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Complexity is an increasingly crucial aspect of societal, environmental and biological phenomena. Using a dense unorganized network of synthetic synapses it is shown that a complex adaptive system can be physically created on a microchip built especially for complex problems. These neuro-inspired atomic switch networks (ASNs) are a dynamic system with inherent and distributed memory, recurrent pathways, and up to a billion interacting elements. We demonstrate key parameters describing self-organized behavior such as non-linearity, power law dynamics, and multistate switching regimes. Device dynamics are then investigated using a feedback loop which provides control over current and voltage power-law behavior. Wide ranging prospective applications include understanding and eventually predicting future events that display complex emergent behavior in the critical regime.

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.