Latent learning drives sleep-dependent plasticity in distinct CA1 subpopulations

Wei, Guilin; Zhang, Jie J.; Newman, Jonathan P.; Wilson, Matthew A.

doi:10.1101/2020.02.27.967794

Cited by 13 publications

(20 citation statements)

References 33 publications

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“…as when adapting to a novel shortcut or barrier. This process can also be viewed as an embedding of sensory experience within a low-dimensional manifold (in this case, 2D space), as observed of place cells during sleep 12 .…”

Section: Introductionmentioning

confidence: 99%

Replay as structural inference in the hippocampal-entorhinal system

Evans¹,

Burgess²

2020

Preprint

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Model-based decision making relies on the construction of an accurate representation of the underlying state-space, and localization of one’s current state within it. One way to localize is to recognize the state with which incoming sensory observations have been previously associated. Another is to update a previous state estimate given a known transition. In practice, both strategies are subject to uncertainty and must be balanced with respect to their relative confidences; robust learning requires aligning the predictions of both models over historic observations. Here, we propose a dual-systems account of the hippocampal-entorhinal system, where sensory prediction errors between these models during online exploration of state space initiate offline probabilistic inference. Offline inference computes a metric embedding on grid cells of an associative place graph encoded in the recurrent connections between place cells, achieved by message passing between cells representing non-local states. We provide testable explanations for coordinated place and grid cell ‘replay’ as efficient message passing, and for distortions, partial rescaling and direction-dependent offsets in grid patterns as the confidence weighted balancing of model priors, and distortions to grid patterns as reflecting inhomogeneous sensory inputs across states.Author SummaryMinimising prediction errors between transition and sensory input (observation) models predicts partial rescaling and direction-dependent offsets in grid cell firing patterns.Inhomogeneous sensory inputs predict distortions of grid firing patterns during online localisation, and local changes of grid scale during offline inference.Principled information propagation during offline inference predicts coordinated place and grid cell ‘replay’, where sequences propagate between structurally related features.

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Section: Introductionmentioning

confidence: 99%

Replay as structural inference in the hippocampal-entorhinal system

Evans¹,

Burgess²

2020

Preprint

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“…Since its publication in 2015 (Lopes et al, 2015 ), Bonsai has been widely used by many labs worldwide not only for tracking animal behavior in different species of rodents, cephalopods, fish, and insects (Dreosti et al, 2015 ; Walker et al, 2015 ; Douglass et al, 2017 ) but also to control and acquire data from multiple streams. Being open-source software, Bonsai is free to use and not proprietary to any one company, thus many users have adopted it to create their specific packages for EEG (Lopes, 2018 ), Miniscopes (Aharoni and Hoogland, 2019 ; Guo et al, 2020 ), Fiber photometry (Carvalho and Lopes, 2019 ), Open Ephys (Neto et al, 2016 ) and real-time video analysis with DeepLabCut (Kane et al, 2020 ). It also encourages good practices for experimental reproducibility by including a built-in package manager and support for portable deployment across rigs.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

New Open-Source Tools: Using Bonsai for Behavioral Tracking and Closed-Loop Experiments

Lopes¹,

Monteiro

2021

Front. Behav. Neurosci.

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The ability to dynamically control a behavioral task based on real-time animal behavior is an important feature for experimental neuroscientists. However, designing automated boxes for behavioral studies requires a coordinated combination of mechanical, electronic, and software design skills which can challenge even the best engineers, and for that reason used to be out of reach for the majority of experimental neurobiology and behavioral pharmacology researchers. Due to parallel advances in open-source hardware and software developed for neuroscience researchers, by neuroscience researchers, the landscape has now changed significantly. Here, we discuss powerful approaches to the study of behavior using examples and tutorials in the Bonsai visual programming language, towards designing simple neuroscience experiments that can help researchers immediately get started. This language makes it easy for researchers, even without programming experience, to combine the operation of several open-source devices in parallel and design their own integrated custom solutions, enabling unique and flexible approaches to the study of behavior, including video tracking of behavior and closed-loop electrophysiology.

