MODELS, STRUCTURE, FUNCTION: THE TRANSFORMATION OF
CORTICAL SIGNALS IN THE DENTATE GYRUS
László Acsády1 and Szabolcs Káli1,2
1
Institute of Experimental Medicine, Hungarian Academy of Sciences, PO Box 67, 1450
Budapest, Hungary,
2
HAS-PPCU-SU Neurobionics Research Group, Budapest, Hungary
Corresponding author: László Acsády
Institute of Experimental Medicine, Hungarian Academy of Sciences, PO Box 67, 1450
Budapest, Hungary, email: acsady@koki.hu; tel: 36-1-210-9413; fax: 36-1-210-9412
2
Abstract
Our central question is why the hippocampal CA3 region is the only
cortical area capable of forming interference-free representations of complex
environmental events (episodes), given that apparently all cortical regions have
recurrent excitatory circuits with modifiable synapses, the basic substrate for
autoassociative memory networks. We review evidence for the radical (but
classic) view that a unique transformation of incoming cortical signals by the
dentate gyrus and the subsequent faithful transfer of the resulting code by the
mossy fibers are absolutely critical for the appropriate association of memory
items by CA3 and, in general, for hippocampal function.
In particular, at the gate of the hippocampal formation, the dentate
gyrus possesses a set of unusual properties which selectively evolved for the task
of code transformation between cortical afferents and the hippocampus. These
evolutionarily-conserved anatomical features enable the dentate gyrus to
translate the noisy signal of the upstream cortical areas into the sparse and
specific code of hippocampal formation, which is indispensable for the efficient
storage and recall of multiple, multidimensional memory items.
To achieve this goal the mossy fiber pathway maximally utilizes the
opportunity to differentially regulate its postsynaptic partners. Selective
innervation of CA3 pyramidal cells and interneurons by distinct terminal types
creates a favorable condition to differentially regulate the short-term and longterm plasticity and the motility of various mossy terminal types. The utility of
this highly dynamic system appears to be the frequency-dependent fine-tuning
of the excitation evoked by the large “detonator” terminals and the inhibition
activated by the small terminals. This will determine exactly which CA3 cell
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population is active and induce permanent modification in the autoassociational
network of the CA3 region.
Introduction
The hippocampus, a peculiar cortical structure, has long been known to be
involved in higher order cognitive functions, most notably, memory formation and
spatial navigation (Scoville & Milner, 1957; O'Keefe & Dostrovsky, 1971). Not all
kinds of memory depend on the hippocampus. The learning of arbitrary associations
of complex, multimodal items during a single exposure (i.e., episodic memory) is
irreversibly compromised following hippocampal lesions, but the acquisition of
simple associative pairings (e.g., classical conditioning) and motor learning remain
intact (Squire, 1992). Hippocampal-dependent memory traces can be used flexibly,
i.e., they can be activated in a context different from the one where they were
learned. Clinical studies in humans, as well as a large number of behavioral and
physiological experiments in other mammalian species (mainly rodents) have also
implicated the hippocampus in the formation and flexible use of world-centered
(allocentric) spatial representations (O'Keefe & Nadel, 1978). It has been argued
repeatedly that these two domains share several important characteristics, and may
have fairly similar computational requirements. In particular, both episodic memory
and spatial navigation may require the fast storage and subsequent recall of specific
conjunctions of environmental stimuli. It has been suggested that the bulk of
neocortex, which is assumed to be involved in continuously creating and refining an
internal representation of the general structure of the observed world, may not be
well-suited for the rapid, interference-free acquisition of such specific memory
traces(McClelland et al., 1995). On the other hand, the hippocampus might be
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optimized for exactly this operation, and could thus complement the generic learning
capabilities of the neocortex.
However, the hippocampus constitutes only a tiny fraction of the cortical
areas, and it has a relatively simple structure. It receives input from and sends
information back to multimodal associational cortical areas. The question is why
neocortex needs the hippocampal loop to implement rapid learning of arbitrary
complex associations? Why is it that other cortical areas with multiple cellular layers
cannot do the same job? What is so special about hippocampus that enables it to
establish the most complex memory traces? In short, what is the “trick” of the
hippocampus?
For a long time, the central role for memory formation was assigned to the
hippocampal CA3 region (Marr, 1971; McNaughton & Morris, 1987; Rolls, 1989).
This hippocampal subfield was favored by most computational neuroscientists and
electrophysiologists since its principal cells, the CA3 pyramidal cells, form a socalled “autoassociative memory network” with their abundant, local recurrent
collaterals (Li et al., 1994). In computational models these types of network were
found to be optimal for the efficient storage of a large number of memory items and
for reactivation of complete memory traces if only part of the trace was provided,
which are key components of episodic memory. Moreover, the synapses among CA3
pyramidal cells, as well as those between pyramidal cells in CA3 and their primary
downstream target CA1, are subject to associative and cooperative long-term
potentiation (LTP), a possible molecular mechanism underlying memory formation
(Debanne et al., 1998). Interestingly, however, many features of CA3 pyramidal cells
are shared by pyramidal cells of other cortical areas. Neocortical pyramidal cells
have just as profuse recurrent local collaterals and their synapses are subject to longterm plastic changes. CA3 and layer II-III cells also have many intrinsic
5
electrophysiological characteristics in common. Thus, autoassociative networks with
plastic synapses are abundant in the cortex. Therefore, it is not immediately obvious
why the formation of complex memory traces is restricted to the hippocampal
formation and cannot be performed by other multimodal associational cortical areas.
Two features of the CA3 area, however, clearly distinguish it from other
cortical regions. CA3 pyramidal cells receive a prominent excitatory input to their
proximal apical dendrites (Ramon y Cajal, 1911; Claiborne et al., 1986), which
enables an unusually strong spike coupling between an upstream region – the dentate
gyrus – and CA3 (Henze et al., 2002). The faithfulness of this synaptic transmission
is unparalleled in excitatory cortical circuits. The second feature (shared by the
principal cells of the other hippocampal subregions) is the way in which the firing
pattern of pyramidal cell codes the environmentally-relevant stimuli. Most pyramidal
cells in other cortical regions are characterized by higher spontaneous firing rates,
and a lower rate of modulation by the appropriate stimuli. In sharp contrast,
background activity in hippocampal principal cells, and granule cells in particular, is
very low (Muller et al., 1987; Barnes et al., 1990; Quirk et al., 1992; Jung &
McNaughton, 1993). However, when hippocampal principal cells participate in
information transfer (e.g. place cells), their activity increases enormously. In
computational terms, the hippocampus uses a sparse code, whereas coding in other
cortical areas is denser. Apparently, the hippocampus and the other cortical regions
“speak” different neuronal languages. Since the environmentally-specific
information reaches the hippocampus via other cortical areas, the immediate
consequence of the difference in coding strategies is that densely-encoded cortical
information reaching the hippocampus needs to be translated into a sparser
hippocampal code. In other words, an interface is needed between the hippocampus
and neocortex. The main task of this interface would be the translation of the
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neocortical code into a hippocampal one, and to transfer the new code as efficiently
as possible to the next station of information processing. According to the classic
view, this interface is the dentate gyrus (DG), the first step of the trisynaptic
hippocampal loop.
We argue that the dentate gyrus is the “odd-man-out” among cortical regions.
In particular, at the gate of the hippocampal formation, the dentate gyrus possesses a
set of unusual properties that selectively evolved for the task of code transformation
between cortical afferents and the hippocampus. These evolutionarily-conserved
anatomical features enable the dentate gyrus to translate the noisy signal of the
upstream cortical areas to the sparse and specific code of hippocampal formation,
which is indispensable for the formation of multiple, multidimensional memory
items.
Computational requirements for the formation of episodic memories
There is a sort of general consensus about how the hippocampus could
contribute to cortical memory functions (Alvarez & Squire, 1994; Treves & Rolls,
1994; McClelland et al., 1995; Kali & Dayan, 2004; Rolls & Kesner, 2006). First,
the hippocampus is assumed to be capable of rapidly creating and storing a memory
trace which is distinct from all existing traces. The memory trace is associated with a
snapshot of activity in medial temporal neocortex (particularly entorhinal cortex),
which, in turn, is thought to represent a compressed version of activity in the rest of
neocortex. Second, the hippocampus is thought to be capable of retrieving particular
stored traces if the entorhinal activity pattern provides only a partial or noisy version
of the corresponding original pattern. Using this cue, the hippocampus reinstates the
original pattern in the entorhinal cortex and, subsequently, the rest of neocortex. This
operation is referred to as pattern completion, or autoassociative memory function.
