Long-term relationships between synaptic tenacity, synaptic remodeling, and network activity

Prolonged Activity Deprivation Causes Pre- and Postsynaptic Compensatory Plasticity at Neocortical Excitatory Synapses

Homeostatic plasticity stabilizes firing rates of neurons, but the pressure to restore low activity rates can significantly alter synaptic and cellular properties. Most previous studies of homeostatic readjustment to complete activity silencing in rodent forebrain have examined changes after 2 d of deprivation, but it is known that longer periods of deprivation can produce adverse effects. To better understand the mechanisms underlying these effects and to address how presynaptic as well as postsynaptic compartments change during homeostatic plasticity, we subjected mouse cortical slice cultures to a more severe 5 d deprivation paradigm. We developed and validated a computational framework to measure the number and morphology of presynaptic and postsynaptic compartments from super-resolution light microscopy images of dense cortical tissue. Using these tools, combined with electrophysiological miniature excitatory postsynaptic current measurements, and synaptic imaging at the electron microscopy level, we assessed the functional and morphological results of prolonged deprivation. Excitatory synapses were strengthened both presynaptically and postsynaptically. Surprisingly, we also observed a decrement in the density of excitatory synapses, both as measured from colocalized staining of pre- and postsynaptic proteins in tissue and from the number of dendritic spines. Overall, our results suggest that cortical networks deprived of activity progressively move toward a smaller population of stronger synapses.

The purpose and ubiquity of turnover

Turnover-constant component production and destruction-is ubiquitous in biology. Turnover occurs across organisms and scales, including for RNAs, proteins, membranes, macromolecular structures, organelles, cells, hair, feathers, nails, antlers, and teeth. For many systems, turnover might seem wasteful when degraded components are often fully functional. Some components turn over with shockingly high rates and others do not turn over at all, further making this process enigmatic. However, turnover can address fundamental problems by yielding powerful properties, including regeneration, rapid repair onset, clearance of unpredictable damage and errors, maintenance of low constitutive levels of disrepair, prevention of stable hazards, and transitions. I argue that trade-offs between turnover benefits and metabolic costs, combined with constraints on turnover, determine its presence and rates across distinct contexts. I suggest that the limits of turnover help explain aging and that turnover properties and the basis for its levels underlie this fundamental component of life.

Progressive Circuit Hyperexcitability in Mouse Neocortical Slice Cultures with Increasing Duration of Activity Silencing

Forebrain neurons deprived of activity become hyperactive when activity is restored. Rebound activity has been linked to spontaneous seizures in vivo following prolonged activity blockade. Here, we measured the time course of rebound activity and the contributing circuit mechanisms using calcium imaging, synaptic staining, and whole-cell patch clamp in organotypic slice cultures of mouse neocortex. Calcium imaging revealed hypersynchronous activity increasing in intensity with longer periods of deprivation. While activity partially recovered 3 d after slices were released from 5 d of deprivation, they were less able to recover after 10 d of deprivation. However, even after the longer period of deprivation, activity patterns eventually returned to baseline levels. The degree of deprivation-induced rebound was age-dependent, with the greatest effects occurring when silencing began in the second week. Pharmacological blockade of NMDA receptors indicated that hypersynchronous rebound activity did not require activation of Hebbian plasticity. In single-neuron recordings, input resistance roughly doubled with a concomitant increase in intrinsic excitability. Synaptic imaging of pre- and postsynaptic proteins revealed dramatic reductions in the number of presumptive synapses with a larger effect on inhibitory than excitatory synapses. Putative excitatory synapses colocalizing PSD-95 and Bassoon declined by 39 and 56% following 5 and 10 d of deprivation, but presumptive inhibitory synapses colocalizing gephyrin and VGAT declined by 55 and 73%, respectively. The results suggest that with prolonged deprivation, a progressive reduction in synapse number is accompanied by a shift in the balance between excitation and inhibition and increased cellular excitability.

Ethylene thiourea exposure induces neurobehavioral toxicity in zebrafish by disrupting axon growth and neuromuscular junctions

Ethylene thiourea (ETU) converted from ethylene bisdithiocarbamate (EBDC) fungicides has aroused great concern because of its prevalence and harmful effects. Although ETU-induced neurotoxicity has been reported, the potential mechanisms remain unclear. This study provided insights into its neurotoxic effects at environmentally relevant concentrations in zebrafish. Our findings showed that embryonic exposure to ETU decreased the hatch rate and delayed somite development. Furthermore, ETU treatment significantly reduced the dark velocity in the locomotion assay. The upregulated tendency of the mitogen-activated protein kinases (MAPK) pathway (mknk1, atf4, mapkapk3) screened by transcriptome analysis implied motor neuron degeneration, which was validated by subsequent morphological observation, as axon length and branches were truncated in the 62.5 µg/L ETU group. However, although the rescue experiment with a p38 MAPK inhibitor (SB203580) successfully ameliorated axon degeneration, it failed to reverse the locomotion behaviors. Further exploration of transcriptome data revealed the varied expression of presynaptic scaffold protein-related genes (pcloa, pclob, bsna), whose downregulation might impair the neuromuscular junction (NMJ). Therefore, we reasonably suspected that ETU-induced neurobehavioral deficits might result from the combined effects of the MAPK pathway and presynaptic proteins. Considering this, we highlighted the necessity to take precautions and early interventions for susceptible ETU-exposed populations.

