Synaptic Tenacity or Lack Thereof: Spontaneous Remodeling of Synapses

Representational drift as a result of implicit regularization

Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To investigate the underlying mechanism, we trained an artificial network on a simplified navigational task. The network quickly reached a state of high performance, and many units exhibited spatial tuning. We then continued training the network and noticed that the activity became sparser with time. Initial learning was orders of magnitude faster than ensuing sparsification. This sparsification is consistent with recent results in machine learning, in which networks slowly move within their solution space until they reach a flat area of the loss function. We analyzed four datasets from different labs, all demonstrating that CA1 neurons become sparser and more spatially informative with exposure to the same environment. We conclude that learning is divided into three overlapping phases: (i) Fast familiarity with the environment; (ii) slow implicit regularization; and (iii) a steady state of null drift. The variability in drift dynamics opens the possibility of inferring learning algorithms from observations of drift statistics.

Representational drift as a result of implicit regularization

Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To investigate the underlying mechanism, we trained an artificial network on a simplified navigational task. The network quickly reached a state of high performance, and many units exhibited spatial tuning. We then continued training the network and noticed that the activity became sparser with time. Initial learning was orders of magnitude faster than ensuing sparsification. This sparsification is consistent with recent results in machine learning, in which networks slowly move within their solution space until they reach a flat area of the loss function. We analyzed four datasets from different labs, all demonstrating that CA1 neurons become sparser and more spatially informative with exposure to the same environment. We conclude that learning is divided into three overlapping phases: (i) Fast familiarity with the environment; (ii) slow implicit regularization; (iii) a steady state of null drift. The variability in drift dynamics opens the possibility of inferring learning algorithms from observations of drift statistics.

Memory: Synaptic or Cellular, That Is the Question

According to the commonly accepted opinion, memory engrams are formed and stored at the level of neural networks due to a change in the strength of synaptic connections between neurons. This hypothesis of synaptic plasticity (HSP), formulated by Donald Hebb in the 1940s, continues to dominate the directions of experimental studies and the interpretations of experimental results in the field. The universal acceptance of the HSP has transformed it from a hypothesis into an incontrovertible theory. In this article, I show that the entire body of experimental and clinical data obtained in studies of long-term memory in mammals and humans is inconsistent with the HSP. Instead, these data suggest that long-term memory is formed and stored at the intracellular level where it is reliably protected from ongoing synaptic activity, including pathological epileptic activity. It seems that the generally accepted HSP became a serious obstacle to understanding the mechanisms of memory and that progress in this field requires rethinking this doctrine and shifting experimental efforts toward exploring the intracellular mechanisms.

A biophysical perspective on the resilience of neuronal excitability across timescales

Neuronal membrane excitability must be resilient to perturbations that can take place over timescales from milliseconds to months (or even years in long-lived animals). A great deal of attention has been paid to classes of homeostatic mechanisms that contribute to long-term maintenance of neuronal excitability through processes that alter a key structural parameter: the number of ion channel proteins present at the neuronal membrane. However, less attention has been paid to the self-regulating 'automatic' mechanisms that contribute to neuronal resilience by virtue of the kinetic properties of ion channels themselves. Here, we propose that these two sets of mechanisms are complementary instantiations of feedback control, together enabling resilience on a wide range of temporal scales. We further point to several methodological and conceptual challenges entailed in studying these processes - both of which involve enmeshed feedback control loops - and consider the consequences of these mechanisms of resilience.

Linking spontaneous and stimulated spine dynamics

Our brains continuously acquire and store memories through synaptic plasticity. However, spontaneous synaptic changes can also occur and pose a challenge for maintaining stable memories. Despite fluctuations in synapse size, recent studies have shown that key population-level synaptic properties remain stable over time. This raises the question of how local synaptic plasticity affects the global population-level synaptic size distribution and whether individual synapses undergoing plasticity escape the stable distribution to encode specific memories. To address this question, we (i) studied spontaneously evolving spines and (ii) induced synaptic potentiation at selected sites while observing the spine distribution pre- and post-stimulation. We designed a stochastic model to describe how the current size of a synapse affects its future size under baseline and stimulation conditions and how these local effects give rise to population-level synaptic shifts. Our study offers insights into how seemingly spontaneous synaptic fluctuations and local plasticity both contribute to population-level synaptic dynamics.

Synaptic weights that correlate with presynaptic selectivity increase decoding performance

The activity of neurons in the visual cortex is often characterized by tuning curves, which are thought to be shaped by Hebbian plasticity during development and sensory experience. This leads to the prediction that neural circuits should be organized such that neurons with similar functional preference are connected with stronger weights. In support of this idea, previous experimental and theoretical work have provided evidence for a model of the visual cortex characterized by such functional subnetworks. A recent experimental study, however, have found that the postsynaptic preferred stimulus was defined by the total number of spines activated by a given stimulus and independent of their individual strength. While this result might seem to contradict previous literature, there are many factors that define how a given synaptic input influences postsynaptic selectivity. Here, we designed a computational model in which postsynaptic functional preference is defined by the number of inputs activated by a given stimulus. Using a plasticity rule where synaptic weights tend to correlate with presynaptic selectivity, and is independent of functional-similarity between pre- and postsynaptic activity, we find that this model can be used to decode presented stimuli in a manner that is comparable to maximum likelihood inference.

Engram neurons: Encoding, consolidation, retrieval, and forgetting of memory

Tremendous strides have been made in our understanding of the neurobiological substrates of memory - the so-called memory "engram". Here, we integrate recent progress in the engram field to illustrate how engram neurons transform across the "lifespan" of a memory - from initial memory encoding, to consolidation and retrieval, and ultimately to forgetting. To do so, we first describe how cell-intrinsic properties shape the initial emergence of the engram at memory encoding. Second, we highlight how these encoding neurons preferentially participate in synaptic- and systems-level consolidation of memory. Third, we describe how these changes during encoding and consolidation guide neural reactivation during retrieval, and facilitate memory recall. Fourth, we describe neurobiological mechanisms of forgetting, and how these mechanisms can counteract engram properties established during memory encoding, consolidation, and retrieval. Motivated by recent experimental results across these four sections, we conclude by proposing some conceptual extensions to the traditional view of the engram, including broadening the view of cell-type participation within engrams and across memory stages. In collection, our review synthesizes general principles of the engram across memory stages, and describes future avenues to further understand the dynamic engram.