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Finally, the hippocampus may also be capable of autonomously reactivating stored
memory traces in the absence of specific retrieval cues, thereby reactivating
complete cortical memory representations during “off-line” behavioral states (e.g.,
slow-wave sleep). Such replay may contribute in various ways to the consolidation
(transfer to a final repository) and maintenance of episodic and semantic memory
(Kali & Dayan, 2004).
But do we have any reason to believe that the hippocampus is even capable of
carrying out these operations, and if so, that it is in some sense optimized for exactly
these tasks? The most widely-cited evidence is the existence of the extensive
recurrent collateral network of pyramidal neurons in area CA3. Such recurrent
networks are the classic examples of autoassociative memory devices, whose
properties have been extensively investigated by theoretical means and computer
simulations (Marr, 1971; Willshaw & Buckingham, 1990; McNaughton & Morris,
1987; Treves & Rolls, 1992; Samsonovich & McNaughton, 1997; Kali & Dayan,
2000). In particular, the storage capacity of such networks, i.e., the number of
patterns they can store and retrieve reliably, has been determined (Treves & Rolls,
1992), and was found to depend substantially on the properties of the set of input
patterns that we attempt to store. Capacity is roughly inversely proportional to the
sparsity of individual patterns (i.e., the proportion of active units in a pattern), and
generally increases as the overlap between different stored patterns decreases.
Therefore, it is reasonable to assume that the need to maximize storage capacity is an
important reason for the conspicuously low activity levels of principal cell
populations in the hippocampus (the proportion of active principal cells in any
hippocampal subfield at any given moment is thought to be on the order of a few
percent (Barnes et al., 1990; Quirk et al., 1992; Jung & McNaughton, 1993, Treves,
1994 #5013).
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However, activity patterns in most areas of neocortex, including entorhinal
cortex, appear to be much denser (involving a larger proportion of neurons at any
given time), which is thought to be beneficial for generalization (McClelland et al.,
1995), an important characteristic of the kind of representational learning that the
neocortex may be engaged in. On the other hand, if the optimal type of activity
pattern (dense vs. sparse) is different in the neocortex and the hippocampus, then the
information contained in the entorhinal dense code needs to be “translated” into a
sparse code when hippocampal representations are created (and vice-versa). In
principle, such translation could be implemented directly by a single set of
projections from the input area to the autoassociative network (i.e., by the direct
perforant path input from entorhinal cortex to the CA3 region), without an
intervening specific interface for sparsification. However, as argued on theoretical
grounds by Treves and Rolls (1992), a projection with the characteristics of the
perforant path – a large number of relatively weak, associatively -modifiable
synapses on each target cell – may be optimal during retrieval, but a different type of
input to the CA3 recurrent network – one with a small number of individually strong
synapses per cell – is probably required for the storage of new memory traces. The
mossy fiber pathway, the projection to CA3 from granule cells, meets the
requirements for this second type of input. Additional theoretical and computer
simulation studies by O'Reilly and McClelland (1994) indicated that the two-stage
pathway from EC to DG to CA3 could perform very effective pattern separation,
provided that individual mossy fiber connections were sufficiently strong to transfer
the benefits of pattern separation in the DG to CA3. They further argued that the
coexistence of this indirect pathway with the direct EC-CA3 connection enables the
hippocampus to avoid an inherent conflict between pattern separation and pattern
completion, both of which are important for the efficient operation of the
9
hippocampal autoassociator. In summary, these studies suggest that a possible role of
the dentate gyrus is to form sparse, pattern-separated representations of entorhinal
activity patterns, and transmit this sparse representation reliably for subsequent
storage in the CA3 recurrent network.
But how does the dentate gyrus create sparse representations from the
relatively dense entorhinal activity patterns which constitute its only major cortical
input? Since no direct experimental investigation of this issue has been undertaken,
we need to rely on indirect evidence and the results of computational studies to try to
answer this question. At an abstract level, well-known computational algorithms
exist which create sparse, pattern-separated representations from distributed input
patterns. A simple example is the competitive-learning pattern classification device
described by Rumelhart and Zipser (1986), which has been shown to be capable of
generating hippocampal place field-like activity patterns from input patterns
resembling neocortical sensory representations (Sharp, 1991). This algorithm
operates on binary input patterns and generates binary output patterns. For each
presentation of an input pattern, the current values of the synaptic weights (which are
initially set to random values) are used to determine the feedforward activation of
units in the output layer. Then the output unit with the highest level of feedforward
input is allowed to become active (this step is assumed to reflect the action of
feedforward and feedback inhibitory circuits), and the incoming weights of this unit
are allowed to change. This weight change is assumed to be Hebbian in nature:
weights from active input units are increased, while weights from inactive units are
decreased in such a way that the sum of all incoming weights remains constant (and
identical to the sum of incoming weights to all other output units). This way, the
“winning” unit will have an even higher level of feedforward activation the next time
the same input pattern appears. It will also show an increased response to other
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similar input patterns; on the other hand, its response to patterns which are very
different (with a low degree of overlap) will diminish. As a result, different output
units will eventually respond to different kinds of patterns, and end up partitioning
the input space among themselves into non-overlapping groups of similar patterns.
The algorithm performs both sparsification – since only a single output unit becomes
active for any given (distributed) input pattern – and orthogonalization (pattern
separation) – since relatively dissimilar, but still overlapping input patterns end up
activating different output units (zero overlap).
However, is there any experimental evidence that sparsification requires a
separate relay station, and that the dentate gyrus has unique features - besides its
well-known “detonator” type of terminals - for code conversion and reliable
transmission? In the following pages we review behavioral, morphological, and
physiological data relevant to these questions.
Lesion studies
Let us first examine whether the putative role of the DG as described above is
consistent with behavioral data on the effects of specific lesions. In general, we
expect that some, but not all tasks that are sensitive to global hippocampal lesions
will also be sensitive to more specific lesions of the DG, and might hope that the
pattern of impairments that occur after selective DG lesions sheds light on the role of
the DG in hippocampal processing. Most lesion studies have taken advantage of the
fact that intrahippocampal injections of colchicine cause a fairly selective destruction
of the granule cells of the DG, but other techniques, such as neonatal X-ray
irradiation and adrenalectomy, which also cause a similar pattern of damage, have
also been used. Perhaps the most consistent finding after DG lesions in rodents has
been a severe impairment in the acquisition of the reference memory task in the
11
Morris water maze (Sutherland et al., 1983; McNaughton et al., 1989; Conrad &
Roy, 1993; Xavier et al., 1999). Working memory versions of the task are also
affected, although perhaps to a lesser extent (Xavier et al., 1999; Jeltsch et al., 2001).
Reference and working memory performance in the radial arm maze are also
compromised (McNaughton et al., 1989; Jeltsch et al., 2001). In some more recent
studies, DG lesions were also found to cause impairment in a delayed-matching-toplace task, in a temporal task (Costa et al., 2005), and in a task which required the
detection of metric distance change between objects (Goodrich-Hunsaker et al.,
2005).
Recently, there have been some attempts to test more directly the proposed
contributions of the DG to hippocampal function. In particular, Gilbert et al. (2001)
designed a short-term spatial memory task where the proximity of relevant locations
could be varied systematically. They found that DG lesions impaired performance at
small, but not at large separations, consistent with the role of the DG in spatial
pattern separation. Lassalle et al. (2000) examined the consequences of selective and
reversible inactivation of mossy fiber synapses in CA3 in mice during various stages
of a reference memory task in the Morris water maze. They found that the mossy
fiber input from DG to CA3 was essential during learning, but not during the
retrieval phase of the task, or in the period directly following learning (the early
stages of consolidation). Similarly, Lee and Kesner (2004) attempted to distinguish
encoding and retrieval deficits during the acquisition of a navigation task in the
Hebb-Williams maze following lesions of various type. They found that lesions of
the DG impaired learning, but not retrieval, in this task; conversely, lesions of the
perforant path input to CA3 affected retrieval, but not learning.