Diverging from the Norm: Reevaluating What Miniature Excitatory Postsynaptic Currents Tell Us about Homeostatic Synaptic Plasticity

The idea that the nervous system maintains a set point of network activity and homeostatically returns to that set point in the face of dramatic disruption-during development, after injury, in pathologic states, and during sleep/wake cycles-is rapidly becoming accepted as a key plasticity behavior, placing it alongside long-term potentiation and depression. The dramatic growth in studies of homeostatic synaptic plasticity of miniature excitatory synaptic currents (mEPSCs) is attributable, in part, to the simple yet elegant mechanism of uniform multiplicative scaling proposed by Turrigiano and colleagues: that neurons sense their own activity and globally multiply the strength of every synapse by a single factor to return activity to the set point without altering established differences in synaptic weights. We have recently shown that for mEPSCs recorded from control and activity-blocked cultures of mouse cortical neurons, the synaptic scaling factor is not uniform but is close to 1 for the smallest mEPSC amplitudes and progressively increases as mEPSC amplitudes increase, which we term divergent scaling. Using insights gained from simulating uniform multiplicative scaling, we review evidence from published studies and conclude that divergent synaptic scaling is the norm rather than the exception. This conclusion has implications for hypotheses about the molecular mechanisms underlying synaptic scaling.

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.

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.

Structural Modularity Tunes Mesoscale Criticality in Biological Neuronal Networks

Numerous studies suggest that biological neuronal networks self-organize toward a critical state with stable recruitment dynamics. Individual neurons would then statistically activate exactly one further neuron during activity cascades termed neuronal avalanches. Yet, it is unclear if and how this can be reconciled with the explosive recruitment dynamics within neocortical minicolumns in vivo and within neuronal clusters in vitro, which indicates that neurons form supercritical local circuits. Theoretical studies propose that modular networks with a mix of regionally subcritical and supercritical dynamics would create apparently critical dynamics, resolving this inconsistency. Here, we provide experimental support by manipulating the structural self-organization process of networks of cultured rat cortical neurons (either sex). Consistent with the prediction, we show that increasing clustering in neuronal networks developing in vitro strongly correlates with avalanche size distributions transitioning from supercritical to subcritical activity dynamics. Avalanche size distributions approximated a power law in moderately clustered networks, indicating overall critical recruitment. We propose that activity-dependent self-organization can tune inherently supercritical networks toward mesoscale criticality by creating a modular structure in neuronal networks.SIGNIFICANCE STATEMENT Critical recruitment dynamics in neuronal networks are considered optimal for information processing in the brain. However, it remains heavily debated how neuronal networks would self-organize criticality by detailed fine-tuning of connectivity, inhibition, and excitability. We provide experimental support for theoretical considerations that modularity tunes critical recruitment dynamics at the mesoscale level of interacting neuron clusters. This reconciles reports of supercritical recruitment dynamics in local neuron clusters with findings on criticality sampled at mesoscopic network scales. Intriguingly, altered mesoscale organization is a prominent aspect of various neuropathological diseases currently investigated in the framework of criticality. We therefore believe that our findings would also be of interest for clinical scientists searching to link the functional and anatomic signatures of such brain disorders.

Mechanical transmission at spine synapses: Short-term potentiation and working memory

Do dendritic spines, which comprise the postsynaptic component of most excitatory synapses, exist only for their structural dynamics, receptor trafficking, and chemical and electrical compartmentation? The answer is no. Simultaneous investigation of both spine and presynaptic terminals has recently revealed a novel feature of spine synapses. Spine enlargement pushes the presynaptic terminals with muscle-like force and augments the evoked glutamate release for up to 20 min. We now summarize the evidence that such mechanical transmission shares critical features in common with short-term potentiation (STP) and may represent the cellular basis of short-term and working memory. Thus, spine synapses produce the force of learning to leave structural traces for both short and long-term memories.

NCK1 Modulates Neuronal Actin Dynamics and Promotes Dendritic Spine, Synapse, and Memory Formation

