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CA1 pyramidal neurons are a major output of the hippocampus and encode features of experience that constitute episodic memories. Feature-selective firing of these neurons results from the dendritic integration of inputs from multiple brain regions. While it is known that synchronous activation of spatially clustered inputs can contribute to firing through the generation of dendritic spikes, there is no established mechanism for spatiotemporal synaptic clustering. Here we show that single presynaptic axons form multiple, spatially clustered inputs onto the distal, but not proximal, dendrites of CA1 pyramidal neurons. These compound connections exhibit ultrastructural features indicative of strong synapses and occur much more commonly in entorhinal than in thalamic afferents. Computational simulations revealed that compound connections depolarize dendrites in a biophysically efficient manner, owing to their inherent spatiotemporal clustering. Our results suggest that distinct afferent projections use different connectivity motifs that differentially contribute to dendritic integration.

Synaptic inputs on cortical dendrites are organized with remarkable subcellular precision at the micron level. This organization emerges during early postnatal development through patterned spontaneous activity and manifests both locally where nearby synapses are significantly correlated, and globally with distance to the soma. We propose a biophysically motivated synaptic plasticity model to dissect the mechanistic origins of this organization during development and elucidate synaptic clustering of different stimulus features in the adult. Our model captures local clustering of orientation in ferret and receptive field overlap in mouse visual cortex based on the receptive field diameter and the cortical magnification of visual space. Including action potential back-propagation explains branch clustering heterogeneity in the ferret and produces a global retinotopy gradient from soma to dendrite in the mouse. Therefore, by combining activity-dependent synaptic competition and species-specific receptive fields, our framework explains different aspects of synaptic organization regarding stimulus features and spatial scales. Synaptic inputs on neuronal dendrites exhibit remarkable organization at different spatial scales, which emerges during the early postnatal development. Kirchner and Gjorgjieva propose a biophysically motivated computational model to explain the different types of organization in mouse and ferret.

Synapses are organized into nanocolumns that control synaptic transmission efficacy through precise alignment of postsynaptic neurotransmitter receptors and presynaptic release sites. Recent evidence show that Leucine-Rich Repeat Transmembrane protein LRRTM2, highly enriched and confined at synapses, interacts with Neurexins through its C-terminal cap, but the role of this binding interface has not been explored in synapse formation and function. Here, we develop a conditional knock-out mouse model (cKO) to address the molecular mechanisms of LRRTM2 regulation, and its role in synapse organization and function. We show that LRRTM2 cKO specifically impairs excitatory synapse formation and function in mice. Surface expression, synaptic clustering, and membrane dynamics of LRRTM2 are tightly controlled by selective motifs in the C-terminal domain. Conversely, the N-terminal domain controls presynapse nano-organization and postsynapse AMPAR sub-positioning and stabilization through the recently identified Neurexin-binding interface. Thus, we identify LRRTM2 as a central organizer of pre- and post- excitatory synapse nanostructure through interaction with presynaptic Neurexins. Synapse nanocolumns control neural transmission through precise alignment of neurotransmitter receptors and release sites. Here, the authors identify the Neurexin-LRRTM2 complex as a central organiser of excitatory synapse nanostructure and function.

In the study of memory engrams, synaptic memory allocation is a newly emerged theme that focuses on how specific synapses are engaged in the storage of a given memory. Cumulating evidence from imaging and molecular experiments indicates that the recruitment of synapses that participate in the encoding and expression of memory is neither random nor uniform. A hallmark observation is the emergence of groups of synapses that share similar response properties and/or similar input properties and are located within a stretch of a dendritic branch. This grouping of synapses has been termed \"synapse clustering\" and has been shown to emerge in many different memory-related paradigms, as well as in studies. The clustering of synapses may emerge from synapses receiving similar input, or many processes which allow for cross-talk between nearby synapses within a dendritic branch, leading to cooperative plasticity. Clustered synapses can act in concert to maximally exploit the nonlinear integration potential of the dendritic branches in which they reside. Their main contribution is to facilitate the induction of dendritic spikes and dendritic plateau potentials, which provide advanced computational and memory-related capabilities to dendrites and single neurons. This review focuses on recent evidence which investigates the role of synapse clustering in dendritic integration, sensory perception, learning, and memory as well as brain dysfunction. We also discuss recent theoretical work which explores the computational advantages provided by synapse clustering, leading to novel and revised theories of memory. As an eminent phenomenon during memory allocation, synapse clustering both shapes memory engrams and is also shaped by the parallel plasticity mechanisms upon which it relies.

The unique characteristics of neocortical pyramidal neurons are thought to be crucial for many aspects of information processing and learning in the brain. Experimental data suggests that their segregation into two distinct compartments, the basal dendrites close to the soma and the apical dendrites branching out from the thick apical dendritic tuft, plays an essential role in cortical organization. A recent hypothesis states that layer 5 pyramidal cells associate top-down contextual information arriving at their apical tuft with features of the sensory input that predominantly arrives at their basal dendrites. It has however remained unclear whether such context association could be established by synaptic plasticity processes. In this work, we formalize the objective of such context association learning through a mathematical loss function and derive a plasticity rule for apical synapses that optimizes this loss. The resulting plasticity rule utilizes information that is available either locally at the synapse, through branch-local NMDA spikes, or through global Ca 2+ events, both of which have been observed experimentally in layer 5 pyramidal cells. We show in computer simulations that the plasticity rule enables pyramidal cells to associate top-down contextual input patterns with high somatic activity. Furthermore, it enables networks of pyramidal neuron models to perform context-dependent tasks and enables continual learning by allocating new dendritic branches to novel contexts.