In summary, the available behavioral data from rodents with lesions to
dentate granule cells or their mossy fiber output are generally consistent with a
12
crucial role of the DG in providing input to area CA3 during the acquisition of
allocentric spatial information, and provide some support for a more specific
function in (spatial) pattern separation. So what are the morphological
peculiarities of the dentate gyrus which support its role in orthogonalization
and make it indispensable for proper CA3 function?
Morphological arguments - Heterogeneous terminal types of the mossy fibers
Let’s see first how Cajal described of the unusual terminals types of the
mossy fibers:
“…there arise either short and thick divergent appendages or quite long fine
threads that end in a swelling. Thus, we reproduced here the arrangement (although
less distinctly) that we described in certain branched fibers of the cerebellum the
mossy fibers. Therefore without further ado let us apply the same name to the axons
of the granules of the fascia dentata.”
It is clear from the above description that the investigator who named the
mossy fibers by examining Golgi-stained material clearly identified that this peculiar
fiber system has more than one terminal type. The well-recognizable giant endings
(the large mossy terminals) gave rise to thin filamentous structures which ended in
terminal-like swellings. Apparently the name “mossy fiber” is based on the presence
of these filopodial extensions. Later electron microscopic work identified that
filopodial terminals indeed establish asymmetrical synapses (Amaral, 1979;
Claiborne et al., 1986) (Figure 1). In addition, a third terminal type has been
described, small “en passant” boutons which resemble the most the conventional
axonal varicosities of cortical pyramidal cells (Claiborne et al., 1986) (Figure1).
Together the two smaller mossy terminal types far outnumber their larger
counterpart, but still, the total number of terminals is actually very low (Claiborne et
13
al., 1986; Acsady et al., 1998). A single granule cell has no more than ~200
terminals, which is at least two orders of magnitude less than the number of
varicosities along the axonal arbor of a cortical pyramidal cell. How can these few
but variable terminals account for the code conversion and efficient transmission as
outlined above?
Unique features for code conversion
The computational studies summarized above suggested that memory
formation in the hippocampus may be a two-step process: the sparsification of the
entorhinal signal by granule cells, followed by an association of the now sparse code
in the CA3 network. Modeling studies suggest that the “winner-take-all” method of
sparsification requires the recruitment of strong feedback inhibition. According to
this scheme, the output of a small population of granule cells that become active in a
given environmental context (e.g., during a couple of theta cycles as the rat passes
through their place fields) activate GABAergic interneurons which exert fast and
strong feedback inhibition on the somata and dendrites of the non-coding granule
cells, shunting their entorhinal inputs and precluding their firing. As a result, synaptic
plasticity only takes place at the perforant path input of the coding (granule) cells.
However, feedback inhibition is well-known in all cortical regions. Is there any
reason to suppose that feedback inhibition is more powerful in the dentate gyrus than
in other cortical regions, which makes it especially useful for pattern separation?
In cortical regions, interneurons constitute 10-20% of all the neurons.
Cortical pyramidal neurons innervate their postsynaptic principal and interneuron
targets in a quasi-random manner, i.e., the incidence of the targets is determined by
the relative distribution of the neuron types (Gulyas et al., 1993; Sik et al., 1993).
14
Thus, the estimated ratio of interneurons among the postsynaptic targets of cortical
pyramidal cells is around 10%.
In the dentate gyrus granule cells have axon collaterals only in the hilus
below the granule cell layer not in stratum granulosum or str. moleculare
(Claiborne et al., 1986; Acsady et al., 1998). As a consequence only inhibitory
neurons having somata and/or dendrites in the hilus can participate in feedback
inhibition . Close to 50% of the hilar neurons are GABAergic (Houser & Esclapez,
1994) which suggests that inhibitory cells may be abundant among the postsynaptic
targets of granule cells. The 5-8 hilar collaterals of the granule cells possess around
7-12 large mossy boutons and 102-147 small terminals (filopodial and en passant
boutons) (Acsady et al., 1998). The postsynaptic targets of the small terminal types
are almost exclusively interneurons, whereas targets of the large mossy terminals are
mainly excitatory mossy cells (Acsady et al., 1998) (Figure 2). Thus, due to the
surprising target selectivity of granule cell terminal types, interneurons may
constitute up to 90% of the postsynaptic targets of the mossy fibers in the hilus, in
contrast to the 10-20% GABAergic targets in other cortical regions. Many of these
hilar neurons provide feedback inhibitory control of the granule cells,
suggesting that proportionally stronger feedback inhibition is recruited here
than in other cortical regions.
A characteristic cell type of the hilus, the somatostatin-immunoreactive
interneuron, provides a good example of the strong recurrent inhibition operating in
this system. The somatostatin-containing hilar neurons restrict their entire dendritic
arbor to the hilus and innervate the dendritic segment of granule cells in the zone
where entorhinal afferents terminate (hence they are also called HIPP cells, i.e., HIlar
interneurons with Perforant Path associated axon terminal) (Han et al., 1993; Sik et
al., 1997). Unlike many other interneuron types that are characterized by a smooth
15
dendritic surface, the dendrites of HIPP cells are densely covered with thousands of
long thin, elaborated spines (Baude et al., 1993). In contrast to the
compartmentalized spines of the pyramidal cells, which usually receive a single
terminal, these spines are contacted by multiple asymmetrical synapses (up to 8-10)
(Acsady et al., 1998). Thus, these spines increase the total synaptic input of the HIPP
cells enormously. Small terminals of granule cells have been described to contact
these spines (at least 30% of the total synaptic output of granule cells contacts HIPP
cells) (Acsady et al., 1998), whereas the axons of CA3 pyramidal cells that project
back to the hilus selectively avoided HIPP cells (Wittner et al., 2006), suggesting
that the majority of the excitatory input of these cells originates from granule cells.
Since the number of contact between a granule cell and a HIPP cell is only 1 or
2 these morphological data indicate the convergence of several thousand granule
cells on any one of these interneurons. The axons of HIPP cells terminate in the same
layers as the perforant path , and therefore are in a critical position to modulate the
information transfer from the entorhinal cortex to the dentate gyrus (Han et al.,
1993). A single HIPP cell may form an unusually large number of axon terminals in
the molecular layer (up to 80,000 compared to 5000-10000 in the case of a basket
cell), the vast majority of which innervate granule cells (Sik et al., 1997). The little
data available about the activity of HIPP cells suggests that these neurons are not
fast-firing cells; rather, their activity is principally driven by the firing pattern of
granule cells, a key feature for appropriately timed feedback inhibition (Buckmaster
& Schwartzkroin, 1995).
A second unique morphological feature of the connectivity in dentate gyrus is
the lack of interaction among the major inhibitory basket cell classes and among
basket cells and other interneurons (Acsady et al., 2000). In other hippocampal
regions as well as in other cortical regions, basket cells densely innervate other
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basket- and non-basket-type interneurons (Sik et al., 1995; Cobb et al., 1997; Tamas
et al., 1998) forming an interacting local GABAergic network. For example, in the
CA1 region the somatic region of parvalbumin-positive basket cells is contacted by
more GABAergic terminals than the somatic region of pyramidal cells (Gulyas et al.,
1999; Megias et al., 2001). In sharp contrast , GABAergic cells in the hilus of the
dentate gyrus receive, on average, 15-40 times less input from local basket cells than
mossy cells, the principal excitatory cell type of this region (Acsady et al., 2000).
Since GABAergic cells inhibit each other, this connectivity pattern suggests minimal
disinhibitory influence in the hilus and different GABAergic network dynamics.
But what are the physiological properties of the granule cell-interneuron
synapses? Examination of synaptic transmission at the granule cell-basket cell
synapses demonstrated very fast kinetics: the postsynaptic conductance of the unitary
current demonstrated submillisecond rise and decay (Geiger et al., 1997). This effect
was largely attributed to the high synchrony of transmitter release and the rapid time
course of AMPA receptor deactivation. The fast postsynaptic response allows
rapid activation of feedback inhibition, which supports a role in pattern
separation.
