Cortical Plasticity under Ketamine: From Synapse to Map

Abstract

Sensory systems need to process signals in a highly dynamic way to efficiently respond to variations in the animal’s environment. For instance, several studies showed that the visual system is subject to neuroplasticity since the neurons’ firing changes according to stimulus properties. This dynamic information processing might be supported by a network reorganization. Since antidepressants influence neurotransmission, they can be used to explore synaptic plasticity sustaining cortical map reorganization. To this goal, we investigated in the primary visual cortex (V1 of mouse and cat), the impact of ketamine on neuroplasticity through changes in neuronal orientation selectivity and the functional connectivity between V1 cells, using cross correlation analyses. We found that ketamine affects cortical orientation selectivity and alters the functional connectivity within an assembly. These data clearly highlight the role of the antidepressant drugs in inducing or modeling short-term plasticity in V1 which suggests that cortical processing is optimized and adapted to the properties of the stimulus.

1. Introduction

Natural animal surroundings provide a variety of external sensory stimuli. Consequently, the brain must dynamically integrate each presented feature with changes in internal patterns of responses which manifests as a change in an animal’s behavioral state [1, 2]. For instance, many studies suggest that visual processing should be optimized and adapted to the properties of the stimulus. Thus, visual object representation arises from the activation of functional domains in the cerebral cortex that encodes feature-specific information such as orientation, color, and motion direction [3, 4, 5, 6, 7, 8]. Such feature-specific units have specific parallel networks [9] and therefore visual processing is based on the activation of multiple circuits. Many manipulations such as visual adaptation or antidepressant applications such as ketamine can alter the neuron’s inherent proprieties, and this might result in a change in correlated and uncorrelated neural activity through changes in firing rates. The effect of ketamine results in NMDAR (N-methyl-D-aspartate receptor) blocking, thus it can be used as a read-out informing visual NMDAR dependent processing or activity mediated processing.

2. Cortical plasticity

Plasticity phenomena in the adult cerebral cortex are known to be heavily correlated to the brain’s capacity for recovery after injuries [10, 11, 12, 13], memory storage [14, 15], and learning [16, 17, 18, 19]. In addition, throughout an animal’s life, cortical representations are continuously modified by experience. In experimental animals, alterations in cortical representations appear following manipulations of inputs and depending on the information locally and globally available to the cortical cells [20, 21, 22]. Many investigations show that the properties of visual cortical neurons are not fixed and can be altered in adulthood [20, 23, 24]. This neuroplasticity has been well documented, as a modification that occurs at many levels from system to molecular, going through the network, cellular and synaptic levels. In this chapter, the experimental electrophysiological work was done in the primary visual cortex of adult cat and mouse so that the responses of visual cortical cells as well as the modification of the cell’s output under different manipulations, particularly antidepressant application, was measured. This has made the visual system a preferred field for experimentation and analysis. Investigations suggest that the enormous architecture of the visual cortex is genetically preprogrammed, however, a minor proportion is shaped by experience and subject to the brain’s plasticity.

2.1 Organization of the visual cortex and visual processing

We do not yet know exactly the ultrastructural connectome of the primary visual cortex and how it processes information. However, there are some general principles of V1 architecture and processing. Visual inputs reach V1 from the lateral geniculate nucleus (LGN). The thalamocortical connections terminate mainly in layer 4 (L4) and less in supra-(L5/6) and infragranular layers (L2/3). This flow of sensory information is common to all the sensory areas. In contrast to this classic scheme, a recent investigation in mouse using an intersectional viral tracing method for ultrastructural connectivity described labeled thalamocortical synapses in all cortical layers with prevalence in L2/3 [25]. The principal vertical flow of information through the cortical layers may be from the granular layer to infragranular (L2/3) to supragranular (L5/6) [26, 27]. Considering that each layer is a level of cortical processing, one might have expected that a proportion of complex cells with larger receptive fields and more complex responses are outside of L4. Hence, at a given stage, each unit is a sampling from a broader input extent, receiving convergent information from the preceding stage, diverging out to the following stage, and in this process, establishing larger and more complex integrated receptive fields, with emerging sharper response properties [28, 29]. In parallel to this vertical flow of information, there is a horizontal connectivity. At each layer, most excitatory projections seem to originate from intra- and interlaminar pyramidal cells. The horizontal connectivity arises from L2/3 and L5 and project to infra- and supragranular levels [30, 31].

