Speaker
Description
Understanding how brain network structure gives rise to function requires measuring connectivity and activity in the same circuit. While inferring connections from spike trains has long been proposed as a solution, correlated inputs cause existing methods to detect many spurious connections. Using spiking network models, we systematically identified features of the spike-time cross-correlogram that distinguish true monosynaptic interactions from correlations due to common inputs, and trained a convolutional network to recognize such features (Dyad).
We validated Dyad using two simulated datasets (one EI balanced network, one ring model) and two published electrophysiological datasets with experimentally identified monosynaptic connectivity (one excitatory, one inhibitory). In all benchmarks, Dyad recovered more than half of the connections at a precision above 95%, substantially outperforming all previous approaches.
We then applied Dyad to two in vivo electrophysiological datasets — recorded in mouse visual cortex and post-subiculum. In both datasets, neurons with putative excitatory outputs had few putative inhibitory outputs, and vice versa, indicating a low rate of spurious connections.
In the visual cortex, when relating synaptic connectivity inferred from spontaneous activity to the pairwise similarity of Gabor responses, we found a like-to-like organization that extends beyond excitatory V1 neurons to span both cell classes across the visual cortex, and that cannot be explained by spatial proximity alone. We additionally identified a subpopulation of off-center, on-surround excitatory neurons that preferentially connect — both to and from — inhibitory neurons with overlapping but oppositely modulated receptive fields.
In the post-subiculum, part of the multi-stage head-direction system, we found sparse recurrent excitation (confirming in-slice work) together with unspecific inhibition, indicating that its head-direction attractor dynamics are inherited from upstream rather than locally generated.
Together, these results establish Dyad as a scalable, validated framework for inferring monosynaptic connectivity from large-scale recordings, opening a tractable route for dissecting circuit organization.
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