Representational drift as a window into neural and behavioural plasticity

Large-scale recordings of neural activity over days and weeks have revealed that neural representations of familiar tasks, precepts and actions continually evolve without obvious changes in behaviour. We hypothesise that this steady drift in neural activity and accompanying physiological changes is due in part to the continuous application of a learning rule at the cellular and population level. Explicit predictions of this drift can be found in neural network models that use iterative learning to optimise weights. Drift therefore provides a measurable signal that can reveal systems-level properties of biological plasticity mechanisms, such as their precision and effective learning rates.

The Role of Nrf2 in Relieving Cerebral Ischemia-Reperfusion Injury

Ischemic stroke includes two related pathological damage processes: brain injury caused by primary ischemia and secondary ischemia reperfusion (I/R) injury. I/R injury has become a worldwide health problem. Unfortunately, there is still a lack of satisfactory drugs for ameliorating cerebral I/R damage. Nrf2 is a vital endogenous antioxidant protein, which combines with Keap1 to maintain a dormant state under physiological conditions. When pathological changes such as I/R occurs, Nrf2 dissociates from Keap1 and activates the expression of downstream antioxidant proteins to exert a protective effect. Recent research have shown that the activated Nrf2 not only effectively inhibits oxidative stress, but also performs the ability to repair the function of compromised mitochondria, alleviate endoplasmic reticulum stress, eliminate inflammatory response, reduce blood-brain barrier permeability, inhibit neuronal apoptosis, enhance the neural network remolding, thereby exerting significant protective effects in alleviating the injuries caused by cell oxygen-glucose deprivation, or animal cerebral I/R. However, no definite clinical application report demonstrated the efficacy of Nrf2 activators in the treatment of cerebral I/R. Therefore, further efforts are needed to elaborate the role of Nrf2 activators in the treatment of cerebral I/R. Here, we reviewed the possible mechanisms underlying its potential pharmacological benefits in alleviating cerebral I/R injury, so as to provide a theoretical basis for studying its mechanism and developing Nrf2 activators.

Quantifying postsynaptic receptor dynamics: insights into synaptic function

The molecular composition of presynaptic and postsynaptic neuronal terminals is dynamic, and yet long-term stabilizations in postsynaptic responses are necessary for synaptic development and long-term plasticity. The need to reconcile these concepts is further complicated by learning- and memory-related plastic changes in the molecular make-up of synapses. Advances in single-particle tracking mean that we can now quantify the number and diffusive properties of specific synaptic molecules, while statistical thermodynamics provides a framework to analyse these molecular fluctuations. In this Review, we discuss the use of these approaches to gain quantitative descriptions of the processes underlying the turnover, long-term stability and plasticity of postsynaptic receptors and show how these can help us to understand the balance between local molecular turnover and synaptic structural identity and integrity.

Unraveling the mysteries of dendritic spine dynamics: Five key principles shaping memory and cognition

Recent research extends our understanding of brain processes beyond just action potentials and chemical transmissions within neural circuits, emphasizing the mechanical forces generated by excitatory synapses on dendritic spines to modulate presynaptic function. From in vivo and in vitro studies, we outline five central principles of synaptic mechanics in brain function: P1: Stability - Underpinning the integral relationship between the structure and function of the spine synapses. P2: Extrinsic dynamics - Highlighting synapse-selective structural plasticity which plays a crucial role in Hebbian associative learning, distinct from pathway-selective long-term potentiation (LTP) and depression (LTD). P3: Neuromodulation - Analyzing the role of G-protein-coupled receptors, particularly dopamine receptors, in time-sensitive modulation of associative learning frameworks such as Pavlovian classical conditioning and Thorndike's reinforcement learning (RL). P4: Instability - Addressing the intrinsic dynamics crucial to memory management during continual learning, spotlighting their role in "spine dysgenesis" associated with mental disorders. P5: Mechanics - Exploring how synaptic mechanics influence both sides of synapses to establish structural traces of short- and long-term memory, thereby aiding the integration of mental functions. We also delve into the historical background and foresee impending challenges.

Computational models of Idling brain activity for memory processing

Studying the underlying neural mechanisms of cognitive functions of the brain is one of the central questions in modern biology. Moreover, it has significantly impacted the development of novel technologies in artificial intelligence. Spontaneous activity is a unique feature of the brain and is currently lacking in many artificially constructed intelligent machines. Spontaneous activity may represent the brain's idling states, which are internally driven by neuronal networks and possibly participate in offline processing during awake, sleep, and resting states. Evidence is accumulating that the brain's spontaneous activity is not mere noise but part of the mechanisms to process information about previous experiences. A bunch of literature has shown how previous sensory and behavioral experiences influence the subsequent patterns of brain activity with various methods in various animals. It seems, however, that the patterns of neural activity and their computational roles differ significantly from area to area and from function to function. In this article, I review the various forms of the brain's spontaneous activity, especially those observed during memory processing, and some attempts to model the generation mechanisms and computational roles of such activities.

Synapse integrity and function: Dependence on protein synthesis and identification of potential failure points

Synaptic integrity and function depend on myriad proteins - labile molecules with finite lifetimes that need to be continually replaced with freshly synthesized copies. Here we describe experiments designed to expose synaptic (and neuronal) properties and functions that are particularly sensitive to disruptions in protein supply, identify proteins lost early upon such disruptions, and uncover potential, yet currently underappreciated failure points. We report here that acute suppressions of protein synthesis are followed within hours by reductions in spontaneous network activity levels, impaired oxidative phosphorylation and mitochondrial function, and, importantly, destabilization and loss of both excitatory and inhibitory postsynaptic specializations. Conversely, gross impairments in presynaptic vesicle recycling occur over longer time scales (days), as does overt cell death. Proteomic analysis identified groups of potentially essential 'early-lost' proteins including regulators of synapse stability, proteins related to bioenergetics, fatty acid and lipid metabolism, and, unexpectedly, numerous proteins involved in Alzheimer's disease pathology and amyloid beta processing. Collectively, these findings point to neuronal excitability, energy supply and synaptic stability as early-occurring failure points under conditions of compromised supply of newly synthesized protein copies.

Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning

Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance. Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-motifs (feed-forward, recurrent, feedback) contributed to learning and performance. We also developed a new biologically-inspired learning rule that significantly enhanced performance, while reducing training time. Our models included visual areas encoding game inputs and relaying the information to motor areas, which used this information to learn to move the racket to hit the ball. Neurons in the early visual area relayed information encoding object location and motion direction across the network. Neuronal association areas encoded spatial relationships between objects in the visual scene. Motor populations received inputs from visual and association areas representing the dorsal pathway. Two populations of motor neurons generated commands to move the racket up or down. Model-generated actions updated the environment and triggered reward or punishment signals that adjusted synaptic weights so that the models could learn which actions led to reward. Here we demonstrate that our biologically-plausible learning rules were effective in training spiking neuronal network models to solve problems in dynamic environments. We used our models to dissect the circuit architectures and learning rules most effective for learning. Our model shows that learning mechanisms involving different neural circuits produce similar performance in sensory-motor tasks. In biological networks, all learning mechanisms may complement one another, accelerating the learning capabilities of animals. Furthermore, this also highlights the resilience and redundancy in biological systems.

Unsupervised learning for robust working memory

Working memory is a core component of critical cognitive functions such as planning and decision-making. Persistent activity that lasts long after the stimulus offset has been considered a neural substrate for working memory. Attractor dynamics based on network interactions can successfully reproduce such persistent activity. However, it requires a fine-tuning of network connectivity, in particular, to form continuous attractors which were suggested for encoding continuous signals in working memory. Here, we investigate whether a specific form of synaptic plasticity rules can mitigate such tuning problems in two representative working memory models, namely, rate-coded and location-coded persistent activity. We consider two prominent types of plasticity rules, differential plasticity correcting the rapid activity changes and homeostatic plasticity regularizing the long-term average of activity, both of which have been proposed to fine-tune the weights in an unsupervised manner. Consistent with the findings of previous works, differential plasticity alone was enough to recover a graded-level persistent activity after perturbations in the connectivity. For the location-coded memory, differential plasticity could also recover persistent activity. However, its pattern can be irregular for different stimulus locations under slow learning speed or large perturbation in the connectivity. On the other hand, homeostatic plasticity shows a robust recovery of smooth spatial patterns under particular types of synaptic perturbations, such as perturbations in incoming synapses onto the entire or local populations. However, homeostatic plasticity was not effective against perturbations in outgoing synapses from local populations. Instead, combining it with differential plasticity recovers location-coded persistent activity for a broader range of perturbations, suggesting compensation between two plasticity rules.

Beyond dimension reduction: Stable electric fields emerge from and allow representational drift

It is known that the exact neurons maintaining a given memory (the neural ensemble) change from trial to trial. This raises the question of how the brain achieves stability in the face of this representational drift. Here, we demonstrate that this stability emerges at the level of the electric fields that arise from neural activity. We show that electric fields carry information about working memory content. The electric fields, in turn, can act as "guard rails" that funnel higher dimensional variable neural activity along stable lower dimensional routes. We obtained the latent space associated with each memory. We then confirmed the stability of the electric field by mapping the latent space to different cortical patches (that comprise a neural ensemble) and reconstructing information flow between patches. Stable electric fields can allow latent states to be transferred between brain areas, in accord with modern engram theory.

Multimodal neuroimaging fusion biomarkers mediate the association between gut microbiota and cognition

Background The field of microbiota-gut-brain research in animals has progressed, while the exact nature of gut microbiota-brain-cognition relationship in humans is not completely elucidated, likely due to small sample sizes and single neuroimaging modality utilized to delineate limited aspects of the brain. We aimed to comprehensively investigate such association in a large sample using multimodal MRI. Methods Fecal samples were collected from 157 healthy young adults and 16S sequencing was used to assess gut microbial diversity and enterotypes. Five brain imaging measures, including regional homogeneity (ReHo) and functional connectivity density (FCD) from resting-state functional MRI, cerebral blood flow (CBF) from arterial spin labeling, gray matter volume (GMV) from structural MRI, and fractional anisotropy (FA) from diffusion tensor imaging, were jointly analyzed with a data-driven multivariate fusion method. Cognition was evaluated by 3-back and digit span tasks. Results We found significant associations of gut microbial diversity with ReHo, FCD, CBF, and GMV within the frontoparietal, default mode and visual networks, as well as with FA in a distributed set of juxtacortical white matter regions. In addition, there were FCD, CBF, GMV, and FA differences between Prevotella- versus Bacteroides-enterotypes in females and between Prevotella- versus Ruminococcaceae-enterotypes in males. Moreover, the identified neuroimaging fusion biomarkers could mediate the associations between microbial diversity and cognition. Conclusions Our findings not only expand existing knowledge of the microbiota-gut-brain axis, but also have potential clinical and translational implications by exposing the gut microbiota as a promising treatment and prevention target for cognitive impairment and related brain disorders.

Environmental enrichment enhances patterning and remodeling of synaptic nanoarchitecture as revealed by STED nanoscopy

Synaptic plasticity underlies long-lasting structural and functional changes to brain circuitry and its experience-dependent remodeling can be fundamentally enhanced by environmental enrichment. It is however unknown, whether and how the environmental enrichment alters the morphology and dynamics of individual synapses. Here, we present a virtually crosstalk-free two-color in vivo stimulated emission depletion (STED) microscope to simultaneously superresolve the dynamics of endogenous PSD95 of the post-synaptic density and spine geometry in the mouse cortex. In general, the spine head geometry and PSD95 assemblies were highly dynamic, their changes depended linearly on their original size but correlated only mildly. With environmental enrichment, the size distributions of PSD95 and spine head sizes were sharper than in controls, indicating that synaptic strength is set more uniformly. The topography of the PSD95 nanoorganization was more dynamic after environmental enrichment; changes in size were smaller but more correlated than in mice housed in standard cages. Thus, two-color in vivo time-lapse imaging of synaptic nanoorganization uncovers a unique synaptic nanoplasticity associated with the enhanced learning capabilities under environmental enrichment.

The Hippocampal Horizon: Constructing and Segmenting Experience for Episodic Memory

How do we recollect specific events that have occurred during continuous ongoing experience? There is converging evidence from non-human animals that spatially modulated cellular activity of the hippocampal formation supports the construction of ongoing events. On the other hand, recent human oriented event cognition models have outlined that our experience is segmented into discrete units, and that such segmentation can operate on shorter or longer timescales. Here, we describe a unification of how these dynamic physiological mechanisms of the hippocampus relate to ongoing externally and internally driven event segmentation, facilitating the demarcation of specific moments during experience. Our cross-species interdisciplinary approach offers a novel perspective in the way we construct and remember specific events, leading to the generation of many new hypotheses for future research.