Memory formation and maintenance is a dynamic process involving the modulation of the actin cytoskeleton at synapses. Understanding the signaling pathways that contribute to actin modulation is important for our understanding of synapse formation and function, as well as learning and memory. Here, we focused on the importance of the actin regulator, noncatalytic region of tyrosine kinase adaptor protein 1 (NCK1), in hippocampal dependent behaviors and development. We report that male mice lacking NCK1 have impairments in both short-term and working memory, as well as spatial learning. Additionally, we report sex differences in memory impairment showing that female mice deficient in NCK1 fail at reversal learning in a spatial learning task. We find that NCK1 is expressed in postmitotic neurons but is dispensable for neuronal proliferation and migration in the developing hippocampus. Morphologically, NCK1 is not necessary for overall neuronal dendrite development. However, neurons lacking NCK1 have lower dendritic spine and synapse densities in vitro and in vivo EM analysis reveal increased postsynaptic density (PSD) thickness in the hippocampal CA1 region of NCK1-deficient mice. Mechanistically, we find the turnover of actin-filaments in dendritic spines is accelerated in neurons that lack NCK1. Together, these findings suggest that NCK1 contributes to hippocampal-dependent memory by stabilizing actin dynamics and dendritic spine formation.SIGNIFICANCE STATEMENT Understanding the molecular signaling pathways that contribute to memory formation, maintenance, and elimination will lead to a better understanding of the genetic influences on cognition and cognitive disorders and will direct future therapeutics. Here, we report that the noncatalytic region of tyrosine kinase adaptor protein 1 (NCK1) adaptor protein modulates actin-filament turnover in hippocampal dendritic spines. Mice lacking NCK1 show sex-dependent deficits in hippocampal memory formation tasks, have altered postsynaptic densities, and reduced synaptic density. Together, our work implicates NCK1 in the regulation of actin cytoskeleton dynamics and normal synapse development which is essential for memory formation.

The molecular memory code and synaptic plasticity: A synthesis

The most widely accepted view of memory in the brain holds that synapses are the storage sites of memory, and that memories are formed through associative modification of synapses. This view has been challenged on conceptual and empirical grounds. As an alternative, it has been proposed that molecules within the cell body are the storage sites of memory, and that memories are formed through biochemical operations on these molecules. This paper proposes a synthesis of these two views, grounded in a computational model of memory. Synapses are conceived as storage sites for the parameters of an approximate posterior probability distribution over latent causes. Intracellular molecules are conceived as storage sites for the parameters of a generative model. The model stipulates how these two components work together as part of an integrated algorithm for learning and inference.

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.

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.

Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding

Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.

The sloppy relationship between neural circuit structure and function

Investigating and describing the relationships between the structure of a circuit and its function has a long tradition in neuroscience. Since neural circuits acquire their structure through sophisticated developmental programmes, and memories and experiences are maintained through synaptic modification, it is to be expected that structure is closely linked to function. Recent findings challenge this hypothesis from three different angles: Function does not strongly constrain circuit parameters, many parameters in neural circuits are irrelevant and contribute little to function, and circuit parameters are unstable and subject to constant random drift. At the same time however, recent work also showed that dynamics in neural circuit activity that is related to function are robust over time and across individuals. Here this apparent contradiction is addressed by considering the properties of neural manifolds that restrict circuit activity to functionally relevant subspaces, and it will be suggested that degenerate, anisotropic and unstable parameter spaces are a closely related to the structure and implementation of functionally relevant neural manifolds. Abstract figure legend What are the relationships between noisy and highly variable microscopic neural circuit variables on the one hand and the generation of behaviour on the other? Here it is proposed that an intermediate level of description exists where this relationship can be understood in terms of low-dimensional dynamics. Recordings of neural activity during unconstrained behaviour and the development of new machine learning methods will help to uncover these links. This article is protected by copyright. All rights reserved.

Anxiety and hippocampal neuronal activity: Relationship and potential mechanisms

The hippocampus has been implicated in modulating anxiety. It interacts with a variety of brain regions, both cortical and subcortical areas regulating emotion and stress responses, including prefrontal cortex, amygdala, hypothalamus, and the nucleus accumbens, to adjust anxiety levels in response to a variety of stressful conditions. Growing evidence indicates that anxiety is associated with increased neuronal excitability in the hippocampus, and alterations in local regulation of hippocampal excitability have been suggested to underlie behavioral disruptions characteristic of certain anxiety disorders. Furthermore, studies have shown that some anxiolytics can treat anxiety by altering the excitability and plasticity of hippocampal neurons. Hence, identifying cellular and molecular mechanisms and neural circuits that regulate hippocampal excitability in anxiety may be beneficial for developing targeted interventions for treatment of anxiety disorders particularly for the treatment-resistant cases. We first briefly review a role of the hippocampus in fear. We then review the evidence indicating a relationship between the hippocampal activity and fear/anxiety and discuss some possible mechanisms underlying stress-induced hippocampal excitability and anxiety-related behavior.

Can biophysical models of dendritic spines be used to explore synaptic changes associated with addiction?

Effective treatments that prevent or reduce drug relapse vulnerability should be developed to relieve the high burden of drug addiction on society. This will only be possible by enhancing the understanding of the molecular mechanisms underlying the neurobiology of addiction. Recent experimental data have shown that dendritic spines, small protrusions from the dendrites that receive excitatory input, of spiny neurons in the nucleus accumbens exhibit morphological changes during drug exposure and withdrawal. Moreover, these changes relate to the characteristic drug-seeking behavior of addiction. However, due to the complexity of the dendritic spines, we do not yet fully understand the processes underlying their structural changes in response to different inputs. We propose that biophysical models can enhance the current understanding of these processes by incorporating different, and sometimes, discrepant experimental data to identify the shared underlying mechanisms and generate experimentally testable hypotheses. This review aims to give an up-to-date report on biophysical models of dendritic spines, focusing on those models that describe their shape changes, which are well-known to relate to learning and memory. Moreover, it examines how these models can enhance our understanding of the effect of the drugs and the synaptic changes during withdrawal, as well as during neurodegenerative disease progression such as Alzheimer's disease.

