Dynamical systems

  • Article
    | Open Access

    How to infer transient cells and cell-fate transitions from snap-shot single cell transcriptome dataset remains a major challenge. Here the authors present a multiscale approach to construct single-cell dynamical manifold, quantify cell stability, and compute transition trajectory and probability between cell states.

    • Peijie Zhou
    • , Shuxiong Wang
    •  & Qing Nie
  • Article
    | Open Access

    Complex biomolecular networks are fundamental to the functioning of living systems, both at the cellular level and beyond. In this paper, the authors develop a systems framework to elucidate the interplay of networks and the spatial localisation of network components.

    • Govind Menon
    •  & J. Krishnan
  • Article
    | Open Access

    Higher-order sequence learning using a structured graph representation - clone-structured cognitive graphs (CSCG) – can explain how the hippocampus learns cognitive maps. CSCG provides novel explanations for transferable schemas and transitive inference in the hippocampus, and for how place cells, splitter cells, lap-cells and a variety of phenomena emerge from the same set of fundamental principles.

    • Dileep George
    • , Rajeev V. Rikhye
    •  & Miguel Lázaro-Gredilla
  • Article
    | Open Access

    A defining human characteristic is the ability to perform diverse cognitively challenging tasks. The authors show that this adaptability relates to a network sampling mechanism, where brain-wide network states transiently blend the unique combinations of neural resources required by different tasks.

    • Eyal Soreq
    • , Ines R. Violante
    •  & Adam Hampshire
  • Article
    | Open Access

    The authors generated a Synthetic Morphogenic Membrane System by encapsulating a dynamic microtubule aster and a light-inducible signaling system driven by GTP/ATP chemical potential into cell-sized liposomes. This reconstitution of artificial proto-cells reveals how non-equilibrium phenomena affect cellular information processing in morphogenesis.

    • Konstantin Gavriljuk
    • , Bruno Scocozza
    •  & Philippe I. H. Bastiaens
  • Article
    | Open Access

    Neural networks trained using predictive models generate representations that recover the underlying low-dimensional latent structure in the data. Here, the authors demonstrate that a network trained on a spatial navigation task generates place-related neural activations similar to those observed in the hippocampus and show that these are related to the latent structure.

    • Stefano Recanatesi
    • , Matthew Farrell
    •  & Eric Shea-Brown
  • Article
    | Open Access

    A principle of neuroanatomy, namely diffuse connectivity, is modeled using a large-scale network of corticothalamic neural masses. We demonstrate that increases in diffuse coupling transition the system through a quasi-critical regime, which coincides with known signatures of complex adaptive brain dynamics, and model fits to human imaging data orient task states to higher levels of diffusivity, consistent with the influence of arousal systems.

    • Eli J. Müller
    • , Brandon R. Munn
    •  & James M. Shine
  • Article
    | Open Access

    Herold et al. present an integrated meta-omics framework to investigate how mixed microbial communities, such as oleaginous bacterial populations in biological wastewater treatment plants, respond with distinct adaptation strategies to disturbances. They show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity.

    • Malte Herold
    • , Susana Martínez Arbas
    •  & Paul Wilmes
  • Article
    | Open Access

    The transition from resting to perceiving one’s milieu requires a fundamental reorganization of brain activity. Here, the authors show how a fundamental reshaping of brain state dynamics supports perceptual engagement in naturalistic stimuli.

    • Johan N. van der Meer
    • , Michael Breakspear
    •  & Luca Cocchi
  • Article
    | Open Access

    Boolean Networks are a well-established model of biological networks, but usual interpretations can preclude the prediction of behaviours observed in quantitative systems. The authors introduce Most Permissive Boolean Networks, which are shown not to miss any behaviour achievable by the corresponding quantitative model.

    • Loïc Paulevé
    • , Juraj Kolčák
    •  & Stefan Haar
  • Article
    | Open Access

    We can flexibly coordinate our movements with external stimuli, but no circuit-level model exists to explain this ability. Inspired by fundamental concepts in control theory, the authors construct a modular neural circuit that captures human behavior in a wide range of temporal coordination tasks.

    • Seth W. Egger
    • , Nhat M. Le
    •  & Mehrdad Jazayeri
  • Article
    | Open Access

    Designing efficient artificial networks able to quickly converge to optimal performance for a given task remains a challenge. Here, the authors demonstrate a relation between criticality, task-performance and information theoretic fingerprint in a spiking neuromorphic network with synaptic plasticity.

