Vocalizations of wild West Indian manatee vary across subspecies and geographic location.

Geographic variation in the vocal behavior of manatees has been reported but is largely unexplored. Vocalizations of wild West Indian manatees (Trichechus manatus) were recorded with hydrophones in Florida from Florida manatees (Trichechus manatus latirostris), and in Belize and Panama from Antillean manatees (Trichechus manatus manatus) to determine if calls varied between subspecies and geographic regions. Calls were visually classified into five categories: squeaks, high squeaks, squeals, squeak-squeals, and chirps.

Antillean manatee calves in captive rehabilitation change vocal behavior in anticipation of feeding.

Captive animals typically develop anticipatory behaviors, actions of increased frequency done in anticipation of an event such as feeding. Anticipatory behaviors can be an indicator of an animal's welfare. However, for rehabilitating animals that are expected to be reintroduced into the wild, these behaviors need to be extinguished to ensure successful release.

Robustness and Flexibility of Neural Function through Dynamical Criticality.

In theoretical biology, refers to the ability of a biological system to function properly even under perturbation of basic parameters (e.g., temperature or pH), which in mathematical models is reflected in not needing to fine-tune basic parameter constants; refers to the ability of a system to switch functions or behaviors easily and effortlessly.

The Relevance of Human Whistled Languages for the Analysis and Decoding of Dolphin Communication.

Humans use whistled communications, the most elaborate of which are commonly called "whistled languages" or "whistled speech" because they consist of a natural type of speech. The principle of whistled speech is straightforward: people articulate words while whistling and thereby transform spoken utterances by simplifying them, syllable by syllable, into whistled melodies. One of the most striking aspects of this whistled transformation of words is that it remains intelligible to trained speakers, despite a reduced acoustic channel to convey meaning.

Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions.

During the COVID-19 pandemic, the scientific community developed predictive models to evaluate potential governmental interventions. However, the analysis of the effects these interventions had is less advanced. Here, we propose a data-driven framework to assess these effects retrospectively.

Learning to localize sounds in a highly reverberant environment: Machine-learning tracking of dolphin whistle-like sounds in a pool.

Tracking the origin of propagating wave signals in an environment with complex reflective surfaces is, in its full generality, a nearly intractable problem which has engendered multiple domain-specific literatures. We posit that, if the environment and sensor geometries are fixed, machine learning algorithms can "learn" the acoustical geometry of the environment and accurately track signal origin. In this paper, we propose the first machine-learning-based approach to identifying the source locations of semi-stationary, tonal, dolphin-whistle-like sounds in a highly reverberant space, specifically a half-cylindrical dolphin pool.

The Antillean manatee produces broadband vocalizations with ultrasonic frequencies.

Antillean manatees produce vocalizations reported to be important for communication, but their vocal behavior throughout their geographic range is poorly understood. A SoundTrap recorder (sample rates: 288/576 kHz) was deployed in Belize to record vocalizations of wild manatees in a seagrass channel and of a young rehabilitated and released manatee in a shallow lagoon. Spectral analysis revealed broadband vocalizations with frequencies up to 150 kHz and a high proportion of calls with ultrasonic components.

Author Correction: Single-trial classification of awareness state during anesthesia by measuring critical dynamics of global brain activity.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Single-trial classification of awareness state during anesthesia by measuring critical dynamics of global brain activity.

In daily life, in the operating room and in the laboratory, the operational way to assess wakefulness and consciousness is through responsiveness. A number of studies suggest that the awake, conscious state is not the default behavior of an assembly of neurons, but rather a very special state of activity that has to be actively maintained and curated to support its functional properties. Thus responsiveness is a feature that requires active maintenance, such as a homeostatic mechanism to balance excitation and inhibition.

Complex spatiotemporal behavior and coherent excitations in critically-coupled chains of neural circuits.

We investigate a critically-coupled chain of nonlinear oscillators, whose dynamics displays complex spatiotemporal patterns of activity, including regimes in which glider-like coherent excitations move about and interact. The units in the network are identical simple neural circuits whose dynamics is given by the Wilson-Cowan model and are arranged in space along a one-dimensional lattice with nearest neighbor interactions. The interactions follow an alternating sign rule, and hence the "synaptic matrix" embodying them is tridiagonal antisymmetric and has purely imaginary (critical) eigenvalues.

Adaptive scales of integration and response latencies in a critically-balanced model of the primary visual cortex.

The primary visual cortex (V1) integrates information over scales in visual space, which have been shown to vary, in an input-dependent manner, as a function of contrast and other visual parameters. Which algorithms the brain uses to achieve this feat are largely unknown and an open problem in visual neuroscience. We demonstrate that a simple dynamical mechanism can account for this contrast-dependent scale of integration in visuotopic space as well as connect this property to two other stimulus-dependent features of V1: extents of lateral integration on the cortical surface and response latencies.

SEGGA: a toolset for rapid automated analysis of epithelial cell polarity and dynamics.

Epithelial remodeling determines the structure of many organs in the body through changes in cell shape, polarity and behavior and is a major area of study in developmental biology. Accurate and high-throughput methods are necessary to systematically analyze epithelial organization and dynamics at single-cell resolution. We developed SEGGA, an easy-to-use software for automated image segmentation, cell tracking and quantitative analysis of cell shape, polarity and behavior in epithelial tissues.

