Editorial: Brain-inspired computing: Neuroscience drives the development of new electronics and artificial intelligence

• Published in: Frontiers in cellular neuroscience 2022-12, Vol.16, p.1115395-1115395
• Main Authors: Gandolfi, Daniela, Puglisi, Francesco Maria, Serb, Alexander, Giugliano, Michele, Mapelli, Jonathan
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; bio-inspired artificial intelligence; Brain research; Cellular Neuroscience; computational neuroscience; Conflicts of interest; Data storage; electronic; Firing pattern; Interfaces; Machine learning; Metabolism; Nanotechnology; Nervous system; Neural networks; neuro-inspired computing; neuromorphic; Neurosciences; Software; Synaptic plasticity
• ISSN: 1662-5102; 1662-5102
• DOI: 10.3389/fncel.2022.1115395

Neuroscience drives the development of new electronics and artificial intelligence The advent of artificial intelligence (AI) applications in everyday life has continuously raised the need for the deployment of advanced machine learning systems like artificial neural networks, which in increasingly numerous tasks outperform humans. [...]the development of humanoid robotics platforms showing intelligent behaviors has boosted research on engineering systems that mimic neural functions. Nonetheless, solutions based on conventional hardware with limited energy efficiency are poorly sustainable, require repeated training cycles, depend on supervised learning rules, and most importantly must be trained offline on vert large datasets to perform online on the desired tasks. Many scientific works have appeared since those initial seminal papers, testifying progresses along four main directions: i) Materials: Novel nanomaterials proposed as candidates for neuromorphic computing devices, showing functionalities that better mimic neuronal behaviors (Sangwan and Hersam, 2020); ii) Synapses: Memristive devices, as alternatives to conventional data storage concepts, capable of reproducing peculiar characteristics of biological synaptic long-term plasticity in both supervised (Florini et al., 2022) and unsupervised regimes (Serb et al., 2016); iii) Neurons: Silicon neurons as the main building block of novel architectures, implementing systems reacting in real-time or bidirectional brain-machine interfaces, where the complexity of their design scales up with the level of detail of neuronal representation (Indiveri et al., 2011); iv) Architecture: Spiking neural networks (SNN) as sustainable solutions, mimicking brain circuits operations, to efficiently perform distributed computation.