Expression of long-term plasticity at individual synapses in hippocampus is graded, bidirectional, and mainly presynaptic: optical quantal analysis

Equal levels of pre- and postsynaptic potentiation produce unequal outcomes

Which proportion of the long-term potentiation (LTP) expressed in the bulk of excitatory synapses is postsynaptic and which presynaptic remains debatable. To understand better the possible impact of either LTP form, we explored a realistic model of a CA1 pyramidal cell equipped with known membrane mechanisms and multiple, stochastic excitatory axo-spinous synapses. Our simulations were designed to establish an input-output transfer function, the dependence between the frequency of presynaptic action potentials triggering probabilistic synaptic discharges and the average frequency of postsynaptic spiking. We found that, within the typical physiological range, potentiation of the postsynaptic current results in a greater overall output than an equivalent increase in presynaptic release probability. This difference grows stronger at lower input frequencies and lower release probabilities. Simulations with a non-hierarchical circular network of principal neurons indicated that equal increases in either synaptic fidelity or synaptic strength of individual connections also produce distinct changes in network activity, although the network phenomenology is likely to be complex. These observations should help to interpret the machinery of LTP phenomena documented in situ. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

Activity-dependent diffusion trapping of AMPA receptors as a key step for expression of early LTP

This review focuses on the activity-dependent diffusion trapping of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) as a crucial mechanism for the expression of early long-term potentiation (LTP), a process central to learning and memory. Despite decades of research, the precise mechanisms by which LTP induction leads to an increase in AMPAR responses at synapses have been elusive. We review the different hypotheses that have been put forward to explain the increased AMPAR responsiveness during LTP. We discuss the dynamic nature of AMPAR complexes, including their constant turnover and activity-dependent modifications that affect their synaptic accumulation. We highlight a hypothesis suggesting that AMPARs are diffusively trapped at synapses through activity-dependent interactions with protein-based binding slots in the post-synaptic density (PSD), offering a potential explanation for the increased synaptic strength during LTP. Furthermore, we outline the challenges still to be addressed before we fully understand the functional roles and molecular mechanisms of AMPAR dynamic nanoscale organization in LTP. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

Fully-primed slowly-recovering vesicles mediate presynaptic LTP at neocortical neurons

Pre- and postsynaptic forms of long-term potentiation (LTP) are candidate synaptic mechanisms underlying learning and memory. At layer 5 pyramidal neurons, LTP increases the initial synaptic strength but also short-term depression during high-frequency transmission. This classical form of presynaptic LTP has been referred to as redistribution of synaptic efficacy. However, the underlying mechanisms remain unclear. We therefore performed whole-cell recordings from layer 5 pyramidal neurons in acute cortical slices of rats and analyzed presynaptic function before and after LTP induction by paired pre- and postsynaptic neuronal activity. LTP was successfully induced in about half of the synaptic connections tested and resulted in increased synaptic short-term depression during high-frequency transmission and a decelerated recovery from short-term depression due to an increased fraction of a slow recovery component. Analysis with a recently established sequential two-step vesicle priming model indicates an increase in the abundance of fully-primed and slowly-recovering vesicles. A systematic analysis of short-term plasticity and synapse-to-synapse variability of synaptic strength at various types of synapses revealed that stronger synapses generally recover more slowly from synaptic short-term depression. Finally, pharmacological stimulation of the cyclic adenosine monophosphate and diacylglycerol signaling pathways, which are both known to promote synaptic vesicle priming, mimicked LTP and slowed the recovery from short-term depression. Our data thus demonstrate that LTP at layer 5 pyramidal neurons increases synaptic strength primarily by enlarging a subpool of fully-primed slowly-recovering vesicles.

A stochastic model of hippocampal synaptic plasticity with geometrical readout of enzyme dynamics

Discovering the rules of synaptic plasticity is an important step for understanding brain learning. Existing plasticity models are either (1) top-down and interpretable, but not flexible enough to account for experimental data, or (2) bottom-up and biologically realistic, but too intricate to interpret and hard to fit to data. To avoid the shortcomings of these approaches, we present a new plasticity rule based on a geometrical readout mechanism that flexibly maps synaptic enzyme dynamics to predict plasticity outcomes. We apply this readout to a multi-timescale model of hippocampal synaptic plasticity induction that includes electrical dynamics, calcium, CaMKII and calcineurin, and accurate representation of intrinsic noise sources. Using a single set of model parameters, we demonstrate the robustness of this plasticity rule by reproducing nine published ex vivo experiments covering various spike-timing and frequency-dependent plasticity induction protocols, animal ages, and experimental conditions. Our model also predicts that in vivo-like spike timing irregularity strongly shapes plasticity outcome. This geometrical readout modelling approach can be readily applied to other excitatory or inhibitory synapses to discover their synaptic plasticity rules.

Adaptive STDP-based on-chip spike pattern detection

A spiking neural network (SNN) is a bottom-up tool used to describe information processing in brain microcircuits. It is becoming a crucial neuromorphic computational model. Spike-timing-dependent plasticity (STDP) is an unsupervised brain-like learning rule implemented in many SNNs and neuromorphic chips. However, a significant performance gap exists between ideal model simulation and neuromorphic implementation. The performance of STDP learning in neuromorphic chips deteriorates because the resolution of synaptic efficacy in such chips is generally restricted to 6 bits or less, whereas simulations employ the entire 64-bit floating-point precision available on digital computers. Previously, we introduced a bio-inspired learning rule named adaptive STDP and demonstrated via numerical simulation that adaptive STDP (using only 4-bit fixed-point synaptic efficacy) performs similarly to STDP learning (using 64-bit floating-point precision) in a noisy spike pattern detection model. Herein, we present the experimental results demonstrating the performance of adaptive STDP learning. To the best of our knowledge, this is the first study that demonstrates unsupervised noisy spatiotemporal spike pattern detection to perform well and maintain the simulation performance on a mixed-signal CMOS neuromorphic chip with low-resolution synaptic efficacy. The chip was designed in Taiwan Semiconductor Manufacturing Company (TSMC) 250 nm CMOS technology node and comprises a soma circuit and 256 synapse circuits along with their learning circuitry.

Targeted sensors for glutamatergic neurotransmission

Optical report of neurotransmitter release allows visualisation of excitatory synaptic transmission. Sensitive genetically-encoded fluorescent glutamate reporters operating with a range of affinities and emission wavelengths are available. However, without targeting to synapses, the specificity of the fluorescent signal is uncertain, compared to sensors directed at vesicles or other synaptic markers. We fused the state-of-the-art reporter iGluSnFR to glutamate receptor auxiliary proteins in order to target it to postsynaptic sites. Chimeras of Stargazin and gamma-8 that we named SnFR-γ2 and SnFR-γ8, were enriched at synapses, retained function and reported spontaneous glutamate release in rat hippocampal cells, with apparently diffraction-limited spatial precision. In autaptic mouse neurons cultured on astrocytic microislands, evoked neurotransmitter release could be quantitatively detected at tens of synapses in a field of view whilst evoked currents were recorded simultaneously. These experiments revealed a specific postsynaptic deficit from Stargazin overexpression, resulting in synapses with normal neurotransmitter release but without postsynaptic responses. This defect was reverted by delaying overexpression. By working at different calcium concentrations, we determined that SnFR-γ2 is a linear reporter of the global quantal parameters and short-term synaptic plasticity, whereas iGluSnFR is not. On average, half of iGluSnFR regions of interest (ROIs) showing evoked fluorescence changes had intense rundown, whereas less than 5% of SnFR-γ2 ROIs did. We provide an open-source analysis suite for extracting quantal parameters including release probability from fluorescence time series of individual and grouped synaptic responses. Taken together, postsynaptic targeting improves several properties of iGluSnFR and further demonstrates the importance of subcellular targeting for optogenetic actuators and reporters.

