Ocular dominance plasticity as a model to study experience-dependent plasticity

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Inhibitory cortical neurons:

Sensory relevant information is carried by long-range glutamatergic excitatory neurons. However, the activity of excitatory neurons is strongly modulated by a rich diversity of local inhibitory GABAergic interneurons. Although encompassing the minority of cortical neurons (10-20%) GABAergic neurons play a critical role in controlling, modulating and shaping the activity of principal neurons. Within brain networks, excitation works in « balance » with inhibition (E/I balance) in order to perform correct circuit computations underlying sensory processing (Isaacson and Scanziani, 2011a). Indeed, an imbalance between the E/I ratio leads to neurological and psychiatric disorders such as epilepsy, autism and schizophrenia (Marín, 2012; Gao and Penzes, 2015; Nelson and Valakh, 2015). Similarly to excitatory neurons, also inhibitory cells share some common characteristics. First, they all use the γ-aminobutyric acid (GABA) as neurotransmitter (Klausberger and Somogyi, 2008). Second, according to Ramón y Cajal’s definition, they are ‘short axon cells’ (Ramon y Cajal, 1899), and indeed they almost exclusively modulate the local neuronal network, due to their restricted axonal and dendritic arborizations. For this reason they are called interneuron (IN). Importantly, however, several studies indicate the existence of long-range GABAergic neurons (Tamamaki and Tomioka, 2010; Lee et al., 2014; Yuan et al., 2017). Another common feature of inhibitory interneurons is the lack or sparse expression of dendritic spines (Kubota et al., 2011). Finally, their soma, contrary to PNs, can be contacted by both glutamatergic and GABAergic synapses (R. Douglas, H. Markram, 2004).
Figure 1.14. Multiple dimensions of interneuron diversity. Interneuron cell types are usually defined using a combination of criteria based on morphology, connectivity pattern, synaptic properties, marker expression and intrinsic firing properties. The highlighted connections define fast-spiking cortical basket cells, from (Kepecs and Fishell, 2014).
Despite these common morpho-functional characteristics, INs form a spectacularly heteregeneous neuronal population. Indeed, INs’ remarkable diversity is based on their morphological, electrophysiological and connectivity properties, as well as the expression of molecular markers, such as Ca²⁺-binding proteins and neuropeptides (Gupta, 2000; Ascoli and Alonso-Nanclares, 2008; DeFelipe et al., 2013). Figure 1.14 resumes the rich diversity of the GABAergic cortical neurons. Due to the overlap of the different morphological and functional features attempting to define different IN subclasses, to date a clear classification of the many cortical IN subtypes is far from being established. Even though the classification of cortical GABAergic interneurons is problematic, perhaps one relevant functional classification relies on their specialized connectivity with different domains of PNs (Fig. 1.15) that generates an efficient division of labor of different forms of inhibition during cortical activity. Indeed, we can typically distinguish the perisomatic-targeting basket cells (BC) and the axo-axonic chandeliers cells. The precise targeting of BCs and chandelier cells on the output region of PNs allows a precise control of PN output spiking activity. Basket cells, which represent the largest population of INs (about 50%) can be divided into two large subclasses: the PV-expressing and the cholecystokinin (CCK)-expressing basket cells that express cannabinoid receptor type 1 (CB1R) (Freund and Katona, 2007). PV⁺ basket cells sustain high-frequency firing, receive strong excitation, release GABA very reliably, and are considered the clockwork of cortical networks, as they synchronize a large population of principal cells (Buzsáki and Draguhn, 2004; Freund and Katona, 2007; Klausberger and Somogyi, 2008). Conversely, basket cells expressing CB1Rs (and CCK) receive less excitation, cannot sustain high-frequency firing, release GABA more asynchronously and unreliably (Hefft and Jonas, 2005), and are negatively modulated by endocannabinoids (Kano et al., 2009). Notably, CCK⁺ cells are the specific target of subcortical neuromodulators, such as acetylcholine and serotonin, and this, together with their more ‘capricious’ GABAergic transmission led to the hypothesis that CCK⁺ cells exert a fine-tuning of cortical activities and might play a key role in the control of mood (Freund and Katona, 