These data suggest that the basic principles of connectivity between
excitatory and inhibitory neurons in the dentate gyrus are significantly different from
those in any other cortical region. The peculiar arrangement of excitatory and
inhibitory connections in the dentate gyrus suggests an unusually strong recruitment
of inhibition that can be used to suppress the activity of granule cells in a competitive
manner. As a result, only granule cells with the strongest entorhinal excitatory drive
will participate in the information transfer to CA3. This small population of active
granule cells could effectively prevent large granule cell populations from reaching
firing threshold via the strong feedback inhibitory system outlined above. Only those
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entorhinal inputs will be potentiated which contact active granule cells. This would
further strengthen the competitive process, which could lead to the sparsification of
the entorhinal signal resulting, e.g., in the sharp and focused place fields of the
granule cells which are in sharp contrast to the grid-like entorhinal signal (see
below). The next step is to faithfully transmit this recoded cortical signal to the
associational station, the CA3 region, and to ensure that only the activated subset of
CA3 pyramidal cells would be included in the autoassociation network responsible
for memory storage.
Unique features for efficient and sparse transmission
The giant mossy terminals display all the morphological features of a classic
“detonator” or “driver” type terminal. No other cortical synapse is comparable to
them, but several subcortical structures (e.g., thalamus, cerebellum) utilize similar
synaptic arrangements to secure faithful synaptic transmission (Sherman & Guillery,
1998). A single large mossy terminal may establish up to 30-40 release sites, all
converging on the proximal dendrite of a single postsynaptic pyramidal cell
(Chicurel & Harris, 1992; Acsady et al., 1998). One would predict that the short
electrotonic distance from the soma would maximize the efficacy of the input in
driving the postsynaptic cell to threshold. Recent in vitro and in vivo data confirm
this assumption. Monosynaptic AMPA/kainate receptor-mediated EPSCs from
granule cell-pyramidal cell pairs had a mean peak amplitude of -163.0 ± 23pA at 70mV in organotypic slice cultures which displayed morphological properties similar
to the in vivo condition (Mori et al., 2004). Strong excitatory action has been
described earlier between granule cells and their excitatory targets in the hilus,
the mossy cells (Scharfman et al., 1990). In the in vivo anesthetized preparation,
repetitive firing in a single granule cell reliably induced action potentials in
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monosynaptically connected CA3 pyramidal cells (Henze et al., 2002). It has to be
emphasized that such strong coupling is extremely rare among excitatory cells in
cortical circuits. The general rule is that a large number of excitatory inputs have to
be simultaneously active to reach postsynaptic spike threshold. But how sparse is the
detonator signal in morphological terms?
The number of large mossy terminals along the single unbranching axon of
granule cells within area CA3 is very low (average:12.3; range: 10-18) (Acsady et
al., 1998). If one considers that a single CA3 pyramidal cell may have up to 60 000
terminals, it is straightforward to conclude that granule cells are a specific cortical
cell type designed to transfer the sparse code generated in dentate gyrus very
effectively to only a restricted set of postsynaptic pyramidal cells.
Two recent studies described additional surprising features of the mossy
fibers. These features not only support faithful transmission through this pathway,
but also demonstrate the computational power of the axon terminals in unexpected
ways. The first study (Engel & Jonas, 2005) describes the active properties of the
large mossy terminals, which facilitates reliable transmission of high frequency
trains of action potentials. Apparently, mossy terminals have a very high density of
specialized Na+ channels with faster activation and inactivation kinetics than somatic
Na+ channels. These Na+ channels enable reliable action potential invasion into large
mossy terminals and increase presynaptic Ca2+ influx, resulting in up to 16-fold
increase of transmitter release. In addition, modeling studies suggest that this active
property of the terminals is absolutely necessary to induce Ca2+ influx into the
filopodial extensions to trigger glutamate release at the mossy fiber-interneuron
synapse. This mechanism may underlie the high release probability of the filopodial
connection compared to the mossy fiber- CA3 pyramidal cell synapse (Jonas et al.,
1993; Lawrence et al., 2004).
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The second study (Alle & Geiger, 2006) demonstrates that mossy fibers
transmit not only action potentials, but also postsynaptic potentials originating in the
soma-dendritic compartment. These “excitatory presynaptic potentials” alter the
transmitter release of the subsequent action potential (within a 10-20 ms delay), thus
enabling the axon to integrate subthreshold and suprathreshold signals provided they
are temporally proximal.
In sum, mossy terminals fulfill all criteria for a classical, sparse “detonator”
synapse. The strong granule cell-pyramidal cell connection, however, poses two
major problems for the autoassociative network of the CA3 region.
Problem 1) Spontaneously-active granule cells, which represent only
background noise, may induce irrelevant spiking of CA3 pyramidal cells, resulting in
non-coding CA3 networks which may overlap with the coding networks.
Problem 2) Spontaneously-active CA3 pyramidal cells may generate action
potentials coincident with the CA3 spikes evoked by granule cell firing, which
represent the given environment. These non-coding EPSPs will be also strengthened
in the autoassociative CA3 network, which would ruin the orthogonalized, sparse
code.
Selective activation of the CA3 network participating in coding – solution to
problem 1 and 2.
A first solution to problem 1 is that granule cells have very low
spontaneous firing rates (0.1-0.01 spike/sec) (Jung & McNaughton, 1993),
probably due to their hyperpolarized resting membrane potential (-80 mV in
vivo) (Penttonen et al., 1997) and/or strong inhibitory control. Still, if we
consider that there are one million granule cells per hippocampus in rodents
(Seress, 1988) and we take the low end of their spontaneous discharge frequency
20
(0.01 Hz), we can calculate that at least 10 granule cells will fire in any one
millisecond, activating ~120 CA3 pyramidal cells, each of which has around
30 000 terminals within the CA3 (Li et al., 1994). Thus, other solutions are
needed to solve problem 1.
Similar to the hilus, the number of small mossy fiber terminals in the CA3
region exceeds the number of large mossy fiber terminals by a factor of at least four
(Acsady et al., 1998). As in the hilus, the small terminals selectively innervate
inhibitory cells. All examined inhibitory cell classes (perisomatic, dendritic and
interneuron-selective) were among the postsynaptic elements of granule cells. Since
a single mossy fiber rarely innervates a postsynaptic interneuron via multiple
contacts (Acsady et al., 1998), granule cell firing activates at least four times as
many inhibitory as excitatory cells. Physiological data confirm strong activation of
this feedforward inhibitory circuit. In paired recordings, a single action potential in
the granule cell induced a biphasic response in the postsynaptic CA3 pyramidal cell:
a brief EPSC followed by a pronounced IPSC (Mori et al., 2004). The direction of
the summated charge transfer was outward, indicating a net inhibitory synaptic
response. The authors calculated that approximately four interneurons fired together
to evoke the measured inhibitory responses, which corresponds well to the
anatomical data. Reliable activation of interneurons was also observed in vivo.
Multiple granule cell spikes induced firing in monosynaptically-coupled interneurons
with quite high probability (Henze et al., 2002)(Figure 3D), suggesting that although
the granule cell-interneuron contact rarely contains multiple release sites, the single
active zone of the small terminals is still efficient enough to induce postsynaptic
firing of the GABAergic cell. Recruitment of more inhibitory than excitatory cells
suggests that the net effect of the excitatory mossy fiber system on the CA3
pyramidal cell population is, counter-intuitively, inhibitory.
21
A peculiar EEG transient, the dentate spike, can be utilized to demonstrate
the impact of granule cell activation on the postsynaptic cell populations along the
dentate-CA3 axis during a “natural” stimulus. Dentate spikes are short-duration,
large-amplitude field potentials caused by synchronous activation of the entorhinal
input, which occur during behavioral immobility and slow wave sleep (Bragin et al.,
1995). Extracellular recordings during dentate spikes demonstrated increased unit
activity in hilar neurons (many of which are GABAergic), but suppressed multiunit
activity in the CA3 region (Bragin et al., 1995). Intracellular studies confirmed
depolarization of granule cells and hilar interneurons but hyperpolarization in CA3
and CA1 pyramidal cells (Penttonen et al., 1997). It is worth mentioning that this
pattern of activity is in sharp contrast to the neuronal behavior that can be observed
during the other major excitatory field transient in the hippocampus, the sharp wave,
which originates in CA3. Since in this case there is no inhibitory “barrier”
comparable to the dentate GABAergic network which could block the spread of
excitation, sharp waves propagate not only to the CA1 region but also to
parahippocampal cortical regions via polysynaptic activation (Chrobak & Buzsaki,
1994; Chrobak & Buzsaki, 1996).