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.

Silent Synapses in Cocaine-Associated Memory and Beyond

Glutamatergic synapses are key cellular sites where cocaine experience creates memory traces that subsequently promote cocaine craving and seeking. In addition to making across-the-board synaptic adaptations, cocaine experience also generates a discrete population of new synapses that selectively encode cocaine memories. These new synapses are glutamatergic synapses that lack functionally stable AMPARs, often referred to as AMPAR-silent synapses or, simply, silent synapses. They are generated de novo in the NAc by cocaine experience. After drug withdrawal, some of these synapses mature by recruiting AMPARs, contributing to the consolidation of cocaine-associated memory. After cue-induced retrieval of cocaine memories, matured silent synapses alternate between two dynamic states (AMPAR-absent vs AMPAR-containing) that correspond with the behavioral manifestations of destabilization and reconsolidation of these memories. Here, we review the molecular mechanisms underlying silent synapse dynamics during behavior, discuss their contributions to circuit remodeling, and analyze their role in cocaine-memory-driven behaviors. We also propose several mechanisms through which silent synapses can form neuronal ensembles as well as cross-region circuit engrams for cocaine-specific behaviors. These perspectives lead to our hypothesis that cocaine-generated silent synapses stand as a distinct set of synaptic substrates encoding key aspects of cocaine memory that drive cocaine relapse.

Distinct in vivo dynamics of excitatory synapses onto cortical pyramidal neurons and parvalbumin-positive interneurons

Cortical function relies on the balanced activation of excitatory and inhibitory neurons. However, little is known about the organization and dynamics of shaft excitatory synapses onto cortical inhibitory interneurons. Here, we use the excitatory postsynaptic marker PSD-95, fluorescently labeled at endogenous levels, as a proxy for excitatory synapses onto layer 2/3 pyramidal neurons and parvalbumin-positive (PV⁺) interneurons in the barrel cortex of adult mice. Longitudinal in vivo imaging under baseline conditions reveals that, although synaptic weights in both neuronal types are log-normally distributed, synapses onto PV⁺ neurons are less heterogeneous and more stable. Markov model analyses suggest that the synaptic weight distribution is set intrinsically by ongoing cell-type-specific dynamics, and substantial changes are due to accumulated gradual changes. Synaptic weight dynamics are multiplicative, i.e., changes scale with weights, although PV⁺ synapses also exhibit an additive component. These results reveal that cell-type-specific processes govern cortical synaptic strengths and dynamics.

Melander et al. use a genetic strategy to visualize excitatory neuronal connections that cannot be inferred from morphology, and they monitor how the connections change over weeks in mice. They find distinct characteristics between synapses onto cells that “suppress” brain activity and those onto cells that “excite” brain activity.

Enhancing motor learning by increasing the stability of newly formed dendritic spines in the motor cortex

Dendritic spine dynamics are thought to be substrates for motor learning and memory, and altered spine dynamics often lead to impaired performance. Here, we describe an exception to this rule by studying mice lacking paired immunoglobulin receptor B (PirB^-/-). Pyramidal neuron dendrites in PirB^-/- mice have increased spine formation rates and density. Surprisingly, PirB^-/- mice learn a skilled reaching task faster than wild-type (WT) littermates. Furthermore, stabilization of learning-induced spines is elevated in PirB^-/- mice. Mechanistically, single-spine uncaging experiments suggest that PirB is required for NMDA receptor (NMDAR)-dependent spine shrinkage. The degree of survival of newly formed spines correlates with performance, suggesting that increased spine stability is advantageous for learning. Acute inhibition of PirB function in M1 of adult WT mice increases the survival of learning-induced spines and enhances motor learning. These results demonstrate that there are limits on motor learning that can be lifted by manipulating PirB, even in adulthood.

STDP and the distribution of preferred phases in the whisker system

Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the whisker’s position. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. We found that under a wide range of parameters, STDP facilitated the transfer of rhythmic information despite the fact that all the synaptic weights remained dynamic. As a result, the preferred phase of the downstream neuron was not fixed, but rather drifted in time at a drift velocity that depended on the preferred phase, thus inducing a distribution of preferred phases. We further analyzed how the STDP rule governs the distribution of preferred phases in the downstream population. This link between the STDP rule and the distribution of preferred phases constitutes a natural test for our theory.

The distribution of preferred phases of whisking neurons in the somatosensory system of rats and mice presents a conundrum: a simple pooling model predicts a distribution that is an order of magnitude narrower than what is observed empirically. Here, we suggest that this non-trivial distribution may result from activity-dependent plasticity in the form of spike timing dependent plasticity (STDP). We show that under STDP, the synaptic weights do not converge to a fixed value, but rather remain dynamic. As a result, the preferred phases of the whisking neurons vary in time, hence inducing a non-trivial distribution of preferred phases, which is governed by the STDP rule. Our results imply that the considerable synaptic volatility which has long been viewed as a difficulty that needs to be overcome, may actually be an underlying principle of the organization of the central nervous system.

Optimal plasticity for memory maintenance during ongoing synaptic change

Synaptic connections in many brain circuits fluctuate, exhibiting substantial turnover and remodelling over hours to days. Surprisingly, experiments show that most of this flux in connectivity persists in the absence of learning or known plasticity signals. How can neural circuits retain learned information despite a large proportion of ongoing and potentially disruptive synaptic changes? We address this question from first principles by analysing how much compensatory plasticity would be required to optimally counteract ongoing fluctuations, regardless of whether fluctuations are random or systematic. Remarkably, we find that the answer is largely independent of plasticity mechanisms and circuit architectures: compensatory plasticity should be at most equal in magnitude to fluctuations, and often less, in direct agreement with previously unexplained experimental observations. Moreover, our analysis shows that a high proportion of learning-independent synaptic change is consistent with plasticity mechanisms that accurately compute error gradients.

