Cellular and circuit remodeling of the primate foveal midget pathway after acute photoreceptor loss.

The retinal fovea in human and nonhuman primates is essential for high acuity and color vision. Within the fovea lies specialized circuitry in which signals from a single cone photoreceptor are largely conveyed to one ON and one OFF type midget bipolar cell (MBC), which in turn connect to a single ON or OFF midget ganglion cell (MGC), respectively. Restoring foveal vision requires not only photoreceptor replacement but also appropriate reconnection with surviving ON and OFF MBCs and MGCs. However, our current understanding of the effects of cone loss on the remaining foveal midget pathway is limited. We thus used serial block-face electron microscopy to determine the degree of plasticity and potential remodeling of this pathway in adult Macaca fascicularis several months after acute photoreceptor loss upon photocoagulation. We reconstructed MBC structure and connectivity within and adjacent to the region of cone loss. We found that MBC dendrites within the scotoma retracted and failed to reach surviving cones to form new connections. However, both surviving cones and ON and OFF MBC dendrites at the scotoma border exhibited remodeling, suggesting that these neurons can demonstrate plasticity and rewiring at maturity. At six months postlesion, disconnected OFF MBCs clearly lost output ribbon synapses with their postsynaptic partners, whereas the majority of ON MBCs maintained their axonal ribbon numbers, suggesting differential timing or extent in ON and OFF midget circuit remodeling after cone loss. Our findings raise rewiring considerations for cell replacement approaches in the restoration of foveal vision.

Neuroscience: The dynamic development of dendrites.

Protocadherins are cell-surface proteins that endow developing neurons with the ability to distinguish self-contacts from non-self. This recognition is critical for dendrite patterning and neuronal function. New research demonstrates the cellular basis for dendrite self-avoidance following protocadherin-mediated self-recognition.

Sub-threshold neuronal activity and the dynamical regime of cerebral cortex.

Cortical neurons exhibit temporally irregular spiking patterns and heterogeneous firing rates. These features arise in model circuits operating in a 'fluctuation-driven regime', in which fluctuations in membrane potentials emerge from the network dynamics. However, it is still debated whether the cortex operates in such a regime. We evaluated the fluctuation-driven hypothesis by analyzing spiking and sub-threshold membrane potentials of neurons in the frontal cortex of mice performing a decision-making task. We showed that while standard fluctuation-driven models successfully account for spiking statistics, they fall short in capturing the heterogeneity in sub-threshold activity. This limitation is an inevitable outcome of bombarding single-compartment neurons with a large number of pre-synaptic inputs, thereby clamping the voltage of all neurons to more or less the same average voltage. To address this, we effectively incorporated dendritic morphology into the standard models. Inclusion of dendritic morphology in the neuronal models increased neuronal selectivity and reduced error trials, suggesting a functional role for dendrites during decision-making. Our work suggests that, during decision-making, cortical neurons in high-order cortical areas operate in a fluctuation-driven regime.

Gamma-protocadherins regulate dendrite self-recognition and dynamics to drive self-avoidance.

Neurons form cell-type-specific morphologies that are shaped by cell-surface molecules and their cellular events governing dendrite growth. One growth rule is dendrite self-avoidance, whereby dendrites distribute uniformly within a neuron's territory by avoiding sibling branches. In mammalian neurons, dendrite self-avoidance is regulated by a large family of cell-recognition molecules called the clustered protocadherins (cPcdhs). Genetic and molecular studies suggest that the cPcdhs mediate homophilic recognition and repulsion between self-dendrites. However, this model has not been tested through direct investigation of self-avoidance during development. Here, we performed live imaging and four-dimensional (4D) quantifications of dendrite morphogenesis to define the dynamics and cPcdh-dependent mechanisms of self-avoidance. We focused on the mouse retinal starburst amacrine cell (SAC), which requires the gamma-Pcdhs (Pcdhgs) and self/non-self-recognition to establish a stereotypic radial morphology while permitting dendritic interactions with neighboring SACs. Through morphogenesis, SACs extend dendritic protrusions that iteratively fill the growing arbor and contact and retract from nearby self-dendrites. Compared to non-self-contacting protrusions, self-contacting events have longer lifetimes, and a subset persists as loops. In the absence of the Pcdhgs, non-self-contacting dynamics are unaffected but self-contacting retractions are significantly diminished. Self-contacting bridges accumulate, leading to the bundling of dendritic processes and disruption to the arbor shape. By tracking dendrite self-avoidance in real time, our findings establish that the γ-Pcdhs mediate self-recognition and retraction between contacting sibling dendrites. Our results also illustrate how self-avoidance shapes stochastic and space-filling dendritic outgrowth for robust pattern formation in mammalian neurons.

