Half a century legacy of long-term potentiation.

In 1973, two papers from Bliss and Lømo and from Bliss and Gardner-Medwin reported that high-frequency synaptic stimulation in the dentate gyrus of rabbits resulted in a long-lasting increase in synaptic strength. This form of synaptic plasticity, commonly referred to as long-term potentiation (LTP), was immediately considered as an attractive mechanism accounting for the ability of the brain to store information. In this historical piece looking back over the past 50 years, we discuss how these two landmark contributions directly motivated a colossal research effort and detail some of the resulting milestones that have shaped our evolving understanding of the molecular and cellular underpinnings of LTP. We highlight the main features of LTP, cover key experiments that defined its induction and expression mechanisms, and outline the evidence supporting a potential role of LTP in learning and memory. We also briefly explore some ramifications of LTP on network stability, consider current limitations of LTP as a model of associative memory, and entertain future research orientations.

Understanding the nervous system: lessons from Frontiers in Neurophotonics.

The Frontiers in Neurophotonics Symposium is a biennial event that brings together neurobiologists and physicists/engineers who share interest in the development of leading-edge photonics-based approaches to understand and manipulate the nervous system, from its individual molecular components to complex networks in the intact brain. In this Community paper, we highlight several topics that have been featured at the symposium that took place in October 2022 in Québec City, Canada.

Low throughput screening in neuroscience: using light to study central synapses one at a time.

Neurophotonic approaches have fostered substantial progress in our understanding of the brain by providing an assortment of means to either monitor or manipulate neural processes. Among these approaches, the development of two-photon uncaging provides a useful and flexible approach to manipulate the activity of individual synapses. In this short piece, we explore how this technique has emerged at the intersection of chemistry, optics, and electrophysiology to enable spatially and temporally precise photoactivation for studying functional aspects of synaptic transmission and dendritic integration. We discuss advantages and limitations of this approach, focusing on our efforts to study several functional aspects of glutamate receptors using uncaging of glutamate. Among other advancements, this approach has contributed to further our understanding of the subcellular regulation, trafficking, and biophysical features of glutamate receptors (e.g., desensitization and silent synapse regulation), the dynamics of spine calcium, and the integrative features of dendrites, and how these functions are altered by several forms of plasticity.

Adaptive spike threshold dynamics associated with sparse spiking of hilar mossy cells are captured by a simple model.

Hilar mossy cells (hMCs) in the dentate gyrus (DG) receive inputs from DG granule cells (GCs), CA3 pyramidal cells and inhibitory interneurons, and provide feedback input to GCs. Behavioural and in vivo recording experiments implicate hMCs in pattern separation, navigation and spatial learning. Our experiments link hMC intrinsic excitability to their synaptically evoked in vivo spiking outputs. We performed electrophysiological recordings from DG neurons and found that hMCs displayed an adaptative spike threshold that increased both in proportion to the intensity of injected currents, and in response to spiking itself, returning to baseline over a long time scale, thereby instantaneously limiting their firing rate responses. The hMC activity is additionally limited by a prominent medium after-hyperpolarizing potential (AHP) generated by small conductance K+ channels. We hypothesize that these intrinsic hMC properties are responsible for their low in vivo firing rates. Our findings extend previous studies that compare hMCs, CA3 pyramidal cells and hilar inhibitory cells and provide novel quantitative data that contrast the intrinsic properties of these cell types. We developed a phenomenological exponential integrate-and-fire model that closely reproduces the hMC adaptive threshold nonlinearities with respect to their threshold dependence on input current intensity, evoked spike latency and long-lasting spike-induced increase in spike threshold. Our robust and computationally efficient model is amenable to incorporation into large network models of the DG that will deepen our understanding of the neural bases of pattern separation, spatial navigation and learning. KEY POINTS: Previous studies have shown that hilar mossy cells (hMCs) are implicated in pattern separation and the formation of spatial memory, but how their intrinsic properties relate to their in vivo spiking patterns is still unknown. Here we show that the hMCs display electrophysiological properties that distinguish them from the other hilar cell types including a highly adaptive spike threshold that decays slowly. The spike-dependent increase in threshold combined with an after-hyperpolarizing potential mediated by a slow K+ conductance is hypothesized to be responsible for the low-firing rate of the hMC observed in vivo. The hMC's features are well captured by a modified stochastic exponential integrate-and-fire model that has the unique feature of a threshold intrinsically dependant on both the stimulus intensity and the spiking history. This computational model will allow future work to study how the hMCs can contribute to spatial memory formation and navigation.

Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling framework.

By means of an expansive innervation, the serotonin (5-HT) neurons of the dorsal raphe nucleus (DRN) are positioned to enact coordinated modulation of circuits distributed across the entire brain in order to adaptively regulate behavior. Yet the network computations that emerge from the excitability and connectivity features of the DRN are still poorly understood. To gain insight into these computations, we began by carrying out a detailed electrophysiological characterization of genetically identified mouse 5-HT and somatostatin (SOM) neurons. We next developed a single-neuron modeling framework that combines the realism of Hodgkin-Huxley models with the simplicity and predictive power of generalized integrate-and-fire models. We found that feedforward inhibition of 5-HT neurons by heterogeneous SOM neurons implemented divisive inhibition, while endocannabinoid-mediated modulation of excitatory drive to the DRN increased the gain of 5-HT output. Our most striking finding was that the output of the DRN encodes a mixture of the intensity and temporal derivative of its input, and that the temporal derivative component dominates this mixture precisely when the input is increasing rapidly. This network computation primarily emerged from prominent adaptation mechanisms found in 5-HT neurons, including a previously undescribed dynamic threshold. By applying a bottom-up neural network modeling approach, our results suggest that the DRN is particularly apt to encode input changes over short timescales, reflecting one of the salient emerging computations that dominate its output to regulate behavior.

A User's Guide to Generalized Integrate-and-Fire Models.

The generalized integrate-and-fire (GIF) neuron model accounts for some of the most fundamental behaviours of neurons within a compact and extensible mathematical framework. Here, we introduce the main concepts behind the design of the GIF model in terms that will be familiar to electrophysiologists, and show why its simple design makes this model particularly well suited to mimicking behaviours observed in experimental data. Along the way, we will build an intuition for how specific neuronal behaviours, such as spike-frequency adaptation, or electrical properties, such as ionic currents, can be formulated mathematically and used to extend integrate-and-fire models to overcome their limitations. This chapter will provide readers with no previous exposure to modelling a clear understanding of the strengths and limitations of GIF models, along with the mathematical intuitions required to digest more detailed and technical treatments of this topic.

Expansion microscopy-based imaging of nuclear structures in cultured cells.

Expansion microscopy is a sample preparation technique in which fixed and immunostained cells or tissues are embedded in a cross-linked network of swellable polyelectrolyte hydrogel that expands isotropically upon addition of deionized water. We utilize the X10 method for tenfold expansion of U2OS cells with concurrent DNA staining. A custom 3D-printed gel cutter and chambered slides minimize gel drift, facilitating analysis of the components of nuclear structures at nanoscale resolution by conventional microscopy or Airyscan confocal imaging. For complete information on the generation and use of this protocol, please refer to Do et al. (2020).

Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package.

The proportion of silent (AMPAR-lacking) synapses is thought to be related to the plasticity potential of neural networks. We created a maximum-likelihood estimator of silent synapse fraction based on simulations of the underlying experimental methodology. Here, we provide a set of guidelines for running a Python package on compatible experimental synaptic data. Compared with traditional failure-rate approaches, this synthetic likelihood estimator improves the validity and accuracy of the estimates of the silent synapse fraction. For complete details on the use and execution of this protocol, please refer to Lynn et al. (2020).

A Synthetic Likelihood Solution to the Silent Synapse Estimation Problem.

Functional features of synaptic populations are typically inferred from random electrophysiological sampling of small subsets of synapses. Are these samples unbiased? Here, we develop a biophysically constrained statistical framework to address this question and apply it to assess the performance of a widely used method based on a failure-rate analysis to quantify the occurrence of silent (AMPAR-lacking) synapses. We simulate this method in silico and find that it is characterized by strong and systematic biases, poor reliability, and weak statistical power. Key conclusions are validated by whole-cell recordings from hippocampal neurons. To address these shortcomings, we develop a simulator of the experimental protocol and use it to compute a synthetic likelihood. By maximizing the likelihood, we infer silent synapse fraction with no bias, low variance, and superior statistical power over alternatives. Together, this generalizable approach highlights how a simulator of experimental methodologies can substantially improve the estimation of physiological properties.

Parsing Out the Variability of Transmission at Central Synapses Using Optical Quantal Analysis.

