Sparse cortical dynamics reveal flexible condition-dependent spike-order codes

Abstract

Cortical networks compute with remarkably sparse spiking activity, yet the circuit mechanisms that organize these few spikes into flexible, condition-dependent temporal codes remain poorly understood. Here we combine analyses of large-scale mouse recordings with a data-driven cortical microcircuit model (CMM) of mouse V1. In recordings, V1 activity exhibits condition-dependent spike-order sequences: peak-latency order varies with task outcome, current image identity, and preceding image identity, while remaining stable under split-half and single-trial analyses. After task optimization by backpropagation through time, the CMM reproduces this sequence-level signature and sparse activity more closely than the matched randomly connected RSNN and rate-RNN controls tested here. Ablations indicate that neuronal heterogeneity and distance-dependent local connectivity each reduce rigid sequential activity, with their combination giving the closest match to measured cortical signatures. Low-dimensional trajectory visualizations and model-silencing experiments further identify high-mutual-information early neurons whose removal perturbs task trajectories and decisions. Together, these results identify a biologically grounded computational principle: neuronal diversity and local connectivity help sparse recurrent networks avoid rigid temporal pipelines and support flexible, condition-dependent spike-order computation, providing candidate design principles for SNNs that exploit flexible temporal codes.