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#calciumimaging

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New #TeachingMaterial available: Functional Imaging Data Analysis – From Calcium Imaging to Network Dynamics. This course covers the entire workflow from raw #imaging data to functional insights, including #SpikeInference & #PopulationAnalysis. Designed for students and for self-guided learning, with a focus on open content and reproducibility. Feel free to use and share it 🤗

🌍 fabriziomusacchio.com/blog/202

📢Hot off the press: "Neuronal correlates of sleep in honey bees"

#CalciumImaging🔬 in sleeping #bees🐝: Antennal lobe neurons synchronise stronger during #sleep, likely due to reduced GABAergic coupling. #SNN💻 simulations show reduced #odour processing, similar to human sleep😴.

📰in Neural Networks: doi.org/10.1016/j.neunet.2025.

🍾Thanks to all collaborators: Sebastian Moguilner, Ettore Tiraboschi, Giacomo Fantoni, Heather Strelevitz, Hamid Soleimani, Luca Del Torre, @urihasson

📍#CIMeC #UniTrento

Quite the big deal:

"Light-field deep learning enables high-throughput, scattering-mitigated calcium imaging", by Howe et al. 2025 (Amanda Foust lab).
biorxiv.org/content/10.1101/20

Transfers one-photon light field images of Ca2+ sensors monitoring neuronal activity, which suffer from scattering in the mouse brain, to two-photon volumes that don't, using machine learning.

Image volumes acquired at 100 Hz demonstrate 10Hz spike rates.

"Forecasting Whole-Brain Neuronal Activity from Volumetric Video", Immer et al. 2025 (with Florian Engert, Jeff Lichtman, Misha Ahrens, Viren Jain and Michal Januszewski)
arxiv.org/abs/2503.00073

"ZAPBench: a benchmark for whole-brain activity prediction in zebrafish", Lueckmann et al. 2025
openreview.net/pdf?id=oCHsDpya

arXiv.orgForecasting Whole-Brain Neuronal Activity from Volumetric VideoLarge-scale neuronal activity recordings with fluorescent calcium indicators are increasingly common, yielding high-resolution 2D or 3D videos. Traditional analysis pipelines reduce this data to 1D traces by segmenting regions of interest, leading to inevitable information loss. Inspired by the success of deep learning on minimally processed data in other domains, we investigate the potential of forecasting neuronal activity directly from volumetric videos. To capture long-range dependencies in high-resolution volumetric whole-brain recordings, we design a model with large receptive fields, which allow it to integrate information from distant regions within the brain. We explore the effects of pre-training and perform extensive model selection, analyzing spatio-temporal trade-offs for generating accurate forecasts. Our model outperforms trace-based forecasting approaches on ZAPBench, a recently proposed benchmark on whole-brain activity prediction in zebrafish, demonstrating the advantages of preserving the spatial structure of neuronal activity.

New preprint with @urihasson : biorxiv.org/cgi/content/short/
First #calciumimaging of the #honeybee brain during #sleep.
#Machinelearning distinguishes sleep from wakefulness with 93% accuracy in #olfactory network. Clearest difference: the #neuralnetwork state. Nodes are more synchronized during sleep.
A simulation shows reduced inhibitory coupling during sleep, meaning less information processing. Increased inhibition during wakefulness leads to highly distinguishable odour maps. #CIMeC #UniTrento

The Schreiter lab did it again:

"A modular chemigenetic calcium indicator for multiplexed in vivo functional imaging", Helen Farrants et al. 2024
nature.com/articles/s41592-024

"WHaloCaMP, a modular chemigenetic calcium indicator built from bright dye-ligands and protein sensor domains. Fluorescence change in WHaloCaMP results from reversible quenching of the bound dye via a strategically placed tryptophan."

I'll have to update my lectures on optogenetics and chemigenetics ...

NatureA modular chemigenetic calcium indicator for multiplexed in vivo functional imaging - Nature MethodsWHaloCaMP is a chemigenetic calcium indicator that can be combined with different rhodamine dyes for multiplexed or FLIM imaging in vivo, as demonstrated for calcium imaging in neuronal cultures, brain slices, Drosophila, zebrafish larvae and the mouse brain.

It's long been known that cooling a brain region reduces activity. But this #preprint claims that the reverse is also true: pulsed infrared light (1875nm) from a glass fiber locally heating neurons can change their activity. Data looks noisy but this might be interesting if true. (Inclusion into optrodes maybe?)

Two-photon imaging of excitatory and inhibitory neural response to infrared neural stimulation
Fu et al., biorxiv preprint 2024
biorxiv.org/content/10.1101/20

bioRxivLearning produces a hippocampal cognitive map in the form of an orthogonalized state machineCognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task understanding and behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence. ### Competing Interest Statement The authors have declared no competing interest.