Remembering what happened on different occasions involves a process in the brain called pattern separation, which allows us to separate and distinguish our memories. One part of the brain where pattern separation occurs is called the dentate gyrus, which sits in the hippocampus—the brain region that is in charge of certain forms of learning and memory.

Neurons called granule cells are thought to play a central role in hippocampal pattern separation. These cells, unlike the majority of nerve cells, can form at any time, and those that form in the mature brain are called adult born granule cells (ABGCs). Although it usually takes 10 weeks for these cells to fully mature, they are capable of communicating with each other about 3–4 weeks after being generated. Previously, it had been reported that while young, 4-week-old ABGCs are required for pattern separation, slightly older (8 week old) ABGCs are not.

What intrinsic properties make ABGCs capable of contributing to pattern separation? Is this property defined by the fate (i.e. a predetermined program) of the cell, or by the cell's experiences and activities?

To investigate these questions, Brunner et al. labeled ABGCs with a fluorescent tag when these neurons were born in adult male rats. Then, when the tagged cells were aged between 3 and 10 weeks old, the electrical properties of the labeled cells were measured from thin brain slices.

Brunner et al. found that ABGCs respond to input signals with two different levels of sensitivity. The youngest cells (3–5 weeks old) are exceptionally sensitive to a narrow range of input signal strengths, which is useful for pattern separation. The oldest investigated cells (10 weeks old), on the other hand, respond incrementally to a wide range of different input signal strengths. Under these experimental conditions, the cells changed how they respond to input signals some time between 5 and 9 weeks after being born. However, they either behaved like the youngest or like the oldest cells: no intermediate behavior was seen.

Unexpectedly, the switch is not directly related to the age of the cells: cells born at the same time don't necessarily change behavior at the same time, and cells born at different times may behave similarly. Thus, Brunner et al. suggest that it is the experience of the cells, and not their fate, that determines how they help the dentate gyrus function during the investigated period.

Adult neurogenesis contributes to certain forms of hippocampus-dependent behavior and is associated with a number of neuro-psychiatric diseases (Parent and Murphy, 2008; Deng et al., 2010; Kheirbek et al., 2012; Spalding et al., 2013). Recent data suggested that young ABGCs (around 4 weeks old) contribute to a discrete pattern separation function, whereas older cells (8 weeks or older) are not necessary for this dentate gyrus-dependent function, therefore functionally different pools of granule cells provide unique plasticity to the hippocampal circuits (Clelland et al., 2009; Aimone et al., 2010; Alme et al., 2010; Sahay et al., 2011a; Nakashiba et al., 2012; Neunuebel and Knierim, 2012). Current theories on adult neurogenesis are based on the provisional correlations between the two distinct physiological functions and age-dependent maturation of cellular (including synaptic, biophysical and molecular) properties (Aimone et al., 2006, 2010; Sahay et al., 2011b). This is supported by numerous observations showing that after their birth, ABGCs undergo a continuous maturation process, lasting for 8–10 weeks. ABGCs acquire neuronal properties including synaptic inputs and outputs, and capability of firing action potentials 3 to 4 weeks after their birth. Notably, ABGCs at this cellular age are highly excitable, show enhanced synaptic plasticity and are differently modulated by inhibition compared to ABGCs at the end of the maturation period (Wang et al., 2000; Schmidt-Hieber et al., 2004; Laplagne et al., 2006; Toni et al., 2008; Mongiat et al., 2009; Gu et al., 2012; Marín-Burgin et al., 2012; Vivar et al., 2012; Dieni et al., 2013). However, it remains unknown how two populations emerge by a continuous maturation of the underlying cellular properties of ABGCs. How do individual ABGCs transform from ‘young’ to ‘old’ properties? There are three testable possibilities. First, their functionally important properties may develop continuously (Figure 1A). However, if this is the case, it may contradict the general notion that two distinct ABGC populations exist, and ABGCs would provide a functional continuum. The second possibility is that, as during their early maturation when becoming functionally integrated (<4 weeks), ABGCs switch function according to a predetermined program (Figure 1B). In this situation there are two clearly distinct populations and they would switch within a short temporal window at a predefined stage of their postmitotic life. Third, ABGCs may be susceptible to extrinsic cues allowing for a functional switch for an extended period (Figure 1C). We tested these hypotheses by resolving the integrative properties of individual ABGCs because data-pooling could mask the differences between above hypotheses. Thus, we here analyzed if (i) all cellular properties develop concomitantly, (ii) if there are biophysical properties that allow the emergence of only two populations between 3 and 10 weeks after their birth, and (iii) how and when ABGCs switch function. Using this approach, we show that ABGCs consist of two functionally distinct populations during an extended period, between 3 and 9 weeks of age in rats, by being sensitive to distinct aspects of their inputs.

