pwc-archive/papers-with-abstracts huggingface dataset:
An analytic physically motivated model of the mammalian cochlea
Authors: Samiya A Alkhairy, Christopher A Shera
Date: 2020-12-25 00:00:00
Tasks: Form

We develop an analytic model of the mammalian cochlea. We use a mixed physical-phenomenological approach by utilizing existing work on the physics of classical box-representations of the cochlea, and behavior of recent data-derived wavenumber estimates. Spatial variation is incorporated through a single independent variable that combines space and frequency. We arrive at closed-form expressions for the organ of Corti velocity, its impedance, the pressure difference across the organ of Corti, and its wavenumber. We perform model tests using real and imaginary parts of chinchilla data from multiple locations and for multiple variables. The model also predicts impedances that are qualitatively consistent with current literature. For implementation, the model can leverage existing efforts for both filter bank and filter cascade models that target improved algorithmic or analog circuit efficiencies. The simplicity of the cochlear model, its small number of model constants, its ability to capture the variation of tuning, its closed-form expressions for physically-interrelated variables, and the form of these expressions that allows for easily determining one variable from another make the model appropriate for analytic and digital auditory filter implementations as discussed here, as well as for extracting macromechanical insights regarding how the cochlea works.
The structure of behavioral data
Authors: Aurélien Defossez, Morteza Ansarinia, Brice Clocher, Emmanuel Schmück, Paul Schrater, Pedro Cardoso-Leite
Date: 2020-12-23 00:00:00

For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replicate, integrate or extend past results. Although behavioral data is fundamental to many scientific fields, there is currently no widely adopted standard for formatting, naming, organizing, describing or sharing such data. This lack of standardization is a major bottleneck for scientific progress. Not only does it prevent the effective reuse of data, it also affects how behavioral data in general are processed, as non-standard data calls for custom-made data analysis code and prevents the development of efficient tools. To address this problem, we develop the Behaverse Data Model (BDM), a standard for structuring behavioral data. Here we focus on major concepts in behavioral data, leaving further details and developments to the project's website (https://behaverse.github.io/data-model/).
Non-equilibrium brain dynamics as a signature of consciousness
Authors: Yonatan Sanz Perl, Hernan Bocaccio, Ignacio Perez-Ipina, Steven Laureys, Helmut Laufs, Morten Kringelbach, Gustavo Deco, Enzo Tagliazucchi
Date: 2020-12-19 00:00:00

The cognitive functions of human and non-human primates rely on the dynamic interplay of distributed neural assemblies. As such, it seems unlikely that cognition can be supported by macroscopic brain dynamics at the proximity of thermodynamic equilibrium. We confirmed this hypothesis by investigating electrocorticography data from non human primates undergoing different states of unconsciousness (sleep, and anesthesia with propofol, ketamine, and ketamine plus medetomidine), and funcional magnetic resonance imaging data from humans, both during deep sleep and under propofol anesthesia. Systematically, all states of reduced consciousness unfolded at higher proximity to equilibrium dynamics than conscious wakefulness, as demonstrated by entropy production and the curl of probability flux in phase space. Our results establish non-equilibrium macroscopic brain dynamics as a robust signature of consciousness, opening the way for the characterization of cognition and awareness using tools from statistical mechanics.
Unconsciousness reconfigures modular brain network dynamics
Authors: Sofia Morena del Pozo, Helmut Laufs, Vincent Bonhomme, Steven Laureys, Pablo Balenzuela, Enzo Tagliazucchi
Date: 2020-12-19 00:00:00

