 2021-08-10)
    Abstract: Deforestation results in habitat fragmentation, decreasing diversity, and functional degradation. For mangroves, no data are available on the impact of deforestation on the diversity and functionality of the specialized invertebrate fauna, critical for their functioning. We compiled a global dataset of mangrove invertebrate fauna comprising 364 species from 16 locations, classified into 64 functional entities (FEs). For each location, we calculated taxonomic distinctness (Δ+), functional richness (FRi), functional redundancy (FRe), and functional vulnerability (FVu) to assess functional integrity. Δ+ and FRi were significantly related to air temperature but not to geomorphic characteristics, mirroring the global biodiversity anomaly of mangrove trees. Neither of those two indices was linked to forest area, but both sharply decreased in human-impacted mangroves. About 60% of the locations showed an average FRe 〈 2, indicating that most of the FEs comprised one species only. Notable exceptions were the Eastern Indian Ocean and west Pacific Ocean locations, but also in this region, 57% of the FEs had no redundancy, placing mangroves among the most vulnerable ecosystems on the planet. Our study shows that despite low redundancy, even small mangrove patches host truly multifunctional faunal assemblages, ultimately underpinning their services. However, our analyses also suggest that even a modest local loss of invertebrate diversity could have significant negative consequences for many mangroves and cascading effects for adjacent ecosystems. This pattern of faunal-mediated ecosystem functionality is crucial for assessing the vulnerability of mangrove forests to anthropogenic impact and provides an approach to planning their effective conservation and restoration.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    RVK:
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    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2021
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Proceedings of the National Academy of Sciences ; 2020
    In:  Proceedings of the National Academy of Sciences Vol. 117, No. 37 ( 2020-09-15), p. 23021-23032
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 117, No. 37 ( 2020-09-15), p. 23021-23032
    Abstract: Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear “ramping” component of each neuron’s firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    RVK:
    RVK:
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2020
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
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  • 7
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2015
    In:  Nature Vol. 522, No. 7556 ( 2015-06-18), p. 309-314
    In: Nature, Springer Science and Business Media LLC, Vol. 522, No. 7556 ( 2015-06-18), p. 309-314
    Type of Medium: Online Resource
    ISSN: 0028-0836 , 1476-4687
    RVK:
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    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2015
    detail.hit.zdb_id: 120714-3
    detail.hit.zdb_id: 1413423-8
    SSG: 11
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  • 8
    Online Resource
    Online Resource
    Proceedings of the National Academy of Sciences ; 2001
    In:  Proceedings of the National Academy of Sciences Vol. 98, No. 7 ( 2001-03-27), p. 4265-4270
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 98, No. 7 ( 2001-03-27), p. 4265-4270
    Abstract: Visual classification is the way we relate to different images in our environment as if they were the same, while relating differently to other collections of stimuli (e.g., human vs. animal faces). It is still not clear, however, how the brain forms such classes, especially when introduced with new or changing environments. To isolate a perception-based mechanism underlying class representation, we studied unsupervised classification of an incoming stream of simple images. Classification patterns were clearly affected by stimulus frequency distribution, although subjects were unaware of this distribution. There was a common bias to locate class centers near the most frequent stimuli and their boundaries near the least frequent stimuli. Responses were also faster for more frequent stimuli. Using a minimal, biologically based neural-network model, we demonstrate that a simple, self-organizing representation mechanism based on overlapping tuning curves and slow Hebbian learning suffices to ensure classification. Combined behavioral and theoretical results predict large tuning overlap, implicating posterior infero-temporal cortex as a possible site of classification.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    RVK:
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    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2001
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
    Location Call Number Limitation Availability
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