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At least 397 records · Page 22Linked to original sources

bioRxiv2024

Changes in the cellular makeup of motor patterning circuits drive courtship song evolution in Drosophila

How evolutionary changes in genes and neurons encode species variation in complex motor behaviors are largely unknown. Here, we develop genetic tools that permit a neural circuit comparison between the model species Drosophila melanogaster and the closely-related species D. yakuba, who has undergone a lineage-specific loss of sine song, one of the two major types of male courtship song in Drosophila. Neuroanatomical comparison of song patterning neurons called TN1 across the phylogeny demonstrates a link between the loss of sine song and a reduction both in the number of TN1 neurons and the neurites serving the sine circuit connectivity. Optogenetic activation confirms that TN1 neurons in D. yakuba have lost the ability to drive sine song, while maintaining the ability to drive the singing wing posture. Single-cell transcriptomic comparison shows that D. yakuba specifically lacks a cell type corresponding to TN1A neurons, the TN1 subtype that is essential for sine song. Genetic and developmental manipulation reveals a functional divergence of the sex determination gene doublesex in D. yakuba to reduce TN1 number by promoting apoptosis. Our work illustrates the contribution of motor patterning circuits and cell type changes in behavioral evolution, and uncovers the evolutionary lability of sex determination genes to reconfigure the cellular makeup of neural circuits.

evolutionary biology↗

bioRxiv2020

Predictive visual motion extrapolation emerges spontaneously and without supervision from a layered neural network with spike-timing-dependent plasticity

The fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localisation of a moving object. One way this problem might be solved is extrapolation: using an objects past trajectory to predict its location in the present moment. Here, we investigate how a simulated in silico layered neural network might implement such extrapolation mechanisms, and how the necessary neural circuits might develop. We allowed an unsupervised hierarchical network of velocity-tuned neurons to learn its connectivity through spike-timing dependent plasticity. We show that the temporal contingencies between the different neural populations that are activated by an object as it moves causes the receptive fields of higher-level neurons to shift in the direction opposite to their preferred direction of motion. The result is that neural populations spontaneously start to represent moving objects as being further along their trajectory than where they were physically detected. Due to the inherent delays of neural transmission, this effectively compensates for (part of) those delays by bringing the represented position of a moving object closer to its instantaneous position in the world. Finally, we show that this model accurately predicts the pattern of perceptual mislocalisation that arises when human observers are required to localise a moving object relative to a flashed static object (the flash-lag effect). Significance StatementOur ability to track and respond to rapidly changing visual stimuli, such as a fast moving tennis ball, indicates that the brain is capable of extrapolating the trajectory of a moving object in order to predict its current position, despite the delays that result from neural transmission. Here we show how the neural circuits underlying this ability can be learned through spike-timing dependent synaptic plasticity, and that these circuits emerge spontaneously and without supervision. This demonstrates how the neural transmission delays can, in part, be compensated to implement the extrapolation mechanisms required to predict where a moving object is at the present moment.

neuroscience↗

bioRxiv2025

Reconstructing the Ischemic Cerebrum by Transplanted Human Neurons

Neural transplantation holds the potential to repair damaged neural circuits in neurological diseases. However, it remains unknown how the grafted neurons project axons to and make functional connections with the appropriate targets to repair the damaged circuit at the adult stage. Here we report that human cortical progenitors, transplanted into the ischemic mouse motor cortex, matured and integrated into cortical and subcortical neural circuits including the corticospinal tract. Neuronal tracing combined with single-nuclei RNA sequencing revealed the close relationship between the transcription profiles of a cortical neuronal subtype, especially those of axon guidance and synapse assembly, with the specific target projection and synapse organization. Machine learning-based regression further identified the transcriptional codes for the targeted projection and circuit integration to reconstruct the damaged circuits. Our finding opens a promising strategy for treating neurological diseases through promoting regeneration and neural transplantation. HighlightsHuman ESC-derived cortical neurons reconstitute the ischemic motor cortex Functionally repaired corticospinal tract restores animal behaviors Transplanted cortical neurons exhibit subtype-specific projection and integration Unique transcriptional coding for axonal navigation in the mature CNS

neuroscience↗

bioRxiv2022

Neural circuit-wide analysis of gene expression during deafening-induced destabilization ofbirdsong

