Our body is a self-regulating system, able to adapt by autonomously adjusting its internal processes in response to changes in the environment, to keep vital internal variables constant, such as body temperature, blood pressure, pH levels, etc. [1] To do so, the body uses sensors, a control center and effectors distributed over the entire organism, together with feedback loops which keep internal conditions stable. This can be considered a kind of biological intelligence through adaptive regulation mechanisms, in which internal feedback loops play a crucial role.^[2,3] Brain itself is a self-regulating system, whose ability to perform efficient and fault-tolerant data processing is strongly related to its peculiar interconnected adaptive architecture, based on redundant neural circuits interacting at different scales. By emulating these systems' processing and learning mechanisms, novel neuromorphic computing technologies can strive to achieve higher levels of energy efficiency and computational performance.^[4]

Here the implementation of neuromorphic data processing devices based on self-assembled nanostructured thin films^[5,6], able to adapt to electrical and environmental stimuli, is explored. In operando observation of the structural and morphological reorganization of the nanostructured films at the nano, meso and macroscale under applied bias and ambient conditions is first reported. Furthermore, an experimental strategy based on micro-thermography is exploited for the study of the spatial and temporal dynamic of the resistive switching (RS) activity of the reorganizing networks. In particular, we investigated the control over the synchronous activity and connectivity of the micrometric active sites which rule the emerging network dynamic, of paramount importance for the correct performance of the data processing devices.

The RS activity of these materials is also described in response to mechanical and environmental stimuli (for instance, temperature and humidity),^[7] allowing the interplay between the sensing and the embodied processing capabilities of these multifaceted materials.

The controlled programmability of ns-Au network’s connectivity, by means of mechanical and electrical inputs, demonstrates the capability of our devices to encode external stimuli into specific connectivity, which can last in time and be reprogrammed on demand. This enables the fabrication of data processing devices based on self-assembled systems, such as reconfigurable nonlinear threshold logic gates,^[4,7,8] featuring tailored and adaptable connectivity that allows the controlled emerging responses of the complex networks. We also demonstrate the potentiality of the use of these self-assembled reconfigurable devices to classify with high accuracy and in real-time neuronal traces corresponding to physiological and evoked spiking activity recorded from the barrel cortex of a rat.^[9]

The use of neuromorphic self-assembled materials with complex wiring and redundant morphological features is proposed for the implementation of energy-efficient reprogrammable edge computing devices.