Hebbian and neuromodulatory mechanisms interact to trigger associative memory formation

A long-standing hypothesis termed “Hebbian plasticity” suggests that memories are formed through strengthening of synaptic connections between neurons with correlated activity. In contrast, other theories propose that coactivation of Hebbian and neuromodulatory processes produce the synaptic strengthening that underlies memory formation. Using optogenetics we directly tested whether Hebbian plasticity alone is both necessary and sufficient to produce physiological changes mediating actual memory formation in behaving animals. Our previous work with this method suggested that Hebbian mechanisms are sufficient to produce aversive associative learning under artificial conditions involving strong, iterative training. Here we systematically tested whether Hebbian mechanisms are necessary and sufficient to produce associative learning under more moderate training conditions that are similar to those that occur in daily life. We measured neural plasticity in the lateral amygdala, a brain region important for associative memory storage about danger. Our findings provide evidence that Hebbian mechanisms are necessary to produce neural plasticity in the lateral amygdala and behavioral memory formation. However, under these conditions Hebbian mechanisms alone were not sufficient to produce these physiological and behavioral effects unless neuromodulatory systems were coactivated. These results provide insight into how aversive experiences trigger memories and suggest that combined Hebbian and neuromodulatory processes interact to engage associative aversive learning.Hebbian plasticity refers to the strengthening of a presynaptic input onto a postsynaptic neuron when both pre- and postsynaptic neurons are coactive (1). This was originally proposed as a mechanism for memory formation. Findings from in vitro and in vivo physiological studies suggest that Hebbian processes control synaptic strengthening (2–10). However, other results and theories suggest that Hebbian mechanisms alone are not normally sufficient for producing synaptic plasticity and that synaptic strengthening mediating memory formation involves interactions between Hebbian and neuromodulatory mechanisms (3, 4, 7, 11–19). Although molecules that may mediate Hebbian processes in memory formation have been identified (3, 11, 16, 17, 20–22), it has been difficult to directly test whether Hebbian plasticity alone or in combination with neuromodulation is necessary and sufficient to produce neural plasticity and memories in behaving animals (especially in mammals). This is because of technical limitations in controlling correlated activity between pre- and postsynaptic neurons involved in memory storage in a temporally/spatially precise manner while measuring behavioral memory formation and neural plasticity.To overcome these problems, we used optogenetic techniques to directly manipulate Hebbian mechanisms in pyramidal neurons in the lateral nucleus of the amygdala (LA), a cell population important for storing aversive memories. Pavlovian auditory threat (fear) conditioning (23, 24) is a form of associative learning during which a neutral auditory conditioned stimulus (CS) is temporally paired with an aversive unconditioned stimulus (US), often a mild electric shock (17, 20, 21, 25–27). Following training, the auditory CS comes to elicit behavioral defense responses (such as freezing) and supporting physiological changes controlled by the autonomic nervous and endocrine systems. These conditioned responses can be used to measure the associative memory created by CS–US pairing.This form of aversive Pavlovian conditioning is a particularly useful model for testing the Hebbian hypothesis because a critical site of associative plasticity underlying the learning has been identified in the LA (17, 22, 28). LA neurons receive convergent input from the auditory system and from aversive nociceptive circuits (29, 30). Auditory inputs to LA neurons are potentiated during threat conditioning (31–34), possibly as a result of auditory-evoked presynaptic activity occurring convergently and contemporaneously with strong activation of postsynaptic LA pyramidal neurons by the aversive shock US (i.e., a Hebbian mechanism). If these neural and behavioral changes are the result of Hebbian plasticity, then activity in LA pyramidal neurons specifically during the aversive US period (when both presynaptic inputs and postsynaptic neurons may be active) should be necessary for aversive memory formation and learning-related plasticity of auditory input synapses in the LA to occur. Reducing activity in LA neurons should disrupt the correlation between presynaptic activity induced by the auditory CS and postsynaptic activity induced by the aversive US. In addition, pairing the auditory CS with direct depolarization of LA pyramidal neurons in place of a shock US should be sufficient to produce aversive memories and plasticity of auditory inputs to the LA. This is because direct stimulation of postsynaptic LA neurons as an US would artificially produce coactivity with concurrently active auditory inputs. Previously, we found that this type of training procedure did produce behavioral learning when many training trials were used (35). However, the behavioral memory acquired under these conditions was somewhat weak, suggesting that other factors, such as neuromodulatory receptor activation, might function in a cooperative way to enhance Hebbian neural plasticity in the LA to possibly regulate the gain of aversive memory formation. Here we optogenetically manipulated correlated activity between auditory inputs and LA postsynaptic pyramidal neurons to directly test whether Hebbian mechanisms are both necessary and sufficient to produce changes in auditory processing in the LA and fear memories.     

Rapid homeostasis by disinhibition during whisker map plasticity

How homeostatic processes contribute to map plasticity and stability in sensory cortex is not well-understood. Classically, sensory deprivation first drives rapid Hebbian weakening of spiking responses to deprived inputs, which is followed days later by a slow homeostatic increase in spiking responses mediated by excitatory synaptic scaling. Recently, more rapid homeostasis by inhibitory circuit plasticity has been discovered in visual cortex, but whether this process occurs in other brain areas is not known. We tested for rapid homeostasis in layer 2/3 (L2/3) of rodent somatosensory cortex, where D-row whisker deprivation drives Hebbian weakening of whisker-evoked spiking responses after an unexplained initial delay, but no homeostasis of deprived whisker responses is known. We hypothesized that the delay reflects rapid homeostasis through disinhibition, which masks the onset of Hebbian weakening of L2/3 excitatory input. We found that deprivation (3 d) transiently increased whisker-evoked spiking responses in L2/3 single units before classical Hebbian weakening (≥5 d), whereas whisker-evoked synaptic input was reduced during both periods. This finding suggests a transient homeostatic increase in L2/3 excitability. In whole-cell recordings from L2/3 neurons in vivo, brief deprivation decreased whisker-evoked inhibition more than excitation and increased the excitation–inhibition ratio. In contrast, synaptic scaling and increased intrinsic excitability were absent. Thus, disinhibition is a rapid homeostatic plasticity mechanism in rodent somatosensory cortex that transiently maintains whisker-evoked spiking in L2/3, despite the onset of Hebbian weakening of excitatory input.During deprivation-induced sensory map plasticity in cerebral cortex, changes in sensory input trigger both homeostatic plasticity mechanisms that maintain stable cortical firing rates and Hebbian mechanisms, in which inactive inputs lose (and active inputs gain) representation in sensory maps (1). Diverse mechanisms for homeostasis exist, including synaptic scaling (2–4), plasticity of intrinsic excitability (5, 6), and changes in sensory-evoked inhibition and excitation–inhibition (E-I) ratio (7–11). How homeostatic and Hebbian mechanisms interact to control map stability and plasticity remains unclear.One key unknown is the relative dynamics of homeostatic and Hebbian plasticity. Homeostasis mediated by synaptic scaling is slow, occurring over hours in vitro and days in vivo. This process is evident in visual cortex, where eyelid closure during the critical period classically drives Hebbian weakening of closed eye spiking responses (after 2 d of deprivation) followed several days later by a slower homeostatic increase in visual responses (12, 13) mediated by excitatory synaptic scaling (3, 14, 15). We investigated whether more rapid forms of homeostasis also exist that shape the earliest stages of cortical plasticity. Recent results in visual cortex show that eyelid closure rapidly weakens inhibitory circuits (within 1 d), and this process increases network excitability and, therefore, is an initial homeostatic response to deprivation (10, 16). This disinhibition correlates with rapid structural plasticity in inhibitory axons and dendrites (17) and is mediated by a reduction in excitatory drive to parvalbumin-positive interneurons (10). Whether rapid homeostasis by disinhibition or other mechanisms is a general feature of cortical plasticity outside the visual cortex is unknown. Theoretical work shows that rapid homeostasis by inhibitory and/or intrinsic plasticity can guide development of realistic sensory tuning and sparse sensory coding in cortical networks, suggesting broad relevance (18).We tested for rapid homeostasis during the onset of whisker map plasticity in the rodent primary somatosensory (S1) cortex, a major model of cortical plasticity. Each cortical column in the S1 whisker map corresponds to one facial whisker, termed its principal whisker (PW). Trimming or plucking a subset of whiskers in young adults weakens spiking responses to deprived PWs in layer 2/3 (L2/3) of deprived columns (19, 20). This process is mediated by Hebbian synaptic weakening at L4–L2/3 and L2/3–L2/3 excitatory synapses (21–23). No homeostatic restoration or strengthening of deprived whisker responses is known. However, PW response weakening is often preceded by an unexplained initial delay of ∼7 d, in which deprived whisker-evoked spiking responses remain stable (24, 25). We hypothesized that this initial delay reflects not a lack of plasticity but rapid homeostasis that (i) masks initial Hebbian weakening of L2/3 excitatory input and (ii) is mediated by loss of inhibition and/or increased intrinsic excitability in L2/3 neurons. Such rapid homeostasis would be a unique component of whisker map plasticity.     

