In the adult mammalian brain, neurogenesis, the production of new neurons, is restricted to a few brain areas, such as the olfactory bulb and the dentate gyrus (Deng et al., 2010). The dentate gyrus is a major entry point of input from cortex, primarily entorhinal cortex (EC), to the hippocampus (Amaral et al., 2007), which is believed to be a substrate of learning and memory (Jarrard, 1993). Adult-born cells in dentate gyrus mostly develop into dentate granule cells (DGCs), the main excitatory cells that project to area CA3 of hippocampus (Deng et al., 2010).

The properties of rodent adult-born DGCs change as a function of their maturation stage, until they become indistinguishable from other mature DGCs at approximately 8 weeks (Deng et al., 2010; Johnston et al., 2016; Figure 1a). Many of them die before they fully mature (Dayer et al., 2003). Their survival is experience dependent and relies on NMDA receptor activation (Tashiro et al., 2006). Initially, newborn DGCs have enhanced excitability (Schmidt-Hieber et al., 2004; Li et al., 2017) and stronger synaptic plasticity than mature DGCs, reflected by a larger long-term potentiation (LTP) amplitude and a lower threshold for induction of LTP (Wang et al., 2000; Schmidt-Hieber et al., 2004; Ge et al., 2007). Furthermore, after 4 weeks of maturation adult-born DGCs have only weak connections to interneurons, while at 7 weeks of age, their activity causes indirect inhibition of mature DGCs (Temprana et al., 2015).

Figure 1 with 1 supplement see all

Download asset Open asset

Newborn DGCs receive no direct connections from mature DGCs (Deshpande et al., 2013; Alvarez et al., 2016) (yet see Vivar et al., 2012), but are indirectly activated via interneurons (Alvarez et al., 2016; Heigele et al., 2016). At about 3 weeks after birth, the γ-aminobutyric acid (GABAergic) input from interneurons to adult-born DGCs switches from excitatory in the early phase to inhibitory in the late phase of maturation (Ge et al., 2006; Deng et al., 2010) (‘GABA-switch’, Figure 1a). Analogous to a similar transition during embryonic and early postnatal stages (Wang and Kriegstein, 2011), the GABA-switch is caused by a change in the expression profile of chloride cotransporters. In the early phase of maturation, newborn cells express the ${\text{Na}}^{+}{\text{-K}}^{+}\text{-}2{\text{Cl}}^{-}$ cotransporter NKCC1, which leads to a high intracellular chloride concentration. Hence, the GABA reversal potential is higher than the resting potential (Ge et al., 2006; Heigele et al., 2016), and GABAergic inputs lead to Cl⁻ ions outflow through the ${\text{GABA}}_A$ ionic receptors, which results in depolarization of the newborn cell (Ben-Ari, 2002; Owens and Kriegstein, 2002). In the late phase of maturation, expression of the ${\text{K}}^{+}{\text{-Cl}}^{-}$-coupled cotransporter KCC2 kicks in, which lowers the intracellular chloride concentration of the newborn cell to levels similar to those of mature cells, leading to a hyperpolarization of the cell membrane due to Cl⁻ inflow upon GABAergic stimulation (Ben-Ari, 2002; Owens and Kriegstein, 2002). The transition from depolarizing (excitatory) to hyperpolarizing (inhibitory) effects of GABA is referred to as the ‘GABA-switch’. It has been shown that GABAergic inputs are crucial for the integration of newborn DGCs into the preexisting circuit (Ge et al., 2006; Chancey et al., 2013; Alvarez et al., 2016; Heigele et al., 2016).

The mammalian dentate gyrus contains – just like hippocampus in general – a myriad of inhibitory cell types (Freund and Buzsáki, 1996; Somogyi and Klausberger, 2005; Klausberger and Somogyi, 2008), including basket cells, chandelier cells, and hilar cells (Figure 1—figure supplement 1). Basket cells can be subdivided in two categories: some express cholecystokinin (CCK) and vasoactive intestinal polypeptide (VIP), while the others express parvalbumin (PV) and are fast-spiking (Freund and Buzsáki, 1996; Amaral et al., 2007). Chandelier cells also express PV (Freund and Buzsáki, 1996). Overall, it has been estimated that PV is expressed in 15–21% of all dentate GABAergic cells (Freund and Buzsáki, 1996) and in 20–25% of the GABAergic neurons in the granule cell layer (Houser, 2007). Amongst the GABAergic hilar cells, 55% express somatostatin (SST) (Houser, 2007) and somatostatin-positive interneurons (SST-INs) represent about 16% of the GABAergic neurons in the dentate gyrus as a whole (Freund and Buzsáki, 1996). While axons of hilar interneurons stay in the hilus and provide perisomatic inhibition onto dentate GABAergic cells, axons of hilar-perforant-path-associated interneurons (HIPP) extend to the molecular layer and provide dendritic inhibition onto both DGCs and interneurons (Yuan et al., 2017). HIPP axons generate lots of synaptic terminals and extend as far as 3.5 mm along the septotemporal axis of the dentate gyrus (Amaral et al., 2007). PV-expressing interneurons (PV-INs) and SST-INs both target adult-born DGCs early (after 2–3 weeks) in their maturation (Groisman et al., 2020). PV-INs provide both feedforward inhibition and feedback inhibition (also called lateral inhibition) to the DGCs (Groisman et al., 2020). In general, SST-INs provide lateral, but not feedforward, inhibition onto DGCs (Stefanelli et al., 2016; Groisman et al., 2020; Figure 1—figure supplement 1).

Adult-born DGCs are preferentially reactivated by stimuli similar to the ones they experienced during their early phase of maturation, up to 3 weeks after cell birth (Tashiro et al., 2007). Even though the amount of newly generated cells per month is rather low (3–6% of the total DGCs population [van Praag et al., 1999; Cameron and McKay, 2001]), adult-born DGCs are critical for behavioral pattern separation (Clelland et al., 2009; Sahay et al., 2011a; Jessberger et al., 2009), in particular in tasks where similar stimuli or contexts have to be discriminated (Clelland et al., 2009; Sahay et al., 2011a). However, the functional role of adult-born DGCs is controversial (Sahay et al., 2011b; Aimone et al., 2011). One view is that newborn DGCs contribute to pattern separation through a modulatory role (Sahay et al., 2011b). Another view suggests that newborn DGCs act as encoding units that become sensitive to features of the environment which they encounter during a critical window of maturation (Kee et al., 2007; Tashiro et al., 2007). Some authors have even challenged the role of newborn DGCs in pattern separation in the classical sense and have proposed a pattern integration effect instead (Aimone et al., 2011), while others suggest a dynamical (Aljadeff et al., 2015; Shani-Narkiss et al., 2020) or forgetting (Akers et al., 2014) role for newborn DGCs. Within that broader controversy, we ask two specific questions: First, why are GABAergic inputs crucial for the integration of newborn DGCs into the preexisting circuit? And second, why are newborn DGCs particularly important in tasks where similar stimuli or contexts have to be discriminated?

To address these questions, we present a model of how newborn DGCs integrate into the preexisting circuit. In contrast to earlier models where synaptic input connections onto newborn cells were assumed to be strong enough to drive them (Chambers et al., 2004; Becker, 2005; Crick and Miranker, 2006; Wiskott et al., 2006; Chambers and Conroy, 2007; Aimone et al., 2009; Appleby and Wiskott, 2009; Weisz and Argibay, 2009; Temprana et al., 2015; Finnegan and Becker, 2015; DeCostanzo et al., 2018), our model uses an unsupervised biologically plausible Hebbian learning rule that makes synaptic connections between EC and newborn DGCs either disappear or grow from small values at birth to values that eventually enable feedforward input from EC to drive DGCs. Contrary to previous modeling studies, our plasticity model does not require an artificial renormalization of synaptic connection weights since model weights are naturally bounded by the synaptic plasticity rule. We show that learning with a biologically plausible plasticity rule is possible thanks to the GABA-switch, which has been overlooked in previous modeling studies. Specifically, the growth of synaptic weights from small values is supported in our model by the excitatory action of GABA, whereas, after the switch, specialization of newborn cells arises from competition between DGCs, triggered by the inhibitory action of GABA. Furthermore, our theory of adult-born DGCs integration yields a transparent explanation of why newborn cells favor pattern separation of similar stimuli, but do not impact pattern separation of distinct stimuli.

We model a small patch of cells within dentate gyrus as a recurrent network of 100 DGCs and 25 GABAergic interneurons, omitting the Mossy cells for the sake of simplicity (Figure 1b). The modeled interneurons correspond to SST-INs from the HIPP category, as they are the providers of feedback inhibition to DGCs through dendritic projections (Stefanelli et al., 2016; Yuan et al., 2017; Groisman et al., 2020; Figure 1—figure supplement 1). The activity of a DGC with index $i$ and an interneuron with index $k$ is described by their continuous firing rates ${\nu }_i$ and ${\nu }_k^I$, respectively. Firing rates are modeled by neuronal frequency–current curves that vanish for weak input and increase if the total input into a neuron is larger than a firing threshold. Since newborn DGCs exhibit enhanced excitability early in maturation (Schmidt-Hieber et al., 2004; Li et al., 2017), the firing threshold of model neurons increases during maturation from a lower to a higher value (Materials and methods). Connectivity in a localized patch of dentate neurons is high: DGCs densely project to GABAergic interneurons (Acsády et al., 1998), and SST-INs heavily project to cells in their neighborhood (Amaral et al., 2007). Hence, in the recurrent network model, each model DGC projects to, and receives input from, a given interneuron with probability 0.9. The exact percentage of GABAergic neurons (or SST-INs) in the dentate gyrus as a whole is not known, but has been estimated at about 10% and only a fraction of these are SST-INs (Freund and Buzsáki, 1996). The number of inhibitory neurons in our model network might therefore seem too high. However, our results are robust to substantial changes in the number of inhibitory neurons (Supplementary file 2).

