Jacquir, S. | bioRxiv Science

bioRxiv2026

Sharp and Fast Dynamic Extraction and Tracking of Emitted Cellular Transients

Genetically encoded fluorescent sensors have expanded our ability to image cellular activity and transmitter release. Yet, sparse and low-salience events remain difficult to resolve against complex and fluctuating fluorescence backgrounds. Here we introduce DETECT, Dynamic Extraction and Tracking of Emitted Cellular Transients, which combines adaptive background suppression, probabilistic classification and multi-object tracking to extract fluorescence events while preserving their identity. Across synthetic datasets, DETECT improved detection and segmentation accuracy and reduced computational cost relative to established event-based methods. We validated DETECT across confocal, two-photon and miniscope imaging, ex vivo and in vivo, using calcium indicators and monoamine sensors. Beyond its technical performance, DETECT extends event-based analysis to low-salience fluorescence signals while resolving events spanning broad ranges of amplitude, morphology and dynamics. By resolving spontaneous dopamine and noradrenaline signals as distinct, trackable release events, DETECT reveals the spatiotemporal organization of neuromodulatory activity and provides a broadly applicable approach to quantitative fluorescence analysis.

neuroscience↗

bioRxiv2024

Spiking Neuron-Astrocyte Networks for Image Recognition

From biological and artificial network perspectives, researchers have started acknowledging astrocytes as computational units mediating neural processes. Here, we propose a novel biologically-inspired neuron-astrocyte network model for image recognition, one of the first attempts at implementing astrocytes in Spiking Neuron Networks (SNNs) using a standard dataset. The architecture for image recognition has three primary units: the pre-processing unit for converting the image pixels into spiking patterns, the neuron-astrocyte network forming bipartite (neural connections) and tripartite synapses (neural and astrocytic connections), and the classifier unit. In the astrocyte-mediated SNNs, an astrocyte integrates neural signals following the simplified Postnov model. It then modulates the Integrate-and-Fire (IF) neurons via gliotransmission, thereby strengthening the synaptic connections of the neurons within the astrocytic territory. We develop an architecture derived from a baseline SNN model for unsupervised digit classification. The Spiking Neuron-Astrocyte Networks (SNANs) display better network performance with an optimal variance-bias trade-off than SNN alone. We demonstrate that astrocytes promote faster learning, support memory formation and recognition, and provide a simplified network architecture. Our proposed SNAN can serve as a benchmark for future researchers on astrocyte implementation in artificial networks, particularly in neuromorphic systems, for its simplified design.

neuroscience↗