A circuit mechanism for independent modulation of excitatory and inhibitory firing rates after sensory deprivation

Diverse interneuron subtypes shape sensory processing in mature cortical circuits. During development, sensory deprivation evokes powerful synaptic plasticity that alters circuitry, but how different inhibitory subtypes modulate circuit dynamics in response to this plasticity remains unclear. We investigate how deprivation-induced synaptic changes affect excitatory and inhibitory firing rates in a microcircuit model of the sensory cortex with multiple interneuron subtypes. We find that with a single interneuron subtype (parvalbumin-expressing [PV]), excitatory and inhibitory firing rates can only be comodulated—increased or decreased together. To explain the experimentally observed independent modulation, whereby one firing rate increases and the other decreases, requires strong feedback from a second interneuron subtype (somatostatin-expressing [SST]). Our model applies to the visual and somatosensory cortex, suggesting a general mechanism across sensory cortices. Therefore, we provide a mechanistic explanation for the differential role of interneuron subtypes in regulating firing rates, contributing to the already diverse roles they serve in the cortex.

Diverse interneurons serve multiple cell-type-specific functions in the cortex (1, 2). The connectivity among excitatory pyramidal neurons and different subtypes of interneurons plays a key role in establishing these functions. In mature cortical circuits, interneurons are involved in disinhibition during locomotion and learning (3, 4), response reversal during top-down modulation (3, 5–7), surround suppression (8, 9), and affect excitatory tuning (10, 11). Inhibitory synapses are plastic (12, 13) and drive plasticity in excitatory circuits (14); however, we still do not understand how the plasticity of connections among the different interneuron subtypes and excitatory neurons shapes circuit dynamics and computations.Cortical circuits are particularly sensitive to perturbations in development and young adulthood during so-called critical periods, when manipulating sensory experience can induce long-lasting changes in circuit connectivity (15–17). Depriving rodents of vision in one eye (known as monocular deprivation, or MD) causes a biphasic response in the monocular region of the primary visual cortex (V1m), driven exclusively by the contralateral eye, that first reduces and then restores excitability (18–20). The plasticity of inhibitory synapses contributes to these processes (21–25). However, it primarily pertains to fast-spiking interneurons, which most likely correspond to parvalbumin-expressing (PV) interneurons, the most abundant and best-studied interneuron subtype in the cortex (2).Previous work has found that the plasticity of recurrent connectivity, and especially the potentiation of intracortical inhibition, dominates over the depression of feedforward connectivity to explain the initial decrease of excitatory and inhibitory activity after MD (24). However, recent experiments show that the network behavior might be more complex with fast-spiking, putative PV inhibitory neurons decreasing their firing rates 1 d after MD (MD1), while excitatory neurons are delayed by an additional day (19, 26). What mechanism lies behind this independent modulation of excitatory and inhibitory firing rates remains unclear.We used a spiking recurrent network with balanced excitation and inhibition to study this process in a microcircuit model of the sensory cortex. Theoretical work has shown that the dynamics of these networks depend on the operating regime, which is determined by the strength of recurrent coupling (27–31). Strong excitatory recurrent coupling needs to be stabilized by sufficiently strong inhibition, giving rise to “inhibition-stabilized networks” (ISNs) (8, 32). A signature of inhibition stabilization is the “paradoxical effect,” which refers to the decrease of inhibitory firing rate following direct excitatory drive to inhibitory interneurons (32). Recent experiments have confirmed the paradoxical effect in cortical circuits, suggesting that they operate in the ISN regime (33, 34). This raises the important question of whether ISNs can explain the independent modulation of excitatory and inhibitory firing rates after brief MD.We found that ISNs cannot capture the independent modulation of excitatory and inhibitory firing rates after brief MD. Even in the presence of heterogeneous connectivity, recurrently driven inhibitory neurons cannot independently modulate their firing rates relative to excitatory neurons. Considering the diversity of interneuron subtypes in the sensory cortex and their role in modulating cortical dynamics, we also modeled somatostatin-expressing (SST) interneurons, the second-most-abundant subtype of interneurons in the cortex (2). Our results demonstrate that the addition of SST interneurons inverts the firing-rate response of PV interneurons relative to excitatory neurons in response to MD-induced plasticity by reversing the paradoxical effect. In contrast to previous work that focused on the paradoxical effect in response to externally injected currents (6, 35, 36), we find that recurrent interactions are the main drivers, specifically, the strength of the feedback from SST interneurons to PV interneurons and excitatory neurons. Importantly, we implement synaptic changes observed experimentally both along the feedforward [from the thalamus (24, 37)] and recurrent [within the cortex (21, 23, 38)] pathways that significantly expand the possibilities for modulating cortical firing rates beyond external drive to the inhibitory population. Hence, our results explain the independent modulation of excitatory and inhibitory firing rates, consistent with their sequential suppression during early MD with inhibitory preceding excitatory firing rates (19, 20). We also applied our model to whisker deprivation (WD) in the somatosensory cortex, which affects interneuron intrinsic excitability rather than synaptic strength onto interneurons (39). Our model predicts similar modulations of the firing rates when changing the intrinsic excitability, suggesting that similar principles might be at work in different sensory cortices. Therefore, our work provides a mechanistic explanation for the experimentally observed temporally offset modulation of excitatory and inhibitory activity after sensory deprivation. It also establishes a more general framework to study how the interaction of three factors—cortical operating regime, interneuron diversity, and plasticity in feedforward and recurrent pathways—shapes circuit dynamics and computations.     

Experience-dependent functional plasticity and visual response selectivity of surviving subplate neurons in the mouse visual cortex

Subplate neurons are early-born cortical neurons that transiently form neural circuits during perinatal development and guide cortical maturation. Thereafter, most subplate neurons undergo cell death, while some survive and renew their target areas for synaptic connections. However, the functional properties of the surviving subplate neurons remain largely unknown. This study aimed to characterize the visual responses and experience-dependent functional plasticity of layer 6b (L6b) neurons, the remnants of subplate neurons, in the primary visual cortex (V1). Two-photon Ca²⁺ imaging was performed in V1 of awake juvenile mice. L6b neurons showed broader tunings for orientation, direction, and spatial frequency than did layer 2/3 (L2/3) and L6a neurons. In addition, L6b neurons showed lower matching of preferred orientation between the left and right eyes compared with other layers. Post hoc 3D immunohistochemistry confirmed that the majority of recorded L6b neurons expressed connective tissue growth factor (CTGF), a subplate neuron marker. Moreover, chronic two-photon imaging showed that L6b neurons exhibited ocular dominance (OD) plasticity by monocular deprivation during critical periods. The OD shift to the open eye depended on the response strength to the stimulation of the eye to be deprived before starting monocular deprivation. There were no significant differences in visual response selectivity prior to monocular deprivation between the OD changed and unchanged neuron groups, suggesting that OD plasticity can occur in L6b neurons showing any response features. In conclusion, our results provide strong evidence that surviving subplate neurons exhibit sensory responses and experience-dependent plasticity at a relatively late stage of cortical development.

The mammalian cerebral cortex consists of six layers, with distinct roles in information processing (1, 2). At the bottom of the neocortex, on the boundary between the gray matter and white matter, there is a thin sheet of neurons called layer 6b (L6b) (3). Layer 6b neurons are thought to be remnants of subplate neurons based on their location and cell-type marker expression (4). During prenatal and early postnatal periods, subplate neurons form transient neuronal circuits that play key roles in cortical maturation (5–7). In the embryonic cortex, subplate neurons form short-lived synapses with early immature neurons to regulate radial migration (8). During perinatal development, subplate neurons transiently receive inputs from ingrowing thalamic axons and innervate layer 4 (L4) to guide thalamic inputs to the eventual target, L4 (5, 6). Thus, the circuits formed by subplate neurons at the perinatal developmental stage are essential to establish basic neuronal circuits before starting experience-dependent refinements (5–7). Subsequently, subplate neurons largely disappear due to programmed cell death, but some survive and reside in L6b (5, 6). In the adult cortex, L6b neurons form neuronal circuits with local and long-distance neurons, which are different from those formed during early development (9–12). Therefore, surviving subplate neurons may acquire a role in information processing after remodeling of neuronal connections. A recent study using three-photon Ca²⁺ imaging demonstrated that L6b neurons show visual responses with broad orientation/direction tuning in the adult mouse primary visual cortex (V1) (13). However, comparable evidence for L6b response properties with other layer neurons in V1 is lacking (14–20). Moreover, L6b neurons have diverse morphology and molecular expression (21–24). Neurons born during subplate neurogenesis show the different expression patterns of subplate markers in postnatal L6b (4). However, the response properties in each subtype of L6b neurons remain unknown.The sensory responsiveness of cortical neurons is considerably refined by sensory experience relatively late in development, referred to as the critical period (25, 26). Previous studies have demonstrated that sensory activities before the onset of the critical period affect the arrangement of subplate neuron neurites in the barrel cortex and local subplate circuits in the auditory cortex (27, 28). However, there is no direct evidence that the sensory responses of surviving subplate neurons are modified by sensory experience during the critical period. If experience-dependent plasticity occurs in subplate neuron responses, they will contribute to the experience-dependent development of sensory functions and possibly to the functions in the mature cortex. Ocular dominance (OD) plasticity in V1 is a canonical model used to examine experience-dependent refinement of sensory responses (25, 26, 29, 30). If one eye is occluded for several days during the critical period, neurons in V1 lose their response to the deprived eye. OD plasticity is robustly preserved across species and cell types. Therefore, OD plasticity is suitable for evaluating experience-dependent plasticity in L6b neurons.This study aimed to characterize the visual responses and OD plasticity of L6b neurons in V1. Toward this goal, two-photon Ca²⁺ imaging was performed in awake juvenile mice, followed by 3D immunohistochemistry with a subplate neuronal marker, connective tissue growth factor (CTGF) (4, 31). L6b neurons showed broader tuning to visual stimuli and lower binocular matching of orientation preference than did layer 2/3 (L2/3) and L6a neurons. Chronic two-photon imaging revealed significant OD plasticity in individual L6b neurons during the critical period. Our results provide strong evidence that L6b neurons, presumed to be subplate neuron remnants, exhibit sensory responses and experience-dependent functional plasticity at a relatively late stage of cortical development.     