Electrophysiology of Dendritic Spines: Information Processing, Dynamic Compartmentalization, and Synaptic Plasticity

For many years, synaptic transmission was considered as information transfer between presynaptic neuron and postsynaptic cell. At the synaptic level, it was thought that dendritic arbors were only receiving and integrating all information flow sent along to the soma, while axons were primarily responsible for point-to-point information transfer. However, it is important to highlight that dendritic spines play a crucial role as postsynaptic components in central nervous system (CNS) synapses, not only integrating and filtering signals to the soma but also facilitating diverse connections with axons from many different sources. The majority of excitatory connections from presynaptic axonal terminals occurs on postsynaptic spines, although a subset of GABAergic synapses also targets spine heads. Several studies have shown the vast heterogeneous morphological, biochemical, and functional features of dendritic spines related to synaptic processing. In this chapter (adding to the relevant data on the biophysics of spines described in Chap. 1 of this book), we address the up-to-date functional dendritic characteristics assessed through electrophysiological approaches, including backpropagating action potentials (bAPs) and synaptic potentials mediated in dendritic and spine compartmentalization, as well as describing the temporal and spatial dynamics of glutamate receptors in the spines related to synaptic plasticity.

A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex

Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie learning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections. In particular, we find that the diverse plasticity outcomes across the different PC types can be explained by cell-type-specific synaptic physiology, cell morphology and innervation patterns, without requiring type-specific plasticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict qualitatively different plasticity dynamics from those observed in vitro. This work provides a first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open framework for further developing models of cortical synaptic plasticity.

Ascorbic Acid Reduces Neurotransmission, Synaptic Plasticity, and Spontaneous Hippocampal Rhythms in In Vitro Slices

Ascorbic acid (AA; a.k.a. vitamin C) is well known for its cellular protection in environments of high oxidative stress. Even though physiological concentrations of AA in the brain are significant (0.2–10 mM), surprisingly little is known concerning the role of AA in synaptic neurotransmission under normal, non-disease state conditions. Here, we examined AA effects on neurotransmission, plasticity and spontaneous network activity (i.e., sharp waves and high frequency oscillations; SPW-HFOs), at the synapse between area 3 and 1 of the hippocampal cornu ammonis region (CA3 and CA1) using an extracellular multi-electrode array in in vitro mouse hippocampal slices. We found that AA decreased evoked field potentials (fEPSPs, IC₅₀ = 0.64 mM) without affecting V₅₀s or paired pulse facilitation indicating normal neurotransmitter release mechanisms. AA decreased presynaptic fiber volleys but did not change fiber volley-to-fEPSP coupling, suggesting reduced fEPSPs resulted from decreased fiber volleys. Inhibitory effects were also observed in CA1 stratum pyramidale where greater fEPSPs were required for population spikes in the presence of AA suggesting an impact on the intrinsic excitability of neurons. Other forms of synaptic plasticity and correlates of memory (i.e., short- and long-term potentiation) were also significantly reduced by AA as was the incidence of spontaneous SPW-HFOs. AA decreased SPW amplitude with a similar IC₅₀ as fEPSPs (0.65 mM). Overall, these results indicate that under normal conditions AA significantly regulates neurotransmission, plasticity, and network activity by limiting excitability. Thus, AA may participate in refinement of signal processing and memory formation, as well as protecting against pathologic excitability.

Determinants of synapse diversity revealed by super-resolution quantal transmission and active zone imaging

Neural circuit function depends on the pattern of synaptic connections between neurons and the strength of those connections. Synaptic strength is determined by both postsynaptic sensitivity to neurotransmitter and the presynaptic probability of action potential evoked transmitter release (Pr). Whereas morphology and neurotransmitter receptor number indicate postsynaptic sensitivity, presynaptic indicators and the mechanism that sets Pr remain to be defined. To address this, we developed QuaSOR, a super-resolution method for determining Pr from quantal synaptic transmission imaging at hundreds of glutamatergic synapses at a time. We mapped the Pr onto super-resolution 3D molecular reconstructions of the presynaptic active zones (AZs) of the same synapses at the Drosophila larval neuromuscular junction (NMJ). We find that Pr varies greatly between synapses made by a single axon, quantify the contribution of key AZ proteins to Pr diversity and find that one of these, Complexin, suppresses spontaneous and evoked transmission differentially, thereby generating a spatial and quantitative mismatch between release modes. Transmission is thus regulated by the balance and nanoscale distribution of release-enhancing and suppressing presynaptic proteins to generate high signal-to-noise evoked transmission.

Online Learning and Memory of Neural Trajectory Replays for Prefrontal Persistent and Dynamic Representations in the Irregular Asynchronous State

In the prefrontal cortex (PFC), higher-order cognitive functions and adaptive flexible behaviors rely on continuous dynamical sequences of spiking activity that constitute neural trajectories in the state space of activity. Neural trajectories subserve diverse representations, from explicit mappings in physical spaces to generalized mappings in the task space, and up to complex abstract transformations such as working memory, decision-making and behavioral planning. Computational models have separately assessed learning and replay of neural trajectories, often using unrealistic learning rules or decoupling simulations for learning from replay. Hence, the question remains open of how neural trajectories are learned, memorized and replayed online, with permanently acting biological plasticity rules. The asynchronous irregular regime characterizing cortical dynamics in awake conditions exerts a major source of disorder that may jeopardize plasticity and replay of locally ordered activity. Here, we show that a recurrent model of local PFC circuitry endowed with realistic synaptic spike timing-dependent plasticity and scaling processes can learn, memorize and replay large-size neural trajectories online under asynchronous irregular dynamics, at regular or fast (sped-up) timescale. Presented trajectories are quickly learned (within seconds) as synaptic engrams in the network, and the model is able to chunk overlapping trajectories presented separately. These trajectory engrams last long-term (dozen hours) and trajectory replays can be triggered over an hour. In turn, we show the conditions under which trajectory engrams and replays preserve asynchronous irregular dynamics in the network. Functionally, spiking activity during trajectory replays at regular timescale accounts for both dynamical coding with temporal tuning in individual neurons, persistent activity at the population level, and large levels of variability consistent with observed cognitive-related PFC dynamics. Together, these results offer a consistent theoretical framework accounting for how neural trajectories can be learned, memorized and replayed in PFC networks circuits to subserve flexible dynamic representations and adaptive behaviors.

The functional organization of excitatory synaptic input to place cells

Hippocampal place cells contribute to mammalian spatial navigation and memory formation. Numerous models have been proposed to explain the location-specific firing of this cognitive representation, but the pattern of excitatory synaptic input leading to place firing is unknown, leaving no synaptic-scale explanation of place coding. Here we used resonant scanning two-photon microscopy to establish the pattern of synaptic glutamate input received by CA1 place cells in behaving mice. During traversals of the somatic place field, we found increased excitatory dendritic input, mainly arising from inputs with spatial tuning overlapping the somatic field, and functional clustering of this input along the dendrites over ~10 µm. These results implicate increases in total excitatory input and co-activation of anatomically clustered synaptic input in place firing. Since they largely inherit their fields from upstream synaptic partners with similar fields, many CA1 place cells appear to be part of multi-brain-region cell assemblies forming representations of specific locations.

