Biology subjects

Ermentrout, B.

Publications and source records attributed to Ermentrout, B..

13 recordsLinked to original sources

bioRxiv2026

Bio-inspired chromatophore network model generates dynamic skin patterns akin to those observed in cephalopods

Cephalopod skin exhibits a wide range of dynamic visual displays, including traveling waves, light/dark flashes, and flickering, all generated by muscle-regulated expansion and retraction of pigment-filled elastic sacs known as chromatophores. While central neural control is essential for high-level body-wide pattern coordination, experimental evidence from excised and denervated skin reveals that intricate spatiotemporal patterns can also emerge spontaneously in the skin. This raises questions about the respective roles of central versus peripheral control. To determine the simplest control necessary to generate such patterns, we developed ChromatoNet, a biophysical model of chromatophore arrays that includes mechanical interactions on 1- and 2-dimensional lattices with the excitability properties of radial muscles. With minimal external input, our modified Morris--Lecar model reproduces a rich repertoire of skin dynamics, including spontaneous flickering as well as traveling and spiral waves. Both spontaneous and stimulus-evoked waves emerge at an intrinsic resonant frequency, determined by model parameters. Propagation speeds remain stable across conditions and are consistent with the range observed in denervated skin preparations. The model generates diverse output states in response to local couplings and the time-dependence and shape of the input stimulation, with little top-down direction. The results provide a proof-of-principle that chromatophore/muscle dynamics with minimal central neural driving can produce rich dynamic patterns.

neuroscience↗

bioRxiv2025

Hidden Spirals Reveal Neurocomputational Mechanisms of Traveling Waves in Human Memory

While traveling waves are often described as planar propagations across cortex, recent theoretical work predicts that more complex spatial patterns, including spiral dynamics, could organize large-scale neural computations but remain difficult to detect in the human brain. To investigate this, we analyzed direct brain recordings from humans performing a working memory task. To characterize traveling wave patterns, we used independent component analysis, and showed that traveling waves propagated along the cortex in complex spatial patterns that correlated with behaviors such as memory encoding, maintenance, and retrieval. We then developed a novel computational framework based on coupled phase oscillators to model these distinct wave patterns. This computational approach revealed hidden spirals that were not visible in the original recordings. The center of these hidden spirals shifted across the cortex to distinguish separate behavioral states, such as memory encoding and retrieval. Together, these findings reveal that cortical traveling waves are governed by latent spiral attractor dynamics and suggest that rotating wave architectures provide a fundamental neurocomputational mechanism for flexible human memory processing.

neuroscience↗

bioRxiv2025

Disinhibition of a recurrent attractor gates a persistent goal signal for navigation

Recurrent attractor networks are widely thought to form the basis of working memory1-3, but how stable attractor activity can be rapidly switched on and off is unclear4-7. Here we investigate how stability and rapid switching can be combined in a discrete recurrent circuit of the fly navigation center8. h{Delta}K and PFG neurons are recurrently connected in a ring structure. Using in vivo imaging, we find that these two populations exhibit shared persistent bump activity that turns on with odor and terminates at the end of a goal-directed upwind run. Using whole-cell recordings, we show that persistence in h{Delta}K depends on recurrent signalling, and that h{Delta}K receives slow recurrent excitation and fast inhibition from its synaptic partners. Computational modeling reveals that this combination of slow excitation with fast inhibition yields persistent attractor dynamics over a range of excitation and inhibition strengths. Next we examine the mechanisms that allow this activity bump to be rapidly turned on and off. We find that while both populations show positionally stable bump activity during goal-directed runs, during turns and rest the PFG bump tracks heading while h{Delta}K is supressed. We can reproduce these differential dynamics in our model by using inhibition to dynamically uncouple activity in h{Delta}K from PFG. When h{Delta}K is inhibited, PFG neurons follow their inputs from the compass system; when h{Delta}K is disinhibited, recurrent interactions lock this input into place, forming a heading memory. Consistent with this model, we find that inhibitory inputs onto h{Delta}K increase during turns and are suppressed during odor input and goal-directed upwind runs. Our work reveals how disinhibition can serve as a gate to rapidly write an ongoing measurement to a recurrent memory circuit.