Cl--dependent amplification of excitatory synaptic potentials at distal dendrites revealed by voltage imaging.

The processing of synaptic signals in somatodendritic compartments determines neuronal computation. Although the amplification of excitatory signals by local voltage-dependent cation channels has been extensively studied, their spatiotemporal dynamics in elaborate dendritic branches remain obscure owing to technical limitations. Using fluorescent voltage imaging throughout dendritic arborizations in hippocampal pyramidal neurons, we demonstrate a unique chloride ion (Cl-)-dependent remote computation mechanism in the distal branches. Excitatory postsynaptic potentials triggered by local laser photolysis of caged glutamate spread along dendrites, with gradual amplification toward the distal end while attenuation toward the soma. Tour de force subcellular patch-clamp recordings from thin branches complemented by biophysical model simulations revealed that the asymmetric augmentation of excitation relies on tetrodotoxin-resistant sodium ion (Na+) channels and Cl- conductance accompanied by a more hyperpolarized dendritic resting potential. Together, this study reveals the cooperative voltage-dependent actions of cation and anion conductance for dendritic supralinear computation, which can locally decode the spatiotemporal context of synaptic inputs.

In Vitro and In Vivo Analysis of Neuronal Polarity.

Neuronal development is characterized by the unidirectional flow of signal from the axon to the dendrites via synapses. Neuronal polarization is a critical step during development that allows the specification of the different neuronal processes as a single axon and multiple dendrites both structurally and functionally, allowing the unidirectional flow of information. Along with extrinsic and intrinsic signaling, a whole network of molecular complexes involved in positive and negative feedback loops play a major role in this critical distinction of neuronal processes. As a result, neuronal morphology is drastically altered during establishment of polarity. In this chapter, we discuss how we can analyze the morphological alterations of neurons in vitro in culture to assess the development and polarity status of the neuron. We also discuss how these studies can be conducted in vivo, where polarity studies pose a greater challenge with promising results for addressing multiple pathological conditions. Our experimental model is limited to rodent hippocampal/cortical neurons in culture and cortical neurons in brain tissues, which are well-characterized model systems for understanding neuronal polarization.

Visualizing and Measuring Dendrite Arborization in Drosophila Somatosensory Neurons.

Dendrites of neurons receive synaptic or sensory inputs and are important sites of neuronal computation. The morphological features of dendrites not only are hallmarks of the neuronal type but also largely determine a neuron's function. Thus, dendrite morphogenesis has been a subject of intensive study in neuroscience. Quantification of dendritic morphology, which is required for accurate assessment of phenotypes, can often be a challenging task, especially for complex neurons. Because manual tracing of dendritic branches is labor-intensive and time-consuming, automated or semiautomated methods are required for efficient analysis of a large number of samples. A popular in vivo model system for studying the mechanisms of dendrite morphogenesis is dendritic arborization (da) neurons in the Drosophila larval peripheral nervous system. In this chapter, we introduce methods for visualizing and measuring the dendritic arbors of these neurons. We begin with an introduction of da neurons and an overview of the methods that have been used for measuring da neuron dendrites. We then discuss the techniques and detailed steps of neuron visualization and image acquisition. Finally, we provide example steps for dendrite tracing and measurement.

Demonstration that sublinear dendrites enable linearly non-separable computations.

Theory predicts that nonlinear summation of synaptic potentials within dendrites allows neurons to perform linearly non-separable computations (LNSCs). Using Boolean analysis approaches, we predicted that both supralinear and sublinear synaptic summation could allow single neurons to implement a type of LNSC, the feature binding problem (FBP), which does not require inhibition contrary to the exclusive-or function (XOR). Notably, sublinear dendritic operations enable LNSCs when scattered synaptic activation generates increased somatic spike output. However, experimental demonstrations of scatter-sensitive neuronal computations have not yet been described. Using glutamate uncaging onto cerebellar molecular layer interneurons, we show that scattered synaptic-like activation of dendrites evoked larger compound EPSPs than clustered synaptic activation, generating a higher output spiking probability. Moreover, we also demonstrate that single interneurons can indeed implement the FBP. Using a biophysical model to explore the conditions in which a neuron might be expected to implement the FBP, we establish that sublinear summation is necessary but not sufficient. Other parameters such as the relative sublinearity, the EPSP size, depolarization amplitude relative to action potential threshold, and voltage fluctuations all influence whether the FBP can be performed. Since sublinear synaptic summation is a property of passive dendrites, we expect that many different neuron types can implement LNSCs.

