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This paper presents some unusual behaviors observed in one single specimen ofO. insularis. While nothing can be concluded rigorously from such data, we share the data and our analysis with the community, in the hope that others will be on the lookout for such rare events. Sleep is a fundamental biological function that is present in all tested vertebrates and most invertebrates.Cephalopods, such as octopuses, are cognitively complex animals that display active and inactive sleep states similar to those of vertebrates. In particular, octopuses have active sleep states during which they display sequences of camouflage patterns and modulation of basal rhythms, while remaining relatively unresponsive to outside stimuli. Some scientists have speculated that these states could be analogous to dreaming in mammals, involving episodic recall with a narrative structure. The convergent evolution of sleep in neurologically complex animals is a striking possibility, but its demonstration requires overcoming significant challenges. Towards this end, capturing abnormal sleep-associated episodes and other parasomnias in cephalopods can provide further insight into the biology of their sleep. This study reports abnormal behavioral episodes associated with transitions between activity states and sleep states observed in a maleOctopus insularis. The study used continuous video monitoring to characterize the animal’s activity patterns and detect rare behavioral episodes. Over the course of a month, four brief episodes (duration range: 44-290 seconds) were identified during which the octopus abruptly emerged from quiescent or active sleep, detached itself from its sleep position, and engaged in antipredator and predatory behaviors (with no predator present). The longest of these episodes resembled the species-typical response to a predatory attack, suggesting that the animal may have been responding to a negative episodic memory or exhibiting a form of parasomnia. These findings, in conjunction with recent evidence for sleep in octopuses, highlight the complexity of possible sleep-associated behavioral episodes. Investigating sleep in invertebrates is crucial to understanding the evolution of sleep across distantly related species.

International audience; 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. It constitutes a natural traditional means of telecommunication that permits spoken communication at long distances in a large diversity of languages of the world. Historically, birdsong has been used as a model for vocal learning and language. But conversely, human whistled languages can serve as a model for elucidating how information may be encoded in dolphin whistle communication. In this paper, we elucidate the reasons why human whistled speech and dolphin whistles are interesting to compare. Both are characterized by similar acoustic parameters and serve a common purpose of long distance communication in natural surroundings in two large brained social species. Moreover, their differences – e.g., how they are produced, the dynamics of the whistles, and the types of information they convey – are not barriers to such a comparison. On the contrary, by exploring the structure and attributes found across human whistle languages, we highlight that they can provide an important model as to how complex information is and can be encoded in what appears at first sight to be simple whistled modulated signals. Observing details, such as processes of segmentation and coarticulation, in whistled speech can serve to advance and inform the development of new approaches for the analysis of whistle repertoires of dolphins, and eventually other species. Human whistled languages and dolphin whistles could serve as complementary test benches for the development of new methodologies and algorithms for decoding whistled communication signals by providing new perspectives on how information may be encoded structurally and organizationally.

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. We use a regularized regression to find a parsimonious model that fits the data with the least changes in the $$R_t$$ R t parameter. Then, we postulate each jump in $$R_t$$ R t as the effect of an intervention. Following the do-operator prescriptions, we simulate the counterfactual case by forcing $$R_t$$ R t to stay at the pre-jump value. We then attribute a value to the intervention from the difference between true evolution and simulated counterfactual. We show that the recommendation to use facemasks for all activities would reduce the number of cases by 200,000 ($$95\%$$ 95 % CI 190,000–210,000) in Connecticut, Massachusetts, and New York State. The framework presented here might be used in any case where cause and effects are sparse in time.

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. Ultrasonic frequency components appear prevalent in their vocal repertoire and may be important to manatee communication.

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. Our algorithm works by supplying a learning network with an overabundance of location "clues", which are then selected under supervised training for their ability to discriminate source location in this particular environment. More specifically, we deliver estimated time-difference-of-arrivals (TDOA's) and normalized cross-correlation values computed from pairs of hydrophone signals to a random forest model for high-feature-volume classification and feature selection, and subsequently deliver the selected features into linear discriminant analysis, linear and quadratic Support Vector Machine (SVM), and Gaussian process models. Based on data from 14 sound source locations and 16 hydrophones, our classification models yielded perfect accuracy at predicting novel sound source locations. Our regression models yielded better accuracy than the established Steered-Response Power (SRP) method when all training data were used, and comparable accuracy along the pool surface when deprived of training data at testing sites; our methods additionally boast improved computation time and the potential for superior localization accuracy in all dimensions with more training data. Because of the generality of our method we argue it may be useful in a much wider variety of contexts.