neuroscience↗

bioRxiv2025

Multi-stable oscillations in cortical networks with two classes of inhibition

In the classic view of cortical rhythms, the interaction between excitatory pyramidal neurons (E) and inhibitory parvalbumin neurons (I) has been shown to be sufficient to generate gamma and beta band rhythms. However, it is now clear that there are multiple inhibitory interneuron subtypes and that they play important roles in the generation of these rhythms. In this paper we develop a spiking network that consists of populations of E, I and an additional interneuron type, the somatostatin (S) internerons that receive excitation from the E cells and inhibit both the E cells and the I cells. These S cells are modulated by a third inhibitory subtype, VIP neurons that receive inputs from other cortical areas. We reduce the spiking network to a system of nine differential equations that characterize the mean voltage, firing rate, and synaptic conductance for each population and using this we find many instances of multiple rhythms within the network. Using tools from nonlinear dynamics, we explore the roles of each of the two classes of inhibition as well as the role of the VIP modulation on the properties of these rhythms. Author summaryRhythmic dynamics in the cortex are crucial for information processing, sensory integration, and cognition. In this paper, we look at a model network consisting of a population of excitatory neurons and two distinct populations of inhibitory neurons. We show that the interactions between these three populations gives rise to multiple coexistent rhythms. We also present a greatly simplified model that can be tuned to have similar properties. Our computational model may provide a mechanism for the experimental appearance of multiple rhythms in the same cortical circuit.

neuroscience↗

bioRxiv2025

Interleaving asynchronous and synchronous activity in balanced cortical networks with short-term synaptic depression

Cortical populations are in a broadly asynchronous state that is sporadically interrupted by brief epochs of coordinated population activity. Cortical models are at a loss to explain this combination of states. At one extreme are network models where recurrent inhibition dynamically stabilizes an asynchronous low activity state. While these networks are widely used, they cannot produce the coherent population-wide activity that is reported in a variety of datasets. At the other extreme are models in which strong recurrent excitation that is quickly tamed by short term synaptic depression between excitatory neurons leads to short epochs of population- wide activity. However, in these networks, inhibition plays only a perfunctory role in network stability, which is at odds with many reports across cortex. In this study we analyze spontaneously active in vitro preparations of primary auditory cortex that show dynamics that are emblematic of this mixture of states. To capture this complex population activity we use firing rate-based networks as well as biologically realistic networks of spiking neuron models where large excitation is balanced by recurrent inhibition, yet we include short-term synaptic depression dynamics of the excitatory connections. These models give very rich nonlinear behavior that mimics the core features of the in vitro data, including the possibility of low frequency (2-12 Hz) rhythmic dynamics within population events. In these networks, synaptic depression enables activity fluctuations to induce a weakening of inhibitory recruitment, which in turn triggers population events. In sum, our study extends balanced network models to account for nonlinear, population-wide correlated activity, thereby providing a critical step in a mechanistic theory of realistic cortical activity.

neuroscience↗

bioRxiv2024

Inhibitory control of locomotor statistics in walking Drosophila

In order to forage for food, many animals regulate not only specific limb movements but the statistics of locomotor behavior over time, switching between long-range dispersal and localized search depending on resource availability. How pre-motor circuits regulate such locomotor statistics is not clear. Here we analyze and model locomotor statistics in walking Drosophila, and their modulation by attractive food odor. Odor evokes three motor regimes in flies: baseline walking, upwind running during odor, and search behavior following odor loss. During search behavior, we find that flies adopt higher angular velocities and slower ground speeds, and tend to turn for longer periods of time in one direction. We further find that flies spontaneously adopt periods of different mean ground speed, and that these changes in state influence the length of odor-evoked runs. We next developed a simple model of neural locomotor control that suggests that contralateral inhibition plays a key role in regulating the statistical features of locomotion. As the fly connectome predicts decussating inhibitory neurons in the lateral accessory lobe (LAL), a pre-motor structure, we gained genetic access to a subset of these neurons and tested their effects on behavior. We identified one population of neurons whose activation induces all three signature of search and that bi-directionally regulates angular velocity at odor offset. We identified a second group of neurons, including a single LAL neuron pair, that bi-directionally regulate ground speed. Together, our work develops a biologically plausible computational architecture that captures the statistical features of fly locomotion across behavioral states and identifies potential neural substrates of these computations.

neuroscience↗

bioRxiv2024

Planar, Spiral, and Concentric Traveling Waves Distinguish Cognitive States in Human Memory

