Multi-scale modelling of the epileptic brain: advantages of computational therapy exploration.

Objective: Epilepsy is a complex disease spanning across multiple scales, from ion channels in neurons to neuronal circuits across the entire brain. Over the past decades, computational models have been used to describe the pathophysiological activity of the epileptic brain from different aspects. Traditionally, each computational model can aid in optimizing therapeutic interventions, therefore, providing a particular view to design strategies for treating epilepsy. As a result, most studies are concerned with generating specific models of the epileptic brain that can help us understand the certain machinery of the pathological state. Those specific models vary in complexity and biological accuracy, with system-level models often lacking biological details.Approach: Here, we review various types of computational model of epilepsy and discuss their potential for different therapeutic approaches and scenarios, including drug discovery, surgical strategies, brain stimulation, and seizure prediction. We propose that we need to consider an integrated approach with a unified modelling framework across multiple scales to understand the epileptic brain. Our proposal is based on the recent increase in computational power, which has opened up the possibility of unifying those specific epileptic models into simulations with an unprecedented level of detail.Main results: A multi-scale epilepsy model can bridge the gap between biologically detailed models, used to address molecular and cellular questions, and brain-wide models based on abstract models which can account for complex neurological and behavioural observations.Significance: With these efforts, we move toward the next generation of epileptic brain models capable of connecting cellular features, such as ion channel properties, with standard clinical measures such as seizure severity.

The brain-inspired decoder for natural visual image reconstruction.

The visual system provides a valuable model for studying the working mechanisms of sensory processing and high-level consciousness. A significant challenge in this field is the reconstruction of images from decoded neural activity, which could not only test the accuracy of our understanding of the visual system but also provide a practical tool for solving real-world problems. Although recent advances in deep learning have improved the decoding of neural spike trains, little attention has been paid to the underlying mechanisms of the visual system. To address this issue, we propose a deep learning neural network architecture that incorporates the biological properties of the visual system, such as receptive fields, to reconstruct visual images from spike trains. Our model outperforms current models and has been evaluated on different datasets from both retinal ganglion cells (RGCs) and the primary visual cortex (V1) neural spikes. Our model demonstrated the great potential of brain-inspired algorithms to solve a challenge that our brain solves.

Bioresorbable thin-film silicon diodes for the optoelectronic excitation and inhibition of neural activities.

Neural activities can be modulated by leveraging light-responsive nanomaterials as interfaces for exerting photothermal, photoelectrochemical or photocapacitive effects on neurons or neural tissues. Here we show that bioresorbable thin-film monocrystalline silicon pn diodes can be used to optoelectronically excite or inhibit neural activities by establishing polarity-dependent positive or negative photovoltages at the semiconductor/solution interface. Under laser illumination, the silicon-diode optoelectronic interfaces allowed for the deterministic depolarization or hyperpolarization of cultured neurons as well as the upregulated or downregulated intracellular calcium dynamics. The optoelectronic interfaces can also be mounted on nerve tissue to activate or silence neural activities in peripheral and central nervous tissues, as we show in mice with exposed sciatic nerves and somatosensory cortices. Bioresorbable silicon-based optoelectronic thin films that selectively excite or inhibit neural tissue may find advantageous biomedical applicability.