Topic: microelectrode arrays

Despite being inspired by biological neuronal networks (BNNs), artificial neural networks (ANNs) have fundamental differences from their biological counterparts. While ANNs excel at solving complex problems across various domains, BNNs outperform them in terms of energy efficiency and processing highly complex parallel inputs. However, the mechanisms behind these advantages remain unclear, making it challenging to integrate them into artificial computation. It has been shown that biological neurons exhibit a characteristic sigmoid-like firing rate response [1], termed bio-sigmoid, as a functi

The emergence of spontaneous activity in the form of synchronized bursting events (SBE) is considered to play a crucial role in activity-dependent development of neuronal networks. As inhibition matures, the spatiotemporal structure of activity becomes increasingly influenced by the balance between excitation and inhibition (E/I). Moreover, intricate interactions between excitatory (ENs) and inhibitory (INs) neurons may arise during their activity-dependent structural differentiation. However, how the resulting spatial embedding of INs and ENs in a network impacts on the spatiotemporal structu

Expanding use of large-scale neural recordings has led to paradigm shifts in how neural computation is understood [1]. Technologies used for collecting these datasets face fundamental limitations ranging from power dissipation, penetration depth, and resolution [2] while facing additional practical challenges related to sensor integration with the subject or patient. While the ultimate goal of simultaneously recording from every neuron in the brain is far out of reach, more modest advancements still need to be made to ensure the proper neural coverage is achieved to construct a sufficient app