Enhanced responses to inflammatory cytokine interleukin-6 in micropatterned networks of cultured cortical neurons.

Cortical neurons in dissociated cultures are an indispensable model system for pharmacological research that provides insights into chemical responses in well-defined environments. However, cortical neurons plated on homogeneous substrates develop an unstructured network that exhibits excessively synchronized activity, which occasionally masks the consequences induced by external substances. Here, we show that hyperactivity and excessive synchrony in cultured cortical networks can be effectively suppressed by growing neurons in microfluidic devices. These devices feature a hierarchically modular design that resembles the in vivo network. We focused on interleukin-6, a pro-inflammatory cytokine, and assessed its acute and chronic effects. Fluorescence calcium imaging of spontaneous neural activity for up to 20 days of culture showed detectable modulation of collective activity events and neural correlation in micropatterned neurons, which was not apparent in neurons cultured on homogeneous substrates. Our results indicate that engineered neuronal networks provide a unique platform for detecting and understanding the fundamental effects of biochemical compounds on neuronal networks.

Modular architecture facilitates noise-driven control of synchrony in neuronal networks.

High-level information processing in the mammalian cortex requires both segregated processing in specialized circuits and integration across multiple circuits. One possible way to implement these seemingly opposing demands is by flexibly switching between states with different levels of synchrony. However, the mechanisms behind the control of complex synchronization patterns in neuronal networks remain elusive. Here, we use precision neuroengineering to manipulate and stimulate networks of cortical neurons in vitro, in combination with an in silico model of spiking neurons and a mesoscopic model of stochastically coupled modules to show that (i) a modular architecture enhances the sensitivity of the network to noise delivered as external asynchronous stimulation and that (ii) the persistent depletion of synaptic resources in stimulated neurons is the underlying mechanism for this effect. Together, our results demonstrate that the inherent dynamical state in structured networks of excitable units is determined by both its modular architecture and the properties of the external inputs.