Sensory feedback is required for stable execution of learned motor skills, and its loss can severely disrupt motor performance. The neural mechanisms that mediate sensorimotor stability have been extensively studied at systems and physiological levels, yet relatively little is known about how disruptions to sensory input alter the molecular properties of associated motor systems. Songbird courtship song, a model for skilled behavior, is a learned and highly structured vocalization that is destabilized following deafening. Here, we sought to determine how the loss of auditory feedback modifies gene expression and its coordination across the birdsong sensorimotor circuit. To facilitate this system-wide analysis of transcriptional responses, we developed a gene expression profiling approach that enables the construction of hundreds of spatially-defined RNA-sequencing libraries. Using this method, we found that deafening preferentially alters gene expression across birdsong neural circuitry relative to surrounding areas, particularly in premotor and striatal regions. Genes with altered expression are associated with synaptic transmission, neuronal spines, and neuromodulation and show a bias toward expression in glutamatergic neurons and Pvalb/Sst-class GABAergic interneurons. We also found that connected song regions exhibit correlations in gene expression that were reduced in deafened birds relative to hearing birds, suggesting that song destabilization alters the inter-region coordination of transcriptional state. Finally, lesioning LMAN, a forebrain afferent of RA required for deafening-induced song plasticity, had the largest effect on groups of genes that were also most affected by deafening. Combined, this integrated transcriptomics analysis demonstrates that the loss of peripheral sensory input drives a distributed gene expression response throughout associated sensorimotor neural circuitry and identifies specific candidate molecular and cellular mechanisms that support stability and plasticity of learned motor skills.

neuroscience↗

bioRxiv2025

Circuit-specific gene editing for precision modulation of neuronal activity with CRISPR-rabies virus

CRISPR gene editing has revolutionized our ability to study and manipulate specific genes, enabling novel insights into gene function and potential therapies for brain disorders. Recent advances in cell-type-specific regulatory elements and viral delivery systems have made precise in vivo gene editing possible. However, neurons with similar molecular profiles can belong to different circuits, complicating efforts to manipulate circuit function and behavior. To address this, we developed CRISPR-rabies virus (CRV), which leverages the trans-synaptic spread of rabies virus to enable gene editing within anatomically defined neural circuits. By pairing CRV with cell type-specific Cas9 expression, we achieved targeted gene modifications in specific circuits. We demonstrate that CRV can modulate sodium and potassium channel expression in parvalbumin interneurons, thereby effectively regulating synaptic transmission of pyramidal neurons in the CA3 region of the hippocampus. Its compatibility with 3'-capture single-cell RNA-seq allows simultaneous circuit perturbation and molecular profiling. In summary, CRV allows precise circuit-level gene modulation, providing a platform for studying gene function in neural circuits and developing novel gene therapies for brain disorders.

neuroscience↗

bioRxiv2020

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Rapid, flexible response to an ever-changing environment is critical for an organisms survival. Recently, multicellular recordings have shown that this rapid, flexible switching between activity patterns is present in neural microcircuits. However, the underlying neural mechanism is not clear. Strikingly, we show in a neural circuit model that mutually inhibitory connections are crucial for rapid and flexible switching between distinct functions without synaptic plasticity. Here, we develop a theoretical framework to explain how inhibitory recurrent circuits give rise to this flexibility and show that mutual inhibition doubles the number of cusp bifurcations in small neural circuits. As a concrete example, we study a commonly observed class of functional motifs we call Coupled Recurrent Inhibitory and Recurrent Excitatory Loops (CRIRELs). These CRIRELs have the advantage of being both multifunctional and controllable, performing a plethora of unique functions. Finally, we demonstrate how mutual inhibition maximizes storage capacity for larger networks.

neuroscience↗

bioRxiv2020

The connectome predicts resting state functional connectivity across the Drosophila brain