Tyrosine phosphatase PTP1B impairs presynaptic NMDA receptor-mediated plasticity in a mouse model of Alzheimer's disease

Mutations in the beta-amyloid protein (APP) cause familial Alzheimer's disease. In hAPP-J20 mice expressing mutant APP, pharmacological inhibition or genetic ablation of the tyrosine phosphatase PTP1B prevents CA3 hippocampus neuron loss and cognitive decline. However, how targeting PTP1B affects the cellular mechanisms underlying these cognitive deficits remains unknown. Changes in synaptic strength at the hippocampus can affect information processing for learning and memory. While prior studies have focused on post-synaptic mechanisms to account for synaptic deficits in Alzheimer's disease models, presynaptic mechanisms may also be affected. Here, using whole cell patch-clamp recording, coefficient of variation (CV) analysis suggested a profound presynaptic deficit in long-term potentiation (LTP) of CA3:CA1 synapses in hAPP-J20 mice. While the membrane-impermeable ionotropic NMDA receptor (NMDAR) blocker norketamine in the post-synaptic recording electrode had no effect on LTP, additional bath application of the ionotropic NMDAR blockers MK801 could replicate the deficit in LTP in wild type mice. In contrast to LTP, the paired-pulse ratio and short-term facilitation (STF) were aberrantly increased in hAPP-J20 mice. These synaptic deficits in hAPP-J20 mice were associated with reduced phosphorylation of NMDAR GluN2B and the synaptic vesicle recycling protein NSF (N-ethylmaleimide sensitive factor). Phosphorylation of both proteins, together with synaptic plasticity and cognitive function, were restored by PTP1B ablation or inhibition by the PTP1B-selective inhibitor Trodusquemine. Taken together, our results indicate that PTP1B impairs presynaptic NMDAR-mediated synaptic plasticity required for spatial learning in a mouse model of Alzheimer's disease. Since Trodusquemine has undergone phase 1/2 clinical trials to treat obesity, it could be repurposed to treat Alzheimer's disease.

Local Design Principles at Hippocampal Synapses Revealed by an Energy-Information Trade-Off

Synapses across different brain regions display distinct structure-function relationships. We investigated the interplay of fundamental design constraints that shape the transmission properties of the excitatory CA3-CA1 pyramidal cell connection, a prototypic synapse for studying the mechanisms of learning in the mammalian hippocampus. This small synapse is characterized by probabilistic release of transmitter, which is markedly facilitated in response to naturally occurring trains of action potentials. Based on a physiologically motivated computational model of the rat CA3 presynaptic terminal, we show how unreliability and short-term dynamics of vesicular release work together to regulate the trade-off of information transfer versus energy use. We propose that individual CA3-CA1 synapses are designed to operate near the maximum possible capacity of information transmission in an efficient manner. Experimental measurements reveal a wide range of vesicular release probabilities at hippocampal synapses, which may be a necessary consequence of long-term plasticity and homeostatic mechanisms that manifest as presynaptic modifications of the release probability. We show that the timescales and magnitude of short-term plasticity (STP) render synaptic information transfer nearly independent of differences in release probability. Thus, individual synapses transmit optimally while maintaining a heterogeneous distribution of presynaptic strengths indicative of synaptically-encoded memory representations. Our results support the view that organizing principles that are evident on higher scales of neural organization percolate down to the design of an individual synapse.

Illuminating Relationships Between the Pre- and Post-synapse

Excitatory synapses in the mammalian cortex are highly diverse, both in terms of their structure and function. However, relationships between synaptic features indicate they are highly coordinated entities. Imaging techniques, that enable physiology at the resolution of individual synapses to be investigated, have allowed the presynaptic activity level of the synapse to be related to postsynaptic function. This approach has revealed that neuronal activity induces the pre- and post-synapse to be functionally correlated and that subsets of synapses are more susceptible to certain forms of synaptic plasticity. As presynaptic function is often examined in isolation from postsynaptic properties, the effect it has on the post-synapse is not fully understood. However, since postsynaptic receptors at excitatory synapses respond to release of glutamate, it follows that they may be differentially regulated depending on the frequency of its release. Therefore, examining postsynaptic properties in the context of presynaptic function may be a useful way to approach a broad range of questions on synaptic physiology. In this review, we focus on how optophysiology tools have been utilized to study relationships between the pre- and the post-synapse. Multiple imaging techniques have revealed correlations in synaptic properties from the submicron to the dendritic level. Optical tools together with advanced imaging techniques are ideally suited to illuminate this area further, due to the spatial resolution and control they allow.

A Practical Guide to Using CV Analysis for Determining the Locus of Synaptic Plasticity

Long-term synaptic plasticity is widely believed to underlie learning and memory in the brain. Whether plasticity is primarily expressed pre- or postsynaptically has been the subject of considerable debate for many decades. More recently, it is generally agreed that the locus of plasticity depends on a number of factors, such as developmental stage, induction protocol, and synapse type. Since presynaptic expression alters not just the gain but also the short-term dynamics of a synapse, whereas postsynaptic expression only modifies the gain, the locus has fundamental implications for circuits dynamics and computations in the brain. It therefore remains crucial for our understanding of neuronal circuits to know the locus of expression of long-term plasticity. One classical method for elucidating whether plasticity is pre- or postsynaptically expressed is based on analysis of the coefficient of variation (CV), which serves as a measure of noise levels of synaptic neurotransmission. Here, we provide a practical guide to using CV analysis for the purposes of exploring the locus of expression of long-term plasticity, primarily aimed at beginners in the field. We provide relatively simple intuitive background to an otherwise theoretically complex approach as well as simple mathematical derivations for key parametric relationships. We list important pitfalls of the method, accompanied by accessible computer simulations to better illustrate the problems (downloadable from GitHub), and we provide straightforward solutions for these issues.

Optical monitoring of glutamate release at multiple synapses in situ detects changes following LTP induction

Information processing and memory formation in the brain relies on release of the main excitatory neurotransmitter glutamate from presynaptic axonal specialisations. The classical Hebbian paradigm of synaptic memory, long-term potentiation (LTP) of transmission, has been widely associated with an increase in the postsynaptic receptor current. Whether and to what degree LTP induction also enhances presynaptic glutamate release has been the subject of debate. Here, we took advantage of the recently developed genetically encoded optical sensors of glutamate (iGluSnFR) to monitor its release at CA3-CA1 synapses in acute hippocampal slices, before and after the induction of LTP. We attempted to trace release events at multiple synapses simultaneously, by using two-photon excitation imaging in fast frame-scanning mode. We thus detected a significant increase in the average iGluSnFR signal during potentiation, which lasted for up to 90 min. This increase may reflect an increased amount of released glutamate or, alternatively, reduced glutamate binding to high-affinity glutamate transporters that compete with iGluSnFR.

Activity Shapes Neural Circuit Form and Function: A Historical Perspective

The brilliant and often prescient hypotheses of Ramon y Cajal have proven foundational for modern neuroscience, but his statement that "In adult centers the nerve paths are something fixed, ended, immutable … " is an exception that did not stand the test of empirical study. Mechanisms of cellular and circuit-level plasticity continue to shape and reshape many regions of the adult nervous system long after the neurodevelopmental period. Initially focused on neurons alone, the field has followed a meteoric trajectory in understanding of activity-regulated neurodevelopment and ongoing neuroplasticity with an arc toward appreciating neuron-glial interactions and the role that each neural cell type plays in shaping adaptable neural circuity. In this review, as part of a celebration of the 50th anniversary of Society for Neuroscience, we provide a historical perspective, following this arc of inquiry from neuronal to neuron-glial mechanisms by which activity and experience modulate circuit structure and function. The scope of this consideration is broad, and it will not be possible to cover the wealth of knowledge about all aspects of activity-dependent circuit development and plasticity in depth.

Locating the engram: Should we look for plastic synapses or information-storing molecules?

