Neuroscience Research

The Drosophila adult central brain contains 240 circadian neurons, of which there are more than 25 different neuron subtypes based on connectomic data. Recent single cell RNA-seq (scRNAseq) characterization of these neurons "around the clock" also indicates a similar number of molecular subtypes of circadian neurons, but other conclusions from these transcriptomic studies warranted verifying and extending with other approaches. To this end: 1) We used a genetic multiplexing strategy to profile the transcriptomes of circadian neurons from multiple time points in a single experiment, reducing confounding technical variation between timepoints; 2) Large numbers of single nuclei were sequenced (snRNA-seq), which was enabled because the new method EL-INTACT purifies nuclei from frozen heads; 3) We assayed 12 time points under both light-dark (LD) and constant darkness (DD) conditions. These approaches showed dramatic transcriptional differences between time points in many circadian neuron types and enhanced time-of-day gene expression analysis. The data indicate that most of this regulation is transcriptional and circadian. There were however a small number of light-dependent transcripts, including a few that correspond to mammalian immediate-early genes. They probably play a role in the light-regulation of gene expression and behavior in specific neurons, perhaps circadian entrainment or phase-shifting. The results taken together provide a more comprehensive picture of gene expression heterogeneity within adult Drosophila circadian neurons including how intrinsic clock mechanisms and light cues are integrated across circadian neuron subtypes.

MicroRNAs (miRNAs) play essential roles in the posttranscriptional regulation of gene expression in organisms. In the process of synthesizing mature miRNAs from miRNA precursors, the miRNA precursors are cleaved via Dicer at their loop structure, after which the miRNA precursors become mature and regulate transcription. However, the consequences of altering the loop sequence are not fully understood. The silkworm Bombyx mori is a lepidopteran insect with many genetic strains. We identified a mutant of the miRNA miR-3260 whose the part of the loop structure was lacking in a silkworm strain with translucent larval skin. Here, we aimed to analyze the role of wild-type miR-3260 and the influence of the mutation of the loop structure in B. mori. First, we identified the genomic region responsible for the translucent larval skin phenotype and determined that the mutated miR-3260 nucleotide sequences. Then, we predicted the binding partners of wild-type miR-3260 using the RNA hybrid tool and found two juvenile hormone (JH)-related genes as targets of wild-type miR-3260. Next, we assessed the relationships between miR-3260 and JH and found that miR-3260 was highly expressed in the Corpora allata and its expression responded to JH treatment. Meanwhile, miR-3260 mimic and inhibitor did not induce the typical phenotypes associated with JH in B. mori. Then, we compared the dicing products from wild-type and mutant miR-3260 precursors and observed that neither form underwent Dicer-mediated cleavage when the loop structure was altered. These results suggest that loop mutations in the miR-3260 precursor may not influence dicing activity, consistent with the lack of observable phenotypic effects.

Recombinant Adeno-associated viruses (AAVs) are widely used for gene delivery in the central nervous system and have become central tools in both gene therapy and basic neuroscience research. However, although AAV serotypes have been extensively characterized in rodent models, their performance in human neurons, particularly those derived from induced pluripotent stem cells (iPSCs), remains poorly characterized. While human iPSC-derived neurons are increasingly used for disease modeling and drug screening, their susceptibility to viral transduction varies and remains difficult to predict. In this study, we systematically evaluated the transduction efficiency and toxicity profiles of 18 wild-type and engineered AAV serotypes across three distinct types of iPSC-derived neurons, relevant to disease modeling and drug discovery: cortical projection neurons, NGN2- induced forebrain-like neurons, and dopaminergic neurons and four doses (1E3, 1E4, 1E5 and 2E5 genome copies per cell). Using automated high-throughput confocal imaging and quantification of reporter gene expression, we identified several serotypes with robust and efficient transduction across all neuronal subtypes. Among these, three serotypes AAV6, AAV6.2 and AAV2.7m8 showed consistently high performance. To assess safety, we quantified cell number and neurite morphology, finding that while high transduction and gene expression correlate with toxicity, sensitivity varied across neuronal subtypes, with NGN2 neurons being most vulnerable and dopaminergic neurons most resilient. Finally, we validated our findings in a more complex 3D model by testing one of the best-performing serotypes, AAV2.7m8, in both whole and dissociated human cerebellar organoids. Together, our results establish a benchmark dataset for AAV performance in human iPSC- derived neurons and provide practical guidance for AAV based gene delivery in human in vitro neural models. This resource will be valuable for both basic research and preclinical applications aiming to manipulate gene expression in human neurons and understanding AAV tropism in disease-relevant cell types.