Figure 1

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To analyze the cellular maturation of ABGCs, we compared a variety of intrinsic biophysical and input–output transformation properties at seven different age-groups (3–10 weeks after cells are born) of individual birth-dated ABGCs in young adult rats using retroviral labeling (Figure 2, Zhao et al., 2006). The majority of the tested parameters (including input resistance, membrane time constant, whole-cell capacitance, resting membrane potential, action potential threshold, peak dV/dt of the spikes, and maximal firing rate) of individual ABGCs changed continuously with age and, consequently, the distribution of the data points from individual cells was wide, without the emergence of distinguishable populations (Figure 3A–B, statistical values are indicated in the figures—see also Supplementary file 1), reflecting the continuous maturation of these properties in accordance with previous observations (Mongiat et al., 2009; Marín-Burgin et al., 2012).

Figure 2

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Figure 3

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In addition to these basic biophysical parameters, we measured the suprathreshold input–output functions in response to sinusoidal current injections to mimic temporally organized input patterns in physiologically relevant frequency ranges (Figure 2F–H, Pernia-Andrade and Jonas, 2014). During these protocols, due to the fluctuating membrane potential, the contribution of voltage-gated ionic channels to the firing is more relevant than in the case of square pulse injection. In contrast to basic membrane properties, the analysis of the gain of the input–output functions of the same cells revealed two significantly distinct populations using Gaussian fits and K-means analysis (Figure 3C). The input–output function of the first group was characterized by a steep average slope (ASL) and highly variable (measured as the variance of the slope, VAR; Figure 2F–H) spike responses, suggesting that this cell population is exceptionally sensitive to certain narrow input strengths, and thus highly suited for disambiguating input–output functions at single cell level (notice the out-of-average values on Figure 2H for the first and third cells, hereafter referred to as S-group, standing for Sensitive). Within the S-group the integrative parameters were independent of the actual age of the individual cells (linear fits on Figure 3C) demonstrating that similar cellular functionality is maintained throughout an extended period (between 3–9 weeks) unless the individual cell switched to the second integration mode. This second group of ABGCs responded with constantly and incrementally increasing, less sensitive spike output (L-group; referring to Linear) enabling them to linearly report various input strengths. A different parameter of the input–output functions, the offset did not divide the same ABGC samples into two populations, indicating that the functional separation of ABGCs is restricted to specific cellular properties (Figure 3B). Altogether, the above results show that ABGCs form two functionally distinct populations during a long period of their maturation based on their different sensitivity to temporally organized inputs.

The above analysis suggested that age alone does not directly determine the functionality of individual ABGCs. However, the probability of whether an individual cell behaved as a member of S- or L-groups shows age-dependence (Figure 4A). Within all analyzed cells, the youngest (3 weeks old) and the oldest (10 weeks old) belonged to the S- or L-group, respectively; however, both functional groups were present with changing probabilities during the intermediate ages between 5 and 9 weeks of age. This observation was further supported by the parabolic distributions of the mean-variance plots of ASL and VAR (Figure 4B, Supplementary file 1). Thus, the continuously increasing probability of L-groups could mask the two functionally distinct groups of ABGCs when global population properties were analyzed as averages (Mongiat et al., 2009).

Figure 4 with 2 supplements see all

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Our data indicate that in 3–9 weeks old ABGCs the gain of the input–output properties is not exclusively determined by the input resistance. ABGCs within the S-group achieved similar input–output computations in spite of largely different input resistances. Strikingly, this becomes clear by the lack of correlations of the gain of individual S-group cells to their input resistance (Figure 4C). However, the input–output function of ABGCs from the L-group showed the expected dependence on the input resistance of individual cells. In contrast to the gain, the offset of the input–output function of individual ABGCs showed a clear dependence on the input resistance across both cell populations (Figure 4D). Thus, the independence of the gain of the input–output transformation from the continuously developing biophysical parameters at the level of individual cells allows for the emergence of only two temporally overlapping and functionally distinct populations within 5- to 9-weeks-old ABGCs.

Importantly, independent experiments in which granule cells were recorded in non-labeled adult animals ('Materials and methods'), confirmed the coexistence of S- and L-functionalities among granule cells based on their ASL and VAR, and these individual cells showed similar correlations (or lack thereof) between their integrative functions and input resistance (Figure 4—figure supplement 1) as in the case of the above birth-dated data set (Figure 4C).

Next, we tested whether all measured biophysical parameters of individual birth-dated ABGCs can cooperatively predict the functional separation at the level of the gain of their input–output functions. We performed cluster analysis of the recorded cells based on seven intrinsic parameters to define two groups (Figure 4E). The two intrinsic parameter groups determined by cluster analysis did not match with the S- and L-group identity of the same individual cells. This latter result shows that consideration of multiple parameters by their arithmetical values is not sufficient to predict the functional separation of S- and L-groups. Thus, a complex and balanced interaction is probably behind the formation of the two states, as also suggested by additional experiments, in which the cellular excitability was altered by decreasing the temperature, adding extracellular calcium or activating background conductances (Figure 4—figure supplement 2A–C). We also tested whether the two distinct input–output functions are due to their distinct synaptic drives (Dieni et al., 2013) by blocking GABA_A and AMPA/kainate receptors. This intervention slightly increased the ASL value (Figure 4—figure supplement 2D); however, the effect was not dependent on the initial state of the tested cells indicating that S- and L-group properties are established largely independent of spontaneous synaptic activity.