The brain processes complex visual information along with different feature aspects, such as orientation, visual motion, color or curvature [32]. Hence, visual inputs are parceled out to different extrastriatal cortical areas for further analysis. The extrastriate visual cortex receives strong direct projections from primary visual cortex which leads to a first-pass computation in the visual processing. The main outputs of V1 are to V2, V3, V4, and V5 (MT). The assumption is that the extrastriate areas which connect with V1 are in lower positions in the processing hierarchy than the extrastriate areas which connect with other extrastriate areas. This idea is superimposed on a recent concept of parallel pathways of visual areas that are implicated in some common dimensions of visual processing, i.e., “what” processing (ventral pathway), or “where” processing (dorsal pathway). From these extrastriate areas, visual inputs are then transferred back by feedback connections to areas V1 and V2 [33]. Visual object recognition depends on developing during processing across a hierarchy of visual areas both selectivity and invariance at each stage. Both simple and complex cells are selective but only complex cells are invariant to a range of object transformations. This invariance allows an object to be recognized even when some of its features (size, orientation, position, etc.) change [34].

In addition to this classical visual cortical hierarchy, it was shown that the stimulus context modulates a cell’s response which suggests the implication of other [33] areas in addition to the higher order of the visual cortical hierarchy [35, 36]. Since a big number of stimuli are present in the visual field at the same time, bottom-up and top-down mechanisms, as visual spatial attention, bias the processing toward a particularly salient stimuli [37].

2.1.1 Primary visual cortex

A key element in the role V1 plays in visual perception is the ability of V1 neurons to integrate information over larger parts of the visual field, since most of them are activated by stimulation of each eye. It was shown that a single oriented bar can induce a V1 neuron to fire. This property of orientation tuning selectivity, first described by Hubel and Wiesel (1968), is an emergent property of V1, seen in an optimal response of a given neuron to a single preferred orientation of the line segment or gratings. Although, orientation selectivity (OS) was shown in retinal ganglion cells, this tuning preference has received much less attention then in the cortex because most retinal ganglion cells are selective only to cardinal orientations: horizontal (pigeon retina) [38], and vertical (rabbit retina) [39]. It was reported that zebrafish retina contains cells with oblique preference in addition to the cardinal types [40].

In addition to the orientation tuning, neurons in primary visual cortex are highly sensitive to other visual stimulus properties such as contrast, the direction of movement, and temporal and spatial frequency. These stimulus properties can interact and influence neuronal responses. For example, it was revealed in ferret visual cortex, that a cell’s orientation-tuning is not affected by contrast level and the temporal-frequency of the visual stimulus. However, direction selectivity decreases, and sometimes reverses, at nonpreferred temporal frequencies [41, 42]. These investigations might support the idea that invariance of OS is a prime aspect of visual processing. However, in the next section, we will see that manipulation and the use of ketamine can alter this intrinsic propriety of V1.