Glutamatergic neuronal synapses in the mouse neocortex mature during the first two months after birth. A key event during synaptic maturation is a change in short-term synaptic plasticity (STP), i.e. a switch from strong synaptic depression to a weaker depression or even facilitation. Glutamatergic pyramidal neurons located in the cortical layers II/III, layer V, and layer VI project axons through the corpus callosum where they release glutamate along their shafts and form glutamatergic synapses with oligodendrocyte precursor cells (OPCs). Here, we used single-cell electrophysiological recordings in brain slices to investigate synaptic plasticity at neuron-OPC synapses along axonal shafts in the white matter, and applied computation approaches to pinpoint the mechanisms of this plasticity. We found that during postnatal development of mice, there is a switch from short-term synaptic depression to short-term synaptic facilitation at glutamatergic neuron-OPC synapses in the corpus callosum. Synaptic delay of phasic neuron-OPC excitatory postsynaptic current shortens, and the amount of asynchronous release at neuron-OPC synapses decrease as animals mature, indicating that glutamate release becomes more synchronized. Our computational modelling suggests that both pre- and postsynaptic changes may contribute to the functional development and changes of plasticity at neuron-OPC synapses in the white matter. Taking together, our findings indicate that synaptic release machineries located at different sites along the same axon (i.e. axonal shaft in the white matter vs synaptic boutons in the grey matter) mature in a very similar fashion, STP occurs at both synaptic sites, and STP dynamics represent an important event during brain maturation.

High and low tides occur twice a day (every [~]12.4 hours), with the largest tidal ranges during spring tides around new and full moons (every [~]14.765 days). While these lunar cycles are known to influence many animal phenotypes, particularly the reproduction of coastal animals, the genetic basis of lunar-related rhythms remains unclear. Since phenotypic variation is a valuable resource for elucidating such mechanisms, we examined geographic variation in the lunar-regulated mass spawning of the grass puffer (Takifugu alboplumbeus) along the Japanese coast. We found that western populations spawn during the first half of the spring tides, whereas eastern populations spawn during the second half. Furthermore, although spawning typically occurs a few hours before high tide, this timing is restricted to a specific time window that is earlier in the western populations than in the eastern ones. Behavioral analysis of larvae also revealed a shorter free-running circadian period ({tau}) in the western population than in the eastern ones. As differences in {tau} affect individual variation in the timing of physiological functions and behaviors, we hypothesized that differences in {tau} could account for the different time windows and consequently the observed difference in spawning days. Population genomics analysis identified proline-rich transmembrane protein 1-like (prrt1l) as a candidate gene. Expression of prrt1l was observed in the circadian pacemaker suprachiasmatic nucleus, and triple CRISPR F0 knockout of prrt1l shortened the free-running period in larvae. These findings suggest a potential mechanism underlying the geographic variation in lunar-synchronized spawning behavior. HighlightsO_LIThe geographic variation exists in the lunar-regulated spawning of the grass puffer, with differences in spawning dates and times between western and eastern Japan. C_LIO_LIThe free-running period of western populations is shorter than that of eastern populations, which is consistent with their earlier spawning timing. C_LIO_LIPopulation genomics analysis identified prrt1l as a candidate gene harboring population-specific missense mutations, the knockout of which shortens the free-running period. C_LI

The brain coordinates animal physiology and behavior via neuronal circuits. To understand and simulate brain functions, it is essential to delineate the synaptic connectivity between neurons. Transsynaptic tracers serve as powerful tools for such purposes. In response to the demand for anterograde tracers for circuit mapping and functional interrogation, we developed WTR, a fusion protein of mammalian codon-optimized WGA, TEV-protease cleavage sequence, and Recombinase. WTR expressed via AAV vectors in cell-type-specific starter neurons reaches their postsynaptic neurons and releases Cre/Flpo upon exposure to TEV-protease expressed in downstream neurons. Accompanied by Cre/Flpo-dependent expression of EGFP, GCaMP7s, or ChR2, the toolkit enables labeling, recording, or manipulation of downstream neurons. We utilized WTR to characterize downstream neurons of either glutamatergic or GABAergic neurons in the preoptic area of anterior hypothalamus for their differential actions in thermoregulation or stress responses, respectively. These results establish WTR as a versatile platform for functional anterograde circuit mapping.

