Neuron

Previews
Beierlein, M., Gibson, J.R., and Connors, B.W.
(2000). Nat. Neurosci. 3, 904–910.

Biol. Sci. 370, 20140164, http://dx.doi.org/10.
1098/rstb.2014.0164.

Chen, J.L., Andermann, M.L., Keck, T., Xu, N.L.,
and Ziv, Y. (2013). J. Neurosci. 33, 17631–17640.

Karnani, M.M., Jackson, J., Ayzenshtat, I., Tucciarone, J., Manoocheri, K., Snider, W.G., and Yuste,R. (2016). Neuron 90, this issue, 86–100.

Roux, L., and Buzsáki, G. (2015). Neuropharmacology 88, 10–23.

Kepecs, A., and Fishell, G. (2014). Nature 505,318–326.

Rudy, B., Fishell, G., Lee, S., and Hjerling-Leffler, J.
(2011). Dev. Neurobiol. 71, 45–61.

Long, M.A., Cruikshank, S.J., Jutras, M.J., andConnors, B.W. (2005). J. Neurosci. 25, 7309–7316.

Swadlow, H.A. (2002). Philos. Trans. R. Soc. Lond.
B Biol. Sci. 357, 1717–1727.

Otsuka, T., and Kawaguchi,J. Neurophysiol. 110, 795–806.

Yoshimura, Y., and Callaway, E.M. (2005). Nat.
Neurosci. 8, 1552–1559.

Galarreta, M., and Hestrin, S. (1999). Nature 402,72–75.
Gibson, J.R., Beierlein, M., and Connors, B.W.
(1999). Nature 402, 75–79.
Jorgenson, L.A., Newsome, W.T., Anderson, D.J.,
Bargmann, C.I., Brown, E.N., Deisseroth, K., Donoghue, J.P., Hudson, K.L., Ling, G.S., MacLeish,P.R., et al. (2015). Philos. Trans. R. Soc. Lond. B

Y.

(2013).

Pfeffer, C.K., Xue, M., He, M., Huang, Z.J., andScanziani, M. (2013). Nat. Neurosci. 16, 1068–1076.

The Young and the Promiscuous
Adam I. Ramsaran1,2 and Paul W. Frankland1,2,3,4,*1Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
2Department of Psychology, University of Toronto, Toronto, ON M5S 3GM, Canada
3Institute of Medical Science
4Department of Physiology
University of Toronto, Toronto, ON M5S 1A8, Canada*Correspondence: paul.frankland@sickkids.ca
http://dx.doi.org/10.1016/j.neuron.2016.03.021

It is now well accepted that new neurons continue to be generated in the adult hippocampus. By visualizing
and manipulating new neurons in behaving mice, Danielson et al. (2016) begin to define how new neuronscontribute to hippocampal function.
In the 1960s a young neurobiologist fromMIT discovered a subpopulation of proliferative cells in the dentate gyrus (DG) thatappeared to generate new neurons wellinto adulthood (Altman, 1963). Although
Joseph Altman’s claims were initially met
with skepticism (Altman, 2011), the introduction of more definitive methods for labeling and phenotyping newborn neurons
in the 1990s led to broad acceptance of
this phenomenon.
With acceptance, attention swiftly
shifted away from whether hippocampal
neurogenesis persists in the adult brain
to how the continued addition of new neurons contributes to hippocampal function.
Given the role of the hippocampus inmemory, initial studies focused largely
on how adult-born granule cells (abGCs)
impact hippocampal memory function:
finding, for example, that global suppression of neurogenesis impairs encodingof some hippocampus-dependent memories (Shors et al., 2001). More recently,
6 Neuron 90, April 6, 2016

