Complex sequencing rules of birdsong can be explained by simple hidden Markov processes

Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical propertiesof the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable sequences, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. This property is shared with other complex sequential behaviors. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model (GMM)), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex sequences with higher-order dependencies

Nel positively regulates the genesis of retinal ganglion cells by promoting their differentiation and survival during development

Peer reviewedPublisher PD

Consolidated bioprocessing of corn cob-derived hemicellulose: engineered industrial Saccharomyces cerevisiae as efficient whole cell biocatalysts

Background Consolidated bioprocessing, which combines saccharolytic and fermentative abilities in a single microorganism, is receiving increased attention to decrease environmental and economic costs in lignocellulosic biorefineries. Nevertheless, the economic viability of lignocellulosic ethanol is also dependent of an efficient utilization of the hemicellulosic fraction, which contains xylose as a major component in concentrations that can reach up to 40% of the total biomass in hardwoods and agricultural residues. This major bottleneck is mainly due to the necessity of chemical/enzymatic treatments to hydrolyze hemicellulose into fermentable sugars and to the fact that xylose is not readily consumed by Saccharomyces cerevisiaethe most used organism for large-scale ethanol production. In this work, industrial S. cerevisiae strains, presenting robust traits such as thermotolerance and improved resistance to inhibitors, were evaluated as hosts for the cell-surface display of hemicellulolytic enzymes and optimized xylose assimilation, aiming at the development of whole-cell biocatalysts for consolidated bioprocessing of corn cob-derived hemicellulose. Results These modifications allowed the direct production of ethanol from non-detoxified hemicellulosic liquor obtained by hydrothermal pretreatment of corn cob, reaching an ethanol titer of 11.1 g/L corresponding to a yield of 0.328 g/g of potential xylose and glucose, without the need for external hydrolytic catalysts. Also, consolidated bioprocessing of pretreated corn cob was found to be more efficient for hemicellulosic ethanol production than simultaneous saccharification and fermentation with addition of commercial hemicellulases. Conclusions These results show the potential of industrial S. cerevisiae strains for the design of whole-cell biocatalysts and paves the way for the development of more efficient consolidated bioprocesses for lignocellulosic biomass valorization, further decreasing environmental and economic costs.This work has been carried out at the Biomass and Bioenergy Research Infrastructure (BBRI)-LISBOA-01-0145-FEDER-022059, supported by Operational Programme for Competitiveness and Internationalization (PORTUGAL2020), by Lisbon Portugal Regional Operational Programme (Lisboa 2020) and by North Portugal Regional Operational Programme (Norte 2020) under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) and has been supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020, the “Contrato-Programa” UIDB/04050/2020, the MIT-Portugal Program (Ph.D. Grant PD/BD/128247/2016 to Joana T. Cunha) and through Project FatVal (POCI-01-0145-FEDER-032506) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte.info:eu-repo/semantics/publishedVersio

A reafferent and feed-forward model of song syntax generation in the Bengalese finch

Adult Bengalese finches generate a variable song that obeys a distinct and individual syntax. The syntax is gradually lost over a period of days after deafening and is recovered when hearing is restored. We present a spiking neuronal network model of the song syntax generation and its loss, based on the assumption that the syntax is stored in reafferent connections from the auditory to the motor control area. Propagating synfire activity in the HVC codes for individual syllables of the song and priming signals from the auditory network reduce the competition between syllables to allow only those transitions that are permitted by the syntax. Both imprinting of song syntax within HVC and the interaction of the reafferent signal with an efference copy of the motor command are sufficient to explain the gradual loss of syntax in the absence of auditory feedback. The model also reproduces for the first time experimental findings on the influence of altered auditory feedback on the song syntax generation, and predicts song- and species-specific low frequency components in the LFP. This study illustrates how sequential compositionality following a defined syntax can be realized in networks of spiking neurons

Coordination Cost and Super-Efficiency in Teamwork: The Role of Communication, Psychological States, Cardiovascular Responses, and Brain Rhythms

To advance knowledge on the psychophysiological markers of “coordination cost” in team settings, we explored differences in meta-communication patterns (i.e., silence, speaking, listening, and overlap), perceived psychological states (i.e., core affect, attention, efficacy beliefs), heart rate variability (i.e., RMSSD), and brain rhythms (i.e., alpha, beta and theta absolute power) across three studies involving 48 male dyads (Mage = 21.30; SD = 2.03). Skilled participants cooperatively played three consecutive FIFA-17 (Xbox) games in a dyad against the computer, or competed against the computer in a solo condition and a dyad condition. We observed that playing in a team, in contrast to playing alone, was associated with higher alpha peak and global efficiency in the brain and, at the same time, led to an increase in focused attention as evidenced by participants’ higher theta activity in the frontal lobe. Moreover, we observed that overtime participants’ brain dynamics moved towards a state of “neural-efficiency” or “flow”, characterized by increased theta and beta activity in the frontal lobe, and high alpha activity across the whole brain. Our findings advance the literature by demonstrating that (1) the notion of coordination cost can be captured at the neural level in the initial stages of team development; (2) by decreasing the costs of switching between tasks, teamwork increases both individuals’ attentional focus and global neural efficiency; and (3) communication dynamics become more proficient and individuals’ brain patterns change towards neural efficiency over time, likely due to team learning and decreases in intra-team conflict

Combining EEG, MIDI, and motion capture techniques for investigating musical performance

This article describes a setup for the simultaneous recording of electrophysiological data (EEG), musical data (MIDI), and three-dimensional movement data. Previously, each of these three different kinds of measurements, conducted sequentially, has been proven to provide important information about different aspects of music performance as an example of a demanding multisensory motor skill. With the method described here, it is possible to record brain-related activity and movement data simultaneously, with accurate timing resolution and at relatively low costs. EEG and MIDI data were synchronized with a modified version of the FTAP software, sending synchronization signals to the EEG recording device simultaneously with keypress events. Similarly, a motion capture system sent synchronization signals simultaneously with each recorded frame. The setup can be used for studies investigating cognitive and motor processes during music performance and music-like tasks—for example, in the domains of motor control, learning, music therapy, or musical emotions. Thus, this setup offers a promising possibility of a more behaviorally driven analysis of brain activity