Biology subjects

Hirano-Iwata, A.

Publications and source records attributed to Hirano-Iwata, A..

3 recordsLinked to original sources

bioRxiv2025

Online supervised learning of temporal patterns in biological neural networks under feedback control

In vitro biological neural networks (BNNs) provide a well-defined model system to constructively investigate how living cells interact with their environment to shape high-dimensional dynamics that could be used to generate a coherent temporal output, such as those required for motor control. Here, we developed a real-time closed-loop BNN system capable of generating periodic and chaotic temporal signals by integrating cultured cortical neurons with microfluidic devices and high-density microelectrode arrays. We show that training a simple linear decoder with fixed feedback weights enables the system to learn and autonomously generate diverse temporal patterns. When feedback was switched on, irregular activity in BNNs is transformed into low-dimensional, structured dynamics, producing coherent trajectories characterized by stable transitions between neural states. BNNs trained on different target frequencies--ranging from 4 to 30 s--could be trained to sustain oscillations at distinct frequencies, demonstrating their adaptability. Importantly, a top-down control of self-organized network formation with microfluidic devices is the key to suppress excessive synchronization and increase dynamical complexity in BNNs, facilitating the training and robust output generation. This work offers a biologically inspired platform for understanding the physical basis of cortical computation and for advancing energy-efficient neuromorphic computation. Significance StatementReservoir computing is a machine learning paradigm that exploits the transient dynamics of high-dimensional nonlinear systems. Although it was originally inspired by the mammalian brain and widely explored in physical systems, its implementations in biological neural networks (BNNs) have been limited due to their excessive connectivity and global synchrony in vitro. Here, we use microfluidic devices to construct modular, nonrandomly connected BNNs and integrate them with microelectrode arrays in a closed-loop reservoir computing environment. We show that the system can be trained to autonomously output various temporal signals, with the modular connectivity that is essential for learning. In vitro BNNs provide unique alternatives for physical reservoirs with dynamic adaptability.

neuroscience↗

bioRxiv2025

Microfluidic platforms for probing spontaneous functional recovery in hierarchically modular neuronal networks

Inherent capacity to flexibly reorganize after injury is a hallmark of brain networks, and recent studies suggest that functional consequences of focal damage are strongly influenced by the networks non-random connectivity. Although many of these insights have been derived from animal models and computational simulations, experimental platforms that enable bottom-up investigations of the structure-function relationships underlying damage and recovery processes remain limited. In this study, we used polydimethylsiloxane microfluidic devices to construct hierarchically modular neuronal networks that mimic the architectural features of the mammalian cortex. Laser microdissection was employed to selectively sever intermodular connections, enabling controlled damage to either hub or peripheral connections. Damage to hub connections led to delayed recovery, requiring more than three days for correlations to re-emerge. In contrast, peripheral damage resulted in faster recovery. Experiments that induced repeated injury to neuronal networks further demonstrated that recovery primarily occurred through the formation of alternative pathways rather than restoration of the original connections. These findings highlight how topological features of neuronal networks shape their response to injury and subsequent reorganization, providing mechanistic insights into the intrinsic self-repair capacity of biological systems.

neuroscience↗

bioRxiv2025

Modular architecture confers robustness to damage and facilitates recovery in spiking neural networks modeling in vitro neurons

Impaired brain function is restored following injury through dynamic processes that involve synaptic plasticity. This restoration is supported by the brains inherent modular organization, which promotes functional separation and redundancy. However, it remains unclear how the modular structure interacts with synaptic plasticity, most notably in the form of spike-timing-dependent plasticity (STDP), to define the damage response and recovery efficiency. In this work, we numerically modeled the response and recovery to damage of a neuronal network in vitro bearing a modular structure. Consistent with the in vitro observations, the in silico numerical model effectively captured the decline and subsequent recovery of spontaneous activity following the injury. We revealed that the modular structure confers robustness to injury, minimizes the decrease in neuronal activity, and promotes recovery via STDP. Finally, using the reservoir computing framework, we show that information representation in the neuronal network improves with the recovery of synchronous activity. Our work provides an experimental-numerical platform for predicting recovery in damaged neuronal networks and may help developing effective models for brain injury.

neuroscience↗