however, the focus has narrowed. Motivated by the idea that the DG is importantfor pattern separation (Leutgeb et al.,2007; Treves and Rolls, 1994)—the process of transforming overlapping input
representations into less overlapping ordistinct output representations—current
studies have asked how hippocampal
neurogenesis regulates this process (Sahay et al., 2011b). These studies reportthat increasing or decreasing hippocampal neurogenesis, respectively, improves
or worsens rodents’ ability to encode
fine spatial discriminations—a behavioral
analog of pattern separation (Niiboriet al., 2012; Sahay et al., 2011a). However, these types of studies are notwithout limitations. First, the interventions
chronically alter levels of hippocampal
neurogenesis, allowing for the possibilityof compensatory circuit-level changes
that may muddy the interpretation. Second, while brain slice preparations haveidentified unique physiological featuresof abGCs (Marı́n-Burgin et al., 2012), it ischallenging to know how these properties
relate to alterations in behavior following
global alteration of hippocampal neurogenesis. In the current issue of Neuron, astudy by Danielson and colleagues begins
to bridge this divide. Combining sophisticated transgenic and imaging strategies,
Danielson et al. (2016) simultaneously
monitor the activity of abGC and mature
granule cell (mGC) populations in the DG
as mice explore virtual contexts and test
whether abGCs are necessary for context
discrimination. Accordingly, the authors
directly compare abGC and mGC activity
in vivo and, in so doing, begin to reveal
how abGCs uniquely contribute to behavioral pattern separation.
Danielson et al. (2016) used two-photon
calcium imaging in awake, behaving mice
to record the activity of DG granule cells
expressing the genetically encoded calcium indicator, GCaMP6f. By using transgenic mice in which abGCs were indeliblyNeuronPreviews
labeled with a red fluorophore (tdTomato),
they directly compared calcium signals
emitted from abGCs and mGCs. As thesehead-fixed mice traversed virtual linear environments (composed of different visual,auditory, and olfactory stimuli), the authors
observed that abGCs were more active
than their more mature counterparts: Calcium transients (a proxy for neuronal firing)were detected at higher rates in abGCs,
and proportionately fewer abGCs were silent. Previous in vitro studies establishedthat abGCs are more excitable than theirmature neighbors (Marı́n-Burgin et al.,2012). The current study shows thatthe same holds true in vivo. Moreover,it suggests that abGCs—by virtue of
their heightened excitability—more readily
respond to stimuli during encoding.
How might this promiscuous activity influence how abGCs encode and represent
information? The major subdivisions of thehippocampus (including CA1, CA3, andDG) contain spatially modulated neurons,or place cells, that preferentially fire in
one or more spatial locations in a given
environment. Although it is clear thatspatial information is carried by DG population activity, it is unclear whether immature and mGCs differentially contribute tothis signal. To answer this question, Danielson et al. (2016) first examined to whatextent GC activity was modulated bylocation in the virtual environments. While
the activity of both abGCs and mGCs
was spatially modulated, this modulation
tended to be less precise in abGCs. New
neurons were less spatially tuned.
Might poorer spatial tuning reduce theability of abGCs to discriminate similar environments? Danielson and colleagues
next monitored GC activity as they alteredthe virtual environments. While mGCs‘‘remapped’’ when environments werealtered, this remapping appeared to beless efficient for abGCs (although thisdepended to some extent on how spatial
tuning was assessed). Interestingly,
though, the authors noted a subpopulation of abGCs that were as discerning as
their mature counterparts, showing no difficulty discriminating efficiently between
contexts. A more detailed analysis of this
subpopulation is necessary to determine
what particular properties support theirsuperior discrimination ability. Nevertheless, these findings emphasize the utilityof the imaging techniques employed by

Danielson et al. (2016) in monitoring subpopulation-specific activity in the DG.
The current findings that mGCs more efficiently discriminate contexts than abGCs
extends previous research by allowing researchers to classify neuronal activity bycell age (Leutgeb et al., 2007).
Although the present study finds thatabGCs are relatively poor discriminators,
paradoxically, elimination of this population of cells results in impaired behavioral
pattern separation (Niibori et al., 2012;
Sahay et al., 2011a). In these studies, neurogenesis levels were typically manipulated prior to training, making it impossible to dissect the role of abGCs indistinct memory processes (e.g., encoding, consolidation, retrieval). Using atransgenic strategy to ‘‘tag’’ abGCs with
the inhibitory opsin Arch, Danielson et al.
(2016) gained temporal control overwhen abGCs could be inhibited during
different stages of a contextual fear
discrimination task. In this kind of task,
mice are repeatedly exposed to a fearful(shock-paired) context and similar neutral
context over the course of several days
and start to show more fearful behavior(e.g., freezing) in the aversive context.
Optogenetically silencing abGCs duringinitial exposures to the neutral, but not
the aversive, context impaired this behavioral discrimination. However, silencing
abGCs after context discrimination was
already acquired, or during exposure toa neutral but dissimilar context, did not
impair pattern separation.
These data extend previous studies
and suggest that abGC activity is necessary during the initial encoding of conflicting contextual stimuli to support patternseparation. But how might this happen?
One possibility is that similar contexts
are sparsely encoded by non-overlapping
ensembles of abGCs, and this might help
to reduce interference during memory
retrieval. However, in light of the present
study’s finding that abGCs more ambiguously represent context information (Danielson et al., 2016), this interpretation
seems unlikely. Instead, abGCs may act
as ‘‘pattern integrators.’’ By virtue of
their increased excitability, informationfrom distinct experiences might be coallocated to ensembles of abGCs thatpartially overlap in their spatial distributions. This idea is supported by computational modeling that predicts that distinct