Syngap1 regulates experience-dependent cortical ensemble plasticity by promoting in vivo excitatory synapse strengthening

A significant proportion of autism risk genes regulate synapse function, including plasticity, which is believed to contribute to behavioral abnormalities. However, it remains unclear how impaired synapse plasticity contributes to network-level processes linked to adaptive behaviors, such as experience-dependent ensemble plasticity. We found that Syngap1, a major autism risk gene, promoted measures of experience-dependent excitatory synapse strengthening in the mouse cortex, including spike-timing–dependent glutamatergic synaptic potentiation and presynaptic bouton formation. Synaptic depression and bouton elimination were normal in Syngap1 mice. Within cortical networks, Syngap1 promoted experience-dependent increases in somatic neural activity in weakly active neurons. In contrast, plastic changes to highly active neurons from the same ensemble that paradoxically weaken with experience were unaffected. Thus, experience-dependent excitatory synapse strengthening mediated by Syngap1 shapes neuron-specific plasticity within cortical ensembles. We propose that other genes regulate neuron-specific weakening within ensembles, and together, these processes function to redistribute activity within cortical networks during experience.

Autism risk genes converge on several neurobiological functions, including the regulation of synapse biology (1–3). Synapse processes directly controlled by autism spectrum disorder (ASD) risk genes include de novo synapse formation, synapse maturation, and activity-driven changes in synapse function (i.e., synapse plasticity). Synapse plasticity, especially in cortical excitatory neurons, is a process enabling neural circuits to store new information, which is essential for experience-dependent modifications of behavior to promote survival (4, 5). Thus, risk genes are thought to contribute to ASD etiology by disrupting how neural circuits change in response to novel experiences, which in turn contributes to maladaptive behaviors. However, the study of risk gene biology and their relationship to neural plasticity is largely restricted to reduced biological preparations that focus on isolated changes to a small subset of synapses. Therefore, it is unclear how risk gene–driven regulation of synapse plasticity contributes to changes in neural dynamics within intact functional networks known to drive adaptive behaviors.Neuronal ensembles, or groups of coactivated neurons, are thought to be the direct neural substrate of cognitive processes and behavior (6). In cortex, ensemble plasticity is a multidimensional process that reflects the distribution of distinct cellular plasticity mechanisms across individual neuronal components within the assembly. For example, neurons within the same sensory-evoked cortical ensemble can undergo either increases or decreases in activity in response to the same sensory experience (7–9). While this general phenomenon has been observed in multiple contexts, it is unclear how neurons within the same functional network can have opposing changes to enduring neuronal activity in response to the same sensory experience. One way that this may occur is through the simultaneous activation of distinct forms of experience-dependent plasticity that are differentially distributed throughout neurons that comprise a functional network. Indeed, sensory experience drives the induction of Hebbian-type synaptic plasticity that can strengthen or weaken excitatory synaptic input onto sensory-responsive neurons (10). Experience-dependent circuit plasticity is not limited to changes in excitatory synaptic strength. Robust changes to the function and connectivity of GABAergic interneurons within cortical microcircuits also occurs in response to novel experience, which in turn regulates the output of pyramidal neurons (11–13). Moreover, intrinsic changes to neuronal excitability have also been observed, and in combination with changes to GABAergic function, these collective processes are thought to maintain a set firing rate within networks even as activity is redistributed among individual neurons (8, 14, 15).We propose that experience induces heterogenous changes in activity within neurons of a cortical assembly through cellular processes controlled, at least in part, by genetic mechanisms linked to ASD risk. This hypothesis originates from the clear overrepresentation of ASD risk genes that regulate the neurobiology of synapses and synapse plasticity (1–3). However, because of the multidimensional nature of cortical network plasticity, one cannot infer how a gene influences experience-dependent changes in distributed network dynamics when the function of the gene has only been studied in isolated subcellular structures, such as synapses. It is therefore important to study major ASD risk genes in the context of intact functional networks. Doing so will help to elucidate how their influence over molecular and cellular functions contribute to intermediate network-level processes more directly linked to behaviors, such as cortical ensemble plasticity.In this study, we investigated how a major ASD risk gene, SYNGAP1/Syngap1 (HUMAN/mouse–mouse only from now on), regulates specific aspects of cellular plasticity in vivo and how this process shapes experience-dependent ensemble plasticity with sensory-responsive cortical networks. The Syngap1 gene, which is a major autism risk factor (16), is a robust regulator of various forms of long-term potentiation (LTP) (17), a cellular model of Hebbian plasticity. It regulates LTP through control of excitatory synapse structure and function by gating NMDA receptor-dependent regulation of AMPA receptor trafficking and dendritic spine size (18–20). The role of Syngap1 in regulating synapse plasticity has been observed by various researchers across distinct neuronal subtypes in a variety of in vitro and ex vivo preparations (21–24). Based on this past work in reduced preparations, we hypothesized that Syngap1 regulates experience-dependent ensemble plasticity by promoting the strengthening of excitatory synapses within functional cortical networks. We found that Syngap1 was required for spike-timing-dependent (STD) synaptic potentiation and experience-mediated synapse bouton formation in layer (L) 2/3 of somatosensory cortex (SSC) but not synaptic depression or synapse bouton elimination. Syngap1 heterozygosity in mice disrupted experience-dependent potentiation of neuronal activity within a subpopulation of L2/3 SSC neurons. Syngap1 loss of function had no effect on plasticity of neurons within the same ensemble that weakens with experience. These findings indicate that disruptions to synapse-level strengthening mechanisms in Syngap1 mice contribute to imbalanced cortical ensemble plasticity driven by novel sensory experience. We propose that a key function of Syngap1 is to promote complex network-level plasticity through the strengthening of excitatory connections within cortical circuits.     

Robust network oscillations during mammalian respiratory rhythm generation driven by synaptic dynamics

How might synaptic dynamics generate synchronous oscillations in neuronal networks? We address this question in the preBötzinger complex (preBötC), a brainstem neural network that paces robust, yet labile, inspiration in mammals. The preBötC is composed of a few hundred neurons that alternate bursting activity with silent periods, but the mechanism underlying this vital rhythm remains elusive. Using a computational approach to model a randomly connected neuronal network that relies on short-term synaptic facilitation (SF) and depression (SD), we show that synaptic fluctuations can initiate population activities through recurrent excitation. We also show that a two-step SD process allows activity in the network to synchronize (bursts) and generate a population refractory period (silence). The model was validated against an array of experimental conditions, which recapitulate several processes the preBötC may experience. Consistent with the modeling assumptions, we reveal, by electrophysiological recordings, that SF/SD can occur at preBötC synapses on timescales that influence rhythmic population activity. We conclude that nondeterministic neuronal spiking and dynamic synaptic strengths in a randomly connected network are sufficient to give rise to regular respiratory-like rhythmic network activity and lability, which may play an important role in generating the rhythm for breathing and other coordinated motor activities in mammals.Central pattern generators (CPGs) are neuronal circuits that generate coordinated activity in the absence of sensory input (1). One such mammalian CPG, the preBötzinger complex (preBötC), gives rise to the eupneic respiratory rhythm (2, 3). Located in the medulla, the preBötC preserves a spontaneous respiratory-like rhythm when isolated in transverse slices, but the precise nature of the cellular and synaptic mechanisms underlying rhythmogenesis remains elusive (3–7). An early hypothesis was that the neuronal activity is driven by intrinsically bursting pacemaker neurons synchronized via excitatory synaptic connections (2, 6, 8, 9). However, electrophysiological and modeling studies (7, 10–12) now suggest the rhythm emerges through stochastic activation of intrinsic currents conveyed by recurrent synaptic connections, without the need for pacemaker neurons (3, 4, 11, 13, 14). In either case, excitatory synapses are required for rhythm generation; the possibility that synaptic properties also underlie periodic burst initiation and termination is yet to be demonstrated.Synaptic transmission relies on the release of vesicles, which can be modulated at the presynaptic terminal. Synaptic depression (SD), based on vesicular release, consists of decaying release probability after sustained activity, which subsequently decreases excitability within the underlying connected network. Conversely, synaptic facilitation (SF) enhances vesicular release probability and promotes neuronal synchronization. These synaptic dynamics are critical for short-term synaptic plasticity, and here they are explored in the context of preBötC rhythm generation.We first consider a randomly connected network where each neuron is modeled using a generalized Hodgkin–Huxley system of equations and exhibits spontaneous spiking activity based on a random process, but the neurons do not have intrinsic bursting mechanisms. These neurons are sparsely connected within a realistically sized network by excitatory synapses. The distinction of this model, from previous preBötC models, is that synapses express SF and SD that is implemented using two separate pools of vesicles and creates dynamic synapses. The first pool is the readily releasable pool (RRP) and the other is the recycling pool (RP) (15), modeled with mass-action kinetics. Synaptic dynamics has been repeatedly used to describe changes in spike rates in neural network populations (16) and emergence of gamma oscillations (17). Furthermore network connectivity can also participate to define bursting or the oscillation frequency in neural networks (18, 19).We show here that random networks connected with these synaptic properties, with random spiking, are sufficient for periodic bursting and examine a variety of experimental scenarios testing this model. The present model shows that an ensemble of excitatory neurons driven by synaptic dynamics can generate population-wide rhythmic activity and behaves in a manner similar to the preBötC under different conditions observed in vitro. Finally, we show experimentally that excitatory inputs to preBötC neurons often exhibit dynamically changing excitatory postsynaptic currents (EPSCs), supporting the modeled concept that SF/SD occurs on a timescale relevant to influence respiratory periods.     

