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. 2009 Jan 29;61(2):187-202.
doi: 10.1016/j.neuron.2008.11.026.

Computational influence of adult neurogenesis on memory encoding

James B Aimone  1 , Janet Wiles, Fred H Gage
Affiliations

Computational influence of adult neurogenesis on memory encoding

James B Aimone et al. Neuron. .

Abstract

Adult neurogenesis in the hippocampus leads to the incorporation of thousands of new granule cells into the dentate gyrus every month, but its function remains unclear. Here, we present computational evidence that indicates that adult neurogenesis may make three separate but related contributions to memory formation. First, immature neurons introduce a degree of similarity to memories learned at the same time, a process we refer to as pattern integration. Second, the extended maturation and change in excitability of these neurons make this added similarity a time-dependent effect, supporting the possibility that temporal information is included in new hippocampal memories. Finally, our model suggests that the experience-dependent addition of neurons results in a dentate gyrus network well suited for encoding new memories in familiar contexts while treating novel contexts differently. Taken together, these results indicate that new granule cells may affect hippocampal function in several unique and previously unpredicted ways.

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Figures

Figure 1
Figure 1. Overview of neural network model
(A) Simplified block diagram of network architecture. (B) Sketch of newborn granule cell (GC) maturation process implemented in model. (C) Growth of the GC layer and cell death. (D) Timeline of model growth initialization and growth. (E) Sample input neuron activity in different environments (Env). Medial entorhinal cortex (mEC) neurons (top) have spatial response, lateral EC (lEC) neurons (bottom) fire at equal rates at all spatial locations. (F) Illustration of how the network is trained and tested. During training (top), model “explores” random paths within an environment. During testing (bottom), network activity is measured in a series of spatial locations that tile the environment
Figure 2
Figure 2. Pattern separation by dentate gyrus (DG) model
(A) Schematic showing pattern separation experiment. Once grown for 160 days, NG and No NG networks were tested at different locations and environments, each providing a different entorhinal cortex (EC) input to the model. (B) Effect of EC similarity (x-axis) on the similarity between DG outputs (y-axis). In networks with neurogenesis (NG, red), very low input similarity results in relatively higher DG similarity, an effect we refer to as pattern integration. Pattern integration does not occur in non-neurogenic networks (No NG, blue). Similarity is measured by the normalized dot product (NDP). The difference between NG and No NG networks was significant (p<0.01). (C) The decrease in pattern separation with neurogenesis occurs with both spatial (medial EC) and contextual (lateral EC) inputs. (D) Cartoon schematic of pattern integration effect. Two events encoded by similar EC populations activate distinct mature DG neurons, yet activate the same immature neurons.
Figure 3
Figure 3. Effect of time between events on pattern separation and pattern integration
(A) Schematic showing the pattern separation experiment extended over time. The model continued to grow with maturation, neurogenesis and cell death between testing sessions, at which time the response of the model was measured at different environments and spatial locations. (B) Effect of time between events on pattern separation of inputs that are 80% (top), 50% (middle), and 10% (bottom) similar. Note how DG similarities between events separated in time are lower than those tested on the same day. Both the decrease in similarity over time and the interaction between time and NG/No NG groups were significant for each of the input similarity groups (p<0.01). (C) Cartoon schematic of temporal separation. Two similar events, when separated by time, will activate distinct mature DG neurons, but also a different population of immature neurons, increasing the separation of the two events.
Figure 4
Figure 4. Response of DG network to familiar and novel environments
Environments were examined in each of the environments used during network growth. Within each environment, firing rates in response to 400 spatial locations were determined. (A) A sample response of a NG network’s granule cell (GC) population upon presentation to 400 spatial locations within each familiar environment (FE) and one novel environment (NE) on Day 160 (gray:>2Hz; green:>4Hz; blue:>6Hz; firing 2Hz or below not shown). Neurons are sorted on the x-axis by age - oldest on the left, youngest on the right. Note how neurons of similar ages respond to the same environments. (B) Response of the same NG network to the same environments on Day 200. Note the increase of the preferring group to Env 4 and the development of a preferring group to Env 5. The young population of GC that respond to all inputs are labeled with an asterisk (‘*’) in (A) and (B). (C–D) The response of a sample No NG network to the four FEs and one NE on Day 160 (C) and Day 200 (D). Note the failure of the No NG to develop a population of neurons that preferred Env 5. (E) Cartoon schematic of DG specialization. Adult-born neurons are involved in the encoding of events during their maturation. Those same adult-born neurons, once mature, are utilized when that event is remembered or experienced again.
Figure 5
Figure 5. Effect of neurogenesis modulation on function
(A) Time-course of neurogenesis in aging study. After day 120, networks were grown for 400 days with either decreasing neurogenesis (red) or constant neurogenesis (blue). (B) Pattern integration in aging networks, measured by ability of network to separate already dissimilar inputs (input similarity of 10%; p<0.01). (C) Temporal dynamics of pattern integration (input similarity of 10%) in young (solid line) and old (dashed line) networks. Pattern integration depends on time in young networks, but time between events has limited effect on old networks. (D) Response of aged network to familiar environments (FEs) after full growth (gray:>2Hz; green:>4Hz; blue:>6Hz; firing 2Hz or below not shown). (E) Time-course of neurogenesis in stress study. After day 120, networks had 60 days of decreased neurogenesis followed by full recovery (red), or no change in neurogenesis (blue). (F) Pattern integration in stressed networks, measured by ability of network to separate already dissimilar inputs (input similarity of 10%; p<0.01). (G) Temporal dynamics of pattern integration (input similarity of 10%) before (solid line), during (dashed line), and after (dotted line) stressful experience. (H) Response of stressed network to familiar environments (FEs) after full growth.
Figure 6
Figure 6. Schematic summarizing possible functions for adult-born neurons
(A) Distinct events (different shapes) occurring at different times (labeled Time 1, Time 2, and Time 3). The events are colored by the time that they are presented. The different events that are experienced will tune the maturing neurons to eventually fire specifically to those events. (B) While immature, the new neurons associate events that occur around the same time (pattern integration). Events encoded at distinct times (Time 1 and Time 2) activate different neurons (temporal separation). (C) The young neurons that matured at time 1 (colored red) will later specify new dimensions specifically tuned to the same events they experienced when young. If the events that occurred during their maturation are re-experienced, the red neurons will be utilized to increase the dimensionality of the memory that is formed.
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References

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