2.1.1.1 Orientation selectivity in cat

OS is a salient propriety of V1. In anesthetized cats, electrophysiological studies using extracellular recordings of V1 cells reveal that neurons are orientation selective (Figure 1). To study OS of neurons, stimulation can be accomplished using blocks of 25 trials of each of eight black–white oriented sine gratings placed in the cat receptive field and covering a span of 157.5° equally spaced at 22.5° (Figure 1a). Spike sorting method allows the separation of a cell’s spikes from multi-unit activity. First, spike-waveforms have to be verified qualitatively by visual control, then the spike sorting is continued by cluster-isolation using first principal components analyses, autocorrelograms (AG) showing absence of events at 0 s on the time-scale (refractory period), peri-stimulus time histograms, (PSTH) and raster plots (RP), denoting for each trial the cell’s spontaneous activity (before the 0 s: stimulus trigger time) and its response to the stimulus presentation (Figure 1b). Based on the raw data, neurons’ responses are determined using Gaussian function that allows precise determination of the preferred orientation of each isolated neuron [43]. Whereas the strength of the OS can be measured by the orientation selectivity index (OSI), whose value is between 0 (orientation-nonselective) and 1(strongest OS) [44, 45], the sharpness of the tuning curve around its peak is measured by the orientation bandwidth from the Gaussian fit based on the full width at half height [46]. In cats, most V1 cells show a strong OS and sharp tuning curves. It was reported that over 82% of V1 neurons were well-tuned to stimulus orientation [47], and all the orientations were represented covering the full 180° [48]. In cats, V1 neurons with similar OS preferences are assembled in orientation columns. This columnar organization, where all cells through all six cortical layers have the same orientation preference, is a well-known characteristic that is shared by cats with ferrets and primates. Such cortical architecture, suggesting a vertical integration of feature selectivity through V1 layers, could reduce cable length, economizing the volume, and maintenance cost of V1 [49, 50]. OS is embedded in a retinotopic map in which information from neighboring locations in the visual field is coded in neighboring locations in the brain onto a two-dimensional surface that retains the image’s spatial organization. In addition, the cortical organization of cats and primates is known as a pinwheel OS map because different orientations columns are organized radially around a central point (showed by a star in Figure 1a) in the retinotopic map.

Figure 1.

Experimental procedures and spike sorting method. (a) V1 stimulation (shown as black and white gratings) and V1 architecture in mouse and cat (shown as cylinders, the black star shows the convergence of different orientations in cat). (b) Spike sorting process on the left for mouse and on the right for cat (from top to bottom): Multiunit activity (MUA), spike wave forms (cyan in mouse and red in cat), principal component analysis of the dissociated waveforms, auto-correlograms, peri-stimulus time histograms, and raster plots for the separated single units.

2.1.1.2 Orientation selectivity in mouse

Unlike cats and primates where the columnar organization is an apparent characteristic of the neocortex, rodents and rabbits have a salt-and-pepper OS map, that is a random distribution of orientation-selective neurons. Hence, cells with different orientation preferences are juxtaposed horizontally across the retinotopic map and vertically through the six cortical layers in a random fashion [51, 52, 53, 54, 55] (Figure 1). Despite the lack of the columnar organization, it was shown, using extracellular recordings, that neurones in V1 of mice are sharply tuned to orientation of drifting gratings but the percentage of orientation-nonselective cells, whose orientation tuning curves were not unimodal, was bigger (63,33% of sorted cells) than in cats (18%) [24, 47]. Therefore, neuronal feature selectivity might be related to the activation of a specific cortical cell’s subtype more than the cortical architecture. Indeed, it was reported that optogenetic activation of parvalbumin-positive (PV⁺) interneurons in the mouse primary visual cortex (V1), that is, the increase of their firing rate, markedly sharpened OS and enhanced perceptual discrimination of nearby neurons [46]. Even in V1, neurons’ responses are well known for their orientation tuning, the results of a recent study in mice seemed to leave little doubt that, in vision, the prominent role of V1 is encoding simple visual stimuli as oriented bars or gratings. It seems that in addition to a simple discrimination between light and dark oriented bars, V1 is involved in learning processes such as categorizing visual stimuli based on perceptual features, functional (semantic) relations, or a combination of both. Hence, the formation of a neuronal category representation in mice occurs in the first stages of visual information processing in the neocortex together with higher cortical association areas [56]. Despite the notion that the salt-and-pepper map is considered the most likely ancestral state, neurons can maintain high values of OS, and they are involved in complex visual processing, such as categorization. It seems that this organization in rodents was favored by their small brain size, that is in this case, the reduced visual field coverage might outweigh the potential advantage of a pinwheel OS map. However, recent studies show that cortical orientation columns perhaps are miniaturized in mouse V1 since orientation preference maps with pinwheel arrangement comparable to the macaque were described in mouse lemur [49, 57]. Hence, the V1 of rodents might represent micro-scale precursors of primate-type functional orientation columns [57]. It is likely that the relative thickness of cortical layers was a predictor for the functional organization. Indeed, an anatomical study showed that layers 2/3 are thicker in carnivores and primates than in rodents, while layers 5/6 are thicker in rodents than in carnivores and primates. The study exhibited that out of the total cortical thickness on average 44% in primates and 35% on average in carnivores were occupied by layers 2/3, but only 26% on average in rodents. In contrast, 34% of the total cortical thickness in primates and 39% in carnivores were occupied by layers 5/6, but 54% in rodents [49] . These anatomical differences between these species might affect intralaminar and cross-laminar networks and the visual cortex organization which evolved to be different in rodents versus primates and carnivores. The question that arises is whether the mechanisms of cortical plasticity, which operate at the level of single cells and the network are similar in mice and cats’ V1, and so independent of the presence of columnar organization. In the next section, we will try to investigate the effect of ketamine on the OS and the synaptic weight between cells in V1 in cats and mice.

