CA3 axonal calcium imaging

Memorizing locations that are harmful or dangerous is a key capability of all organisms, and requires an integration of affective and spatial information. In mammals, the dorsal hippocampus mainly processes spatial information, while the intermediate to ventral hippocampal divisions receive affective information via the amygdala. However, how spatial and aversive information is integrated is currently unknown.
To address this question, we recorded the activity of hippocampal long-range CA3 axons at single axon resolution in mice forming an aversive spatial memory. We show that intermediate CA3 to dorsal CA3 (i-dCA3) projections rapidly overrepresent areas preceding the location of an aversive stimulus, due to a spatially selective addition of newly place-coding axons, followed by a spatially nonspecific stabilization. This sequence significantly improves the encoding of location by the i-dCA3 axon population.
These results suggest that i-dCA3 axons transmit a precise, denoised and stabl..., The calcium imaging data was obtained through chronic two-photon awake imaging of CA3 axons within the murine hippocampus. Concurrently, behavioral data was gathered from the experimental setup using a LabView-based customized software. The analysis of the imaging data was performed in MATLAB, utilizing standard toolboxes, open-access toolboxes, and custom-written code. The treadmill behavioral data was partially analyzed in MATLAB and partially in Python., , # CA3 axonal calcium imaging

[https://doi.org/10.5061/dryad.stqjq2c72](https://doi.org/10.5061/dryad.stqjq2c72)

This data set contains the calcium imaging and behavioral data used in Nikbakht et al.,'s \"**Efficient encoding of aversive location by CA3 long-range projections**\". The data set contains the preprocessed imaging and behavior data for 8 animals over 8 behavioral sessions. The formatting of the data set is MATLAB files (*.m).

Code available on: [https://github.com/negarniki/Efficient-encoding-of-aversive-location-by-CA3-long-range-projections](https://github.com/negarniki/Efficient-encoding-of-aversive-location-by-CA3-long-range-projections)

## Description of the data and file structure

The **BigFatCluster.m** is structured in a way that the columns represent 8 animals (1-4: i-d, 5-8:d-d) over 8 behavioral sessions.

**cclustID.m** contains the table guide headers.

The tracked data over session pairs are named after the respective sessions.

**BigFatPCA.m** contains the ...