Major efforts to reproduce the brain performances in terms of classification and pattern recognition have been focused on the development of artificial neuromorphic systems based on topdown lithographic technologies typical of highly integrated components of digital computers. Unconventional computing has been proposed as an alternative exploiting the complexity and collective phenomena originating from various classes of physical substrates. Materials composed of a large number of non-linear nanoscale junctions are of particular interest: these systems, obtained by the self-assembling of nano-objects like nanoparticles and nanowires, results in non-linear conduction properties characterized by spatiotemporal correlation in their electrical activity. This appears particularly useful for classification of complex features: nonlinear projection into a high-dimensional space can make data linearly separable, providing classification solutions that are computationally very expensive with digital computers. Recently we reported that nanostructured Au films fabricated from the assembling of gold clusters by supersonic cluster beam deposition show a complex resistive switching behaviour. Their non-linear electric behaviour is remarkably stable and reproducible allowing the facile training of the devices on precise resistive states. Here we report about the fabrication and characterization of a device that allows the binary classification of Boolean functions by exploiting the properties of cluster-assembled Au films interconnecting a generic pattern of electrodes. This device, that constitutes a generalization of the perceptron, can receive inputs from different electrode configurations and generate a complete set of Boolean functions of n variables for classification tasks. We also show that the non-linear and non-local electrical conduction of clusterassembled gold films, working at room temperature, allows the classification of non-linearly separable functions without previous training of the device.

Among unconventional computing platforms, neuromorphic artificial systems aim at the reproduction of the human brain functions in terms of classification and pattern recognition capabilities, overcoming the limitations of traditional digital computers and closing the gap with the energetic efficiency of biological systems. Here we present a model, the receptron, based on a generalization of the perceptron, and its physical implementation via a neuromorphic system which opens the way for the exploitation of complex networks of reconfigurable elements. Recently we have reported that nanostructured Au films, fabricated from gold clusters produced in the gas phase, have non-linear and non-local electric conduction properties caused by the extremely high density of grain boundaries and the resulting complex arrangement of nanojunctions. Exploiting these non-linear and non-local properties we produced and tested a receptron that can receive inputs from different electrode configurations and generate a complete set of Boolean functions of n variables for classification tasks. The receptron allows also the classification of non-linearly separable functions without previous training of the device.

The study was carried out to determine the concentrations of cefodizime (single 2-g intravenous [i.v.] dose) and ceftriaxone (single 2-g i.v. dose) in the sera and bones of 42 patients (18 women and 24 men) undergoing hip arthroplasty. The concentrations of cefodizime and ceftriaxone in cancellous and cortical bone appear to be related to the free levels in serum but not to the total levels in serum, so the concentrations of cephalosporins in bone must be compared with the free concentrations in serum. Both drugs diffuse well into bone, with concentrations exceeding the MIC at which 90% of the major pathogenic infecting organisms are inhibited.

Nanostructured Au films fabricated by the assembling of nanoparticles produced in the gas phase have shown properties suitable for neuromorphic computing applications: they are characterized by a non-linear and non-local electrical behavior, featuring switches of the electric resistance whose activation is typically triggered by an applied voltage over a certain threshold. These systems can be considered as complex networks of metallic nanojunctions where thermal effects at the nanoscale cause the continuous rearrangement of regions with low and high electrical resistance. In order to gain a deeper understanding of the electrical properties of this nano granular system, we developed a model based on a large three dimensional regular resistor network with non-linear conduction mechanisms and stochastic updates of conductances. Remarkably, by increasing enough the number of nodes in the network, the features experimentally observed in the electrical conduction properties of nanostructured gold films are qualitatively reproduced in the dynamical behavior of the system. In the activated non-linear conduction regime, our model reproduces also the growing trend, as a function of the subsystem size, of quantities like Mutual and Integrated Information, which have been extracted from the experimental resistance series data via an information theoretic analysis. This indicates that nanostructured Au films (and our model) possess a certain degree of activated interconnection among different areas which, in principle, could be exploited for neuromorphic computing applications.