The information theory of developmental pruning: Optimizing global network architectures using local synaptic rules

During development, biological neural networks produce more synapses and neurons than needed. Many of these synapses and neurons are later removed in a process known as neural pruning. Why networks should initially be over-populated, and the processes that determine which synapses and neurons are ultimately pruned, remains unclear. We study the mechanisms and significance of neural pruning in model neural networks. In a deep Boltzmann machine model of sensory encoding, we find that (1) synaptic pruning is necessary to learn efficient network architectures that retain computationally-relevant connections, (2) pruning by synaptic weight alone does not optimize network size and (3) pruning based on a locally-available measure of importance based on Fisher information allows the network to identify structurally important vs. unimportant connections and neurons. This locally-available measure of importance has a biological interpretation in terms of the correlations between presynaptic and postsynaptic neurons, and implies an efficient activity-driven pruning rule. Overall, we show how local activity-dependent synaptic pruning can solve the global problem of optimizing a network architecture. We relate these findings to biology as follows: (I) Synaptic over-production is necessary for activity-dependent connectivity optimization. (II) In networks that have more neurons than needed, cells compete for activity, and only the most important and selective neurons are retained. (III) Cells may also be pruned due to a loss of synapses on their axons. This occurs when the information they convey is not relevant to the target population.

Biological neural networks need to be efficient and compact, as synapses and neurons require space to store and energy to operate and maintain. This favors an optimized network topology that minimizes redundant neurons and connections. Large numbers of extra neurons and synapses are produced during development, and later removed as the brain matures. A key question to understand this process is how neurons determine which synapses are important. We used statistical models of neural networks to simulate developmental pruning. We show that neurons in such networks can use locally available information to measure the importance of their synapses in a biologically plausible way. We demonstrate that this pruning rule, which is motivated by information theoretic considerations, retains network topologies that can efficiently encode sensory inputs. In contrast, pruning at random, or based on synaptic weights alone, was less able to identify redundant neurons.

Representational drift in the mouse visual cortex

Recent studies have shown that neuronal representations gradually change over time despite no changes in the stimulus, environment, or behavior. However, such representational drift has been assumed to be a property of high-level brain structures, whereas earlier circuits, such as sensory cortices, have been assumed to stably encode information over time. Here, we analyzed large-scale optical and electrophysiological recordings from six visual cortical areas in behaving mice that were repeatedly presented with the same natural movies. Contrary to the prevailing notion, we found representational drift over timescales spanning minutes to days across multiple visual areas, cortical layers, and cell types. Notably, neural-code stability did not reflect the hierarchy of information flow across areas. Although individual neurons showed time-dependent changes in their coding properties, the structure of the relationships between population activity patterns remained stable and stereotypic. Such population-level organization may underlie stable visual perception despite continuous changes in neuronal responses.

The prevalence and specificity of local protein synthesis during neuronal synaptic plasticity

To supply proteins to their vast volume, neurons localize mRNAs and ribosomes in dendrites and axons. While local protein synthesis is required for synaptic plasticity, the abundance and distribution of ribosomes and nascent proteins near synapses remain elusive. Here, we quantified the occurrence of local translation and visualized the range of synapses supplied by nascent proteins during basal and plastic conditions. We detected dendritic ribosomes and nascent proteins at single-molecule resolution using DNA-PAINT and metabolic labeling. Both ribosomes and nascent proteins positively correlated with synapse density. Ribosomes were detected at ~85% of synapses with ~2 translational sites per synapse; ~50% of the nascent protein was detected near synapses. The amount of locally synthesized protein detected at a synapse correlated with its spontaneous Ca²⁺ activity. A multifold increase in synaptic nascent protein was evident following both local and global plasticity at respective scales, albeit with substantial heterogeneity between neighboring synapses.

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.

Improved Method for Efficient Generation of Functional Neurons from Murine Neural Progenitor Cells

Neuronal culture was used to investigate neuronal function in physiological and pathological conditions. Despite its inevitability, primary neuronal culture remained a gold standard method that requires laborious preparation, intensive training, and animal resources. To circumvent the shortfalls of primary neuronal preparations and efficiently give rise to functional neurons, we combine a neural stem cell culture method with a direct cell type-conversion approach. The lucidity of this method enables the efficient preparation of functional neurons from mouse neural progenitor cells on demand. We demonstrate that induced neurons (NPC-iNs) by this method make synaptic connections, elicit neuronal activity-dependent cellular responses, and develop functional neuronal networks. This method will provide a concise platform for functional neuronal assessments. This indeed offers a perspective for using these characterized neuronal networks for investigating plasticity mechanisms, drug screening assays, and probing the molecular and biophysical basis of neurodevelopmental and neurodegenerative diseases.