Most recently the efficacy of the dentate inhibitory “barrier” was
demonstrated by comparing the correlation of intracellular activity of neurons
in various cortical fields with the UP and DOWN states in the EEG during slow
cortical oscillation (Isomura et al., 2006). Surprisingly, all cortical regions
(including entorhinal cortex and dentate gyrus) changed intracellular activity
coherently with the EEG states with the exception of CA3 region. In contrast to
basically all cortical cells CA3 pyramidal cells were not active during the UP
states. Apparently the inhibitory network launched by the dentate during the
22
UP states is able to shield CA3 even from the most synchronous excitatory
events of the cortical mantle.
In summary, anatomical and physiological data provide convergent evidence
for the conclusion that the output of the granule cells activates an unusually strong
feedforward inhibition to area CA3. Since even a single basket cell is able to block
action potential generation in a large number of pyramidal cells (Miles et al., 1996),
feedforward inhibition will effectively reduce the number of active CA3 pyramidal
cells during dentate activation. In addition, in the CA3 region, specialized
interneuron types exist which restrict their dendritic (Gulyas et al., 1991) or axonal
(Vida & Frotscher, 2000) arbor to stratum lucidum of CA3, suggesting selective
control of this pathway. Thus, the likely solution of Problems 1 and 2 is that
“background” or “non-coding” EPSPs and action potentials in CA3 are actively
inhibited during dentate-CA3 information transfer by the strong feedforward
inhibition. In this way only the small population of CA3 cells receiving the
decorrelated sparse dentate signal will be active, and, following the Hebbian rule,
only the recurrent synapses between the activated CA3 cells will be potentiated.
According to the modeling studies (see above) the matrix of potentiated synapses
will represent the memory trace.
However, what happens to the EPSPs generated by the spontaneous activity
of CA3 pyramidal cells immediately before the dentate input arrives? The EPSPs
among pyramidal cells are quite slow (half duration, 27 ms) (Miles & Wong, 1986)
and accidentally the mossy fiber input can arrive together with their peak, thus
these early background (“unwanted”) EPSPs can be potentiated before the
disynaptic feed forward inhibition arrives. A recent report provides a possible
solution for this problem (Kobayashi & Poo, 2004). This study describes LTP at the
recurrent synapses of the CA3 network induced by paired stimulation of mossy fiber
23
input and the associational/commissural input. First, this study provides direct and
elegant evidence for the role of mossy fibers in changing the synaptic strength of the
autoassociative CA3 matrix. Second, an interesting observation from this study
helps resolve the problem of early EPSPs. The results demonstrate that the
potentiation of associational input depends on the relative timing of mossy fiber and
associational spike trains. The potentiation was smaller if additional associational
spikes were added before the paired stimulation, compared to the protocol that added
spikes after the paired (associational/mossy fiber) pulses. The effect depended on
mGluR1 activation. Thus, apparently the system favors the potentiation of CA3
EPSPs arriving coincidentally or after the dentate signal. Since the mossy fiber input
is able to induce CA3 spiking in vivo, these EPSPs will mostly represent the spiking
of CA3 pyramidal cells evoked by the sparse, orthogonalized dentate input. In this
way only the associational EPSPs representing dentate activity will be potentiated,
but EPSPs arriving earlier, representing spontaneous CA3 activity, will not.
Target-dependent plasticity - the meaning of various terminals types
If we consider the strong feedforward inhibition operating along the
dentate-CA3 axis the following, third problem arises:
Problem 3: How to overcome the strong feedforward inhibition when the
specific dentate pattern has to activate the pyramidal cell?
Apparently the solution to Problem 3 is that short- and long-term plasticity
at large and small types of mossy fiber ending are different. The physiological data
clearly demonstrate that the small terminals are not only distinct morphological
units, evolved to contact a large number of interneurons, but discrete compartments
which harbor distinct molecular machinery for plasticity different from their giant
cousins.
24
Small and large mossy terminals display different types of short-term
plasticity. The giant mossy terminal-pyramidal cell connection express very strong
short-term facilitation (Salin et al., 1996; Toth et al., 2000), reaching up to threefold
amplitude increase on average at the fifth EPSC in case of 20 Hz stimulation. (This is
highly unusual in the case of giant “driver”-like terminals; e.g., in the thalamus
excitatory terminals with a similar synaptic arrangement show strong depression in
relay cells (Reichova & Sherman, 2004)). In contrast the small mossy terminalinterneuron connection hows short-term depression after repetitive mossy fiber
stimulation in about 50% of the interneurons. Others show modest facilitation at 20
Hz (Toth et al., 2000), but at 40 Hz, all responses are depressed after the 8th action
potential (Mori et al., 2004). The third synapse in the feedforward inhibitory circuit,
the interneuron-CA3 pyramidal cell contact, expresses pronounced short-term
depression (Mori et al., 2004) at all frequencies tested.
This variability in short-term plasticity at different mossy fiber synapses
favors conditions for a single granule cell action potential to induce weaker
excitation, but relatively strong feedforward inhibition. Repetitive firing, however,
rapidly increases the effect of excitation and at the same time decreases the efficacy
of inhibition. Thus, the net effect of mossy fiber activation on CA3 pyramidal cells
depends heavily on the frequency of granule cell firing. The system appears to act as
a high-pass filter, where spike transmission from granule cell to pyramidal cell is
blocked at low frequencies but favored when the frequency of granule cell discharge
increases. Recently this assumption was tested directly in in vitro paired recordings
of granule cells and pyramidal cells (Mori et al., 2004). The postsynaptic potentials
evoked in pyramidal cells by granule cell stimulation were measured at increasing
frequencies (10-40 Hz). The inhibitory dominant PSPs observed during low
frequency trains switched to excitatory dominant PSPs at high frequencies. At 40 Hz
25
EPSPs dominated the response already after the third granule cell action potentials,
whereas at 10 Hz the response remained inhibitory even at the 15th action potential.
Frequency-dependent facilitation of mossy fiber transmission was
demonstrated in monosynaptically-coupled granule cell - pyramidal cell pairs in vivo
as well (Henze et al., 2002) (Figure 3). The probability of postsynaptic pyramidal
cell spikes rapidly increased with increasing granule cell firing and reached 0.8 at
100 Hz (Figure 3E), which is an extremely high value in cortical circuits and results
in an almost one-to-one relay of the granule cell activity. Within the spike train the
probability of CA3 pyramidal spikes increased with the number of presynaptic
granule cell spikes. The maximum spike transmission probability was reached after
the 4-5th spike (Figure 3F). In contrast, in the case of interneurons, the probability of
transmission did not increase with an increasing number of presynaptic spikes at 100
Hz and remained lower than that of the pyramidal cell (Figure 3F). These in vitro and
in vivo data indicate that differential short-term plasticity in this feed-forward circuit
result in a frequency-dependent shift of the polarity of postsynaptic response.
In the freely-moving condition, granule cells have very low spontaneous
firing rates, which can rapidly increase to 40 Hz (Jung & McNaughton, 1993) as the
animal enters the place field of the neuron. As a consequence of the frequencydependent switch from excitation to inhibition described above, the probability of
spike transmission between the DG and CA3 is very low at low firing rates, despite
the “detonator” nature of the synapse. When granule cells code the specific
information of the environment, the probability of spike transmission to CA3
becomes very high. Thus, granule cells can act as a “conditional detonator” as
suggested by Henze et al. (2004). This mechanism solves both the problem of
filtering out non-coding spontaneous activity (Problem 1), and resolves the issue that
26
strong feedforward inhibition must be overcome when specific information must be
transmitted from dentate to CA3 (Problem 3).