Karl Lashley began the search for the engram nearly seventy years ago. In the time since, much has been learned but divisions remain. In the contemporary neurobiology of learning and memory, two profoundly different conceptions contend: the associative/connectionist (A/C) conception and the computational/representational (C/R) conception. Both theories ground themselves in the belief that the mind is emergent from the properties and processes of a material brain. Where these theories differ is in their description of what the neurobiological substrate of memory is and where it resides in the brain. The A/C theory of memory emphasizes the need to distinguish memory cognition from the memory engram and postulates that memory cognition is an emergent property of patterned neural activity routed through engram circuits. In this model, learning re-organizes synapse association strengths to guide future neural activity. Importantly, the version of the A/C theory advocated for here contends that synaptic change is not symbolic and, despite normally being necessary, is not sufficient for memory cognition. Instead, synaptic change provides the capacity and a blueprint for reinstating symbolic patterns of neural activity. Unlike the A/C theory, which posits that memory emerges at the circuit level, the C/R conception suggests that memory manifests at the level of intracellular molecular structures. In C/R theory, these intracellular structures are information-conveying and have properties compatible with the view that brain computation utilizes a read/write memory, functionally similar to that in a computer. New research has energized both sides and highlighted the need for new discussion. Both theories, the key questions each theory has yet to resolve and several potential paths forward are presented here.

Optical Quantal Analysis

Understanding the mechanisms by which long-term synaptic plasticity is expressed remains an important objective in neuroscience. From a physiological perspective, the strength of a synapse can be considered a consequence of several parameters including the probability that a presynaptic action potential (AP) evokes the release of neurotransmitter, the mean number of quanta of transmitter released when release is evoked, and the mean amplitude of a postsynaptic response to a single quantum. Various methods have been employed to estimate these quantal parameters from electrophysiological recordings; such "quantal analysis" has been used to support competing accounts of mechanisms of expression of long-term plasticity. Because electrophysiological recordings, even with minimal presynaptic stimulation, can reflect responses arising at multiple synaptic sites, these methods are open to alternative interpretations. By combining intracellular electrical recording with optical detection of transmission at individual synapses, however, it is possible to eliminate such ambiguity. Here, we describe methods for such combined optical and electrical monitoring of synaptic transmission in brain slice preparations and illustrate how quantal analyses thereby obtained permit more definitive conclusions about the physiological changes that underlie long-term synaptic plasticity.

Amyloid β oligomers suppress excitatory transmitter release via presynaptic depletion of phosphatidylinositol-4,5-bisphosphate

Amyloid β (Aβ) oligomer-induced aberrant neurotransmitter release is proposed to be a crucial early event leading to synapse dysfunction in Alzheimer's disease (AD). In the present study, we report that the release probability (Pr) at the synapse between the Schaffer collateral (SC) and CA1 pyramidal neurons is significantly reduced at an early stage in mouse models of AD with elevated Aβ production. High nanomolar synthetic oligomeric Aβ₄₂ also suppresses Pr at the SC-CA1 synapse in wild-type mice. This Aβ-induced suppression of Pr is mainly due to an mGluR5-mediated depletion of phosphatidylinositol-4,5-bisphosphate (PIP₂) in axons. Selectively inhibiting Aβ-induced PIP₂ hydrolysis in the CA3 region of the hippocampus strongly prevents oligomeric Aβ-induced suppression of Pr at the SC-CA1 synapse and rescues synaptic and spatial learning and memory deficits in APP/PS1 mice. These results first reveal the presynaptic mGluR5-PIP₂ pathway whereby oligomeric Aβ induces early synaptic deficits in AD.

Optical Quantal Analysis Using Ca2+ Indicators: A Robust Method for Assessing Transmitter Release Probability at Excitatory Synapses by Imaging Single Glutamate Release Events

Despite evidence that presynaptic efficacy and plasticity influence circuit function and behavior in vivo, studies of presynaptic function remain challenging owing to the difficulty of assessing transmitter release in intact tissue. Electrophysiological analyses of transmitter release are indirect and cannot readily resolve basic presynaptic parameters, most notably transmitter release probability (p r), at single synapses. These issues can be circumvented by optical quantal analysis, which uses the all-or-none optical detection of transmitter release in order to calculate p r. Over the past two decades, we and others have successfully demonstrated that Ca2+ indicators can be strategically implemented to perform optical quantal analysis at single glutamatergic synapses in ex vivo and in vitro preparations. We have found that high affinity Ca2+ indicators can reliably detect spine Ca2+ influx generated by single quanta of glutamate, thereby enabling precise calculation of pr at single synapses. Importantly, we have shown this method to be robust to changes in postsynaptic efficacy, and to be sensitive to activity-dependent presynaptic changes at central synapses following the induction of long-term potentiation (LTP) and long-term depression (LTD). In this report, we describe how to use Ca2+-sensitive dyes to perform optical quantal analysis at single synapses in hippocampal slice preparations. The general technique we describe here can be applied to other glutamatergic synapses and can be used with other reporters of glutamate release, including recently improved genetically encoded Ca2+ and glutamate sensors. With ongoing developments in imaging techniques and genetically encoded probes, optical quantal analysis is a promising strategy for assessing presynaptic function and plasticity in vivo.

Early life stress impairs fear memory and synaptic plasticity; a potential role for GluN2B

Programming of the brain by early life stress has been associated with alterations in structure and function of the dorsal hippocampus. Yet, the underlying molecular mechanisms remain largely elusive. In this study, we examined the effects of early life stress (ELS) - by housing mouse dams with limited nesting and bedding material from postnatal days 2-9 and examined in 6 month old offspring; 1) auditory fear conditioning, 2) expression of the hippocampal N-methyl-d-aspartate receptor (NMDA-R) subunits 2A and 2B (GluN2A, GluN2B), and expression of PSD-95 and synaptophysin, and 3) short- and long-term (LTP) synaptic plasticity. Given its critical role in NMDA receptor function and synaptic plasticity, we further examined the role of GluN2B in effects of ELS on synaptic plasticity and fear memory formation. We demonstrate that ELS impaired fear memory in 6 month old mice and decreased hippocampal LTP as well as the paired-pulse ratio (PPR). ELS also reduced hippocampal GluN2B expression. Interestingly, pharmacological blockade of GluN2B with the selective antagonist Ro25 6981 was less effective to reduce synaptic plasticity in ELS mice, and was also ineffective to impair memory retrieval in ELS mice. These studies suggest that ELS reduces hippocampal synaptic plasticity and fear memory formation and hampers GluN2B receptor function. As such, GluN2B may provide an important target for future strategies to prevent lasting ELS effects on cognition.

The Synaptic Theory of Memory: A Historical Survey and Reconciliation of Recent Opposition

Trettenbrein (2016) has argued that the concept of the synapse as the locus of memory is outdated and has made six critiques of this concept. In this article, we examine these six critiques and suggest that the current theories of the neurobiology of memory and the empirical data indicate that synaptic activation is the first step in a chain of cellular and biochemical events that lead to memories formed in cell assemblies and neural networks that rely on synaptic modification for their formation. These neural networks and their modified synaptic connections can account for the cognitive basis of learning and memory and for memory deterioration in neurological disorders. We first discuss Hebb's (1949) theory that synaptic change and the formation of cell assemblies and phase sequences can link neurophysiology to cognitive processes. We then examine each of Trettenbrein's (2016) critiques of the synaptic theory in light of Hebb's theories and recent empirical data. We examine the biochemical basis of memory formation and the necessity of synaptic modification to form the neural networks underlying learning and memory. We then examine the use of Hebb's theories of synaptic change and cell assemblies for integrating neurophysiological and cognitive conceptions of learning and memory. We conclude with an examination of the applications of the Hebb synapse and cell assembly theories to the study of the neuroscience of learning and memory, the development of computational models of memory and the construction of "intelligent" robots. We conclude that the synaptic theory of memory has not met its demise, but is essential to our understanding of the neural basis of memory, which has two components: synaptic plasticity and intrinsic plasticity.