Connectomic datasets have emerged as invaluable tools for understanding neural circuits in many systems. What constraints does the connectome place on information processing and routing in a large scale neural circuit? For mesoscale brain networks, the relationship between cell and synaptic level connectivity and brain function is not well understood. Here, we use data from the Drosophila connectome in conjunction with whole-brain in vivo imaging to relate structural and functional connectivity in the central brain. We find that functional connectivity is strongly associated with the strength of both direct and indirect anatomical pathways. We also show that some brain regions, including the mushroom body and central complex, show considerably higher functional connectivity to other brain regions than is predicted based on their direct anatomical connections. We find several key topological similarities between mesoscale brain networks in flies and mammals, revealing conserved principles relating brain structure and function.

neuroscience↗

bioRxiv2023

Acute ampakines increase voiding function and coordination in a rat model of SCI

Neurogenic bladder dysfunction causes urological complications and reduces the quality of life in persons with spinal cord injury (SCI). Glutamatergic signaling via AMPA receptors is fundamentally important to the neural circuits controlling bladder voiding. Ampakines are positive allosteric modulators of AMPA receptors that can enhance the function of glutamatergic neural circuits after SCI. We hypothesized that ampakines can acutely stimulate bladder voiding that has been impaired due to thoracic contusion SCI. Adult female Sprague Dawley rats received a unilateral contusion of the T9 spinal cord (n=10). Bladder function (cystometry) and coordination with the external urethral sphincter (EUS) were assessed five days post-SCI under urethane anesthesia. Data were compared to responses in spinal intact rats (n=8). The "low impact" ampakine CX1739 (5, 10, or 15 mg/kg) or vehicle (HPCD) was administered intravenously. The HPCD vehicle had no discernable impact on voiding. In contrast, following CX1739, the pressure threshold for inducing bladder contraction, voided volume, and the interval between bladder contractions were significantly reduced. These responses occurred in a dose-dependent manner. We conclude that modulating AMPA receptor function using ampakines can rapidly improve bladder voiding capability at sub-acute time points following contusion SCI. These results may provide a new and translatable method for therapeutic targeting of bladder dysfunction acutely after SCI.

neuroscience↗

bioRxiv2025

Brainwide genetic capture for conscious state transitions

Neural circuits underlying unconsciousness remain poorly defined. We test the hypothesis that unconsciousness arises from specific, distributed circuits using general anesthesia in mice as a reproducible model. We identify a cortical-to-subcortical shift in neural activity during isoflurane anesthesia that is organized into nine discrete functional communities mapped at single-cell resolution. The lateral parabrachial nucleus (LPB) emerges as a central hub, exhibiting high interconnectivity, spontaneous firing under anesthesia, and preferential recruitment during reactivation of the brain-wide ensemble confirmed by single unit recordings. Chemogenetic reactivation of the captured brain-wide ensemble induces sedation, slow wave oscillations, hypothermia, and analgesia, which are components of anesthesia-induced unconsciousness. Reactivation of the LPB ensemble alone recapitulates a subset of these effects. Together, we define a global neural substrate for unconsciousness and recapitulate its dissociable autonomic, neurophysiologic, and behavioral effects using brain-wide ensemble manipulations. These results establish a neural circuit framework for anesthesia-induced unconsciousness in the mammalian brain.

neuroscience↗

bioRxiv2025

Coordinated dynamics of excitatory and inhibitory synapse assembly

Diverse synaptic connections self-organize into neural circuits during brain development. A balance between excitatory and inhibitory synaptic function is required for information processing by these neural circuits. Despite the importance of this balance, the interplay between excitatory and inhibitory synaptic assembly during circuit establishment remains unclear due to a lack of means to monitor both processes simultaneously. Here, we develop imaging and analysis methods to visualize and track excitatory and inhibitory synapses. By applying these approaches, we find that despite continual dynamics, excitatory and inhibitory synaptic density remain at steady-state levels during synapse maturation. These results indicate balanced excitatory and inhibitory synapse assembly despite continual synaptic turnover.

neuroscience↗

bioRxiv2016

Predictive representations can link model-based reinforcement learning to model-free mechanisms