A dendritic mechanism for balancing synaptic flexibility and stability.

Biological and artificial neural networks learn by modifying synaptic weights, but it is unclear how these systems retain previous knowledge and also acquire new information. Here, we show that cortical pyramidal neurons can solve this plasticity-versus-stability dilemma by differentially regulating synaptic plasticity at distinct dendritic compartments. Oblique dendrites of adult mouse layer 5 cortical pyramidal neurons selectively receive monosynaptic thalamic input, integrate linearly, and lack burst-timing synaptic potentiation. In contrast, basal dendrites, which do not receive thalamic input, exhibit conventional NMDA receptor (NMDAR)-mediated supralinear integration and synaptic potentiation. Congruently, spiny synapses on oblique branches show decreased structural plasticity in vivo. The selective decline in NMDAR activity and expression at synapses on oblique dendrites is controlled by a critical period of visual experience. Our results demonstrate a biological mechanism for how single neurons can safeguard a set of inputs from ongoing plasticity by altering synaptic properties at distinct dendritic domains.

Fast-spiking interneuron detonation drives high-fidelity inhibition in the olfactory bulb.

Inhibitory circuits in the mammalian olfactory bulb (OB) dynamically reformat olfactory information as it propagates from peripheral receptors to downstream cortex. To gain mechanistic insight into how specific OB interneuron types support this sensory processing, we examine unitary synaptic interactions between excitatory mitral and tufted cells (MTCs), the OB projection neurons, and a conserved population of anaxonic external plexiform layer interneurons (EPL-INs) using pair and quartet whole-cell recordings in acute mouse brain slices. Physiological, morphological, neurochemical, and synaptic analyses divide EPL-INs into distinct subtypes and reveal that parvalbumin-expressing fast-spiking EPL-INs (FSIs) perisomatically innervate MTCs with release-competent dendrites and synaptically detonate to mediate fast, short-latency recurrent and lateral inhibition. Sparse MTC synchronization supralinearly increases this high-fidelity inhibition, while sensory afferent activation combined with single-cell silencing reveals that individual FSIs account for a substantial fraction of total network-driven MTC lateral inhibition. OB output is thus powerfully shaped by detonation-driven high-fidelity perisomatic inhibition.

Competitive processes shape multi-synapse plasticity along dendritic segments.

Neurons receive thousands of inputs onto their dendritic arbour, where individual synapses undergo activity-dependent plasticity. Long-lasting changes in postsynaptic strengths correlate with changes in spine head volume. The magnitude and direction of such structural plasticity - potentiation (sLTP) and depression (sLTD) - depend upon the number and spatial distribution of stimulated synapses. However, how neurons allocate resources to implement synaptic strength changes across space and time amongst neighbouring synapses remains unclear. Here we combined experimental and modelling approaches to explore the elementary processes underlying multi-spine plasticity. We used glutamate uncaging to induce sLTP at varying number of synapses sharing the same dendritic branch, and we built a model incorporating a dual role Ca2+-dependent component that induces spine growth or shrinkage. Our results suggest that competition among spines for molecular resources is a key driver of multi-spine plasticity and that spatial distance between simultaneously stimulated spines impacts the resulting spine dynamics.

A novel multivariate time series forecasting dendritic neuron model for COVID-19 pandemic transmission tendency.