Homomeric and Heteromeric α7 Nicotinic Acetylcholine Receptors in Health and Some Central Nervous System Diseases

Nicotinic acetylcholine receptors (nAChRs) are pentameric ligand-gated ion channels involved in the modulation of essential brain functions such as memory, learning, and attention. Homomeric α7 nAChR, formed exclusively by five identical α7 subunits, is involved in rapid synaptic transmission, whereas the heteromeric oligomers composed of α7 in combination with β subunits display metabotropic properties and operate in slower time frames. At the cellular level, the activation of nAChRs allows the entry of Na⁺ and Ca²⁺; the two cations depolarize the membrane and trigger diverse cellular signals, depending on the type of nAChR pentamer and neurons involved, the location of the intervening cells, and the networks of which these neuronal cells form part. These features make the α7 nAChR a central player in neurotransmission, metabolically associated Ca²⁺-mediated signaling, and modulation of diverse fundamental processes operated by other neurotransmitters in the brain. Due to its ubiquitous distribution and the multiple functions it displays in the brain, the α7 nAChR is associated with a variety of neurological and neuropsychiatric disorders whose exact etiopathogenic mechanisms are still elusive.

Stochastic self-assembly of reaction-diffusion patterns in synaptic membranes

Synaptic receptor and scaffold molecules self-assemble into membrane protein domains, which play an important role in signal transmission across chemical synapses. Experiment and theory have shown that the formation of receptor-scaffold domains of the characteristic size observed in nerve cells can be understood from the receptor and scaffold reaction and diffusion processes suggested by experiments. We employ here kinetic Monte Carlo (KMC) simulations to explore the self-assembly of synaptic receptor-scaffold domains in a stochastic lattice model of receptor and scaffold reaction-diffusion dynamics. For reaction and diffusion rates within the ranges of values suggested by experiments we find, in agreement with previous mean-field calculations, self-assembly of receptor-scaffold domains of a size similar to that observed in experiments. Comparisons between the results of our KMC simulations and mean-field solutions suggest that the intrinsic noise associated with receptor and scaffold reaction and diffusion processes accelerates the self-assembly of receptor-scaffold domains, and confers increased robustness to domain formation. In agreement with experimental observations, our KMC simulations yield a prevalence of scaffolds over receptors in receptor-scaffold domains. Our KMC simulations show that receptor and scaffold reaction-diffusion dynamics can inherently give rise to plasticity in the overall properties of receptor-scaffold domains, which may be utilized by nerve cells to regulate the receptor number at chemical synapses.

A large-scale nanoscopy and biochemistry analysis of postsynaptic dendritic spines

Dendritic spines, the postsynaptic compartments of excitatory neurotransmission, have different shapes classified from 'stubby' to 'mushroom-like'. Whereas mushroom spines are essential for adult brain function, stubby spines disappear during brain maturation. It is still unclear whether and how they differ in protein composition. To address this, we combined electron microscopy and quantitative biochemistry with super-resolution microscopy to annotate more than 47,000 spines for more than 100 synaptic targets. Surprisingly, mushroom and stubby spines have similar average protein copy numbers and topologies. However, an analysis of the correlation of each protein to the postsynaptic density mass, used as a marker of synaptic strength, showed substantially more significant results for the mushroom spines. Secretion and trafficking proteins correlated particularly poorly to the strength of stubby spines. This suggests that stubby spines are less likely to adequately respond to dynamic changes in synaptic transmission than mushroom spines, which possibly explains their loss during brain maturation.

Computational roles of intrinsic synaptic dynamics

Conventional theories assume that long-term information storage in the brain is implemented by modifying synaptic efficacy. Recent experimental findings challenge this view by demonstrating that dendritic spine sizes, or their corresponding synaptic weights, are highly volatile even in the absence of neural activity. Here, we review previous computational works on the roles of these intrinsic synaptic dynamics. We first present the possibility for neuronal networks to sustain stable performance in their presence, and we then hypothesize that intrinsic dynamics could be more than mere noise to withstand, but they may improve information processing in the brain.

Stable but not rigid: Chronic in vivo STED nanoscopy reveals extensive remodeling of spines, indicating multiple drivers of plasticity

Excitatory synapses on dendritic spines of pyramidal neurons are considered a central memory locus. To foster both continuous adaption and the storage of long-term information, spines need to be plastic and stable at the same time. Here, we advanced in vivo STED nanoscopy to superresolve distinct features of spines (head size and neck length/width) in mouse neocortex for up to 1 month. While LTP-dependent changes predict highly correlated modifications of spine geometry, we find both, uncorrelated and correlated dynamics, indicating multiple independent drivers of spine remodeling. The magnitude of this remodeling suggests substantial fluctuations in synaptic strength. Despite this high degree of volatility, all spine features exhibit persistent components that are maintained over long periods of time. Furthermore, chronic nanoscopy uncovers structural alterations in the cortex of a mouse model of neurodegeneration. Thus, at the nanoscale, stable dendritic spines exhibit a delicate balance of stability and volatility.

Spine dynamics in the brain, mental disorders and artificial neural networks

In the brain, most synapses are formed on minute protrusions known as dendritic spines. Unlike their artificial intelligence counterparts, spines are not merely tuneable memory elements: they also embody algorithms that implement the brain's ability to learn from experience and cope with new challenges. Importantly, they exhibit structural dynamics that depend on activity, excitatory input and inhibitory input (synaptic plasticity or 'extrinsic' dynamics) and dynamics independent of activity ('intrinsic' dynamics), both of which are subject to neuromodulatory influences and reinforcers such as dopamine. Here we succinctly review extrinsic and intrinsic dynamics, compare these with parallels in machine learning where they exist, describe the importance of intrinsic dynamics for memory management and adaptation, and speculate on how disruption of extrinsic and intrinsic dynamics may give rise to mental disorders. Throughout, we also highlight algorithmic features of spine dynamics that may be relevant to future artificial intelligence developments.

Nanoscale 3D EM reconstructions reveal intrinsic mechanisms of structural diversity of chemical synapses

Chemical synapses of shared cellular origins have remarkably heterogeneous structures, but how this diversity is generated is unclear. Here, we use three-dimensional (3D) electron microscopy and artificial intelligence algorithms for image processing to reconstruct functional excitatory microcircuits in the mouse hippocampus and microcircuits in which neurotransmitter signaling is permanently suppressed with genetic tools throughout the lifespan. These nanoscale analyses reveal that experience is dispensable for morphogenesis of synapses with different geometric shapes and contents of membrane organelles and that arrangement of morphologically distinct connections in local networks is stochastic. Moreover, loss of activity increases the variability in sizes of opposed pre- and postsynaptic structures without disrupting their alignments, suggesting that inherently variable weights of naive connections become progressively matched with repetitive use. These results demonstrate that mechanisms for the structural diversity of neuronal synapses are intrinsic and provide insights into how circuits essential for memory storage assemble and integrate information.