AMPA Receptor Trafficking for Postsynaptic Potentiation

Long-term potentiation (LTP) of excitatory synaptic strength, which has long been considered a synaptic correlate for learning and memory, requires a fast recruitment of additional α-amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA) receptors (AMPARs) to the postsynaptic sites. As cell biological concepts have been applied to the field and genetic manipulation and microscopic imaging technologies have been advanced, visualization of the trafficking of AMPARs to synapses for LTP has been investigated intensively over the last decade. Recycling endosomes have been reported as intracellular storage organelles to supply AMPARs for LTP through the endocytic recycling pathway. In addition, exocytic domains in the spine plasma membrane, where AMPARs are inserted from the intracellular compartment, and nanodomains, where diffusing AMPARs are trapped and immobilized inside synapses for LTP, have been described. Furthermore, cell surface lateral diffusion of AMPARs from extrasynaptic to synaptic sites has been reported as a key step for AMPAR location to the synaptic sites for LTP. This review article will discuss recent findings and views on the reservoir(s) of AMPARs and their trafficking for LTP expression by focusing on the exocytosis and lateral diffusion of AMPARs, and provide some future directions that need to be addressed in the field of LTP.

Discovering long-term potentiation (LTP) - recollections and reflections on what came after

Chance events led me to a lifelong career in scientific research. They paved the way for being the first to see long-term potentiation of synaptic efficiency (LTP) in Per Andersen's laboratory in Oslo in 1966. Here I describe my way to this discovery and the experiments with Tim Bliss in 1968-1969 that led to Bliss and Lømo, 1973. Surprisingly, we later failed to reproduce these results. I discuss possible reasons for this failure, which made us both leave LTP research, in my case for good, in Tim's case for several years. After 30 years of work in a different field, I renewed my interest in the hippocampus and LTP in the early 2000s and published, for the first time, results that I had obtained 40 years earlier. Here I present my take on how interest in and research on LTP evolved after the early years. This includes a discussion of the functions of hippocampus as seen in those early days, the case of patient H.M., Donald Hebb's place in the story, the search for 'memory molecules' such as PKMζ, and the primary site for LTP expression (pre- and/or post-synaptic?). Throughout, I reflect on my life in science, how science is done and what drives it. The reflections are quite personal and I admit to mixed feelings about broadcasting them.

Gradation (approx. 10 size states) of synaptic strength by quantal addition of structural modules

Memory storage involves activity-dependent strengthening of synaptic transmission, a process termed long-term potentiation (LTP). The late phase of LTP is thought to encode long-term memory and involves structural processes that enlarge the synapse. Hence, understanding how synapse size is graded provides fundamental information about the information storage capability of synapses. Recent work using electron microscopy (EM) to quantify synapse dimensions has suggested that synapses may structurally encode as many as 26 functionally distinct states, which correspond to a series of proportionally spaced synapse sizes. Other recent evidence using super-resolution microscopy has revealed that synapses are composed of stereotyped nanoclusters of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors and scaffolding proteins; furthermore, synapse size varies linearly with the number of nanoclusters. Here we have sought to develop a model of synapse structure and growth that is consistent with both the EM and super-resolution data. We argue that synapses are composed of modules consisting of matrix material and potentially one nanocluster. LTP induction can add a trans-synaptic nanocluster to a module, thereby converting a silent module to an AMPA functional module. LTP can also add modules by a linear process, thereby producing an approximately 10-fold gradation in synapse size and strength.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.

Glutamate is required for depression but not potentiation of long-term presynaptic function

Hebbian plasticity is thought to require glutamate signalling. We show this is not the case for hippocampal presynaptic long-term potentiation (LTP_pre), which is expressed as an increase in transmitter release probability (P_r). We find that LTP_pre can be induced by pairing pre- and postsynaptic spiking in the absence of glutamate signalling. LTP_pre induction involves a non-canonical mechanism of retrograde nitric oxide signalling, which is triggered by Ca²⁺ influx from L-type voltage-gated Ca²⁺ channels, not postsynaptic NMDA receptors (NMDARs), and does not require glutamate release. When glutamate release occurs, it decreases P_r by activating presynaptic NMDARs, and promotes presynaptic long-term depression. Net changes in P_r, therefore, depend on two opposing factors: (1) Hebbian activity, which increases P_r, and (2) glutamate release, which decreases P_r. Accordingly, release failures during Hebbian activity promote LTP_pre induction. Our findings reveal a novel framework of presynaptic plasticity that radically differs from traditional models of postsynaptic plasticity.

Neurons communicate with one another at junctions called synapses. One neuron at the synapse releases a chemical substance called a neurotransmitter, which binds to and activates the other neuron. The release of neurotransmitter thus enables the electrical activity of one cell to influence the electrical activity of another. The efficiency of this communication can change over time, as is thought to occur during learning. If the neurons on both sides of a synapse are repeatedly active at the same time, the ability of the neurons to transmit electrical signals to each other increases.

One way that communication between neurons can become more efficient is if the first neuron becomes more likely to release neurotransmitter. Most synapses in the brain release a neurotransmitter called glutamate, and most types of learning involve changes in the efficiency of communication at glutamatergic synapses. But glutamate release is unreliable. Active glutamatergic neurons fail to release glutamate about 80% of the time. If glutamate has a key role in learning, how does the brain learn efficiently when glutamate release is so unlikely?

To find out, Padamsey et al. studied glutamatergic synapses in slices of tissue from mouse and rat brains. When both neurons at a synapse were repeatedly active at the same time, the first neuron would sometimes become more likely to release glutamate. But this only happened at synapses in which the first neuron usually failed to release glutamate in the first place. This suggests that communication failures help to drive change at synapses. When two neurons that are often active at the same time do not communicate efficiently, this failure triggers molecular changes that make future communication more reliable.

Previous results have shown that synapses can change when glutamate release occurs. The current results show that they can also change when it does not. This means that the brain can continue to learn despite frequent communication failures between neurons. Many neurological disorders, including Alzheimer’s disease, show altered glutamate signalling at synapses. Padamsey et al. hope that a better understanding of this process will lead to new therapies for these disorders.

Optimal structure of metaplasticity for adaptive learning

Learning from reward feedback in a changing environment requires a high degree of adaptability, yet the precise estimation of reward information demands slow updates. In the framework of estimating reward probability, here we investigated how this tradeoff between adaptability and precision can be mitigated via metaplasticity, i.e. synaptic changes that do not always alter synaptic efficacy. Using the mean-field and Monte Carlo simulations we identified ‘superior’ metaplastic models that can substantially overcome the adaptability-precision tradeoff. These models can achieve both adaptability and precision by forming two separate sets of meta-states: reservoirs and buffers. Synapses in reservoir meta-states do not change their efficacy upon reward feedback, whereas those in buffer meta-states can change their efficacy. Rapid changes in efficacy are limited to synapses occupying buffers, creating a bottleneck that reduces noise without significantly decreasing adaptability. In contrast, more-populated reservoirs can generate a strong signal without manifesting any observable plasticity. By comparing the behavior of our model and a few competing models during a dynamic probability estimation task, we found that superior metaplastic models perform close to optimally for a wider range of model parameters. Finally, we found that metaplastic models are robust to changes in model parameters and that metaplastic transitions are crucial for adaptive learning since replacing them with graded plastic transitions (transitions that change synaptic efficacy) reduces the ability to overcome the adaptability-precision tradeoff. Overall, our results suggest that ubiquitous unreliability of synaptic changes evinces metaplasticity that can provide a robust mechanism for mitigating the tradeoff between adaptability and precision and thus adaptive learning.