Each of the 100 model DGCs receives input from a set of 144 model EC cells (Figure 1b). In the rat, the number of DGCs has been estimated to be about 10⁶, while the number of EC input cells is estimated to be about 2 · 10⁵ (Andersen et al., 2007), yielding an expansion factor from EC to dentate gyrus of about 5. Theoretical analysis suggests that the expansion of the number of neurons enhances decorrelation of the representation of input patterns (Marr, 1969; Albus, 1971; Marr, 1971; Rolls and Treves, 1998) and promotes pattern separation (Babadi and Sompolinsky, 2014). Our standard network model does not reflect this expansion because we want to highlight the particular ability of adult neurogenesis in combination with the GABA-switch to decorrelate input patterns independently of specific choices of the network architecture. However, we show later that an enlarged network with an expansion from 144 model EC cells to 700 model DGCs (similar to the anatomical expansion factor) yields similar results.

At birth, a DGC with index $i$ does not receive synaptic glutamatergic input yet. Hence, the connection from any model EC cell with index $j$ is initialized at $w_{ij}=0$. The growth or decay of the synaptic strength $w_{ij}$ of the connection from $j$ to $i$ is controlled by a Hebbian plasticity rule (Figure 1c):

where x_j is the firing rate of the presynaptic EC neuron, η (‘learning rate’) is the susceptibility of a cell to synaptic plasticity, and $\alpha ,\beta ,\gamma$ are positive parameters (Materials and methods, Table 1). The first term on the right-hand side of Equation (1) describes LTP whenever the presynaptic neuron is active ($x_j>0)$ and the postsynaptic firing ${\nu }_i$ is above a threshold θ; the second term on the right-hand side of Equation (1) describes long-term depression (LTD) whenever the presynaptic neuron is active and the postsynaptic firing rate is positive but below the threshold θ; LTD stops if the synaptic weight is zero. Such a combination of LTP and LTD is consistent with experimental data (Artola et al., 1990; Sjöström et al., 2001) as shown in earlier rate-based (Bienenstock et al., 1982) or spike-based (Pfister and Gerstner, 2006) plasticity models. The third term on the right-hand side of Equation (1) implements heterosynaptic plasticity (Chistiakova et al., 2014; Zenke and Gerstner, 2017): whenever strong presynaptic input arriving at synapses $k\ne j$ drives the firing of postsynaptic neuron $i$ at a rate above θ, the weight of a synapse $j$ is downregulated if synapse $j$ does not receive any input, while the weights of synapses $k\ne j$ are simultaneously increased due to the first term (Lynch et al., 1977). Importantly, the threshold condition for the third term (postsynaptic rate above θ) is the same as that for induction of LTP in the first term so that if some synapses are potentiated, silent synapses are depressed. In the model, heterosynaptic interaction between synapses is induced since information about postsynaptic activity is shared across synapses. This could be achieved in biological neurons via backpropagating action potentials or similar depolarization of the postsynaptic membrane potential at several synaptic locations; alternatively, heterosynaptic crosstalk could be implemented by signaling molecules. Note that since our neuron model is a point neuron, all synapses are neighbors of each other. In our model, the ‘heterosynaptic’ term has a negative sign which ensures that the weights cannot grow without bounds (Materials and methods). In this sense, the third term has a ‘homeostatic’ function (Zenke and Gerstner, 2017), yet acts on a time scale faster than experimentally observed homeostatic synaptic plasticity (Turrigiano et al., 1998).

We ask whether such a biologically plausible plasticity rule enables adult-born DGCs to be integrated in an existing network of mature cells. To address this question, we exploit two observations (Figure 1a): first, the effect of interneurons onto newborn DGCs exhibits a GABA-switch from excitatory to inhibitory after about three weeks of maturation (Ge et al., 2006; Deng et al., 2010) and, second, newborn DGCs receive input from interneurons early in their maturation (before the third week), but project back to interneurons only later (Temprana et al., 2015). For simplicity, no plasticity rule was implemented within the dentate gyrus: connections between newborn DGCs and inhibitory cells are either absent or present with a fixed value (see below). However, before integration of adult-born DGCs can be addressed, an adult-stage network where mature cells already store some memories has to be constructed.

Newborn neurons become selective for novel patterns during maturation

In our main neurogenesis model, we replace unresponsive model units by plastic newborn DGCs ($\eta >0$ in Equation (1)), which receive lateral GABAergic input but do not receive feedforward input yet (all weights from EC are set to zero). The replacement of unresponsive neurons reflects the fact that unresponsive units have weak synaptic connections and, experimentally, a lack of NMDA receptor activation has been shown to be deleterious for the survival of newborn DGCs (Tashiro et al., 2006). To mimic exposure of an animal to a novel set of stimuli, we now add input patterns from digit 5 to the set of presented stimuli, which was previously limited to patterns of digits 3 and 4. The novel patterns from digit 5 are randomly interspersed into the sequence of patterns from digits 3 and 4; in other words, the presentation sequence was not optimized with a specific goal in mind.

We postulate that functional integration of newborn DGCs requires the two-step maturation process caused by the GABA-switch from excitation to inhibition. Since excitatory GABAergic input potentially increases correlated activity within the dentate gyrus network, we predict that newborn DGCs respond to familiar stimuli during the early phase of maturation, but not during the late phase, when inhibitory GABAergic input leads to competition.

To test this hypothesis, our model newborn DGCs go through two maturation phases (Materials and methods). The early phase of maturation is cooperative because, for each pattern presentation, activated mature DGCs indirectly excite the newborn DGCs via GABAergic interneurons. We assume that in natural settings, the activation of ${\text{GABA}}_A$ receptors is low enough that the mean membrane potential remains below the chloride reversal potential at which shunting inhibition would be induced (Heigele et al., 2016). In this regime, the net effect of synaptic activity is hence excitatory. This lateral activation of newborn DGCs drives the growth of their receptive fields in a direction similar to those of the currently active mature DGCs. Consistent with our hypothesis we find that, at the end of the early phase of maturation, newborn DGCs show a receptive field corresponding to a mixture of several input patterns (Figure 2a).

Figure 2 with 2 supplements see all

Download asset Open asset

In the late phase of maturation, model newborn DGCs receive inhibitory GABAergic input from interneurons, similar to the input received by mature DGCs. Given that at the end of the early phase, newborn DGCs have receptive fields similar to those of mature DGCs, lateral inhibition induces competition with mature DGCs for activation during presentation of patterns from the novel digit. Because model newborn DGCs start their late phase of maturation with a higher excitability (lower threshold) compared to mature DGCs, consistent with observed enhanced excitability of newborn cells (Schmidt-Hieber et al., 2004; Li et al., 2017), the activation of newborn DGCs is facilitated for those input patterns for which no mature DGC has preexisting selectivity. Therefore, in the late phase of maturation, competition drives the synaptic weights of most newborn DGCs toward receptive fields corresponding to different subcategories of the ensemble of input patterns of the novel digit 5 (Figure 2b).

The total strength of the receptive field of a given DGC can be characterized by the sum of the squared synaptic weights of all feedfoward projections onto the cell (i.e. the square of the L2-norm). During maturation, the L2-norm of the feedforward weights onto newborn DGCs increases (Figure 2e) indicating an increase in total glutamatergic innervation, e.g., through an increase in the number and size of spines (Zhao et al., 2006). Nevertheless, the distribution of firing rates of newborn DGCs is shifted to lower values at the end of the late phase compared to the end of the early phase of maturation (Figure 2c,d), consistent with in vivo calcium imaging recordings showing that newborn DGCs are more active than mature DGCs (Danielson et al., 2016).

We emphasize that upon presentation of a pattern of a given digit, only those DGCs with a receptive field similar to the specific writing style of the presented pattern become strongly active, others fire at a medium firing rate, yet others at a low rate (Figure 2g). As a consequence, the firing rate of a particular newborn DGC at the end of its maturation to a pattern from digit 5 is strongly modulated by the specific choice of stimulation pattern within the class of ‘5’s. Analogous results are obtained for patterns from pretrained digits 3 and 4 (Figure 2—figure supplement 1). Hence, the ensemble of DGCs is effectively performing pattern separation within each digit class as opposed to a simple ternary classification task. The selectivity of newborn DGCs develops during maturation. Indeed, during the late, competitive, phase, the percentage of active newborn DGCs decreases, both upon presentation of familiar patterns (digits 3 and 4), as well as upon presentation of novel patterns (digit 5) (Figure 2f). This reflects the development of the selectivity of our model newborn DGCs from broad to narrow tuning, consistent with experimental observations (Marín-Burgin et al., 2012; Danielson et al., 2016).

