Data-Driven Classification of Spectral Profiles Reveals Brain Region-Specific Plasticity in Blindness

Congenital blindness has been shown to result in behavioral adaptation and neuronal reorganization, but the underlying neuronal mechanisms are largely unknown. Brain rhythms are characteristic for anatomically defined brain regions and provide a putative mechanistic link to cognitive processes. In a novel approach, using magnetoencephalography resting state data of congenitally blind and sighted humans, deprivation-related changes in spectral profiles were mapped to the cortex using clustering and classification procedures. Altered spectral profiles in visual areas suggest changes in visual alpha-gamma band inhibitory-excitatory circuits. Remarkably, spectral profiles were also altered in auditory and right frontal areas showing increased power in theta-to-beta frequency bands in blind compared with sighted individuals, possibly related to adaptive auditory and higher cognitive processing. Moreover, occipital alpha correlated with microstructural white matter properties extending bilaterally across posterior parts of the brain. We provide evidence that visual deprivation selectively modulates spectral profiles, possibly reflecting structural and functional adaptation

Spontaneous synchronization to speech reveals neural mechanisms facilitating language learning

We introduce a deceptively simple behavioral task that robustly identifies two qualitatively different groups within the general population. When presented with an isochronous train of random syllables, some listeners are compelled to align their own concurrent syllable production with the perceived rate, whereas others remain impervious to the external rhythm. Using both neurophysiological and structural imaging approaches, we show group differences with clear consequences for speech processing and language learning. When listening passively to speech, high synchronizers show increased brain-to-stimulus synchronization over frontal areas, and this localized pattern correlates with precise microstructural differences in the white matter pathways connecting frontal to auditory regions. Finally, the data expose a mechanism that underpins performance on an ecologically relevant word-learning task. We suggest that this task will help to better understand and characterize individual performance in speech processing and language learning

Repita la sílaba “ta” y le diremos cómo funciona su cerebro

Un atributo innato en los seres humanos es la habilidad de sincronizar nuestros movimientos con los sonidos que percibimos. Imaginemos, por ejemplo, cuando movemos el pie o la cabeza al ritmo de una canción. Este fenómeno sucede sin esfuerzo ni entrenamiento previo: ¡incluso los bebés lo hacen

Stabilization of the J-V characteristic of a perovskite solar cell using an intelligent control loop

The phenomena related to charge trapping are among the most relevant open issues that affect the long-term stability of perovskite-based devices. According to this, the objective of this paper is to report experimental results in which a charge control strategy is used for the first time in a solar cell structure that has a high trap density perovskite absorber. This device has also noticeable J-V hysteresis, produced by non-capacitive effects. The control strategy proposed, based on sigma-delta modulation, applies to the device an appropriate sequence of voltage waveforms determined after periodical current measurements made at a constant voltage. The experimental results obtained and the fittings made with a phenomenological model indicate that this approach allows controlling several charge-related effects. As a consequence, the J-V characteristic of the device is successfully shifted and stabilized to predetermined positions.This research was funded by the Spanish Ministry MINECO (under grant RTI2018-098728- B-C33), the Severo Ochoa program (under grant SEV-2015-0522), MINECO and FEDER (under grant MAT2017-89522-R), the Fundacio Privada Cellex and the CERCA program from the Generalitat de Catalunya.Peer ReviewedPostprint (published version

Low temperature back-surface-field contacts deposited by hot-wire CVD for heterojunction solar cells

The growing interest in using thinner wafers (< 200 µm) requires the development of low temperature passivation strategies for the back contact of heterojunction solar cells. In this work, we investigate low temperature deposited back contacts based on boron-doped amorphous silicon films obtained by Hot-Wire CVD. The influence of the deposition parameters and the use of an intrinsic buffer layer have been considered. The microstructure of the deposited thin films has been comprehensively studied by Spectroscopic Ellipsometry in the UV–visible range. The effective recombination velocity at the back surface has been measured by the Quasi-Steady-State Photoconductance technique. Complete double-side heterojunction solar cells (1 cm2) have been fabricated and characterized by External Quantum Efficiency and current–voltage measurements. Total-area conversion efficiencies up to 14.5% were achieved in a fully low temperature process (< 200 °C).Peer ReviewedPostprint (author's final draft

Low temperature back-surface-field contacts deposited by Hot-wire CVD for heterojunction solar cells

