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: Robot & Humanoid 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.

Extracellular matrix molecules, their receptors, and secreted proteases in synaptic plasticity.

Abstract: Neural cells secrete diverse molecules, which accumulate in the extracellular space and form the extracellular matrix (ECM). Interactions between cells and the ECM are well recognized to play the crucial role in cell migration and guidance of growing axons, whereas formation of mature neural ECM in the form of perineuronal nets is believed to restrict certain forms of developmental plasticity. On the other hand, major components of perineuronal nets and other ECM molecules support induction of functional plasticity, the most studied form of which is long-term potentiation. Here, we review the underlying mechanisms by which ECM molecules, their receptors and remodeling proteases regulate the induction and maintenance of synaptic modifications. In particular, we highlight that activity-dependent secretion and activation of proteases leads to a local cleavage of the ECM and release of signaling proteolytic fragments. These molecules regulate transmitter receptor trafficking, actin cytoskeleton, growth of dendritic spines, and formation of dendritic filopodia.

Charger-mediated energy transfer for quantum batteries: An open-system approach

Abstract: The energy charging of a quantum battery is analyzed in an open quantum setting, where the interaction between the battery element and the external power source is mediated by an ancilla system (the quantum charger) that acts as a controllable switch. Different implementations are analyzed putting emphasis on the interplay between coherent energy pumping mechanisms and thermalization.

Inhibition of α-Synuclein Fibrillization by Dopamine Is Mediated by Interactions with Five C-Terminal Residues and with E83 in the NAC Region

Abstract: The interplay between dopamine and α-synuclein (AS) plays a central role in Parkinson's disease (PD). PD results primarily from a severe and selective devastation of dopaminergic neurons in substantia nigra pars compacta. The neuropathological hallmark of the disease is the presence of intraneuronal proteinaceous inclusions known as Lewy bodies within the surviving neurons, enriched in filamentous AS. In vitro, dopamine inhibits AS fibril formation, but the molecular determinants of this inhibition remain obscure. Here we use molecular dynamic (MD) simulations to investigate the binding of dopamine and several of its derivatives onto conformers representative of an NMR ensemble of AS structures in aqueous solution. Within the limitations inherent to MD simulations of unstructured proteins, our calculations suggest that the ligands bind to the 125YEMPS129 region, consistent with experimental findings. The ligands are further stabilized by long-range electrostatic interactions with glutamate 83 (E83) in the NAC region. These results suggest that by forming these interactions with AS, dopamine may affect AS aggregation and fibrillization properties. To test this hypothesis, we investigated in vitro the effects of dopamine on the aggregation of mutants designed to alter or abolish these interactions. We found that point mutations in the 125YEMPS129 region do not affect AS aggregation, which is consistent with the fact that dopamine interacts non-specifically with this region. In contrast, and consistent with our modeling studies, the replacement of glutamate by alanine at position 83 (E83A) abolishes the ability of dopamine to inhibit AS fibrillization.

Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks.

Abstract: Models of networks of Leaky Integrate-and-Fire neurons (LIF) are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single-neuron and neural population dynamics of conductance-based networks (COBN) and current-based networks (CUBN) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-sensitive in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, COBN showed stronger neuronal population synchronization in the gamma band, and their spectral information about the network input was higher and spread over a broader range of frequencies. These results suggest that second order properties of network dynamics depend strongly on the choice of synaptic model.

Self-limited single nanowire systems combining all-in-one memristive and neuromorphic functionalities

Abstract: The ability for artificially reproducing human brain type signals' processing is one of the main challenges in modern information technology, being one of the milestones for developing global communicating networks and artificial intelligence. Electronic devices termed memristors have been proposed as effective artificial synapses able to emulate the plasticity of biological counterparts. Here we report for the first time a single crystalline nanowire based model system capable of combining all memristive functions - non-volatile bipolar memory, multilevel switching, selector and synaptic operations imitating Ca2+ dynamics of biological synapses. Besides underlying common electrochemical fundamentals of biological and artificial redox-based synapses, a detailed analysis of the memristive mechanism revealed the importance of surfaces and interfaces in crystalline materials. Our work demonstrates the realization of self-assembled, self-limited devices feasible for implementation via bottom up approach, as an attractive solution for the ultimate system miniaturization needed for the hardware realization of brain-inspired systems.