The delicate frequency-dependent balance between excitation and inhibition
substantially increases the computational power of the mossy fibers. It raises the
possibility that they not only participate in the faithful transmission of an
orthogonalized dentate signal but they themselves participate in creating the nonoverlapping representations in the CA3 region. Recent data suggest (Leutgeb et al.,
2006, see below) that the representation of the environment is more orthogonalized
in CA3 than in the dentate gyrus, which questions the role of dentate as the sole
contributor to this process. Due to its high-pass filter nature, the dentate-CA3 circuit
may refine the dentate code, and, e.g., participate in creating the single receptive
field observed in CA3 place cells as opposed to the multiple place fields of dentate
place cells. The critical variable in this process will be the exact firing pattern of the
dentate granule cell. For instance, assume that a granule cell fires at 40 Hz in one of
its receptive fields but only 10 Hz in the other. Based on the data discussed above,
only the higher firing rate will induce firing in the CA3 pyramidal cell, the lower
activity will be filtered out, and the CA3 pyramidal cell will display a single
receptive field as a result.
What about long term changes? One presentation of the dentate code may
not be sufficient to induce long-term changes in the CA3 recurrent network. Multiple
presentations (e.g., crossing the place field several times) or autonomous replay of
the memory trace in absence of the original condition, may be necessary to induce
long-term plasticity. Replay of a given firing pattern may occur during different EEG
states, as has been shown for theta and sharp wave activity (Nadasdy et al., 1999), or
during the subsequent sleep episodes (Wilson & Mcnaughton, 1993).
27
Tetanic stimulation of mossy fibers induces long-term potentiation in
pyramidal neurons, but is either without effect, or it induces depression at synapses
onto interneurons (Maccaferri et al., 1998). Since the mossy fiber-LTP onto
pyramidal cells critically depends on cAMP, the effect can be explained by the
absence of the adenylyl cyclase-cAMP cascade from the filopodial and “en passant”
small terminals. As a result of this differential LTP, the critical frequency at which
inhibition switches to excitation may change and/or the steepness of the high-pass
filter cut-off may increase. In this way, presynaptic mossy fiber -LTP may help the
orthogonalization process performed by the dentate-CA3 circuit and creates a good
opportunity for the faithful activation of the same CA3 circuit in case of repetitive
presentation.
Finally, morphological plasticity of the small terminals provides yet another
way to fine-tune the balance of excitation and inhibition in the mossy fiber pathway.
The structure of the filopodial terminals strongly resembles rapidly advancing and
retracting axonal filopodia observed during axonal development and synapse
formation. Recent studies of slice cultures indeed demonstrated that over one-third of
the filopodia are highly active (De Paola et al., 2003; Tashiro et al., 2003). In
addition, in mature slices, approximately 9% of the small en passant boutons were
also labile (half-life, approximately 1 day), in contrast to the stability of large
terminals . Interestingly, in mature cultures, the total number of synapses remained
stable in the presence of substantial turnover of individual terminal structures.
Motility of the filopodial extensions was observed not only in slice cultures but also
in the acute whole-mount hippocampal preparation and acute hippocampal slices.
This actin-based motility can be regulated by brain-derived neurotrophic factor
(BDNF), AMPA and/or kainate receptors, in a cAMP dependent manner (De Paola et
al., 2003; Tashiro et al., 2003). These data strongly suggest that target selectivity of
28
the small terminal types of the mossy fiber is based on the dynamic morphological
properties of these axonal elements. In addition, they suggest that even in mature
animals activity change, traumatic injury or cell loss may induce rapid remodeling of
the mossy fiber-to-interneuron connections from granule cells. Indeed, ischemic
damage, which results in the loss of hilar and stratum lucidum interneurons,
dramatically reduces the number of filopodia (Arabadzisz & Freund, 1999).
In summary, the mossy fiber pathway maximally utilizes the opportunity to
differentially regulate its postsynaptic partners. Selective innervation of pyramidal
cells and interneurons by distinct terminal types creates a favorable condition to
differentially regulate short-term and long-term plasticity and the motility of various
mossy terminal types. The “bottom-line” of this highly dynamic system appears to be
the fine-tuning of the balance between the excitation evoked by the large “detonator”
terminals and the feedforward inhibition activated by the small terminals. This will
determine exactly which dentate firing patterns will induce permanent modification
of the autoassociational network of the CA3 region. The final test for all predictions
regarding dentate function is the examination of firing activity in the freely-moving
animal.
Neuronal activity patterns in vivo and the processing of entorhinal input
by the dentate gyrus
Compared to the vast amount of data available on spatial (and non-spatial)
representations in area CA1 of the hippocampus, relatively little is known about the
in vivo firing properties of neurons in the dentate gyrus under various behavioral
conditions. During exploration, granule cells display spatially-selective activity, and
their firing behavior is, at least under most conditions studied so far, qualitatively
29
quite similar to that of pyramidal cells (“place cells”) in areas CA1 and CA3. In
particular, in the radial arm maze, granule cells have directional place fields,
although the average number of subfields is somewhat higher and the average size of
these subfields is slightly smaller than in CA3 place cells (Jung & McNaughton,
1993). The firing activity of dentate granule cells is modulated by local field
potential oscillations in the theta frequency range, and the timing of individual action
potentials changes during traversals of the place field similar to phase precession
observed in CA1 pyramidal cells (Skaggs et al., 1996). When different spatial cues
were put in conflict by manipulating the environment, sudden coherent transitions
(known as reference frame shifts) could be observed in the activity of the set of
simultaneously-recorded granule cells, also analogous to the behavior of the CA1
cell population under these conditions (Gothard et al., 2001). In an experiment where
an explicit attempt was made to identify non-spatial as well as spatial responses, a
subpopulation of granule cells showed position-selective or position-independent
reward site responses, whereas another population showed pure place responses
(Tabuchi et al., 2003).
When rats are allowed to explore a novel environment for the first time, both
dentate granule cells and CA1 pyramidal cells acquire distinct spatial preferences
within the first few minutes (Nitz & McNaughton, 2004). However, concurrentlyrecorded interneurons showed very different behavior in the two regions: while most
CA1 interneurons transiently decreased their activity while the animal was exploring
a novel environment, the majority of interneurons in the DG significantly increased
their activity during the same epoch. These data are congruent with the role of strong
feedback inhibition in establishing sparse and decorrelated output in the DG.
In order to understand the nature of the computations performed by the DG, it
is essential to have an accurate description of the cortical input it receives. The last
30
few years have witnessed a major revolution in our understanding of spatial
representations in entorhinal cortex. Until a few years ago, existing data on EC
representations indicated that, although neurons in EC were spatially selective, their
place fields were much larger and less clearly defined than those in hippocampal
areas (Quirk et al., 1992; Frank et al., 2000). Indeed, this was perhaps the most direct
experimental evidence for the assumption that sparse, orthogonal, "hippocampaltype" representations are created first in the dentate gyrus. However, when spatial
firing patterns were measured in the part of medial entorhinal cortex (mEC) which
projects to the dorsal HC (where place fields are normally recorded), much smaller
place fields, similar in size to corresponding hippocampal place fields, were found
(Fyhn et al., 2004), while areas of mEC with large place fields projected to more
ventral parts of the HC, which itself was found to have large place fields (Maurer et
al., 2005). From these data, it appeared that there was in fact no major transformation
of spatial representations from EC to the DG (and the rest of the hippocampus),
although subtler differences (especially in response to environmental manipulations)
could not be ruled out. However, it soon emerged that if spatial firing patterns are
recorded over a larger spatial scale, entorhinal and hippocampal representations are
again fundamentally different. In particular, EC place fields were found to repeat
periodically at the vertices of a regular hexagonal lattice, and different EC "grid
cells" differ in the center location ("phase"), orientation, and scale of the grid
(Hafting et al., 2005). In contrast, hippocampal place cells have at most a few
discrete place fields even in these larger environments, and their fields typically do
not have any special geometrical relationship. Thus, it currently appears that
hippocampal spatial representations are in fact different in nature, and, in particular,
much sparser over a large environment than representations in the (medial)
entorhinal cortex, and a transformation (in fact, a pattern separation operation) by the
31
DG is still required. Indeed, if we consider two locations in the environment which
are separated by the grid period (assumed to be relatively invariable) in the area of
entorhinal cortex which projects to a given part of the HC, the activity of the grid cell
population will be quite similar, while the hippocampal activity pattern will be
distinct, reflecting the outcome of some kind of pattern separation process. In fact,
the need for pattern separation becomes even more obvious if we consider two
environments. The root of the problem is the observation that the relative grid
parameters (phase and orientation) of different grid cells appear to be fixed in all
environments, so, in principle, there must be corresponding locations (and
orientations) in two distinct environments where the activity of the entire grid cell
population is identical. This raises an obvious question: how distinct spatial
representations of the two environments could be formed in the HC based on a single
entorhinal grid cell representation. One possible answer to this question is based on
the fact that the entorhinal grids are not perfectly regular; for instance, peaks in the
grid vary in amplitude, so that, in principle, there could be sufficient spatial
information present in the amplitudes to distinguish different environments. Another,
perhaps more plausible explanation is that the hippocampus (and, in particular, the
dentate gyrus) receives, in addition to input from medial EC, where grid cells are
located, input from lateral EC, where neuronal firing patterns have a lower spatial
information content, but, instead, carry more information about relevant objects
(Hargreaves et al., 2005). Such object-based information is probably sufficient to
disambiguate different environments, and create distinct codes in the HC.
