Fig. 6: Fitting Drosophila resting-state neural activity by training whole-brain spiking networks.
From: Model-agnostic linear-memory online learning in spiking neural networks
A Representative ΔF/F traces from atlas ROIs in a single fly over 800 seconds, obtained from calcium imaging experiments17,18. B Deconvolved firing rates for the corresponding neuropils shown in A. C Schematic of the training and testing paradigm. During the training phase, the model receives the experimental firing rate at time t (ExpFRt) and predicts the rate at time t + 1 (SimFRt) by minimizing the mean squared error. In the testing phase, the model autonomously generates sequential activations from its most recent prediction. D Loss convergence of whole-brain neural activity fitting using the pp-prop algorithm. Firing rate dynamics for the Mushroom body neuropil: (E) experimental data, (F) outputs from the trained model, and (G) outputs from an untrained control. The initial Warmup phase (80 seconds) initializes network activity with experimental data. In the subsequent Train phase, the network generates activity at time t + 1 based on its prior output at time t. The Test phase evaluates the network’s ability to generate activity over unseen periods. H Quantitative comparison of similarity between experimental data and model-generated outputs, contrasting trained versus untrained models. Functional connectivity matrices derived from (I) experimental recordings, (J) the trained whole-brain network, and (K) the untrained network, underscoring the efficacy of the network in capturing intrinsic connectivity patterns.