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. In this work we developed a method for monitoring such maintenance processes, focusing on a specific signature of their behavior derived from the theory of dynamical systems: stability analysis of dynamical modes. When such mechanisms are at work, their modes of activity are at marginal stability, neither damped (stable) nor exponentially growing (unstable) but rather hovering in between. We have previously shown that, conversely, under induction of anesthesia those modes become more stable and thus less responsive, then reversed upon emergence to wakefulness. We take advantage of this effect to build a single-trial classifier which detects whether a subject is awake or unconscious achieving high performance. We show that our approach can be developed into a means for intra-operative monitoring of the depth of anesthesia, an application of fundamental importance to modern clinical practice.

In recent years, considerable evidence has accrued indicating that brain function can be flexibly reconfigured on the fly: many brain areas are capable of carrying out a variety of distinct functions and are able to switch between those functions in a context-sensitive, dynamic fashion. Some evidence has also emerged that ongoing brain activity, the ceaseless background brain dynamics, may be implicated in setting and controlling these flexible functions as well as attentional gain. A crucial link between them is dynamics. Here we review both the accumulating evidence as well as propose a theoretical outline of dynamical mechanisms for functional self-reconfiguration of neural networks, including reconfiguration of logic function, reconfiguration of information routing, and poising at critical points to switch 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. SEGGA is free, open source, and provides a full suite of tools that allow users with no prior computational expertise to independently perform all steps of automated image segmentation, semi-automated user-guided error correction, and data analysis. Here we use SEGGA to analyze changes in cell shape, cell interactions and planar polarity during convergent extension in the Drosophila embryo. These studies demonstrate that planar polarity is rapidly established in a spatiotemporally regulated pattern that is dynamically remodeled in response to changes in cell orientation. These findings reveal an unexpected plasticity that maintains coordinated planar polarity in actively moving populations through the continual realignment of cell polarity with the tissue axes.

Relative to individually distinctivesignature whistles, little is known about the “non-signature” calls – particularly the non-signature whistles – of the common Atlantic bottlenose dolphin,Tursiops truncatus. While such calls are suspected to serve social function, tracking their exchange among conspecifics and correlating their usage with non-acoustic behavior has proven challenging, given both their relative scarcity in the dolphin repertoire and their characteristic shared use among dolphins, which precludes the unique identification of callers on the basis of whistle properties alone. Towards the goal of robustly identifying the callers of non-signature whistles (equivalently, attributing non-signature whistles to callers), we present a new, long-term audiovisual monitoring system designed for and tested at the Dolphin Discovery exhibit of the National Aquarium in Baltimore, Maryland. In this paper, we confirm the system’s ability to spatially localize impulse-like sounds using traditional signal processing approaches that have already been used to localize dolphin echolocation clicks. We go on to provide the first rigorous experimental evaluation of the component time-difference-of-arrival-(TDOA) extraction methods on whistle-like tonal sounds in a (reverberant) aquatic environment, showing that they are generally not suited to sound localization. Nevertheless, we find that TDOA extraction under these circumstances is performed significantly better using a Generalized Cross-Correlation with Phase Transform (GCC-PHAT) method than a standard circular cross-correlation method, a potentially important result.