If two novel ensembles of digits (instead of a single one) are introduced during maturation of newborn DGCs, we observe that some newborn DGCs become selective for one of the novel digits, while others become selective for the other novel digit (Figure 2—figure supplement 2). This was expected, since we have found earlier that DGCs are becoming selective for different prototype writing styles even within a digit category; hence introducing several additional digit categories of novel patterns simply increases the prototype diversity. Therefore, newborn DGCs can ultimately promote separation of several novel overarching categories of patterns, no matter if they are learned simultaneously or sequentially (Figure 2—figure supplement 2).

Adult-born neurons promote better discrimination

As above, we compute classification performance of our model network as a surrogate for behavioral discrimination (Woods et al., 2020). At the end of the late phase of maturation of newborn DGCs, we obtain an overall classification performance of 94.56% for the three ensembles of digits (classification performance for digit 3: 90.50%; digit 4: 98.17%; digit 5: 95.18%). Confusion matrices show that although novel patterns are not well classified at the end of the early phase of maturation (Figure 3e), they are as well classified as pretrained patterns at the end of the late phase of maturation (Figure 3f).

Figure 3

Download asset Open asset

We compare this performance with that of a network where all three digit ensembles are directly simultaneously pretrained starting from random weights (Figure 3a, control 1). In this case, the overall classification performance is 92.09% (classification performance for digit 3: 86.83%; digit 4: 98.78%; digit 5: 90.70%). The confusion matrix shows that all three digits are decently classified, but with an overall lower performance (Figure 3d). Across 10 simulation experiments, classification performance is significantly higher when a novel ensemble of patterns is learned sequentially by newborn DGCs (P₂; Supplementary file 1), than if all patterns are learned simultaneously (P₁; Supplementary file 1). Indeed, the distribution of $P_2-P_1$ for the 10 simulation experiments has a mean which is significantly different from zero (Wilcoxon signed rank test: p-val = 0.0020, Wilcoxon signed rank = 55; one-way t-test: p-val = 0.0269, t-stat = 2.6401, df = 9; Supplementary file 1).

Maturation of newborn neurons shapes the representation of novel patterns

Since each input pattern stimulates slightly different, yet overlapping, subsets of the 100 model DGCs in a sparse code such that about 20 DGCs respond to each pattern (Figure 2g), there is no simple one-to-one assignment between neurons and patterns. In order to visualize the activity patterns of the ensemble of DGCs, we perform dimensionality reduction. We construct a two-dimensional space using the activity patterns of the network at the end of the late phase of maturation of newborn DGCs trained with ‘3’s, ‘4’s and ‘5’s. One axis connects the center of mass (in the 100-dimensional activity space) of all DGC responses to ‘3’s with all responses to ‘5’s (arbitrarily called ‘axis 1’) and the other axis those from ‘4’s to ‘5’s (arbitrarily called ‘axis 2’). We then project the activity of the 100 model DGCs upon presentation of MNIST testing patterns onto those two axes, both at the end of the early and late phase of maturation of newborn DGCs (Materials and methods). Each two-dimensional projection is illustrated by a dot whose color corresponds to the digit class of the presented input pattern (blue for digit 3, green for digit 4, red for digit 5). Different input patterns within the same digit class cause different activation patterns of the DGCs, as depicted by extended clouds of dots of the same color (Figure 4a,b). Interestingly, an example pattern of a ‘5’ that is visually similar to a ‘4’ (characterized by the green cross) yields a DGC representation that lies closer to other ‘4’s (green cloud of dots) than to typical ‘5’s (red cloud of dots) (Figure 4b). Noteworthy the separation of the representation of ‘5’s from ‘3’s and ‘4’s is better at end of the late phase (Figure 4b) when compared to the end of the early phase of maturation (Figure 4a). For instance, even though the pattern ‘5’ corresponding to the orange cross is represented close to representations of ‘4’s at the end of the early phase of maturation (green cloud of dots, Figure 4a), it is represented far from any ‘3’s and ‘4’s at the end of maturation (Figure 4b). The expansion of the representation of ‘5’s into a previously empty subspace evolves as a function of time during the late phase of maturation (Figure 4d).

Figure 4 with 2 supplements see all

Download asset Open asset

Newborn DGCs become selective for similar novel patterns

To investigate whether our theory for integration of newborn DGCs can explain why adult dentate gyrus neurogenesis promotes discrimination of similar stimuli, but does not affect discrimination of distinct patterns (Clelland et al., 2009; Sahay et al., 2011a), we use a simplified competitive winner-take-all network (Materials and methods). It contains only as many DGCs as trained clusters, and the GABAergic inhibitory neurons are implicitly modeled through direct DGC-to-DGC inhibitory connections. DGCs are either silent or active (binary activity state, while in the detailed network DGCs had continuous firing rates). The synaptic plasticity rule is however the same as for the detailed network, with different parameter values (Materials and methods). We also construct an artificial data set (Figure 5a,b) that allows us to control the similarity $s$ of pairs of clusters (Materials and methods). The MNIST data set is not appropriate to distinguish similar from dissimilar patterns, because all digit clusters are similar and highly overlapping, reflected by a high within cluster dispersion (e.g. across the set of all ‘3’) compared to the separation between clusters (e.g. typical ‘3’ versus typical ‘5’).

Figure 5

Download asset Open asset

After a pretraining period, a first mature DGC responds to patterns of cluster 1 and a second mature DGC to those of cluster 2 (Figure 5e,f). We then fix the feedforward weights of those two DGCs and introduce a newborn DGC in the network. Thereafter, we present patterns from three clusters (the two pretrained ones, as well as a novel one), while the plastic feedforward weights of the newborn DGC are the only ones that are updated. We observe that the newborn DGC ultimately becomes selective for the novel cluster if it is similar ($s=0.8$) to the two pretrained clusters (Figure 5i), but not if it is distinct ($s=0.2$, Figure 5j). The selectivity develops in two phases. In the early phase of maturation of the newborn model cell, a pattern from the novel cluster that is similar to one of the pretrained clusters activates the mature DGC that has a receptive field closest to the novel pattern. The activated mature DGC drives the newborn DGC via lateral excitatory GABAergic connections to a firing rate where LTP is triggered at active synapses onto the newborn DGC. LTP also happens when a pattern from one of the pretrained clusters is presented. Thus, synaptic plasticity leads to a receptive field that reflects the average of all stimuli from all three clusters (Figure 5g).

To summarize our findings in a more mathematical language, we characterize the receptive field of the newborn cell by the vector of its feedforward weights. Analogous to the notion of a firing rate vector that represents the set of firing rates of an ensemble of neurons, the feedforward weight vector represents the set of weights of all synapses projecting onto a given neuron (Figure 1b). In the early phase of maturation, for similar clusters, the feedforward weight vector onto the newborn DGC grows in the direction of the center of mass of all three clusters (the two pretrained ones and the novel one), because for each pattern presentation, be it a novel pattern or a familiar one, one of the mature DGCs becomes active and stimulates the newborn cell (compare Figure 5g and Figure 5k). However, if the novel cluster has a low similarity to pretrained clusters, patterns from the novel cluster do not activate any of the mature DGCs. Therefore, the receptive field of the newborn cell reflects the average of stimuli from the two pretrained clusters only (compare Figure 5h and Figure 5l).

As a result of the different orientation of the feedforward weight vector onto the newborn DGC at the end of the early phase of maturation, two different situations arise in the late phase of maturation, when lateral GABAergic connections are inhibitory. If the novel cluster is similar to the pretrained clusters, the weight vector onto the newborn DGC at the end of the early phase of maturation lies at the center of mass of all the patterns across the three clusters. Thus, it is closer to the novel cluster than the weight vector onto either of the mature DGCs (Figure 5g). So if a novel pattern is presented, the newborn DGC wins the competition between the three DGCs, and its feedforward weight vector moves toward the center of mass of the novel cluster (Figure 5i). By contrast, if the novel cluster is distinct, the weight vector onto the newborn DGC at the end of the early phase of maturation is located at the center of mass of the two pretrained clusters (Figure 5h). If a novel pattern is presented, no output unit is activated since their receptive fields are not similar enough to the input pattern. Therefore, the newborn DGC always stays silent and does not update its feedforward weights (Figure 5j). These results are consistent with studies that have suggested that dentate gyrus is only involved in the discrimination of similar stimuli, but not distinct stimuli (Gilbert et al., 2001; Hunsaker and Kesner, 2008). For discrimination of distinct stimuli, another pathway might be used, such as the direct EC to CA3 connection (Yeckel and Berger, 1990; Fyhn et al., 2007).

In conclusion, our model suggests that adult dentate gyrus neurogenesis promotes discrimination of similar patterns because newborn DGCs can ultimately become selective for novel stimuli, which are similar to already learned stimuli. On the other hand, newborn DGCs fail to represent novel distinct stimuli, precisely because they are too distinct from other stimuli already represented by the network. Presentation of novel distinct stimuli in the late phase of maturation therefore does not induce synaptic plasticity of the newborn DGC feedforward weight vector toward the novel stimuli. In the simplified network, the transition between similar and distinct can be determined analytically (Materials and methods). This analysis clarifies the importance of the switch from cooperative dynamics (excitatory interactions) in the early phase to competitive dynamics (inhibitory interactions) in the late phase of maturation.