The growing interest in using thinner wafers (< 200 μm) requires the development of low temperature passivation strategies for the back contact of heterojunction solar cells. In this work, we investigate low temperature deposited back contacts based on boron-doped amorphous silicon films obtained by Hot-Wire CVD. The influence of the deposition parameters and the use of an intrinsic buffer layer have been considered. The microstructure of the deposited thin films has been comprehensively studied by Spectroscopic Ellipsometry in the UV-visible range. The effective recombination velocity at the back surface has been measured by the Quasi-Steady-State Photoconductance technique. Complete double-side heterojunction solar cells (1 cm 2) have been fabricated and characterized by External Quantum Efficiency and current-voltage measurements. Total-area conversion efficiencies up to 14.5% were achieved in a fully low temperature process (< 200 °C)

Analysis of bias stress on thin-film transistors obtained by Hot-Wire Chemical Vapour Deposition

The stability under gate bias stress of unpassivated thin film transistors was studied by measuring the transfer and output characteristics at different temperatures. The active layer of these devices consisted of in nanocrystalline silicon deposited at 125 °C by Hot-Wire Chemical Vapour Deposition. The dependence of the subthreshold activation energy on gate bias for different gate bias stresses is quite different from the one reported for hydrogenated amorphous silicon. This behaviour has been related to trapped charge in the active layer of the thin film transistor

Analysis of bias stress on thin-film transistors obtained by Hot-Wire Chemical Vapour Deposition

The stability under gate bias stress of unpassivated thin film transistors was studied by measuring the transfer and output characteristics at different temperatures. The active layer of these devices consisted of in nanocrystalline silicon deposited at 125°C by Hot-Wire Chemical Vapour Deposition. The dependence of the subthreshold activation energy on gate bias for different gate bias stresses is quite different from the one reported for hydrogenated amorphous silicon. This behaviour has been related to trapped charge in the active layer of the thin film transistor.Peer ReviewedPostprint (published version

Language statistical learning responds to reinforcement learning principles rooted in the striatum

Statistical learning (SL) is the ability to extract regularities from the environment. In the domain of language, this ability is fundamental in the learning of words and structural rules. In lack of reliable online measures, statistical word and rule learning have been primarily investigated using offline (post-familiarization) tests, which gives limited insights into the dynamics of SL and its neural basis. Here, we capitalize on a novel task that tracks the online SL of simple syntactic structures combined with computational modeling to show that online SL responds to reinforcement learning principles rooted in striatal function. Specifically, we demonstrate-on 2 different cohorts-that a temporal difference model, which relies on prediction errors, accounts for participants' online learning behavior. We then show that the trial-by-trial development of predictions through learning strongly correlates with activity in both ventral and dorsal striatum. Our results thus provide a detailed mechanistic account of language-related SL and an explanation for the oft-cited implication of the striatum in SL tasks. This work, therefore, bridges the long-standing gap between language learning and reinforcement learning phenomena

Decoding imagined speech reveals speech planning and production mechanisms

Speech imagery (the ability to generate internally quasi-perceptual experiences of speech) is a fundamental ability linked to cognitive functions such as inner speech, phonological working memory, and predictive processing. Speech imagery is also considered an ideal tool to test theories of overt speech. The study of speech imagery is challenging, primarily because of the absence of overt behavioral output as well as the difficulty in temporally aligning imagery events across trials and individuals. We used magnetoencephalography (MEG) paired with temporal-generalization-based neural decoding and a simple behavioral protocol to determine the processing stages underlying speech imagery. We monitored participants’ lip and jaw micromovements during mental imagery of syllable production using electromyography. Decoding participants’ imagined syllables revealed a sequence of task-elicited representations. Importantly, participants’ micromovements did not discriminate between syllables. The decoded sequence of neuronal patterns maps well onto the predictions of current computational models of overt speech motor control and provides evidence for hypothesized internal and external feedback loops for speech planning and production, respectively. Additionally, the results expose the compressed nature of representations during planning which contrasts with the natural rate at which internal productions unfold. We conjecture that the same sequence underlies the motor-based generation of sensory predictions that modulate speech perception as well as the hypothesized articulatory loop of phonological working memory. The results underscore the potential of speech imagery, based on new experimental approaches and analytical methods, and further pave the way for successful non-invasive brain-computer interfaces