5 papers
Physical Neural Networks Need Nonlinearity, Amplification, and Suppression for Learning
Nex Chiaki Xijana Stuhlmüller, Marjolein Dijkstra
The exponential growth in energy consumption of artificial intelligence systems has spurred interest in physical computing paradigms that exploit the relaxation of physical systems…
Nonlinear iontronic signal processing with neuromorphic Spike Rate-Dependent Plasticity
T. M. Kamsma, Y. Gu, D. Shi +4
We present an integrated iontronic memristor circuit that reproduces biologically inspired Spike Rate-Dependent Plasticity (SRDP) and functions as a physical nonlinear frequency ke…
Energy-efficient time series processing in real-time with fluidic iontronic memristor circuits
T. M. Kamsma, Y. Gu, C. Spitoni +3
Iontronic neuromorphic computing has emerged as a rapidly expanding paradigm. The arrival of angstrom-confined iontronic devices enables ultra-low power consumption with dynamics a…
Neuromorphic Computing with Microfluidic Memristors
Nex C. X. Stuhlmüller, René van Roij, Marjolein Dijkstra
Conical microfluidic channels filled with electrolytes exhibit volatile memristive behavior, offering a promising platform for energy-efficient, neuromorphic computing. Here, we in…
Intelligent Soft Matter: Towards Embodied Intelligence
Vladimir A. Baulin, Achille Giacometti, Dmitry Fedosov +24
Intelligent soft matter stands at the intersection of materials science, physics, and cognitive science, promising to change how we design and interact with materials. This transfo…