Upon successful integration the receptive field of a newborn DGC represents an average of novel stimuli

With the simplified model network, it is possible to analytically compute the maximal strength of the DGC receptive field via the L2-norm of the feedforward weight vector onto the newborn DGC (Materials and methods). In addition, the angle between the center of mass of the novel patterns and the feedforward weight vector onto the adult-born DGC can also be analytically computed (Materials and methods). To illustrate the analytical results and characterize the evolution of the receptive field of the newborn DGC, we thus examine the angle φ of the feedforward weight vector with the center of mass of the novel cluster (i.e. the average of the novel stimuli), as a function of maturation time (Figure 6b,c, Figure 6—figure supplement 1).

Figure 6 with 1 supplement see all

Download asset Open asset

In the early phase of maturation, the feedforward weight vector onto the newborn DGC grows, while its angle with the center of mass of the novel cluster stays constant (Figure 6—figure supplement 1). In the late phase of maturation, the angle φ between the center of mass of the novel cluster and the feedforward weight vector onto the newborn DGC decreases in the case of similar patterns (Figure 6c, Figure 6—figure supplement 1), but not in the case of distinct patterns (Figure 6—figure supplement 1), indicating that the newborn DGC becomes selective for the novel cluster for similar but not for distinct patterns.

The analysis of the simplified model thus leads to a geometric picture that helps us to understand how the similarity of patterns influences the evolution of the receptive field of the newborn DGC before and after the switch from excitation to inhibition of the GABAergic input. For novel patterns that are similar to known patterns, the receptive field of a newborn DGC at the end of maturation represents the average of novel stimuli.

While experimental studies, such as manipulating the ratio of NKCC1 to KCC2, suggest that the switch from excitation to inhibition of the GABAergic input onto adult-born DGCs is crucial for their integration into the preexisting circuit (Ge et al., 2006; Alvarez et al., 2016) and that adult dentate gyrus neurogenesis promotes pattern separation (Clelland et al., 2009; Sahay et al., 2011a; Jessberger et al., 2009), the link between channel properties and behavior has remained puzzling (Sahay et al., 2011b; Aimone et al., 2011). Our modeling work shows that the GABA-switch enables newborn DGCs to become selective for novel stimuli, which are similar to familiar, already-stored, representations, consistent with the experimentally observed function of pattern separation (Clelland et al., 2009; Sahay et al., 2011a; Jessberger et al., 2009).

Previous modeling studies already suggested that newborn DGCs integrate novel inputs into the representation in dentate gyrus (Chambers et al., 2004; Becker, 2005; Crick and Miranker, 2006; Wiskott et al., 2006; Chambers and Conroy, 2007; Appleby and Wiskott, 2009; Aimone et al., 2009; Weisz and Argibay, 2009; Temprana et al., 2015; Finnegan and Becker, 2015; DeCostanzo et al., 2018). However, our work differs from them in four important aspects. First of all, we implement an unsupervised biologically plausible plasticity rule, while many studies used supervised algorithmic learning rules (Chambers et al., 2004; Becker, 2005; Chambers and Conroy, 2007; Weisz and Argibay, 2009; Finnegan and Becker, 2015; DeCostanzo et al., 2018). Second, as we model the formerly neglected GABA-switch, the connection weights from EC to newborn DGCs are grown from small values through cooperativity in the early phase of maturation. This integration step was mostly bypassed in earlier models by initialization of the connectivity weights toward newborn DGCs to random, yet fully grown values (Crick and Miranker, 2006; Aimone et al., 2009; Weisz and Argibay, 2009; Finnegan and Becker, 2015). Third, as the dentate gyrus network is commonly modeled as a competitive network, weight normalization is crucial. In our framework, competition occurs during the late phase of maturation. Previous modeling works either applied algorithmic weight normalization or hard bounds on the weights at each iteration step (Crick and Miranker, 2006; Aimone et al., 2009; Weisz and Argibay, 2009; Temprana et al., 2015; Finnegan and Becker, 2015). Instead, our plasticity rule includes heterosynaptic plasticity, which intrinsically softly bounds connectivity weights by a homeostatic effect. Finally, although some earlier computational models of adult dentate gyrus neurogenesis could explain the pattern separation abilities of newborn cells, separation was obtained independently of the similarity between the stimuli. Contrarily to experimental data, no distinction was made between similar and distinct patterns (Chambers et al., 2004; Becker, 2005; Crick and Miranker, 2006; Wiskott et al., 2006; Chambers and Conroy, 2007; Aimone et al., 2009; Appleby and Wiskott, 2009; Weisz and Argibay, 2012; Temprana et al., 2015; Finnegan and Becker, 2015; DeCostanzo et al., 2018). To our knowledge, we present the first model that can explain both (1) how adult-born DGCs integrate into the preexisting network and (2) why they promote pattern separation of similar stimuli and not distinct stimuli.

Our work emphasizes why a two-phase maturation of newborn DGCs is beneficial for proper integration in the preexisting network. From a computational perspective, the early phase of maturation, when GABAergic inputs onto newborn DGCs are excitatory, corresponds to cooperative unsupervised learning. Therefore, the synapses grow in the direction of patterns that indirectly activate the newborn DGCs via GABAergic interneurons (Figure 6a). At the end of the early phase of maturation, the receptive field of a newborn DGC represents the center of mass of all input patterns that led to its (indirect) activation. In the late phase of maturation, GABAergic inputs onto newborn DGCs become inhibitory, so that lateral interactions change from cooperation to competition, causing a shift of the receptive fields of the newborn DGCs toward novel features (Figure 6b). At the end of maturation, newborn DGCs are thus selective for novel inputs. This integration mechanism is in agreement with the experimental observation that newborn DGCs are broadly tuned early in maturation, yet highly selective at the end of maturation (Marín-Burgin et al., 2012; Danielson et al., 2016). Loosely speaking, the cooperative phase of excitatory GABAergic input promotes the growth of the synaptic weights coarsely in the relevant direction, whereas the competitive phase of inhibitory GABAergic input helps to specialize on detailed, but potentially important differences between patterns.

In the context of theories of unsupervised learning, the switch of lateral GABAergic input to newborn DGCs from excitatory to inhibitory provides a biological solution to the ‘problem of unresponsive units’ (Hertz et al., 1991). Unsupervised competitive learning has been used to perform clustering of input patterns into a few categories (Rumelhart and Zipser, 1985; Grossberg, 1987; Kohonen, 1989; Hertz et al., 1991; Du, 2010). Ideally, after learning of the feedforward weights between an input layer and a competitive network, input patterns that are distinct from each other activate different neuron assemblies of the competitive network. After convergence of competitive Hebbian learning, the vector of feedforward weights onto a given neuron points to the center of mass of the cluster of input patterns for which it is selective (Kohonen, 1989; Hertz et al., 1991). Yet, if the synaptic weights are randomly initialized, it is possible that the set of feedforward weights onto some neurons of the competitive network point in a direction ‘quasi-orthogonal’ (Materials and methods) to the subspace of the presented input patterns. Therefore, those neurons, called ‘unresponsive units’, will never get active during pattern presentation. Different learning strategies have been developed in the field of artificial neural networks to avoid this problem (Grossberg, 1976; Bienenstock et al., 1982; Rumelhart and Zipser, 1985; Grossberg, 1987; DeSieno, 1988; Kohonen, 1989; Hertz et al., 1991; Du, 2010). However, most of these algorithmic approaches lack a biological interpretation. In our model, weak synapses onto newborn DGCs form spontaneously after neuronal birth. The excitatory GABAergic input in the early phase of maturation drives the growth of the synaptic weights in the direction of the subspace of presented patterns that succeed in activating some of the mature DGCs. Hence, the early cooperative phase of maturation can be seen as a smart initialization of the synaptic weights onto newborn DGCs, close enough to novel patterns so as to become selective for them in the late competitive phase of maturation. However, the cooperative phase is helpful only if the novel patterns are similar to the input statistics defined by the set of known (familiar) patterns.

Our results are in line with the classic view that dentate gyrus is responsible for decorrelation of inputs (Marr, 1969; Albus, 1971; Marr, 1971; Rolls and Treves, 1998), a necessary step for differential storage of similar memories in CA3, and with the observation that dentate gyrus lesions impair discrimination of similar but not distinct stimuli (Gilbert et al., 2001; Hunsaker and Kesner, 2008). To discriminate distinct stimuli, another pathway might be involved, such as the direct EC to CA3 connection (Yeckel and Berger, 1990; Fyhn et al., 2007).

The parallel of neurogenesis in dentate gyrus and olfactory bulb suggests that similar mechanisms could be at work in both areas. Yet, even though adult olfactory bulb neurogenesis seems to have a similar functional role to adult dentate gyrus neurogenesis (Sahay et al., 2011b), follow a similar integration sequence and undergo a GABA-switch from excitatory to inhibitory, the circuits are different in several aspects. First, while newborn neurons in dentate gyrus are excitatory, newborn cells in the olfactory bulb are inhibitory. Second, the newborn olfactory cells start firing action potentials only once they are well integrated (Carleton et al., 2003). Therefore, in view of a transfer of results to the olfactory bulb, it would be interesting to adjust our model of adult dentate gyrus neurogenesis accordingly. For example, a voltage-based synaptic plasticity rule could be used to account for subthreshold plasticity mechanisms (Clopath et al., 2010).