A fundamental challenge in neuroscience is explaining how widespread brain regions flexibly interact to support behaviors. We hypothesize that traveling waves of oscillations are a key mechanism of neural coordination, such that they propagate across the cortex in distinctive patterns that control how different regions interact. To test this hypothesis, we used direct brain recordings from humans performing multiple memory experiments and an analytical framework that flexibly measures the propagation patterns of traveling waves. We found that traveling waves propagated along the cortex in not only plane waves, but also spirals, sources and sinks, and more complex patterns. The propagation patterns of traveling waves correlated with novel aspects of behavior, with specific wave shapes reflecting particular cognitive processes and even individual remembered items. Our findings suggest that large-scale cortical patterns of traveling waves reveal the spatial organization of cognitive processes in the brain and may be relevant for neural decoding.

neuroscience↗

bioRxiv2022

The inhibitory control of traveling waves in cortical networks

Propagating waves of activity can be evoked and can occur spontaneously in vivo and in vitro. We examine the properties of these waves as inhibition varies in a cortical slice and then develop several computational models. We first show that in the slice, inhibition controls the velocity of propagation as well as the magnitude of the local field potential. We introduce a spiking model of sparsely connected excitatory and inhibitory theta neurons which are distributed on a one-dimensional domain and illustrate both evoked and spontaneous waves. The excitatory neurons have an additional spike-frequency adaptation current which limits their maximal activity. We show that increased inhibition slows the waves down and limits the participation of excitatory cells in this spiking network. Decreased inhibition leads to large amplitude faster moving waves similar to those seen in seizures. To gain further insight into the mechanism that control the waves, we then systematically reduce the model to a Wilson-Cowan type network using a mean-field approach. We simulate this network directly and by using numerical continuation to follow traveling waves in a moving coordinate system as we vary the strength and spread of inhibition and the strength of adaptation. We find several types of instability (bifurcations) that lead to the loss of waves and subsequent pattern formation. We approximate the smooth nonlinearity by a step function and obtain expressions for the velocity, wave-width, and stability. Author summaryStimuli and other aspects of neuronal activity are carried across areas in the brain through the concerted activity of recurrently connected neurons. The activity is controlled through negative feedback from both inhibitory neurons and intrinsic currents in the excitatory neurons. Evoked activity often appears in the form of a traveling pulse of activity. In this paper we study the speed, magnitude, and other properteis of these waves as various aspects of the negative feedback are altered. Inhibition enables information to be readily transmitted across distances without the neural activity blowing up into a seizure-like state.

neuroscience↗

bioRxiv2022

Chaotic dynamics in spatially distributed neuronal networks generate population-wide shared variability

Neural activity in the cortex is highly variable in response to repeated stimuli. Population recordings across the cortex demonstrate that the variability of neuronal responses is shared among large groups of neurons and concentrates in a low dimensional space. However, the source of the populationwide shared variability is unknown. In this work, we analyzed the dynamical regimes of spatially distributed networks of excitatory and inhibitory neurons. We found chaotic spatiotemporal dynamics in networks with similar excitatory and inhibitory projection widths, an anatomical feature of the cortex. The chaotic solutions contain broadband frequency power in rate variability and have distancedependent and low-dimensional correlations, in agreement with experimental findings. In addition, rate chaos can be induced by globally correlated noisy inputs. These results suggest that spatiotemporal chaos in cortical networks can explain the shared variability observed in neuronal population responses.

neuroscience↗

bioRxiv2021

An Oscillatory Mechanism for Multi-level Storage in Short-term Memory

Oscillatory activity is commonly observed during the maintenance of information in short-term memory, but its role remains unclear. Non-oscillatory models of short-term memory storage are able to encode stimulus identity through their spatial patterns of activity, but are typically limited to either an all-or-none representation of stimulus amplitude or exhibit a biologically implausible exact-tuning condition. Here, we demonstrate a simple phase-locking mechanism by which oscillatory input enables a circuit to generate persistent or sequential activity patterns that encode information not only in their location but also in their discretely graded amplitudes. SignificanceA core observation in many memory systems and tasks is the presence of oscillations during memory maintenance. Here, we demonstrate a mechanism for the accumulation and storage of information in short-term memory in which oscillatory activity enables a solution to long-standing challenges in modeling the persistent neural activity underlying working memory. These challenges include the ability to encode information with low firing rates, multi-level storage of stimulus amplitude without extreme fine tuning, and multi-level storage of information in sequential activity. Altogether, this work proposes a new class of models for the storage of information in working memory, a new potential role for brain oscillations, and a novel dynamical mechanism for multi-stability.