Properties of synaptic release dictates the core of information transfer in neural circuits. Despite decades of technical and theoretical advances, distinguishing bona fide information content from the multiple sources of synaptic variability remains a challenging problem. Here, we employed a combination of computational approaches with cellular electrophysiology, two-photon uncaging of MNI-Glutamate and imaging at single synapses. We describe and calibrate the use of the fluorescent glutamate sensor iGluSnFR and found that its kinetic profile is close to that of AMPA receptors, therefore providing several distinct advantages over slower methods relying on NMDA receptor activation (i.e., chemical or genetically encoded calcium indicators). Using an array of statistical methods, we further developed, and validated on surrogate data, an expectation-maximization algorithm that, by biophysically constraining release variability, extracts the quantal parameters n (maximum number of released vesicles) and p (unitary probability of release) from single-synapse iGluSnFR-mediated transients. Together, we present a generalizable mathematical formalism which, when applied to optical recordings, paves the way to an increasingly precise investigation of information transfer at central synapses.

Classes of dendritic information processing.

Dendrites are much more than passive neuronal components. Mounting experimental evidence and decades of computational work have decisively shown that dendrites leverage a host of nonlinear biophysical phenomena and actively participate in sophisticated computations, at the level of the single neuron and at the level of the network. However, a coherent view of their processing power is still lacking and dendrites are largely neglected in neural network models. Here, we describe four classes of dendritic information processing and delineate their implications at the algorithmic level. We propose that beyond the well-known spatiotemporal filtering of their inputs, dendrites are capable of selecting, routing and multiplexing information. By separating dendritic processing from axonal outputs, neuron networks gain a degree of freedom with implications for perception and learning.

Loss of Adult 5-HT1A Autoreceptors Results in a Paradoxical Anxiogenic Response to Antidepressant Treatment.

Selective serotonin (5-HT) reuptake inhibitors (SSRIs) are first-line antidepressants but require several weeks to elicit their actions. Chronic SSRI treatment induces desensitization of 5-HT1A autoreceptors to enhance 5-HT neurotransmission. Mice (both sexes) with gene deletion of 5-HT1A autoreceptors in adult 5-HT neurons (1AcKO) were tested for response to SSRIs. Tamoxifen-induced recombination in adult 1AcKO mice specifically reduced 5-HT1A autoreceptor levels. The 1AcKO mice showed a loss of 5-HT1A autoreceptor-mediated hypothermia and electrophysiological responses, but no changes in anxiety- or depression-like behavior. Subchronic fluoxetine (FLX) treatment induced an unexpected anxiogenic effect in 1AcKO mice in the novelty suppressed feeding and elevated plus maze tests, as did escitalopram in the novelty suppressed feeding test. No effect was seen in wild-type (WT) mice. Subchronic FLX increased 5-HT metabolism in prefrontal cortex, hippocampus, and raphe of 1AcKO but not WT mice, suggesting hyperactivation of 5-HT release. To detect chronic cellular activation, FosB+ cells were quantified. FosB+ cells were reduced in entorhinal cortex and hippocampus (CA2/3) and increased in dorsal raphe 5-HT cells of 1AcKO mice, suggesting increased raphe activation. In WT but not 1AcKO mice, FLX reduced FosB+ cells in the median raphe, hippocampus, entorhinal cortex, and median septum, which receive rich 5-HT projections. Thus, in the absence of 5-HT1A autoreceptors, SSRIs induce a paradoxical anxiogenic response. This may involve imbalance in activation of dorsal and median raphe to regulate septohippocampal or fimbria-fornix pathways. These results suggest that markedly reduced 5-HT1A autoreceptors may provide a marker for aberrant response to SSRI treatment.SIGNIFICANCE STATEMENT Serotonin-selective reuptake inhibitors (SSRIs) are effective in treating anxiety and depression in humans and mouse models. However, in some cases, SSRIs can increase anxiety, but the mechanisms involved are unclear. Here we show that, rather than enhancing SSRI benefits, adulthood knockout (KO) of the 5-HT1A autoreceptor, a critical negative regulator of 5-HT activity, results in an SSRI-induced anxiety effect that appears to involve a hyperactivation of the 5-HT system in certain brain areas. Thus, subjects with very low levels of 5-HT1A autoreceptors, such as during childhood or adolescence, may be at risk for an SSRI-induced anxiety response.

Adult hippocampal neurogenesis occurs in the absence of Presenilin 1 and Presenilin 2.

