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Chapter 1
General Introduction
Partly adapted from: Johan Winnubst, Christian Lohmann
Frontiers in Molecular Neuroscience, 2012, 5:70
11
Chapter 1. General Introduction
Preface
Parents often mention how observant or ‘curious’ new born babies seem to be about
their surroundings. Indeed, even though a newborns eyesight is still limited, they are
already able to track moving objects, perceive depth, imitate finger movement and
even recognize faces (Campos et al., 1970; Field et al., 1984; Nagy et al., 2005). While
these abilities are usually sufficient to impress proud parents, they pale in comparison
to the capabilities of other mammalian offspring. For instance, calves of the blue
wildebeest, who have similar gestation periods as humans, are able to stand steady
on their feet within mere minutes after birth (Estes, 1991). Calves learn to walk and
sprint on the very same day, allowing them to avoid predators come nightfall. How
is it possible that these animals are able to perform such complex sensorimotor
tasks without any prior experience? To answer these questions we must look at the
processes that shape neuronal connectivity in our brains during early development.
These processes do not only involve properly connecting the right anatomical regions
with each other (for instance connecting the eyes with the visual cortex), but can
even involve fine-scale subcellular connectivity (connecting one cell onto a specific
part of another cell). Interestingly, this connection specificity arises not only through
molecular mechanisms but also through internally generated brain activity. Studying
these developmental mechanisms will not only aid our efforts to combat disorders
in which neurodevelopment goes awry (autism, schizophrenia etc.), but can also
increase our understanding of the functioning of the healthy adult brain.
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Network connectivity and function
Network connectivity and function
In order for our brain to integrate and parse the information from our sensory
organs, neurons must form precise synaptic connections with each other. I will first
discuss this link between connectivity and function in the context of the two brain
areas that I will focus on in this thesis: the hippocampus and visual cortex.
Hippocampus
The hippocampus is part of the limbic system and it is involved in long-term memory
formation and spatial navigation (Cohen and Eichenbaum, 1995; Moser et al., 2008).
All mammals have two hippocampi located on both sides of the brain underneath
the cortex surface of the medial temporal lobe (2006). Each hippocampus consists
of two parts: the entorhinal cortex (EC) and the Ammonis horn, so called because it
resembles a rams horn (Cornu Ammonis, CA), which is subdivided into 4 regions
(CA1-4). The entorhinal cortex is the main connectivity hub of the hippocampus and
is heavily interconnected with the cortex, thalamus, hypothalamus and several nuclei
in the brainstem. Connectivity inside the hippocampus is remarkably unidirectional
and is usually considered to propagate along a ‘trisynaptic’ pathway (Figure 1). The
main output path of the entorhinal cortex, the perforant path, heavily innervates the
Ammonis horn and acts as its main input source. Perforant path connections from
the EC mainly innervate the granule cell layer in the dentate gyrus whose axons,
called mossy fibbers, connect to the dendrites of CA3 pyramidal neurons. Axons
from the CA3 in turn send connections to the CA1 (Schaffer collaterals) and also
make reciprocal connections with other CA3 neurons. CA1 neurons then close the
loop by innervating the EC through the subiculum.
Due to these well-defined connections, the hippocampus is a favoured model
system for studying activity-dependent changes in synaptic strength (Malenka
and Bear, 2004). Since the discovery of the synapse it has been postulated that the
strengthening of synaptic connections between simultaneously firing cells could
act as physiological correlate of memory formation. This type of ‘fire together, wire
together’ synaptic strengthening is often referred to as Hebbian plasticity after
Donald Hebb who formalized the possible role of such a plasticity rule in learning
and behavior (Hebb, 1949). The hippocampus was the first system in which such
long-term changes in synaptic strength were observed in response to brief strong
stimulation, a phenomenon known as long-term potentiation (LTP; Bliss and Lomo,
1973). Since the hippocampus receives information from many sensory modalities
it is thought to facilitate memory formation by creating associative links between
13
Chapter 1. General Introduction
A
B
CA1
Perforant Path
CA3
CA1
DG
SC
CA3
MF
EC
DG
Perforant Path
Figure 1. Anatomical structure of the hippocampus.
