On How the Dentate Gyrus Contributes to Memory Discrimination

Dijk, Milenna T. van; Fenton, A. G.

doi:10.2139/ssrn.3155582

Cited by 30 publications

(75 citation statements)

References 33 publications

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“…It was recently shown that the mixed selectivity component of the neuronal responses is important in complex cognitive tasks 1,3 because it is a signature of the high dimensionality of the neural representations. Place cell discharges are also highly variable 5 to the extent that the variability, not the spatial tuning alone, can captures changes due to learning in a spatial memory task [7][8][9] . These recent studies naturally pose the question of how position is encoded within population activity of the hippocampus.…”

Section: Introductionmentioning

confidence: 99%

“…The responses of some of its cells are easily interpretable as these tend to fire only when the animal is at one or more locations in an environment (place cells). However, it is becoming clear that in many brain areas, which include the hippocampus and entorhinal cortex, the neural responses are very diverse [1][2][3][4] and highly variable in time [5][6][7] . Place cells might respond at single or multiple locations, in an orderly (grid cells) or disorderly way and multiple passes through the same location typically elicit different responses.…”

Section: Introductionmentioning

confidence: 99%

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A distributed neural code in the dentate gyrus and in CA1

Kheirbek

Kushnir²,

Jimenez

et al. 2018

Preprint

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Traditionally, neurons have been viewed as specialized for single functions or a few highly related functions. However, at the higher levels of processing, neural specialization is less obvious, rather a mix of disparate, seemingly unrelated, sensory, cognitive and behavioural quantities drive neural activity. Task relevant variables can be decoded by reading out populations of neurons as the neural code is highly distributed. Here we show that this seems to be the case also in the dentate gyrus subregion of the hippocampus. Using calcium imaging we simultaneously recorded the activity of hundreds of cells from the dentate gyrus of mice while they freely explored an arena. Their instantaneous position, direction of motion and speed could be accurately decoded from the neural activity. Ranking neurons by their contribution to the decoding accuracy revealed that the response properties of individual neurons were often not predictive of their importance for encoding position. Furthermore, we could decode position from neurons that were important for encoding the direction of motion and vice versa, showing that these quantities are encoded by largely overlapping populations. Our analysis indicates that classical methods of analysis based on single cell response properties might be insufficient to characterize the neural code and that the lack of observation of easily interpretable place cells in one brain area is not necessarily the indication that position is not efficiently encoded in that area.All rights reserved. No reuse allowed without permission.was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A distributed neural code in the dentate gyrus and in CA1

Kheirbek

Kushnir²,

Jimenez

et al. 2018

Preprint

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“…Such an operation, termed pattern separation, has been ascribed to the hippocampal dentate gyrus in species ranging from rodents to humans [2][3][4] . In the dentate gyrus, polysensory inputs are mapped onto a large number of granule cells which exhibit extremely sparse firing patterns, resulting in a high probability of non-overlapping output patterns [5][6][7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

Dentate gyrus population activity during immobility supports formation of precise memories