Reciprocal stabilization of glycine receptors and gephyrin scaffold proteins at inhibitory synapses

Postsynaptic scaffold proteins immobilize neurotransmitter receptors in the synaptic membrane opposite to presynaptic vesicle release sites, thus ensuring efficient synaptic transmission. At inhibitory synapses in the spinal cord, the main scaffold protein gephyrin assembles in dense molecule clusters that provide binding sites for glycine receptors (GlyRs). Gephyrin and GlyRs can also interact outside of synapses, where they form receptor-scaffold complexes. Although several models for the formation of postsynaptic scaffold domains in the presence of receptor-scaffold interactions have been advanced, a clear picture of the coupled dynamics of receptors and scaffold proteins at synapses is lacking. To characterize the GlyR and gephyrin dynamics at inhibitory synapses, we performed fluorescence time-lapse imaging after photoconversion to directly visualize the exchange kinetics of recombinant Dendra2-gephyrin in cultured spinal cord neurons. Immuno-immobilization of endogenous GlyRs with specific antibodies abolished their lateral diffusion in the plasma membrane, as judged by the lack of fluorescence recovery after photobleaching. Moreover, the cross-linking of GlyRs significantly reduced the exchange of Dendra2-gephyrin compared with control conditions, suggesting that the kinetics of the synaptic gephyrin pool is strongly dependent on GlyR-gephyrin interactions. We did not observe any change in the total synaptic gephyrin levels after GlyR cross-linking, however, indicating that the number of gephyrin molecules at synapses is not primarily dependent on the exchange of GlyR-gephyrin complexes. We further show that our experimental data can be quantitatively accounted for by a model of receptor-scaffold dynamics that includes a tightly interacting receptor-scaffold domain, as well as more loosely bound receptor and scaffold populations that exchange with extrasynaptic pools. The model can make predictions for single-molecule data such as typical dwell times of synaptic proteins. Taken together, our data demonstrate the reciprocal stabilization of GlyRs and gephyrin at inhibitory synapses and provide a quantitative understanding of their dynamic organization.

Microbial memories: Sex-dependent impact of the gut microbiome on hippocampal plasticity

Germ‐free rodents, raised in the absence of a measurable gut microbiome, have been a key model to study the microbiome‐gut‐brain axis. Germ‐free mice exhibit marked behavioural and neurochemical differences to their conventionally raised counterparts. It is as yet unclear how these neurochemical differences lead to the behavioural differences. Here, we test the electrophysiological properties of hippocampal plasticity in adult germ‐free mice and compare them to conventionally raised counterparts. Whilst basal synaptic efficacy and pre‐synaptic short‐term plasticity appear normal, we find a striking alteration of hippocampal long‐term potentiation specifically in male germ‐free slices. However, the spike output of these neurons remains normal along with altered input‐output coupling, potentially indicating homeostatic compensatory mechanisms, or an altered excitation/inhibition balance. To our knowledge this is the first time the electrophysiological properties of the hippocampus have been assessed in a microbiome deficient animal. Our data indicate that the absence of a microbiome alters integration of dendritic signalling in the CA1 region in mice.

The brain in motion: How ensemble fluidity drives memory-updating and flexibility

While memories are often thought of as flashbacks to a previous experience, they do not simply conserve veridical representations of the past but must continually integrate new information to ensure survival in dynamic environments. Therefore, 'drift' in neural firing patterns, typically construed as disruptive 'instability' or an undesirable consequence of noise, may actually be useful for updating memories. In our view, continual modifications in memory representations reconcile classical theories of stable memory traces with neural drift. Here we review how memory representations are updated through dynamic recruitment of neuronal ensembles on the basis of excitability and functional connectivity at the time of learning. Overall, we emphasize the importance of considering memories not as static entities, but instead as flexible network states that reactivate and evolve across time and experience.

Shedding light on learning and memory: optical interrogation of the synaptic circuitry

In the quest for the physical substrate of learning and memory, a consensus gradually emerges that memory traces are stored in specific neuronal populations and the synaptic circuits that connect them. In this review, we discuss recent progresses in understanding the reorganization of synaptic circuits and neuronal assemblies associated with learning and memory, with an emphasis on optical techniques for in vivo interrogations. We also highlight some open questions on the missing link between synaptic modifications and neuronal coding, and how stable memory persists despite synaptic and neuronal fluctuations.

Activity Dependent and Independent Determinants of Synaptic Size Diversity

The extraordinary diversity of excitatory synapse sizes is commonly attributed to activity-dependent processes that drive synaptic growth and diminution. Recent studies also point to activity-independent size fluctuations, possibly driven by innate synaptic molecule dynamics, as important generators of size diversity. To examine the contributions of activity-dependent and independent processes to excitatory synapse size diversity, we studied glutamatergic synapse size dynamics and diversification in cultured rat cortical neurons (both sexes), silenced from plating. We found that in networks with no history of activity whatsoever, synaptic size diversity was no less extensive than that observed in spontaneously active networks. Synapses in silenced networks were larger, size distributions were broader, yet these were rightward-skewed and similar in shape when scaled by mean synaptic size. Silencing reduced the magnitude of size fluctuations and weakened constraints on size distributions, yet these were sufficient to explain synaptic size diversity in silenced networks. Model-based exploration followed by experimental testing indicated that silencing-associated changes in innate molecular dynamics and fluctuation characteristics might negatively impact synaptic persistence, resulting in reduced synaptic numbers. This, in turn, would increase synaptic molecule availability, promote synaptic enlargement, and ultimately alter fluctuation characteristics. These findings suggest that activity-independent size fluctuations are sufficient to fully diversify glutamatergic synaptic sizes, with activity-dependent processes primarily setting the scale rather than the shape of size distributions. Moreover, they point to reciprocal relationships between synaptic size fluctuations, size distributions, and synaptic numbers mediated by the innate dynamics of synaptic molecules as they move in, out, and between synapses.SIGNIFICANCE STATEMENT Sizes of glutamatergic synapses vary tremendously, even when formed on the same neuron. This diversity is commonly thought to reflect the outcome of activity-dependent forms of synaptic plasticity, yet activity-independent processes might also play some part. Here we show that in neurons with no history of activity whatsoever, synaptic sizes are no less diverse. We show that this diversity is the product of activity-independent size fluctuations, which are sufficient to generate a full repertoire of synaptic sizes at correct proportions. By combining modeling and experimentation we expose reciprocal relationships between size fluctuations, synaptic sizes and synaptic counts, and show how these phenomena might be connected through the dynamics of synaptic molecules as they move in, out, and between synapses.