neuroscience↗

bioRxiv2021

A Multiscale Multicellular Spatiotemporal Model of Local Influenza Infection and Immune Response

Respiratory viral infections pose a serious public health concern, from mild seasonal influenza to pandemics like those of SARS-CoV-2. Spatiotemporal dynamics of viral infection impact nearly all aspects of the progression of a viral infection, like the dependence of viral replication rates on the type of cell and pathogen, the strength of the immune response and localization of infection. Mathematical modeling is often used to describe respiratory viral infections and the immune response to them using ordinary differential equation (ODE) models. However, ODE models neglect spatially-resolved biophysical mechanisms like lesion shape and the details of viral transport, and so cannot model spatial effects of a viral infection and immune response. In this work, we develop a multiscale, multicellular spatiotemporal model of influenza infection and immune response by combining non-spatial ODE modeling and spatial, cell-based modeling. We employ cellularization, a recently developed method for generating spatial, cell-based, stochastic models from non-spatial ODE models, to generate much of our model from a calibrated ODE model that describes infection, death and recovery of susceptible cells and innate and adaptive responses during influenza infection, and develop models of cell migration and other mechanisms not explicitly described by the ODE model. We determine new model parameters to generate agreement between the spatial and original ODE models under certain conditions, where simulation replicas using our model serve as microconfigurations of the ODE model, and compare results between the models to investigate the nature of viral exposure and impact of heterogeneous infection on the time-evolution of the viral infection. We found that using spatially homogeneous initial exposure conditions consistently with those employed during calibration of the ODE model generates far less severe infection, and that local exposure to virus must be multiple orders of magnitude greater than a uniformly applied exposure to all available susceptible cells. This strongly suggests a prominent role of localization of exposure in influenza A infection. We propose that the particularities of the microenvironment to which a virus is introduced plays a dominant role in disease onset and progression, and that spatially resolved models like ours may be important to better understand and more reliably predict future health states based on susceptibility of potential lesion sites using spatially resolved patient data of the state of an infection. We can readily integrate the immune response components of our model into other modeling and simulation frameworks of viral infection dynamics that do detailed modeling of other mechanisms like viral internalization and intracellular viral replication dynamics, which are not explicitly represented in the ODE model. We can also combine our model with available experimental data and modeling of exposure scenarios and spatiotemporal aspects of mechanisms like mucociliary clearance that are only implicitly described by the ODE model, which would significantly improve the ability of our model to present spatially resolved predictions about the progression of influenza infection and immune response.

systems biology↗

bioRxiv2019

Mouse navigation strategies for odor source localization

Navigating an odor landscape is a critical behavior for the survival of many species, including mice. One ethologically relevant mouse behavior is locating food using odor concentration gradients. To model this behavior, we use a naturalistic open field odor-based spot-finding task, examining navigation strategies as mice search for and approach an odor source. Mice were trained to navigate to odor sources paired with food reward. We detected behavioral changes consistent with localization of the odor source when mice were [~]10cm away from the source. These behaviors included both orientation towards the source and increased exploration time. We found that the amplitude of casting, lateral back and forth head movement, increased exponentially with proximity to the source. We then created concentration-dependent models to simulate mouse behavior, which provided evidence for a serial-sniffing strategy (sampling concentration, moving in space, then sampling again) and a stereo-sniffing strategy (inter-nostril comparison of concentration in a single sniff). Together, these results elucidate key components of behavioral strategies for odor-based navigation. SUMMARY STATEMENTUse of a naturalistic odor-source localizing task uncovers key strategies underlying successful mouse navigation. Concentration-dependent models successfully recapitulate mouse behavior and reveal important behavioral components.

neuroscience↗

bioRxiv2018

Oscillations in working memory and neural binding: a mechanism for multiple memories and their interactions

Neural oscillations have been implicated in many different basic brain and cognitive processes. Oscillatory activity has been suggested to play a role in neural binding, and more recently in the maintenance of information in working memory. This latter work has focused primarily on oscillations in terms of providing a "code" in working memory. However, oscillations may additionally play a fundamental role in essential properties and behaviors that neuronal networks must exhibit in order to produce functional working memory. In the present work, we present a biologically plausible working memory model and demonstrate that specific types of stable oscillatory dynamics may play a critical role in facilitating properties of working memory, including transitions between different memory states and a multi-item working memory capacity. We also show these oscillatory dynamics may facilitate and provide an underlying mechanism to enable a range of different types of binding in the context of working memory.

neuroscience↗