A novel coronavirus discovered in late 2019 (COVID-19) quickly spread into a global epidemic and, thankfully, was brought under control by 2022. Because of the virus's unknown mutations and the vaccine's waning potency, forecasting is still essential for resurgence prevention and medical resource management. Computational efficiency and long-term accuracy are two bottlenecks for national-level forecasting. This study develops a novel multivariate time series forecasting model, the densely connected highly flexible dendritic neuron model (DFDNM) to predict daily and weekly positive COVID-19 cases. DFDNM's high flexibility mechanism improves its capacity to deal with nonlinear challenges. The dense introduction of shortcut connections alleviates the vanishing and exploding gradient problems, encourages feature reuse, and improves feature extraction. To deal with the rapidly growing parameters, an improved variation of the adaptive moment estimation (AdamW) algorithm is employed as the learning algorithm for the DFDNM because of its strong optimization ability. The experimental results and statistical analysis conducted across three Japanese prefectures confirm the efficacy and feasibility of the DFDNM while outperforming various state-of-the-art machine learning models. To the best of our knowledge, the proposed DFDNM is the first to restructure the dendritic neuron model's neural architecture, demonstrating promising use in multivariate time series prediction. Because of its optimal performance, the DFDNM may serve as an important reference for national and regional government decision-makers aiming to optimize pandemic prevention and medical resource management. We also verify that DFDMN is efficiently applicable not only to COVID-19 transmission prediction, but also to more general multivariate prediction tasks. It leads us to believe that it might be applied as a promising prediction model in other fields.

Clustered synapses develop in distinct dendritic domains in visual cortex before eye opening.

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.

Functional architecture of intracellular oscillations in hippocampal dendrites.

Fast electrical signaling in dendrites is central to neural computations that support adaptive behaviors. Conventional techniques lack temporal and spatial resolution and the ability to track underlying membrane potential dynamics present across the complex three-dimensional dendritic arbor in vivo. Here, we perform fast two-photon imaging of dendritic and somatic membrane potential dynamics in single pyramidal cells in the CA1 region of the mouse hippocampus during awake behavior. We study the dynamics of subthreshold membrane potential and suprathreshold dendritic events throughout the dendritic arbor in vivo by combining voltage imaging with simultaneous local field potential recording, post hoc morphological reconstruction, and a spatial navigation task. We systematically quantify the modulation of local event rates by locomotion in distinct dendritic regions, report an advancing gradient of dendritic theta phase along the basal-tuft axis, and describe a predominant hyperpolarization of the dendritic arbor during sharp-wave ripples. Finally, we find that spatial tuning of dendritic representations dynamically reorganizes following place field formation. Our data reveal how the organization of electrical signaling in dendrites maps onto the anatomy of the dendritic tree across behavior, oscillatory network, and functional cell states.

Dorsoventral Heterogeneity of Synaptic Connectivity in Hippocampal CA3 Pyramidal Neurons.

The hippocampal CA3 region plays an important role in learning and memory. CA3 pyramidal neurons (PNs) receive two prominent excitatory inputs-mossy fibers (MFs) from dentate gyrus (DG) and recurrent collaterals (RCs) from CA3 PNs-that play opposing roles in pattern separation and pattern completion, respectively. Although the dorsoventral heterogeneity of the hippocampal anatomy, physiology, and behavior has been well established, nothing is known about the dorsoventral heterogeneity of synaptic connectivity in CA3 PNs. In this study, we performed Timm's sulfide silver staining, dendritic and spine morphological analyses, and ex vivo electrophysiology in mice of both sexes to investigate the heterogeneity of MF and RC pathways along the CA3 dorsoventral axis. Our morphological analyses demonstrate that ventral CA3 (vCA3) PNs possess greater dendritic lengths and more complex dendritic arborization, compared with dorsal CA3 (dCA3) PNs. Moreover, using ChannelRhodopsin2 (ChR2)-assisted patch-clamp recording, we found that the ratio of the RC-to-MF excitatory drive onto CA3 PNs increases substantially from dCA3 to vCA3, with vCA3 PNs receiving significantly weaker MFs, but stronger RCs, excitation than dCA3 PNs. Given the distinct roles of MF versus RC inputs in pattern separation versus completion, our findings of the significant dorsoventral variations of MF and RC excitation in CA3 PNs may have important functional implications for the contribution of CA3 circuit to the dorsoventral difference in hippocampal function.

Activity-dependent dendrite patterning in the postnatal barrel cortex.