Most research into bottlenose dolphins’ (Tursiops truncatus’) capacity for communication has centered on tonal calls termedwhistles, in particular individually distinctive contact calls referred to assignature whistles. While “non-signature” whistles exist, and may be important components of bottlenose dolphins’ communicative repertoire, they have not been studied extensively. This is in part due to the difficulty of attributing whistles to specific individuals, a challenge that has limited the study of not only non-signature whistles but the study of general acoustic exchanges among socializing dolphins. In this paper, we propose the first machine-learning-based approach to identifying the source locations of semi-stationary, tonal, whistle-like sounds in a highly reverberant space, specifically a half-cylindrical dolphin pool. We deliver estimated time-difference-of-arrivals (TDOA’s) and normalized cross-correlation values computed from pairs of hydrophone signals to a random forest model for high-feature-volume classification and feature selection, and subsequently deliver the selected features into linear discriminant analysis, linear and quadratic Support Vector Machine (SVM), and Gaussian process models. In our 14-source-location setup, we achieve perfect accuracy in localization by classification and high accuracy in localization by regression (median absolute deviation of 0.66 m, interquartile range of 0.34 m - 1.57 m), with fewer than 10,000 features. By building a parsimonious (minimum-feature) classification tree model for the same task, we show that a minimally sufficient feature set is consistent with the information valued by a strictly geometric, time-difference-of-arrival-based approach to sound source localization. Ultimately, our regression models yielded better accuracy than the established Steered-Response Power (SRP) method when all training data were used, and comparable accuracy along the pool surface when deprived of training data at testing sites; our methods additionally boast improved computation time and the potential for superior localization accuracy in all dimensions with more training data.

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. In this work we developed a method for monitoring such maintenance processes, focusing on a specifc signature of their behavior derived from the theory of dynamical systems: stability analysis of dynamical modes. When such mechanisms are at work, their modes of activity are at marginal stability, neither damped (stable) nor exponentially growing (unstable) but rather hovering in between. We have previously shown that, conversely, under induction of anesthesia those modes become more stable and thus less responsive, then reversed upon emergence to wakefulness. We take advantage of this efect to build a single-trial classifer which detects whether a subject is awake or unconscious achieving high performance. We show that our approach can be developed into a means for intra-operative monitoring of the depth of anesthesia, an application of fundamental importance to modern clinical practice. Fil: Alonso, Leandro M.. The Rockefeller University; Estados Unidos. Brandeis University; Estados Unidos Fil: Solovey, Guillermo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; Argentina Fil: Yanagawa, Toru. Riken Center For Brain Science; Japón Fil: Proekt, Alex. University of Pennsylvania; Estados Unidos Fil: Cecchi, Guillermo A.. IBM Research. Thomas J. Watson Research Center; Estados Unidos Fil: Magnasco, Marcelo O.. Brandeis University; Estados Unidos. The Rockefeller University; Estados Unidos

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. In this work we developed a method for monitoring such maintenance processes, focusing on a specific signature of their behavior derived from the theory of dynamical systems: stability analysis of dynamical modes. When such mechanisms are at work, their modes of activity are at marginal stability, neither damped (stable) nor exponentially growing (unstable) but rather hovering in between. We have previously shown that, conversely, under induction of anesthesia those modes become more stable and thus less responsive, then reversed upon emergence to wakefulness. We take advantage of this effect to build a single-trial classifier which detects whether a subject is awake or unconscious achieving high performance. We show that our approach can be developed into a mean for intra-operative monitoring of the depth of anesthesia, an application of fundamental importance to modern clinical practice.

International audience; 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. However, there are inherent difficulties in simulating their networks' dynamics in silico with standard numerical discretization schemes. Indeed, the well-posedness of the evolution of such networks requires temporally ordering every neuronal interaction, whereas the order of interactions is highly sensitive to the random variability of spiking times. Here, we answer these issues for perfect stochastic integrate-and-fire neurons by designing an exact event-driven algorithm for the simulation of recurrent networks, with delayed Dirac-like interactions. In addition to being exact from the mathematical standpoint, our proposed method is highly efficient numerically. We envision that our algorithm is especially indicated for studying the emergence of polychronized motifs in networks evolving under spike-timing-dependent plasticity with intrinsic noise.