A, Basic anatomy of Ammon’s Horn in the hippocampus showing the major pathways and
principal neurons. Original by Ramon y Cajal. B, Schematic structure of the hippocampus and
the ‘trisynaptic’ pathway. EC = entorhinal cortex, DG = Dentate gyrus, MF = Mossy fibers, SC =
Schaffer collaterals.
different patterns of inputs. Understanding the modification of synaptic inputs by
cellular activity is therefore crucial in elucidating the physiological correlates of
memory.
Another important function of the hippocampal formation is related to its role in
spatial navigation. Electrical recordings made in freely moving rodents have shown
that a large number of neurons in the hippocampus and entorhinal cortex fire action
potentials on specific spatial locations (Moser et al., 2008). Broadly speaking these
cells can be divided into four main categories: 1) place cells, which encode for a
specific location in space; 2) grid cells, which are active on multiple locations in a
grid-like pattern; 3) border cells, that fire on close proximity to a physical border
and 4) head-direction cells that encode for head orientation in space. Together these
cells can create a representation of the physical space of the animals surroundings
in a ‘cognitive map’ (O’Keefe and Nadel, 1978). Interestingly, recent micro-electrode
recordings in rat pups that explore their surroundings for the first time have found
near adult-like grid and place cells firing in the hippocampus (Wills et al., 2010).
Since these animals had no prior experience with spatial navigation the connection
specificity required for this sensory processing must have already been established
during early neuronal development.
Visual Cortex
In mammals visual information from the eyes travels through the lateral geniculate
nucleus (LGN) in the thalamus to the most caudal part of the cortex (Maunsell
14
Network connectivity and function
and Newsome, 1987). Here the
A
B
visual information first arrives in
L1
L1
the primary visual cortex (V1)
L2/3
where primary features of the L2/3
visual scene are processed (lines,
L4
orientation etc.; Hübener, 2003).
As the information is sent along L4/5
L5
to more anterior parts of the
L6
cortex (V2-V6) the features that
are processed become increasingly
L6
complex and abstract (e.g. shapes,
Thalamus
faces, motion). Anatomically
V1 consists of 6 distinct layers,
which are numbered from top to Figure 2. Organization of the striate visual cortex.
bottom (L1-6), and differ in their A, Layered anatomical organization of the visual
cell type content and input/output cortex. Left) groups of cells are shown in their respective
sources (Métin et al., 1988). The layers. Right) axonal fiber pathways. Original by
organization between these layers Ramon y Cajal. B, Canonical circuit diagram showing
is best described in what is known the information flow in the neocortex. Adapted with
permission from (Constantinople and Bruno, 2013).
as the ‘canonical circuit’ (Figure
2). First, retinal inputs from the
LGN arrive in layer 4, where the information is passed along to excitatory neurons
in layer 2 and 3 (L2/3). These L2/3 neurons further receive modulatory inputs from
other cortical areas on their dendritic branches that extend into layer 1. Axons from
L2/3 heavily innervate other cortical regions and neurons in layer 5 and 6 which have
outputs back to the thalamus and other subcortical regions.
Historically V1 has been one of the most studied and best understood areas of the
brain (Alonso, 2009; Hubel, 1959). This is in large part due to the clear functional
organization in V1, as many cells only respond to visual stimuli within a specific
region of the visual scene (Hubel, 1959). These ‘receptive fields’ are caused by selective
innervation from connections arising from specific parts of the retina. Interestingly,
spatially adjacent cells in V1 often share comparatively similar receptive fields that
overlap in space (Hübener, 2003). Together these smaller regions of V1 cooperate
to form a representation of the visual field in a so called retinotopic map. Other
non-cortical structures, such as the LGN and brainstem show a similar retinotopic
organization, indicating that outputs from the retina are highly specific and preserve
their spatial organization in higher brain areas.
