From Functional Architecture to Organizing Principles of Neuronal Ensembles in Mouse Area V1

While single-neuron responses in mouse V1 are well characterized, less is known about how functional ensembles-- groups of neurons that co-activate more frequently than expected by chance--emerge as computational units within laminar V1 circuits. Even with increasingly detailed knowledge of structural connectivity, the rules governing ensemble organization and interactions remain unclear. We imaged pyramidal neurons across granular (L4) and supragranular (L2/3) layers of mouse V1 and applied pairwise functional connectivity analysis to identify multi-neuronal ensembles as putative information-processing modules. In the absence of visual stimulation, 19-34% of pyramidal pairs within 300{micro}m were functionally connected, declining to 10% at 1 mm. Layer 2 to 4 laminar networks exhibited a small-world architecture, L4 displaying slightly denser connectivity and a near-uniform degree-of-connectivity distribution. We propose that neurons together with their first-order functionally connected (1FC) partners constitute putative elementary units of cortical computation. The firing probability of layer 2/3 neurons exhibits a ReLU-like nonlinearity, emerging when [≥] 13% of L4-1FC "putative inputs" co-fire, yielding sparse yet reliable responses. Moreover, L2/3 neuronal responses depend on the count (N), not the identity, of co-active L4-1FC partners, with response sensitivity scaling as a power law in N. These properties persist during visual stimulation and across different states of alertness. Interestingly, L2/3 neurons with L4-1FC modules of different sizes exhibit distinct coupling to brain-state and different computational signatures. This framework yields mechanistic insight into cortical circuit organization, complementary to structural connectivity, helping to link biological circuitry to deep-learning models of artificial intelligence.