Mutations in the presenilin genes (PS1 and PS2) are a major cause of familial-Alzheimer's disease (FAD). Presenilins regulate neurogenesis in the developing brain, with loss of PS1 inducing aberrant premature differentiation of neural progenitor cells, and additional loss of PS2 exacerbating this effect. It is unclear, however, whether presenilins are involved in adult neurogenesis, a process that may be impaired in Alzheimer's disease within the hippocampus. To investigate the requirement of presenilins in adult-generated dentate granule neurons, we examined adult neurogenesis in the PS2-/- adult brain and then employ a retroviral approach to ablate PS1 selectively in dividing progenitor cells of the PS2-/- adult brain. Surprisingly, the in vivo ablation of both presenilins resulted in no defects in the survival and differentiation of adult-generated neurons. There was also no change in the morphology or functional properties of the retroviral-labeled presenilin-null cells, as assessed by dendritic morphology and whole-cell electrophysiology analyses. Furthermore, while FACS analysis showed that stem and progenitor cells express presenilins, inactivation of presenilins from these cells, using a NestinCreERT2 inducible genetic approach, demonstrated no changes in the proliferation, survival, or differentiation of adult-generated cells. Therefore, unlike their significant role in neurogenesis during embryonic development, presenilins are not required for cell-intrinsic regulation of adult hippocampal neurogenesis.

Excitable Adult-Generated GABAergic Neurons Acquire Functional Innervation in the Cortex after Stroke.

Ischemic stroke enhances the proliferation of adult-generated precursor cells that ectopically migrate toward the infarct. Studies have correlated precursor cell proliferation and subsequent adult neurogenesis with enhanced stroke recovery, yet it remains unclear whether stroke can generate new neurons capable of functional integration into the injured cortex. Here, using single and bitransgenic reporter mice, we identify spatial and temporal features of a multilineage cellular response to focal ischemia. We reveal that a small population of stroke-induced immature neurons accumulate within the peri-infarct region of the adult sensorimotor cortex, exhibit voltage-dependent conductances, fire action potentials, express GABAergic markers, and receive sparse GABAergic synaptic inputs. Collectively, these findings reveal that GABAergic neurons arising from the lateral ventricle have the capacity to integrate into the stroke-injured cortex, although their limited number and exiguous synaptic integration may limit their ability to participate in stroke recovery.

The Eloquent Silent Synapse.

The ability of central synapses to undergo long-term potentiation (LTP) still captures the imagination of scientists and has become one of the most fascinating and deeply studied questions in modern neuroscience. By the mid-1990s, however, the field was deeply ensnarled in trying to answer a passionately dichotomous question: is LTP expressed by a pre- or a postsynaptic mechanism? Experimental results that could only be seen by many as being incontrovertibly contradictory presented a perplexing conundrum. However, two papers published in 1995 fundamentally redefined critical assumptions and provided a cunningly simple and elegant solution to an otherwise inextricable impasse.

PGE2 EP1 receptor inhibits vasopressin-dependent water reabsorption and sodium transport in mouse collecting duct.

PGE2 regulates glomerular hemodynamics, renin secretion, and tubular transport. This study examined the contribution of PGE2 EP1 receptors to sodium and water homeostasis. Male EP1-/- mice were bred with hypertensive TTRhRen mice (Htn) to evaluate blood pressure and kidney function at 8 weeks of age in four groups: wildtype (WT), EP1-/-, Htn, HtnEP1-/-. Blood pressure and water balance were unaffected by EP1 deletion. COX1 and mPGE2 synthase were increased and COX2 was decreased in mice lacking EP1, with increases in EP3 and reductions in EP2 and EP4 mRNA throughout the nephron. Microdissected proximal tubule sglt1, NHE3, and AQP1 were increased in HtnEP1-/-, but sglt2 was increased in EP1-/- mice. Thick ascending limb NKCC2 was reduced in the cortex but increased in the medulla. Inner medullary collecting duct (IMCD) AQP1 and ENaC were increased, but AVP V2 receptors and urea transporter-1 were reduced in all mice compared to WT. In WT and Htn mice, PGE2 inhibited AVP-water transport and increased calcium in the IMCD, and inhibited sodium transport in cortical collecting ducts, but not in EP1-/- or HtnEP1-/- mice. Amiloride (ENaC) and hydrochlorothiazide (pendrin inhibitor) equally attenuated the effect of PGE2 on sodium transport. Taken together, the data suggest that EP1 regulates renal aquaporins and sodium transporters, attenuates AVP-water transport and inhibits sodium transport in the mouse collecting duct, which is mediated by both ENaC and pendrin-dependent pathways.

