arXiv subjects

Chun-Chung Chen

Publications and source records attributed to Chun-Chung Chen.

14 recordsLinked to original sources

arXiv2021

Ubiquitous proximity to a critical state for collective neural activity in the CA1 region of freely moving mice

Using miniscope recordings of calcium fluorescence signals in the CA1 region of the hippocampus of mice, we monitor the neural activity of hippocampal regions while the animals are freely moving in an open chamber. Using a data-driven statistical modeling approach, the statistical properties of the recorded data are mapped to spin-glass models with pairwise interactions. Considering the parameter space of the model, the observed system is generally near a critical state between two distinct phases. The close proximity to the criticality is found to be robust against different ways of sampling and segmentation of the measured data. By independently altering the coupling distribution and the network structure of the statistical model, the network structures are found to be vital to maintain the proximity to the critical state. We further find the observed assignment of the coupling strengths makes the net coupling at each site more balanced with slight variation, which likely helps the maintenance of the critical state. Network analysis on the connectivity obtained by thresholding the coupling strengths find the connectivity of the networks to be well described by a random network model. These results are consistent across different experiments, sampling and segmentation choices in our analysis.

q-bio.NC↗

arXiv2020

Anticipation and Negative Group Delay in a Retina

The mechanism of negative group delay (NGD) is used to understand the anticipatory capability of a retina. Experiments with retinas from bull frogs are performed to compare with the predictions of the NGD model. In particulars, whole field stochastic stimulation with various time correlations are used to probe anticipatory responses from the retina. We find that the NGD model can reproduce essential features of experimental observations characterized by the cross correlations between the stimulation and the retinal responses. The prediction horizon of a retina is found to depend on the correlation time of the stimulation as predicted by the NGD model. Experiments with dark and bright Gaussian light pulses further support the NGD mechanism; but only for the dark pulses indicating that the NGD effect of a retina might originate from its OFF response. Our finding suggests that sensory systems capable of using negative feedback for adaptation can give rise to anticipation as a consequence of the delay in the system.

physics.bio-ph↗

arXiv2016

Positive Feedback and Synchronized Bursts in Neuronal Cultures

Synchronized bursts (SBs) with complex structures are common in neuronal cultures. Although the origin of SBs is still unclear, they have been studied for their information processing capabilities. Here, we investigate the properties of these SBs in a culture on multi-electrode array system. We find that structures of these SBs are related to the different developmental stages of the cultures. A model based on short term synaptic plasticity, recurrent connections and astrocytic recycling of neurotransmitters has been developed successfully to understand these structures. A phase diagram obtained from this model shows that networks exhibiting SBs are in an oscillatory state due to large enough positive feedback provided by synaptic facilitation and recurrent connections. In this model, the structures of the SBs are the results of intrinsic synaptic interactions; not information stored in the network.

q-bio.NC↗

arXiv2016

Measurement of Anticipative Power of a Retina by Predictive Information

The predictive properties of a retina are studied by measuring the mutual information (MI) between its stimulation and the corresponding firing rates while it is being probed by a train of short pulses with stochastic intervals. Features of the measured MI at various time shifts between the stimulation and the response are used to characterize the predictive properties of the retina. By varying the statistical properties of the pulse train, our experiments show that a retina has the ability to predict future events of the stimulation if the information rate of the stimulation is low enough. Also, this predictive property of the retina occurs at a time scale similar to the well established anticipative phenomenon of omitted stimulus response in a retina. Furthermore, a retina can make use of its predictive ability to distinguish between time series created by an Ornstein--Uhlenbeck and a hidden Markovian process.

q-bio.NC↗

arXiv2016

Propagation and synchronization of reverberatory bursts in developing cultured networks