2007; Varga et al., 2009). This functional classification of PV and CCK BCs derive mostly from studies in the hippocampus (Freund, 2003; Szabadics J, Varga C, Molnar G, Olah S, Barzo P, 2006; Freund and Katona, 2007). Indeed, a deep knowledge of the different distribution of CCK and PV cells in different neocortical layers and areas is missing. Yet, we know that CCK/CB1 BCs are mostly located in superficial cortical layers (L1 and L2/3), where they share the perisomatic control of PN excitability with PV BCs. In contrast, L5 PNs are almost exclusively modulated by PV BCs (Allene et al., 2015). Importantly, both PV and CCK cells include several subtypes that can be classified by their specific connectivity patterns.
Another major subclass of cortical inhibitory cells is represented by dendrite-targeting interneurons such as Martinotti cells (which express the neuropeptide somatostatin or SST) and neurogliaform cells (which express high levels of neuronal nitric oxide synthase). Dendrite-targeting interneurons filter glutamatergic synaptic input, which, in PNs, mostly occurs at dendritic spines. Therefore, dendritic inhibition plays a crucial role in modulating dendritic non-linearity and input-output transformations, as well as plasticity of glutamatergic synapses (Palmer et al., 2012). Also SST cells are not a uniform cell type, and a clear picture of their different subtypes is only now beginning to emerge (Scheyltjens and Arckens, 2016). For a review on the different subtypes of INs see (Markram et al., 2004; Druga, 2009).
In addition to the connectivity logic of interneurons onto PNs, cortical inhibitory neurons can be recruited by distinct excitatory circuits (Isaacson and Scanziani, 2011b; Roux and Buzsáki, 2015). Excitatory inputs arising from cortical and subcortical regions can diverge onto both principal cells and interneurons, giving rise to feed-forward inhibition (Fig. 1.16A). This form of inhibition is ubiquitous, is triggered by long-range connections, and plays an important role in shaping and controlling the precise time window of PN spiking activity. This type of inhibition is exceptionally strong in L4 in which PV interneurons are potently recruited by thalamic fibers (Pouille, 2001; Gabernet et al., 2005; Cruikshank et al., 2007). Feedback inhibition is divided in recurrent inhibition (Fig. 1.16B) or lateral inhibition (Fig. 1.16C). In both cases a PN fires first and recruits a postsynaptic inhibitory neuron, which in turn suppresses the activity of the same (recurrent) or a neighboring PN (lateral) (Silberberg and Markram, 2007). Lateral inhibition is important for example in the visual cortex where it drives surround inhibition (Adesnik et al., 2012), which is a basic mechanism for setting and modulating the receptive fields. Furthermore, a form of direct inhibition arises when long-range GABAergic inputs from distant regions drive local inhibition in the circuit (Fig. 1.16D). Another important circuit, in which inhibitory neurons are involved, is disinhibition, which takes place when GABAergic neurons target other GABAergic neurons (Fig. 1.16E). This can mediate network synchrony or disinhibition of principal neurons (Sohn et al., 2016).
Figure 1.16. Main forms of inhibitory microcircuits. (A) Feed-forward inhibition (B) Feed-back inhibition. (C) Lateral inhibition. (D) Direct inhibition. (E) Disinhibition. Interneurons are in red, afferent excitatory inputs from an external source in green and local principal neurons in black. Modified from (Roux and Buzsáki, 2015).
Inhibition of inhibition is a common feature in cortical circuits. In particular, visual cortex activity relies on distinct inhibitory connectivity patterns (Pfeffer et al., 2013). For example, we know that PV INs inhibit strongly themselves via autaptic transmission and mutual inhibition between PV cells (see paragraph below dedicated to PV cells). Moreover, SST INs have a preference for other types of INs, and tend to avoid other SST cells. An important disinhibitory cortical circuit involves interneurons expressing the vasoactive intestinal polypeptide (VIP). These interneurons are specialized in contacting other GABAergic neurons selectively, and they have a particular preference for SST cells, although they also inhibit PV cells with a lower extent (Pfeffer et al., 2013; Kepecs and Fishell, 2014). VIP IN-dependent disinhibition has been recently described to underlie several cognitive functions, including auditory discrimination (Pi et al., 2013), sensory-motor integration (Lee et al., 2013) and working memory (Kamigaki and Dan, 2017).