Hippocampal pattern separation between environments of varying degrees of
similarity has recently been investigated in a series of experiments (Leutgeb et al.,
2004; Leutgeb et al., 2005c; Leutgeb et al., 2005b), and the initial analysis in areas
CA3 and CA1 has now been partially extended to mEC and the DG (Hafting et al.,
32
2006; Leutgeb et al., 2005a; Moser et al., 2006). By analyzing the spatial firing fields
of neurons within environments of varying degrees of similarity (manipulations
included switching between different recording locations, as well as changes in the
shape and/or the color of the enclosure), Moser and colleagues found that firing
patterns of neurons in all hippocampal areas distinguished between different
environments much better than those of grid cells in mEC. Essentially no pattern
separation was detected in EC, as population firing patterns in enclosures of different
shapes, colors, or even in different rooms were not significantly different (any
observed changes occurred coherently in all recorded neurons; (Hafting et al.,
2006)). However, they also found that the basic properties of pattern separation were
different between different subfields of the hippocampus. In area CA3, the
phenomenon termed "rate remapping" was observed when the shape of the enclosure
was varied continuously: the firing rate pattern of the active cell population changed
continuously as the environment was gradually transformed, while place field
locations remained constant (Leutgeb et al., 2005b). In the end, environments of
clearly distinct shapes (e.g., circle vs. square) activated CA3 pyramidal cell
populations with relatively little overlap - indeed, when recordings were made in two
different rooms, the two populations of active CA3 neurons appeared to be chosen
independently, a phenomenon referred to as "global remapping" (Leutgeb et al.,
2005c).
Interestingly, preliminary data from recent experiments have revealed a
radically different type of pattern separation in the DG. First, unlike in CA3, the
same dentate cells were found to be active in different environments (Moser et al.,
2006). Second, DG neurons typically had multiple place fields even in a single
environment, consistent with earlier data (Jung and McNaughton, 1993). Finally, the
peak firing rate within any one place field of a single cell varied with even small
33
changes in the shape of the enclosure, independently of rate changes in other fields of
the same cell (Leutgeb et al., 2005a). These results show that the DG performs a
pattern separation operation on its entorhinal inputs, but pattern separation appears to
work differently from what was previously assumed. The observation that the
proportion of simultaneously active cells is much lower in the DG than in EC (a fact
that was confirmed by the data described above, and independently by another recent
study, which measured immediate early gene expression in the DG following spatial
experience;(Chawla et al., 2005)) suggested that different environments might
activate different sets of neurons in the DG, thereby implementing a particularly
efficient type of pattern separation. The results of Moser and colleagues now suggest
that this might not be the case, and different environments (as well as different
locations within these environments) might be encoded by different firing rate
patterns in essentially the same population of active DG neurons. This latter scheme
potentially also allows a fine discrimination of different environments (and locations)
based on the DG population activity pattern (especially since the firing rates of DG
neurons appear to be rather sensitive to changes in environmental features).
However, since the CA3 code appears to be more efficiently orthogonalized than the
DG code, an additional processing step may be needed. The physiological data
reviewed above indicate that the mossy fiber projection may be utilized to arrive at
the sparser, more completely pattern-separated representation recorded in area CA3,
but further contributions from other sources (temporo-ammonic pathway and local
network connections) cannot be excluded.
The different behavior of spatial representations at different stages of
hippocampal processing clearly argues against the simple view that all properties of
hippocampal place cells originate in the dentate gyrus, and downstream areas simply
inherit these properties. A more complex view of the formation of hippocampal
34
representations is also indicated by recordings of neuronal activity patterns following
selective lesions. In particular, following colchicine lesions of dentate granule cells
as described above, place cell representations could still be observed in area CA1
(McNaughton et al., 1989). Similarly, the spatial representation in area CA1 was
largely intact after surgical separation from all other hippocampal areas, which left it
with the direct projection from entorhinal cortex as its sole cortical input (Brun et al.,
2002). Therefore, hippocampal areas other than the DG must be capable of creating
sparse, distributed, place-field-like representations on their own under some
circumstances, probably based on their direct entorhinal inputs. However, it has to be
kept in mind that despite the presence of a proper place cell representation in CA1,
navigation memory was compromised following both types of lesion, suggesting that
utilization of the spatial code at the behavioral level requires intact dentate-CA3
interaction.
In summary, these data suggest that the DG does perform a pattern separation
of the entorhinal signal, which, however, needs further processing to achieve the
sparse and decorrelated activity pattern observed in the CA3 region.
The dentate gyrus: an evolutionary-developmental perspective
Apparently the basic organization of the dentate-CA3 network has the
deepest phylogenetic root among cortical regions. Before the divergence of
reptilian-mammalian lineages which apparently preceded the mass extinction of
the Permian period (~250 million years ago) neurons in all cortical areas were
most probably packed in a single cellular layer. During the ontogenesis they likely
followed an outside-in pattern of histogenesis, where newly-generated neurons settle
below the older ones, like in extant reptiles (Goffinet et al., 1986). Their main
excitatory afferents entered and terminated in the embryonic marginal zone, above
35
the cortical plate, i.e., in the same zone where the apical dendrites of their main
targets (excitatory principal cells) were present (Ten Donkelaar, 1998). As suggested
recently, this arrangement does not support the evolution of a cortical structure with
multiple cellular layers and distinct areas (Super & Uylings, 2001).
As the mammalian nervous system evolved, the dorsal and lateral cortex of
the ancient reptilian cortex underwent a significant modification that opened up
tremendous opportunities for areal and cellular diversification (Super & Uylings,
2001). This included changing the pattern of histogenesis to the inside-out pattern,
where newly generated neurons settle above the older ones, and changing the pattern
of axonal ingrowth (for a review see Super and Uylings, 2001). In the mammalian
neocortex, most of the afferents fibers enter not above but below the cortical plate,
via the subplate (Allendoerfer & Shatz, 1994; Molnar, 2000). This new
developmental scheme allowed the development of multilayered cortical
structures with highly variable cell types, rich reciprocal interaction with the
thalamus and the establishment of new cortical regions.
Little developmental change occurred, however, in the medial and
dorsomedial cortex, which became the mammalian allocortex. In the dorsomedial
cortex that is homologous with the Cornu Ammonis, the pattern of histogenesis
changed to the inside-out pattern, but the pattern of axonal ingrowth did not
(Super et al., 1998). However, in the most “stubborn” structure, the dentate gyrus,
the basic reptilian developmental pattern was retained (Bayer, 1980) histogenesis
follows the outside-in pattern and the main excitatory afferents enter above the
cortical plate.
Neocortex has evolved rapidly to become a highly complex multilayered
structure, with extensive intracortical and thalamo-cortical reciprocal connections
(Butler, 1994; Nieuwenhuys, 1994; Rakic, 1995; Northcutt & Kaas, 1995).