2.2 Induction of plasticity by ketamine

Antidepressant drugs are often used to treat mental and affective disorders such as maladaptive responses to stress. Although the drugs have different mechanisms of action, the “monoaminergic hypothesis” is commonly accepted to underline the antidepressant effect [58]. Ketamine is a rapidly-acting antidepressant, and its effect is profound and sustainable [59, 60]. It is used for treatment-resistant symptoms of mood disorders in patients who are resistant to typical antidepressants [45, 59, 61]. Ketamine is a blocker of glutamatergic NMDAR (N-methyl-D-aspartate receptor) activity as it acts as a non-competitive antagonist. Many findings reveal that ketamine, in addition to its antidepressant effect, induces visual cortical plasticity. It was shown, in adult mouse, that single-dose ketamine promotes functional recovery of visual acuity from amblyopia [62]. Another investigation provided evidence that ketamine enhanced visual sensory-evoked Long-Term Potentiation (LTP) in depressive patients [63]. By contrast, other investigations showed that ketamine altered or blocked some visual processing and disturbed cortical plasticity. For example, it was reported that ketamine blocked the induction of LTP in layer 2/3 of the adult rat visual cortex in vitro [64]. In addition, in kitten, it prevented the ocular dominance shift toward the open eye which suggests a retrograde effect on cortical plasticity [65]. Moreover, in humans, ketamine interfering with top-down processes distorted object recognition [66], and it altered the neuronal processing of facial emotion recognition due to the reduced activity in visual brain regions involved in emotion processing [67]. The effect of ketamine on the brain remains uncertain and sometimes contradictory according to investigations. This might be due to several variables such as the region of interest in the brain, the dose administrated, the administration mode (local, intraveinal, acute, or chronic, etc.) or the animal model. The effect of ketamine on the OS of V1 cells was tested in cat and mice and is explained in the next section.

2.2.1 Under ketamine influence cortical cells exhibit neuroplasticity by acquiring new selectivity

To examine the impact of the antidepressant on the orientation preferences of V1 cells, the drug can be applied locally over the animal’s cortex. Ketamine application can be executed using a strip of filter paper (1 × 1 mm) impregnated with the drug (10 mM) and placed next to the recording sites. Test orientations can be presented, and recordings can be performed in the control conditions and ten minutes after ketamine administration [68]. As a result, cortical neurons selectively responding to the exposed orientations were altered by ketamine in that the cells acquired a new preference and showed a shift in the peak of their tuning curve. Based on the simulation results, we obtained evidence that ketamine induced orientation plasticity in mice (Figure 2a) and cat V1. It is shown that the ketamine effect on V1 cells is local since it does not exceed 0.7 mm, and transient since a recovery state was observed [68]. The question is whether the observed changes of the cells’ tuning properties were observed after visual adaptation, that is, could ketamine alter the adaptation effects? To implement adaptation, an imposed orientation can be exposed for several minutes. Results showed that restricted exposure of V1 cells to vertical orientation (90°) for 12 minutes shifted their original preferred orientations toward the exposed orientation (attractive shift). Contrarily, the tuning curve peaks of a few cells shifted away from the original preferred orientation (repulsive shift). Dual mechanisms have been proposed for repulsive and attractive shifts in cat. While the repulsive shift results in a decrease of excitation at the adapted flank of the tuning curve, the attractive shift is the result of the parallel facilitation of responses on the adapted flank and a depression on the opposite flank [69]. This effect of adaptation is known as a push–pull mechanism [69, 70]. In cats, Dragoi et al. [23] reported larger repulsive shifts near the pinwheels of orientation maps than in an iso-orientation domain in cats. This systematic change in V1 was attributed to a higher degree of plasticity near pinwheels because of the convergence of a broad spectrum of orientation inputs [23]. Comparing the cells’ orientation preferences in control, post-adaptation, and post-ketamine, the collected data showed that ketamine abolished the adaptation effects, that it changes the new preferred orientation. Apart from this general effect, electrophysiological studies reveal a more varied scenario. Indeed, the effect of ketamine categorizes cells into two groups according to the amplitude of the adaptation-induced shift: for cells exhibiting large shifts (superior to 24°), ketamine decreases the post-adaptation shift amplitude in that it alters their new preferred orientations toward the original preference, but for cells exhibiting small shifts (inferior to 24°), ketamine increases the post-adaptation shifts. Thus, while ketamine facilitates the cell’s recovery for large shifts, it potentiates the small shifts (Figure 2b). This might suggest that ketamine application leads to weakening or amplifying the adaptation effects according to the amplitude of the adaptation-induced shift.

