Biology subjects

Marin, B.

Publications and source records attributed to Marin, B..

4 recordsLinked to original sources

bioRxiv2024

Sufficiency of ITD for (biased) human auditory azimuthal perception

Experiments on human auditory perception have shown that interaural time difference (ITD) is sufficient to generate spatial percepts, even though stimuli containing only the ITD cue are perceived as being emitted from inside the head instead of from external locations at specific azimuths. These experiments are thus interpreted as "lateralization" instead of "localization" tasks. In fact, lateralized spatial perception has been quantified using tasks in which participants have to report their estimates by selecting a putative location inside the head, or matching the perceived position to sounds with a given interaural level difference. Therefore, these estimates are made with respect to internal frames of reference, but it is unclear whether these percepts have any significance for the more ecological problem of locating an external sound source. In order to investigate the link between internalized spatial percepts and sound localization, we designed a new task in which subjects are instructed to report externalized azimuthal location for sounds containing only ITD cues. Despite the mismatch between an internalized percept having to be reported as emanating from an external location, subjects were able to estimate azimuths consistently. Furthermore, normalized estimates were indistinguishable from those obtained using traditional lateralization tasks. Our results revealed a direct relationship between perceived azimuths and ITD, which deviates from that obtained from acoustical analysis of binaural recordings, revealing estimation biases. Intriguingly, these results indicate that externalized percepts are not required for the generation of azimuthal percepts.

neuroscience↗

bioRxiv2023

The NeuroML ecosystem for standardized multi-scale modeling in neuroscience

Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes make the construction and reuse of biologically detailed models challenging. A wide range of tools have been developed to aid their construction and simulation, but differences in design and internal representation act as technical barriers to those who wish to use data-driven models in their research workflows. NeuroML, a model description language for computational neuroscience, was developed to address this fragmentation in modeling tools. Since its inception, NeuroML has evolved into a mature community standard that encompasses a wide range of model types and approaches in computational neuroscience. It has enabled the development of a large ecosystem of interoperable open source software tools for the creation, visualization, validation and simulation of data-driven models. Here, we describe how the NeuroML ecosystem can be incorporated into research workflows to simplify the construction, testing and analysis of standardized models of neural systems, and supports the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, thus promoting open, transparent and reproducible science.

neuroscience↗

bioRxiv2022

Low noise HRTFs and delay line corrections are detrimental to the prediction of ITD discrimination thresholds from environmental statistics

Interaural Time Difference (ITD) is the main cue for azimuthal auditory perception in humans. ITDs at each frequency contribute differently to azimuth discrimination, which can be quantified by their azimuthal Fisher Information. Consistently, human ITD discrimination thresholds are predicted by the azimuthal information. However, this prediction is poor for frequencies below 500 Hz. Such poor prediction could be ascribed to the strategy of quantifying azimuthal information using HRTFs obtained in unnaturalistic anechoic chambers or by using a direct method which does not incorporate the delay lines proposed by the Jeffress-Colburn model. In the present study, we obtained ITD discrimination thresholds from extensive sampling across frequency and ITD, and applied multiple strategies for quantifying azimuthal information. These strategies employed HRTFs obtained in realistic and anechoic chambers, with and without considering delay lines. We found that ITD discriminability thresholds across the complete range of frequencies are better predicted by azimuthal information conveyed by ITD cues when (1) we use naturalistic high-noise HRTFs, and (2) ITD delay compensation is not applied. Our results support that auditory perception is shaped by natural environments, which include high reverberation in low frequencies. Moreover, we also suggest that delay lines are not a crucial feature for determining ITD discrimination thresholds in the human auditory system. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=186 SRC="FIGDIR/small/507313v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1540172org.highwire.dtl.DTLVardef@2aea34org.highwire.dtl.DTLVardef@175ff15org.highwire.dtl.DTLVardef@1bdf2d1_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2018

Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits

Computational models are powerful tools for investigating brain function in health and disease. However, biologically detailed neuronal and circuit models are complex and implemented in a range of specialized languages, making them inaccessible and opaque to many neuroscientists. This has limited critical evaluation of models by the scientific community and impeded their refinement and widespread adoption. To address this, we have combined advances in standardizing models, open source software development and web technologies to develop Open Source Brain, a platform for visualizing, simulating, disseminating and collaboratively developing standardized models of neurons and circuits from a range of brain regions. Model structure and parameters can be visualized and their dynamical properties explored through browser-controlled simulations, without writing code. Open Source Brain makes neural models transparent and accessible and facilitates testing, critical evaluation and refinement, thereby helping to improve the accuracy and reproducibility of models, and their dissemination to the wider community.

neuroscience↗