The dynamic core hypothesis posits that consciousness is correlated with simultaneously integrated and differentiated assemblies of transiently synchronized brain regions. We represented time-dependent functional interactions using dynamic brain networks, and assessed the integrityof the dynamic core by means of the flexibility and largest multilayer module of these networks. As a first step, we constrained parameter selection using a newly developed benchmark for module detection in heterogeneous temporal networks. Next, we applied a multilayer modularity maximization algorithm to dynamic brain networks computed from functional magnetic resonance imaging (fMRI) data acquired during deep sleep and under propofol anesthesia. We found that unconsciousness reconfigured network flexibility and reduced the size of the largest spatiotemporal module, which we identified with the dynamic core. Our results present a first characterization of modular brain network dynamics during states of unconsciousness measured with fMRI, adding support to the dynamic core hypothesis of human consciousness.
How does neural activity encode spontaneous motor behavior in zebrafish larvae ?
Authors: Selma Mehyaoui
Date: 2020-12-19 00:00:00

The origins of spontaneous movements have been investigated in human as well as in other vertebrates. Studies have reported an increase in neuronal activity one second before the onset of a given movement: this is known as readiness potential. The mechanisms underlying this increase are still unclear. Zebrafish larva is an ideal animal model to study the neuronal basis of spontaneous movements. Because of its small size and transparency, this vertebrate is an ideal candidate to apply optical recording methods. In order to understand what neuronal activity causes the execution of a specific tail movement at a given time, we will mainly use a prediction approach.
Non-genetic acoustic stimulation of single neurons by a tapered fiber optoacoustic emitter
Authors: Linli Shi, Ying Jiang, Fernando R. Fernandez, Lu Lan, Guo Chen, Heng-ye Man, John A. White, Ji-Xin Cheng, Chen Yang
Date: 2020-12-17 00:00:00

As an emerging technology, transcranial focused ultrasound has been demonstrated to successfully evoke motor responses in mice, rabbits, and sensory/motor responses in humans. Yet, the spatial resolution of ultrasound does not allow for high-precision stimulation. Here, we developed a tapered fiber optoacoustic emitter (TFOE) for optoacoustic stimulation of neurons with an unprecedented spatial resolution of 20 microns, enabling selective activation of single neurons or subcellular structures, such as axons and dendrites. A single acoustic pulse of 1 microsecond converted by the TFOE from a single laser pulse of 3 nanoseconds is shown as the shortest acoustic stimuli so far for successful neuron activation. The highly localized ultrasound generated by the TFOE made it possible to integrate the optoacoustic stimulation and highly stable patch clamp recording on single neurons. Direct measurements of electrical response of single neurons to acoustic stimulation, which is difficult for conventional ultrasound stimulation, have been demonstrated for the first time. By coupling TFOE with ex vivo brain slice electrophysiology, we unveil cell-type-specific response of excitatory and inhibitory neurons to acoustic stimulation. These results demonstrate that TFOE is a non-genetic single-cell and sub-cellular modulation technology, which could shed new insights into the mechanism of neurostimulation.
An information-theoretic framework to measure the dynamic interaction between neural spike trains
Authors: Gorana Mijatovic, Yuri Antonacci, Tatjana Loncar-Turukalo, Ludovico Minati, Luca Faes
Date: 2020-12-15 00:00:00
Tasks: Point Processes

Understanding the interaction patterns among simultaneous recordings of spike trains from multiple neuronal units is a key topic in neuroscience. However, an optimal approach of assessing these interactions has not been established, as existing methods either do not consider the inherent point process nature of spike trains or are based on parametric assumptions that may lead to wrong inferences if not met. This work presents a framework, grounded in the field of information dynamics, for the model-free, continuous-time estimation of both undirected (symmetric) and directed (causal) interactions between pairs of spike trains. The framework decomposes the overall information exchanged dynamically between two point processes X and Y as the sum of the dynamic mutual information (dMI) between the histories of X and Y, plus the transfer entropy (TE) along the directions X->Y and Y->X. Building on recent work which derived theoretical expressions and consistent estimators for the TE in continuous time, we develop algorithms for estimating efficiently all measures in our framework through nearest neighbor statistics. These algorithms are validated in simulations of independent and coupled spike train processes, showing the accuracy of dMI and TE in the assessment of undirected and directed interactions even for weakly coupled and short realizations, and proving the superiority of the continuous-time estimator over the discrete-time method. Then, the usefulness of the framework is illustrated in a real data scenario of recordings from in-vitro preparations of spontaneously-growing cultures of cortical neurons, where we show the ability of dMI and TE to identify how the networks of undirected and directed spike train interactions change their topology through maturation of the neuronal cultures.
A case for robust translation tolerance in humans and CNNs. A commentary on Han et al
Authors: Ryan Blything, Valerio Biscione, Jeffrey Bowers
Date: 2020-12-10 00:00:00
Tasks: Translation

