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.

Bilevel Programming for Hyperparameter Optimization and Meta-Learning

Abstract: We introduce a framework based on bilevel programming that unifies gradient-based hyperparameter optimization and meta-learning. We show that an approximate version of the bilevel problem can be solved by taking into explicit account the optimization dynamics for the inner objective. Depending on the specific setting, the outer variables take either the meaning of hyperparameters in a supervised learning problem or parameters of a meta-learner. We provide sufficient conditions under which solutions of the approximate problem converge to those of the exact problem. We instantiate our approach for meta-learning in the case of deep learning where representation layers are treated as hyperparameters shared across a set of training episodes. In experiments, we confirm our theoretical findings, present encouraging results for few-shot learning and contrast the bilevel approach against classical approaches for learning-to-learn.

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models.

Abstract: Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best “LFP proxy”, we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with “ground-truth” LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

Deletion of TrkB in adult progenitors alters newborn neuron integration into hippocampal circuits and increases anxiety-like behavior

Abstract: New neurons in the adult dentate gyrus are widely held to incorporate into hippocampal circuitry via a stereotypical sequence of morphological and physiological transitions, yet the molecular control over this process remains unclear. We studied the role of brain-derived neurotrophic factor (BDNF)/TrkB signaling in adult neurogenesis by deleting the full-length TrkB via Cre expression within adult progenitors in TrkBlox/lox mice. By 4 weeks after deletion, the growth of dendrites and spines is reduced in adult-born neurons demonstrating that TrkB is required to create the basic organization of synaptic connections. Later, when new neurons normally display facilitated synaptic plasticity and become preferentially recruited into functional networks, lack of TrkB results in impaired neurogenesis-dependent long-term potentiation and cell survival becomes compromised. Because of the specific lack of TrkB signaling in recently generated neurons a remarkably increased anxiety-like behavior was observed in mice carrying the mutation, emphasizing the contribution of adult neurogenesis in regulating mood-related behavior.

Methods and Technologies for the Implementation of Large-Scale Robot Tactile Sensors

Abstract: Even though the sense of touch is crucial for humans, most humanoid robots lack tactile sensing. While a large number of sensing technologies exist, it is not trivial to incorporate them into a robot. We have developed a compliant “skin” for humanoids that integrates a distributed pressure sensor based on capacitive technology. The skin is modular and can be deployed on nonflat surfaces. Each module scans locally a limited number of tactile-sensing elements and sends the data through a serial bus. This is a critical advantage as it reduces the number of wires. The resulting system is compact and has been successfully integrated into three different humanoid robots. We have performed tests that show that the sensor has favorable characteristics and implemented algorithms to compensate the hysteresis and drift of the sensor. Experiments with the humanoid robot iCub prove that the sensors can be used to grasp unmodeled, fragile objects.

Artificial honeycomb lattices for electrons, atoms and photons

Abstract: Artificial honeycomb lattices offer a tunable platform for studying massless Dirac quasiparticles, and their topological and correlated phases.