15
Chapter 1. General Introduction
Next to being retinotopically selective, many neurons preferentially fire in response
to stimuli with specific orientations and spatial frequency. These neurons are called
‘simple cells’ and are mainly found in layer 4 of the visual cortex (Hubel and Wiesel,
1962). Cells in L2/3 often respond to a wider variety of orientations and phases
(‘complex cells’) and emerge through selective innervation by simple cells in layer
4. Recently, recordings made in V1 of neonatal mice pups showed that even on the
first day that the animals open their eyes (postnatal day (P) 14), L2/3 pyramidal
neurons already possess receptive fields and orientation selectivity (Ko et al., 2013).
The emergence of this functional specificity was found to be independent of sensory
activity as it could also be observed in dark-reared animals (Ko et al., 2014). These
findings suggest that highly specific synaptic connectivity can arise during early
development in a way that prepares the network for future sensory processing.
Spontaneous activity during development
The previous descriptions of the hippocampus and visual cortex show that
neurons need to form highly specific connections with each other in order to form
a functional network. This connection specificity arises during brain development
through successive organizational processes. First, the axon from a presynaptic cell
is guided to its target location through the presence of both attracting and repellent
molecular cues in its surrounding (Lowery and Van, 2009; Maness and Schachner,
2007). Once the axon has reached its destination it must then make connections with
the right cells from a larger pool of possible synaptic partners. In part, this specificity
is achieved using different variations of the so-called lock-and-key principle, whereby
matching membrane-bound recognition proteins on both the pre- and postsynaptic
side allows for the restricted formation of connections between appropriate cell types.
Indeed, such ‘chemoaffinity’ mechanisms have been found in various developing
systems (Shishido et al., 1998; Yamagata and Sanes, 2008). However, the fine-tuning
of synaptic connectivity has been found not to be solely dependent on molecular
factors but is later also shaped by synaptic activity itself (Cline, 2003; Goodman and
Shatz, 1993; Yamagata and Sanes, 2008).
Interestingly, many developing systems, before they receive sensory information,
internally generate spontaneous network activity (Ben Ari, 2001; Hua and Smith,
2004; Huberman et al., 2008; Katz and Shatz, 1996). Spontaneous activity can usually
be observed as repetitive network events where large proportions of the network
become activated at the same time (Ben-Ari et al., 1989; Galli and Maffei, 1988;
Garaschuk et al., 1998; O’Donovan et al., 1994; Yuste et al., 1992). The initial burst
of activity quickly travels through the network in a wave-like fashion as neighboring
16
Spontaneous activity during development
A
B
Visual cortex
4
1
2
5
6
3
Thalamus
Retina
50 μm
C
1
2
3
4
5
6
50 sec
D
3
2
1
5
50 μm
Hippocampus
4
1
2
3
CA3
DG
4
5
10 sec
Figure 3. Spontaneous activity during brain development.
A, Example of calcium imaging recording in the visual cortex in vivo. B, Example traces of
changes in calcium fluorescence over time from cells labelled in A. Coloured markers show
clear distinction between low and high participation events. Adapted from (Siegel et al.,
2012). C, Example of spontaneous activity recorded in vitro from hippocampal slice cultures.
D, Activity traces of neurons labelled in C.
cells become activated. The resulting synchronicity of neuronal firing was recognized
early on as an important feature for activity-dependent refinement of synaptic
connectivity.
The best studied form of activity-dependent refinement by spontaneous activity
takes place in the visual system. Even before animals open their eyes, waves of
spontaneous activity travel across the vertebrate retina and spread from cell to cell
(Galli and Maffei, 1988). Spatial relationships are hereby maintained in the axonal
firing patterns of neighboring retinal cells, which project to higher order brain
structures and thereby aid retinotopic map formation (Stellwagen and Shatz, 2002;
Triplett et al., 2009). The information that is present in spontaneous activity is
therefore thought to have an instructive role in shaping synaptic connectivity in the
visual system through Hebbian plasticity mechanisms. Indeed studies that affected
not the presence of spontaneous activity but the patterns within the activity have
shown such instructive influences (Stellwagen and Shatz, 2002; Weliky and Katz,
1997; Xu et al., 2011).