Biophysical properties of ligand-gated receptors can be profoundly modified by auxiliary subunits or by the lipid microenvironment of the membrane. Hence, it is sometimes challenging to relate the properties of receptors reconstituted in heterologous expression systems to those of their native counterparts. Here we show that the properties of Caenorhabditis elegans levamisole-sensitive acetylcholine receptors (L-AChRs), the ionotropic acetylcholine receptors targeted by the cholinergic anthelmintic levamisole at neuromuscular junctions, can be profoundly modified by their clustering machinery. We uncovered that L-AChRs exhibit a strong outward rectification in vivo, which was not previously described in heterologous systems. This unusual feature for an ionotropic AChR is abolished by disrupting the interaction of the receptors with the extracellular complex required for their synaptic clustering. When recorded at 260 mV, levamisole-induced currents are similar in the wild type and in L-AChR-clustering– defective mutants, while they are halved in these mutants at more depolarized physiological membrane potentials. Consequently, levamisole causes a strong muscle depolarization in the wild type, which leads to complete inactivation of the voltage-gated calcium channels and to an irreversible flaccid paralysis. In mutants defective for L-AChR clustering, the levamisole-induced depolarization is weaker, allowing voltage-gated calcium channels to remain partially active, which eventually leads to adaptation and survival of the worms. This explains why historical screens for C. elegans mutants resistant to levamisole identified the components of the L-AChR clustering machinery, in addition to proteins required for receptor biosynthesis or efficacy. This work further emphasizes the importance of pursuing ligand-gated channel characterization in their native environment.

Synaptic inputs to cortical neurons are highly structured in adult sensory systems, such that neighboring synapses along dendrites are activated by similar stimuli. This organization of synaptic inputs, called synaptic clustering, is required for high-fidelity signal processing, and clustered synapses can already be observed before eye opening. However, how clustered inputs emerge during development is unknown. Here, we employed concurrent in vivo whole-cell patch-clamp and dendritic calcium imaging to map spontaneous synaptic inputs to dendrites of layer 2/3 neurons in the mouse primary visual cortex during the second postnatal week until eye opening. We found that the number of functional synapses and the frequency of transmission events increase several fold during this developmental period. At the beginning of the second postnatal week, synapses assemble specifically in confined dendritic segments, whereas other segments are devoid of synapses. By the end of the second postnatal week, just before eye opening, dendrites are almost entirely covered by domains of co-active synapses. Finally, co-activity with their neighbor synapses correlates with synaptic stabilization and potentiation. Thus, clustered synapses form in distinct functional domains presumably to equip dendrites with computational modules for high-capacity sensory processing when the eyes open.

Treatment-resistant depression (TRD) is a severe form of major depressive disorder (MDD) with substantial public health impact and poor treatment outcome. Treatment outcome in MDD is significantly heritable, but genome-wide association studies have failed to identify replicable common marker alleles, suggesting a potential role for uncommon variants. Here we investigated the hypothesis that uncommon, putatively functional genetic variants are associated with TRD. Whole-exome sequencing data was obtained from 182 TRD cases and 2021 psychiatrically healthy controls. After quality control, the remaining 149 TRD cases and 1976 controls were analyzed with tests designed to detect excess burdens of uncommon variants. At the gene level, 5 genes, ZNF248 , PRKRA , PYHIN1 , SLC7A8 , and STK19 each carried exome-wide significant excess burdens of variants in TRD cases (q < 0.05). Analysis of 41 pre-selected gene sets suggested an excess of uncommon, functional variants among genes involved in lithium response. Among the genes identified in previous TRD studies, ZDHHC3 was also significant in this sample after multiple test correction. ZNF248 and STK19 are involved in transcriptional regulation, PHYIN1 and PRKRA are involved in immune response, SLC7A8 is associated with thyroid hormone transporter activity, and ZDHHC3 regulates synaptic clustering of GABA and glutamate receptors. These results implicate uncommon, functional alleles in TRD and suggest promising novel targets for future research.

Sleep-related hypermotor epilepsy (SHE) is a focal epilepsy syndrome. The underlying pathophysiology is presumed to be closely related with disruption of GABAergic neurotransmission, which is mainly medicated by γ-aminobutyric acid type A receptor (GABAAR). Thus, it is reasonable to assume that rare GABAAR variants might contribute to the pathogenesis of SHE. To test this hypothesis, we performed next-generation sequencing in 58 SHE patients and analyzed the functional effects of the identified variants in both neuronal and non-neuronal cells using a combination of electrophysiology recordings, western blot, flow cytometry, and confocal microscopy. In our study, we detected three rare variants (NM_198904.2: c.269C > T, p.T90M; NM_198904.2: c.950C > A, p.T317N and NM_198903.2: c.649C > T, p.Q217X) in GABRG2 (MIM:137,164, encoding GABAAR γ2 subunit) in three unrelated patients. Two of the three rare variants were transmitted unaffected maternally (T90M) or unaffected paternally (Q217X), whereas the T317N variant arose de novo. The mother of proband carrying the T90M variant was unaffected and being mosaicism for this variant. Functional analysis showed that T90M and T317N variants decreased GABA-evoked current amplitudes by diverse mechanisms including impaired surface expression, endoplasmic reticulum retention, and channel gating defects. And Q217X variant reduced synaptic clustering and distribution of GABAAR. While a causal role of these variants cannot be established directly from these results, the functional assessment together with the genetic sequencing suggests that these rare GABRG2 variants may constitute genetic risk factors for SHE. Our study further expands the GABRG2 phenotypic spectrum and supports the view that GABAergic neurotransmission participates in the epileptogenesis of SHE.

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