    • Benjamin Cramer
    • , David Stöckel
    •  & Viola Priesemann
  • Article
    | Open Access

    Local activity of the DNA methylation machinery remains poorly understood. Here, the authors present a theoretical and experimental framework to infer methylation and demethylation rates at genome scale in mouse embryonic stem cells, finding that maintenance methylation activity is reduced at transcription factor binding sites, while methylation turnover is elevated in transcribed gene bodies.

    • Paul Adrian Ginno
    • , Dimos Gaidatzis
    •  & Dirk Schübeler
  • Article
    | Open Access

    That attention is a rhythmic process has received abundant evidence. Here, the authors reveal the natural sampling rate of auditory and visual periodic temporal attention. Both are antagonistically modulated by overt motor activity, a result generalised in a dynamical model of coupled oscillators.

    • Arnaud Zalta
    • , Spase Petkoski
    •  & Benjamin Morillon
  • Article
    | Open Access

    Feedback mechanisms for synthetic gene circuits are necessary to provide robustness to external perturbations. Here the authors validate a biomolecular controller based on a sigma and anti-sigma factor to achieve stable gene expression in the face of external disturbances in an in vitro synthetic gene circuit.

    • Deepak K. Agrawal
    • , Ryan Marshall
    •  & Eduardo D Sontag
  • Article
    | Open Access

    There are many examples of cell populations exhibiting density-dependent collective oscillatory behaviour. Here, the authors show that sustained collective oscillations emerge when cells anticipate variation in signal and attempt to amplify it, a property that can be linked to adaptation.

    • Shou-Wen Wang
    •  & Lei-Han Tang
  • Article
    | Open Access

    It is difficult to fit mechanistic, biophysically constrained circuit models to spike train data from in vivo extracellular recordings. Here the authors present analytical methods that enable efficient parameter estimation for integrate-and-fire circuit models and inference of the underlying connectivity structure in subsampled networks.

    • Josef Ladenbauer
    • , Sam McKenzie
    •  & Srdjan Ostojic
  • Article
    | Open Access

    How are stable memories maintained in the brain despite significant ongoing fluctuations in synaptic strengths? Here, the authors show that a model consistent with fluctuations, homeostasis and biologically plausible learning rules, naturally leads to memories implemented as dynamic attractors.

    • Lee Susman
    • , Naama Brenner
    •  & Omri Barak
  • Article
    | Open Access

    The nature of the signals that propagate through feedforward networks is not well understood. Here, the authors combine microfabrication, multilayer cortical cultures, and optogenetic stimulation to show that NMDA-mediated synaptic current generates a sustained phase of activity that propagates firing rate signals.

    • Jérémie Barral
    • , Xiao-Jing Wang
    •  & Alex D. Reyes
  • Article
    | Open Access

    By examining the organization of bird song and human speech, the authors show that the two types of communication signals have similar sequential structures, following both hierarchical and Markovian organization.

    • Tim Sainburg
    • , Brad Theilman
    •  & Timothy Q. Gentner
  • Article
    | Open Access

    Neural representations in working memory are susceptible to internal noise, which scales with memory load. Here, the authors show that attractor dynamics mitigate the influence of internal noise by pulling memories towards a few stable representations.

    • Matthew F. Panichello
    • , Brian DePasquale
    •  & Timothy J. Buschman
  • Article
    | Open Access

    Motor preparation processes guide movement. Here, by recording neural activity in monkeys reaching toward targets that can change location, the authors provide evidence that changing a prepared movement midway through completion reengages motor preparation.

    • K. Cora Ames
    • , Stephen I. Ryu
    •  & Krishna V. Shenoy
  • Article
    | Open Access

    NREM sleep in rodents is characterized by internal dynamics in the form of UP/DOWN states in the neocortex and SWRs in the hippocampus. Here, the authors report that a mean field model with excitable dynamics captures the transition probabilities between these states from rodent sleep data.

    • Daniel Levenstein
    • , György Buzsáki
    •  & John Rinzel
  • Article
    | Open Access

    We make decisions with varying degrees of confidence and, if our confidence in a decision falls, we may change our mind. Here, the authors present a neuronal circuit model to account for how change of mind occurs under particular low-confidence conditions.