The parvalbumin-positive fast-spiking interneurons:

In this section, I will expand on one subtype of neocortical INs, which is of particular interest for my thesis work: the fast-spiking (FS), PV-positive basket cell, which contacts the perisomatic region of principal neuron with a basket-like axonal structure (Fig. 1.17A). These cells have a multipolar morphology and extended dendritic arborization that often spans several cortical layers, allowing them to be recruited by different excitatory afferent pathways, such as feedforward and feedback circuits (Pouille, 2001; Gabernet et al., 2005; Cruikshank et al., 2007). They express the Ca²⁺-binding protein parvalbumin, defining them as belonging to the PV cell classes (Kawaguchi et al., 1987). PV is an endogenous Ca²⁺ -binding protein that controls both synchronous and asynchronous GABA release (Manseau et al., 2010). PV expression starts in the second week of postnatal age in mice, and, in the hippocampus, PV levels are inversely correlated with the degree of plasticity of the network: PV expression diminishes when the network is plastic and increases with reduced plasticity (Donato et al., 2013, 2015). The specific expression of PV is generally used as a marker to recognize these cells in anatomical studies, and allowed the generation of transgenic mice in which PV cells can be identified and/or manipulated (Taniguchi et al., 2011).
Fast-spiking cells owe their name to their firing pattern in response to depolarizing current steps, as they can display abrupt high firing frequency (several hundred Hz). Importantly, FS cells can fire up to 600-800 Hz in vivo during seizure activity (Timofeev et al., 2002). In vitro, spike trains are typically non-adapting, and action potentials exhibit a large and fast afterhyperpolarization, as shown in figure 1.17B (McCormick et al., 1985). These INs are endowed with everything is needed to make them “fast machines”: i) on their dendrites, they express a high density of voltage-gated potassium channels of the Kv3 subtype (Chow et al., 1999) which have high activation threshold, fast activation, and fast deactivation properties, thus allowing fast repetitive firing (Rudy and McBain, 2001) ii) they express high levels of sodium channels allowing fast AP propagation along the entire axonal length (Hu and Jonas, 2014) iii) they possess short membrane time constants and low input resistance (Nörenberg et al., 2010) iv) they express ultra-fast AMPA receptor conductance (and no or little NMDA conductance) v) they release rapidly and reliably GABA at their terminals. Furthermore, by targeting the soma and proximal dendrites of principal neurons, they provide powerful perisomatic inhibition (Freund and Katona, 2007) controlling and synchronizing the firing of a large number of PNs during gamma activity (Cardin et al., 2009; Sohal et al., 2009) as well as in gain control of sensory processes (Atallah et al., 2012).
A recent study (Hioki, 2015) analyzed the compartmentalization of inputs onto PV cells: in neocortical L4, they receive more cortical inhibitory synapses onto proximal dendrites, whereas excitatory inputs from cortical PNs are located on the most distal dendritic portions. In contrast, excitatory thalamic axons have no preferences in the targeting PV cells (Bagnall et al., 2011). Importantly, PV cells are strongly coupled to other PV cells via chemical synapses and gap junctions (Galarreta and Hestrin, 2002) and show a high degree of self-inhibition via functional autaptic GABAergic synapses (Tamás et al., 1997; Bacci et al., 2003; Bacci and Huguenard, 2006) that drive a particular type of disinhibition in the circuits. For review see (Deleuze et al., 2014). Figure 1.18 illustrates anatomical (A and B) and functional (C) evidence of autapses in neocortical PV interneurons.
Finally, PV cells have implication in several neurologic disorders including epilepsy and schizophrenia (Marín, 2012) and are therefore important therapeutic targets. They are also essential in mediating experience-dependent plasticity (Hensch, 2005a). The next chapter of the manuscript is dedicated to cortical plasticity and the fundamental role of PV cells.
Figure 1.18. Massive self-inhibition of PV cells. (A) Subcellular distribution of autaptic contacts on a FS cell (1-8). Soma and dendrites are in red, axon in black. (B) Electron microscope image illustrating a self-innervating bouton (α5) targeting its own dendrite. Scale bar: 0.3 mm. (C) Representative traces illustrating synchronous GABAergic (gabazine-sensitive) autaptic release on a FS interneuron from rat neocortex. Autaptic transmission is induced by voltage steps eliciting fast inward Na currents (truncated). The dotted line indicates the peak of the response, showing fixed latency and peak- amplitude fluctuation. From (Deleuze et al., 2014).