Electrophysiology has recently evolved into an interactive, high-throughput endeavor. Recording from dozens to hundreds of electrodes is today routine; novel means of manipulating the system in real time, through electrical stimulation, optogenetics or sensory manipulation are allowing us to decipher neural circuit function at an unparalleled rate. To contribute to the wide dissemination of such techniques, we present an open hardware project, High-Channel Count Electrophysiology (HiCCE), aiming to produce low-cost, high-channel count (≥128 channels) electrophysiology instrumentation capable of fast data acquisition rates, real-time processing and feedback capabilities. Our design is centered on an open standard, FPGA Mezzanine Card (FMC), which permits a varied choice of FPGA carrier architectures suited to different laboratory experimental needs. The HiCCE-128, a low-cost highperformance 128-channel data acquisition board for small voltage signals, is being introduced. It is a FMC module that can be operated from any FPGA carrier conforming to the FMC/VITA57 standard. This specialized board with a low input referred noise of about 3 μV is capable of acquiring 128 channels simultaneously at 31.25 kS/s per channel with 16 effective bits of resolution. We present the global architecture and some preliminary measurement to illustrate its potential for electrophysiological and medical applications.

All the Smells of the World How many odorant stimuli can a normal human being discriminate? During psychophysical tests of odor mixture discrimination, Bushdid et al. (p. 1370 ) were surprised to find that humans can discriminate among more than a trillion different smells. Because the authors reduced the complexity by investigating only mixtures of 10, 20, or 30 components drawn from a collection of 128 odorous molecules, this astonishingly large number is probably the lower limit of the potential number of olfactory stimuli that humans can distinguish.

The metrization of the space of neural responses is an ongoing research program seeking to find natural ways to describe, in geometrical terms, the sets of possible activities in the brain. One component of this program is spike metrics—notions of distance between two spike trains recorded from a neuron. Alignment spike metrics work by identifying “equivalent” spikes in both trains. We present an alignment spike metric having [Formula: see text] underlying geometrical structure; the [Formula: see text] version is Euclidean and is suitable for further embedding in Euclidean spaces by multidimensional scaling methods or related procedures. We show how to implement a fast algorithm for the computation of this metric based on bipartite graph matching theory.

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” M embodying them is tridiagonal antisymmetric and has purely imaginary (critical) eigenvalues. The model illustrates the interplay of two properties: circuits with a complex internal dynamics, such as multiple stable periodic solutions and period doubling bifurcations, and coupling with a “critical” synaptic matrix, i.e., having purely imaginary eigenvalues. In order to identify the dynamical underpinnings of these behaviors, we explored a discrete-time coupled-map lattice inspired by our system: the dynamics of the units is dictated by a chaotic map of the interval, and the interactions are given by allowing the critical coupling to act for a finite period τ, thus given by a unitary matrix U = exp ⁡ ( τ 2 M ). It is now explicit that such critical couplings are volume-preserving in the sense of Liouville’s theorem. We show that this map is also capable of producing a variety of complex spatiotemporal patterns including gliders, like our original chain of neural circuits. Our results suggest that if the units in isolation are capable of featuring multiple dynamical states, then local critical couplings lead to a wide variety of emergent spatiotemporal phenomena.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” M embodying them is tridiagonal antisymmetric and has purely imaginary (critical) eigenvalues. The model illustrates the interplay of two properties: circuits with a complex internal dynamics, such as multiple stable periodic solutions and period doubling bifurcations, and coupling with a “critical” synaptic matrix, i.e., having purely imaginary eigenvalues. In order to identify the dynamical underpinnings of these behaviors, we explored a discrete-time coupled-map lattice inspired by our system: the dy...

Even for simple diffusion processes, treating first-passage problems analytically proves intractable for generic barriers and existing numerical methods are inaccurate and computationally costly. Here, we present a novel numerical method that is faster and has more tightly controlled accuracy. Our algorithm is a probabilistic variant of dichotomic search for the computation of first passage times through non-negative homogeneously Holder continuous boundaries by Gauss-Markov processes. These include the Ornstein-Uhlenbeck process underlying the ubiquitous “leaky integrate-and-fire” model of neuronal excitation. Our method evaluates discrete points in a sample path exactly, and refines this representation recursively only in regions where a passage is rigorously estimated to be probable (e.g. when close to the boundary).