Developing networks of neural systems can exhibit spontaneous, synchronous activities called neural bursts, which can be important in the organization of functional neural circuits. Before the network matures, the activity level of a burst can reverberate in repeated rise-and-falls in periods of hundreds of milliseconds following an initial wave-like propagation of spiking activity, while the burst itself lasts for seconds. To investigate the spatiotemporal structure of the reverberatory bursts, we culture dissociated, rat cortical neurons on a high-density multi-electrode array to record the dynamics of neural activity over the growth and maturation of the network. We find the synchrony of the spiking significantly reduced following the initial wave and the activities become broadly distributed spatially. The synchrony recovers as the system reverberates until the end of the burst. Using a propagation model we infer the spreading speed of the spiking activity, which increases as the culture ages. We perform computer simulations of the system using a physiological model of spiking networks in two spatial dimensions and find the parameters that reproduce the observed resynchronization of spiking in the bursts. An analysis of the simulated dynamics suggests that the depletion of synaptic resources causes the resynchronization. The spatial propagation dynamics of the simulations match well with observations over the course of a burst and point to an interplay of the synaptic efficacy and the noisy neural self-activation in producing the morphology of the bursts.

q-bio.NC↗

arXiv2015

Adaptive Synchronization and Anticipatory Dynamical System

Many biological systems can sense periodical variations in a stimulus input and produce well-timed, anticipatory responses after the input is removed. Such systems show memory effects for retaining timing information in the stimulus and cannot be understood from traditional synchronization consideration of passive oscillatory systems. To understand this anticipatory phenomena, we consider oscillators built from excitable systems with the addition of an adaptive dynamics. With such systems, well-timed post-stimulus responses similar to those from experiments can be obtained. Furthermore, a well-known model of working memory is shown to possess similar anticipatory dynamics when the adaptive mechanism is identified with synaptic facilitation. The last finding suggests that this type of oscillators can be common in neuronal systems with plasticity.

q-bio.NC↗

arXiv2014

Reconstruction of network structures from repeating spike patterns in simulated bursting dynamics

Repeating patterns of spike sequences from a neuronal network have been proposed to be useful in the reconstruction of the network topology. Reverberations in a physiologically realistic model with various physical connection topologies (from random to scale-free) have been simulated to study the effectiveness of the pattern-matching method in the reconstruction of network topology from network dynamics. Simulation results show that functional networks reconstructed from repeating spike patterns can be quite different from the original physical networks; even global properties, such as the degree distribution, cannot always be recovered. However, the pattern-matching method can be effective in identifying hubs in the network. Since the form of reverberations are quite different for networks with and without hubs, the form of reverberations together with the reconstruction by repeating spike patterns might provide a reliable method to detect hubs in neuronal cultures.

q-bio.NC↗

arXiv2011

Event-driven simulations of a plastic, spiking neural network

We consider a fully-connected network of leaky integrate-and-fire neurons with spike-timing-dependent plasticity. The plasticity is controlled by a parameter representing the expected weight of a synapse between neurons that are firing randomly with the same mean frequency. For low values of the plasticity parameter, the activities of the system are dominated by noise, while large values of the plasticity parameter lead to self-sustaining activity in the network. We perform event-driven simulations on finite-size networks with up to 128 neurons to find the stationary synaptic weight conformations for different values of the plasticity parameter. In both the low and high activity regimes, the synaptic weights are narrowly distributed around the plasticity parameter value consistent with the predictions of mean-field theory. However, the distribution broadens in the transition region between the two regimes, representing emergent network structures. Using a pseudophysical approach for visualization, we show that the emergent structures are of "path" or "hub" type, observed at different values of the plasticity parameter in the transition region.

q-bio.NC↗

arXiv2010

Variational ansatz for quasispecies in the Eigen model

We investigate the error threshold for the emergence of quasispecies in the Eigen model. By mapping to to an effective Hamiltonian ruled by the "imaginary-time" Schrödinger equation, a variational ansatz is proposed and applied to calculate various quantities associated with the quasispecies. The variational ansatz gives correct predictions for the survival population of the wild-type sequence and also reveals an unexpected universal scaling behaviors near the error threshold. We check the validity of the variational ansatz by numerical methods and find excellent agreement. Though the emergence of the scaling behaviors is not yet fully understood, it is remarkable that the universal scaling function reigns even for relatively short genome length such as L=16. Further investigations may reveal the mechanism of the universal scaling and extract the essential ingredients for the emergence of the quasispecies in molecular evolution.