Cortical plasticity – Experience-dependent visual plasticity

Critical period of plasticity

Why is it much easier for a child to learn a foreign language as well as a music instrument for instance? Why can young individuals learn something new so easily? Why are certain pathological conditions reversible in kids, but not in adults, like for instance recovering from visual deficits?
A hallmark of sensory systems is their sensitivity to experience which shapes and influence their maturation and affects their structures. Indeed, any perturbation of sensory experience has profound long lasting modifications in the organization and functioning of the system. This sensitivity and increased susceptibility to certain stimuli is present only during a specific time window where the brain and circuits are highly plastic and sensitive to the external world. This is the so called critical period (CP) of plasticity (Fig. 1.19), typical of juvenile’s brain and very limited in adulthood. This window of heightened plasticity, during which neuronal circuits are sculpted and reorganized, requires environmental inputs for the proper maturation of brain connectivity, function and behavior. CPs are present across a variety of species, from humans to drosophila and their duration (from weeks to years) is proportional to the lifespan of the species (Berardi et al., 2000). As indicated in figure 1.19, there are different CPs, depending on specific brain functions and circuits.
As illustrated in figure 1.20, in the mouse visual system, retinotopic maps form well before eye opening which occurs around the second postnatal week (P14). Orientation-selective and contralateral-eye-driven neurons are already present at eye opening. During the subsequent days, neurons become more sensitive to visual stimulation, more selective for orientation tuning and their responses to inputs from the ipsilateral eye increase. During the CP (between P22 and P35 in rodents, with a peak typically around P28), the orientation preference of cortical neurons goes from mismatched eye-specific preferred orientations to binocular matching (Smith and Trachtenberg, 2007; Bhaumik and Shah, 2014). Importantly, an abnormal visual experience early in life leads to amblyopia (a condition known as lazy eye), a severe and permanent visual deficit resulting in a number of defects in spatial vision, such as decreased visual acuity and depth perception (McKee 2003).
In summary, the pre-CP is guided by molecular and neuronal signals that are genetically determined and mostly independent from external stimuli, while the maintenance and refinement of the neuronal microcircuits for binocular vision are driven by experience and environmental inputs during the CP.

Ocular dominance plasticity as a model to study experience-dependent plasticity

For a long time, the visual system has been the gold-standard model to study experience-dependent plasticity (Priebe and McGee, 2014). As mentioned at the beginning of the manuscript, pioneering experiments by Hubel and Wiesel showed that neurons in the visual cortex respond preferentially to the stimulation of the contralateral eye, providing an OD bias of neuronal responses. Their seminal work on kitten showed that the occlusion of one eye (by means of lid suture) during the CP induces a shift in the responses of cortical neurons: cells that in normal conditions would have been activated by the closed eye respond to the input coming from the non-deprived, opened eye (Wiesel and Hubel, 1963). This phenomenon, typical of young animals, is limited in adults. The shift in spiking toward the non-deprived eye is usually detected by single-unit electrophysiological recordings in V1 and quantified with an OD score (contralateral bias index), as represented in figure 1.21.
Sensory deprivation is widely used as a paradigm to study plasticity in the visual system and is very well documented. Indeed, only two or three days of a monocular deprivation (MD) in young mice are sufficient to produce a pronounced shift in OD, by creating a discordant binocular integration of inputs from the two eyes (Gordon and Stryker, 1996; Wang et al., 2010). This produces a loss of the responses throughout the deprived eye followed by an increase in the input from the open eye. The rapid functional effects of MD are underlined by anatomical rewiring of cortical circuits (Trachtenberg and Stryker, 2001; Maffei et al., 2004; Nahmani and Turrigiano, 2014) and thalamic afferents (Antonini and Stryker, 1996; Coleman et al., 2010). This paradigm will be better discussed later in section 1.2.3.2, in relation with the experiments we performed. For review on experience-dependent plasticity see (Hensch, 2005a; Hensch and Fagiolini, 2005; Levelt and Hübener, 2012).
Figure 1.21. Ocular dominance (OD) plasticity in the visual cortex of juvenile mice. (A) Monocular deprivation (MD) produces a loss of response to the deprived eye and a gain of open-eye input, as measured by the neuronal discharge of single units from the mouse visual cortex. The OD of cells, rated on a seven-point scale of neuronal responsiveness, indicates a typical bias toward the contralateral eye (1–3) in the rodent. After 3 or more days of MD, the distribution shifts toward the open, ipsilateral eye (4–7). From (Takao K Hensch, 2005b). (B) Recordings are made from electrodes implanted in L4 of the binocular zone of V1 (green), receiving independent input from the contralateral (blue) and ipsilateral (yellow) eyes. From (Cooke and Bear, 2013).