Figure 2.

Effect of ketamine on orientation selectivity in the mouse. (a) The control preferred orientation of cells changes after ketamine application. (b) The effect of ketamine on post-adaptation preferred orientation depends on the post-adaptation shifts. Shifts inferior to 24° are amplified under ketamine while shifts superior to 24° are reduced, that is, ketamine favors cells’ recovery.

Because the results are similar in mouse and cat, we assumed that the mechanisms of cortical plasticity induced by ketamine, which operate at the level of single cells, are similar, independent of the presence of columnar organization.

2.2.2 Crosscorrelation analyses

Cross-correlogram (CCG) analysis is an efficient tool to reveal the putative functional coupling between neurons that display time relationships between their respective spike trains [71, 72, 73, 74, 75]. The stimulus-dependent synchrony should be suppressed in the shift-corrected cross-correlation histograms [76]; this allowed the measurement of synchrony excluding latencies evoked by stimuli onset. The CCG is performed between simultaneously recorded spike trains of two neurons where one cell is set as reference and the second as target. In CCGs, the time axis (X axis) is divided into bins of 1 ms and the Y-axis corresponds to the probability (p) of a neuron firing in the small bin of the size bconsidering the spike train is a Poisson process [77]; this probability pis calculated as follows:

$$p=F\times b$$E1

$$F=N/T$$E2

where Fis the neuronal firing rate, bis the bin size of the calculated firing of the neuron, Tis the total time interval and Nis the number of spikes in that interval. The functional connection between neuron-pairs is illustrated by a significant peak of at least one bin [78] within a window of 5 ms offset from zero. The statistical threshold for the significance peak was set at 95% and presented by the red line in Figure 3a. Cross-correlation function can also reveal neuronal synchrony which is generated when units receive a common input from other cells embedded in the network. In neuronal synchrony, the central peak of the CCG is significant within the time window −1 to +1 ms bin adjoining the central zero point (Figure 3b).

2.2.3 Ketamine reorganizes the cortical network

In cat and mouse, CCG analysis performed before and following ketamine application shows that this drug alters the putative synaptic links between neurons following visual adaptation. Thus, ketamine modulates the neuronal assembly by strengthening or weakening synaptic weight and/or adding new cells to connectomes (Figure 3c). The redistribution of synaptic weights between neurons after ketamine application suggests a reassignment of functions of each neuron pair inside the microcircuits. Ketamine not only enables altering the original network but also the post-adaptation microcircuits. This implies that when a single unit changes its selectivity after experience-dependent plasticity, its wiring changes according to its new preferred orientation (Figure 3c and d).

Ketamine might disturb cells’ activity which in turn redeploys the strength of projections between cells to restructure the entire wiring-dynamic of the neuronal assembly. We conclude that, despite the organizational difference between mouse and cat, ketamine remaps the connectivity of visual cortex microcircuits, and leads to a new configuration of the functional networks.

