Natural Forgetting of an Object Memory

(a) Object recognition paradigm (b) Object memory test for recall at 24 h or 2 wk (c) Discrimination index (d) Schematic of c-Fos-tTA engram labelling system (e) Representative image of engram labeling within the dentate gyrus (f) Engram labeling of object memory and c-Fos detection following recall (g) Object memory test for recall at 24 h or 2 wk (h) Discrimination index (i) Correlation between Discrimination Index and Engram reactivation (j) Representative image eYFP+ cells, c-Fos+ cells and Merged eYFP+ and c-Fos+ for both 24 h and 2 wk test (k) Engram cells (l) c-Fos+ cells (m) Engram reactivation (n) Engram spine density average per mouse, (o) Engram spine volume average per dendrite (p) Representative image of engram dendrite for morphological analysis. Bar graphs indicates average values in n = 4-11 per group (**p<0.01, ***p<0.001). Data graphed as means ± SEM. Scale bar 100um.

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Dentate gyrus engrams are necessary and sufficient for recall of an object memory

(a) Engram labelling for optogenetic inhibition and behavioral timeline (b) Object memory test (c) Discrimination index (d) Engram labelling for optogenetic activation and behavioral timeline (e) Object memory test (f) Discrimination index Bar graphs indicate average values in n = 9-12 per group (*p<0.05). Data graphed as means ± SEM. Scale bar 100um.

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Environmental enrichment reduces the rate of forgetting by increasing engram activation

(a) Enrichment behavioral paradigm (b) Natural forgetting curve of an object memory at 24 h, 1 wk, 2 wk and 3 wk (c) Object memory test (d) Discrimination index (e) Engram labelling within the dentate gyrus (f) Engram cells (g) c-Fos+ cells (h) Engram reactivation. Bar graphs indicate average values in n = 4-12 per group (*p<0.05, ***p<0.001). Data graphed as means ± SEM. Scale Bar 100um.

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Exposure to the original stimuli facilities memory recall

(a) Behavioral timeline for a brief reminder exposure (b) Object memory test (c) Discrimination index (d) Engram labelling within the dentate gyrus (e) Engram cells (f) c-Fos+ cells (g) Engram reactivation. Bar graphs indicates average values in n = 5-11 per group (*p<0.05, **p<0.01, ***p<0.001). Data graphed as means ± SEM. Scale Bars 100um.

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Repeated exposure to the training environment facilitates forgetting

(a) Behavioral paradigm (b) Object memory test (c) Discrimination index (d) Behavioral paradigm for optogenetic inhibition during repeated context exposure (e) Object memory test (f) Discrimination index. Bar graphs indicates average values in n = 8-12 per group (*p<0.05, **p<0.01). Data graphed as means ± SEM.

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Rac1 modulates forgetting through engram activation

(a) Experimental timeline and drug administration for Rac1 inhibition (b) Object memory test (c) Discrimination index (d) Engram cells (e) c-Fos+ cells (f) Engram reactivation (h) Engram labelling within the dentate gyrus (i) Experimental timeline and drug administration for Rac1 inhibition (j) Object memory test (k) Discrimination index (l) Engram cells (m) c-Fos+ cells (n) Engram reactivation. Bar graphs indicates average values in n = 8-9 per group (**p<0.01, ***p<0.001). Data graphed as means ± SEM. Scale Bar 100um.

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Forgetting as adaptive learning.

Our model assumes that animals create and update memory engrams to flexibly adjust their behavior to their environment. (a) We used a Rescorla-Wagner model that learns object-context associations in the object-based memory task. The key assumption is that stronger associations lead to higher memory performance, while weaker associations lead to more forgetting. (b) The Rescorla-Wagner model updated the strength of object-context associations ("engram strength") as a function of the prediction error (difference between experienced object and engram strength) and the learning rates governing the influence of the prediction error. (c) Based on learned representations, animals constantly predict what happens in the environment (e.g., the occurrence of objects), and if predictions are violated (prediction errors), engrams are updated to improve the accuracy of future predictions. Here, established engram cells are shown in green; non-engram cells in gray. (d) Positive prediction errors signaling the occurrence of an unexpected event (e.g., new object) induce a learning process that increases the probability of remembering. This might rely on the recruitment of new engram cells (shown in yellow). In contrast, negative prediction errors signaling the absence of an expected event (e.g., predicted object did not appear) induce forgetting. This might rely on “forgetting” plasticity reducing access to engrams (light green cells). (e) Our model formalizes this perspective based on the notion of “engram relevancy”, i.e., the strength of the object-context association. Higher engram relevancy makes it more likely that an engram is behaviorally expressed, e.g., through exploration behavior. The presentation of a novel object (upper panel) leads to a high engram relevancy (middle panel) in response to a positive prediction error (lower panel). The absence of an expected object decreases engram relevancy through negative prediction errors. (f) Model simulations corroborate the behavioral effects of our data (Figure 3a). Gray lines and bars show the average exploration probability for the familiar and novel object according to the model; markers show simulated mice.

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Learning model captures the dynamics of forgetting.

Environmental, optogenetic, and pharmacological manipulations might modulate the speed of forgetting by altering key parameters of our model. Simulations with different learning-rate parameters explain the forgetting dynamics of the different experimental conditions. (a) The enrichment and Rac1-inhibition conditions were successfully captured using a low learning rate (0.01, similar to the empirical estimates). (c) In contrast, assuming a larger learning rate (0.5), we could capture faster forgetting as observed in the Rac1-activator and context-only conditions. (e) Moreover, improved memory performance after reminder cues can be explained by assuming that these interventions induce a positive prediction error boosting object relevancy. Here, we assumed a learning rate of 0.07 (based on the empirical estimate). (b) Development of engram relevancy and (d) prediction errors across conditions. (f) Probability of exploring the novel object plotted separately for each condition.