Organic transistor technology has been the subject of intense research in the last decade paving the way for
industrialization of organic electronics applications characterized by low fabrication costs, additive manufacturing
processes at low-energy, high flexibility and application versatility. A dedicated technology platform has been developed
at ST and fully characterized, which is devoted to the manufacturing of all-organic transistor devices with sub-micron
feature size as multilayered structures, obtained through a sequential combination of deposition from solution and
patterning steps through stamps. The design and manufacturing platform is actually being assessed through the
development of the first all-organic 'reduced complexity' microprocessor. An outline of the architecture and major
building blocks will be presented.
The study of spatio-temporal patterns generation and processing in systems with high parallelism like biological neuronal networks gives birth to a new technology able to realize architectures with robust performance even in noisy environments. The behavioural properties of neural assemblies warrant an effective exchange and use of information in presence of high-level neuronal noise.
Neuron population processing and self-organization have been reproduced by connecting several neuron through synaptic connections, which can be either electrical or chemical, in artificial information processing architectures based on Field Programmable Gate Arrays (FPGA). The adopted neuron model is based on Izhikevich’s description of cortical neuron dynamics .
The development of biological neuronal network models has been focused on architecture features like changes over time of topologies, uniformity of the connections, node diversity, etc. The hardware reproduction of neuron dynamical behaviour, by giving high computation performance, allows the development of innovative computational methods and models based on self-organizing nonlinear architectures.
In this work neuron information exchanges have been investigated by modeling nonlinear lattices by connecting Hindmarsh-Rose neurons. In the first phase, regular network models have been characterized by varying the coupling strength in bidimensional arrays of neurons. In the second one, dynamical behavior of 2-D small world configurations,
with long term connections, has been considered. Several experiments have been characterized by quantifying a synchronization index. A trade off between network architecture complexity and information exchange performances has been discussed.
The concept of stochastic resonance introduced the idea that the presence of noise in nonlinear systems may have benefic effects. In this paper different regular topologies of populations of FitzHugh-Nagumo neurons have been investigated with respect to the presence of noise in the network. Each neuron is subjected to an independent source of noise. In these conditions the behavior of the population depend on the connection among the elements. In population of uncoupled neurons the so-called stochastic resonance without tuning was observed. Moreover, we show that globally coupled neurons have increasing response-to-stimulus coherence for increasing values of the coupling strength. In locally coupled neurons the performance depend on the neighborhood radius and in general are higher than in the case of uncoupled neurons.