events may be encoded by largely overlapping ensembles of highly excitable
abGCs but non-overlapping ensemblesof mGCs (Aimone et al., 2009) and would
explain why in the current study neuronal
activity in new neuron population less efficiently disambiguated context experiences. The small percentage of abGCs
that could discriminate contexts may
represent a cohort of neurons on thecusp of maturity and therefore display
the mGC phenotype, but more temporally
precise labeling of immature granulecells is needed to test this possibility.
Such experiments will determine whether
a specific cohort of abGCs in the heterogeneous immature neuron population
can support pattern separation.
Why would the elimination of a population of ‘‘pattern integrators’’ lead to deficits in context discrimination? One idea
is that abGCs help ‘‘sparsify’’ the DG
code. Immature granule cells form connections with hilar and CA3 interneurons
which exert inhibitory drive on mGC andCA3 pyramidal neuron populations toeffectively constrain principal cell activation during encoding (Drew et al., 2015; Ikrar et al., 2013; Restivo et al., 2015).
Consistent with this idea, increased levelsof neurogenesis are associated withreduced DG activation both in vitro (Ikraret al., 2013) and in vivo (Drew et al.,2015). Conversely, reduced levels of neurogenesis are associated with elevated
DG activation both in vitro (Ikrar et al.,2013) and in vivo (Drew et al., 2015).
Therefore, pattern separation deficits
stemming from optogenetic inhibition ofabGCs (Danielson et al., 2016) could be
attributed to unrestrained mGC activity
during encoding of similar environments
and poor separation of memory traces in
CA3 (see also Niibori et al., 2012).
What new neurons do in the adulthippocampus has been the subject ofmuch conjecture and debate (Cameronand Glover, 2015). However, just as
improved techniques allowed the field
to move on from questioning whether
neurogenesis persists in the adult hippocampus to acceptance of this phenomenon, improved techniques for imaging
new neurons in behaving animals—inessence allowing researchers to watch
thought in real time at a cellular level ofresolution—will accelerate progress toward defining their function.
Neuron 90, April 6, 2016 7

NeuronPreviews
REFERENCES
Aimone, J.B., Wiles, J., and Gage, F.H. (2009).
Neuron 61, 187–202.
Altman, J. (1963). Anat. Rec. 145, 573–591.
Altman, J. (2011). The discovery of adult mammalian neurogenesis. In Neurogenesis in the Adult
Brain I (Springer), pp. 3–46.
Cameron, H.A., and Glover, L.R. (2015). Annu. Rev.
Psychol. 66, 53–81.
Danielson, N.B., Kaifosh, P., Zaremba, J.D.,
Lovett-Barron, M., Tsai, J., Denny, C.A., Balough,E.M., Goldberg, A.R., Drew, L.J., Hen, R., et al.
(2016). Neuron. Published online March 8, 2016.
http://dx.doi.org/10.1016/j.neuron.2016.02.019.

Drew, L.J., Kheirbek, M.A., Luna, V.M., Denny,C.A., Cloidt, M.A., Wu, M.V., Jain, S., Scharfman,H.E., and Hen, R. (2015). Hippocampus. http://dx.doi.org/10.1002/hipo.22557.

Restivo, L., Niibori, Y., Mercaldo, V., Josselyn,S.A., and Frankland, P.W. (2015). J. Neurosci. 35,10600–10612.