The brain processes visual inputs having structure over a large range of spatial scales. The precise mechanisms or algorithms used by the brain to achieve this feat are largely unknown and an open problem in visual neuroscience. In particular, the spatial extent in visual space over which primary visual cortex (V1) performs evidence integration has been shown to change as a function of contrast and other visual parameters, thus adapting scale in visual space in an input-dependent manner. We demonstrate that a simple dynamical mechanism---dynamical criticality---can simultaneously account for the well-documented input-dependence characteristics of three properties of V1: scales of integration in visuotopic space, extents of lateral integration on the cortical surface, and response latencies.

The initial synapses of the auditory system, which connect hair cells to afferent nerve fibers, display two unusual features. First, synaptic transmission occurs in a multiquantal fashion: the contents of multiple synaptic vesicles are discharged simultaneously. Second, synaptic transmission may be tuned to specific frequencies of stimulation. We developed a minimal theoretical model to explore the possibility that hair-cell synapses achieve both multiquantal release and frequency selectivity through a cooperative mechanism for the exocytotic release of neurotransmitter. We first characterized vesicle release as a four-step cycle at each release site, then generalized the result to an arbitrary number of steps. The cyclic process itself induces some degree of resonance, and may display a stable, underdamped fixed point of the release dynamics associated with a pair of complex eigenvalues. Cooperativity greatly enhances the frequency selectivity by moving the eigenvalues toward the imaginary axis; spontaneously oscillatory release can arise beyond a Hopf bifurcation. These phenomena occur both in the macroscopic limit, when the number of release sites involved is very large, and in the more realistic stochastic regime, when only a limited number of release sites participate at each synapse. It is thus possible to connect multiquantal release with frequency selectivity through the mechanism of cooperativity.

The classical Haar construction of Brownian motion uses a binary tree of triangular wedge-shaped functions. This basis has compactness properties which make it especially suited for certain classes of numerical algorithms. We present a similar basis for the Ornstein-Uhlenbeck process, in which the basis elements approach asymptotically the Haar functions as the index increases, and preserve the following properties of the Haar basis: all basis elements have compact support on an open interval with dyadic rational endpoints; these intervals are nested and become smaller for larger indices of the basis element, and for any dyadic rational, only a finite number of basis elements is nonzero at that number. Thus the expansion in our basis, when evaluated at a dyadic rational, terminates in a finite number of steps. We prove the covariance formulae for our expansion and discuss its statistical interpretation and connections to asymptotic scale invariance., 20 pages, 3 figures

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. The stimuli varied in spectro‐temporal density and cyclo‐temporal statistics over several orders of magnitude, corresponding to a range of water‐like percepts, from pattering of rain to a slow stream. We recorded neuronal activity in the primary auditory cortex of awake rats presented with these stimuli. The responses of the majority of individual neurons were selective for a subset of stimuli with specific statistics. However, as a neuronal population, the responses were remarkably stable over large changes in stimulus statistics, exhibiting a similar range in firing rate, response strength, variability and information rate, and only minor variation in receptive field parameters. This pattern of neuronal responses suggests a potentially general principle for cortical encoding of complex acoustic scenes: while individual cortical neurons exhibit selectivity for specific statistical features, a neuronal population preserves a constant response structure across a broad range of statistical parameters.

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. We argue that the brain, as a dynamical system, is self-regulated at the boundary between stable and unstable regimes, allowing it in particular to maintain high susceptibility to stimuli. To test this hypothesis, we performed stability analysis of high-density electrocorticography recordings covering an entire cerebral hemisphere in monkeys during reversible loss of consciousness. We show that, during loss of consciousness, the number of eigenmodes at the edge of instability decreases smoothly, independently of the type of anesthetic and specific features of brain activity. The eigenmodes drift back toward the unstable line during recovery of consciousness. Furthermore, we show that stability is an emergent phenomenon dependent on the correlations among activity in different cortical regions rather than signals taken in isolation. These findings support the conclusion that dynamics at the edge of instability are essential for maintaining consciousness and provide a novel and principled measure that distinguishes between the conscious and the unconscious brain. Significance Statement: What distinguishes brain activity during consciousness from that observed during unconsciousness? Answering this question has proven difficult because neither consciousness nor lack thereof have universal signatures in terms of most specific features of brain activity. For instance, different anesthetics induce different patterns of brain activity. We demonstrate that loss of consciousness is universally and reliably associated with stabilization of cortical dynamics regardless of the specific activity characteristics. To give an analogy, our analysis suggests that loss of consciousness is akin to depressing the damper pedal on the piano, which makes the sounds dissipate quicker regardless of the specific melody being played. This approach may prove useful in detecting consciousness on the basis of brain activity under anesthesia and other settings. Fil: Solovey, Guillermo. The Rockefeller University; Estados Unidos. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Neurociencia Integrativa; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Alonso, Leandro Martín. The Rockefeller University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Yanagawa, Toru. RIKEN Brain Science Institute; Japón Fil: Fujii, Naotaka. RIKEN Brain Science Institute; Japón Fil: Magnasco, Marcelo Osvaldo. The Rockefeller University; Estados Unidos Fil: Cecchi, Guillermo Alberto. IBM Research. Thomas J. Watson Research Center; Estados Unidos Fil: Proekt, Alex. The Rockefeller University; Estados Unidos. Weill Medical Center; Estados Unidos