Han et al. (2020) reported a behavioral experiment that assessed the extent to which the human visual system can identify novel images at unseen retinal locations (what the authors call "intrinsic translation invariance") and developed a novel convolutional neural network model (an Eccentricity Dependent Network or ENN) to capture key aspects of the behavioral results. Here we show that their analysis of behavioral data used inappropriate baseline conditions, leading them to underestimate intrinsic translation invariance. When the data are correctly interpreted they show near complete translation tolerance extending to 14{\deg} in some conditions, consistent with earlier work (Bowers et al., 2016) and more recent work Blything et al. (in press). We describe a simpler model that provides a better account of translation invariance.
App-based saccade latency and error determination across the adult age spectrum
Authors: Hsin-Yu Lai, Gladynel Saavedra-Pena, Charles G. Sodini, Thomas Heldt, Vivienne Sze
Date: 2020-12-14 00:00:00
Tasks: Specificity

We aid in neurocognitive monitoring outside the hospital environment by enabling app-based measurements of visual reaction time (saccade latency) and error rate in a cohort of subjects spanning the adult age spectrum. Methods: We developed an iOS app to record subjects with the frontal camera during pro- and anti-saccade tasks. We further developed automated algorithms for measuring saccade latency and error rate that take into account the possibility that it might not always be possible to determine the eye movement from app-based recordings. Results: To measure saccade latency on a tablet, we ensured that the absolute timing error between on-screen task presentation and the camera recording is within 5 ms. We collected over 235,000 eye movements in 80 subjects ranging in age from 20 to 92 years, with 96% of recorded eye movements either declared good or directional errors. Our error detection code achieved a sensitivity of 0.97 and a specificity of 0.97. Confirming prior reports, we observed a positive correlation between saccade latency and age while the relationship between error rate and age was not significant. Finally, we observed significant intra- and inter-subject variations in saccade latency and error rate distributions, which highlights the importance of individualized tracking of these visual digital biomarkers. Conclusion and Significance: Our system and algorithms allow ubiquitous tracking of saccade latency and error rate, which opens up the possibility of quantifying patient state on a finer timescale in a broader population than previously possible.
Quantum information theoretic approach to the mind-brain problem
Authors: Danko D. Georgiev
Date: 2020-12-13 00:00:00

The brain is composed of electrically excitable neuronal networks regulated by the activity of voltage-gated ion channels. Further portraying the molecular composition of the brain, however, will not reveal anything remotely reminiscent of a feeling, a sensation or a conscious experience. In classical physics, addressing the mind-brain problem is a formidable task because no physical mechanism is able to explain how the brain generates the unobservable, inner psychological world of conscious experiences and how in turn those conscious experiences steer the underlying brain processes toward desired behavior. Yet, this setback does not establish that consciousness is non-physical. Modern quantum physics affirms the interplay between two types of physical entities in Hilbert space: unobservable quantum states, which are vectors describing what exists in the physical world, and quantum observables, which are operators describing what can be observed in quantum measurements. Quantum no-go theorems further provide a framework for studying quantum brain dynamics, which has to be governed by a physically admissible Hamiltonian. Comprising consciousness of unobservable quantum information integrated in quantum brain states explains the origin of the inner privacy of conscious experiences and revisits the dynamic timescale of conscious processes to picosecond conformational transitions of neural biomolecules. The observable brain is then an objective construction created from classical bits of information, which are bound by Holevo's theorem, and obtained through the measurement of quantum brain observables. Thus, quantum information theory clarifies the distinction between the unobservable mind and the observable brain, and supports a solid physical foundation for consciousness research.
Estimating the contribution of early and late noise in vision from psychophysical data
Authors: Jesus Malo, Jose Juan Esteve-Taboada, Guillermo Aguilar, Marianne Maertens, Felix A. Wichmann
Date: 2020-12-11 00:00:00