Rehabilitation drives enhancement of neuronal structure in functionally relevant neuronal subsets

We determined whether rehabilitation after cortical injury also drives dynamic dendritic and spine changes in functionally distinct subsets of neurons, resulting in functional recovery. Moreover, given known requirements for cholinergic systems in mediating complex forms of cortical plasticity, including skilled motor learning, we hypothesized that cholinergic systems are essential mediators of neuronal structural and functional plasticity associated with motor rehabilitation. Adult rats learned a skilled forelimb grasping task and then, underwent destructive lesions of the caudal forelimb region of the motor cortex, resulting in nearly complete loss of grasping ability. Subsequent intensive rehabilitation significantly enhanced both dendritic architecture and spine number in the adjoining rostral forelimb area compared with that in the lesioned animals that were not rehabilitated. Cholinergic ablation markedly attenuated rehabilitation-induced recovery in both neuronal structure and motor function. Thus, rehabilitation focused on an affected limb robustly drives structural compensation in perilesion cortex, enabling functional recovery.Studies over the past decade have indicated that the adult brain is structurally dynamic (1–3). Indeed, dendritic spines dynamically turn over in the adult brain (3, 4), and learning of novel tasks is associated with further increases in spine turnover (4). Moreover, total and stable increases in spine number together with enhanced dendritic complexity can be detected when analyses are focused specifically on neuronal subpopulations that are functionally related to a newly learned motor skill (5). For example, we recently reported that cortical layer V pyramidal neurons, which project to spinal segment C8 and are specifically engaged when learning a skilled forelimb grasping task, elaborate a 22% increase in apical dendritic spines and exhibit significant increases in dendritic branching and total dendritic length (5); an adjoining control population of cortical layer V pyramidal neurons that project to C4, which are not specifically shaped by the skilled motor task, exhibits no change in spines or dendritic complexity when the same task is learned (5). The detection of stable structural increases in neurons engaged by skilled motor learning in contrast to a lack of change in adjacent neurons that are not engaged by learning advances our understanding of mechanisms underlying experience-dependent cortical plasticity.Damage to the adult CNS also generates adaptive brain plasticity. For example, focal cortical lesions evoke cortical map plasticity (6, 7), extension of new axonal connections (7, 8), and neurogenesis (9). A very important and unresolved question in the neural plasticity and injury fields is whether rehabilitation—that is, specific retraining of injured neural circuits—can drive, alter, or enhance neural plasticity subsequent to brain lesions. Whereas extensive literature has shown that rehabilitation can increase the numbers of dendritic spines and dendritic complexity in the cortical hemisphere opposite a brain lesion (10–13) and is associated with improved skill in the limb unaffected by the lesion, effects of rehabilitation on neuronal structure in perilesioned cortex have not been described. Indeed, some studies suggest either stability or early loss of dendritic structure in perilesion cortex (14–16). However, knowing whether rehabilitation can drive adaptive brain plasticity could be essential in improving outcomes of numerous CNS disorders acquired in adulthood, including stroke, traumatic brain injury, and spinal cord injury.Prior studies that have sought to interrogate neuronal structure after injury have been limited by their use of nonspecific cellular sampling methods, such as Golgi–Cox staining or EM; these approaches lack the ability to specifically sample structural changes in neurons associated with specific tasks that are practiced in rehabilitation. Sampling from subpopulations of neurons mediating specific behaviors, such as skilled grasping in the motor cortex, may yield far more sensitive measures of changes in dendritic structure and spine number as a function of rehabilitation, fundamentally advancing our understanding of the role of experience and rehabilitation on structural neuronal plasticity.Another consideration in understanding cortical mechanisms underlying plasticity after CNS injury is the contribution of subcortical systems that modulate cortical activity, including cholinergic inputs. Studies have identified an essential role for cholinergic activation in modulating cortical plasticity associated with learning (17–19) and motor map plasticity that is evoked after lesions of the caudal forelimb region of the motor cortex (6, 20). These observations raise the possibility that cholinergic inputs to the motor cortex are also essential for generating neuronal structural adaptations in response to rehabilitation training after injury.In this study, we hypothesized that rehabilitation after injury to the adult brain drives adaptive plasticity, rebuilding spines and enhancing dendritic architecture in neurons surrounding the lesion site. We further hypothesize that these changes are cholinergic-dependent. We examined specific subpopulations of layer V cortical neurons directly related to the learning, loss, and subsequent relearning of skilled forelimb grasping, allowing detailed and specific sampling of structural parameters among subpopulations of neurons specifically engaged in the skilled grasping task.     

Olfactory learning promotes input-specific synaptic plasticity in adult-born neurons

The production of new neurons in the olfactory bulb (OB) through adulthood is a major mechanism of structural and functional plasticity underlying learning-induced circuit remodeling. The recruitment of adult-born OB neurons depends not only on sensory input but also on the context in which the olfactory stimulus is received. Among the multiple steps of adult neurogenesis, the integration and survival of adult-born neurons are both strongly influenced by olfactory learning. Conversely, optogenetic stimulation of adult-born neurons has been shown to specifically improve olfactory learning and long-term memory. However, the nature of the circuit and the synaptic mechanisms underlying this reciprocal influence are not yet known. Here, we showed that olfactory learning increases the spine density in a region-restricted manner along the dendritic tree of adult-born granule cells (GCs). Anatomical and electrophysiological analysis of adult-born GCs showed that olfactory learning promotes a remodeling of both excitatory and inhibitory inputs selectively in the deep dendritic domain. Circuit mapping revealed that the malleable dendritic portion of adult-born neurons receives excitatory inputs mostly from the regions of the olfactory cortex that project back to the OB. Finally, selective optogenetic stimulation of olfactory cortical projections to the OB showed that learning strengthens these inputs onto adult-born GCs. We conclude that learning promotes input-specific synaptic plasticity in adult-born neurons, which reinforces the top-down influence from the olfactory cortex to early stages of olfactory information processing.Within the framework of Hebbian theory, information processing, learning, and memory all depend on dramatic changes in the synaptic weights throughout life. Recent progress in microscopy has extended this notion to structural synaptic plasticity, as synaptic networks were reported to be highly dynamic because of ongoing mechanisms that encompass synapse formation, stabilization, and elimination (1). Therefore, when studying synaptic plasticity today, one has to take into account both the functional and structural changes that lead to continuous remodeling of a given synaptic network.Developing cortical networks are molded by experience or activity patterns during a “sensitive period” in development (2). Similarly, newly formed neurons in the adult olfactory bulb (OB) may be subject to plastic changes during a restricted time window after generation (3). Identifying the neural mechanisms and the nature of signals that trigger changes in developing and adult brain circuits during critical periods is a matter of intense debate (4). To address this fundamental question of circuit plasticity, we used the adult OB circuit, which receives about 30,000 new neurons per day in rodents, as a model system. As in embryonic cell development, adult-born neuron integration is under a strong selection process in which half of the young neuronal population is eliminated (5). Sensory experience might promote cell survival during a specific critical window, when new neurons receive synaptic inputs from preexisting circuits (6–9) and exhibit long-term potentiation (LTP) (10). During this window, an odor-reward association task (but not a mere odor exposure) promotes cell survival (11, 12) and specific activation of adult-born neurons monitored through immediate early gene labeling (13, 14). As a result, olfactory memory is impaired when adult neurogenesis is compromised (13, 15). Conversely, the selective stimulation of adult-born neurons improves olfactory learning and memory (16), suggesting that these neurons are part of the olfactory memory engram (17). Although recent transsynaptic strategies have revealed the presynaptic connectivity of adult-born neurons (18, 19), little is known about how learning affects their structural and synaptic plasticity or which circuits and presynaptic cells are functionally recruited with them during associative learning. By demonstrating that olfactory learning triggers both selective structural rearrangements and changes in synaptic transmission onto adult-born neurons, this study represents a first attempt to link associative learning to functional plasticity of circuits endowed with adult neurogenesis.     

Homeostatic regulation of axonal Kv1.1 channels accounts for both synaptic and intrinsic modifications in the hippocampal CA3 circuit

Homeostatic plasticity of intrinsic excitability goes hand in hand with homeostatic plasticity of synaptic transmission. However, the mechanisms linking the two forms of homeostatic regulation have not been identified so far. Using electrophysiological, imaging, and immunohistochemical techniques, we show here that blockade of excitatory synaptic receptors for 2 to 3 d induces an up-regulation of both synaptic transmission at CA3–CA3 connections and intrinsic excitability of CA3 pyramidal neurons. Intrinsic plasticity was found to be mediated by a reduction of Kv1.1 channel density at the axon initial segment. In activity-deprived circuits, CA3–CA3 synapses were found to express a high release probability, an insensitivity to dendrotoxin, and a lack of depolarization-induced presynaptic facilitation, indicating a reduction in presynaptic Kv1.1 function. Further support for the down-regulation of axonal Kv1.1 channels in activity-deprived neurons was the broadening of action potentials measured in the axon. We conclude that regulation of the axonal Kv1.1 channel constitutes a major mechanism linking intrinsic excitability and synaptic strength that accounts for the functional synergy existing between homeostatic regulation of intrinsic excitability and synaptic transmission.