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.

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.

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.

Lifetime of a structure evolving by cluster aggregation and particle loss, and application to postsynaptic scaffold domains

The dynamics of several mesoscopic biological structures depend on the interplay of growth through the incorporation of components of different sizes laterally diffusing along the cell membrane, and loss by component turnover. In particular, a model of such an out-of-equilibrium dynamics has recently been proposed for postsynaptic scaffold domains, which are key structures of neuronal synapses. It is of interest to estimate the lifetime of these mesoscopic structures, especially in the context of synapses where this time is related to memory retention. The lifetime of a structure can be very long as compared to the turnover time of its components and it can be difficult to estimate it by direct numerical simulations. Here, in the context of the model proposed for postsynaptic scaffold domains, we approximate the aggregation-turnover dynamics by a shot-noise process. This enables us to analytically compute the quasistationary distribution describing the sizes of the surviving structures as well as their characteristic lifetime. We show that our analytical estimate agrees with numerical simulations of a full spatial model, in a regime of parameters where a direct assessment is computationally feasible. We then use our approach to estimate the lifetime of mesoscopic structures in parameter regimes where computer simulations would be prohibitively long. For gephyrin, the scaffolding protein specific to inhibitory synapses, we estimate a lifetime longer than several months for a scaffold domain when the single gephyrin protein turnover time is about half an hour, as experimentally measured. While our focus is on postsynaptic domains, our formalism and techniques should be applicable to other biological structures that are also formed by a balance of condensation and turnover.

Glutamatergic Dysfunction and Synaptic Ultrastructural Alterations in Schizophrenia and Autism Spectrum Disorder: Evidence from Human and Rodent Studies

The correlation between dysfunction in the glutamatergic system and neuropsychiatric disorders, including schizophrenia and autism spectrum disorder, is undisputed. Both disorders are associated with molecular and ultrastructural alterations that affect synaptic plasticity and thus the molecular and physiological basis of learning and memory. Altered synaptic plasticity, accompanied by changes in protein synthesis and trafficking of postsynaptic proteins, as well as structural modifications of excitatory synapses, are critically involved in the postnatal development of the mammalian nervous system. In this review, we summarize glutamatergic alterations and ultrastructural changes in synapses in schizophrenia and autism spectrum disorder of genetic or drug-related origin, and briefly comment on the possible reversibility of these neuropsychiatric disorders in the light of findings in regular synaptic physiology.

Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring

The connectivity structure of neuronal networks in cortex is highly dynamic. This ongoing cortical rewiring is assumed to serve important functions for learning and memory. We analyze in this article a model for the self-organization of synaptic inputs onto dendritic branches of pyramidal cells. The model combines a generic stochastic rewiring principle with a simple synaptic plasticity rule that depends on local dendritic activity. In computer simulations, we find that this synaptic rewiring model leads to synaptic clustering, that is, temporally correlated inputs become locally clustered on dendritic branches. This empirical finding is backed up by a theoretical analysis which shows that rewiring in our model favors network configurations with synaptic clustering. We propose that synaptic clustering plays an important role in the organization of computation and memory in cortical circuits: we find that synaptic clustering through the proposed rewiring mechanism can serve as a mechanism to protect memories from subsequent modifications on a medium time scale. Rewiring of synaptic connections onto specific dendritic branches may thus counteract the general problem of catastrophic forgetting in neural networks.

Site-specific ubiquitination of pathogenic huntingtin attenuates its deleterious effects

Huntington's disease (HD) is a progressive incurable neurodegenerative disorder characterized by motor and neuropsychiatric symptoms. It is caused by expansion of a cytosine-adenine-guanine triplet in the N-terminal domain of exon 1 in the huntingtin (HTT) gene that codes for an expanded polyglutamine stretch in the protein product which becomes aggregation prone. The mutant Htt (mHtt) aggregates are associated with components of the ubiquitin-proteasome system, suggesting that mHtt is marked for proteasomal degradation and that, for reasons still debated, are not properly degraded. We used a novel HD rat model, proteomic analysis, and long-term live neuronal imaging to characterize the effects of ubiquitination on aggregation of mHtt and subsequent cellular responses. We identified two lysine residues, 6 and 9, in the first exon of mHtt that are specifically ubiquitinated in striatal and cortical brain tissues of mHtt-transgenic animals. Expression of mHtt exon 1 lacking these ubiquitination sites in cortical neurons and cultured cells was found to slow aggregate appearance rates and reduce their size but at the same time increase the number of much smaller and less visible ones. Importantly, expression of this form of mHtt was associated with elevated death rates. Proteomic analysis indicated that cellular reactions to mHtt expression were weaker in cells expressing the lysineless protein, possibly implying a reduced capacity to cope with the proteotoxic stress. Taken together, the findings suggest a novel role for ubiquitination-attenuation of the pathogenic effect of mHtt.

