Topology-Dependent Communication and Computing in Bioengineered Neuronal Networks

Abstract

Bioengineered neuronal systems are increasingly explored as living computational substrates, yet their end-to-end communication properties remain poorly characterized beyond global activity statistics. We model a mechanosensitive spiking neuronal network as a noisy multiple-input multiple-output (MIMO) communication system to quantify how topology and substrate mechanics shape information transfer and computation. Using a mechanosensitive Izhikevich network with time-varying stiffness, we compare random, clustered, and disintegrated topologies under matched stimulation and observation noise. Communication is quantified using transfer entropy, achievable mutual information rate from binned spike observations, and information redundancy. Computation is evaluated using a receiver-level separability metric posed as a transmitter-identification task. Across noise levels, clustered networks more consistently maintain higher achievable rates and exhibit more concentrated transmitter–receiver separability than random and disintegrated substrates. These results show that topology and mechanical state jointly regulate communication and computational separability in bioengineered neuronal networks, providing design-relevant metrics for bioengineered intelligence systems.