Ultrasonic vocalizations (USVs) are widely used in rodent social communication, yet the functional significance of male-male vocal interactions in mice remains unclear. Here, we investigated whether USVs produced during specific social behaviors influence the behavior of conspecifics. Using playback experiments, we compared responses to vocalizations recorded during chasing and being chased in male-male interactions. We found that USVs emitted by chased intruders consistently elicited approach behavior in receiver mice, whereas those emitted by chasing individuals did not. Acoustic analyses revealed that these vocalizations differed in syllable composition, with intruder calls containing a higher proportion of upward frequency-modulated syllables and exhibiting higher mean frequencies. In addition, the temporal organization of syllables appeared to contribute to the behavioral response. Together, these results suggest that male mice respond selectively to certain USV patterns associated with specific social contexts, indicating that acoustic features and temporal structure may jointly influence social approach behavior in mice. HighlightsO_LIBehavioral context (chased vs. chasing) shapes the composition of USV syllable types C_LIO_LIMale mice selectively approach USVs from chased intruders, but not chasing residents C_LIO_LIThe approach response exhibits high temporal synchrony across individual receivers C_LIO_LITemporal organization of syllables modulates approach behavior based on acoustic features C_LI

Resource sharing is a fundamental form of social exchange underlying the formation and maintenance of social bonds in humans and other species. While reciprocity has long been proposed as a key mechanism in group interactions, the dynamic processes underlying resource allocation remain poorly understood. In this study, we employed computational modeling to investigate the temporal dynamics of resource sharing in a novel group decision-making task across three experiments. We found that, beyond the well-documented reciprocity, participants exhibited consistent alternating behavior, characterized by the switching between potential recipients. This alternation was not driven by fairness concerns but reflected a strategic balance between maintaining stable partnerships and exploring alternatives. Crucially, a reinforcement learning model incorporating Theory of Mind (ToM) consistently outperformed all alternative models. These findings highlight the critical role of ToM in social decision-making and suggest that mentalizing others intentions may be essential for effective resource sharing and social bond formation.

Background: The rapid expansion of large-scale neuroscience datasets has increased the need for automated, accurate, and standardized quality control (QC). Manual proofreading of 3-dimensional neural morphology (SWC files) remains labor-intensive, error-prone, and non-scalable. We developed and evaluated a fully automated, machine-learning driven QC pipeline to standardize neural reconstructions, detect and correct structural anomalies, and rectify dendritic labeling in pyramidal neurons. Methods: We developed an end-to-end, cloud-deployed pipeline for automated QC, correction, and standardization of SWC-formatted neural morphologies. The framework integrates deterministic structural normalization, topology repair, geometric correction, quantitative morphometric analysis, and graph-based dendritic relabeling within a containerized React/Flask architecture deployed on Amazon Web Services. Rule-based algorithms systematically detect, classify, and correct structural irregularities including overlapping nodes, spurious side branches, non-positive radii, disconnected components, and anomalously long parent-child connections. A graph convolutional network, trained on Sholl-derived features from 20,500 pyramidal neurons, performs dendritic relabeling. Model training employed an 80/10/10 train-validation-test split with adaptive learning-rate scheduling and distributed execution across ten runs to evaluate stability and reproducibility. The pipeline generates images of the final product and computes quantitative morphometrics using L-Measure. Results: All neuronal reconstructions were processed without manual intervention. Automated normalization and topology repair restored structurally coherent and biologically accurate morphologies suitable for quantitative analysis and visualization without data loss. Dendritic relabeling achieved a mean accuracy of 99.51%, consistent between validation and test sets, with class-weighted precision of 0.978, recall of 0.977, and F1-score of 0.977. Enforcing a single apical dendritic tree per neuron improved anatomical consistency without reducing classification performance. Distributed training completed all runs in approximately 25 hours, demonstrating scalability and reproducibility for large datasets. Conclusions: We present a fully automated and cloud-scalable open-source pipeline for standardizing neural reconstructions and performing biologically consistent dendritic classification with near-perfect accuracy. The automated correction and relabeling procedures do not alter or compromise the size or unaffected morphological detail of the original SWC files, ensuring geometric fidelity and compatibility with downstream analysis tools. This open-access framework provides a robust foundation for high-throughput neural morphology curation and large-scale neuroanatomical analysis.