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation.\n\nAuthor SummaryAccording to standard models, when confronted with a choice, animals and humans rely on two separate, distinct processes to come to a decision. One process deliberatively evaluates the consequences of each candidate action and is thought to underlie the ability to flexibly come up with novel plans. The other process gradually increases the propensity to perform behaviors that were previously successful and is thought to underlie automatically executed, habitual reflexes. Although computational principles and animal behavior support this dichotomy, at the neural level, there is little evidence supporting a clean segregation. For instance, although dopamine -- famously implicated in drug addiction and Parkinsons disease -- currently only has a well-defined role in the automatic process, evidence suggests that it also plays a role in the deliberative process. In this work, we present a computational framework for resolving this mismatch. We show that the types of behaviors associated with either process could result from a common learning mechanism applied to different strategies for how populations of neurons could represent candidate actions. In addition to demonstrating that this account can produce the full range of flexible behavior observed in the empirical literature, we suggest experiments that could detect the various approaches within this framework.

neuroscience↗

bioRxiv2018

A Diverse Range of Factors Affect the Nature of Neural Representations Underlying Short-Term Memory

Sequential and persistent activity models are two prominent models of short-term memory in neural circuits. In persistent activity models, memories are represented in persistent or nearly persistent activity patterns across a population of neurons, whereas in sequential models, memories are represented dynamically by a sequential pattern of activity across the population. Experimental evidence for both types of model in the brain has been reported previously. However, it has been unclear under what conditions these two qualitatively different types of solutions emerge in neural circuits. Here, we address this question by training recurrent neural networks on several short-term memory tasks under a wide range of circuit and task manipulations. We show that sequential and nearly persistent solutions are both part of a spectrum that emerges naturally in trained networks under different conditions. Fixed delay durations, tasks with higher temporal complexity, strong network coupling, motion-related dynamic inputs and prior training in a different task favor more sequential solutions, whereas variable delay durations, tasks with low temporal complexity, weak network coupling and symmetric Hebbian short-term synaptic plasticity favor more persistent solutions. Our results help clarify some seemingly contradictory experimental results on the existence of sequential vs. persistent activity based memory mechanisms in the brain.

neuroscience↗

bioRxiv2022

Neural Activity in Afferent Projections to the Infralimbic Cortex is Associated with Individual Differences in the Recall of Fear Extinction.

Post-traumatic stress disorder (PTSD) is characterized by an impaired ability to extinguish fear responses to trauma-associated cues. Studies in humans and non-human animals point to differences in the engagement of certain frontal cortical regions as key mediators determining whether or not fear extinction is successful, however the neural circuit interactions that dictate the differential involvement of these regions are not well understood. To better understand how individual differences in extinction recall are reflected in differences in neural circuit activity, we labeled projections to the infralimbic cortex (IL) in rats using a retrograde tracer and compared neural activity within, and outside, of IL-projecting neurons. We analyzed these data in groups separated on the basis of how well rats retained extinction memory. We found that within IL-projecting cells, neurons in the posterior paraventricular thalamus showed heightened activity in rats that showed good extinction recall. Outside of the IL-projecting cells, increased Fos activity was observed in good extinction rats in select regions of the claustrum and ventral hippocampus. Our results indicate that differences in extinction recall are associated with a specific pattern of neural activity both within and outside of projections to the IL.

neuroscience↗

bioRxiv2016

A qualitative model of the rod photoreceptor in natural context

Given the intricacies of the retinal neural circuit, which bears a striking resemblance to that of the brain, it is proposed that retinal function goes beyond mere spatiotemporal prefiltering. We hypothesise that aspects related to motion detection and discrimination, anticipation and adaptation to environmental and contextual conditions, which have traditionally been ascribed to the brain, may be supported by neurons in the retina. Such early computations may be dependent on compensative and adaptive mechanisms that stem from qualities intrinsic to the retinal neural circuit and its interaction with the environment (neural transduction time, connectivity patterns, regularities in the input signal, temporal dynamics and light variations).\n\nWith a view to investigating the contribution of the photoreceptor population to the processing performed by the retina in natural scotopic conditions, we present a continuous model of the rod photoreceptor. Our model permits the reproduction and exploration of a set of qualitative features displayed in vitro, such as excitation-dependent activation level and time-to-membrane current integration. We captured qualitative aspects of key features selected for their presumed importance in early visual function. Further, we subjected our model to extensive parameter sensitivity analyses, aiming to provide a visual representation of their contribution to the observed qualitative behavior.\n\nAuthor SummaryPrimate rod photoreceptor cells constitute the very first processing step in retinal function. This layer influences most of the visual field and presents itself as a tightly packed photosensitive array. Yet, no computational formulation of this neuron is suited for large-scale, time continuous modeling. With a view to studying retinal function in natural contexts, we describe a qualitative, continuous model of the rod. We subject this model to parameter analyses against selected behavioral features. We aim to provide a model that can integrate in further experiments, as well as in large scale network simulation.