Stable task information from an unstable neural population

Over days and weeks, neural activity representing an animal's position and movement in sensorimotor cortex has been found to continually reconfigure or 'drift' during repeated trials of learned tasks, with no obvious change in behavior. This challenges classical theories, which assume stable engrams underlie stable behavior. However, it is not known whether this drift occurs systematically, allowing downstream circuits to extract consistent information. Analyzing long-term calcium imaging recordings from posterior parietal cortex in mice (Mus musculus), we show that drift is systematically constrained far above chance, facilitating a linear weighted readout of behavioral variables. However, a significant component of drift continually degrades a fixed readout, implying that drift is not confined to a null coding space. We calculate the amount of plasticity required to compensate drift independently of any learning rule, and find that this is within physiologically achievable bounds. We demonstrate that a simple, biologically plausible local learning rule can achieve these bounds, accurately decoding behavior over many days.

A non-fluorescent HaloTag blocker for improved measurement and visualization of protein synthesis in living cells

Background: HaloTag is a modified bacterial enzyme that binds rapidly and irreversibly to an array of synthetic ligands, including chemical dyes. When expressed in live cells in conjunction with a protein of interest, HaloTag can be used to study protein trafficking, synthesis, and degradation. For instance, sequential HaloTag labeling with spectrally separable dyes can be used to separate preexisting protein pools from proteins newly synthesized following experimental manipulations or the passage of time. Unfortunately, incomplete labeling by the first dye, or labeling by residual, trapped dye pools can confound interpretation.

Methods: Labeling specificity of newly synthesized proteins could be improved by blocking residual binding sites. To that end, we synthesized a non-fluorescent, cell permeable blocker (1-chloro-6-(2-propoxyethoxy)hexane; CPXH), essentially the HaloTag ligand backbone without the reactive amine used to attach fluorescent groups.

Results: High-content imaging was used to quantify the ability of CPXH to block HaloTag ligand binding in live HEK cells expressing a fusion protein of mTurquoise2 and HaloTag. Full saturation was observed at CPXH concentrations of 5-10 µM at 30 min. No overt effects on cell viability were observed at any concentration or treatment duration. The ability of CPXH to improve the reliability of newly synthesized protein detection was then demonstrated in live cortical neurons expressing the mTurquoise2-HaloTag fusion protein, in both single and dual labeling time lapse experiments. Practically no labeling was observed after blocking HaloTag binding sites with CPXH when protein synthesis was suppressed with cycloheximide, confirming the identification of newly synthesized protein copies as such, while providing estimates of protein synthesis suppression in these experiments.

Conclusions: CPXH is a reliable (and inexpensive) non-fluorescent ligand for improving assessment of protein-of-interest metabolism in live cells using HaloTag technology.

A non-fluorescent HaloTag blocker for improved measurement and visualization of protein synthesis in living cells

Background: HaloTag is a modified bacterial enzyme that binds rapidly and irreversibly to an array of synthetic ligands, including chemical dyes. When expressed in live cells in conjunction with a protein of interest, HaloTag can be used to study protein trafficking, synthesis, and degradation. For instance, sequential HaloTag labeling with spectrally separable dyes can be used to separate preexisting protein pools from proteins newly synthesized following experimental manipulations or the passage of time. Unfortunately, incomplete labeling by the first dye, or labeling by residual, trapped dye pools can confound interpretation. Methods: Labeling specificity of newly synthesized proteins could be improved by blocking residual binding sites. To that end, we synthesized a non-fluorescent, cell permeable blocker (1-chloro-6-(2-propoxyethoxy)hexane; CPXH), essentially the HaloTag ligand backbone without the reactive amine used to attach fluorescent groups. Results: High-content imaging was used to quantify the ability of CPXH to block HaloTag ligand binding in live HEK cells expressing a fusion protein of mTurquoise2 and HaloTag. Full saturation was observed at CPXH concentrations of 5-10 µM at 30 min. No overt effects on cell viability were observed at any concentration or treatment duration. The ability of CPXH to improve the reliability of newly synthesized protein detection was then demonstrated in live cortical neurons expressing the mTurquoise2-HaloTag fusion protein, in both single and dual labeling time lapse experiments. Practically no labeling was observed after blocking HaloTag binding sites with CPXH when protein synthesis was suppressed with cycloheximide, confirming the identification of newly synthesized protein copies as such, while providing estimates of protein synthesis suppression in these experiments. Conclusions: CPXH is a reliable (and inexpensive) non-fluorescent ligand for improving assessment of protein-of-interest metabolism in live cells using HaloTag technology.