Our bodies have evolved to maintain homeostasis through regulatory systems that continuously adapt to keep physiological processes within a normal range. From this perspective, complex chronic disease can be understood as a breakdown of compensatory mechanisms, resulting in loss of homeostasis. Here we propose that adaptive receptor expression dynamics may serve as one such compensatory mechanism, increasing receptor surface expression when external ligand is insufficient, and clearing it when signaling is excessive. To explore this, we adapt a previously published agent-based model and simulate it under a range of scenarios. We find that the system of adaptive receptor expression is robust to oscillatory perturbations but not to chronic stress. We propose that receptor turnover dynamics may be better understood as an adaptive, environmentally responsive process rather than a fixed biological property, and that in some cases, disease manifests only after compensatory mechanisms have been pushed past their limits. We conclude with a discussion of implications for understanding complex chronic diseases, for thinking about epigenetic and mutational change as escalating layers of adaptation, and for how we model receptor dynamics in the context of receptor-mediated drug activity.

Neurotransmitter co-transmission contributes to diverse physiological processes throughout the mammalian brain, including sensory integration, motivational control, and social behaviors. Projections from the globus pallidus internus (GPi; the entopeduncular nucleus, EPN, in rodents) to the lateral habenula (LHb) are well-characterized by the co-transmission of both GABA and glutamate. These dual-release inputs modulate behavioral states in chronically learned helpless (cLH) rats, influencing both the onset and recovery of pathological phenotypes. Here, we employed confocal 3D reconstructions that confirmed the presence of both vesicular transporters VGAT and VGLUT2 in EPN axon terminals within the LHb. Further investigation revealed that GABA and glutamate are packaged in distinct vesicle populations within individual presynaptic terminals. Notably, the calcium (Ca{superscript 2}) sensors Synaptotagmin-2 (Syt2) and Synaptotagmin-3 (Syt3) are highly expressed in the EPN whereas expression of the canonical Ca{superscript 2} sensor, Synaptotagmin 1 (Syt1), is downregulated. Additionally, using confocal microscopy, we observed selective spatial correlations of Syt2 and VGLUT2 and between Syt3 and VGAT in LHb axon terminals. These observations strongly suggested that Syt2 serves as the predominant Ca{superscript 2} sensor for glutamatergic vesicle fusion, and Syt3 serves as the predominant Ca{superscript 2} sensor for GABAergic vesicle fusion in the LHb. To test this hypothesis, we employed targeted antisense oligonucleotide (ASO) knockdown of Syt2 and Syt3 in EPN neurons and measured LHb glutamatergic and GABAergic currents. Syt2 knockdown resulted in an increase in mEPSC frequency, amplitude, half-width and decay, suggesting increased glutamate vesicle release probability and increased glutamate vesicle packing. However, Syt2 knockdown had no influence on mIPSCs amplitude or frequency. On the other hand, Syt3 knockdown had no apparent effect on glutamate release but caused an increase in mIPSC frequency suggesting increased quantal release probability of GABA. Together, these findings identify a molecular mechanism by which synaptotagmin isoforms govern differential glutamate and GABA release at EPN dual-transmitter terminals in the LHb. These results provide evidence for presynaptic mechanisms regulating excitatory-inhibitory balance within this brain structure and importantly provide molecular targets for pharmacological intervention.

Animals exposed to pairings of a neutral stimulus with reward acquire a conditioned response to the neutral stimulus. A prominent hypothesis, formalized in the Temporal Difference (TD) learning algorithm, is that animals learn to predict the future reward associated with the neutral stimulus ("value"). Though the TD algorithm does not explicitly specify what drives conditioned responding, a typical assumption is that it reflects the animals estimate of value. In TD learning, value estimates are updated using reward prediction error (RPE, the discrepancy between observed and predicted reward), and are thought to be signaled by the phasic activity of midbrain dopamine neurons. This hypothesis posits that dopamines effects on conditioned responding are mediated entirely by its effects on learning. However, recent experimental and theoretical evidence suggests that dopamine may play a more direct role in modulating conditioned responding. We use a combination of data analysis and computational modeling to probe the relationship between dopamine and conditioned responding. Our results suggest that dopamine directly modulates conditioned responding, in addition to its role in learning. These findings can be captured by a model in which dopamine RPE acts both indirectly (via learning) and directly on conditioned responding.