Chronic modulation of activity regimes in neuronal circuits induces homeostatic plasticity. This implicates a regulation of both intrinsic excitability (homeostatic plasticity of intrinsic excitability) and synaptic transmission (homeostatic plasticity of synaptic transmission) to maintain network activity within physiological bounds (1). In most cases, these two forms of homeostatic plasticity act synergistically but involve different molecular actors. Homeostatic intrinsic plasticity is associated with the regulation of voltage-gated ion channels (2–9), while homeostatic synaptic plasticity involves the regulation of postsynaptic receptors to neurotransmitters (10–17) or the regulation of the readily releasable pool of synaptic vesicles (18–20). However, the function of voltage-gated ion channels is not limited to the control of intrinsic excitability. Several studies point to the role of axonal voltage-gated channels in shaping presynaptic action potential (AP) waveform and subsequently controlling neurotransmitter release and synaptic transmission (21–35). Moreover, some studies describe homeostatic plasticity of the AP waveform via voltage-gated channel regulation (36–38), while other studies report an absence of this phenomenon (39).Kv1.1 channels are responsible for the fast-activating, slow-inactivating D-type current (I_D) in CA3 neurons. This current has been shown to create a delay in the onset of the first AP and to determine intrinsic excitability in various neuronal types, including CA1 and CA3 pyramidal neurons of the hippocampus (3, 40), L5 pyramidal neurons of the cortex (26, 34), and L2/3 fast-spiking interneurons of the somatosensory cortex (41, 42). Furthermore, Kv1.1 channels have been shown to control axonal AP width and subsequently presynaptic calcium entry and neurotransmitter release. In fact, pharmacological blockade of Kv1.1 channels broadens presynaptic APs and increases synaptic transmission at neocortical and hippocampal glutamatergic synapses and at cerebellar GABAergic synapses (21, 22, 26, 30, 32, 41, 43, 44). Moreover, Kv1.1 channels have been shown to be responsible for the phenomenon of depolarization-induced analog digital facilitation of synaptic transmission (d-ADF). In fact, at CA3–CA3 and L5–L5 synapses, a somatic subthreshold depolarization of the presynaptic cell leads to inactivation of axonal Kv1.1 channels, inducing the broadening of the presynaptic AP, an increase in spike-evoked calcium entry, and a facilitation of presynaptic glutamate release (26, 31, 33, 34, 45, 46). Therefore, Kv1.1 channels control both intrinsic excitability and glutamate release in CA3 pyramidal neurons.Kv1.1 channels have been shown to be involved in homeostatic regulation of neuronal excitability. Chronic activity enhancement by kainate application leads to an increase in I_D current and a decrease in excitability in dentate gyrus granule cells (47). Conversely, chronic sensory deprivation leads to Kv1.1 channel down-regulation and enhancement of excitability in the avian cochlear nucleus (48). In this study, we examined whether the increase in synaptic transmission could also be due to Kv1.1 channel down-regulation, which would possibly explain the observed synergy between homeostatic plasticity of excitability and synaptic transmission.We show here that chronic activity deprivation induced with an antagonist of ionotropic glutamate receptors (kynurenate) in hippocampal organotypic cultures provokes both an increase in CA3 pyramidal cells excitability and an enhancement of synaptic transmission at monosynaptically connected CA3 neurons. Deprived cultures display a decrease in Kv1.1 channel staining in the axon initial segment. Bath application of dendrotoxin-K (DTX-K), a selective blocker of Kv1.1 channels, leads to a larger excitability increase in control cultures than in deprived cultures. Focal puffing of DTX-K on the axon increases excitability in control but not in deprived cultures, showing that homeostatic plasticity of excitability in deprived cultures is partly due to the down-regulation of axonal Kv1.1 channels. In addition, we found that axonal Kv1.1 down-regulation in deprived cultures is responsible for a spike broadening in CA3 neurons, leading to elevated release probability at CA3–CA3 synapses. Consistent with these observations, d-ADF, a Kv1.1-dependent form of synaptic facilitation, is present in control cultures but not in deprived cultures. Altogether, these results show that chronic activity blockade of the hippocampal CA3 circuit induces the down-regulation of axonal Kv1.1 channels leading to a homeostatic increase in both excitability and presynaptic release probability.     

Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation

Change is ubiquitous in living beings. In particular, the connectome and neural representations can change. Nevertheless, behaviors and memories often persist over long times. In a standard model, associative memories are represented by assemblies of strongly interconnected neurons. For faithful storage these assemblies are assumed to consist of the same neurons over time. Here we propose a contrasting memory model with complete temporal remodeling of assemblies, based on experimentally observed changes of synapses and neural representations. The assemblies drift freely as noisy autonomous network activity and spontaneous synaptic turnover induce neuron exchange. The gradual exchange allows activity-dependent and homeostatic plasticity to conserve the representational structure and keep inputs, outputs, and assemblies consistent. This leads to persistent memory. Our findings explain recent experimental results on temporal evolution of fear memory representations and suggest that memory systems need to be understood in their completeness as individual parts may constantly change.

Organisms change over time, on many different levels. This holds in particular for the synapses in neural networks (1): They change their impact and also appear and vanish. On the one hand, this weight and structural plasticity are activity dependent. Such forms have been argued and directly shown to be crucial for learning (2). On the other hand, weight changes and turnover of connections with similar magnitude occur spontaneously, in excitatory and inhibitory synapses, independent of previous spiking activity and in its absence (3–7). A similar dichotomy exists for neural representations: They change due to adaptive learning to improve task performance, but also spontaneously, often without affecting behavior. The latter has been observed in areas storing long-term memories (8), in sensory areas, for place cells, for location and goal-selective cells, and in motor areas (9, 10). The changes over the durations of the experiments were mostly only partial.Environments change as well. To flexibly adapt, higher animals acquire information and retain it by forming memories in the brain. In a widely used model, a memory is represented by one or several (depending on their definition) neuronal assemblies, ensembles of strongly interconnected neurons (11, 12). If an assembly is partially excited, for example by an external input, the remainder of the neurons follow, leading to associative memory recall. For faithful memory storage the ensemble of neurons forming an assembly is assumed to remain the same (13). Previous theoretical analysis has carefully studied the formation and maintenance of such static neuronal assemblies (14–22). In particular, it has been suggested that in the presence of noisy autonomous (without receiving external stimulation or feedback) network activity (15–17) and spontaneous (activity-independent) synaptic changes (21, 23), assemblies are preserved with the help of activity-dependent synaptic plasticity.Based on the experimentally observed changes of synaptic weights and connections and neural representations we develop a contrasting associative memory model where assemblies are ever and completely changing; they drift or “swim.” This happens gradually, by successive exchange of individual neurons. The neuron ensembles forming the same assembly at distant times are not directly related, but indirectly via the ensembles forming the assembly at the times in between. Using an analogy of ref. 24 (SI Appendix, Note 1), this is comparable to a thread, which consists of many rather short overlapping fibers; the ensembles of fibers in spatially distant parts are not directly related. In our model, the participation of single neurons in the memory representation overlaps (Fig. 1A), like the participation of fibers in the thread. As a consequence, viewed over time the representation looks like a continuous thread (Fig. 1B). While fibers adhere together due to the friction between them, neurons in the assembly adhere due to increased synaptic weights. The inputs and outputs track the course of the “assembly thread” to keep behavior and memory stable (Fig. 1C); they connect at each time to the correct ensemble of neurons that currently forms the required neural representation. Stable input neurons may be located in the sensory periphery, but also within the brain (9), for example in the primary visual cortex and the dentate gyrus, the input area of the hippocampus (25); motor neurons are candidates for stable output neurons. We refer to both input and output neurons (Fig. 1C) as periphery neurons and to the assembly-forming neurons (Fig. 1A) as interior ones.Open in a separate windowFig. 1Assembly drift and persistent memory. (A) At two nearby times a similar ensemble of neurons forms the neural representation of, for example, “apple” (compare the blue-colored assembly neurons at the first and the second time point). At distant times the representation consists of completely different ensembles (blue-colored assembly neurons at the first and the third time point). Due to their gradual change, temporally distant representations are indirectly related via ensembles in the time period between them. (B) Parts of a thread possess the same form of indirect relation: Nearby parts are composed of similar ensembles of fibers, while distant ones consist of different ensembles, which are connected by those in between. (C) The complete change of memory representations still allows for stable behavior. In the schematic, a tasty apple is perceived. At different times, this triggers different ensembles that presently form the representation of “apple”; see A. Assembly activation initiates a reaching movement toward the apple, despite the dissimilarity of the activated neuron ensembles. Memory and behavior are conserved because the gradual change of assembly neurons enables the inputs (green) and outputs (orange) to track the neural representation.We demonstrate the feasibility of our memory model using neural networks at different levels of complexity and with different types of dynamics. Numerical simulations and theoretical analysis reveal that assembly drift can be driven by synaptic weight fluctuations due to noisy autonomous activity or by spontaneous synaptic turnover (activity-independent appearance and disappearance of synaptic connections). The overall representational structure and memory are maintained by activity-dependent and homeostatic synaptic plasticity. Furthermore, we find that assembly drift can be directly related to the evolution of fear memory representations uncovered in recent experiments (8) and that drifting assemblies are suitable for computation.     