Metaplasticity at CA1 Synapses by Homeostatic Control of Presynaptic Release Dynamics.

Hebbian and homeostatic forms of plasticity operate on different timescales to regulate synaptic strength. The degree of mechanistic overlap between these processes and their mutual influence are still incompletely understood. Here, we report that homeostatic synaptic strengthening induced by prolonged network inactivity compromised the ability of CA1 synapses to exhibit LTP. This effect could not be accounted for by an obvious deficit in the postsynaptic capacity for LTP expression, since neither the fraction of silent synapses nor the ability to induce LTP by two-photon glutamate uncaging were reduced by the homeostatic process. Rather, optical quantal analysis reveals that homeostatically strengthened synapses display a reduced capacity to maintain glutamate release fidelity during repetitive stimulation, ultimately impeding the induction, and thus expression, of LTP. By regulating the short-term dynamics of glutamate release, the homeostatic process thus influences key aspects of dynamic network function and exhibits features of metaplasticity.

Abrogated Freud-1/Cc2d1a Repression of 5-HT1A Autoreceptors Induces Fluoxetine-Resistant Anxiety/Depression-Like Behavior.

Freud-1/Cc2d1a represses the gene transcription of serotonin-1A (5-HT1A) autoreceptors, which negatively regulate 5-HT tone. To test the role of Freud-1 in vivo, we generated mice with adulthood conditional knock-out of Freud-1 in 5-HT neurons (cF1ko). In cF1ko mice, 5-HT1A autoreceptor protein, binding and hypothermia response were increased, with reduced 5-HT content and neuronal activity in the dorsal raphe. The cF1ko mice displayed increased anxiety- and depression-like behavior that was resistant to chronic antidepressant (fluoxetine) treatment. Using conditional Freud-1/5-HT1A double knock-out (cF1/1A dko) to disrupt both Freud-1 and 5-HT1A genes in 5-HT neurons, no increase in anxiety- or depression-like behavior was seen upon knock-out of Freud-1 on the 5-HT1A autoreceptor-negative background; rather, a reduction in depression-like behavior emerged. These studies implicate transcriptional dysregulation of 5-HT1A autoreceptors by the repressor Freud-1 in anxiety and depression and provide a clinically relevant genetic model of antidepressant resistance. Targeting specific transcription factors, such as Freud-1, to restore transcriptional balance may augment response to antidepressant treatment.SIGNIFICANCE STATEMENT Altered regulation of the 5-HT1A autoreceptor has been implicated in human anxiety, major depression, suicide, and resistance to antidepressants. This study uniquely identifies a single transcription factor, Freud-1, as crucial for 5-HT1A autoreceptor expression in vivo Disruption of Freud-1 in serotonin neurons in mice links upregulation of 5-HT1A autoreceptors to anxiety/depression-like behavior and provides a new model of antidepressant resistance. Treatment strategies to reestablish transcriptional regulation of 5-HT1A autoreceptors could provide a more robust and sustained antidepressant response.

The aPKC-CBP Pathway Regulates Post-stroke Neurovascular Remodeling and Functional Recovery.

Epigenetic modifications have emerged as attractive molecular substrates that integrate extrinsic changes into the determination of cell identity. Since stroke-related brain damage releases micro-environmental cues, we examined the role of a signaling-induced epigenetic pathway, an atypical protein kinase C (aPKC)-mediated phosphorylation of CREB-binding protein (CBP), in post-stroke neurovascular remodeling. Using a knockin mouse strain (CbpS436A) where the aPKC-CBP pathway was defective, we show that disruption of the aPKC-CBP pathway in a murine focal ischemic stroke model increases the reprogramming efficiency of ischemia-activated pericytes (i-pericytes) to neural precursors. As a consequence of enhanced cellular reprogramming, CbpS436A mice show an increased transient population of locally derived neural precursors after stroke, while displaying a reduced number of i-pericytes, impaired vascular remodeling, and perturbed motor recovery during the chronic phase of stroke. Together, this study elucidates the role of the aPKC-CBP pathway in modulating neurovascular remodeling and functional recovery following focal ischemic stroke.