Bioinformatics↗

bioRxiv2024

Comparison of the cortical hierarchy between macaque monkeys and mice based on cell-type specific microcircuits

The primate neocortex contains a hierarchy of cortical areas, with feedforward connections running from lower to higher levels, and feedback connections running in the opposite direction. The relative hierarchical position of cortical areas has been well established by retrograde tracing studies that allow to determine whether type of output from different source areas is predominantly feedforward or feedback. This method can not determine whether the cortico-cortical input within target areas is predominantly feedforward or feedback. We here make use of cell-type specific microcircuits to provide an intrinsic measure of the strength of feedforward versus feedback processing within cortical areas in macaque monkeys and mice. This allows a more complete map of the cortical hierarchy of different species, and therefore a more direct cross-species comparison. Parvalbumin-expressing interneurons were used as a marker of feedforward processing and calretinin-expressing interneurons as a marker of feedback processing. We found steep gradients in the distributions of these two interneuron types across macaque monkey cortical areas, indicating a deep cortical hierarchy where early visual areas are dominated by feedforward neural circuits and higher cortical areas become dominated by top-recurrent circuits. In contrast, the gradient in interneuron distribution across mouse cortical areas was limited, indicating a shallow cortical hierarchy and remaining dominated by feedforward neural circuits. This implies that the mouse cortex is comparable to early visual areas in primates, remaining dominated by bottom-up input. While primates are unique in having a deep cortical hierarchy that allows neural processing in higher cortical areas to become predominantly internal. Significance statementThe cortex in primates is known to contain a hierarchy of cortical areas, where bottom-up processing allows sensory information to get more and more compressed towards higher cortical areas, and where top-down processing allows lower areas to remain informed about the internal goals of the animal. However, it has been challenging to directly compare cortical hierarchies between animal species. Here, we developed an intrinsic measure of the strength of bottom-up and top-down processing within cortical areas, based on the density of different types of interneurons within areas. Or results indicate that the mouse cortical hierarchy is shallow and is driven by bottom-up processing, while the primate cortical hierarchy is deep and slowly becomes dominated by internal processing towards the top.

neuroscience↗

bioRxiv2021

Computational mechanisms of osmoregulation: a reinforcement learning model for sodium appetite

Homeostatic control with oral nutrient intake is a vital complex system involving the orderly interactions between the external and internal senses, behavioral control, and reward learning. Sodium appetite is a representative system and has been intensively investigated in animal models of homeostatic systems and oral nutrient intake. However, the system-level mechanisms for regulating sodium intake behavior and homeostatic control remain unclear. In the current study, we attempted to provide a mechanistic understanding of sodium appetite behavior by using a computational model, the homeostatic reinforcement learning model, in which homeostatic behaviors are interpreted as reinforcement learning processes. Through simulation experiments, we confirmed that our homeostatic reinforcement learning model successfully reproduced homeostatic behaviors by regulating sodium appetite. These behaviors include the approach and avoidance behaviors to sodium according to the internal states of individuals. In addition, based on the assumption that the sense of taste is a predictor of changes in the internal state, the homeostatic reinforcement learning model successfully reproduced the previous paradoxical observations of the intragastric infusion test, which cannot be explained by the classical drive reduction theory. Moreover, we extended the homeostatic reinforcement learning model to multi-modal data, and successfully reproduced the behavioral tests in which water and sodium appetite were mediated by each other. Finally, through an experimental simulation of chemical manipulation in a specific neural population in the brain stem, we proposed a testable hypothesis for the function of neural circuits involving sodium appetite behavior. The study results support the idea that osmoregulation via sodium appetitive behavior can be understood as a reinforcement learning process and provide a mechanistic explanation for the underlying neural mechanisms of sodium appetite and homeostatic behavior. Author SummaryThe taste of high-concentration saltwater is rewarding during sodium depletion, while it is aversive in a sodium sufficient state. This "sodium appetite" is a clear manifestation of homeostasis maintenance and proper action selection in animals, reflecting the internal environment. To reveal the computational mechanism of this property, we applied a machine learning model, in which homeostatic stability is a reward and the goal is to maximize the sum of the reward, and simulated animal behavioral experiments. The results suggest that the mechanism of sodium-appetite behavior is based on the machine learning model. Specifically, by replicating the results of neural circuit manipulation, which controls sodium appetite, an algorithm in which the function of a neural population affects sodium appetite behaviors was proposed. Our results provide a fundamental computational model for a mechanism by a function of a neural cell type to regulate animal behavior. More generally, this study can be fundamental to understanding the computational process of decision making reflecting the internal environment.