Cellular, molecular and structural mechanisms of critical period

Role of inhibition

Fast synaptic inhibition shapes all forms of cortical activity (Isaacson and Scanziani, 2011a) and the modulation of experience-dependent structural plasticity is not an exception, as an optimal E/I balance is required for plasticity. It is now known that the onset of the CP is determined by the maturation of local inhibitory GABAergic circuits (Fagiolini and Hensch, 2000; Hensch, 2005b; Levelt et al., 2011). Accordingly, it is possible to shift the timing of the CP.
In particular, PV-positive interneurons, which maturate in parallel with the onset of the CP, have been indicated as key modulators of experience-dependent plasticity. Indeed, the specific blockade of the potassium channel Kv3.1, highly expressed in PV cells, leads to impaired ODP (Takao K Hensch, 2005b). In addition, in mice where the α-1 subunit of the GABA_AR is insensitive to benzodiazepines, CP cannot be induced (Fagiolini, 2004), suggesting again that PV interneurons are involved, as this subunit is enriched in inhibitory synapses formed by PV basket cells on the soma and proximal dendrites of PNs (Klausberger et al., 2002).
Another intriguing major actor in PV cell maturation and visual plasticity is the transcription factor Otx2. Interestingly, this function is accomplished through a non-cell autonomous mechanism. Otx2 is a homeoprotein, and the Otx2 gene locus is silent in juvenile and adult cortex. The Otx2 protein reaches cortical structures from extra-cortical sources: Otx2 is globally synthetized in the choroid plexus (Spatazza et al., 2013), which secretes the protein in the cerebrospinal fluid. Otx2 is then internalized preferentially in cortical PV cells, triggering their maturation and subsequently initiating the CP (Sugiyama et al., 2009; Prochiantz et al., 2014). Otx2 can be detected in PV cells of V1 before P20, increases during the CP, and plateaus thereafter. In mice, cortical infusion of recombinant Otx2 protein before CP onset accelerates CP timing, while the extracellular blocking of Otx2 protein delays CP onset (Sugiyama et al., 2008).
Others factors, related to PV cells’ correct maturation and functioning, are important for the regulation of the CP in the visual cortex. Among them, we can cite: the circadian clock genes (Kobayashi et al., 2015), the immediate early gene NARP (Gu et al., 2013), the polysialic acid (Di Cristo et al., 2007), the Neuregulin-1/ErbB4 signaling (Sun et al., 2016) as well as the CREB-mediated gene transcription. In particular, recent evidence indicates that CREB regulated microRNAs, such as miR-132, are involved in visual plasticity (Tognini et al., 2011; Tognini and Pizzorusso, 2012).
To conclude, inhibition, which is mediated essentially by PV interneurons, is crucial to shape the CP of visual plasticity by initiating and terminating this window of increased plasticity. In the next paragraph, I will summarize how inhibition acts to regulate plasticity, at the network and circuit level.