2.2.4 Functional connectivity within an assembly changes in response to different orientations

In this section, the network-dynamics of the assembly are related to the orientation changes in each condition (control, post-adaptation, post-ketamine). Thus, we investigated whether the strength of connections between units in an assembly is related to stimulus orientation. Results, in cat and mouse, show a unique network was activated at every orientation whatever the condition. Therefore, feature-specific connectivity was generated for each input stimulus. Thus, connections are activated or deactivated depending on the feature stimulus. Figure 3c-e illustrates the dynamic interactions between neurons within an assembly in response to different orientations in cat and mouse. In short, in mouse, as shown in Figure 3d, some connections were largely maintained despite the change in orientation, whereas, and independently of the condition, other links emerged specifically for some orientations (e.g., unit (e)—unit (c) at 67.5°). The connection disclosed between (f) and (d) units at 0° disappeared at other grating orientations. Furthermore, some connections were characterized by a change in their peak-strength (p) from one orientation to another as depicted by the changing colors in the connectivity matrices (Figure 3d) and the weights numbers over connecting lines (Figure 3e). For instance, the connection between unit (a) and unit (b) (p = 3.5% at 45°) weakens (p = 1.4% at 67.5°) as shown in Figure 3e.

Figure 3.

Cell assembly dynamics. (a) The functional network between a reference cell (green) and a target cell (orange on the left and cyan on the right) revealed by CCG analysis. (b) Neuronal synchrony revealed by a significant bin within the time window −1 to +1 ms adjoining the central zero point. In (a) and (b) the confidence limit is shown by the red curved line. (c and d) Strength matrices of a cells (6 × 6 cells simultaneously) in mouse, at all the tested gratings and in all conditions: Control (C), and post-ketamine (K) in c, and control (C), post-adaptation (a), and post-ketamine (K) in d. the colored scale (to the right) represents normalized peaks-strengths of connections. (e and f) Functional network between neurons according to the presented orientation in mouse (e) and in cat (f). The number above the black line indicates the probability of the connection between two units. For cat, cells were simultaneously recorded from two layers (L2/3) and (L5/6) separated in the scheme by the interrupted black line.

Similarly, in cat, some links were maintained at all presented orientations, implying the stability of distinctive connections between specific neurons (dark and light gray units), others were activated (black cell—light gray cell at 0°) or deactivated only at some orientations (the connectivity between dark gray cell—white cell disappeared at 45°, 67.5° and 90° (Figure 3f). All previous examples were drawn from the control condition. However, similar results were observed following adaptation and ketamine, depending on the orientation applied. We conclude that the functional links between pairs at a particular orientation (here 0°) show a unique network activated by a particular condition. Thus, adaptation changes the initial network and induces a new one; in addition, these cellular relationship modifications occur in both supra- and infra-granular layers (separated by the dotted horizontal black lines). This network acquired following adaptation was modified after ketamine application and a new pattern of connections emerges. It is worth mentioning that the effect of ketamine on the network dynamics is reversible since after recovery the connections between reference and targets return to the original pattern.

The change in the probabilities of connection (p) from one grating to another reflects a modification of synaptic weights between neurons in the assembly [78], wherein new neurons join and others leave in relation to the presented orientation. Accordingly, the unit output is the result of synaptic weights distributed over its dendritic tree for each grating. It was reported that within a cell-assembly, some connections are weak, therefore their feeble activation might confer flexibility to the assembly as the stimulus changes [79]. Thus, in the cortex, the functional units are neuronal ensembles rather than individual cells [80] and because of the synaptic flexibility of these neuronal groups, a dynamic salient microcircuit is involved for each visual stimulus. In line with a previous report [81], the encoding sensory stimuli might require a coordinated activity of specific groups of neurons that represent the building block of visual processing. Conclusively, all the above findings imply that the flexibility of the neuronal circuit keeps it permanently ready to receive the input efficiently and that the output is related to the assembly organization. In mouse, the proximity of neurons with different orientation preferences (salt-and-pepper organization) may favor each orientation grating, the activation of a specific group of synapses, and thus the emergence of a specific functional microcircuit. It is worth noting the activation of a specific functional network between co-active neurons as the orientation changes is a general property of stimulus processing that would be applicable to all mammals. It must be underlined that connectivity weights are independent of firing rates [79].