Progressive maturation of silent synapses governs the duration of a critical period

During critical periods, all cortical neural circuits are refined to optimize their functional properties. The prevailing notion is that the balance between excitation and inhibition determines the onset and closure of critical periods. In contrast, we show that maturation of silent glutamatergic synapses onto principal neurons was sufficient to govern the duration of the critical period for ocular dominance plasticity in the visual cortex of mice. Specifically, postsynaptic density protein-95 (PSD-95) was absolutely required for experience-dependent maturation of silent synapses, and its absence before the onset of critical periods resulted in lifelong juvenile ocular dominance plasticity. Loss of PSD-95 in the visual cortex after the closure of the critical period reinstated silent synapses, resulting in reopening of juvenile-like ocular dominance plasticity. Additionally, silent synapse-based ocular dominance plasticity was largely independent of the inhibitory tone, whose developmental maturation was independent of PSD-95. Moreover, glutamatergic synaptic transmission onto parvalbumin-positive interneurons was unaltered in PSD-95 KO mice. These findings reveal not only that PSD-95–dependent silent synapse maturation in visual cortical principal neurons terminates the critical period for ocular dominance plasticity but also indicate that, in general, once silent synapses are consolidated in any neural circuit, initial experience-dependent functional optimization and critical periods end.Immature cortical neural networks, which are formed primarily under genetic control (1), require experience and training to shape and optimize their functional properties. This experience-dependent refinement is considered to be a general developmental process for all functional cortical domains and typically peaks during their respective critical periods (CPs) (2, 3). Known examples for CPs span functional domains as diverse as filial imprinting and courtship song learning in birds (4, 5); cognitive functions, such as linguistic or musical skills in humans (6, 7); and likely best studied, the different features of sensory modalities (3). CPs are characterized by the absolute requirement for experience in a restricted time window for neural network optimization. Lack of visual experience during the CP for visual cortex refinements can, for example, cause irreversible visual impairment (8). Refinements during the CP play an essential role (9). Although some functions can be substantially ameliorated after the CP, they are rarely optimally restored.It is believed that the neural network refinement is based on synapse stabilization and elimination (10–12) and includes forms of long-term synaptic plasticity to remodel excitatory synapses of principal neurons (13, 14). Although long-term plasticity at these excitatory synapses is instructive for shaping neural networks for functional output and their expression coincides with CPs, it is not known whether the remodeling itself governs the duration of CPs. In contrast, only permissive mechanisms have been shown to terminate CPs. Among these, the developmental increase of local inhibition appears to be the dominating mechanism to regulate cortical plasticity and CPs (15–17). Additionally, extracellular matrix remodeling is involved, as well as receptors of immune signaling, such as paired Ig-like receptor B (PirB), or axon pathfinding, such as Nogo (18–21). However, a specific function to directly regulate synapse remodeling during initial neural network optimization is not known and a potential instructive function of PirB was described for adult cortical plasticity but not plasticity of the initial synapse remodeling during CPs (22).AMPA receptor-silent synapses have been proposed to be efficient plasticity substrates during early cortical network refinements (13, 23, 24). Silent synapses are thought to be immature, still-developing excitatory synapses containing only NMDA receptors (NMDARs) but lacking AMPA receptors (AMPARs) (23, 24). They are functionally dormant but can evolve into fully transmitting synapses by experience-dependent insertion of AMPARs, a plasticity process thought to occur frequently in developing cortices (10). Although they appear as the ideal synaptic substrate for CP plasticity and their maturation correlates with sensory experience (10, 25), it has not been experimentally tested whether maturation of silent synapses indeed causes the termination of critical periods. This conceptual model contrasts with the current view that increased local inhibition and the expression of plasticity brakes ends critical periods (18–20, 26). We hypothesize that experience-dependent unsilencing of silent synapses, which results in strengthening and maturation of excitatory synapses, governs network stabilization and refinement during critical periods, and that the progressive decrease of silent synapses leads to the closure of critical periods.Experience-dependent cortical plasticity is classically tested with ocular dominance (OD) plasticity (ODP) in the primary visual cortex (V1), induced by monocular deprivation (MD). In the binocular region of mouse V1, neurons respond to sensory inputs from both eyes, but activity is dominated by afferents from the contralateral eye. During the critical period, a brief MD induces an OD shift of visually evoked responses in V1 toward the open eye (27–29). This juvenile ODP is mediated by a reduction of deprived eye responses in V1 and is temporally confined to a critical period (30, 31).A molecular candidate regulating the cellular basis of critical period plasticity is postsynaptic density protein-95 (PSD-95), whose expression in the visual cortex increases on eye opening and thus the onset of visual experience (32). PSD-95 promotes the maturation of AMPA receptor-silent excitatory synapses in hippocampal neurons and is required for activity-driven synapse stabilization (33–35). In juvenile PSD-95 KO mice, ODP displays the same features as in WT mice (36). However, as adult PSD-95 KO mice have not yet been analyzed, it is unknown whether PSD-95 is essential for the closure of critical periods. Thus, PSD-95 appeared to be the ideal molecular candidate to test our conceptual model that progressive silent synapse maturation marks the closure of critical periods.     

PirB regulates a structural substrate for cortical plasticity

Experience-driven circuit changes underlie learning and memory. Monocular deprivation (MD) engages synaptic mechanisms of ocular dominance (OD) plasticity and generates robust increases in dendritic spine density on L5 pyramidal neurons. Here we show that the paired immunoglobulin-like receptor B (PirB) negatively regulates spine density, as well as the threshold for adult OD plasticity. In PirB^−/− mice, spine density and stability are significantly greater than WT, associated with higher-frequency miniature synaptic currents, larger long-term potentiation, and deficient long-term depression. Although MD generates the expected increase in spine density in WT, in PirB^−/− this increase is occluded. In adult PirB^−/−, OD plasticity is larger and more rapid than in WT, consistent with the maintenance of elevated spine density. Thus, PirB normally regulates spine and excitatory synapse density and consequently the threshold for new learning throughout life.Experience generates both functional and structural changes in neural circuits. The learning process is robust at younger ages during developmental critical periods and continues, albeit at a lower level, into adulthood and old age (1–3). For example, young barn owls exposed to horizontally shifting prismatic spectacles can adapt readily to altered visual input, but adult owls cannot. The experience in the young owls results in a rearranged audiovisual map in tectum that is accompanied by ectopic axonal projections (1). Experience-dependent structural changes have also been observed in the mammalian cerebral cortex. Enriched sensory experience or motor learning are both associated with an increase in dendritic spine density, and a morphological shift from immature thin spines to mushroom spines which harbor larger postsynaptic densities (PSDs) and stronger synapses (4–7). On the flip side, bilateral sensory deprivation induces spine loss (8, 9). Abnormal sensory experience also results in structural modification of inhibitory synapses and circuitry that is temporally and spatially coordinated with changes in excitatory synapses on dendritic spines (10–13).These experience-driven spine changes are thought to involve synaptic mechanisms of long-term potentiation (LTP) and long-term depression (LTD). In hippocampal slices, induction of LTP causes new spines to emerge, as well as spine head enlargement on existing spines (14–16); induction of LTD results in rapid spine regression (14, 17). Importantly, the emergence or regression of spines starts soon after the induction of LTP or LTD, suggesting that these structural changes underlie the persistent expression of long-term plasticity (14, 17).Little is known about molecular mechanisms that restrict experience-dependent plasticity at circuit and synaptic levels and connect it to spine stability. Paired Ig-like receptor B (PirB), a receptor expressed in cortical pyramidal neurons, is known to limit ocular dominance (OD) plasticity both during the critical period and in adulthood (18). PirB binds major histocompatibility class I (MHCI) ligands, whose expression is regulated by visual experience and neural activity (19–21) and thus could act as a key link connecting functional to structural plasticity. If so, mice lacking PirB might be expected to have altered synaptic plasticity rules on the one hand and changes in the density and stability of dendritic spines on the other.     

Hierarchical timescales in the neocortex: Mathematical mechanism and biological insights

A cardinal feature of the neocortex is the progressive increase of the spatial receptive fields along the cortical hierarchy. Recently, theoretical and experimental findings have shown that the temporal response windows also gradually enlarge, so that early sensory neural circuits operate on short timescales whereas higher-association areas are capable of integrating information over a long period of time. While an increased receptive field is accounted for by spatial summation of inputs from neurons in an upstream area, the emergence of timescale hierarchy cannot be readily explained, especially given the dense interareal cortical connectivity known in the modern connectome. To uncover the required neurobiological properties, we carried out a rigorous analysis of an anatomically based large-scale cortex model of macaque monkeys. Using a perturbation method, we show that the segregation of disparate timescales is defined in terms of the localization of eigenvectors of the connectivity matrix, which depends on three circuit properties: 1) a macroscopic gradient of synaptic excitation, 2) distinct electrophysiological properties between excitatory and inhibitory neuronal populations, and 3) a detailed balance between long-range excitatory inputs and local inhibitory inputs for each area-to-area pathway. Our work thus provides a quantitative understanding of the mechanism underlying the emergence of timescale hierarchy in large-scale primate cortical networks.

