Psychotherapy Commission ACPEFeb 11

Recent findings show that hippocampal neurons shift their activity backward in time as learning occurs, enabling anticipation of rewards before they happen. Using calcium imaging in mice performing a delayed nonmatching-to-location task, the study tracked the same neurons over weeks and observed a backpropagation of tuning from reward delivery to earlier moments, including the moment of correct touchscreen choice. The results portray the hippocampus as a dynamic predictive map that reorganizes with experience to guide future behavior.

This work is of interest to psychology because it provides concrete neural evidence for predictive coding and memory-based anticipation, illustrating how learning reshapes mental representations to forecast future events. It links memory, prediction, and action in a measurable neural framework.

Article Title: Hippocampal neurons shift their activity backward in time to anticipate rewards

Link to PsyPost Article: ift dot tt/fVBpnxF

Copy and paste broken link above into your browser and replace "dot" with "." for link to work. We have to do it this way to avoid displaying copyrighted images.

#Hippocampus #PredictiveCoding #NeuralPlasticity #CalciumImaging #RewardLearning

Fabrizio MusacchioOct 27

🧠 New preprint by Kim et al. (2025) from David Anderson’s lab: A line #attractor maintains aggressiveness during feeding in “hangry” mice 🍔🐁. Using in vivo #CalciumImaging and #rSLDS modeling, they show how moderate fasting stabilizes an aggression-related attractor in #VMHvl, while prolonged fasting collapses it, linking hunger, motivation, and aggression through #PopulationDynamics:

🌍 https://doi.org/10.1101/2025.10.16.682711

#Neuroscience #CompNeuro #Behavior #AttractorDynamics #Hypothalamus #2p #imaging

El Duvelle NeuroJul 16, 2025

If you want to study #HippocampalReplay... Use ephys, not #CalciumImaging!!

(Calcium imaging doesn't detect single spikes well, but replay mostly involves single spikes)
#Neuroscience #SpatialCognition #Hippocampus

Fabrizio MusacchioJul 14, 2025

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 🤗

🌍 https://www.fabriziomusacchio.com/blog/2025-07-13-function_image_analysis/

#Python #DataScience #MachineLearning #Neuroscience #OpenSource #calciumimaging #CompNeuro

Fabrizio MusacchioJun 1, 2025

📢 Our new study is now published in Communications Biology (Nature Portfolio):
We demonstrate deep in vivo #ThreePhoton imaging 🔬 of neurons 🧠 and glia in the medial prefrontal cortex with subcellular resolution!

👉 https://www.nature.com/articles/s42003-025-08079-8

#Neuroscience #Microscopy #CalciumImaging #Microglia #DZNE @dzne

NeurophysicsLab Albrecht HaaseMay 14, 2025

📢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: https://doi.org/10.1016/j.neunet.2025.107575

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

📍#CIMeC #UniTrento

Albert CardonaMar 26, 2025

Quite the big deal:

"Light-field deep learning enables high-throughput, scattering-mitigated calcium imaging", by Howe et al. 2025 (Amanda Foust lab).
https://www.biorxiv.org/content/10.1101/2025.03.17.643718v1.full

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.

#neuroscience #mouse #CalciumImaging

Light-field deep learning enables high-throughput, scattering-mitigated calcium imaging

Light field microscopy enables volumetric, high throughput functional imaging. However, the computational burden and vulnerability to scattering limit light field's application to neuroscience. We present a strategy for volumetric, scattering-mitigated neural circuit activity monitoring. A physics-based deep neural network, LNet, is trained with two-photon volumes and one-photon light fields. A processing pipeline uses LNet to extract calcium activity from light-field videos of jGCaMP8f-expressing neurons in acute cortical slices. The extracted time series have high signal-to-noise ratios and reduced optical crosstalk compared to conventional volume reconstruction. Imaging 100 volumes per second, we observed putative spikes fired at up to 10 Hz and the spatial intermingling of putative ensembles throughout 530 x 530 x 100-micron volumes. Compared to iterative algorithms, LNet LFM cuts light-field video processing time from hours to minutes and hence advances the goal of real-time, scattering-robust volumetric neural circuit imaging for closed-loop and adaptive experimental paradigms. ### Competing Interest Statement The authors have declared no competing interest.

bioRxiv
Albert CardonaMar 11, 2025

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

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

#ZAPBench #neuroscience #zebrafish #CalciumImaging #CompNeurosci

Forecasting Whole-Brain Neuronal Activity from Volumetric Video

Large-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.

arXiv.org
NeurophysicsLab Albrecht HaaseJan 17, 2025

Comparative study between D. melanogaster and the invasive D. suzukii. Significant differences in structure and function of the antennal lobes could be the basis for their different host-seeking behaviour causing huge crop damage: https://www.mdpi.com/2075-4450/16/1/84

#CalciumImaging #Olfaction #Drosophila #CIMeC #UniTrento

Differential Coding of Fruit, Leaf, and Microbial Odours in the Brains of Drosophila suzukii and Drosophila melanogaster

Drosophila suzukii severely damages the production of berry and stone fruits in large parts of the world. Unlike D. melanogaster, which reproduces on overripe and fermenting fruits on the ground, D. suzukii prefers to lay its eggs in ripening fruits still on the plants. Flies locate fruit hosts by their odorant volatiles, which are detected and encoded by a highly specialised olfactory system before being translated into behaviour. The exact information-processing pathway is not yet fully understood, especially the evaluation of odour attractiveness. It is also unclear what differentiates the brains of D. suzukii and D. melanogaster to cause the crucial difference in host selection. We hypothesised that the basis for different behaviours is already formed at the level of the antennal lobe of D. suzukii and D. melanogaster by different neuronal responses to volatiles associated with ripe and fermenting fruit. We thus investigated by 3D in vivo two-photon calcium imaging how both species encoded odours from ripe fruits, leaves, fermented fruits, bacteria, and their mixtures in the antennal lobe. We then assessed their behavioural responses to mixtures of ripe and fermenting odours. The neural responses reflect species-dependent shifts in the odour code. In addition to this, morphological differences were also observed. However, this was not directly reflected in different behavioural responses to the odours tested.

MDPI
NeurophysicsLab Albrecht HaaseOct 14, 2024
New preprint with @urihasson : https://biorxiv.org/cgi/content/short/2024.10.11.617548v1
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