A developmental model of audio-visual attention (MAVA) for bimodal language learning in infants and robots.

A social individual needs to effectively manage the amount of complex information in his or her environment relative to his or her own purpose to obtain relevant information. This paper presents a neural architecture aiming to reproduce attention mechanisms (alerting/orienting/selecting) that are efficient in humans during audiovisual tasks in robots. We evaluated the system based on its ability to identify relevant sources of information on faces of subjects emitting vowels.

Therapeutics of the future: Navigating the pitfalls of extracellular vesicles research from an osteoarthritis perspective.

Extracellular vesicles have gained wide momentum as potential therapeutics for osteoarthritis, a highly prevalent chronic disease that still lacks an approved treatment. The membrane-bound vesicles are secreted by all cells carrying different cargos that can serve as both disease biomarkers and disease modifiers. Nonetheless, despite a significant peak in research regarding EVs as OA therapeutics, clinical implementation seems distant.

Brain-inspired model for early vocal learning and correspondence matching using free-energy optimization.

We propose a developmental model inspired by the cortico-basal system (CX-BG) for vocal learning in babies and for solving the correspondence mismatch problem they face when they hear unfamiliar voices, with different tones and pitches. This model is based on the neural architecture INFERNO standing for Iterative Free-Energy Optimization of Recurrent Neural Networks. Free-energy minimization is used for rapidly exploring, selecting and learning the optimal choices of actions to perform (eg sound production) in order to reproduce and control as accurately as possible the spike trains representing desired perceptions (eg sound categories).

Gated spiking neural network using Iterative Free-Energy Optimization and rank-order coding for structure learning in memory sequences (INFERNO GATE).

We present a framework based on iterative free-energy optimization with spiking neural networks for modeling the fronto-striatal system (PFC-BG) for the generation and recall of audio memory sequences. In line with neuroimaging studies carried out in the PFC, we propose a genuine coding strategy using the gain-modulation mechanism to represent abstract sequences based solely on the rank and location of items within them. Based on this mechanism, we show that we can construct a repertoire of neurons sensitive to the temporal structure in sequences from which we can represent any novel sequences.

Elastic, electronic, chemical bonding and thermodynamic properties of the ternary nitride CaTiN: Ab initio predictions.

In order to shed light on the unexplored properties of the ternary nitride CaTiN, we report for the first time the results of an ab initio study of its structural, electronic, elastic, chemical bonding and thermodynamic properties. Calculated equilibrium structural parameters are in excellent concordance with available experimental data. Electronic properties were explored through the calculation of the energy band dispersions and density of states.