    • Nadim A. A. Atiya
    • , Iñaki Rañó
    •  & KongFatt Wong-Lin
  • Article
    | Open Access

    An unresolved problem in neuroscience is to determine the relevant timescale for understanding spatiotemporal dynamics of the brain. Here, the authors introduce a new framework to study the different timescales through binning the output of a generative model of neural activity.

    • Gustavo Deco
    • , Josephine Cruzat
    •  & Morten L. Kringelbach
  • Article
    | Open Access

    To explain the neural correlates of behavior and its variability, one must analyze single-trial population dynamics. Here, the authors develop a statistical method that extracts low-dimensional dynamics that explain behavior better than high-dimensional neural activity revealing unexpected structure.

    • Ziqiang Wei
    • , Hidehiko Inagaki
    •  & Shaul Druckmann
  • Article
    | Open Access

    Noisy gene expression leading to phenotypic variability can help organisms to survive in changing environments. Here, Patange et al. show that noisy expression of a stress response regulator, RpoS, allows E. coli cells to modulate their growth rates to survive future adverse environments.

    • Om Patange
    • , Christian Schwall
    •  & James C. W. Locke
  • Article
    | Open Access

    Causality inference in time series analysis based on temporal precedence principle between cause and effect fails to detect mutual causal interactions. Here, Yang et al. introduce a causal decomposition approach based on the covariation principle of cause and effect that overcomes this limitation.

    • Albert C. Yang
    • , Chung-Kang Peng
    •  & Norden E. Huang
  • Article
    | Open Access

    FORCE training is a . Here the authors implement FORCE training in models of spiking neuronal networks and demonstrate that these networks can be trained to exhibit different dynamic behaviours.

    • Wilten Nicola
    •  & Claudia Clopath
  • Article
    | Open Access

    Though the amplitude and frequency of neural oscillations in the alpha band are related to dissociable visual processes, they are not independent mathematically. Here, the authors show that fluctuations in instantaneous frequency predict alpha amplitude during visual discrimination tasks.

    • Stephanie Nelli
    • , Sirawaj Itthipuripat
    •  & John T. Serences
  • Article
    | Open Access

    Cybergenetics aims to monitor and regulate cellular processes in real-time using computer monitoring and feedback of biological readouts. Here the authors use a feedback loop and periodic forcing to maintain cells with a bistable synthetic circuit near its unstable state.

    • Jean-Baptiste Lugagne
    • , Sebastián Sosa Carrillo
    •  & Pascal Hersen
  • Article
    | Open Access

    Active locomotion requires closed-loop sensorimotor co ordination between perception and action. Here the authors show using behavioural, imaging and modelling approaches that gaze orientation during phototaxis behaviour in larval zebrafish is related to oscillatory dynamics of a neuronal population in the hindbrain.

    • Sébastien Wolf
    • , Alexis M. Dubreuil
    •  & Georges Debrégeas
  • Article
    | Open Access

    Focal cortical seizures result from local and widespread propagation of excitatory activity. Here the authors employ widefield calcium imaging in mouse visual areas to demonstrate that these seizures start as local synchronous activation and then propagate along the connectivity that underlies normal sensory processing.

    • L. Federico Rossi
    • , Robert C. Wykes
    •  & Matteo Carandini
  • Article
    | Open Access

    The influence of insulin on food preference and the corresponding underlying neural circuits are unknown in humans. Here, the authors show that increasing insulin changes food preference by modulating mesolimbic neural circuits, and that this pattern is changed in insulin-resistant individuals.

    • Lena J. Tiedemann
    • , Sebastian M. Schmid
    •  & Stefanie Brassen
  • Article
    | Open Access

    Protein RNA interactions are dynamic and regulated in response to environmental changes. Here the authors describe ‘kinetic CRAC’, an approach that allows time resolved analyses of protein RNA interactions with minute time point resolution and apply it to gain insight into the function of the RNA-binding protein Nab3.

    • Rob van Nues
    • , Gabriele Schweikert
    •  & Sander Granneman
  • Article
    | Open Access

    The paradigm of reservoir computing shows that, like the human brain, complex networks can perform efficient information processing. Here, a simple delay dynamical system is demonstrated that can efficiently perform computations capable of replacing a complex network in reservoir computing.

    • L. Appeltant
    • , M.C. Soriano
    •  & I. Fischer