Topic: micro-electrode arrays

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurological disorder characterized by the gradual degeneration of cells in the spinal cord, brainstem, and motor cortex, leading to manifestations such as weakness, muscle atrophy, and spasticity (Elisen, 2009). A comprehensive understanding of the disease process is currently lacking, making preclinical ALS models crucial to study the mechanisms of pathology insurgence, characterize the disease and test therapeutics approaches. Pathological in vitro models emerged as viable alternative to in vivo studies, consolidating a non-invasive appro

Interest in using biological neuronal networks as artificial learning systems has surged [e.g. 1,2,3]. However, networks of biological neurons differ from artificial neural networks: they are intrinsically noisy and have non-trivial physical embeddings. The question thus arises whether biological neurons are usable for artificial intelligence. A basic requirement for a system to be a good learning machine is the capacity to respond distinctly to different inputs, yet reproduce similar activity to similar stimuli. Liquid state [4] and reservoir [5] computing make direct and explicit use of this

Sleep is a recurring state of inactivity in which the brain undergoes a period of rest and restoration; its disturbances can severely affect health. Sleep and wake cycles can be mimicked in vitro: cortical neurons plated on Micro-Electrode Arrays (MEAs) show spontaneously synchronized, low-frequency firing patterns resembling the slow wave oscillations typical of NREM sleep. With specific compounds (e.g., Carbachol, orexin), network activity can be manipulated to switch to a wake state. To reduce the costs and ethical issues related to in vitro studies and to be more sustainable in research, i

In the brain, the neuronal activity is kept and controlled by a precise and well-defined “Excitatory/Inhibitory balance” among neurons, which holds importance in neurodegenerative and neurodevelopmental disorders. With the final aim of generating a reliable in vitro system of the human brain, this work was focused on the interplay between excitatory (E) and inhibitory (I) neurons in neural networks derived from human induced pluripotent stem cells (hiPSCs), deepening the critical role of heterogeneity. We exploited high-density Micro-Electrode Arrays (MEAs, 2304 electrodes) to explore two neur

In vitro models of neuronal networks have proven to be a valuable tool for gaining deeper insights into the intricate mechanisms governing the human brain. Notably, the integration of human induced pluripotent stem cells (hiPSCs) with micro-electrode arrays (MEAs) provides a controlled in vitro environment to replicate and analyse both the structural and functional elements of the human brain. As neuronal communication relies on the emission of electrical (and chemical) stimuli, the employment of electrical stimulation becomes crucial for a comprehensive exploration of neuronal assemblies, to

Cancer remains a significant challenge in the medical field (Siegel et al., 2012). Conventional treatments frequently have serious side effects that lower patients' quality of life and cannot totally eradicate the disease (Hendijani, 2015). Additionally, neural progenitor cells might be adversely affected by chemotherapeutic drugs, extinguishing any possibility of nervous tissue regeneration (Dietrich et al., 2006). This emphasizes the importance of precisely targeting cancer cells while preserving healthy brain cells. This work aims at addressing this challenge through the development of poly

The human brain comprises about 86 billion neurons organized in well-defined spatial locations that define clusters of neurons (modules). Each cognitive and motor function is possible thanks to the correct interaction among them. The disruption, loss, or alteration of these connections can produce pathological conditions. To understand how the information is transmitted and computed, we need to investigate the communication among the different modules. Another essential feature to understand is how the electrophysiological signals are transmitted is the 3D spatial organization. Many studies ma