Successful learning from our experience and feedback from the environment requires that the reward value assigned to a given option or action to be updated by a precise amount after each feedback. In the standard model for reward-based learning known as reinforcement learning, the learning rates determine the strength of such update. A large learning rate allows fast update of values (large adaptability) but introduces noise (small precision), whereas a small learning rate does the opposite. Thus, learning seems to be bounded by a tradeoff between adaptability and precision. Here, we asked whether there are synaptic mechanisms that are capable of adjusting the brain’s level of plasticity according to reward statistics, and, therefore, allow the learning process to be adaptive. We showed that metaplasticity, changes in the synaptic state that shape future synaptic modifications without any observable changes in the strength of synapses, could provide such a mechanism and furthermore, identified the optimal structure of such metaplasticity. We propose that metaplasticity, which sometimes causes no observable changes in behavior and thus could be perceived as a lack of learning, can provide a robust mechanism for adaptive learning.

Astrocytic IP3 Rs: Contribution to Ca2+ signalling and hippocampal LTP

Astrocytes regulate hippocampal synaptic plasticity by the Ca²⁺ dependent release of the N-methyl d-aspartate receptor (NMDAR) co-agonist d-serine. Previous evidence indicated that d-serine release would be regulated by the intracellular Ca²⁺ release channel IP₃ receptor (IP₃ R), however, genetic deletion of IP₃ R2, the putative astrocytic IP₃ R subtype, had no impact on synaptic plasticity or transmission. Although IP₃ R2 is widely believed to be the only functional IP₃ R in astrocytes, three IP₃ R subtypes (1, 2, and 3) have been identified in vertebrates. Therefore, to better understand gliotransmission, we investigated the functionality of IP₃ R and the contribution of the three IP₃ R subtypes to Ca²⁺ signalling. As a proxy for gliotransmission, we found that long-term potentiation (LTP) was impaired by dialyzing astrocytes with the broad IP₃ R blocker heparin, and rescued by exogenous d-serine, indicating that astrocytic IP₃ Rs regulate d-serine release. To explore which IP₃ R subtypes are functional in astrocytes, we used pharmacology and two-photon Ca²⁺ imaging of hippocampal slices from transgenic mice (IP₃ R2^-/- and IP₃ R2^-/- ;3^-/- ). This approach revealed that underneath IP₃ R2-mediated global Ca²⁺ events are an overlooked class of IP₃ R-mediated local events, occurring in astroglial processes. Notably, multiple IP₃ Rs were recruited by high frequency stimulation of the Schaffer collaterals, a classical LTP induction protocol. Together, these findings show the dependence of LTP and gliotransmission on Ca²⁺ release by astrocytic IP₃ Rs. GLIA 2017;65:502-513.

Presynaptic PICK1 facilitates trafficking of AMPA-receptors between active zone and synaptic vesicle pool

Previous studies have indicated that presynaptic α-amino-3-hydroxy-5-methyl-4-isoxazole propionate receptors (AMPARs) contribute to the regulation of neurotransmitter release. In hippocampal synapses, the presynaptic surface expression of several AMPAR subunits, including GluA2, is regulated in a ligand-dependent manner. However, the molecular mechanisms underlying the presynaptic trafficking of AMPARs are still unknown. Here, using bright-field immunocytochemistry, western blots, and quantitative immunogold electron microscopy of the hippocampal CA1 area from intact adult rat brain, we demonstrate the association of AMPA receptors with the presynaptic active zone and with small presynaptic vesicles, in Schaffer collateral synapses in CA1 of the hippocampus. Furthermore, we show that GluA2 and protein interacting with C kinase 1 (PICK1) are colocalized at presynaptic vesicles. Similar to postsynaptic mechanisms, overexpression of either PICK1 or pep2m, which inhibit the N-ethylmaleimide sensitive fusion protein (NSF)-GluA2 interaction, decreases the concentration of GluA2 in the presynaptic active zone membrane. These data suggest that the interacting proteins PICK1 and NSF act as regulators of presynaptic GluA2-containing AMPAR trafficking between the active zone and a vesicle pool that may provide the basis of presynaptic components of synaptic plasticity.

Mitochondrial support of persistent presynaptic vesicle mobilization with age-dependent synaptic growth after LTP

Mitochondria support synaptic transmission through production of ATP, sequestration of calcium, synthesis of glutamate, and other vital functions. Surprisingly, less than 50% of hippocampal CA1 presynaptic boutons contain mitochondria, raising the question of whether synapses without mitochondria can sustain changes in efficacy. To address this question, we analyzed synapses from postnatal day 15 (P15) and adult rat hippocampus that had undergone theta-burst stimulation to produce long-term potentiation (TBS-LTP) and compared them to control or no stimulation. At 30 and 120 min after TBS-LTP, vesicles were decreased only in presynaptic boutons that contained mitochondria at P15, and vesicle decrement was greatest in adult boutons containing mitochondria. Presynaptic mitochondrial cristae were widened, suggesting a sustained energy demand. Thus, mitochondrial proximity reflected enhanced vesicle mobilization well after potentiation reached asymptote, in parallel with the apparently silent addition of new dendritic spines at P15 or the silent enlargement of synapses in adults.

DOI:http://dx.doi.org/10.7554/eLife.15275.001

Direct Current-Induced Calcium Trafficking in Different Neuronal Preparations

The influence of direct current (DC) stimulation on radioactive calcium trafficking in sciatic nerve in vivo and in vitro, spinal cord, and synaptosomes was investigated. The exposure to DC enhanced calcium redistribution in all of these preparations. The effect was dependent on the strength of the stimulation and extended beyond the phase of exposure to DC. The DC-induced increase in calcium sequestration by synaptosomes was significantly reduced by cobalt and rupture of synaptosomes by osmotic shock. Although both anodal and cathodal currents were effective, the experiments with two electrodes of different areas revealed that cathodal stimulation exerted stronger effect. The exposure to DC induced not only relocation but also redistribution of calcium within segments of the sciatic nerve. Enzymatic removal of sialic acid by preincubation of synaptosomes with neuroaminidase, or carrying out the experiments in sodium-free environment, amplified DC-induced calcium accumulation.

Storing structured sparse memories in a multi-modular cortical network model

We study the memory performance of a class of modular attractor neural networks, where modules are potentially fully-connected networks connected to each other via diluted long-range connections. On this anatomical architecture we store memory patterns of activity using a Willshaw-type learning rule. P patterns are split in categories, such that patterns of the same category activate the same set of modules. We first compute the maximal storage capacity of these networks. We then investigate their error-correction properties through an exhaustive exploration of parameter space, and identify regions where the networks behave as an associative memory device. The crucial parameters that control the retrieval abilities of the network are (1) the ratio between the number of synaptic contacts of long- and short-range origins (2) the number of categories in which a module is activated and (3) the amount of local inhibition. We discuss the relationship between our model and networks of cortical patches that have been observed in different cortical areas.

The Corticohippocampal Circuit, Synaptic Plasticity, and Memory

Synaptic plasticity serves as a cellular substrate for information storage in the central nervous system. The entorhinal cortex (EC) and hippocampus are interconnected brain areas supporting basic cognitive functions important for the formation and retrieval of declarative memories. Here, we discuss how information flow in the EC-hippocampal loop is organized through circuit design. We highlight recently identified corticohippocampal and intrahippocampal connections and how these long-range and local microcircuits contribute to learning. This review also describes various forms of activity-dependent mechanisms that change the strength of corticohippocampal synaptic transmission. A key point to emerge from these studies is that patterned activity and interaction of coincident inputs gives rise to associational plasticity and long-term regulation of information flow. Finally, we offer insights about how learning-related synaptic plasticity within the corticohippocampal circuit during sensory experiences may enable adaptive behaviors for encoding spatial, episodic, social, and contextual memories.