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“…The explosion in the use of in vivo calcium imaging has allowed the visualization of hundreds of identified neurons over long time periods [1][2][3][4][5][6][7][8]. Calcium (Ca2+) imaging using GCaMP calcium indicators and miniature microscopes has been used to image cellular populations during long timescales [9][10][11][12][13][14][15] and in different task phases [16][17][18], as well as to determine neuronal circuit topogrophy and organization [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The hippocampus (HPC) has been studied with cellular resolution using in vivo electrophysiology, but this technique does not allow the same cells to be definitively followed over days, nor does it allow the visualization of cellular organization within the structure (O’Keefe, 1976; Wilson and McNaughton, 1993; McEchron and Disterhoft, 1999; Leutgeb et al, 2004; Schimanski et al, 2013). In vivo calcium imaging is particularly well suited for imaging the rodent HPC, as the orientation of the horizontal cell layer permits imaging of large numbers of neurons with insertion of a 1mm diameter or smaller lens (Guo et al, 2020; Kinsky et al, 2020; Sheintuch et al, 2020; Stefanini et al, 2020). Because the hippocampus is essential for many tasks involving memory (Scoville and Milner, 1957; Squire, 1992; Eichenbaum et al, 1999; Hasselmo and McClelland, 1999; Ferbinteanu and Shapiro, 2003; Ferbinteanu et al, 2006; Smith and Mizumori, 2006; Vann, 2013; Cai et al, 2016; Josselyn and Tonegawa, 2020), spatial navigation (Foster et al, 2000; Rosenzweig et al, 2003; McNaughton et al, 2006; Foster and Knierim, 2012; Moser et al, 2017; Avigan et al, 2020), and learning (Moyer et al, 1990; Chan et al, 2001; Ito et al, 2005; Andrzejewski and Ryals, 2016), Ca2+ imaging of large populations of HPC neurons can shed new insight on cell changes and organization over time during these tasks.…”

Section: Introductionmentioning

confidence: 99%

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In Vivo Multi-Day Calcium Imaging of CA1 Hippocampus in Freely Moving Rats Reveals a High Preponderance of Place Cells with Consistent Place Fields

Wirtshafter

Disterhoft

2021

Preprint

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Calcium imaging using GCaMP calcium indicators and miniature microscopes has been used to image cellular populations during long timescales and in different task phases, as well as to determine neuronal circuit topology and organization. Because the hippocampus (HPC) is essential for many tasks of memory, spatial navigation, and learning, calcium imaging of large populations of HPC neurons can provide new insight on cell changes and organization over time during these tasks. To our knowledge, all reported HPC in vivo calcium imaging experiments have been done in mouse. However, rats have many behavioral and physiological experimental advantages over mice, and, due to their larger size, rats are able to support larger implants, thereby enabling the recording of a greater number of cells. In this paper, we present the first in vivo calcium imaging from CA1 hippocampus in freely moving rats. Using GCaMP7c and the UCLA Miniscope, we demonstrate that hundreds of cells (mean 240+-90 cells per session, maximum 428 cells) can reliably be visualized and held across weeks, and that calcium events in these cells are correlated with periods of movement. We additionally show proof of method by showing that an extremely high percent of place cells (82.3%+-8.1%, far surpassing the percent seen during mouse calcium imaging) can be recorded on a navigational task, and that these place cells enable accurately decoding of animal position. Finally, we show that calcium imaging is rats is not prone to photobleaching during hour-long recordings and that cells can be reliably recorded for an hour or more per session. A detailed protocol for this technique, including notes on the numerous parameter changes needed to use Ca2+ in rats, is included in the Materials and Methods section, and implications of these advancements are discussed.

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Latent learning drives sleep-dependent plasticity in distinct CA1 subpopulations

Cited by 13 publications

References 33 publications

Replay as structural inference in the hippocampal-entorhinal system

Replay as structural inference in the hippocampal-entorhinal system

New Open-Source Tools: Using Bonsai for Behavioral Tracking and Closed-Loop Experiments

In Vivo Multi-Day Calcium Imaging of CA1 Hippocampus in Freely Moving Rats Reveals a High Preponderance of Place Cells with Consistent Place Fields

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