A new open-source Python toolkit, DRIADA, aims to connect analyses across biological scales: individual neurons, population activity, and network dynamics, all anchored to the same behavioral variables. The developers built it to address the challenge of linking cellular selectivity to emergent population codes, and they report results from both biological and simulated data.
The authors validated DRIADA on synthetic populations with known ground truth, calcium-imaging recordings from dorsal hippocampal CA1 in 13 mice across 39 sessions, and a simulated continuous attractor neural network with known toroidal organization. Across most hippocampal sessions, embeddings of spatially selective neurons predicted held-out position better than equally sized random subsets or all recorded neurons. This suggests that focusing on selective cells can improve behavioral decoding.
The same workflow allowed the team to trace population dimensions back to individual neurons, including cells that did not show detected selectivity for the measured behavioral variables. In the simulated network, complementary analyses produced features consistent with its known toroidal organization. The authors propose DRIADA as a common framework for relating neuronal selectivity, population structure, and network activity to behavioral dynamics.