Abstract Developments in computing hardware are constrained by the operating principles of complementary metal oxide semiconductor (CMOS) technology, fabrication limits of nanometer scaled features, and difficulties in effective utilization of high density interconnects. This set of obstacles has promulgated a search for alternative, energy efficient approaches to computing inspired by natural systems including the mammalian brain. Atomic switch network (ASN) devices are a unique platform specifically developed to overcome these current barriers to realize adaptive neuromorphic technology. ASNs are composed of a massively interconnected network of atomic switches with a density of ∼10⁹ units/cm² and are structurally reminiscent of the neocortex of the brain. ASNs possess both the intrinsic capabilities of individual memristive switches, such as memory capacity and multi-state switching, and the characteristics of large-scale complex systems, such as power-law dynamics and non-linear transformations of input signals. Here we describe the successful nanoarchitectonic fabrication of next-generation ASN devices using combined top-down and bottom-up processing and experimentally demonstrate their utility as reservoir computing hardware. Leveraging their intrinsic dynamics and transformative input/output (I/O) behavior enabled waveform regression of periodic signals in the absence of embedded algorithms, further supporting the potential utility of ASN technology as a platform for unconventional approaches to computing.