Our model of transition from an early cooperative phase to a late competitive phase makes specific predictions, at the behavioral and cellular level. In our model, the early cooperative phase of maturation can only drive the growth of synaptic weights onto newborn cells if they are indirectly activated by mature DGCs through GABAergic input, which has an excitatory effect due to the high NKCC1/KCC2 ratio early in maturation. Therefore, our model predicts that NKCC1-knockout mice would be impaired in discriminating similar contexts or objects because newborn cells stay silent due to lack of indirect activation. The feedforward weight vector onto newborn DGCs could not grow in the early phase and newborn DGCs could not become selective for novel inputs. Therefore, our model predicts that since newborn DGCs are poorly integrated into the preexisting circuit, they are unlikely to survive. If, however, in the same paradigm newborn cells are activated by light-induced or electrical stimulation, we predict that they become selective to novel patterns. Thus discrimination abilities would be restored and newborn DGCs are likely to survive. Analogously, we predict that using inducible NKCC1-knockout mice, animals would gradually be impaired in discrimination tasks after induced knockout and reach a stable maximum impairment about 3 weeks after the start of induced knockout.

Experimental observations support the importance of the switch from early excitation to late inhibition of the GABAergic input onto newborn DGCs. An absence of early excitation using NKCC1-knockout mice has been shown to strongly affect synapse formation and dendritic development in vivo (Ge et al., 2006). Conversely, a reduction in inhibition in the dentate gyrus through decrease in KCC2 expression has been associated with epileptic activity (Pathak et al., 2007; Barmashenko et al., 2011). An analogous switch of the GABAergic input has been observed during development, and its proper timing has been shown to be crucial for sensorimotor gating and cognition (Wang and Kriegstein, 2011; Furukawa et al., 2017). In addition to early excitation and late inhibition, our theory also critically depends on the time scale of the switching process. In our model, the switch makes an instantaneous transition between early and late phase of maturation. Several experimental results have suggested that the switch is indeed sharp and occurs within a single day, both during development (Khazipov et al., 2004; Tyzio et al., 2007; Leonzino et al., 2016) and adult dentate gyrus neurogenesis (Heigele et al., 2016). Furthermore, in hippocampal cell cultures, expression of KCC2 is upregulated by GABAergic activity but not affected by glutamatergic activity (Ganguly et al., 2001). A similar process during adult dentate gyrus neurogenesis would increase the number of newborn DGCs available for representing novel features by advancing the timing of their switch. In this way, instead of a few thousands of newborn DGCs ready to switch (3–6% of the whole population [van Praag et al., 1999; Cameron and McKay, 2001], divided by 30 days), a larger fraction of newborn DGCs would be made available for coding, if appropriate stimulation occurs. Finally, while neurotransmitter switching has been observed following sustained stimulation for hours to days (Li et al., 2020), it is still unclear if it has the same functional role as the GABA-switch in our model. In particular, it remains an open question if neurotransmitter switching promotes the integration of neurons in the same way as our model GABA-switch does in the context of adult dentate gyrus neurogenesis.

To conclude, our theory for integration of adult-born DGCs suggests that newborn cells have a coding – rather than a modulatory – role during dentate gyrus pattern separation function. Our theory highlights the importance of GABAergic input in adult dentate gyrus neurogenesis and links the switch from excitation to inhibition to the integration of newborn DGCs into the preexisting circuit. Finally, it illustrates how Hebbian plasticity of EC to DGC synapses along with the switch make newborn cells suitable to promote pattern separation of similar but not distinct stimuli, a long-standing mystery in the field of adult dentate gyrus neurogenesis (Sahay et al., 2011b; Aimone et al., 2011).

Simulation and plotting scripts can be found at: https://github.com/ogozel/NeurogenesisModel (copy archived at https://archive.softwareheritage.org/swh:1:rev:e46f2dfc10c21d69ac057f31c5800f46644b004a).

1. 1. LeCun Y
   2. Cortes C
   3. Burges CJC

   (1999) THE MNIST DATABASE

   ID yann.lecun.com/exdb/mnist/. The MNIST database of handwritten digits.

   http://yann.lecun.com/exdb/mnist/

1. 1. Acsády L
   2. Kamondi A
   3. Sík A
   4. Freund T
   5. Buzsáki G

   (1998) GABAergic cells are the major postsynaptic targets of mossy fibers in the rat Hippocampus

   The Journal of Neuroscience 18:3386–3403.

   https://doi.org/10.1523/JNEUROSCI.18-09-03386.1998

   • PubMed
   • Google Scholar
2. 1. Aimone JB
   2. Wiles J
   3. Gage FH

   (2009) Computational influence of adult neurogenesis on memory encoding

   Neuron 61:187–202.

   https://doi.org/10.1016/j.neuron.2008.11.026

   • PubMed
   • Google Scholar
3. 1. Aimone JB
   2. Deng W
   3. Gage FH

   (2011) Resolving new memories: a critical look at the Dentate Gyrus, adult neurogenesis, and pattern separation

   Neuron 70:589–596.

   https://doi.org/10.1016/j.neuron.2011.05.010

   • PubMed
   • Google Scholar
4. 1. Akers KG
   2. Martinez-Canabal A
   3. Restivo L
   4. Yiu AP
   5. De Cristofaro A
   6. Hsiang HL
   7. Wheeler AL
   8. Guskjolen A
   9. Niibori Y
   10. Shoji H
   11. Ohira K
   12. Richards BA
   13. Miyakawa T
   14. Josselyn SA
   15. Frankland PW

   (2014) Hippocampal neurogenesis regulates forgetting during adulthood and infancy

   Science 344:598–602.

   https://doi.org/10.1126/science.1248903

   • PubMed
   • Google Scholar
5. 1. Albus JS

   (1971) A theory of cerebellar function

   Mathematical Biosciences 10:25–61.

   https://doi.org/10.1016/0025-5564(71)90051-4

   • Google Scholar
6. 1. Aljadeff J
   2. Stern M
   3. Sharpee T

   (2015) Transition to chaos in random networks with cell-type-specific connectivity

   Physical Review Letters 114:088101.

   https://doi.org/10.1103/PhysRevLett.114.088101

   • PubMed
   • Google Scholar
7. 1. Alvarez DD
   2. Giacomini D
   3. Yang SM
   4. Trinchero MF
   5. Temprana SG
   6. Büttner KA
   7. Beltramone N
   8. Schinder AF

   (2016) A disynaptic feedback network activated by experience promotes the integration of new granule cells

   Science 354:459–465.

   https://doi.org/10.1126/science.aaf2156

   • PubMed
   • Google Scholar
8. 1. Amaral DG
   2. Scharfman HE
   3. Lavenex P

   (2007) The dentate gyrus: fundamental neuroanatomical organization (dentate gyrus for dummies)

   Progress in Brain Research 163:3–22.

   https://doi.org/10.1016/S0079-6123(07)63001-5

   • PubMed
   • Google Scholar
9. Book

   1. Andersen P
   2. Morris R
   3. Amaral D
   4. Bliss T
   5. O’Keefe J

   (2007) The Hippocampus Book

   Oxford University Press.

   https://doi.org/10.1093/acprof:oso/9780195100273.001.0001

   • Google Scholar
10. 1. Appleby PA
    2. Wiskott L

    (2009) Additive neurogenesis as a strategy for avoiding interference in a sparsely-coding dentate gyrus

    Network: Computation in Neural Systems 20:137–161.

    https://doi.org/10.1080/09548980902993156

    • Google Scholar
11. 1. Artola A
    2. Bröcher S
    3. Singer W

    (1990) Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex

    Nature 347:69–72.

    https://doi.org/10.1038/347069a0

    • PubMed
    • Google Scholar
12. 1. Babadi B
    2. Sompolinsky H

    (2014) Sparseness and expansion in sensory representations

    Neuron 83:1213–1226.

    https://doi.org/10.1016/j.neuron.2014.07.035

    • PubMed
    • Google Scholar
13. 1. Barmashenko G
    2. Hefft S
    3. Aertsen A
    4. Kirschstein T
    5. Köhling R

    (2011) Positive shifts of the GABAA receptor reversal potential due to altered chloride homeostasis is widespread after status epilepticus

    Epilepsia 52:1570–1578.

    https://doi.org/10.1111/j.1528-1167.2011.03247.x

    • PubMed
    • Google Scholar
14. 1. Becker S

    (2005) A computational principle for hippocampal learning and neurogenesis

    Hippocampus 15:722–738.

    https://doi.org/10.1002/hipo.20095

    • PubMed
    • Google Scholar
15. 1. Ben-Ari Y

    (2002) Excitatory actions of gaba during development: the nature of the nurture

    Nature Reviews Neuroscience 3:728–739.

    https://doi.org/10.1038/nrn920

    • PubMed
    • Google Scholar
16. 1. Bienenstock EL
    2. Cooper LN
    3. Munro PW

    (1982) Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex

    The Journal of Neuroscience 2:32–48.

    https://doi.org/10.1523/JNEUROSCI.02-01-00032.1982

    • PubMed
    • Google Scholar
17. 1. Cameron HA
    2. McKay RD

    (2001) Adult neurogenesis produces a large pool of new granule cells in the dentate gyrus

    The Journal of Comparative Neurology 435:406–417.