Ikrar, T., Guo, N., He, K., Besnard, A., Levinson, S.,Hill, A., Lee, H.K., Hen, R., Xu, X., and Sahay, A.
(2013). Front. Neural Circuits 7, 204.

Sahay, A., Scobie, K.N., Hill, A.S., O’Carroll, C.M.,
Kheirbek, M.A., Burghardt, N.S., Fenton, A.A., Dranovsky, A., and Hen, R. (2011a). Nature 472,466–470.

Leutgeb, J.K., Leutgeb, S., Moser, M.B., andMoser, E.I. (2007). Science 315, 961–966.

Sahay, A., Wilson, D.A., and Hen, R. (2011b).
Neuron 70, 582–588.

Marı́n-Burgin, A., Mongiat, L.A., Pardi, M.B., andSchinder, A.F. (2012). Science 335, 1238–1242.

Shors, T.J., Miesegaes, G., Beylin, A., Zhao, M.,
Rydel, T., and Gould, E. (2001). Nature 410,372–376.

Niibori, Y., Yu, T.S., Epp, J.R., Akers, K.G., Josselyn, S.A., and Frankland, P.W. (2012). Nat. Commun. 3, 1253.

Treves, A., and Rolls, E.T. (1994). Hippocampus 4,374–391.

Optogenetics Advances in Primate Visual Pathway
Mehrdad Jazayeri1,2,* and Evan Remington1
1McGovern Institute for Brain Research
2Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology, Cambridge, MA 02139, USA*Correspondence: mjaz@mit.edu
http://dx.doi.org/10.1016/j.neuron.2016.03.024

In this issue of Neuron, Klein et al. (2016) used cell-type-specific optogenetics and electrical microstimulation
to characterize the koniocellular geniculocortical projections in nonhuman primates. Their work offers apowerful platform for refining our understanding of the mechanisms of visual information processing in thelateral geniculate nucleus and primary visual cortex.
Optogenetics has revolutionized neuroscience research in transgenic animalmodels and is slowly becoming moresophisticated in nontransgenic models.
In nonhuman primates (NHPs), the application of optogenetics has advanced
from the initial proof of principle (Diesteret al., 2011; Han et al., 2009) to perturbing
behavior (Cavanaugh et al., 2012; Geritset al., 2012), evoking percepts (Jazayeriet al., 2012), and, most recently, projection-specific targeting in the oculomotor
system (Inoue et al., 2015). Cell-typespecific targeting, however, has remained
a challenge (but see Lerchner et al., 2014).
In this issue of Neuron, Klein et al. (2016)use optogenetics and electrical microstimulation to target the koniocellularcells of the lateral geniculate nucleus(LGN) in NHPs.
The macaque visual system is among
the most thoroughly characterized model
8 Neuron 90, April 6, 2016

systems for thalamocortical processing.
Thalamocortical projections to the primary visual cortex (V1) originate fromdistinct and alternating bands of magno,parvo, and koniocellular cells in the LGN.
Decades of research have shown thatthe magno- and parvocellular projections terminate in the granule layer of V1,
whereas koniocellular cells (K-cells) project to the supragranular layers. In addition
to their distinct anatomical projections,K-cells are the only cells in the LGN that
express the calcium binding protein
CamKIIa (Hendry and Reid, 2000). Theauthors took advantage of this biochemical property and used a viral vector(rAAV5) with an effective CamKII promoter
to target the expression of Channelrhodopsin-2 (ChR2) to K-cells. The vectortransduced many K-cells with reasonable(but imperfect) specificity and reliability,
and reporter gene expression was foundthroughout the membrane of transducedcells. In addition to K-cells and nearby
CamKII-positive cells, the vector wasalso able to transduce distant layer 6 pyramidal cells of V1 and retinal ganglion
cells, presumably through retrograde
transport mechanisms.
K-cells have heterogeneous visual
response properties and are thus difficult
to identify and characterize with visualstimuli. Klein et al. (2016) used optogenetics as an assay to identify K-cells
based on their responsiveness to optogenetic stimulation. Optogenetic stimulation
can activate both cells of interest that
express ChR2 and interconnected cells
that do not express ChR2. Klein et al.
(2016), therefore, focused their analysis
on cells with short-latency responses to
optogenetic stimulation that are more
likely to have been K-cells with ChR2
expression. They characterized K-cells’