Reactivation of Rate Remapping in CA3

Schwindel, C. Daniela; Navratilova, Zaneta; Ali, Karim; Tatsuno, Masami; McNaughton, Bruce L.

doi:10.1523/jneurosci.1678-15.2016

Cited by 15 publications

(14 citation statements)

References 30 publications

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“…This is perhaps not surprising given that the behaviorally relevant information for the animal is not just the position along the track but also the way in which it is facing; for example this determines how far away a potential reward is, if located at one or both ends of the track. Studies using periodic tracks have shown no such global remapping, but rather some degree of rate remapping ( Schwindel et al, 2016 ), that is the direction of motion affects the shape and amplitude of place fields, but not their position. In the data we have analyzed there is very weak remapping, see Figure 7—figure supplements 1c and 2b , and so the assumption of invariance of place field to direction of motion is a good one.…”

Section: Discussionmentioning

confidence: 99%

Theta-modulation drives the emergence of connectivity patterns underlying replay in a network model of place cells

Theodoni

Rovira

Wang

et al. 2018

eLife

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Place cells of the rodent hippocampus fire action potentials when the animal traverses a particular spatial location in any environment. Therefore for any given trajectory one observes a repeatable sequence of place cell activations. When the animal is quiescent or sleeping, one can observe similar sequences of activation known as replay, which underlie the process of memory consolidation. However, it remains unclear how replay is generated. Here we show how a temporally asymmetric plasticity rule during spatial exploration gives rise to spontaneous replay in a model network by shaping the recurrent connectivity to reflect the topology of the learned environment. Crucially, the rate of this encoding is strongly modulated by ongoing rhythms. Oscillations in the theta range optimize learning by generating repeated pre-post pairings on a time-scale commensurate with the window for plasticity, while lower and higher frequencies generate learning rates which are lower by orders of magnitude.

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Section: Discussionmentioning

confidence: 99%

Theta-modulation drives the emergence of connectivity patterns underlying replay in a network model of place cells

Theodoni

Rovira

Wang

et al. 2018

eLife

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“…As we could not explain this effect based on changes in behavioral variables, this suggests a certain amount of trial‐unique content, as has been shown previously with different analyses (Manns et al, ; Mankin et al, ; Ziv et al, ). If the contribution of time, or trial‐unique features more generally, to internally generated sequences is large (Takahashi, ; Schwindel et al, ), then averaging across many trials may limit and/or bias the detection of trial‐specific replay content. As it is not yet clear to what extent internally generated sequences reflect trial‐unique experience, it is difficult to convert this possibility into specific recommendations when interpreting decoded replay data.…”

Section: Discussionmentioning

confidence: 99%

Optimizing for generalization in the decoding of internally generated activity in the hippocampus

2017

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The decoding of a sensory or motor variable from neural activity benefits from a known ground truth against which decoding performance can be compared. In contrast, the decoding of covert, cognitive neural activity, such as occurs in memory recall or planning, typically cannot be compared to a known ground truth. As a result, it is unclear how decoders of such internally generated activity should be configured in practice. We suggest that if the true code for covert activity is unknown, decoders should be optimized for generalization performance using cross-validation. Using ensemble recording data from hippocampal place cells, we show that this cross-validation approach results in different decoding error, different optimal decoding parameters, and different distributions of error across the decoded variable space. In addition, we show that a minor modification to the commonly used Bayesian decoding procedure, which enables the use of spike density functions, results in substantially lower decoding errors. These results have implications for the interpretation of covert neural activity, and suggest easy-to-implement changes to commonly used procedures across domains, with applications to hippocampal place cells in particular. © 2017 Wiley Periodicals, Inc.

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“…As we could not explain this effect based on changes in behavioral variables, this suggests a certain amount of trial-unique content, as has been shown previously with different analyses (Manns et al, 2007; Mankin et al, 2012; Ziv et al, 2013). If the contribution of time, or trial-unique features more generally, to internally generated sequences is large (Takahashi, 2015; Schwindel et al, 2016), then averaging across many trials may limit and/or bias the detection of trial-specific replay content. As it is not yet clear to what extent internally generated sequences reflect trial-unique experience, it is difficult to convert this possibility into specific recommendations when interpreting decoded replay data.…”

Section: Discussionmentioning

confidence: 99%

Optimizing for generalization in the decoding of internally generated activity in the hippocampus

Meer

Carey

Tanaka

2016

Preprint

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1The decoding of a sensory or motor variable from neural activity benefits from a known ground truth against 2 which decoding performance can be compared. In contrast, the decoding of covert, cognitive neural activity, 3 such as occurs in memory recall or planning, typically cannot be compared to a known ground truth. As a 4 result, it is unclear how decoders of such internally generated activity should be configured in practice. We 5 suggest that if the true code for covert activity is unknown, decoders should be optimized for generalization 6 performance using cross-validation. Using ensemble recording data from hippocampal place cells, we show 7 that this cross-validation approach results in different decoding error, different optimal decoding parameters, 8 and different distributions of error across the decoded variable space. In addition, we show that a minor 9 modification to the commonly used Bayesian decoding procedure, which enables the use of spike density 10 functions, results in substantially lower decoding errors. These results have implications for the interpreta-11 tion of covert neural activity, and suggest easy-to-implement changes to commonly used procedures across 12 domains, with applications to hippocampal place cells in particular. 13 65 (Zhang et al., 1998; Johnson and Redish, 2007; Pfeiffer and Foster, 2013). 66 Materials and Methods 67Overview 68 Our aim is to describe how the output of decoding hippocampal ensemble activity depends on the configu-69 ration of the decoder. In particular, we examine two components: (1) the split between training and testing 70 data, and (2) the parameters associated with the estimation of firing rates and tuning curves (the encoding 71 model). Both are described in the Analysis section. All analyses are performed on multiple single unit data 72 recorded from rats performing a T-maze task, described in the Behavior section. Data acquisition, annotation, 73 and pre-processing steps are described in the Neural data section. 74All preprocessing and analysis code is publicly available on our GitHub repository, https://github. 75 com/vandermeerlab/papers. Data files are available from our lab server on request by e-mail to the 76 corresponding author. 77 Neural data 78 Subjects and overall timeline. Four male Long-Evans rats (Charles River and Harlan Laboratories), weigh-79ing 439-501 g at the start of the experiment, were first introduced to the behavioral apparatus (described 80 below; 3-11 days) before being implanted with an electrode array targeting the CA1 area of the dorsal hip-81 5 pocampus (details below). Following recovery (4-9 days) rats were reintroduced to the maze until they ran 82 proficiently (0-3 days), at which point daily recording sessions began. On alternate days, rats were water-83 or food-restricted. In parallel with the maze task, some rats (R042, R044, R050) were trained on a simple 84 Pavlovian conditioning task in a separate room (data not analyzed). 85Behavioral task. The apparatus was an elevated T-maze, constructed from wood, paint...

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Reactivation of Rate Remapping in CA3

Cited by 15 publications

References 30 publications

Theta-modulation drives the emergence of connectivity patterns underlying replay in a network model of place cells

Theta-modulation drives the emergence of connectivity patterns underlying replay in a network model of place cells

Optimizing for generalization in the decoding of internally generated activity in the hippocampus

Optimizing for generalization in the decoding of internally generated activity in the hippocampus

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