    https://doi.org/10.1002/cne.1040

    • PubMed
    • Google Scholar
18. 1. Carleton A
    2. Petreanu LT
    3. Lansford R
    4. Alvarez-Buylla A
    5. Lledo PM

    (2003) Becoming a new neuron in the adult olfactory bulb

    Nature Neuroscience 6:507–518.

    https://doi.org/10.1038/nn1048

    • PubMed
    • Google Scholar
19. 1. Chambers RA
    2. Potenza MN
    3. Hoffman RE
    4. Miranker W

    (2004) Simulated apoptosis/neurogenesis regulates learning and memory capabilities of adaptive neural networks

    Neuropsychopharmacology 29:747–758.

    https://doi.org/10.1038/sj.npp.1300358

    • PubMed
    • Google Scholar
20. 1. Chambers RA
    2. Conroy SK

    (2007) Network modeling of adult neurogenesis: shifting rates of neuronal turnover optimally gears network learning according to novelty gradient

    Journal of Cognitive Neuroscience 19:1–12.

    https://doi.org/10.1162/jocn.2007.19.1.1

    • PubMed
    • Google Scholar
21. 1. Chancey JH
    2. Adlaf EW
    3. Sapp MC
    4. Pugh PC
    5. Wadiche JI
    6. Overstreet-Wadiche LS

    (2013) GABA depolarization is required for experience-dependent synapse unsilencing in adult-born neurons

    Journal of Neuroscience 33:6614–6622.

    https://doi.org/10.1523/JNEUROSCI.0781-13.2013

    • PubMed
    • Google Scholar
22. 1. Chawla MK
    2. Guzowski JF
    3. Ramirez-Amaya V
    4. Lipa P
    5. Hoffman KL
    6. Marriott LK
    7. Worley PF
    8. McNaughton BL
    9. Barnes CA

    (2005) Sparse, environmentally selective expression of Arc RNA in the upper blade of the rodent fascia dentata by brief spatial experience

    Hippocampus 15:579–586.

    https://doi.org/10.1002/hipo.20091

    • PubMed
    • Google Scholar
23. 1. Chistiakova M
    2. Bannon NM
    3. Bazhenov M
    4. Volgushev M

    (2014) Heterosynaptic plasticity: multiple mechanisms and multiple roles

    The Neuroscientist : A Review Journal Bringing Neurobiology, Neurology and Psychiatry 20:483–498.

    https://doi.org/10.1177/1073858414529829

    • PubMed
    • Google Scholar
24. 1. Clelland CD
    2. Choi M
    3. Romberg C
    4. Clemenson GD
    5. Fragniere A
    6. Tyers P
    7. Jessberger S
    8. Saksida LM
    9. Barker RA
    10. Gage FH
    11. Bussey TJ

    (2009) A functional role for adult hippocampal neurogenesis in spatial pattern separation

    Science 325:210–213.

    https://doi.org/10.1126/science.1173215

    • PubMed
    • Google Scholar
25. 1. Clopath C
    2. Büsing L
    3. Vasilaki E
    4. Gerstner W

    (2010) Connectivity reflects coding: a model of voltage-based STDP with homeostasis

    Nature Neuroscience 13:344–352.

    https://doi.org/10.1038/nn.2479

    • PubMed
    • Google Scholar
26. 1. Crick C
    2. Miranker W

    (2006) Apoptosis, neurogenesis, and information content in Hebbian networks

    Biological Cybernetics 94:9–19.

    https://doi.org/10.1007/s00422-005-0026-8

    • PubMed
    • Google Scholar
27. 1. Danielson NB
    2. Kaifosh P
    3. Zaremba JD
    4. Lovett-Barron M
    5. Tsai J
    6. Denny CA
    7. Balough EM
    8. Goldberg AR
    9. Drew LJ
    10. Hen R
    11. Losonczy A
    12. Kheirbek MA

    (2016) Distinct contribution of adult-born hippocampal granule cells to context encoding

    Neuron 90:101–112.

    https://doi.org/10.1016/j.neuron.2016.02.019

    • PubMed
    • Google Scholar
28. Book

    1. Dayan P
    2. Abbott LF

    (2001)

    Theoretical Neuroscience

    MIT Press.

    • Google Scholar
29. 1. Dayer AG
    2. Ford AA
    3. Cleaver KM
    4. Yassaee M
    5. Cameron HA

    (2003) Short-term and long-term survival of new neurons in the rat dentate gyrus

    The Journal of Comparative Neurology 460:563–572.

    https://doi.org/10.1002/cne.10675

    • PubMed
    • Google Scholar
30. 1. DeCostanzo AJ
    2. Fung CCA
    3. Fukai T

    (2018) Hippocampal neurogenesis reduces the dimensionality of sparsely coded representations to enhance memory encoding

    Frontiers in Computational Neuroscience 12:99.

    https://doi.org/10.3389/fncom.2018.00099

    • PubMed
    • Google Scholar
31. 1. Deng W
    2. Aimone JB
    3. Gage FH

    (2010) New neurons and new memories: how does adult hippocampal neurogenesis affect learning and memory?

    Nature Reviews Neuroscience 11:339–350.

    https://doi.org/10.1038/nrn2822

    • PubMed
    • Google Scholar
32. 1. Deshpande A
    2. Bergami M
    3. Ghanem A
    4. Conzelmann KK
    5. Lepier A
    6. Götz M
    7. Berninger B

    (2013) Retrograde monosynaptic tracing reveals the temporal evolution of inputs onto new neurons in the adult dentate gyrus and olfactory bulb

    PNAS 110:E1152–E1161.

    https://doi.org/10.1073/pnas.1218991110

    • PubMed
    • Google Scholar
33. Conference

    1. DeSieno D

    (1988) Adding a conscience to competitive learning

    IEEE International Conference on Neural Networks. pp. 117–124.

    https://doi.org/10.1109/ICNN.1988.23839

    • Google Scholar
34. 1. Du KL

    (2010) Clustering: a neural network approach

    Neural Networks 23:89–107.

    https://doi.org/10.1016/j.neunet.2009.08.007

    • PubMed
    • Google Scholar
35. 1. Finnegan R
    2. Becker S

    (2015) Neurogenesis paradoxically decreases both pattern separation and memory interference

    Frontiers in Systems Neuroscience 9:136.

    https://doi.org/10.3389/fnsys.2015.00136

    • PubMed
    • Google Scholar
36. 1. Freund TF
    2. Buzsáki G

    (1996) Interneurons of the hippocampus

    Hippocampus 6:347–470.

    https://doi.org/10.1002/(SICI)1098-1063(1996)6:4<347::AID-HIPO1>3.0.CO;2-I

    • PubMed
    • Google Scholar
37. 1. Furukawa M
    2. Tsukahara T
    3. Tomita K
    4. Iwai H
    5. Sonomura T
    6. Miyawaki S
    7. Sato T

    (2017) Neonatal maternal separation delays the GABA excitatory-to-inhibitory functional switch by inhibiting KCC2 expression

    Biochemical and Biophysical Research Communications 493:1243–1249.

    https://doi.org/10.1016/j.bbrc.2017.09.143

    • PubMed
    • Google Scholar
38. 1. Fyhn M
    2. Hafting T
    3. Treves A
    4. Moser MB
    5. Moser EI

    (2007) Hippocampal remapping and grid realignment in entorhinal cortex

    Nature 446:190–194.

    https://doi.org/10.1038/nature05601

    • PubMed
    • Google Scholar
39. 1. Ganguly K
    2. Schinder AF
    3. Wong ST
    4. Poo M

    (2001) GABA itself promotes the developmental switch of neuronal GABAergic responses from excitation to inhibition

    Cell 105:521–532.

    https://doi.org/10.1016/S0092-8674(01)00341-5

    • PubMed
    • Google Scholar
40. 1. Ge S
    2. Goh EL
    3. Sailor KA
    4. Kitabatake Y
    5. Ming GL
    6. Song H

    (2006) GABA regulates synaptic integration of newly generated neurons in the adult brain

    Nature 439:589–593.

    https://doi.org/10.1038/nature04404

    • PubMed
    • Google Scholar
41. 1. Ge S
    2. Yang CH
    3. Hsu KS
    4. Ming GL
    5. Song H

    (2007) A critical period for enhanced synaptic plasticity in newly generated neurons of the adult brain

    Neuron 54:559–566.

    https://doi.org/10.1016/j.neuron.2007.05.002

    • PubMed
    • Google Scholar
42. Book

    1. Gerstner W
    2. Kistler W
    3. Naud R
    4. Paninski L

    (2014) Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition

    Cambridge University Press.

    https://doi.org/10.1017/CBO9781107447615

    • Google Scholar
43. 1. Gilbert PE
    2. Kesner RP
    3. Lee I

    (2001) Dissociating hippocampal subregions: double dissociation between dentate gyrus and CA1

    Hippocampus 11:626–636.

    https://doi.org/10.1002/hipo.1077

    • PubMed
    • Google Scholar
44. 1. Groisman AI
    2. Yang SM
    3. Schinder AF

    (2020) Differential coupling of adult-born granule cells to parvalbumin and somatostatin interneurons

    Cell Reports 30:202–214.

    https://doi.org/10.1016/j.celrep.2019.12.005

    • PubMed
    • Google Scholar
45. 1. Grossberg S

    (1976) Adaptive pattern classification and universal recoding II: feedback, expectation, olfaction, illusions

    Biological Cybernetics 23:187–202.

    https://doi.org/10.1007/BF00340335

    • PubMed
    • Google Scholar
46. Book

    1. Grossberg S

    (1987)

    The Adaptive Brain I: Cognition, Learning, Reinforcement, and Rhythm

    Elsevier.