We recently presented an estimate of the number of mutually discriminable olfactory stimuli at one trillion (1). Subjects were asked to sniff mixtures of molecules with increasing component overlap selected from a panel of 128 isointense structurally and perceptually diverse monomolecular odorants (2). We considered stimulus pairs discriminable when the majority of subjects could significantly discriminate them at p=0.05, a conventional statistical threshold given our sample size. From these empirical data, we estimated that human discriminative capacity exceeds one trillion olfactory stimuli. Several readers have pointed out that such extrapolations are sensitive to underlying assumptions about the chosen significance threshold (3) and the dimensionality of olfaction (4). It is important to note that any exponential function will be sensitive in this way, and the goal of our model was not to identify the exact number of discriminable olfactory stimuli, or even the exact mathematical bounds, but an estimate of the order of magnitude of human discriminatory power across a population of human subjects. This was not clearly stated in our paper, and we agree that contradictory references to a “lower limit” and an “upper bound” were confusing. The central argument in (4) is that our estimation method assumes that the dimensionality of olfactory space is large. We agree that the high-dimensional nature of olfaction is indeed an assumption, and we should have stated this explicitly in our paper (1). Even if we follow this logic of the models presented in (4), purely geometrical calculations show that our results hold if the dimensionality of olfactory representations is D≥25. The dimensionality of olfaction is a question of interest to everyone, and while we do not know for sure, all available evidence suggests that olfaction is a high-dimensional sense. The olfactory system is wired to keep information from the ~400 odorant receptors strictly separated, so it is plausible that olfaction operates at least in 400-dimensional space. This is an important topic of discussion in olfaction, and we welcome continued debate of the dimensionality of smell and how this impacts human olfactory perception.

Effector CD4+ T cells rapidly activate high-level cytokine expression following TCR stimulation. Consistent with accelerated protein production in these cells, global mRNA profiles revealed that, after cytokines, the most impressive cluster of activated genes encode rRNA-maturation factors. Activation of these genes was ERK-MAPK dependent, accompanied by increased rRNA transcription and faster maturation kinetics, and much greater in effector CD4+ T cells than in naive cells. Ribosomal protein subunit (RPS) synthesis was also ERK-MAPK dependent and increased to match rRNA production, but without evident increase in RPS mRNA. Instead, stimulation promoted polysome loading of RPS mRNA via cis-acting, 5′-terminal oligopyrimidines. These results demonstrate how, in response to extracellular signals, effector CD4+ T cells coordinately increase multiple ribosomal components to accommodate burgeoning cytokine production.