Network Architecture of Gap Junctional Coupling among Parallel Processing Channels in the Mammalian Retina

Gap junctions are ubiquitous throughout the nervous system, mediating critical signal transmission and integration, as well as emergent network properties. In mammalian retina, gap junctions within the Aii amacrine cell-ON cone bipolar cell (CBC) network are essential for night vision, modulation of day vision, and contribute to visual impairment in retinal degenerations, yet neither the extended network topology nor its conservation is well established. Here, we map the network contribution of gap junctions using a high-resolution connectomics dataset of an adult female rabbit retina. Gap junctions are prominent synaptic components of ON CBC classes, constituting 5%-25% of all axonal synaptic contacts. Many of these mediate canonical transfer of rod signals from Aii cells to ON CBCs for night vision, and we find that the uneven distribution of Aii signals to ON CBCs is conserved in rabbit, including one class entirely lacking direct Aii coupling. However, the majority of gap junctions formed by ON CBCs unexpectedly occur between ON CBCs, rather than with Aii cells. Such coupling is extensive, creating an interconnected network with numerous lateral paths both within, and particularly across, these parallel processing streams. Coupling patterns are precise with ON CBCs accepting and rejecting unique combinations of partnerships according to robust rulesets. Coupling specificity extends to both size and spatial topologies, thereby rivaling the synaptic specificity of chemical synapses. These ON CBC coupling motifs dramatically extend the coupled Aii-ON CBC network, with implications for signal flow in both scotopic and photopic retinal networks during visual processing and disease.SIGNIFICANCE STATEMENT Electrical synapses mediated by gap junctions are fundamental components of neural networks. In retina, coupling within the Aii-ON CBC network shapes visual processing in both the scotopic and photopic networks. In retinal degenerations, these same gap junctions mediate oscillatory activity that contributes to visual impairment. Here, we use high-resolution connectomics strategies to identify gap junctions and cellular partnerships. We describe novel, pervasive motifs both within and across classes of ON CBCs that dramatically extend the Aii-ON CBC network. These motifs are highly specific with implications for both signal processing within the retina and therapeutic interventions for blinding conditions. These findings highlight the underappreciated contribution of coupling motifs in retinal circuitry and the necessity of their detection in connectomics studies.

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.

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.

Size-Dependent Axonal Bouton Dynamics following Visual Deprivation In Vivo

Persistent synapses are thought to underpin the storage of sensory experience, yet little is known about their structural plasticity in vivo. We investigated how persistent presynaptic structures respond to the loss of primary sensory input. Using in vivo two-photon (2P) imaging, we measured fluctuations in the size of excitatory axonal boutons in L2/3 of adult mouse visual cortex after monocular enucleation. The average size of boutons did not change after deprivation, but the range of bouton sizes was reduced. Large boutons decreased, and small boutons increased. Reduced bouton variance was accompanied by a reduced range of correlated calcium-mediated neural activity in L2/3 of awake animals. Network simulations predicted that size-dependent plasticity may promote conditions of greater bidirectional plasticity. These predictions were supported by electrophysiological measures of short- and long-term plasticity. We propose size-dependent dynamics facilitate cortical reorganization by maximizing the potential for bidirectional plasticity.

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The range of persistent axonal bouton sizes is reduced following visual deprivation

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Bouton sizes move toward the mean in a size-dependent manner

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Bouton plasticity is accompanied by a reduced range of correlated network activity

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Deprived cortex exhibits greater bidirectional functional presynaptic plasticity

Multicontact Co-operativity in Spike-Timing-Dependent Structural Plasticity Stabilizes Networks

Excitatory synaptic connections in the adult neocortex consist of multiple synaptic contacts, almost exclusively formed on dendritic spines. Changes of spine volume, a correlate of synaptic strength, can be tracked in vivo for weeks. Here, we present a combined model of structural and spike-timing–dependent plasticity that explains the multicontact configuration of synapses in adult neocortical networks under steady-state and lesion-induced conditions. Our plasticity rule with Hebbian and anti-Hebbian terms stabilizes both the postsynaptic firing rate and correlations between the pre- and postsynaptic activity at an active synaptic contact. Contacts appear spontaneously at a low rate and disappear if their strength approaches zero. Many presynaptic neurons compete to make strong synaptic connections onto a postsynaptic neuron, whereas the synaptic contacts of a given presynaptic neuron co-operate via postsynaptic firing. We find that co-operation of multiple synaptic contacts is crucial for stable, long-term synaptic memories. In simulations of a simplified network model of barrel cortex, our plasticity rule reproduces whisker-trimming–induced rewiring of thalamocortical and recurrent synaptic connectivity on realistic time scales.

Reduced sensory-evoked structural plasticity in the aging barrel cortex

Impairments in synaptic connectivity have been linked to cognitive deficits in age-related neurodegenerative disorders and healthy aging. However, the anatomical and structural bases of these impairments have not been identified yet. A hallmark of neural plasticity in young adults is short-term synaptic rearrangement, yet aged animals already display higher synaptic turnover rates at the baseline. Using two-photon excitation (2PE) microscopy, we explored if this elevated turnover alters the aged brain's response to plasticity. Following a sensory-evoked plasticity protocol involving whisker stimulation, aged mice display reduced spine dynamics (gain, loss, and turnover), decreased spine clustering, and lower spine stability when compared to young adult mice. These results suggest a deficiency of the cortical neurons of aged mice to structurally incorporate new sensory experiences, in the form of clustered, long-lasting synapses, into already existing cortical circuits. This research provides the first evidence linking experience-dependent plasticity with in vivo spine dynamics in the aged brain and supports a model of both reduced synaptic plasticity and reduced synaptic tenacity in the aged somatosensory system.

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.

