Topic: high-density microelectrode arrays

Artificial neural networks simplify complex biological circuits into tractable models for computational exploration and experimentation. However, the simplification of artificial models also undermines their applicability to real brain dynamics. Typical efforts to address this mismatch add complexity to increasingly unwieldy models. Here, we take a different approach; by reducing the complexity of a biological cortical culture, we aim to distil the essential factors of neuronal dynamics and plasticity. We leverage recent advances in growing neurons from human induced pluripotent stem cells (hi

Recent advances in neuronal recording technology have facilitated the development of large-scale, high-density microelectrode arrays resulting in a substantial increase (>10x) in the number of neurons that can be studied simultaneously within a localized area of neural tissue. A prime example is the recent development of the Neuropixels probe, which consists of a high-channel count Si shank with continuous, dense, programmable recording sites (~1000/cm). Neuropixels probes have transformed neurophysiological studies in rodent models by enabling recording from large populations of neurons anywh

Precise timing of action potentials is crucial for processing sensory information. Axonal length and propagation speed largely determine the time necessary for action potentials to reach postsynaptic neurons. Within the retina, retinal ganglion cell (RGC) axons form the retinal nerve fiber layer (RNFL), a highly organized layer with species-specific axonal arrangements. The human RNFL is characterized by unmyelinated axons, resulting in slow signal transmission, and by the presence of the fovea, a specialized region enabling high-resolution vision located temporally to the optic nerve head (i.

Artificial intelligence (AI) has proven to be effective in solving complex problems in fields such as computer vision, natural language processing and medicine. However, in terms of energy efficiency, deep learning approaches are still outmatched by their biological counterparts. Yet, the underlying fundamental mechanisms of information storage, processing and computation of biological neuronal networks (BNNs) remain poorly understood.In bottom-up neuroscience, we tackle this challenge by analyzing low-density, engineered BNNs in vitro. Primary hippocampal rat neurons or human induced pluripot

While dissociated cultures of rodent neurons on microelectrode arrays (MEAs) have become a prominent method for studying neuronal network properties, establishing human neuronal cultures with mature electrophysiological features compatible with MEAs has posed a significant challenge. The aim of this study was to establish a reliable protocol for generating human-derived neuronal networks on High Density Microelectrode Arrays (HD-MEA) and to characterize their electrophysiological features for potential applications in disease modeling and therapeutic testing. Human-induced pluripotent stem cel

The advent of large-scale and high-density ex-vivo experiments enables the unveiling of neural network dynamics at unprecedented resolution. It is now routinely possible to grow neural network cultures from individual human subjects, allowing us to compare the dynamics of subtly different genetic backgrounds. As the analysis of network dynamics becomes more fine-grained, we observed unexplained variability of our results during the development stages of the cultures, beyond the boundaries of genotypic differences. Our work leveraged breakthroughs in culturing cortical neurons from human induce

Human induced pluripotent stem cell (hiPSC)-derived neural models have emerged as invaluable tools for studying neurological disorders, such as epilepsy, Alzheimer’s, and Parkinson’s disease.Real-time, label-free measurement of electrical activity in self-organizing in vitro cellular models provides critical insight into the complexity of their neuronal networks. High-density microelectrode arrays (HD-MEAs) enable non-invasive electrophysiological recordings from various electrogenic samples, including iPSC-derived neurons, retinal explants, brain slices, and neural organoids.In this study, we

While normal pain perception is necessary for increasing survival possibility of organisms, chronic pain is a great source of disability and is a serious social problem. On pain perception, dorsal root ganglion (DRG) neurons generate electrical activity by nociceptive stimuli, and transmit signals toward spinal cord. Alteration in pain processing circuit of spinal cord network can cause a type of chronic pain called nociplastic pain. Conventional studies using model animals are limited to investigate the complex mechanisms of functional changes in neural networks. In this study, we aimed to re