Although mRNA decay rates are a key determinant of the steady-state concentration for any given mRNA species, relatively little is known, on a population level, about what factors influence turnover rates and how these rates are integrated into cellular decisions. We decided to measure mRNA decay rates in two human cell lines with high-density oligonucleotide arrays that enable the measurement of decay rates simultaneously for thousands of mRNA species. Using existing annotation and the Gene Ontology hierarchy of biological processes, we assign mRNAs to functional classes at various levels of resolution and compare the decay rate statistics between these classes. The results show statistically significant organizational principles in the variation of decay rates among functional classes. In particular, transcription factor mRNAs have increased average decay rates compared with other transcripts and are enriched in “fast-decaying” mRNAs with half-lives Saccharomyces cerevisiae mRNA decay shows the same functional organization of decay rates, implying that it is a general organizational scheme for eukaryotes. Additionally, we investigated the dependence of decay rates on sequence composition, that is, the presence or absence of short mRNA motifs in various regions of the mRNA transcript. Our analysis recovers the positive correlation of mRNA decay with known AU-rich mRNA motifs, but we also uncover further short mRNA motifs that show statistically significant correlation with decay. However, we also note that none of these motifs are strong predictors of mRNA decay rate, indicating that the regulation of mRNA decay is more complex and may involve the cooperative binding of several RNA-binding proteins at different sites.

Oligonucleotide microarrays are based on the hybridization of labeled mRNA molecules to short length oligonucleotide probes on a glass surface. Two effects have been shown to affect the raw data: the sequence dependence of the probe hybridization properties and the chemical saturation resulting from surface adsorption processes. We address both issues simultaneously using a physically motivated hybridization model. Based on publicly available calibration data sets, we show that Langmuir adsorption accurately describes GeneChip hybridization, with model parameters that we predict from the sequence composition of the probes. Because these parameters have physical units, we are able to estimate absolute mRNA concentrations in picomolar. Additionally, by accounting for chemical saturation, we substantially reduce the compressive bias of differential expression estimates that normally occurs toward high concentrations.

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. In addition, we find that the long term equilibration dynamics exhibit behavior reminiscent of glassy systems characterized by long periods of slow changes punctuated by bursts of reorganization events.

In this work we analyze electro-corticography (ECoG) recordings in human sub- jects as they are anesthetized. We hypothesize that the decrease in responsiveness that defines anesthesia induction 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 decompo- sition of the autoregressive matrices, independently fitted to short sliding temporal windows. Consistent with the hypothesis we show that while the subject is awake, many modes of oscillations of neuronal activity are found at the edge of instabil- ity, but as the subject becomes anesthetized the fitted dynamics becomes more damped. Analysis of eigenmode distributions in the awake and anesthetized brain revealed statistically significant stabilization not present in surrogate data. Sta- bility analysis thus offer a novel way of quantifying changes in neuronal activity that characterize loss of consciousness induced by general anesthetics. Specifically, our analysis suggests that the effect of the anesthetic procedure is to damp out high frequency activity while still allowing for low frequency modes to perform a function.

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., 2000 ; Tiesinga, 2002 ; Fellous, Rudolph, Destexhe, & Sejnowski, 2003 ). In order to investigate this interplay, we focus on the stochastic leaky integrate-and-fire neuron and identify the Hölder exponent H of the integrated input as the key mathematical property dictating the regime of firing of a single-unit neuron. We have recently provided numerical evidence (Taillefumier & Magnasco, 2013 ) for the existence of a phase transition when [Formula: see text] becomes less than the statistical Hölder exponent associated with internal gaussian white noise (H=1/2). Here we describe the theoretical and numerical framework devised for the study of a neuron that is periodically driven by frozen noisy inputs with exponent H>0. In doing so, we account for the existence of a transition between two regimes of firing when H=1/2, and we show that spiking times have a continuous density when the Hölder exponent satisfies H>1/2. The transition at H=1/2 formally separates rate codes, for which the neural firing probability varies smoothly, from temporal codes, for which the neuron fires at sharply defined times regardless of the intensity of internal noise.

Natural and man-made transport webs are frequently dominated by dense sets of nested cycles. The architecture of these networks, as defined by the topology and edge weights, determines how efficiently the networks perform their function. Yet, the set of tools that can characterize such a weighted cycle-rich architecture in a physically relevant, mathematically compact way is sparse. In order to fill this void, we have developed a new algorithm that rests on an abstraction of the physical `tiling' in the case of a two dimensional network to an effective tiling of an abstract surface in space that the network may be thought to sit in. Generically these abstract surfaces are richer than the flat plane and as a result there are now two families of fundamental units that may aggregate upon cutting weakest links -- the plaquettes of the tiling and the longer `topological' cycles associated with the abstract surface itself. Upon sequential removal of the weakest links, as determined by the edge weight, neighboring plaquettes merge and a tree characterizing this merging process results. The properties of this characteristic tree can provide the physical and topological data required to describe the architecture of the network and to build physical models. The new algorithm can be used for automated phenotypic characterization of any weighted network whose structure is dominated by cycles, such as mammalian vasculature in the organs, the root networks of clonal colonies like quaking aspen, or the force networks in jammed granular matter.