In the hippocampus the earliest forms of synaptic activity are also spontaneously
17
Chapter 1. General Introduction
generated and are known as giant depolarizing potentials (GDPs, P0-10, Figure 3CD). These events are mainly generated in CA3 and are heavily dependent on the
influence of GABAergic signaling in the network. Due to the higher intercellular
concentration of chloride in developing neurons GABA signaling actually leads to
excitation of the cell instead of the inhibitory action seen in mature neurons (BenAri, 2014). Interestingly, the functional significance of GDPs remain largely unclear
especially since sensory information is not yet present in the network at the time they
predominate.
While the tuning of neuronal connectivity by spontaneous activity is thought to
be facilitated through Hebbian plasticity (Butts et al., 2007; Kasyanov et al., 2004;
Kirkby et al., 2013), the precise plasticity mechanisms remain largely unknown.
Furthermore, most studies have investigated the effects of spontaneous activity on
the network level and rarely examined plasticity at the level of individual synapses.
Synaptic clustering
The connection specificity considered so far has dealt with the formation of
connections between specific brain structures or between different cell types.
However, previous studies have predicted that synaptic organization could extend
far beyond simply connecting the right axon with the correct cell, but might entail
a more precise subcellular organization. The prediction stems from both modeling
and experimental studies in which individual segments of dendrite are thought to
function as independent computational subunits (Branco and Hausser, 2010; Larkum
and Nevian, 2008; Losonczy and Magee, 2006; Polsky et al., 2004). Interestingly, local
plasticity mechanisms have recently been described that could lead to the activitydependent establishment of such a clustered organization. The following section will
give an overview of the research behind these new insights.
The dendritic compartmentalization model
The dendritic tree receives the bulk of synaptic inputs and plays an important role
in the integration of incoming signals. It is therefore not surprising that the way the
dendrite processes synaptic activity to influence somatic firing has been the topic of
much research and debate. In early views the dendrite was seen as a linear integrator,
summing the received inputs independently of their position on the dendritic tree as
they are transmitted towards the soma (Cash and Yuste, 1999; Urban and Barrionuevo,
1998). When the linear sum of these inputs reaches a certain threshold value at the
soma a non-linear processing step takes place as an action potential is generated
18
Synaptic clustering
in an all-or-none fashion (Figure 4). In this model, the impact of a single synapse
on somatic firing is low since it is merely one input amongst many available ones.
Consequently, the information and memory of a single-cell is stored in the changing
patterns of synaptic weights spanning the entire cell. It is clear that for this ‘synaptic
democracy’ to work the dendrite must propagate and integrate the synaptic signals
in a linear and neutral way (Yuste, 2011). Interestingly, while the described model
is the standard model by which many neuroscientists consider synaptic integration,
several properties of the dendritic tree have been discovered that seem to question its
role as a linear integrator. Specifically, it has been known for some time that dendrites
contain ionic conductances capable of generating active dendritic events in response
to local synaptic activity (Hausser et al., 2000; Nevian et al., 2007; Schiller et al., 1997).
These regenerative events are possible due to the presence of voltage gated channels,
consisting of voltage-gated calcium channels; sodium channels and N-methyl-Daspartate (NMDA) receptor channels, spread along the entire dendrite. In particular,
dendritic NMDA receptor channels can be activated by the synchronous activation
of spatially clustered synapses, due to the voltage-sensitive release of the Mg2+ block
along a 10-20 µm stretch of dendrite (Losonczy and Magee, 2006; Nevian et al., 2007).
