Papers for

neural simulation developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Spiking network structural changes depend on input and time window

Input and Horizon Dependent Structural Fidelity in Chunk Coupled Spiking Networks

Abstract: A fixed structural edit can preserve a spike sequence under one input and change it under another. We study this dependence using statistics-preserving edge swaps, controlled inputs and finite-horizon parameter certificates. The original 125-chunk numerical model is tested on ten artificial graphs from three connection rules. Despite identical total external drive at every slot, global and focal inputs change the visibility of 4--13 of nineteen fixed swaps per graph. A paired homogeneous and heterogeneous leaky integrate-and-fire validation in brian2-atlas reuses three originals and extends observation from 30 to 300 ms under a constant background drive sufficient for isolated-cell firing. Heterogeneous short-window switching persists, but by 300 ms every tested swap differs from its reference, saturating the binary visibility readout. Fixed-support weight families provide a complementary regional guarantee. Positive certified intervals cover 4,308 of 4,320 LIF direction conditions and include distinct floating-point configurations. A post hoc comparison finds a long-window failure inside the short-window certified interval in 710 of 720 heterogeneous conditions; 394 of 829 direction pairs ordered under both matched-drive inputs change order at 300 ms. Input-specific filtering and threshold-margin scores retain useful ordering but degrade with horizon and do not consistently beat activity controls. Earlier temporal-error, paired-dose and unsuccessful structural-selection results delimit what these findings imply. Together, the evidence links input-conditioned structural effects to finite-horizon fidelity regions in two deterministic kernels, without establishing universal topology rankings, model compression or asymptotic robustness.

Mon 9 NovComputational Engineering, Finance, and Science
The gist
The paper finds that changing connections in certain brain-like networks can either keep or alter their activity patterns, depending on the input they receive and the observation time. The authors tested a network model with fixed connection changes under different input signals, showing that these changes affect how similar the activity is to the original over short and long times. They also developed ways to certify when connections preserve behavior for specific time frames and inputs. This reveals that the network's responses depend strongly on both the input and how long you watch the activity, rather than having fixed global rankings of connection importance.
Open → 2611.12813v1