For neural circuit construction in the brain, coarse neuronal connections are assembled prenatally following genetic programs, being reorganized postnatally by activity-dependent mechanisms to implement area-specific computational functions. Activity-dependent dendrite patterning is a critical component of neural circuit reorganization, whereby individual neurons rearrange and optimize their presynaptic partners. In the rodent primary somatosensory cortex (barrel cortex), driven by thalamocortical inputs, layer 4 (L4) excitatory neurons extensively remodel their basal dendrites at neonatal stages to ensure specific responses of barrels to the corresponding individual whiskers. This feature of barrel cortex L4 neurons makes them an excellent model, significantly contributing to unveiling the activity-dependent nature of dendrite patterning and circuit reorganization. In this review, we summarize recent advances in our understanding of the activity-dependent mechanisms underlying dendrite patterning. Our focus lays on the mechanisms revealed by in vivo time-lapse imaging, and the role of activity-dependent Golgi apparatus polarity regulation in dendrite patterning. We also discuss the type of neuronal activity that could contribute to dendrite patterning and hence connectivity.

Conductance-based dendrites perform Bayes-optimal cue integration.

A fundamental function of cortical circuits is the integration of information from different sources to form a reliable basis for behavior. While animals behave as if they optimally integrate information according to Bayesian probability theory, the implementation of the required computations in the biological substrate remains unclear. We propose a novel, Bayesian view on the dynamics of conductance-based neurons and synapses which suggests that they are naturally equipped to optimally perform information integration. In our approach apical dendrites represent prior expectations over somatic potentials, while basal dendrites represent likelihoods of somatic potentials. These are parametrized by local quantities, the effective reversal potentials and membrane conductances. We formally demonstrate that under these assumptions the somatic compartment naturally computes the corresponding posterior. We derive a gradient-based plasticity rule, allowing neurons to learn desired target distributions and weight synaptic inputs by their relative reliabilities. Our theory explains various experimental findings on the system and single-cell level related to multi-sensory integration, which we illustrate with simulations. Furthermore, we make experimentally testable predictions on Bayesian dendritic integration and synaptic plasticity.

Differences in the consolidation by spontaneous and evoked ripples in the presence of active dendrites.

Ripples are a typical form of neural activity in hippocampal neural networks associated with the replay of episodic memories during sleep as well as sleep-related plasticity and memory consolidation. The emergence of ripples has been observed both dependent as well as independent of input from other brain areas and often coincides with dendritic spikes. Yet, it is unclear how input-evoked and spontaneous ripples as well as dendritic excitability affect plasticity and consolidation. Here, we use mathematical modeling to compare these cases. We find that consolidation as well as the emergence of spontaneous ripples depends on a reliable propagation of activity in feed-forward structures which constitute memory representations. This propagation is facilitated by excitable dendrites, which entail that a few strong synapses are sufficient to trigger neuronal firing. In this situation, stimulation-evoked ripples lead to the potentiation of weak synapses within the feed-forward structure and, thus, to a consolidation of a more general sequence memory. However, spontaneous ripples that occur without stimulation, only consolidate a sparse backbone of the existing strong feed-forward structure. Based on this, we test a recently hypothesized scenario in which the excitability of dendrites is transiently enhanced after learning, and show that such a transient increase can strengthen, restructure and consolidate even weak hippocampal memories, which would be forgotten otherwise. Hence, a transient increase in dendritic excitability would indeed provide a mechanism for stabilizing memories.

Calcium plays an essential role in early-stage dendrite injury detection and regeneration.

Dendrites are injured in a variety of clinical conditions such as traumatic brain and spinal cord injuries and stroke. How neurons detect injury directly to their dendrites to initiate a pro-regenerative response has not yet been thoroughly investigated. Calcium plays a critical role in the early stages of axonal injury detection and is also indispensable for regeneration of the severed axon. Here, we report cell and neurite type-specific differences in laser injury-induced elevations of intracellular calcium levels. Using a human KCNJ2 transgene, we demonstrate that hyperpolarizing neurons only at the time of injury dampens dendrite regeneration, suggesting that inhibition of injury-induced membrane depolarization (and thus early calcium influx) plays a role in detecting and responding to dendrite injury. In exploring potential downstream calcium-regulated effectors, we identify L-type voltage-gated calcium channels, inositol triphosphate signaling, and protein kinase D activity as drivers of dendrite regeneration. In conclusion, we demonstrate that dendrite injury-induced calcium elevations play a key role in the regenerative response of dendrites and begin to delineate the molecular mechanisms governing dendrite repair.