Towards Generalized Synapse Detection Across Invertebrate Species

• URL: http://arxiv.org/abs/2509.17041v1
• Date: Sun, 21 Sep 2025 11:40:49 GMT
• Title: Towards Generalized Synapse Detection Across Invertebrate Species
• Authors: Samia Mohinta, Daniel Franco-Barranco, Shi Yan Lee, Albert Cardona,
• Abstract summary: SimpSyn is a single-stage Residual U-Net trained to predict dual-channel spherical masks around pre- and post-synaptic sites.<n>It consistently outperforms Synful in F1-score across all volumes for synaptic site detection.
• Score: 0.07999703756441755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Behavioural differences across organisms, whether healthy or pathological, are closely tied to the structure of their neural circuits. Yet, the fine-scale synaptic changes that give rise to these variations remain poorly understood, in part due to persistent challenges in detecting synapses reliably and at scale. Volume electron microscopy (EM) offers the resolution required to capture synaptic architecture, but automated detection remains difficult due to sparse annotations, morphological variability, and cross-dataset domain shifts. To address this, we make three key contributions. First, we curate a diverse EM benchmark spanning four datasets across two invertebrate species: adult and larval Drosophila melanogaster, and Megaphragma viggianii (micro-WASP). Second, we propose SimpSyn, a single-stage Residual U-Net trained to predict dual-channel spherical masks around pre- and post-synaptic sites, designed to prioritize training and inference speeds and annotation efficiency over architectural complexity. Third, we benchmark SimpSyn against Buhmann et al.'s Synful [1], a state-of-the-art multi-task model that jointly infers synaptic pairs. Despite its simplicity, SimpSyn consistently outperforms Synful in F1-score across all volumes for synaptic site detection. While generalization across datasets remains limited, SimpSyn achieves competitive performance when trained on the combined cohort. Finally, ablations reveal that simple post-processing strategies - such as local peak detection and distance-based filtering - yield strong performance without complex test-time heuristics. Taken together, our results suggest that lightweight models, when aligned with task structure, offer a practical and scalable solution for synapse detection in large-scale connectomic pipelines.

Related papers

• On the Mechanism and Dynamics of Modular Addition: Fourier Features, Lottery Ticket, and Grokking [49.1352577985191]
  We present a comprehensive analysis of how two-layer neural networks learn features to solve the modular addition task.<n>Our work provides a full mechanistic interpretation of the learned model and a theoretical explanation of its training dynamics.
  arXiv  Detail & Related papers  (2026-02-18T20:25:13Z)
• Towards Syn-to-Real IQA: A Novel Perspective on Reshaping Synthetic Data Distributions [74.00222571094437]
  Blind Image Quality Assessment (BIQA) has advanced significantly through deep learning, but the scarcity of large-scale labeled datasets remains a challenge.<n>We make a key observation that representations learned from synthetic datasets often exhibit a discrete and clustered pattern that hinders regression performance.<n>We introduce a novel framework SynDR-IQA, which reshapes synthetic data distribution to enhance BIQA generalization.
  arXiv  Detail & Related papers  (2026-01-01T06:11:16Z)
• SWAN: Self-supervised Wavelet Neural Network for Hyperspectral Image Unmixing [0.2624902795082451]
  We present SWAN: a three-stage, self-supervised wavelet neural network for estimation of endmembers and abundances from hyperspectral imagery.<n>The idea is to exploit latent symmetries from thus obtained invariant and covariant features using a self-supervised learning paradigm.<n> Experiments are conducted on two benchmark synthetic data sets with different signal-to-noise ratios as well as on three real benchmark hyperspectral data sets.
  arXiv  Detail & Related papers  (2025-10-26T10:05:48Z)
• SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learning [50.69448058071441]
  Deciphering how visual stimuli are transformed into cortical responses is a fundamental challenge in computational neuroscience.<n>We propose SynBrain, a generative framework that simulates the transformation from visual semantics to neural responses.<n>We show that SynBrain surpasses state-of-the-art methods in subject-specific visual-to-fMRI encoding performance.
  arXiv  Detail & Related papers  (2025-08-14T03:01:05Z)
• HetSyn: Versatile Timescale Integration in Spiking Neural Networks via Heterogeneous Synapses [3.744763853474646]
  Spiking Neural Networks (SNNs) offer a biologically plausible and energy-efficient framework for temporal information processing.<n>We introduce HetSyn, a framework that models synaptic heterogeneity with synapse-specific time constants.<n>We demonstrate that HetSynLIF improves the performance of SNNs across a variety of tasks.
  arXiv  Detail & Related papers  (2025-08-01T10:19:56Z)
• Multi-Modal Graph Convolutional Network with Sinusoidal Encoding for Robust Human Action Segmentation [10.122882293302787]
  temporal segmentation of human actions is critical for intelligent robots in collaborative settings.<n>We propose a Multi-Modal Graph Convolutional Network (MMGCN) that integrates low-frame-rate (e.g., 1 fps) visual data with high-frame-rate (e.g., 30 fps) motion data.<n>Our approach outperforms state-of-the-art methods, especially in action segmentation accuracy.
  arXiv  Detail & Related papers  (2025-07-01T13:55:57Z)
• Extending Spike-Timing Dependent Plasticity to Learning Synaptic Delays [50.45313162890861]
  We introduce a novel learning rule for simultaneously learning synaptic connection strengths and delays.<n>We validate our approach by extending a widely-used SNN model for classification trained with unsupervised learning.<n>Results demonstrate that our proposed method consistently achieves superior performance across a variety of test scenarios.
  arXiv  Detail & Related papers  (2025-06-17T21:24:58Z)
• MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection [30.77558600436759]
  We introduce a novel and lightweight pipeline that generates synthetic anomalies through Math-Phys model guidance.<n>Our method produces realistic defect masks, which are subsequently enhanced in two phases.<n>To validate our method, we conduct experiments on three anomaly detection benchmarks: MVTec AD, VisA, and BTAD.
  arXiv  Detail & Related papers  (2025-04-17T14:22:27Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Revisiting the Evaluation of Image Synthesis with GANs [55.72247435112475]
  This study presents an empirical investigation into the evaluation of synthesis performance, with generative adversarial networks (GANs) as a representative of generative models. In particular, we make in-depth analyses of various factors, including how to represent a data point in the representation space, how to calculate a fair distance using selected samples, and how many instances to use from each set.
  arXiv  Detail & Related papers  (2023-04-04T17:54:32Z)
• Understanding Self-supervised Learning with Dual Deep Networks [74.92916579635336]
  We propose a novel framework to understand contrastive self-supervised learning (SSL) methods that employ dual pairs of deep ReLU networks. We prove that in each SGD update of SimCLR with various loss functions, the weights at each layer are updated by a emphcovariance operator. To further study what role the covariance operator plays and which features are learned in such a process, we model data generation and augmentation processes through a emphhierarchical latent tree model (HLTM)
  arXiv  Detail & Related papers  (2020-10-01T17:51:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.