In many psychophysical detection and discrimination tasks human performance is thought to be limited by internal or inner noise when neuronal activity is converted into an overt behavioural response. It is unclear, however, to what extent the behaviourally limiting inner noise arises from early noise in the photoreceptors and the retina, or from late noise in cortex at or immediately prior to the decision stage. Presumably, the behaviourally limiting inner noise is a non-trivial combination of both early and late noises. Here we propose a method to quantify the contributions of early and late noise purely from psychophysical data. Our analysis generalizes classical results for linear systems (Burgess and Colborne, 1988) by combining the theory of noise propagation through a nonlinear network (Ahumada, 1987) with the expressions to obtain the perceptual metric along the nonlinear network (Malo and Simoncelli, 2006; Laparra et al., 2010). We show that from threshold-only data the relative contribution of early and late noise can only be determined if the experiments include substantial external noise in some of the stimuli used during experiments. If experimenters collected full psychometric functions, however, then early and late noise sources can be quantified even in the absence of external noise. Our psychophysical estimate of the magnitude of the early noise assuming a standard cascade of linear and nonlinear model stages is substantially lower than the noise in cone photocurrents computed via an accurate model of retinal physiology (Brainard and Wandell, 2020, ISETBIO). This is consistent with the idea that one of the fundamental tasks of early vision is to reduce the comparatively large retinal noise.
Quantifying Synchronization in a Biologically Inspired Neural Network
Authors: Pranav Mahajan, Advait Rane, Swapna Sasi, Basabdatta Sen Bhattacharya
Date: 2020-12-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), SSVEP, Time Series, Time Series Analysis

We present a collated set of algorithms to obtain objective measures of synchronisation in brain time-series data. The algorithms are implemented in MATLAB; we refer to our collated set of 'tools' as SyncBox. Our motivation for SyncBox is to understand the underlying dynamics in an existing population neural network, commonly referred to as neural mass models, that mimic Local Field Potentials of the visual thalamic tissue. Specifically, we aim to measure the phase synchronisation objectively in the model response to periodic stimuli; this is to mimic the condition of Steady-state-visually-evoked-potentials (SSVEP), which are scalp Electroencephalograph (EEG) corresponding to periodic stimuli. We showcase the use of SyncBox on our existing neural mass model of the visual thalamus. Following our successful testing of SyncBox, it is currently being used for further research on understanding the underlying dynamics in enhanced neural networks of the visual pathway
Chaos stabilizes synchronization in systems of coupled inner-ear hair cells
Authors: Justin Faber, Hancheng Li, Dolores Bozovic
Date: 2020-12-08 00:00:00

Hair cells of the auditory and vestibular systems display astonishing sensitivity, frequency selectivity, and temporal resolution to external signals. These specialized cells utilize an internal active amplifier to achieve highly sensitive mechanical detection. One of the manifestations of this active process is the occurrence of spontaneous limit-cycle motion of the hair cell bundle. As hair bundles under in vivo conditions are typically coupled to each other by overlying structures, we explore the role of this coupling on the dynamics of the system, using a combination of theoretical and experimental approaches. Our numerical model suggests that the presence of chaotic dynamics in the response of individual bundles enhances their ability to synchronize when coupled, resulting in significant improvement in the system's ability to detect weak signals. This synchronization persists even for a large frequency dispersion and a large number of oscillators comprising the system. Further, the amplitude and coherence of the active motion is not reduced upon increasing the number of oscillators. Using artificial membranes, we impose mechanical coupling on groups of live and functional hair bundles, selected from in vitro preparations of the sensory epithelium, allowing us to explore the role of coupling experimentally. Consistent with the numerical simulations of the chaotic system, synchronization occurs even for large frequency dispersion and a large number of hair cells. Further, the amplitude and coherence of the spontaneous oscillations are independent of the number of hair cells in the network. We therefore propose that hair cells utilize their chaotic dynamics to stabilize the synchronized state and avoid the amplitude death regime, resulting in collective coherent motion that could play a role in generating spontaneous otoacoustic emissions and an enhanced ability to detect weak signals.
Dynamic and Thermodynamic Models of Adaptation
Authors: A. N. Gorban, T. A. Tyukina, L. I. Pokidysheva, E. V. Smirnova
Date: 2021-03-02 00:00:00
Tasks: Time Series Analysis