The charge generated by such a regenerative ‘NMDA spike’ has been shown to be
much larger than the linear sum of the synapses involved. Furthermore, the extended
time course of NMDA activation causes the charge to be more effectively passed along
towards the soma. Even though one NMDA spike might not be sufficient to trigger
an action potential, the influence of these spatially clustered synapses on somatic
firing is hereby significantly increased.
The described findings show that, much like the non-linear process of action
potential generation at the soma, the synchronous activation of neighboring
synapses on a sub-branch of the dendrite can lead to their non-linear summation.
This allows for the implementation of a spatio-temporal coding scheme, resulting in
a greater specificity in spiking responses and increased computational capabilities.
A new model of synaptic integration has therefore been proposed in which the
dendritic tree is thought to consist of local compartments, each functioning as an
individual computational subunit (Figure 4; Govindarajan et al., 2006; Losonczy
et al., 2008; Poirazi and Mel, 2001). In this view the extensive arborization of the
dendritic tree becomes a feature, not an obstacle, of the synaptic integration process
(Hausser and Mel, 2003). Modeling studies have shown that neurons utilizing such a
compartmentalization model can perform mathematical transformations of synaptic
inputs that would normally require multiple neurons connected in a network to be
achieved (Poirazi and Mel, 2001; Wu and Mel, 2009).
19
Chapter 1. General Introduction
Plasticity mechanisms favoring synaptic clustering
While the dendritic compartmentalization model increases the computational
capabilities of an individual neuron, it places additional demands on the synaptic
organization that is established during development. Namely, it requires synapses
with a synchronized activity pattern to be clustered close together on the dendrite.
Interestingly, several local plasticity mechanisms have been described which could aid
the formation of such a synaptic organization through activity dependent processes.
Point Neuron Model
+
Compartmentalization Model
+
Figure 4. Models of dendritic integration.
A pyramidal neuron is depicted with synaptic inputs, represented as colored circles, making
distributed contacts along its dendrite. The process of dendritic integration according to the
“synaptic democracy” and the “compartmentalization” model are shown. Time points of
synaptic input events are represented by the colored bars on top. The blue traces underneath
show excitatory postsynaptic potentials as one would record in current clamp. When synaptic
inputs reach a certain threshold value (dashed line) an action potential is generated (shown
in red) in a non-linear fashion. In the case of the synaptic democracy model, integration is
independent of the location of the synaptic inputs. However, when two neighboring synapses
are active at the same time in the compartmentalization model (time points highlighted in
grey) an additional non-linear step takes place as these inputs are super linearly summated
giving them a larger influence on somatic firing.
20
Plasticity mechanisms favoring synaptic clustering
Synaptic tagging and capture
Changes in synaptic weight have long been seen as the mechanism through which
memory engrams are stored. However, not all these changes in synaptic weight are
equally stable and can have different life-times. In the case of LTP, it is possible to
distinguish both an immediate,- early phase of potentiation (E-LTP, lasting less than
3 hours) and a long-term,- late phase (L-LTP; Davis and Squire, 1984; Frey and
Morris, 1997; Frey et al., 1988; Krug et al., 1984). These can be dissociated from
each other since protein synthesis is required for the induction of L-LTP while the
expression of E-LTP is protein-synthesis independent (Frey et al., 1988; Krug et al.,
1984). Interestingly, a classic study by Frey and Morris found that cooperation can
take place between synapses in these different states of potentiation. Specifically, field
stimulation experiments in the Schaffer collateral – CA1 pathway showed that L-LTP
inducing stimulation in one set of synapses before the application of anisomycin (a
protein synthesis inhibitor) could rescue the expression of L-LTP in a later stimulated
set of synapses. The authors proposed that the induction of LTP leads to the creation
of a protein-synthesis independent synaptic ‘tag’ which can appropriate, or ‘capture’,
the relevant plasticity related proteins (PrPs) needed for the induction of L-LTP (Frey
and Morris, 1997).