Mechanisms of ocular dominance plasticity at the circuit level

Although it is clear that PV interneurons are central actors in the control of CP shaping and timing, how these cells regulate plasticity is not completely understood. Recent studies have proposed that a transient suppression of PV cell firing may gate cortical plasticity (Kuhlman et al., 2013). Monocular deprivation results in a biphasic profile of neuronal responses: first, within 3 days of MD, neurons in the binocular region of V1 initially decrease their responses to the contralateral closed eye. Then, after 7 days, neuronal responses to both the open and deprived eyes are enhanced (Mrsic-Flogel et al., 2007). These findings were confirmed, in vivo, by two recent papers that provide evidence that CP is triggered by a disinhibitory microcircuit (Hengen et al., 2013; Kuhlman et al., 2013). In both cases, excitatory neurons’ responses after MD decrease and then gradually return to baseline level. This restoration of binocular-like firing rates is due to a rapid and transient inhibition of PV cells (which decrease their activity) which results into disinhibition of pyramidal neurons. Thus, deprivation-induced CP is defined by a rapid drop in PV cell inhibition, a subsequent loss of deprived eye responses, followed by potentiation of responses to both eyes.
It is now widely accepted that the loss of cortical responsiveness to deprived eye stimulation after MD involves some form of Hebbian-like LTD (Yoon et al., 2009; Cooke and Bear, 2013; Hengen et al., 2013) involving NMDAR-mediated endocytosis of postsynaptic AMPA and mGluR5 receptors (Sidorov et al., 2015), as well as a strengthening of PV to PYR synapses (Maffei et al., 2006, 2010; Lefort et al., 2013; Nahmani and Turrigiano, 2014). This enhancement of inhibition after MD can be detected across different cortical layers, including layer 2/3 (Kannan et al., 2016). In contrast, the delayed synaptic potentiation is due to homeostatic scaling and requires the GluA2 subunit of AMPARs.
To summarize, MD-mediated plasticity during the CP is accompanied by Hebbian LTD mechanisms and a gain in PV to pyramidal cell inhibition that, together, will depress principal cell firing. This elicits a homeostatic process that restores V1 cortical firing.
ODP during the CP is also accompanied by anatomical and structural changes. 3 days of MD during the CP are sufficient to induce ODP and anatomical alterations of thalamocortical (TC) synapses, such as reduction of TC bouton density in V1b (Coleman et al., 2010), accompanied by synaptic depression at TC synapses (Khibnik et al., 2010). Similarly, plasticity can be seen at the level of dendrites: spine motility from L5 pyramidal neurons is increased after short MD in V1b (Majewska and Sur, 2003; Oray et al., 2004).
Finally, a theory has been recently proposed to explain the role of inhibition in opening the CP (Toyoizumi et al., 2013). The transition from pre-CP to CP plasticity is possible, because the maturation of inhibition selectively suppresses spontaneous neuronal activity, but not visual environmentally-evoked inputs, switching learning cues from internal (spontaneous activity) to external (visual inputs) sources.

Adult plasticity and the reopening of windows of plasticity

Even though neuronal plasticity is a key feature of the CP, it is not exclusively restricted to this time window of development. Indeed, mature brains also present plasticity, to some extent. In the visual system, ODP during adulthood has been reported by several studies. Indeed, in vivo long term formation and turnover of dendritic spines in adult visual cortex after sensory experience has been reported (Trachtenberg et al., 2002). Moreover, a mature brain is more susceptible to plasticity if it has already been subject to a MD earlier in life (Hofer et al., 2006a, 2009). Importantly, adult visual plasticity depends on NMDA receptors, which strengthen the inputs originating from the open eye (Sawtell et al., 2003). Finally, adult plasticity is correlated with the developmental decline of the transcriptional factor CREB (Pham et al., 2004).
What mostly differentiates juvenile versus adult ODP is that, in mature brain, longer periods of MD are required to obtain the shift of the responses (> 4-7 days in adult versus 2-3 days in young mice) (Pham et al., 2004; Hofer et al., 2006a; Sato and Stryker, 2008). Furthermore, the magnitude of the OD shift is smaller in adults, as compared to juvenile subjects (Pham et al., 2004; Hofer et al., 2006a; Sato and Stryker, 2008). Mechanistically, MD-induced plasticity shows also other disparities between adult and young mice. In young animals, the effect is biphasic with a rapid loss in deprived-eye responses followed by a delayed increase of open-eye response strength (Frenkel and Bear, 2004; Mrsic-Flogel et al., 2007; Sato and Stryker, 2008). Conversely, in adult mice, the dominant change is a reversible potentiation of responses from the open eye (Sawtell et al., 2003; Pham et al., 2004; Hofer et al., 2006a; Sato and Stryker, 2008). At the level of neuronal morphology, MD in adults leads to a decrease in spine motility and either an increase (Mataga et al., 2004; Oray et al., 2004) in spine density or no change (Hofer et al., 2009), depending on the cell type; the effects are opposite during the CP. Moreover, no changes in TC bouton density were reported in adults. Finally, adult visual plasticity is more sensitive to anesthesia affecting GABAergic neurotransmission and therefore experiments in adult anesthetized mice can influence the detectability of adult plasticity. For reviews on adult visual plasticity see (Fischer et al., 2007; Morishita and Hensch, 2008; Hübener and Bonhoeffer, 2014).
Nevertheless, adult brain capacity for plasticity remains relatively limited. Once cortical circuits are mature, molecular brakes appear gradually and are responsible for the restriction of plasticity and the closure of the CP. Such brakes can be both structural (PNNs, myelin factors) and functional (Lynx protein for instance), by providing physical impairment to neurotransmission between previously formed connections and by acting on neuro-modulatory systems respectively (see below). Manipulating these brakes allows reopening of windows of plasticity in adult brains (Bavelier et al., 2010; Takesian and Hensch, 2013) and this field of research is of great interest for nervous system damage recovery as well as the developmental of therapeutic strategies for several brain disorders (Fig. 1.23).