q-bio.PE↗

arXiv2009

Mean-field theory of a plastic network of integrate-and-fire neurons

We consider a noise driven network of integrate-and-fire neurons. The network evolves as result of the activities of the neurons following spike-timing-dependent plasticity rules. We apply a self-consistent mean-field theory to the system to obtain the mean activity level for the system as a function of the mean synaptic weight, which predicts a first-order transition and hysteresis between a noise-dominated regime and a regime of persistent neural activity. Assuming Poisson firing statistics for the neurons, the plasticity dynamics of a synapse under the influence of the mean-field environment can be mapped to the dynamics of an asymmetric random walk in synaptic-weight space. Using a master-equation for small steps, we predict a narrow distribution of synaptic weights that scales with the square root of the plasticity rate for the stationary state of the system given plausible physiological parameter values describing neural transmission and plasticity. The dependence of the distribution on the synaptic weight of the mean-field environment allows us to determine the mean synaptic weight self-consistently. The effect of fluctuations in the total synaptic conductance and plasticity step sizes are also considered. Such fluctuations result in a smoothing of the first-order transition for low number of afferent synapses per neuron and a broadening of the synaptic weight distribution, respectively.

q-bio.NC↗

arXiv2002

Cohesion induced deepening transition of avalanches

A directed avalanche model with a control parameter is introduced to describe the transition between cohesive and noncohesive granular material. The underlying dynamics of the process can be mapped to interface growth model. In that representation, a continuous phase transition separates the rough phase and the flat phase. In the avalanche formulation, this corresponds to a transition from deep to shallow avalanches. The scaling exponents of the avalanches indeed follow those of the underlying interface growth in both phases and at the transition point. However, the mass hyperscaling relation is broken at the transition point due to the fractal nature of the avalanche and a hierarchy of critical directed percolation processes.

cond-mat.stat-mech↗

arXiv2002

Directed avalanche processes with underlying interface dynamics

We describe a directed avalanche model; a slowly unloading sandbox driven by lowering a retaining wall. The directness of the dynamics allows us to interpret the stable sand surfaces as world sheets of fluctuating interfaces in one lower dimension. In our specific case, the interface growth dynamics belongs to the Kardar-Parisi-Zhang (KPZ) universality class. We formulate relations between the critical exponents of the various avalanche distributions and those of the roughness of the growing interface. The nonlinear nature of the underlying KPZ dynamics provides a nontrivial test of such generic exponent relations. The numerical values of the avalanche exponents are close to the conventional KPZ values, but differ sufficiently to warrant a detailed study of whether avalanche correlated Monte Carlo sampling changes the scaling exponents of KPZ interfaces. We demonstrate that the exponents remain unchanged, but that the traces left on the surface by previous avalanches give rise to unusually strong finite-size corrections to scaling. This type of slow convergence seems intrinsic to avalanche dynamics.

cond-mat.stat-mech↗

arXiv2001

An Interface View of Directed Sandpile Dynamics

We present a directed unloading sand box type avalanche model, driven by slowly lowering the retaining wall at the bottom of the slope. The avalanche propagation in the two dimensional surface is related to the space-time configurations of one dimensional Kardar-Parisi-Zhang (KPZ) type interface growth dynamics. We express the scaling exponents for the avalanche cluster distributions into that framework. The numerical results agree closely with KPZ scaling, but not perfectly.

cond-mat.stat-mech↗

arXiv1999

Active Width at a Slanted Active Boundary in Directed Percolation

The width W of the active region around an active moving wall in a directed percolation process diverges at the percolation threshold p_c as W \simeq A ε^{-ν_\parallel} \ln(ε_0/ε), with ε=p_c-p, ε_0 a constant, and ν_\parallel=1.734 the critical exponent of the characteristic time needed to reach the stationary state ξ_\parallel \sim ε^{-ν_\parallel}. The logarithmic factor arises from screening of statistically independent needle shaped sub clusters in the active region. Numerical data confirm this scaling behaviour.

cond-mat.stat-mech↗