36
Allocortex remained a unilayered structure with a basically unidirectional
information flow, dentate gyrus being the only cortical structure without thalamic
input (Amaral & Witter, 1989). Neocortex has been significantly expanded laterally
and was parceled into numerous functionally segregated areas, but the hippocampus
retained the original two major subfields, the dentate gyrus and Cornu Ammonis. For
these two regions, only the Cornu Ammonis showed some areal segregation, and the
original reptilian dorso-medial cortex became CA3, CA2, CA1 and subiculum
(Amaral et al., 1990). Again, the dentate gyrus showed no areal segregation.
But why did the DG-CA3 connection remain essentially unchanged during
the course of evolution, when the rest of the cortical mantle underwent a dramatic
reorganization? Here we would like to propose that the reason behind the protracted
evolutionary pattern of the hippocampus, and especially the dentate gyrus, is the
structural constraints of hippocampal function. Apparently, the formation of freely
accessible multidimensional memory traces can only be performed in a two-step
process that includes a segregation of input followed by an associative step. The
reciprocally-coupled, multilayered cortical structures evolved for a different role (for
a more complex interaction with the environment). Apparently, the basic plan of the
two-step information processing through the hippocampal formation remained
essentially the same from lizard to human. Similarly to mammals, in reptiles, the
dentate-equivalent medial cortex receives the cortical input. This cortical region
lacks an autoassociative network and projects the recoded information
unidirectionally, to the reptilian analogue of the Cornu Ammonis, the dorsomedial cortex (Lopez-Garcia & Martinez-Guijarro, 1988; Martinez-Guijarro et al.,
1991; de la Iglesia et al., 1994). Association functions in the reptile may take place in
the next step here in the dorso-medial cortex, where an extensive recurrent collateral
system exists, similar to the CA3 region in mammals (Martinez Guijarro et al.,
37
1984). Interestingly, damage to the reptilian homologue of hippocampus causes
similar learning problems as hippocampal lesions do in mammals (Rodriguez et al.,
2002), suggesting an analogous structural-functional relationship.
Has anything changed in dentate gyrus during the mammalian evolution? In
primates, the volumetric ratio of the dentate gyrus and CA3 has changed in favor of
the first. Indeed, dentate gyrus is “dentate” sensu stricto only in primates, where it
includes numerous infoldings. Among these areas the hilus showed the largest
relative increase in volume and cell number (Seress, 1988) underlying the
importance of the region, where most of the peculiarities in microcircuits have been
noticed. Apparently, the feed back regulation of granule cells became more
elaborated with the increasing complexity of information to be categorized by the
system.
Conclusions, unresolved questions
The dentate gyrus appears to be an ancient cortical structure from the
phylogenetic perspective, yet it displays a number of unique features not found in
other cortical regions. Its morphological and physiological properties are highlyspecialized, and these can explain, at least in part, the role assigned to the DG and
CA3 by computational theories. In particular, mossy fibers are characterized by
distinct mechanisms of signal transfer at excitatory vs. inhibitory targets, and
unusually strong activation of GABAergic circuits. This arrangement allows a
delicate balance of excitation and inhibition, which is utilized for code conversion
and sparsification at the entorhinal-dentate connection and for frequency-dependent
spike transfer at the dentate-CA3 connection.
Several issues, however, remain unresolved. More comparable data are
needed from freely-moving animals to understand the precise computation that takes
38
place in the DG and in CA3. Since DG-CA3 transmission appears to be dependent on
the frequency of granule cell discharge, DG firing patterns should be carefully
analyzed, and particularly with respect to multiple receptive fields. The role of two
major excitatory inputs of the granule cells, not discussed here, the mossy cell input
and the supramammillary afferents, is necessary to clarify DG information
processing comprehensively. Both mossy cells and supramammillary afferents
contact the proximal dendrites of granule cells, and therefore are likely to exert a
powerful influence. Mossy cells of the hilus have highly divergent axons, and thus
may link distant DG populations involved in coding similar environmental events,
whereas the supramammillary input may mediate the modulation of granule cells by
the theta rhythm. The role of rhythmic EEG activities (theta, gamma) in mediating
signal transfer, code conversion, and the short- and long-term plasticity is unclear at
the present time. Similarly, the role of dentate spikes is not explored. It is tempting to
speculate that they may participate in memory replay, like the sharp wave in the
CA3-CA1 network (Buzsaki, 1989), but definitive proof is currently unavailable.
From the computational perspective, it is not clear how the lack of connectivity
among granule cells and the relative paucity of the direct backprojection from
the CA3 to the DG (Li et al., 1994) helps the categorization function in DG. In
conclusion, this peculiar neocortex-archicortex interface will likely keep us busy for
a long time.
Acknowledgement:
This work was supported by the Wellcome Trust (A.L. is the recipient of a Wellcome
Trust International Senior Fellowship), the Institut de Cerveau et de la Moelle
épiniere, the Hungarian Scientific Research Fund (OTKA T 049100) and the EU
Framework 6.
39
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46
Figure Legends
Figure 1. Electron micrographs of different terminal types along the mossy fibers in
the CA3 region (A-C, E) and of a CA3 pyramidal cell terminal (D) for comparison.
All electron micrographs have the same magnification. A, B, A small en passant
terminal establishes a single asymmetrical synapse on a dendritic shaft, showing the
characteristic long perforated postsynaptic density of small terminal types on
interneurons (arrows). C, A filopodial extension of a mossy fiber terminal forms a
synapse (arrow) with an Substance P receptor-immunoreactive interneuron. D, A
CA3 pyramidal cell establishes asymmetrical synapse on a simple spine of a CA1
pyramidal neuron. E, A large, double-headed mossy fiber terminal forms multiple
contacts (arrows) with thorny excrescences of a CA3 pyramidal cell. All active zones
converged on the same pyramidal cell. The individual release sites are short. Scale
bars: A-D, 0.5 µm; E, 1 µm. Reprinted with permission from Acsády et al., 1998,
Society for Neuroscience.
Figure 2. Filopodial extensions of mossy fiber terminals are specialized to innervate
GABAergic cells. Artistic rendition of two large mossy terminals, each equipped
with four filopodial extensions (large arrowheads). The mossy fibers were labeled
by intracellular injection of biocytin into two neighboring granule cells. All
filopodial terminals were examined in the electron microscope (not shown) and all
contacted the dendrites or spines of altogether six GABAergic neurons. Four of the
GABAergic neurons were identified by their Substance P receptor-content and two
of them by ultrastructural characteristics. Five of the six postsynaptic interneurons
were spiny cells. Arrows point to the main axons. Reprinted with permission from
Acsády et al., 1998, Society for Neuroscience.
47
Figure 3. Spike transmission dynamics between a granule cell and its interneuron
and pyramidal cell targets in CA3c in vivo. A) Camera lucida reconstruction of the
extracellular electrode track and biocytin-labeled granule cell. Inset, a higher-power
view of the mossy fiber axon near the probe track. Arrowheads, mossy fiber boutons.
B) Superimposed (n = 60) intracellularly evoked action potentials in a granule cell
(bottom traces) and simultaneously recorded extracellular units (filtered 0.8−8 kHz).
Note the time-locked response of a putative pyramidal cell to the granule cell action
potentials. C-D) Cross-correlograms between the evoked granule cell action
potentials and the activity of a putative CA3c pyramidal cell (C) or interneuron (D).
The values are shuffle corrected and expressed as probability (number of unit spikes
per bin/total number of granule cell spikes). Arrowhead, peak time of the granule
cell action potential. E) Representative results of the effect of intratrain frequency on
spike transmission probability for a putative pyramidal cell. F) Spike transmission
probability (shuffle-corrected probability of spike in 6 ms following granule cell
spike) as a function of spike number in evoked 100 Hz train (
s.e.m). Solid line,
putative interneurons (n = 24); dotted line, putative pyramidal cells (n = 21). Scale
bars, A) 50 m; inset 20 m; B) 1 ms, 25 mV, 75 V. m, molecular layer; g, granule
cell layer; h, hilus; IC, intracellular electrode track; EC, extracellular electrode track.