Istituto Italiano di Tecnologia

About: Istituto Italiano di Tecnologia is a facility organization based out in Genoa, Italy. It is known for research contribution in the topics: Humanoid robot & Robot. The organization has 4561 authors who have published 14595 publications receiving 437558 citations. The organization is also known as: Italian Institute of Technology & IIT.

Water-repellent cellulose fiber networks with multifunctional properties.

Abstract: We demonstrate a simple but highly efficient technique to introduce multifunctional properties to cellulose fiber networks by wetting them with ethyl-cyanoacrylate monomer solutions containing various suspended organic submicrometer particles or inorganic nanoparticles. Solutions can be applied on cellulosic surfaces by simple solution casting techniques or by dip coating, both being suitable for large area applications. Immediately after solvent evaporation, ethyl-cyanoacrylate starts cross-linking around cellulose fibers under ambient conditions because of naturally occurring surface hydroxyl groups and adsorbed moisture, encapsulating them with a hydrophobic polymer shell. Furthermore, by dispersing various functional particles in the monomer solutions, hydrophobic ethyl-cyanoacrylate nanocomposites with desired functionalities can be formed around the cellulose fibers. To exhibit the versatility of the method, cellulose sheets were functionalized with different ethyl-cyanoacrylate nanocomposite shells...

Neuromorphic electronic circuits for building autonomous cognitive systems

Abstract: Several analog and digital brain-inspired electronic systems have been recently proposed as dedicated solutions for fast simulations of spiking neural networks. While these architectures are useful for exploring the computational properties of large-scale models of the nervous system, the challenge of building low-power compact physical artifacts that can behave intelligently in the real-world and exhibit cognitive abilities still remains open. In this paper we propose a set of neuromorphic engineering solutions to address this challenge. In particular, we review neuromorphic circuits for emulating neural and synaptic dynamics in real-time and discuss the role of biophysically realistic temporal dynamics in hardware neural processing architectures; we review the challenges of realizing spike-based plasticity mechanisms in real physical systems and present examples of analog electronic circuits that implement them; we describe the computational properties of recurrent neural networks and show how neuromorphic Winner-Take-All circuits can implement working-memory and decision-making mechanisms. We validate the neuromorphic approach proposed with experimental results obtained from our own circuits and systems, and argue how the circuits and networks presented in this work represent a useful set of components for efficiently and elegantly implementing neuromorphic cognition.

Emergent functional properties of neuronal networks with controlled topology.

Abstract: The interplay between anatomical connectivity and dynamics in neural networks plays a key role in the functional properties of the brain and in the associated connectivity changes induced by neural diseases. However, a detailed experimental investigation of this interplay at both cellular and population scales in the living brain is limited by accessibility. Alternatively, to investigate the basic operational principles with morphological, electrophysiological and computational methods, the activity emerging from large in vitro networks of primary neurons organized with imposed topologies can be studied. Here, we validated the use of a new bio-printing approach, which effectively maintains the topology of hippocampal cultures in vitro and investigated, by patch-clamp and MEA electrophysiology, the emerging functional properties of these grid-confined networks. In spite of differences in the organization of physical connectivity, our bio-patterned grid networks retained the key properties of synaptic transmission, short-term plasticity and overall network activity with respect to random networks. Interestingly, the imposed grid topology resulted in a reinforcement of functional connections along orthogonal directions, shorter connectivity links and a greatly increased spiking probability in response to focal stimulation. These results clearly demonstrate that reliable functional studies can nowadays be performed on large neuronal networks in the presence of sustained changes in the physical network connectivity.

Controlling Soft Robots: Balancing Feedback and Feedforward Elements

Abstract: Soft robots (SRs) represent one of the most significant recent evolutions in robotics Designed to embody safe and natural behaviors, they rely on compliant physical structures purposefully designed to embody desirable and sometimes variable impedance characteristics This article discusses the problem of controlling SRs We start by observing that most of the standard methods of robotic control-eg, high-gain robust control, feedback linearization, backstepping, and active impedance control-effectively fight against or even completely cancel the physical dynamics of the system, replacing them with a desired model This defeats the purpose of introducing physical compliance After all, what is the point of building soft actuators if we then make them stiff by control?