Here, we show that the gain of the input–output transformation of 3- to 10-weeks-old ABGCs exists at two functionally distinct states, allowing for the translation of similar excitatory drives into highly distinct action potential outputs in a manner that is not directly predicted by the cellular age alone (refer to the alternative maturation hypotheses depicted in Figure 1A–C). This finding is in contrast to the continuous maturation hypothesis (Figure 1A). Around the third postmitotic week, ABGCs represent a functionally homogeneous population (S-group) characterized by highly variable and sensitive output, which potentially underlies the effective disambiguation of input patterns because the output of S-group cells represent certain input ranges with exceptional efficacy. Importantly, this functional parameter is similar for an extended time period at the level of individual ABGCs, despite the continuous maturation of other biophysical parameters suggesting precise homeostatic tuning and complex interactions of the biophysical properties (Marder and Goaillard, 2006). However, between the fifth and ninth week (under our experimental conditions), ABGCs switch function by losing their sensitivity to a particular input strength as their output incrementally reports a wide input range (L-group). Thus, in the theoretical case of identical input strengths and patterns to S- and L-cells (which allowed us to investigate the integrative properties of individual ABGCs in isolation), sensitivity of ABGCs in the S-group to certain input ranges, in opposed to linear reporting in cells of the L-group, allows a certain level of input disambiguation at the level of single granule cells. Furthermore, previously described distinct input rules (Dieni et al., 2013) may synergistically promote two distinct functions within the ABGC populations, if these rules are specifically associated with S- or L-group properties.

Strikingly, our data indicate that ‘classmate’ cells (born during the same period) can contribute to the network with fundamentally different functions during an extended period after cells are born (5–9 weeks). Conversely, these data suggest that similar cellular functions can be served by ABGCs that were born at different periods during the animal's life. Therefore, the properties of individual cell rather than the cells' age determine how certain input strengths are computed; either by, disambiguating certain input combinations (S-functionality) or by representing all of its inputs by similar rate changes toward the downstream networks (L-functionality). This observation challenges previous hypotheses on the plasticity provided by adult neurogenesis from being predominantly determined by the postmitotic cellular age and predicting similar functions of ABGCs born at the same time (Aimone et al., 2006, 2010; Sahay et al., 2011b). Moreover, environmental conditions that increase or reduce hippocampal neurogenesis may affect the relative contribution of newly generated granule cells to the S- or L-groups that may explain distinct behavioral consequences of altered neurogenesis (Kempermann et al., 1997; Gould and Tanapat, 1999; van Praag et al., 1999).

All experimental procedures were made in accordance with the ethical guidelines of the Institute of Experimental Medicine Protection of Research Subjects Committee (permission: 22.1/1760/003/2009) and were approved by the local virus safety committee.

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Author details

1. János Brunner

   1. Lendület Laboratory of Cellular Neuropharmacology, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary
   2. János Szentágothai School of Neurosciences, Semmelweis University School of PhD Studies, Budapest, Hungary

   Contribution

   JB, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Máté Neubrandt

   Competing interests

   The authors declare that no competing interests exist.

2. Máté Neubrandt

   1. Lendület Laboratory of Cellular Neuropharmacology, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary
   2. János Szentágothai School of Neurosciences, Semmelweis University School of PhD Studies, Budapest, Hungary

   Contribution

   MN, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   János Brunner

   Competing interests

   The authors declare that no competing interests exist.

3. Susan Van-Weert

   Lendület Laboratory of Cellular Neuropharmacology, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary

   Contribution

   SV-W, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

4. Tibor Andrási

   1. Lendület Laboratory of Cellular Neuropharmacology, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary
   2. János Szentágothai School of Neurosciences, Semmelweis University School of PhD Studies, Budapest, Hungary

   Contribution

   TA, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

5. Felix B Kleine Borgmann

   Brain Research Institute, Faculty of Medicine and Science, University of Zurich, Zurich, Switzerland

   Contribution

   FBKB, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

6. Sebastian Jessberger

   Brain Research Institute, Faculty of Medicine and Science, University of Zurich, Zurich, Switzerland

   Contribution

   SJ, Conception and design, Drafting or revising the article

   For correspondence

   jessberger@hifo.uzh.ch

   Competing interests

   The authors declare that no competing interests exist.

7. János Szabadics

   Lendület Laboratory of Cellular Neuropharmacology, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary

   Contribution

   JS, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   szabadics.janos@koki.mta.hu

   Competing interests

   The authors declare that no competing interests exist.

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