Assuming PrPs can spread from the location of the synapse whose activity lead
to their generation it could be hypothesized that this synaptic tagging and capture
plasticity (STC) preferentially occurs at clusters of neighboring synapses on the
dendrite. A study by Govindarajan et. al. aimed to investigate this possibility by
looking at the spatial and temporal characteristics of STC at the level of individual
spines. In order to induce L-LTP at a single spine the authors combined high-frequency
glutamate uncaging with bath application of forskolin (GLU+FSK; Govindarajan et
al., 2011). This protocol resulted in significant L-LTP expression at the stimulated
spine. Conversely, glutamate uncaging in the absence of forskolin (GLU) resulted
in shorter lived E-LTP expression. The authors found, in accordance with the STC
model, that GLU+FSK stimulation of one spine followed by GLU stimulation of a
second spine lead to L-LTP expression in both spines (Govindarajan et al., 2011). In
addition, the efficiency of STC was found to be inversely proportional to the distance
between the 2 stimulated spines and was almost completely undetectable at 70 µm.
These findings show that STC occurs more readily at neighboring synapses, making
the dendritic branch the preferred site for cooperativity and association between
synapses. Furthermore, this cooperativity acts on time scales that are much longer
than the time window in which classical plasticity mechanisms take place (hours vs.
21
Chapter 1. General Introduction
ms).
Metaplasticity through local synaptic activity
The induction of long-term plasticity requires the activation of various signaling
cascades in order to stimulate the necessary protein synthesis. Interestingly, synaptic
stimuli leading to L-LTP have been shown to activate specific signaling kinases
namely mitogen-activated protein kinase (MAPK) and mammalian target of
rapamycin (mTOR; Kelleher et al., 2004a, 2004b). Since MAPK and mTOR remain
active for several minutes it has been suggest that their activation by plasticity
inducing stimulation could lower the threshold for plasticity in neighboring synaptic
activity (Govindarajan et al., 2006; Wu et al., 2001). To investigate this possibility one
study by Harvey et. al. looked at the spike-timing-dependent potentiated (STDP) of
neighboring spines in hippocampal pyramidal neurons (Harvey and Svoboda, 2007).
Here, STDP was induced in single spines using two-photon glutamate uncaging
followed by action potential initiation. Similarly to classical experiments of STDP,
the magnitude of plasticity was found to decrease as the time between glutamate
uncaging and action potential initiation increased. Specifically, an increase in the
excitatory postsynaptic current and spine volume could not be observed with a
spike time window larger than 5 milliseconds. However, it was found that previous
induction of LTP in one spine broadened the spike time window of STDP in
neighboring spines (up to 35 ms.). Additionally, no change in STDP was observed
when the time between stimulation of the two spines increased up to 10 minutes or
when their relative distance exceeded 10 µm. The authors called this form of local
plasticity cross talk, in order to distinguish it from STC plasticity mechanisms.
One of the early activators of the MAPK signaling pathway is the guanosine
triphosphatase (GTPase) RAS. This GTPase becomes activated by NMDAR-induced
calcium influx and is involved in the induction of LTP (Zhu et al., 2002). In order
to uncover if activated RAS could spread between neighboring spines, and possibly
facilitate crosstalk, a later study by Harvey et. al. used a fluorescence resonance
energy transfer (FRET) based indicator of RAS activation (Harvey et al., 2008). Using
this method a robust increase of RAS activity was observed after LTP induction in
stimulated spines that peaked within 1 minute and returned to baseline after 15
minutes. Once activated, RAS spread over several micrometers in both directions and
subsequently invaded neighboring spines. Both the spatial and temporal activation
profile of RAS closely matched those observed in the crosstalk experiments, indicating
that RAS activation could be involved in its expression. This was confirmed in a
subsequent experiment where crosstalk was prevented by the local application of a
22
Thesis outline
pharmacological blocker for MAPK and extracellular signal-regulated kinase (ERK;
Harvey et al., 2008). These findings show that Ca2+-activated signaling machinery
can regulate the threshold for plasticity induction in local stretches of dendrite. In
this way the occurrence of strong LTP-inducing activity at one synapse can lower
the threshold for potentiation at neighboring synapses, thereby aiding the storage of
memory engrams in clusters of synapses on the dendrite.