Investigating learning-related neural circuitry with chronic in vivo optical imaging

Fundamental aspects of brain function, including development, plasticity, learning, and memory, can take place over time scales of days to years. Chronic in vivo imaging of neural activity with cellular resolution is a powerful method for tracking the long-term activity of neural circuits. We review recent advances in our understanding of neural circuit function from diverse brain regions that have been enabled by chronic in vivo cellular imaging. Insight into the neural basis of learning and decision-making, in particular, benefit from the ability to acquire longitudinal data from genetically identified neuronal populations, deep brain areas, and subcellular structures. We propose that combining chronic imaging with further experimental and computational innovations will advance our understanding of the neural circuit mechanisms of brain function.

Cortical Synaptic AMPA Receptor Plasticity during Motor Learning

Modulation of synaptic strength through trafficking of AMPA receptors (AMPARs) is a fundamental mechanism underlying synaptic plasticity, learning, and memory. However, the dynamics of AMPAR trafficking in vivo and its correlation with learning have not been resolved. Here, we used in vivo two-photon microscopy to visualize surface AMPARs in mouse cortex during the acquisition of a forelimb reaching task. Daily training leads to an increase in AMPAR levels at a subset of spatially clustered dendritic spines in the motor cortex. Surprisingly, we also observed increases in spine AMPAR levels in the visual cortex. There, synaptic potentiation depends on the availability of visual input during motor training, and optogenetic inhibition of visual cortex activity impairs task performance. These results indicate that motor learning induces widespread cortical synaptic potentiation by increasing the net trafficking of AMPARs into spines, including in non-motor brain regions.

The Microbiota-Gut-Brain Axis

The importance of the gut-brain axis in maintaining homeostasis has long been appreciated. However, the past 15 yr have seen the emergence of the microbiota (the trillions of microorganisms within and on our bodies) as one of the key regulators of gut-brain function and has led to the appreciation of the importance of a distinct microbiota-gut-brain axis. This axis is gaining ever more traction in fields investigating the biological and physiological basis of psychiatric, neurodevelopmental, age-related, and neurodegenerative disorders. The microbiota and the brain communicate with each other via various routes including the immune system, tryptophan metabolism, the vagus nerve and the enteric nervous system, involving microbial metabolites such as short-chain fatty acids, branched chain amino acids, and peptidoglycans. Many factors can influence microbiota composition in early life, including infection, mode of birth delivery, use of antibiotic medications, the nature of nutritional provision, environmental stressors, and host genetics. At the other extreme of life, microbial diversity diminishes with aging. Stress, in particular, can significantly impact the microbiota-gut-brain axis at all stages of life. Much recent work has implicated the gut microbiota in many conditions including autism, anxiety, obesity, schizophrenia, Parkinson’s disease, and Alzheimer’s disease. Animal models have been paramount in linking the regulation of fundamental neural processes, such as neurogenesis and myelination, to microbiome activation of microglia. Moreover, translational human studies are ongoing and will greatly enhance the field. Future studies will focus on understanding the mechanisms underlying the microbiota-gut-brain axis and attempt to elucidate microbial-based intervention and therapeutic strategies for neuropsychiatric disorders.

Stable memory with unstable synapses

What is the physiological basis of long-term memory? The prevailing view in Neuroscience attributes changes in synaptic efficacy to memory acquisition, implying that stable memories correspond to stable connectivity patterns. However, an increasing body of experimental evidence points to significant, activity-independent fluctuations in synaptic strengths. How memories can survive these fluctuations and the accompanying stabilizing homeostatic mechanisms is a fundamental open question. Here we explore the possibility of memory storage within a global component of network connectivity, while individual connections fluctuate. We find that homeostatic stabilization of fluctuations differentially affects different aspects of network connectivity. Specifically, memories stored as time-varying attractors of neural dynamics are more resilient to erosion than fixed-points. Such dynamic attractors can be learned by biologically plausible learning-rules and support associative retrieval. Our results suggest a link between the properties of learning-rules and those of network-level memory representations, and point at experimentally measurable signatures.

How are stable memories maintained in the brain despite significant ongoing fluctuations in synaptic strengths? Here, the authors show that a model consistent with fluctuations, homeostasis and biologically plausible learning rules, naturally leads to memories implemented as dynamic attractors.

The hidden repertoire of brain dynamics and dysfunction

The purpose of this paper is to describe a framework for the understanding of rules that govern how neural system dynamics are coordinated to produce behavior. The framework, structured flows on manifolds (SFM), posits that neural processes are flows depicting system interactions that occur on relatively low-dimension manifolds, which constrain possible functional configurations. Although this is a general framework, we focus on the application to brain disorders. We first explain the Epileptor, a phenomenological computational model showing fast and slow dynamics, but also a hidden repertoire whose expression is similar to refractory status epilepticus. We suggest that epilepsy represents an innate brain state whose potential may be realized only under certain circumstances. Conversely, deficits from damage or disease processes, such as stroke or dementia, may reflect both the disease process per se and the adaptation of the brain. SFM uniquely captures both scenarios. Finally, we link neuromodulation effects and switches in functional network configurations to fast and slow dynamics that coordinate the expression of SFM in the context of cognition. The tools to measure and model SFM already exist, giving researchers access to the dynamics of neural processes that support the concomitant dynamics of the cognitive and behavioral processes.

