FIGURE

Figure 6.

ID

ZDB-FIG-250307-15

Publication

O'Neill et al., 2025 - A deep learning framework for automated and generalized synaptic event analysis

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Figure 6.

Synaptic event detection for neurons in a full-brain explant preparation of adult zebrafish.

(A) Application of TL to facilitate event detection for EPSC recordings. (B) Extraction of amplitudes, event frequencies, decay times, and rise time for all neurons in the dataset (n = 34). Bars are means and error bars denote SD. (C) Typical (mean) event kinetics for the analyzed neurons, ordered by decay times. Event traces are peak-normalized. (D–E) Example recordings with slow (dark blue) and fast (light blue) event kinetics. (F) Examples of events taken from (D–E), illustrating the diversity of kinetics within and across neurons. (G) Distribution of event decay kinetics across single events for two example neurons (traces shown in (D–E)). (H) Distribution of event rise kinetics across single events for same two example neurons. (I) Mapping of decay times (color-coded as in (C)) onto the anatomical map of the recording subregion of the telencephalon. (J) Mean decay and rise times are correlated across neurons. (K) Decay time distributions are broader (SD of decay time distributions) when mean decay times are larger. (L) Input resistance as a proxy for cell size is negatively correlated with the decay time.

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