Table of contents :

Chapter 1: INTRODUCTION
1.1 The mouse visual system
1.1.1 Some considerations on sensory system and vision
1.1.2 Architecture and properties of the mouse visual system
1.1.2.1 The retina: first element of the chain
1.1.2.2 The dorso lateral geniculate nucleus: relay between the eye and the cortex
1.1.2.3 The primary visual cortex: first cortical level of visual processing
1.2 Cortical plasticity – Experience-dependent visual plasticity
1.2.1 Critical period of plasticity
1.2.2 Ocular dominance plasticity as a model to study experience-dependent plasticity
1.2.3 Cellular, molecular and structural mechanisms of critical period
1.2.3.1 Role of inhibition
1.2.3.2 Mechanisms of ocular dominance plasticity at the circuit level
1.2.4 Adult plasticity and the reopening of windows of plasticity
1.3 Extracellular matrix and Perineuronal Nets
1.3.1 Extracellular matrix in the central nervous system
1.3.2 Perineuronal Nets
1.3.2.1 Overview
1.3.2.2 Molecular and structural organization of PNNs
1.3.2.3 Formation and development of PNNs
1.3.3 Roles of perineuronal nets
1.4 Why looking at layer 4 of primary visual cortex?
1.5 Aim of the study
Chapter II: MATERIALS AND METHODS
2.1 Animals
2.2 In vivo enzymatic degradation of PNNs in V1
2.3 In vivo expression of the light-sensitive channel ChR2 in the dLGN
2.4 Sensory deprivation in adult mice by monocular deprivation
2.5 Preparation of acute slices for electrophysiology
2.6 Electrophysiology and optogenetic stimulation
2.7 In vivo recordings
2.8 Immunohistochemistry
2.9 Data analysis
2.10 Statistical tests
Chapter III: RESULTS
3.1 In vivo enzymatic removal of PNN in V1 of adult mice
3.2 PNN removal alters the visual gain adaptation curve in vivo, and increases the power of visually-evoked oscillations
3.3 Developmental changes of firing dynamics and passive properties of PV cells and PNs
3.3.1 Developmental changes
3.3.2 Firing dynamics and passive properties are not altered by PNN removal
3.4 Developmental changes of action potential waveform of both cell types. No apparent effect induced by disruption of PNNs
3.5 Developmental maturation of synaptic transmission onto PV cells and PNs, and alterations induced by PNN digestion
3.5.1 Development of glutamatergic transmission onto L4 neurons and effects of PNN removal
3.5.2 Development of GABAergic transmission onto L4 neurons and effects of PNN removal
3.5.3 Effects of PNN removal on quantal synaptic transmission onto PV cells
3.6 Does monocular deprivation alter the E/I balance after PNN removal?
3.6.1 Firing dynamics, passive properties and action potential shape are not altered
3.6.2 Effects of monocular deprivation on synaptic transmission onto PV cells and PNs following enzymatic PNN removal in adult mice
3.7 PNN removal does not alter unitary GABAergic connections in L4
3.8 In vivo expression of the light-sensitive channel ChR2 in the dLGN. Double injections
3.9 PNN disruption increases thalamocortical glutamatergic synapses specifically onto PV cells
3.10 PNN disruption increases thalamocortical feed-forward inhibition onto PV cells
Chapter IV: DISCUSSION

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