The brain’s 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. In such a system, individual neurons count for little; it is population activity that matters. Part of the difficulty in understanding population coding is that neurons are noisy, which means that the same pattern of activity never occurs twice, even when the same stimulus is presented.¹ In this context, correlation and simultaneity within spiking activity of different neurons may play a crucial role in decreasing/increasing the amount of information that is decoded by population activity, in a non-trivial way.¹ Furthermore, since cells are recurrently connected, forming a certain number of recursive loops, their own connectivity plays also a crucial role in the coordinated and synchronous firing of the population.²

On the artificial side, the exploitation of self-assembled systems with nonlinear dynamics has already been demonstrated as a possible unconventional strategy for energy efficient data processing tasks, relying on the emerging behavior of stochastic network activity.^3,4  However, the encoding of the stimulus and the decoding of information in the response of such systems is still a challenge.

Here I will present and discuss results obtained on the spatial and temporal dynamic of the resistive switching activity of a nanostructured gold network, combining micro-thermography and electrical measurements. Cluster-assembled films produced by Supersonic Cluster Beam Deposition (SCBD)⁵ have been widely studied from both experimental and theoretical perspectives, as resistive switching systems that can be used for data processing tasks.^6–10 The main mechanism at the nanoscale which causes the reversible changes in electric conductivity (10% to 10⁵% from its initial value) is the stochastic reorganization of the grain boundaries in the nanogranular matrix of gold cluster-assembled thin films.^11,12

The description and the understanding of the dynamic of local active sites which lead, at a larger scale, to the emerging electrical behavior of the network will be shown for the first time. The control obtained over the adaptive reorganization of the network will be proposed as an effective strategy to control the reversible connectivity and the tunable correlation between local active sites which promote the activity of the network at the microscale. This control allows the programmability of these systems used as reversible electronic switches and reconfigurable threshold logic gates (TLGs), implemented on both hard⁶ and soft substrates¹³ as complete functional systems for Boolean function classification tasks.¹⁴ We also demonstrated the potentiality of the use of these nanostructured devices to classify with high accuracy and in real-time neuronal traces, requiring limited datasets for training and memory storage.

The hardware implementation of such self-assembled neuromorphic materials will be proposed for the development of novel hybrid reprogrammable electronic architectures for data processing tasks employed on edge systems to efficiently interact with the environment.