    • Google Scholar
47. 1. Heigele S
    2. Sultan S
    3. Toni N
    4. Bischofberger J

    (2016) Bidirectional GABAergic control of action potential firing in newborn hippocampal granule cells

    Nature Neuroscience 19:263–270.

    https://doi.org/10.1038/nn.4218

    • PubMed
    • Google Scholar
48. 1. Henze DA
    2. Wittner L
    3. Buzsáki G

    (2002) Single granule cells reliably discharge targets in the hippocampal CA3 network in vivo

    Nature Neuroscience 5:790–795.

    https://doi.org/10.1038/nn887

    • PubMed
    • Google Scholar
49. Book

    1. Hertz J
    2. Krogh A
    3. Palmer RG

    (1991)

    Introduction to the Theory of Neural Computation

    Addison-Wesley.

    • Google Scholar
50. 1. Houser CR

    (2007) Interneurons of the dentate gyrus: an overview of cell types, terminal fields and neurochemical identity

    Progress in Brain Research 163:217–232.

    https://doi.org/10.1016/S0079-6123(07)63013-1

    • PubMed
    • Google Scholar
51. 1. Hunsaker MR
    2. Kesner RP

    (2008) Evaluating the differential roles of the dorsal dentate gyrus, dorsal CA3, and dorsal CA1 during a temporal ordering for spatial locations task

    Hippocampus 18:955–964.

    https://doi.org/10.1002/hipo.20455

    • PubMed
    • Google Scholar
52. 1. Jarrard LE

    (1993) On the role of the hippocampus in learning and memory in the rat

    Behavioral and Neural Biology 60:9–26.

    https://doi.org/10.1016/0163-1047(93)90664-4

    • PubMed
    • Google Scholar
53. 1. Jessberger S
    2. Clark RE
    3. Broadbent NJ
    4. Clemenson GD
    5. Consiglio A
    6. Lie DC
    7. Squire LR
    8. Gage FH

    (2009) Dentate gyrus-specific knockdown of adult neurogenesis impairs spatial and object recognition memory in adult rats

    Learning & Memory 16:147–154.

    https://doi.org/10.1101/lm.1172609

    • PubMed
    • Google Scholar
54. 1. Johnston ST
    2. Shtrahman M
    3. Parylak S
    4. Gonçalves JT
    5. Gage FH

    (2016) Paradox of pattern separation and adult neurogenesis: a dual role for new neurons balancing memory resolution and robustness

    Neurobiology of Learning and Memory 129:60–68.

    https://doi.org/10.1016/j.nlm.2015.10.013

    • PubMed
    • Google Scholar
55. 1. Kee N
    2. Teixeira CM
    3. Wang AH
    4. Frankland PW

    (2007) Preferential incorporation of adult-generated granule cells into spatial memory networks in the dentate gyrus

    Nature Neuroscience 10:355–362.

    https://doi.org/10.1038/nn1847

    • PubMed
    • Google Scholar
56. 1. Khazipov R
    2. Khalilov I
    3. Tyzio R
    4. Morozova E
    5. Ben-Ari Y
    6. Holmes GL

    (2004) Developmental changes in GABAergic actions and seizure susceptibility in the rat hippocampus

    European Journal of Neuroscience 19:590–600.

    https://doi.org/10.1111/j.0953-816X.2003.03152.x

    • PubMed
    • Google Scholar
57. 1. Klausberger T
    2. Somogyi P

    (2008) Neuronal diversity and temporal dynamics: the unity of hippocampal circuit operations

    Science 321:53–57.

    https://doi.org/10.1126/science.1149381

    • PubMed
    • Google Scholar
58. Book

    1. Kohonen T

    (1989) Self-Organization and Associative Memory

    Springer-Verlag.

    https://doi.org/10.1007/978-3-642-88163-3

    • Google Scholar
59. 1. Lecun Y
    2. Bottou L
    3. Bengio Y
    4. Haffner P

    (1998) Gradient-based learning applied to document recognition

    Proceedings of the IEEE 86:2278–2324.

    https://doi.org/10.1109/5.726791

    • Google Scholar
60. 1. Leonzino M
    2. Busnelli M
    3. Antonucci F
    4. Verderio C
    5. Mazzanti M
    6. Chini B

    (2016) The timing of the excitatory-to-inhibitory GABA switch is regulated by the oxytocin receptor via KCC2

    Cell Reports 15:96–103.

    https://doi.org/10.1016/j.celrep.2016.03.013

    • PubMed
    • Google Scholar
61. 1. Li Y
    2. Stam FJ
    3. Aimone JB
    4. Goulding M
    5. Callaway EM
    6. Gage FH

    (2013) Molecular layer perforant path-associated cells contribute to feed-forward inhibition in the adult dentate gyrus

    PNAS 110:9106–9111.

    https://doi.org/10.1073/pnas.1306912110

    • PubMed
    • Google Scholar
62. 1. Li L
    2. Sultan S
    3. Heigele S
    4. Schmidt-Salzmann C
    5. Toni N
    6. Bischofberger J

    (2017) Silent synapses generate sparse and orthogonal action potential firing in adult-born hippocampal granule cells

    eLife 6:e23612.

    https://doi.org/10.7554/eLife.23612

    • PubMed
    • Google Scholar
63. 1. Li HQ
    2. Pratelli M
    3. Godavarthi S
    4. Zambetti S
    5. Spitzer NC

    (2020) Decoding neurotransmitter switching: the road forward

    The Journal of Neuroscience 40:4078–4089.

    https://doi.org/10.1523/JNEUROSCI.0005-20.2020

    • PubMed
    • Google Scholar
64. 1. Litwin-Kumar A
    2. Harris KD
    3. Axel R
    4. Sompolinsky H
    5. Abbott LF

    (2017) Optimal degrees of synaptic connectivity

    Neuron 93:1153–1164.

    https://doi.org/10.1016/j.neuron.2017.01.030

    • PubMed
    • Google Scholar
65. 1. Lynch GS
    2. Dunwiddie T
    3. Gribkoff V

    (1977) Heterosynaptic depression: a postsynaptic correlate of long-term potentiation

    Nature 266:737–739.

    https://doi.org/10.1038/266737a0

    • PubMed
    • Google Scholar
66. Book

    1. Mardia KV
    2. Jupp PE

    (2009) Directional Statistics

    John Wiley & Sons.

    https://doi.org/10.1002/9780470316979

    • Google Scholar
67. 1. Marín-Burgin A
    2. Mongiat LA
    3. Pardi MB
    4. Schinder AF

    (2012) Unique processing during a period of high excitation/inhibition balance in adult-born neurons

    Science 335:1238–1242.

    https://doi.org/10.1126/science.1214956

    • PubMed
    • Google Scholar
68. 1. Marr D

    (1969) A theory of cerebellar cortex

    The Journal of Physiology 202:437–470.

    https://doi.org/10.1113/jphysiol.1969.sp008820

    • PubMed
    • Google Scholar
69. 1. Marr D

    (1971) Simple memory: a theory for archicortex

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 262:23–81.

    https://doi.org/10.1098/rstb.1971.0078

    • PubMed
    • Google Scholar
70. 1. Mazzucato L
    2. Fontanini A
    3. La Camera G

    (2016) Stimuli reduce the dimensionality of cortical activity

    Frontiers in Systems Neuroscience 10:11.

    https://doi.org/10.3389/fnsys.2016.00011

    • PubMed
    • Google Scholar
71. 1. McHugh TJ
    2. Jones MW
    3. Quinn JJ
    4. Balthasar N
    5. Coppari R
    6. Elmquist JK
    7. Lowell BB
    8. Fanselow MS
    9. Wilson MA
    10. Tonegawa S

    (2007) Dentate gyrus NMDA receptors mediate rapid pattern separation in the hippocampal network

    Science 317:94–99.

    https://doi.org/10.1126/science.1140263

    • PubMed
    • Google Scholar
72. 1. Miller KD
    2. Fumarola F

    (2012) Mathematical equivalence of two common forms of firing rate models of neural networks

    Neural Computation 24:25–31.

    https://doi.org/10.1162/NECO_a_00221

    • PubMed
    • Google Scholar
73. 1. Owens DF
    2. Kriegstein AR

    (2002) Is there more to GABA than synaptic inhibition?