2.2.5 Ketamine affects the pair-wise synchrony

To investigate the effect of ketamine on the pair-wise synchrony, a computation of the number of connections and the CCG magnitudes of all summed pairs was performed at all presented orientations and compared between control, post-adaptation, and post-ketamine conditions. Results show that, contrasting with adaptation, under ketamine, the magnitude and the number of synchronous inputs was increased in cat but not in mouse. This increase might reflect a more coordinate activity of the recipient units with each other [82], which might lead to expand and upgrade the cortical processing and thus more efficient information transfer. Synchrony is energy demanding. Indeed, neuronal synchrony requires resources to time firing initiation accurately, aligned anatomical pathways to transfer the spikes, and energy expenditures for redundant action potentials [83]. Since in biological systems, the costs should not outweigh benefits, these energy costs should be counterbalanced by an information rate increase and more efficient information transfer. Moreover, it has been shown previously that in addition to the firing rate, the precise timing of firing potentially encoded visual information (the visual information is encoded in temporal patterns of firing) [84, 85, 86]. It seems that columnar, and not salt-pepper organization where cells with different orientation preferences are locally intermixed, favors the pair-wise synchrony. In the cat visual cortex, neurons with similar features are clustered together, forming columns, and are likely to be interconnected [78, 87, 88]. Thus, it is more likely to encounter close neurons with similar tuning then in mouse and this organization favors synchronization since it was shown that the latter is due in part to specific horizontal connections between cortical domains having similar tuning properties. Indeed, it was reported that cells exhibiting similar orientation preference showed a significant pair-wise synchrony [89].

2.2.6 Ketamine affects downstream signaling events and leads to plasticity

Antidepressants, in particular ketamine, influence neurotransmission since it blocks NMDAR activity. Investigators have made many important strides toward understanding the molecular mechanisms governing the induction of plasticity by ketamine in stimulus processing.

It was reported that excitation (E)inhibition (I) ratios (E/I) are equalized across visual cortical neurons [90] and that matched inhibition is mediated by PV interneurons [91]. Since it was demonstrated that ketamine alters the neurochemical phenotype of PV cells [92], and modulates cortical circuit E/Iratios [93, 94], a new equilibrium of E/Iratios might be a putative explanation for the neuronal microcircuits’ dynamics observed following ketamine administration. E/I ratio is activity-dependent [90], and the blockage of NMDA mediated activities might rebalance it. The effects of ketamine could also be explained by the increase in the expression of several molecules involved in neuronal plasticity, in particular, the neurotrophin BDNF, and its receptor TrkB. Thus, reactivation of activity-dependent and BDNF-mediated cortical plasticity by ketamine leads to the alteration of neuronal networks to better adapt to environmental challenges [95]. Furthermore, ketamine increases neurogenesis [96, 97, 98] and synaptogenesis [60, 99, 100, 101].

3. Conclusion

In the primary cortical areas, cells are fed by the feedforward thalamic drive while their intrinsic properties are further shaped through the local recurrent network. The most striking effects of ketamine are the imposition of new intrinsic properties of individual neurons and the abolition of adaptation effects. The core of the representational question is whether the changes in synaptic strengths, under ketamine, constitute an engram of a new encoding of inputs in the visual processing. Experimental findings show that in parallel to tuning shifts of V1 orientation-selective cells, ketamine reorganizes the connectomes, that is, cells modifying their synaptic weight, and therefore a change of the synaptic links between units was observed. These results might implicitly provide that synaptic rewiring plasticity underlies cortical map reorganization and that the modification of a cell’s selectivity by ketamine may be better viewed in relationship to neuronal connections. In the cat primary visual cortex, it was reported that long-range horizontal axons preferentially bind to distant columns of similar tuning preferences which favors synchrony of cells’ activity under ketamine. This could suggest that ketamine through activity-dependent synaptic plasticity can redistribute connections to preferentially link neurons with similar response properties.

Acknowledgments

We acknowledge the Conseil de Recherche en Sciences Naturelles et en Genie du Canada (CRSNG) to support the completion of this study and Steve Itaya for his comments on the early version of the manuscript.

Conflict of interest

Authors declare that they have no conflict of interest