Balance of functional integrability and spatial segregation mediates dynamical richness in modular cortical networks.

Reservoir computing is a machine learning paradigm that transforms the transient dynamics of high-dimensional nonlinear systems for processing time-series data. Although the paradigm was initially proposed to model information processing in the mammalian cortex, it remains unclear how the nonrandom network architecture, such as the modular architecture, in the cortex integrates with the biophysics of living neurons to characterize the function of biological neuronal networks (BNNs). Here, we used optogenetics and calcium imaging to record the multicellular responses of cultured BNNs and employed the reservoir computing framework to decode their computational capabilities. Micropatterned substrates were used to embed the modular architecture in the BNNs. We first show that the dynamics of modular BNNs in response to static inputs can be classified with a linear decoder and that the modularity of the BNNs positively correlates with the classification accuracy. We then used a timer task to verify that BNNs possess a short-term memory of several 100 ms and finally show that this property can be exploited for spoken digit classification. Interestingly, BNN-based reservoirs allow categorical learning, wherein a network trained on one dataset can be used to classify separate datasets of the same category. Such classification was not possible when the inputs were directly decoded by a linear decoder, suggesting that BNNs act as a generalization filter to improve reservoir computing performance. Our findings pave the way toward a mechanistic understanding of information representation within BNNs and build future expectations toward the realization of physical reservoir computing systems based on BNNs.

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.

The self-assembled bilayer lipid membrane (BLM) is the basic component of the cell membrane. The reconstitution of ion channel proteins in artificially formed BLMs represents a well-defined system for the functional analysis of ion channels and screening the effects of drugs that act on them. However, because BLMs are unstable, this limits the experimental throughput of BLM reconstitution systems. Here we report on the formation of mechanically stable solvent-free BLMs in microfabricated apertures with defined nano- and micro-tapered edge structures. The role of such nano- and micro-tapered structures on the stability of the BLMs was also investigated. Finally, this BLM system was combined with a cell-free synthesized human ether-a-go-go-related gene channel, a cardiac potassium channel whose relation to arrhythmic side effects following drug treatment is well recognized. Such stable BLMs as these, when combined with a cell-free system, represent a potential platform for screening the effects of drugs that act on various ion-channel genotypes.