The concept of biological adaptation was closely connected to some mathematical, engineering and physical ideas from the very beginning. Cannon in his "The wisdom of the body" (1932) used the engineering vision of regulation. In 1938, Selye enriched this approach by the notion of adaptation energy. This term causes much debate when one takes it literally, i.e. as a sort of energy. Selye did not use the language of mathematics, but the formalization of his phenomenological theory in the spirit of thermodynamics was simple and led to verifiable predictions. In 1980s, the dynamics of correlation and variance in systems under adaptation to a load of environmental factors were studied and the universal effect in ensembles of systems under a load of similar factors was discovered: in a crisis, as a rule, even before the onset of obvious symptoms of stress, the correlation increases together with variance (and volatility). During 30 years, this effect has been supported by many observations of groups of humans, mice, trees, grassy plants, and on financial time series. In the last ten years, these results were supplemented by many new experiments, from gene networks in cardiology and oncology to dynamics of depression and clinical psychotherapy. Several systems of models were developed: the thermodynamic-like theory of adaptation of ensembles and several families of models of individual adaptation. Historically, the first group of models was based on Selye's concept of adaptation energy and used fitness estimates. Two other groups of models are based on the idea of hidden attractor bifurcation and on the advection--diffusion model for distribution of population in the space of physiological attributes. We explore this world of models and experiments, starting with classic works, with particular attention to the results of the last ten years and open questions.
B.1.258$Δ$, a SARS-CoV-2 variant with $Δ$H69/$Δ$V70 in the Spike protein circulating in the Czech Republic and Slovakia
Authors: Broňa Brejová, Viktória Hodorová, Kristína Boršová, Viktória Čabanová, Lenka Reizigová, Evan D. Paul, Pavol Čekan, Boris Klempa, Jozef Nosek, Tomáš Vinař
Date: 2021-02-09 00:00:00

SARS-CoV-2 mutants carrying the $\Delta$H69/$\Delta$V70 deletion in the amino terminal domain of the Spike protein emerged independently in at least six lineages of the virus (namely, B.1.1.7, B.1.1.298, B.1.160, B.1.177, B.1.258, B.1.375). Routine RT-qPCR tests including TaqPath or similar assays based on a drop-out of the Spike gene target are incapable of distinguishing among these lineages and often lead to the false conclusion that clinical samples contain the B.1.1.7 variant, which recently emerged in the United Kingdom and is quickly spreading through the human population. We analyzed SARS-CoV-2 samples collected from various regions of Slovakia between November and December 2020 that were presumed to contain the B.1.1.7 variant due to traveling history of the virus carriers or their contacts. Sequencing of these isolates revealed that although in some cases the samples were indeed confirmed as B.1.1.7, a substantial fraction of isolates contained another $\Delta$H69/$\Delta$V70 carrying mutant belonging to the lineage B.1.258, which has been circulating in Central Europe since August 2020, long before the import of B.1.1.7. Phylogenetic analysis shows that the early sublineage of B.1.258 acquired the N439K substitution in the receptor binding domain (RBD) of the Spike protein and, later on, also the deletion $\Delta$H69/$\Delta$V70 in the Spike N-terminal domain (NTD). This variant is particularly common in several European countries including Czech Republic and Slovakia, and we propose to name it B.1.258$\Delta$.