Thesis outline
In this thesis I will investigate how spontaneous activity shapes synaptic
connectivity during early development. The focus here will be specifically on the
role of spontaneous activity in fine-scale synaptic clustering. Such an approach has
only recently become possible due to the advances in calcium imaging techniques
which allow us to follow the activity of a large population of synapses over time.
Furthermore, I will attempt to overcome some of the shortcomings of previous studies
which relied on blocking spontaneous activity during development in order to study
its effect on synaptic connectivity. These manipulations have made it difficult to make
causal statement about the instructive role of spontaneous activity since neuronal
activity also drives genetic changes during neuronal development. An overarching
strategy that I will therefore employ is to start out by studying the spontaneous
activity patterns that occur naturally in the intact system. These observations then
inform the design of further experiments to test the correlational relationships that
we observe. Throughout the following chapters I will return to the important link
between connectivity and neuronal processing specifically in regards to synaptic
clustering. In this way I hope to show that studying activity-dependent neuronal
development can aid our understanding of neuronal processing and plasticity in
adulthood. Here, I aimed to answer the following research questions:
•
Can spontaneous synaptic activity be imaged on the dendrites of individual
pyramidal neurons (Chapter 2)?
•
Is there an underlying structure to the spatiotemporal activity pattern of
spontaneous synaptic inputs (Chapter 3)?
•
What mechanism leads to the clustering of co-active synaptic inputs during
spontaneous activity (Chapter 4)?
In chapter 2 I will give a detailed description of the imaging techniques that we
used to perform the experiments in this thesis. By combining calcium imaging
23
Chapter 1. General Introduction
with whole-cell electrophysiology we could distinguish synaptic calcium signals
(e.g. calcium influx through activated NMDA channels) from non-synaptic sources
(calcium release from internal stores). We can therefore not only see when synapses
are active (electrophysiology) but also where these synapses are located on the
dendrite (calcium imaging). In this way we can map the functional synaptic sites on
the dendritic tree (the “synaptome”) and follow their activity over time.
Chapter 3 uses the method described above to investigate the patterns of synaptic
activity that occur during spontaneous network bursts in CA3 pyramidal neurons of
the hippocampus. We found that while the identity of the synapses that are activated
vary from burst to burst, the resulting patterns are not completely random. Instead,
synapses that are located close together on the dendrite tend to be more often active
within the same burst then synapses that are farther apart. Furthermore, this synaptic
organization was dependent on spontaneous activity and NMDA receptor activation
during development. We therefore find that spontaneous activity can connect neurons
even with subcellular precision.
Chapter 4 investigates the plasticity mechanism responsible for the synaptic
clustering organization that we observed in the previous chapter. We start by
following the activity of a large number of synapses on layer 2/3 neurons in the
visual cortex of live mice. Strikingly, we find that synapses that are not often coactive with neighboring synapses on the dendrite get suppressed in their activity
on the subsequent part of the recording. We performed similar experiments in the
hippocampus in vitro and found that this is a general local plasticity mechanism
driven by spontaneous activity. By utilizing a closed-loop stimulation experiment,
where we manipulate the co-activity rate of individual synapses, we show that this
is due to a decrease in the synaptic transmission efficiency of ‘out-of-sync’ synapses.
Spontaneous activity therefore drives an ‘out-of-sync, lose-your-link’ local plasticity
mechanism in a way that facilitates synaptic clustering
Chapter 5 consists of a general discussion of the presented research. Here I will
focus in particular on recent research concerning non-linear dendritic integration
in functional processing in vivo. Synaptic clustering is likely a requirement for this
form of dendritic processing and therefore sensory processing in the adult animal.
Finally, I will discuss possibilities for future research that should help to clarify the
link between these two important topics.
24
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