Pofahl

Nikbakht

et al. 2020

Preprint

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All vertebrates are capable of generating dissimilar patterns of neuronal activity from similar sensory-driven input patterns, a phenomenon called pattern separation. It is unclear, however, how these separated patterns are transformed into lasting memories that retain the initial discrimination. Using dual-color in-vivo two-photon Ca 2+ imaging, we show that the dentate gyrus, a region implicated in pattern separation, generates in immobile mice sparse, synchronized activity patterns driven by entorhinal cortex activity. These population events are structured and modified by changes in the environment; they incorporate place-and speed cells and are similar to population patterns evoked during self-motion. Inhibiting only granule cells in immobile mice impairs formation of pattern-separated memories. These patterns, thus, support the creation of precise memories by replaying the population codes of the current environment on a short time scale. Results Sparse, structured dentate network events in immobile animalsWe imaged the activity of >300 granule cells (GCs) using a Thy1-GCaMP6s mouse line (GP4.12Dkim/J,15 ). In addition, we monitored the activity of the major input system into the dentate gyrus, the medial perforant path (MPP). To this end, we expressed the red-shifted Ca 2+ indicator jRGECO1a 16 in the medial entorhinal cortex using viral gene transfer (see Methods section, Fig. 1A, B, Supplementary Fig. 1A). To allow efficient excitation of both genetically encoded Ca 2+ indicators, we established excitation with two pulsed laser sources at 940 and 1070 nm (see Supplementary Fig. 1B). The mice were placed under a two photon microscope and ran on a linear track (see Supplementary Fig. 1C, Supplementary Movie 1).As previously described, the firing of GCs was generally sparse 7-9,17 , both when animals were immobile and running on a textured belt without additional cues (mean event frequency 1.38±0.19 events/min and 0.97±0.2 events/min, respectively, n=9 mice, Fig. 1B, Supplementary Fig. 2C-F). Despite the sparse activity of granule cells, we observed synchronized activity patterns. To rigorously define such events, we used an algorithm that detects synchronized network events within a 200 ms time window (see Methods). Such synchronous network events could readily be observed in the dentate gyrus ( Fig. 1C, D, network events depicted in different colors, see corresponding Supplementary Movie 2).Network events were sparse, incorporating only 5.7±0.09 % of the active GC population.Notably, network events occurred mainly during immobility and were much less prevalent during running (Fig. 1D, E). Shuffling analysis (see Methods) confirmed that network events cannot arise by chance (Fig 1E, grey bars correspond to shuffled data, ANOVA F (3,25) =30.12, p=4*10 -14 , Bonferroni post-test resting vs. shuffled p=2.2*10 -10 indicated with asterisk, running vs. shuffled p=1). Simultaneous imaging of MPP and GC activity showed that network events were strongly correlated with MPP activity increases (Fig. 1F). Moreover, ...

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“…So far, all pattern separation analyses were conducted on the population rate vectors during a 600 ms time window. However, many neural computations are likely to occur on shorter timescales, such as within individual theta cycles (~100 ms) 19,59 . Indeed, the time window in which correlation is recorded can nontrivially affect the resulting correlation, depending on the timing of spikes within it 58 .…”

Section: Frequency Dependent Pattern Separation Is Robust Over Analysmentioning

confidence: 99%

“…Feedback circuits can i) implement direct competition between active cells through lateral inhibition and can ii) integrate information about the actual global activity level in a population allowing efficient normalization [7][8][9] . Indeed, in the insect olfactory system a critical role of such a circuit has been causally demonstrated 10,11 In mammals, substantial evidence points towards a role of the hippocampal dentate gyrus (DG) for pattern separation during memory formation and spatial discrimination [12][13][14][15][16][17][18][19][20] . The DG is thought to subserve this task by converting different types of inputs to sparse, nonoverlapping activity patterns of granule cells (GCs).…”

Section: Introductionmentioning

confidence: 99%

Quantitative properties of a feedback circuit predict frequency-dependent pattern separation

Braganza

Müller-Komorowska

Kelly

et al. 2019

Preprint

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Feedback inhibitory motifs are thought to be important for pattern separation across species. How feedback circuits may implement pattern separation of biologically plausible, temporally structured input in mammals is, however, poorly understood. We have quantitatively determined key properties of net feedback inhibition in the mouse dentate gyrus, a region critically involved in pattern separation. Feedback inhibition is recruited steeply with a low dynamic range (0 to 4% of active GCs), and with a non-uniform spatial profile. Additionally, net feedback inhibition shows frequency-dependent facilitation, driven by strongly facilitating mossy fiber inputs. Computational analyses show a significant contribution of the feedback circuit to pattern separation of theta modulated inputs, even within individual theta cycles. Moreover, pattern separation was selectively boosted at gamma frequencies, in particular for highly similar inputs. This effect was highly robust, suggesting that frequency dependent pattern separation is a key feature of the feedback inhibitory microcircuit.

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On How the Dentate Gyrus Contributes to Memory Discrimination

Cited by 30 publications

References 33 publications

A distributed neural code in the dentate gyrus and in CA1

A distributed neural code in the dentate gyrus and in CA1

Dentate gyrus population activity during immobility supports formation of precise memories

Quantitative properties of a feedback circuit predict frequency-dependent pattern separation

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