Speaker
Description
Closed-loop control of cortical population activity is increasingly investigated for circuit-specific neuromodulation and neural prostheses, and as a causal tool to probe learning and plasticity. Classical control theory, however, requires prior knowledge of circuit connectivity and dynamics (Gu,2015). Baggio, 2019 showed that finite-horizon controllers can instead be learned directly from input–output data, without identifying the underlying system. Barzon, 2026 recently implemented this framework as a real-time brain-computer interface in macaque cortex, using patterned microstimulation and multi-electrode recordings to identify the manifold of reachable population states and compute the stimulation sequences required to reach selected targets. What remains unresolved is which circuit properties determine whether stimulation recruits population patterns already expressed spontaneously, or accesses directions outside the circuit's intrinsic repertoire.
We address this question in parallel using primary rat hippocampal cultures on High-Density MEAs, and in silico biophysically grounded spiking networks with short-term synaptic depression, tunable intermodular mixing, and external drive.
In our Data-Driven Control (DDC) protocol, a Poisson GLM estimates the finite-horizon controllability matrix (the mapping from stimulation sequences to terminal population spike-counts) from random stimulation–response trials; then the sequence best approximating each target is identified via optimal control. We tested DDC across 38 sessions in vitro and ~100 in silico networks, and analyzed intrinsic–reachable manifold overlap on different topologies.
DDC reliably evoked target-specific patterns in both systems, outperforming shuffled stimulation–response mappings. In vitro, reachable activity remained predominantly within the intrinsic manifold, indicating that control largely recombines spontaneously expressed patterns. Off-manifold recruitment was limited but increased with strong modularity, as simultaneous stimulation coactivated segregated clusters rarely recruited together spontaneously. In silico, mixing and external drive reshaped intrinsic dimensionality and its overlap with the reachable set. Neural reachability therefore emerges from the interaction between circuit architecture, spontaneous dynamics, and stimlation constraints; whether more channels expand the manifold remains to be tested.
| Preferred Presentation | Oral Presentation |
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