The brain is organized with a delicate structure to integrate and process both spatial and temporal information received from the external world. For spatial information processing, neurons along cortical visual pathways possess increasingly large spatial receptive fields, and its underlying mechanism has been understood as neurons in higher-level visual areas receive input from many neurons with smaller receptive fields in lower-level visual areas, thereby aggregating information across space (1). More recently, a computational model (2) revealed that the timescale over which neural integration occurs also gradually increases from area to area along the cortical hierarchy. The model was based on the anatomically measured directed- and weighted-interareal connectivity of the macaque cortex (3) and incorporated heterogeneity of synaptic excitation calibrated by spine count per pyramidal neuron (4). It has been observed that the decay times increased progressively along the cortical hierarchy when signals propagate in the network, and the temporal hierarchy could change dynamically in response to different types of sensory inputs (e.g., different hierarchy of timescales for somatosensory input versus visual input) (2). By manipulating parameters of the model, simulation results further demonstrated that both within and between regions of anatomical properties could affect the hierarchy of timescales in neuronal population activity (2). A hierarchy of temporal receptive windows is functionally desirable, so that the circuit dynamics operate on short timescales in early sensory areas to encode and process rapidly changing external stimuli, whereas parietal and frontal areas can accumulate information over a relatively long period of time in decision-making and other cognitive processes (5, 6).Despite the accumulating evidence in support of timescale hierarchy across cortical areas in mice (7, 8), monkeys (9–15), and humans (16–23), its underlying mechanism remains unclear. In particular, since interareal connections are dense, with roughly 65% of all possible connections present in the macaque cortex (3) and even higher connection density in the mouse cortex (24), what circuit properties are required to ensure that dynamical modes with disparate time constants are spatially localized? How do intraareal anatomical properties determine the intrinsic timescale of each area, and how do these intrinsic timescales remain to be segregated rather than mixed up in the presence of dense interareal connections? In this work, we addressed these questions by a mathematical analysis of the model (2). Using a perturbation method, we identified key required conditions, in particular a detailed excitation–inhibition balance for long-distance interareal connections that is experimentally testable.     

Brain state limits propagation of neural signals in laminar cortical circuits

Our perception of the environment relies on the efficient propagation of neural signals across cortical networks. During the time course of a day, neural responses fluctuate dramatically as the state of the brain changes to possibly influence how electrical signals propagate across neural circuits. Despite the importance of this issue, how patterns of spiking activity propagate within neuronal circuits in different brain states remains unknown. Here, we used multielectrode laminar arrays to reveal that brain state strongly modulates the propagation of neural activity across the layers of early visual cortex (V1). We optogenetically induced synchronized state transitions within a group of neurons and examined how far electrical signals travel during wakefulness and rest. Although optogenetic stimulation elicits stronger neural responses during wakefulness relative to rest, signals propagate only weakly across the cortical column during wakefulness, and the extent of spread is inversely related to arousal level. In contrast, the light-induced population activity vigorously propagates throughout the entire cortical column during rest, even when neurons are in a desynchronized wake-like state prior to light stimulation. Mechanistically, the influence of global brain state on the propagation of spiking activity across laminar circuits can be explained by state-dependent changes in the coupling between neurons. Our results impose constraints on the conclusions of causal manipulation studies attempting to influence neural function and behavior, as well as on previous computational models of perception assuming robust signal propagation across cortical layers and areas.

The extent and accuracy with which neural signals propagate within and across neural circuits play a critical role in shaping behavior and cognition. One key variable that could potentially influence signal propagation across neural networks is global brain state (1–4). Indeed, during the time course of a day, the state of the brain undergoes dramatic changes from wakefulness to drowsiness and sleep (3, 5–7). Multiple lines of evidence in rodents and monkeys have shown that distinct brain states are associated with specific changes in neural responses (2–4, 8). Neurons strongly respond during wakefulness when animals are in an aroused state, and responses diminish during drowsiness and sleep (6, 9–11). However, despite significant progress in our understanding of state-dependent sensory coding across neural circuits (2–5, 8, 12, 13), the influence of brain state on the propagation of electrical signals remains unknown.The cortical column constitutes an ideal locus to examine the propagation of neural signals. For over a century, neuroscientists have observed remarkable regularity in the cortical microarchitecture: Clusters of cells are synaptically connected to form small columns orthogonal to the cortical surface (14, 15). These microcolumns constitute the elementary functional units of cortical circuitry (16) and consist of distinct layers that each contain a characteristic distribution of cell types and connections with other layers (15, 17–19). Understanding how neural signals propagate across laminar circuits would greatly contribute to deciphering the functional principles of cortical column operation.In principle, the strong intracortical connections within and between cortical layers (17–20) imply that signals emitted by individual neurons would vigorously propagate across the entire microcolumn. Indeed, during wakefulness, the input granular (G) cortical layers relay stimulus information to the output supragranular (SG) layers, which send feedforward projections to downstream areas (18, 20). Furthermore, neurons in SG layers project back to infragranular (IG) layers, which in turn project to granular layers; hence, signals are circulated across the entire microcolumn (17, 18). Thus, from a theoretical standpoint, it can be argued that electrical signals are robustly transmitted during wakefulness across cortical layers to contribute to perception and cognition. In reality, how robustly signals travel across layers in different states of wakefulness, and especially when the state of the brain undergoes dramatic changes, such as during drowsiness and sleep, remains unknown.Previous studies were unable to address these issues due to inherent restrictions of techniques such as in vitro slice recordings [e.g., (21)] and in vivo recordings during anesthesia (6, 10, 22) that severely limit the behavioral repertoire and hence the interpretation of cortical dynamics across laminar circuits. Even studies focused on in vivo laminar recordings failed to investigate state-dependent signal propagation across cortical layers (23–25). Here, we examined the propagation of neural signals across the cortical column in different brain states using multielectrode laminar arrays. We discovered that the global brain state strongly modulates the propagation of neural activity across the layers of the early visual cortex (area V1). We optogenetically activated specific cell populations during wakefulness to find that even though the elicited neural signals were stronger than those during rest, they propagated to other layers only weakly. Further, arousal was inversely related to the extent of signal spread. In contrast, the light-induced activity of the same neural population robustly propagated throughout the entire cortical column during rest, even when neurons were in a desynchronized wake-like state prior to light stimulation. The differential propagation of electrical signals in different brain states can be explained by state-dependent changes in the degree of coupling between individual neurons and their local population. Our results impose constraints on the conclusions of causal manipulation studies attempting to influence neural function and behavior, as well as on previous computational models of perception assuming robust signal propagation across cortical layers and areas.     

Sleep promotes the formation of dendritic filopodia and spines near learning-inactive existing spines

Changes in synaptic connections are believed to underlie long-term memory storage. Previous studies have suggested that sleep is important for synapse formation after learning, but how sleep is involved in the process of synapse formation remains unclear. To address this question, we used transcranial two-photon microscopy to investigate the effect of postlearning sleep on the location of newly formed dendritic filopodia and spines of layer 5 pyramidal neurons in the primary motor cortex of adolescent mice. We found that newly formed filopodia and spines were partially clustered with existing spines along individual dendritic segments 24 h after motor training. Notably, posttraining sleep was critical for promoting the formation of dendritic filopodia and spines clustered with existing spines within 8 h. A fraction of these filopodia was converted into new spines and contributed to clustered spine formation 24 h after motor training. This sleep-dependent spine formation via filopodia was different from retraining-induced new spine formation, which emerged from dendritic shafts without prior presence of filopodia. Furthermore, sleep-dependent new filopodia and spines tended to be formed away from existing spines that were active at the time of motor training. Taken together, these findings reveal a role of postlearning sleep in regulating the number and location of new synapses via promoting filopodial formation.

Learning and memory consolidation are associated with the rewiring of neuronal network connectivity (1–3). Previous studies have shown that motor training leads to the formation and elimination of postsynaptic dendritic spines of pyramidal neurons in the primary motor cortex (M1) (4–8). Learning-induced new spines stabilize and persist over long periods of time (4). The extent of spine remodeling correlates with behavioral improvement after learning (4, 9), and the disruption of spine remodeling impairs learned motor behavior (10–12). These studies suggest that learning-induced new synapses contribute to changes in neuronal circuits that are likely important for the retention of learned behaviors (13, 14).Cumulative evidence suggests that sleep affects synaptic structural plasticity in many brain regions (15–17). For example, sleep has been shown to promote spine formation and elimination in developing somatosensory and visual cortices (18, 19). In the motor cortex, sleep promotes branch-specific formation of new dendritic spines following motor learning and selectively stabilizes learning-induced new synaptic connections (11, 12). Sleep has also been shown to regulate dendritic spine numbers in hippocampal CA1 area (20–22). In addition, many lines of evidence have revealed the function of sleep in increasing, decreasing, or stabilizing synaptic strength and neuronal firing in various brain regions (23–31). Together, these studies strongly suggest that sleep has an important role in promoting synaptic structural plasticity in neuronal circuits during development and after learning.While sleep promotes the formation of new spines after learning (12), it remains unknown how postlearning sleep regulates new synapse formation along dendritic branches. Synapse formation is a prolonged process often involving the generation of dendritic filopodia, thin and long protrusions without bulbous heads (32–35). These highly dynamic filopodia have been shown to initiate the contact with presynaptic axonal terminals and transform into new spines (36, 37). It is not known whether sleep promotes new spine formation via filopodia formation and subsequent transformation. Furthermore, it is also unclear whether sleep-dependent formation of new dendritic protrusions (filopodia and spines) is distributed on dendritic branches in a random or nonrandom manner. On the one hand, new synapses may be formed in clusters with synapses of similar functions to allow nonlinear summation of inputs important for increasing memory storage capacity (9, 38–43). On the other hand, new connections may be formed preferentially near less active/strong synapses to avoid competition for limited synaptic resources (44–47).In this study, we found that dendritic filopodia and spines formed after motor training were partially clustered with existing spines on apical tuft dendrites of layer 5 (L5) pyramidal neurons in the mouse primary motor cortex. Posttraining sleep was critical for the clustered formation of new filopodia, some of which were transformed into new spines. In addition, the clustered new filopodia and spines tended to be formed near existing spines that were inactive at the time of motor training. These findings reveal a role for sleep in neuronal circuit plasticity by promoting clustered spine formation via dendritic filopodia near learning-inactive existing spines.     