Electrical behaviour of dendritic spines as revealed by voltage imaging

Thousands of dendritic spines on individual neurons process information and mediate plasticity by generating electrical input signals using a sophisticated assembly of transmitter receptors and voltage-sensitive ion channel molecules. Our understanding, however, of the electrical behaviour of spines is limited because it has not been possible to record input signals from these structures with adequate sensitivity and spatiotemporal resolution. Current interpretation of indirect data and speculations based on theoretical considerations are inconclusive. Here we use an electrochromic voltage-sensitive dye which acts as a transmembrane optical voltmeter with a linear scale to directly monitor electrical signals from individual spines on thin basal dendrites. The results show that synapses on these spines are not electrically isolated by the spine neck to a significant extent. Electrically, they behave as if they are located directly on dendrites.

Biochemical Computation for Spine Structural Plasticity

The structural plasticity of dendritic spines is considered to be essential for various forms of synaptic plasticity, learning, and memory. The process is mediated by a complex signaling network consisting of numerous species of molecules. Furthermore, the spatiotemporal dynamics of the biochemical signaling are regulated in a complicated manner because of geometrical restrictions from the unique morphology of the dendritic branches and spines. Recent advances in optical techniques have enabled the exploration of the spatiotemporal aspects of the signal regulations in spines and dendrites and have provided many insights into the principle of the biochemical computation that underlies spine structural plasticity.

Atypical PKCs in memory maintenance: the roles of feedback and redundancy

Memories that last a lifetime are thought to be stored, at least in part, as persistent enhancement of the strength of particular synapses. The synaptic mechanism of these persistent changes, late long-term potentiation (L-LTP), depends on the state and number of specific synaptic proteins. Synaptic proteins, however, have limited dwell times due to molecular turnover and diffusion, leading to a fundamental question: how can this transient molecular machinery store memories lasting a lifetime? Because the persistent changes in efficacy are synapse-specific, the underlying molecular mechanisms must to a degree reside locally in synapses. Extensive experimental evidence points to atypical protein kinase C (aPKC) isoforms as key components involved in memory maintenance. Furthermore, it is evident that establishing long-term memory requires new protein synthesis. However, a comprehensive model has not been developed describing how these components work to preserve synaptic efficacies over time. We propose a molecular model that can account for key empirical properties of L-LTP, including its protein synthesis dependence, dependence on aPKCs, and synapse-specificity. Simulations and empirical data suggest that either of the two aPKC subtypes in hippocampal neurons, PKMζ and PKCι/λ, can maintain L-LTP, making the system more robust. Given genetic compensation at the level of synthesis of these PKC subtypes as in knockout mice, this system is able to maintain L-LTP and memory when one of the pathways is eliminated.

Activation of InsP₃ receptors is sufficient for inducing graded intrinsic plasticity in rat hippocampal pyramidal neurons

The synaptic plasticity literature has focused on establishing necessity and sufficiency as two essential and distinct features in causally relating a signaling molecule to plasticity induction, an approach that has been surprisingly lacking in the intrinsic plasticity literature. In this study, we complemented the recently established necessity of inositol trisphosphate (InsP3) receptors (InsP3R) in a form of intrinsic plasticity by asking if InsP3R activation was sufficient to induce intrinsic plasticity in hippocampal neurons. Specifically, incorporation of d-myo-InsP3 in the recording pipette reduced input resistance, maximal impedance amplitude, and temporal summation but increased resonance frequency, resonance strength, sag ratio, and impedance phase lead. Strikingly, the magnitude of plasticity in all these measurements was dependent on InsP3 concentration, emphasizing the graded dependence of such plasticity on InsP3R activation. Mechanistically, we found that this InsP3-induced plasticity depended on hyperpolarization-activated cyclic nucleotide-gated channels. Moreover, this calcium-dependent form of plasticity was critically reliant on the release of calcium through InsP3Rs, the influx of calcium through N-methyl-d-aspartate receptors and voltage-gated calcium channels, and on the protein kinase A pathway. Our results delineate a causal role for InsP3Rs in graded adaptation of neuronal response dynamics, revealing novel regulatory roles for the endoplasmic reticulum in neural coding and homeostasis.

Biochemical principles underlying the stable maintenance of LTP by the CaMKII/NMDAR complex

Memory involves the storage of information at synapses by an LTP-like process. This information storage is synapse specific and can endure for years despite the turnover of all synaptic proteins. There must, therefore, be special principles that underlie the stability of LTP. Recent experimental results suggest that LTP is maintained by the complex of CaMKII with the NMDAR. Here we consider the specifics of the CaMKII/NMDAR molecular switch, with the goal of understanding the biochemical principles that underlie stable information storage by synapses. Consideration of a variety of experimental results suggests that multiple principles are involved. One switch requirement is to prevent spontaneous transitions from the off to the on state. The highly cooperative nature of CaMKII autophosphorylation by Ca(2+) (Hill coefficient of 8) and the fact that formation of the CaMKII/NMDAR complex requires release of CaMKII from actin are mechanisms that stabilize the off state. The stability of the on state depends critically on intersubunit autophosphorylation, a process that restores any loss of pT286 due to phosphatase activity. Intersubunit autophosphorylation is also important in explaining why on state stability is not compromised by protein turnover. Recent evidence suggests that turnover occurs by subunit exchange. Thus, stability could be achieved if a newly inserted unphosphorylated subunit was autophosphorylated by a neighboring subunit. Based on other recent work, we posit a novel mechanism that enhances the stability of the on state by protection of pT286 from phosphatases. We posit that the binding of the NMNDAR to CaMKII forces pT286 into the catalytic site of a neighboring subunit, thereby protecting pT286 from phosphatases. A final principle concerns the role of structural changes. The binding of CaMKII to the NMDAR may act as a tag to organize the binding of further proteins that produce the synapse enlargement that underlies late LTP. We argue that these structural changes not only enhance transmission, but also enhance the stability of the CaMKII/NMDAR complex. Together, these principles provide a mechanistic framework for understanding how individual synapses produce stable information storage. This article is part of a Special Issue entitled SI: Brain and Memory.

Dynamics of nascent and active zone ultrastructure as synapses enlarge during long-term potentiation in mature hippocampus

Nascent zones and active zones are adjacent synaptic regions that share a postsynaptic density, but nascent zones lack the presynaptic vesicles found at active zones. Here dendritic spine synapses were reconstructed through serial section electron microscopy (3DEM) and EM tomography to investigate nascent zone dynamics during long-term potentiation (LTP) in mature rat hippocampus. LTP was induced with theta-burst stimulation, and comparisons were made with control stimulation in the same hippocampal slices at 5 minutes, 30 minutes, and 2 hours post-induction and to perfusion-fixed hippocampus in vivo. Nascent zones were present at the edges of ∼35% of synapses in perfusion-fixed hippocampus and as many as ∼50% of synapses in some hippocampal slice conditions. By 5 minutes, small dense-core vesicles known to transport active zone proteins moved into more presynaptic boutons. By 30 minutes, nascent zone area decreased, without significant change in synapse area, suggesting that presynaptic vesicles were recruited to preexisting nascent zones. By 2 hours, both nascent and active zones were enlarged. Immunogold labeling revealed glutamate receptors in nascent zones; however, average distances from nascent zones to docked presynaptic vesicles ranged from 170 ± 5 nm in perfusion-fixed hippocampus to 251 ± 4 nm at enlarged synapses by 2 hours during LTP. Prior stochastic modeling suggests that decrease in glutamate concentration reduces the probability of glutamate receptor activation from 0.4 at the center of release to 0.1 just 200 nm away. Thus, conversion of nascent zones to functional active zones likely requires the recruitment of presynaptic vesicles during LTP.