Difficult-to-treat (D2T) rheumatic disease affects approximately 12% of rheumatoid arthritis patients and resists sequential biologic therapy, yet no mechanistic model explains this refractoriness as a system-level phenomenon. Here we present the 3-Axis Integrative Framework (3-AIF), a six-variable ordinary differential equation system integrating mucosal tolerance, energy-gated neuroimmune danger sensing, and integrated stress response pathways coupled through Hill-function metabolic gating. Stability analysis reveals bistable dynamics with two co-existing attractors separated by a saddle point. Bifurcation analysis demonstrates fold catastrophe with hysteresis: recovery requires greater therapeutic effort than disease prevention. Sensitivity analysis identifies three dominant parameters mapping to neuroimmune activation, energy drain, and recovery capacity. Cross-disease transcriptomic consistency analysis across six public datasets (n=310, five rheumatic diseases, four tissue types) reveals compartment-specific axis dysregulation -- circulating cells show integrated stress response activation while target tissues show pathway exhaustion -- and disease-specific axis dominance patterns consistent with model predictions.

1Markerless pose estimation has emerged as a powerful technique for animal behavior quantification, capable of high resolution tracking of body parts. Many neuroscience labs rely on tools like DeepLabCut and SLEAP, which provide accessible interfaces but restrict users to a narrow set of models and configurations. In this work, we adopt MMPose an open source, general-purpose computer vision library to build a workflow for training and evaluating multiple state-of-the-art models on animal video datasets. We benchmark these models in two scenarios: (1) a complex maze assay with occlusions and varied backgrounds, and (2) a simpler open field arena with a high-contrast background. Our results show that a bottomup model (DEKR) delivers the highest accuracy in the complex task, whereas lighter-weight models (e.g., SLEAP) offer superior speed highlighting a clear trade-off between accuracy and throughput. We also evaluate a recently published foundation model (TopViewMouse-5K) trained on a large top-view mouse dataset to test its generalization. It performs poorly on our tasks at zero-shot, and even when we combine its data with our training set, we observe no consistent benefit. These findings emphasize the importance of context-specific model selection and the need for more diverse training data to create generalizable pose models. By leveraging a general-purpose vision library, researchers can flexibly choose models that best suit their experimental needs. This work illustrates how adopting advanced computer vision frameworks can accelerate behavioral neuroscience and genetics research, paving the way for more scalable, reproducible, and sensitive analysis of animal behavior.

Animals produce different vocalization types, which differ in their acoustic features and are produced in different behavioral contexts. How vocalization-related brain circuits are organized to enable the production of different vocalization types remains poorly understood. The nucleus retroambiguus is a hindbrain premotor region that regulates the production of both ultrasonic vocalizations (USVs) and distress calls (squeaks) in adult mice, but whether distinct or overlapping populations of RAm neurons are recruited during the production of these two vocalization types is unknown. In the current study, we used Fos immunohistochemistry to compare the counts and spatial distributions of Fos-positive RAm neurons in males and females that produced USVs and females that produced courtship squeaks. We also combined in vivo activity-dependent (TRAP2) labeling with Fos immunohistochemistry to directly compare Fos expression associated with the production of USVs and courtship squeaks in the same females. Our findings suggest that RAm contains three vocalization-related populations of neurons: squeak-related neurons, USV-related neurons, and shared neurons that are recruited during both vocalization types. These findings refine current models of the premotor control of vocalization and set the stage for future work to explore anatomical and functional heterogeneity within RAm.

We examined the overlap in the genes associated with daily rhythms and with behavioral plasticity in ants. We first investigated the daily rhythms of gene expression in the harvester ant, Pogonomyrmex barbatus, and how the rhythmic genes overlap with others previously shown to be associated with plasticity of foraging behavior. Then, to consider whether the overlap is conserved across ant species, we compared rhythms of gene expression in the diurnal, desert harvester ants with those previously reported for a distantly related nocturnal, subtropical carpenter ant, Camponotus floridanus. First, daily transcriptomes in P. barbatus showed that most genes were expressed in light-dark (LD) and constantly dark (DD) conditions at about the same levels; only 11 genes showed at least a two-fold change in expression. Network analysis identified eleven modules of P. barbatus genes under LD conditions. Of these 11 clusters, modules C1 and C2 seem to be central nodes of the gene expression network, because they are the most highly connected in LD, and show the strongest preservation in DD vs. LD, and contain core clock gene Period. Only one module, C2, showed significant overlap with P. barbatus genes that have 24h-rhythmic expression in both LD and DD. There was significant overlap between modules C1, C2, C10, C11, and P. barbatus genes found previously to be associated with plasticity in the regulation of foraging activity to manage water loss. A comparison of the daily transcriptome of P. barbatus with that of C. floridanus showed significant overlap of 24h-rhythmic genes in LD. Modules C1 and C2 of P. barbatus also overlap with C. floridanus genes previously shown to differ in expression rhythms in nurses and foragers. In combination, these results indicate that genes linking plasticity of the circadian clock and of behavior may be broadly conserved in ants.