Intrinsic Spine Dynamics Are Critical for Recurrent Network Learning in Models With and Without Autism Spectrum Disorder

It is often assumed that Hebbian synaptic plasticity forms a cell assembly, a mutually interacting group of neurons that encodes memory. However, in recurrently connected networks with pure Hebbian plasticity, cell assemblies typically diverge or fade under ongoing changes of synaptic strength. Previously assumed mechanisms that stabilize cell assemblies do not robustly reproduce the experimentally reported unimodal and long-tailed distribution of synaptic strengths. Here, we show that augmenting Hebbian plasticity with experimentally observed intrinsic spine dynamics can stabilize cell assemblies and reproduce the distribution of synaptic strengths. Moreover, we posit that strong intrinsic spine dynamics impair learning performance. Our theory explains how excessively strong spine dynamics, experimentally observed in several animal models of autism spectrum disorder, impair learning associations in the brain.

Fundamental bounds on learning performance in neural circuits

How does the size of a neural circuit influence its learning performance? Larger brains tend to be found in species with higher cognitive function and learning ability. Intuitively, we expect the learning capacity of a neural circuit to grow with the number of neurons and synapses. We show how adding apparently redundant neurons and connections to a network can make a task more learnable. Consequently, large neural circuits can either devote connectivity to generating complex behaviors or exploit this connectivity to achieve faster and more precise learning of simpler behaviors. However, we show that in a biologically relevant setting where synapses introduce an unavoidable amount of noise, there is an optimal size of network for a given task. Above the optimal network size, the addition of neurons and synaptic connections starts to impede learning performance. This suggests that the size of brain circuits may be constrained by the need to learn efficiently with unreliable synapses and provides a hypothesis for why some neurological learning deficits are associated with hyperconnectivity. Our analysis is independent of specific learning rules and uncovers fundamental relationships between learning rate, task performance, network size, and intrinsic noise in neural circuits.

Self-organized reactivation maintains and reinforces memories despite synaptic turnover

Long-term memories are believed to be stored in the synapses of cortical neuronal networks. However, recent experiments report continuous creation and removal of cortical synapses, which raises the question how memories can survive on such a variable substrate. Here, we study the formation and retention of associative memory in a computational model based on Hebbian cell assemblies in the presence of both synaptic and structural plasticity. During rest periods, such as may occur during sleep, the assemblies reactivate spontaneously, reinforcing memories against ongoing synapse removal and replacement. Brief daily reactivations during rest-periods suffice to not only maintain the assemblies, but even strengthen them, and improve pattern completion, consistent with offline memory gains observed experimentally. While the connectivity inside memory representations is strengthened during rest phases, connections in the rest of the network decay and vanish thus reconciling apparently conflicting hypotheses of the influence of sleep on cortical connectivity.

Medial Prefrontal Cortex Population Activity Is Plastic Irrespective of Learning

The prefrontal cortex (PFC) is thought to learn the relationships between actions and their outcomes. But little is known about what changes to population activity in PFC are specific to learning these relationships. Here we characterize the plasticity of population activity in the medial PFC (mPFC) of male rats learning rules on a Y-maze. First, we show that the population always changes its patterns of joint activity between the periods of sleep either side of a training session on the maze, regardless of successful rule learning during training. Next, by comparing the structure of population activity in sleep and training, we show that this population plasticity differs between learning and nonlearning sessions. In learning sessions, the changes in population activity in post-training sleep incorporate the changes to the population activity during training on the maze. In nonlearning sessions, the changes in sleep and training are unrelated. Finally, we show evidence that the nonlearning and learning forms of population plasticity are driven by different neuron-level changes, with the nonlearning form entirely accounted for by independent changes to the excitability of individual neurons, and the learning form also including changes to firing rate couplings between neurons. Collectively, our results suggest two different forms of population plasticity in mPFC during the learning of action-outcome relationships: one a persistent change in population activity structure decoupled from overt rule-learning, and the other a directional change driven by feedback during behavior.SIGNIFICANCE STATEMENT The PFC is thought to represent our knowledge about what action is worth doing in which context. But we do not know how the activity of neurons in PFC collectively changes when learning which actions are relevant. Here we show, in a trial-and-error task, that population activity in PFC is persistently changing, regardless of learning. Only during episodes of clear learning of relevant actions are the accompanying changes to population activity carried forward into sleep, suggesting a long-lasting form of neural plasticity. Our results suggest that representations of relevant actions in PFC are acquired by reward imposing a direction onto ongoing population plasticity.

In Vivo Volume Dynamics of Dendritic Spines in the Neocortex of Wild-Type and Fmr1 KO Mice

Excitatory synapses are often formed at small protrusions of dendrite, called dendritic spines, in most projection neurons, and the spine-head volumes show strong correlations with synaptic connectivity. We examined the dynamics of spine volume in the adult mouse visual cortex using time-lapse in vivo two-photon imaging with a resonant Galvano scanner. Contrary to expectations, we found that the spines in the adult neocortex showed fluctuations to a similar degree as that observed in young hippocampal preparations, but there were systematic differences in how the dynamics were dependent on spine volumes, thus allowing for fewer fluctuations in small spines, which could account for the relatively low turnover rates of neocortical spines in vivo. We found that spine volumes fluctuated to a greater extent in a mouse model (Fmr1 knockout) of fragile X mental retardation than in wild-type mice, and the spine turnover rates were also higher in Fmr1 knock-out mice. Such features of spine dynamics in Fmr1 knock-out mice could be represented by a single slope factor in our model. Our data and model indicate a small but significant change in the average spine volume and more eminent differences in the statistical distribution in Fmr1 knock-out mice even in adulthood, which reflects the abnormal in vivo dynamics of spine volumes.

Competition for synaptic building blocks shapes synaptic plasticity

Changes in the efficacies of synapses are thought to be the neurobiological basis of learning and memory. The efficacy of a synapse depends on its current number of neurotransmitter receptors. Recent experiments have shown that these receptors are highly dynamic, moving back and forth between synapses on time scales of seconds and minutes. This suggests spontaneous fluctuations in synaptic efficacies and a competition of nearby synapses for available receptors. Here we propose a mathematical model of this competition of synapses for neurotransmitter receptors from a local dendritic pool. Using minimal assumptions, the model produces a fast multiplicative scaling behavior of synapses. Furthermore, the model explains a transient form of heterosynaptic plasticity and predicts that its amount is inversely related to the size of the local receptor pool. Overall, our model reveals logistical tradeoffs during the induction of synaptic plasticity due to the rapid exchange of neurotransmitter receptors between synapses.