Stable encoding of sounds over a broad range of statistical parameters in the auditory cortex.

Natural auditory scenes possess highly structured statistical regularities, which are dictated by the physics of sound production in nature, such as scale-invariance. We recently identified that natural water sounds exhibit a particular type of scale invariance, in which the temporal modulation within spectral bands scales with the centre frequency of the band. Here, we tested how neurons in the mammalian primary auditory cortex encode sounds that exhibit this property, but differ in their statistical parameters.

Structural self-assembly and avalanchelike dynamics in locally adaptive networks.

Transport networks play a key role across four realms of eukaryotic life: slime molds, fungi, plants, and animals. In addition to the developmental algorithms that build them, many also employ adaptive strategies to respond to stimuli, damage, and other environmental changes. We model these adapting network architectures using a generic dynamical system on weighted graphs and find in simulation that these networks ultimately develop a hierarchical organization of the final weighted architecture accompanied by the formation of a system-spanning backbone.

Loss of Consciousness Is Associated with Stabilization of Cortical Activity.

Unlabelled: What aspects of neuronal activity distinguish the conscious from the unconscious brain? This has been a subject of intense interest and debate since the early days of neurophysiology. However, as any practicing anesthesiologist can attest, it is currently not possible to reliably distinguish a conscious state from an unconscious one on the basis of brain activity. Here we approach this problem from the perspective of dynamical systems theory.

Dynamical criticality during induction of anesthesia in human ECoG recordings.

In this work we analyze electro-corticography (ECoG) recordings in human subjects during induction of anesthesia with propofol. We hypothesize that the decrease in responsiveness that defines the anesthetized state is concomitant with the stabilization of neuronal dynamics. To test this hypothesis, we performed a moving vector autoregressive analysis and quantified stability of neuronal dynamics using eigenmode decomposition of the autoregressive matrices, independently fitted to short sliding temporal windows.

A transition to sharp timing in stochastic leaky integrate-and-fire neurons driven by frozen noisy input.

The firing activity of intracellularly stimulated neurons in cortical slices has been demonstrated to be profoundly affected by the temporal structure of the injected current (Mainen & Sejnowski, 1995 ). This suggests that the timing features of the neural response may be controlled as much by its own biophysical characteristics as by how a neuron is wired within a circuit. Modeling studies have shown that the interplay between internal noise and the fluctuations of the driving input controls the reliability and the precision of neuronal spiking (Cecchi et al.

Degraded time-frequency acuity to time-reversed notes.

Time-reversal symmetry breaking is a key feature of many classes of natural sounds, originating in the physics of sound production. While attention has been paid to the response of the auditory system to "natural stimuli," very few psychophysical tests have been performed. We conduct psychophysical measurements of time-frequency acuity for stylized representations of "natural"-like notes (sharp attack, long decay) and the time-reversed versions of these notes (long attack, sharp decay).

A phase transition in the first passage of a Brownian process through a fluctuating boundary with implications for neural coding.

Finding the first time a fluctuating quantity reaches a given boundary is a deceptively simple-looking problem of vast practical importance in physics, biology, chemistry, neuroscience, economics, and industrial engineering. Problems in which the bound to be traversed is itself a fluctuating function of time include widely studied problems in neural coding, such as neuronal integrators with irregular inputs and internal noise. We show that the probability p(t) that a Gauss-Markov process will first exceed the boundary at time t suffers a phase transition as a function of the roughness of the boundary, as measured by its Hölder exponent H.

Topological phase transition in the Scheidegger model of river networks.

Transport networks are found at the heart of myriad natural systems, yet are poorly understood, except for the case of river networks. The Scheidegger model, in which rivers are convergent random walks, has been studied only in the case of flat topography, ignoring the variety of curved geometries found in nature. Embedding this model on a cone, we find a convergent and a divergent phase, corresponding to few, long basins and many, short basins, respectively, separated by a singularity, indicating a phase transition.

Input-dependent wave attenuation in a critically-balanced model of cortex.

A number of studies have suggested that many properties of brain activity can be understood in terms of critical systems. However it is still not known how the long-range susceptibilities characteristic of criticality arise in the living brain from its local connectivity structures. Here we prove that a dynamically critically-poised model of cortex acquires an infinitely-long ranged susceptibility in the absence of input.

Exact event-driven implementation for recurrent networks of stochastic perfect integrate-and-fire neurons.

In vivo cortical recording reveals that indirectly driven neural assemblies can produce reliable and temporally precise spiking patterns in response to stereotyped stimulation. This suggests that despite being fundamentally noisy, the collective activity of neurons conveys information through temporal coding. Stochastic integrate-and-fire models delineate a natural theoretical framework to study the interplay of intrinsic neural noise and spike timing precision.

Self-Regulated Dynamical Criticality in Human ECoG.

Mounting experimental and theoretical results indicate that neural systems are poised near a critical state. In human subjects, however, most evidence comes from functional MRI studies, an indirect measurement of neuronal activity with poor temporal resolution. Electrocorticography (ECoG) provides a unique window into human brain activity: each electrode records, with high temporal resolution, the activity resulting from the sum of the local field potentials of ∼10(5) neurons.