neuroscience↗

bioRxiv2019

From behavior to circuit modeling of light-seeking navigation in zebrafish larvae

Bridging brain-scale circuit dynamics and organism-scale behavior is a central challenge in neuroscience. It requires the concurrent development of minimal behavioral and neural circuit models that can quantitatively capture basic sensorimotor operations. Here we focus on light-seeking navigation in zebrafish larvae. Using a virtual reality assay, we first characterize how motor and visual stimulation sequences govern the selection of discrete swim-bout events that subserve the fish navigation in the presence of a distant light source. These mechanisms are combined into a comprehensive Markov-chain model of navigation that quantitatively predict the stationary distribution of the fishs body orientation under any given illumination profile. We then map this behavioral description onto a neuronal model of the ARTR, a small neural circuit involved in the orientation-selection of swim bouts. We demonstrate that this visually-biased decision-making circuit can similarly capture the statistics of both spontaneous and contrast-driven navigation.

animal behavior and cognition↗

bioRxiv2026

Protocadherin 9 promotes cell survival of different bipolar subtypes in the developing mouse retina

Neural circuit assembly relies on different neuronal subtypes coming together to form a functional circuit. The question of how the appropriate number of each subtype is integrated into an emerging circuit remains relatively unknown. To answer this question, we used the mouse retina to uncover the molecular mechanisms responsible for neuron subtype integration in a developing circuit. In the mammalian retina, bipolar neurons are a class of interneurons that relay visual information from photoreceptors to ganglion cells. Extensive studies have shown there are 15 distinct bipolar subtypes: 6 types of OFF cone bipolars, 8 types of ON cone bipolars, and 1 type of rod bipolar. During retinal development, bipolar neurons are born in excess and through programmed cell death, a precise number of each subtype remains to give rise to the retinal circuit. Although this process has been well-described, little is known about the key molecules responsible for bipolar subtype integration in the developing retina. Our work uncovered a new role for the autism-associated risk gene, Protocadherin 9 (Pcdh9) in bipolar subtype integration. Deletion of Pcdh9 using a floxed allele leads to loss of OFF and ON cone bipolars; however, disruption in the extracellular binding of Pcdh9 leads to selective loss of ON cone bipolars but not rod bipolars. Moreover, we found this later function of Pcdh9 is mediated by homophilic interactions between ON cone bipolars and their known synaptic partners. Taken together, our work revealed a new role for Pcdh9 in bipolar subtype integration during retinal development. SUMMARY STATEMENTNeural circuits are comprised of multiple neuronal subtypes where a specific number need to come together to give rise to a functional circuit. Although this is a critical process during neurodevelopment, little is known about the molecular mechanisms that determines the precise number of each subtype during circuit development. In the present study, we identified the autism risk gene, Protocadherin 9 as a critical molecule in subtype integration of bipolar neurons within the developing mouse retina. Using newly generated mouse lines, we found distinct requirements of Pcdh9 to promote survival in different bipolar subtypes during retinal circuit assembly. The significance of this work is that it shed lights into how different neuronal subtypes are integrated in nascent neural circuits.

neuroscience↗