The striatum, a critical hub for motor skill learning, is located deep within the subcortical region, making noninvasive stimulation particularly challenging. Nevertheless, recent studies suggest that transcranial magnetic stimulation (TMS) can modulate subcortical activity indirectly by targeting functionally connected cortical areas. In this study, we applied TMS to the dorsolateral prefrontal cortex (DLPFC) immediately before the fMRI session measuring task-related activity in the striatum during motor learning. We examined whether continuous theta-burst stimulation (cTBS) and high-frequency stimulation (20 Hz) could modulate motor learning and associated striatal responses with opposing effects. There was no significant effect of either stimulation condition on the overall motor learning performance. However, cTBS significantly reduced performance-related striatal activity, while 20 Hz stimulation did not show any modulatory effect. These findings demonstrate that cTBS targeting the corticostriatal network can suppress striatal activity and suggest its potential use in clinical trials for treating disorders such as addiction associated with hyperactive striatal responses.

The subiculum (SUB) is the main output structure of the hippocampus, influencing diverse behaviors through its widespread cortical and subcortical connections. Our previous work creating the mouse Hippocampus Gene Expression Atlas (HGEA) identified four genetically distinct cellular layers across five columnar domains in the SUB, with gene expression boundaries corresponding to distinct connectivity patterns and brain-wide networks involved in spatial navigation, social behavior, and neuroendocrine regulation (Bienkowski et al., 2018). Using the Digital Brain Mouse Projectome Atlas (MPA) tool, we conducted virtual tract-tracing to assess whether connectivity patterns of single-neuron 3D reconstructions aligned with HGEA-defined SUB cell types (Qiu et al., 2024). We classified 689 SUB projection neurons into 12 HGEA cell-type groups based on their laminar and columnar distributions, whose spatial organization recapitulated HGEA-defined 3D boundaries. Using this population sample, we performed a SUB cell-type census, characterized neuronal heterogeneity and projection prevalence, identified common and rare connectivity motifs and axonal collateralization patterns, and defined distinct projection themes for each SUB cell type. Together, this analysis integrates single-neuron and population-level data to advance understanding of SUB cell type organization and its contributions to brain-wide networks regulating diverse behaviors.

Kinesin-3 motor proteins are increasingly recognized for their important roles in cilia. The mammalian kinesin-3 motor KIF13B moves bidirectionally in primary cilia and regulates ciliary content, but its relationship to the intraflagellar transport (IFT) machinery is unclear. Here, we combine quantitative live-cell imaging with a new kymograph analysis based on dynamic mode decomposition (DMD) to separate mobile from immobile protein populations in primary cilia. This approach simplifies extraction of molecular velocities from kymographs and reveals that a KIF13B deletion mutant retaining only the motor domain and part of the forkhead-associated domain does not alter steady-state IFT velocity or frequency. However, when retrograde dynein-2 function is inhibited by Ciliobrevin D, both anterograde and retrograde IFT velocities decrease in parental cells, as expected, but remain unchanged in KIF13B mutant cells. Structured illumination, confocal, and STED microscopy further show that KIF13B localizes to the ciliary membrane and concentrates at the periciliary membrane region and the centriolar subdistal appendages, below the distal appendage marker FBF1. Our improved kymograph approach provides new insight into KIF13B ciliary function and simplifies the quantitative analysis of ciliary protein transport.

Building a simple model that precisely and functionally characterizes a neuron is a challenging and important task to select the best concise and computationally efficient model. However, this type of work has only been done for subthreshold properties of neurons. Here, we take a different perspective and suggest a method to obtain point-neuron models from morphologically-detailed models with dendrites. To do this, we focus on the functional characterization of the neuron response under in vivo conditions, and compute the transfer function of the detailed model. The parameters of this transfer function, in terms of mean voltage, voltage standard deviation and correlation time, can be used to compute the "best" point-neuron model that generates a transfer function very close to that of the morphologically-detailed model. We illustrate this approach for two very different neuronal morphologies, one from Drosophila larvae and one from mammals. In conclusion, this approach provides a tool to generate point-neuron models from detailed models, based on a functional characterization of the neuron response. Significance StatementThis study provides a new computational method to reduce morphological models into point-neuron models. To do so, we calculate the transfer function parameters, ie the voltage standard deviation, the mean voltage and the correlation time, of the morphological model and fit a point neuron-model onto this data. Here, we successfully apply this approach for two very different neuron morphologies, a drosophila neuron and a rat motoneuron.