Critical role for Piccolo in synaptic vesicle retrieval

Loss of function of the active zone protein Piccolo has recently been linked to a disease, Pontocerebellar Hypoplasia type 3, which causes brain atrophy. Here, we address how Piccolo inactivation in rat neurons adversely affects synaptic function and thus may contribute to neuronal loss. Our analysis shows that Piccolo is critical for the recycling and maintenance of synaptic vesicles. We find that boutons lacking Piccolo have deficits in the Rab5/EEA1 dependent formation of early endosomes and thus the recycling of SVs. Mechanistically, impaired Rab5 function was caused by reduced synaptic recruitment of Pra1, known to interact selectively with the zinc finger domains of Piccolo. Importantly, over-expression of GTPase deficient Rab5 or the Znf1 domain of Piccolo restores the size and recycling of SV pools. These data provide a molecular link between the active zone and endosome sorting at synapses providing hints to how Piccolo contributes to developmental and psychiatric disorders.

Long-Term Activity Dynamics of Single Neurons and Networks

The firing rate of neuronal spiking in vitro and in vivo significantly varies over extended timescales, characterized by long-memory processes and complex statistics, and appears in spontaneous as well as evoked activity upon repeated stimulus presentation. These variations in response features and their statistics, in face of repeated instances of a given physical input, are ubiquitous in all levels of brain-behavior organization. They are expressed in single neuron and network response variability but even appear in variations of subjective percepts or psychophysical choices and have been described as stemming from history-dependent, stochastic, or rate-determined processes.But what are the sources underlying these temporally rich variations in firing rate? Are they determined by interactions of the nervous system as a whole, or do isolated, single neurons or neuronal networks already express these fluctuations independent of higher levels? These questions motivated the application of a method that allows for controlled and specific long-term activation of a single neuron or neuronal network, isolated from higher levels of cortical organization.This chapter highlights the research done in cultured cortical networks to study (1) the inherent non-stationarity of neuronal network activity, (2) single neuron response fluctuations and underlying processes, and (3) the interface layer between network and single cell, the non-stationary efficacy of the ensemble of synapses impinging onto the observed neuron.

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.

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning

Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity. These data are inconsistent with common models for network plasticity and raise the following questions: how can neural circuits maintain a stable computational function in spite of these continuously ongoing processes, and what could be functional uses of these ongoing processes? Here, we present a rigorous theoretical framework for these seemingly stochastic spine dynamics and rewiring processes in the context of reward-based learning tasks. We show that spontaneous synapse-autonomous processes, in combination with reward signals such as dopamine, can explain the capability of networks of neurons in the brain to configure themselves for specific computational tasks, and to compensate automatically for later changes in the network or task. Furthermore, we show theoretically and through computer simulations that stable computational performance is compatible with continuously ongoing synapse-autonomous changes. After reaching good computational performance it causes primarily a slow drift of network architecture and dynamics in task-irrelevant dimensions, as observed for neural activity in motor cortex and other areas. On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations.

Associative properties of structural plasticity based on firing rate homeostasis in recurrent neuronal networks

Correlation-based Hebbian plasticity is thought to shape neuronal connectivity during development and learning, whereas homeostatic plasticity would stabilize network activity. Here we investigate another, new aspect of this dichotomy: Can Hebbian associative properties also emerge as a network effect from a plasticity rule based on homeostatic principles on the neuronal level? To address this question, we simulated a recurrent network of leaky integrate-and-fire neurons, in which excitatory connections are subject to a structural plasticity rule based on firing rate homeostasis. We show that a subgroup of neurons develop stronger within-group connectivity as a consequence of receiving stronger external stimulation. In an experimentally well-documented scenario we show that feature specific connectivity, similar to what has been observed in rodent visual cortex, can emerge from such a plasticity rule. The experience-dependent structural changes triggered by stimulation are long-lasting and decay only slowly when the neurons are exposed again to unspecific external inputs.

Variance and invariance of neuronal long-term representations

The brain extracts behaviourally relevant sensory input to produce appropriate motor output. On the one hand, our constantly changing environment requires this transformation to be plastic. On the other hand, plasticity is thought to be balanced by mechanisms ensuring constancy of neuronal representations in order to achieve stable behavioural performance. Yet, prominent changes in synaptic strength and connectivity also occur during normal sensory experience, indicating a certain degree of constitutive plasticity. This raises the question of how stable neuronal representations are on the population level and also on the single neuron level. Here, we review recent data from longitudinal electrophysiological and optical recordings of single-cell activity that assess the long-term stability of neuronal stimulus selectivities under conditions of constant sensory experience, during learning, and after reversible modification of sensory input. The emerging picture is that neuronal representations are stabilized by behavioural relevance and that the degree of long-term tuning stability and perturbation resistance directly relates to the functional role of the respective neurons, cell types and circuits. Using a 'toy' model, we show that stable baseline representations and precise recovery from perturbations in visual cortex could arise from a 'backbone' of strong recurrent connectivity between similarly tuned cells together with a small number of 'anchor' neurons exempt from plastic changes.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.