    Nature Reviews Neuroscience 3:715–727.

    https://doi.org/10.1038/nrn919

    • PubMed
    • Google Scholar
74. 1. Pathak HR
    2. Weissinger F
    3. Terunuma M
    4. Carlson GC
    5. Hsu FC
    6. Moss SJ
    7. Coulter DA

    (2007) Disrupted dentate granule cell chloride regulation enhances synaptic excitability during development of temporal lobe epilepsy

    Journal of Neuroscience 27:14012–14022.

    https://doi.org/10.1523/JNEUROSCI.4390-07.2007

    • PubMed
    • Google Scholar
75. 1. Pfister JP
    2. Gerstner W

    (2006) Triplets of spikes in a model of spike timing-dependent plasticity

    Journal of Neuroscience 26:9673–9682.

    https://doi.org/10.1523/JNEUROSCI.1425-06.2006

    • PubMed
    • Google Scholar
76. Book

    1. Rolls ET
    2. Treves A

    (1998) Neural Networks and Brain Function

    Oxford: Oxford University Press.

    https://doi.org/10.1093/acprof:oso/9780198524328.001.0001

    • Google Scholar
77. 1. Rumelhart DE
    2. Zipser D

    (1985) Feature discovery by competitive learning

    Cognitive Science 9:75–112.

    https://doi.org/10.1207/s15516709cog0901_5

    • Google Scholar
78. 1. Sahay A
    2. Scobie KN
    3. Hill AS
    4. O'Carroll CM
    5. Kheirbek MA
    6. Burghardt NS
    7. Fenton AA
    8. Dranovsky A
    9. Hen R

    (2011a) Increasing adult hippocampal neurogenesis is sufficient to improve pattern separation

    Nature 472:466–470.

    https://doi.org/10.1038/nature09817

    • PubMed
    • Google Scholar
79. 1. Sahay A
    2. Wilson DA
    3. Hen R

    (2011b) Pattern separation: a common function for new neurons in hippocampus and olfactory bulb

    Neuron 70:582–588.

    https://doi.org/10.1016/j.neuron.2011.05.012

    • PubMed
    • Google Scholar
80. 1. Schmidt-Hieber C
    2. Jonas P
    3. Bischofberger J

    (2004) Enhanced synaptic plasticity in newly generated granule cells of the adult hippocampus

    Nature 429:184–187.

    https://doi.org/10.1038/nature02553

    • PubMed
    • Google Scholar
81. 1. Senzai Y
    2. Buzsáki G

    (2017) Physiological properties and behavioral correlates of hippocampal granule cells and mossy cells

    Neuron 93:691–704.

    https://doi.org/10.1016/j.neuron.2016.12.011

    • PubMed
    • Google Scholar
82. 1. Shani-Narkiss H
    2. Vinograd A
    3. Landau ID
    4. Tasaka G
    5. Yayon N
    6. Terletsky S
    7. Groysman M
    8. Maor I
    9. Sompolinsky H
    10. Mizrahi A

    (2020) Young adult-born neurons improve odor coding by mitral cells

    Nature Communications 11:5867.

    https://doi.org/10.1038/s41467-020-19472-8

    • PubMed
    • Google Scholar
83. 1. Sjöström PJ
    2. Turrigiano GG
    3. Nelson SB

    (2001) Rate, timing, and cooperativity jointly determine cortical synaptic plasticity

    Neuron 32:1149–1164.

    https://doi.org/10.1016/S0896-6273(01)00542-6

    • PubMed
    • Google Scholar
84. 1. Somogyi P
    2. Klausberger T

    (2005) Defined types of cortical interneurone structure space and spike timing in the hippocampus

    The Journal of Physiology 562:9–26.

    https://doi.org/10.1113/jphysiol.2004.078915

    • PubMed
    • Google Scholar
85. 1. Stefanelli T
    2. Bertollini C
    3. Lüscher C
    4. Muller D
    5. Mendez P

    (2016) Hippocampal somatostatin interneurons control the size of neuronal memory ensembles

    Neuron 89:1074–1085.

    https://doi.org/10.1016/j.neuron.2016.01.024

    • PubMed
    • Google Scholar
86. 1. Tashiro A
    2. Sandler VM
    3. Toni N
    4. Zhao C
    5. Gage FH

    (2006) NMDA-receptor-mediated, cell-specific integration of new neurons in adult dentate gyrus

    Nature 442:929–933.

    https://doi.org/10.1038/nature05028

    • PubMed
    • Google Scholar
87. 1. Tashiro A
    2. Makino H
    3. Gage FH

    (2007) Experience-specific functional modification of the dentate gyrus through adult neurogenesis: a critical period during an immature stage

    Journal of Neuroscience 27:3252–3259.

    https://doi.org/10.1523/JNEUROSCI.4941-06.2007

    • PubMed
    • Google Scholar
88. 1. Temprana SG
    2. Mongiat LA
    3. Yang SM
    4. Trinchero MF
    5. Alvarez DD
    6. Kropff E
    7. Giacomini D
    8. Beltramone N
    9. Lanuza GM
    10. Schinder AF

    (2015) Delayed coupling to feedback inhibition during a critical period for the integration of adult-born granule cells

    Neuron 85:116–130.

    https://doi.org/10.1016/j.neuron.2014.11.023

    • PubMed
    • Google Scholar
89. 1. Turrigiano GG
    2. Leslie KR
    3. Desai NS
    4. Rutherford LC
    5. Nelson SB

    (1998) Activity-dependent scaling of quantal amplitude in neocortical neurons

    Nature 391:892–896.

    https://doi.org/10.1038/36103

    • PubMed
    • Google Scholar
90. 1. Tyzio R
    2. Holmes GL
    3. Ben-Ari Y
    4. Khazipov R

    (2007) Timing of the developmental switch in GABA_A mediated signaling from excitation to inhibition in CA3 rat hippocampus using gramicidin perforated patch and extracellular recordings

    Epilepsia 48:96–105.

    https://doi.org/10.1111/j.1528-1167.2007.01295.x

    • PubMed
    • Google Scholar
91. 1. van Praag H
    2. Kempermann G
    3. Gage FH

    (1999) Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus

    Nature Neuroscience 2:266–270.

    https://doi.org/10.1038/6368

    • PubMed
    • Google Scholar
92. 1. Vivar C
    2. Potter MC
    3. Choi J
    4. Lee JY
    5. Stringer TP
    6. Callaway EM
    7. Gage FH
    8. Suh H
    9. van Praag H

    (2012) Monosynaptic inputs to new neurons in the dentate gyrus

    Nature Communications 3:1107.

    https://doi.org/10.1038/ncomms2101

    • PubMed
    • Google Scholar
93. 1. Wang Y
    2. Chik DT
    3. Wang ZD

    (2000) Coherence resonance and noise-induced synchronization in globally coupled Hodgkin-Huxley neurons

    Physical Review E 61:740–746.

    https://doi.org/10.1103/PhysRevE.61.740

    • PubMed
    • Google Scholar
94. 1. Wang DD
    2. Kriegstein AR

    (2011) Blocking early GABA depolarization with bumetanide results in permanent alterations in cortical circuits and sensorimotor gating deficits

    Cerebral Cortex 21:574–587.

    https://doi.org/10.1093/cercor/bhq124

    • PubMed
    • Google Scholar
95. 1. Weisz VI
    2. Argibay PF

    (2009) A putative role for neurogenesis in neuro-computational terms: inferences from a hippocampal model

    Cognition 112:229–240.

    https://doi.org/10.1016/j.cognition.2009.05.001

    • PubMed
    • Google Scholar
96. 1. Weisz VI
    2. Argibay PF

    (2012) Neurogenesis interferes with the retrieval of remote memories: forgetting in neurocomputational terms

    Cognition 125:13–25.

    https://doi.org/10.1016/j.cognition.2012.07.002

    • PubMed
    • Google Scholar
97. 1. Wiskott L
    2. Rasch MJ
    3. Kempermann G

    (2006) A functional hypothesis for adult hippocampal neurogenesis: avoidance of catastrophic interference in the dentate gyrus

    Hippocampus 16:329–343.

    https://doi.org/10.1002/hipo.20167

    • PubMed
    • Google Scholar
98. 1. Woods NI
    2. Stefanini F
    3. Apodaca-Montano DL
    4. Tan IMC
    5. Biane JS
    6. Kheirbek MA

    (2020) The dentate gyrus classifies cortical representations of learned stimuli

    Neuron 107:173–184.

    https://doi.org/10.1016/j.neuron.2020.04.002

    • PubMed
    • Google Scholar
99. 1. Yeckel MF
    2. Berger TW

    (1990) Feedforward excitation of the hippocampus by afferents from the entorhinal cortex: redefinition of the role of the trisynaptic pathway

    PNAS 87:5832–5836.

    https://doi.org/10.1073/pnas.87.15.5832

    • PubMed
    • Google Scholar
100. 1. Yuan M
     2. Meyer T
     3. Benkowitz C
     4. Savanthrapadian S
     5. Ansel-Bollepalli L
     6. Foggetti A
     7. Wulff P
     8. Alcami P
     9. Elgueta C
     10. Bartos M

     (2017) Somatostatin-positive interneurons in the dentate gyrus of mice provide local- and long-range septal synaptic inhibition

     eLife 6:e21105.

     https://doi.org/10.7554/eLife.21105

     • PubMed
     • Google Scholar
101. 1. Zenke F
     2. Gerstner W

     (2017) Hebbian plasticity requires compensatory processes on multiple timescales

     Philosophical Transactions of the Royal Society B: Biological Sciences 372:20160259.

     https://doi.org/10.1098/rstb.2016.0259

     • PubMed
     • Google Scholar
102. 1. Zhao C
     2. Teng EM
     3. Summers RG
     4. Ming GL
     5. Gage FH

     (2006) Distinct morphological stages of dentate granule neuron maturation in the adult mouse hippocampus

     Journal of Neuroscience 26:3–11.

     https://doi.org/10.1523/JNEUROSCI.3648-05.2006

     • PubMed
     • Google Scholar