To understand how the human visual system analyzes images, it is essential to know the structure of the visual environment. In particular, natural images display consistent statistical properties that distinguish them from random luminance distributions. We have studied the geometric regularities of oriented elements (edges or line segments) present in an ensemble of visual scenes, asking how much information the presence of a segment in a particular location of the visual scene carries about the presence of a second segment at different relative positions and orientations. We observed strong long-range correlations in the distribution of oriented segments that extend over the whole visual field. We further show that a very simple geometric rule, cocircularity, predicts the arrangement of segments in natural scenes, and that different geometrical arrangements show relevant differences in their scaling properties. Our results show similarities to geometric features of previous physiological and psychophysical studies. We discuss the implications of these findings for theories of early vision., Comment: 3 figures, 2 large figures not included

Adult neurogenesis has long been documented in the vertebrate brain and recently even in humans. Although it has been conjectured for many years that its functional role is related to the renewing of memories, no clear mechanism as to how this can be achieved has been proposed. Using the mammalian olfactory bulb as a paradigm, we present a scheme in which incorporation of new neurons proceeds at a constant rate, while their survival is activity-dependent and thus contingent on new neurons establishing suitable connections. We show that a simple mathematical model following these rules organizes its activity so as to maximize the difference between its responses and can adapt to changing environmental conditions in unsupervised fashion, in agreement with current neurophysiological data.

Our hearing organ, the cochlea, evidently poises itself at a Hopf bifurcation to maximize tuning and amplification. We show that in this condition several effects are expected to be generic: compression of the dynamic range, infinitely sharp tuning at zero input, and generation of combination tones. These effects are >essentially> nonlinear in that they become more marked the smaller the forcing: there is no audible sound soft enough not to evoke them. All the well-documented nonlinear aspects of hearing therefore appear to be consequences of the same underlying mechanism., We acknowledge partial support from the Mathers Foundation (M. M.), the Spanish DGICyT Grant No. PB94-1167 (V. M. S.), the NSF (Y. C.), NIH Grant No. DC00241, and the Howard Hughes Medical Institute (A. J. H., M. O.).

We describe here an automated system that accurately maps tissue sections stained by immunocytochemistry for an inducible nuclear protein. The sections are scanned with a computer-controlled microscope setup hooked to a CCD camera. Raw images captured at high resolution are filtered using highly selective criteria for the recognition of labeled cell nuclei. The total population of recognized labeled nuclei is then divided into separate bins, according to their labeling intensities. Finally, information about both the position and labeling intensity of labeled nuclei is represented in average density maps. The system was optimized for the quantitative mapping of neuronal cells expressing the inducible gene ZENK in the brain of songbirds, in response to stimulation with song, but should be of general applicability for the mapping of inducible nuclear proteins.

While many papers in the last few years have dealt with various equations euphemistically called “ratchets,” the original Feyman two-temperature setup has been left largely unchallenged. We present here a look at the details of how this famous engine actually generates motion from a temperature difference.

We show that presentation of individual canary song syllables results in distinct expression patterns of the immediate-early gene ZENK in the caudomedial neostriatum (NCM) of adult canaries. Information on the spatial distribution and labeling of stained cells provides for a classification of ZENK patterns that (1) accords to the organization of stimuli into families, (2) preserves the stimuli intrafamily relationships, and (3) confers salience to natural over artificial stimuli, resulting in a nonclassical tonotopic map. Moreover, complex syllable maps cannot be reduced to any linear combinations of simple syllable maps. These properties arise from the collective response of NCM neurons to auditory stimuli, rather than from the behavior of single neurons. The syllabic representation described here may constitute an important step toward deciphering the rules of birdsong auditory representation.