A synaptic mechanism for network synchrony

Within neural networks, synchronization of activity is dependent upon the synaptic connectivity of embedded microcircuits and the intrinsic membrane properties of their constituent neurons. Synaptic integration, dendritic Ca²⁺ signaling, and non-linear interactions are crucial cellular attributes that dictate single neuron computation, but their roles promoting synchrony and the generation of network oscillations are not well understood, especially within the context of a defined behavior. In this regard, the lamprey spinal central pattern generator (CPG) stands out as a well-characterized, conserved vertebrate model of a neural network (Smith et al., 2013a), which produces synchronized oscillations in which neural elements from the systems to cellular level that control rhythmic locomotion have been determined. We review the current evidence for the synaptic basis of oscillation generation with a particular emphasis on the linkage between synaptic communication and its cellular coupling to membrane processes that control oscillatory behavior of neurons within the locomotor network. We seek to relate dendritic function found in many vertebrate systems to the accessible lamprey central nervous system in which the relationship between neural network activity and behavior is well understood. This enables us to address how Ca²⁺ signaling in spinal neuron dendrites orchestrate oscillations that drive network behavior.

Memory capacity of networks with stochastic binary synapses

In standard attractor neural network models, specific patterns of activity are stored in the synaptic matrix, so that they become fixed point attractors of the network dynamics. The storage capacity of such networks has been quantified in two ways: the maximal number of patterns that can be stored, and the stored information measured in bits per synapse. In this paper, we compute both quantities in fully connected networks of N binary neurons with binary synapses, storing patterns with coding level , in the large and sparse coding limits (). We also derive finite-size corrections that accurately reproduce the results of simulations in networks of tens of thousands of neurons. These methods are applied to three different scenarios: (1) the classic Willshaw model, (2) networks with stochastic learning in which patterns are shown only once (one shot learning), (3) networks with stochastic learning in which patterns are shown multiple times. The storage capacities are optimized over network parameters, which allows us to compare the performance of the different models. We show that finite-size effects strongly reduce the capacity, even for networks of realistic sizes. We discuss the implications of these results for memory storage in the hippocampus and cerebral cortex.

Two central hypotheses in neuroscience are that long-term memory is sustained by modifications of the connectivity of neural circuits, while short-term memory is sustained by persistent neuronal activity following the presentation of a stimulus. These two hypotheses have been substantiated by several decades of electrophysiological experiments, reporting activity-dependent changes in synaptic connectivity in vitro, and stimulus-selective persistent neuronal activity in delayed response tasks in behaving monkeys. They have been implemented in attractor network models, that store specific patterns of activity using Hebbian plasticity rules, which then allow retrieval of these patterns as attractors of the network dynamics. A long-standing question in the field is how many patterns (or equivalently, how much information) can be stored in such networks? Here, we compute the storage capacity of networks of binary neurons and binary synapses. Synapses store information according to a simple stochastic learning process that consists of transitions between synaptic states conditioned on the states of pre- and post-synaptic neurons. We consider this learning process in two limits: a one shot learning scenario, where each pattern is presented only once, and a slow learning scenario, where noisy versions of a set of patterns are presented multiple times, but transition probabilities are small. The two limits are assumed to represent, in a simplified way, learning in the hippocampus and neocortex, respectively. We show that in both cases, the information stored per synapse remains finite in the large limit, when the coding is sparse. Furthermore, we characterize the strong finite size effects that exist in such networks.

Activity-dependent dendritic spine neck changes are correlated with synaptic strength

Most excitatory inputs in the mammalian brain are made on dendritic spines, rather than on dendritic shafts. Spines compartmentalize calcium, and this biochemical isolation can underlie input-specific synaptic plasticity, providing a raison d'etre for spines. However, recent results indicate that the spine can experience a membrane potential different from that in the parent dendrite, as though the spine neck electrically isolated the spine. Here we use two-photon calcium imaging of mouse neocortical pyramidal neurons to analyze the correlation between the morphologies of spines activated under minimal synaptic stimulation and the excitatory postsynaptic potentials they generate. We find that excitatory postsynaptic potential amplitudes are inversely correlated with spine neck lengths. Furthermore, a spike timing-dependent plasticity protocol, in which two-photon glutamate uncaging over a spine is paired with postsynaptic spikes, produces rapid shrinkage of the spine neck and concomitant increases in the amplitude of the evoked spine potentials. Using numerical simulations, we explore the parameter regimes for the spine neck resistance and synaptic conductance changes necessary to explain our observations. Our data, directly correlating synaptic and morphological plasticity, imply that long-necked spines have small or negligible somatic voltage contributions, but that, upon synaptic stimulation paired with postsynaptic activity, they can shorten their necks and increase synaptic efficacy, thus changing the input/output gain of pyramidal neurons.

Stochasticity in Ca2+ increase in spines enables robust and sensitive information coding

A dendritic spine is a very small structure (∼0.1 µm³) of a neuron that processes input timing information. Why are spines so small? Here, we provide functional reasons; the size of spines is optimal for information coding. Spines code input timing information by the probability of Ca²⁺ increases, which makes robust and sensitive information coding possible. We created a stochastic simulation model of input timing-dependent Ca²⁺ increases in a cerebellar Purkinje cell's spine. Spines used probability coding of Ca²⁺ increases rather than amplitude coding for input timing detection via stochastic facilitation by utilizing the small number of molecules in a spine volume, where information per volume appeared optimal. Probability coding of Ca²⁺ increases in a spine volume was more robust against input fluctuation and more sensitive to input numbers than amplitude coding of Ca²⁺ increases in a cell volume. Thus, stochasticity is a strategy by which neurons robustly and sensitively code information.

Prolonged ampakine exposure prunes dendritic spines and increases presynaptic release probability for enhanced long-term potentiation in the hippocampus

CX 546, an allosteric positive modulator of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid-type ionotropic glutamate receptors (AMPARs), belongs to a drug class called ampakines. These compounds have been shown to enhance long-term potentiation (LTP), a cellular model of learning and memory, and improve animal learning task performance, and have augmented cognition in neurodegenerative patients. However, the chronic effect of CX546 on synaptic structures has not been examined. The structure and integrity of dendritic spines are thought to play a role in learning and memory, and their abnormalities have been implicated in cognitive disorders. In addition, their structural plasticity has been shown to be important for cognitive function, such that dendritic spine remodeling has been proposed as the morphological correlate for LTP. Here, we tested the effect of CX546 on dendritic spine remodeling following long-term treatment. We found that, with prolonged CX546 treatment, organotypic hippocampal slice cultures showed a significant reduction in CA3-CA1 excitatory synapse and spine density. Electrophysiological approaches revealed that the CA3-CA1 circuitry compensates for this synapse loss by increasing synaptic efficacy through enhancement of presynaptic release probability. CX546-treated slices showed prolonged and enhanced potentiation upon LTP induction. Furthermore, structural plasticity, namely spine head enlargement, was also more pronounced after CX546 treatment. Our results suggest a concordance of functional and structural changes that is enhanced with prolonged CX546 exposure. Thus, the improved cognitive ability of patients receiving ampakine treatment may result from the priming of synapses through increases in the structural plasticity and functional reliability of hippocampal synapses.

Dendritic spines: the locus of structural and functional plasticity

The introduction of high-resolution time lapse imaging and molecular biological tools has changed dramatically the rate of progress towards the understanding of the complex structure-function relations in synapses of central spiny neurons. Standing issues, including the sequence of molecular and structural processes leading to formation, morphological change, and longevity of dendritic spines, as well as the functions of dendritic spines in neurological/psychiatric diseases are being addressed in a growing number of recent studies. There are still unsettled issues with respect to spine formation and plasticity: Are spines formed first, followed by synapse formation, or are synapses formed first, followed by emergence of a spine? What are the immediate and long-lasting changes in spine properties following exposure to plasticity-producing stimulation? Is spine volume/shape indicative of its function? These and other issues are addressed in this review, which highlights the complexity of molecular pathways involved in regulation of spine structure and function, and which contributes to the understanding of central synaptic interactions in health and disease.