NMDARs in granule cells contribute to parallel fiber–Purkinje cell synaptic plasticity and motor learning

Long-term synaptic plasticity is believed to be the cellular substrate of learning and memory. Synaptic plasticity rules are defined by the specific complement of receptors at the synapse and the associated downstream signaling mechanisms. In young rodents, at the cerebellar synapse between granule cells (GC) and Purkinje cells (PC), bidirectional plasticity is shaped by the balance between transcellular nitric oxide (NO) driven by presynaptic N-methyl-D-aspartate receptor (NMDAR) activation and postsynaptic calcium dynamics. However, the role and the location of NMDAR activation in these pathways is still debated in mature animals. Here, we show in adult rodents that NMDARs are present and functional in presynaptic terminals where their activation triggers NO signaling. In addition, we find that selective genetic deletion of presynaptic, but not postsynaptic, NMDARs prevents synaptic plasticity at parallel fiber-PC (PF-PC) synapses. Consistent with this finding, the selective deletion of GC NMDARs affects adaptation of the vestibulo-ocular reflex. Thus, NMDARs presynaptic to PCs are required for bidirectional synaptic plasticity and cerebellar motor learning.

The ability of an organism to adjust its behavior to environmental demands depends on its capacity to learn and execute coordinated movements. The cerebellum plays a central role in this process by optimizing motor programs through trial-and-error learning (1). Within the cerebellum, the synaptic output from granule cells (GCs) to Purkinje cells (PCs) shapes computational operations during basal motor function and serves as a substrate for motor learning (2). Several forms of motor learning depend on changes in the strength of the parallel fiber (PF), the axon of GCs, to the PC synapse (3, 4).In the mammalian forebrain, synaptic plasticity typically relies on postsynaptic N-methyl-D-aspartate receptor (NMDAR) activation, which alters AMPA receptor (AMPAR) turnover at the postsynaptic site (5). However, this may not extend to the cerebellar synapse between GCs and PCs, since no functional postsynaptic NMDARs have been identified in young or adult rodents (6, 7). Pharmacological approaches, however, have shown that both long-term depression (LTD) and long-term potentiation (LTP) induction depend on NMDAR activation at the PF-PC synapse in young rodents (8–12). Hence, the alternative mechanisms for NMDAR-dependent synaptic modulation may involve presynaptic NMDARs activation [(12–15); for review: refs. 16 and 17]. Indeed, cell-specific deletion of NMDARs in GCs abolishes LTP in young rodents (12). In addition to NMDARs, PF-PC synaptic plasticity also requires nitric-oxide (NO) signaling (18–20). As nitric-oxide synthase (NOS) is expressed in GCs, but not in PCs (21), the activation of presynaptic NMDARs might allow Ca²⁺ influx that activates NO synthesis, which in turn may act upon the PCs. However, in the mature cerebellum, the existence of presynaptic NMDARs on PFs and the role of NO in PF-PC plasticity remains a matter of debate. Previously, we have proposed that the activation of putatively presynaptic NMDARs in young rodents is necessary for inducing PF-PC synaptic plasticity without affecting transmitter release (8, 9, 11, 12). More recently, it has been shown that a subset of PFs express presynaptic NMDARs containing GluN2A subunits and that these receptors are functional (11, 12). Thus, in contrast to their role at other synapses, at least in young rodent, presynaptic NMDARs as part of the PF-PC synapses might act via the production of NO to induce postsynaptic plasticity, without altering neurotransmitter release (9, 11, 12, 18–22). However, a causal link between NMDARs activation in PFs, NO synthesis, and synaptic plasticity induction is still missing.In the cerebral cortex, the expression of presynaptic NMDARs is developmentally regulated (23, 24). However, little is known about the presence and function of presynaptic NMDARs in adult tissue. In the adult cerebellum, PCs only express postsynaptic NMDARs at their synapse with climbing fibers (CFs) (25). It has been proposed that the activation of these receptors could have heterosynaptic effects during PF-PC LTD. This mechanism would explain why LTD in adults depends on NMDARs. According to this model, presynaptic NMDARs would be a transient feature of developing tissue and not necessary for induction of synaptic plasticity and motor learning in adult animals (25).Here, we combine electron microscopy, two-photon calcium imaging, synaptic plasticity experiments, and behavioral measurements to show that presynaptic NMDARs are not developmentally regulated but are required for cerebellar motor learning in adults. We demonstrate that presynaptic NMDARs are present and functional in PFs of mature rodents. By specifically deleting the NMDAR subunit GluN1 either in the post- (PC) or the presynaptic cells (GCs), we demonstrate that NMDAR activation in GCs plays a key role in bidirectional synaptic plasticity and in vestibulo-ocular reflex (VOR) adaptation, an important paradigm for testing cerebellar motor learning (26–28). In contrast, NMDARs in PCs are neither involved in PF-PC synaptic plasticity nor required for cerebellar motor learning.     

Lactate supply overtakes glucose when neural computational and cognitive loads scale up

Neural computational power is determined by neuroenergetics, but how and which energy substrates are allocated to various forms of memory engram is unclear. To solve this question, we asked whether neuronal fueling by glucose or lactate scales differently upon increasing neural computation and cognitive loads. Here, using electrophysiology, two-photon imaging, cognitive tasks, and mathematical modeling, we show that both glucose and lactate are involved in engram formation, with lactate supporting long-term synaptic plasticity evoked by high-stimulation load activity patterns and high attentional load in cognitive tasks and glucose being sufficient for less demanding neural computation and learning tasks. Indeed, we show that lactate is mandatory for demanding neural computation, such as theta-burst stimulation, while glucose is sufficient for lighter forms of activity-dependent long-term potentiation (LTP), such as spike timing–dependent plasticity (STDP). We find that subtle variations of spike number or frequency in STDP are sufficient to shift the on-demand fueling from glucose to lactate. Finally, we demonstrate that lactate is necessary for a cognitive task requiring high attentional load, such as the object-in-place task, and for the corresponding in vivo hippocampal LTP expression but is not needed for a less demanding task, such as a simple novel object recognition. Overall, these results demonstrate that glucose and lactate metabolism are differentially engaged in neuronal fueling depending on the complexity of the activity-dependent plasticity and behavior.

Brain activity and performance are tightly constrained by neurovasculature–neuroenergetic coupling (1–3). Neuroenergetics, that is, brain energy metabolism, relies on the blood supply of glucose from the circulation. Evidence accrued over the last two decades has indicated that blood glucose is taken up during synaptic activity (4, 5), mainly by glial cells (astrocytes and oligodendrocytes), and metabolized by aerobic glycolysis, resulting in the release of lactate before transport to neurons as an energy substrate (6–13) necessary for optimized neuronal coding and memory consolidation (14–22). When astrocytes constitute the source of lactate, this process is known as the astrocyte–neuron lactate shuttle in which lactate is transferred from astrocytes to neurons through monocarboxylate transporters, providing an energy substrate for neurons (7). Indeed, lactate can be rapidly metabolized to pyruvate, enter the tricarboxylic acid cycle, and feed the mitochondrial respiratory chain to produce ATP. Other fates of glucose include its glial storage in the form glycogen (7, 23, 24); some degree of glucose uptake occurs in neurons via transporters mainly aimed at feeding the pentose phosphate shunt to produce reducing equivalents (25–27), which is involved in olfactory memory in Drosophila (28). Nevertheless, the nature of the energy substrate, glucose versus lactate, allocated to various forms of memory engram and cognitive load is not known.Here, we tested various forms of activity patterns (rate- and time-coding) for Hebbian long-term synaptic plasticity expression in rat cornu ammonis 1 (CA1) hippocampal pyramidal cells and behavioral tasks with increasing cognitive loads to determine under which conditions glucose and/or lactate are crucial for engram formation and memory. To this end, using brain slice and in vivo electrophysiology, two-photon imaging, mathematical modeling, and recognition memory tasks, we show that astrocytic lactate is mandatory for demanding neural computation, while glucose is sufficient for lighter forms of activity-dependent long-term potentiation (LTP) and that subtle variations of action potential amount or frequency are sufficient to direct the energetic dependency from glucose to lactate. Furthermore, we demonstrate that lactate is necessary for a cognitive task requiring high attentional load (object-in-place [OiP] task) and for the corresponding in vivo hippocampal potentiation but is not needed for a less demanding task (novel object recognition [NOR]). Our results demonstrate that glucose and lactate metabolism are differentially engaged in neuronal fueling depending on the complexity of the activity-dependent plasticity and behavior. Beyond reconciling a decades-long debate (7, 11, 26, 27), our results demonstrate the importance of distinguishing specific cellular and molecular mechanisms because the corresponding cognitive perturbations might depend on whether lactate or glucose metabolism is perturbed.