678 research outputs found
Beta-rhythm oscillations and synchronization transition in network models of Izhikevich neurons: effect of topology and synaptic type
Despite their significant functional roles, beta-band oscillations are least
understood. Synchronization in neuronal networks have attracted much attention
in recent years with the main focus on transition type. Whether one obtains
explosive transition or a continuous transition is an important feature of the
neuronal network which can depend on network structure as well as synaptic
types. In this study we consider the effect of synaptic interaction (electrical
and chemical) as well as structural connectivity on synchronization transition
in network models of Izhikevich neurons which spike regularly with beta
rhythms. We find a wide range of behavior including continuous transition,
explosive transition, as well as lack of global order. The stronger electrical
synapses are more conducive to synchronization and can even lead to explosive
synchronization. The key network element which determines the order of
transition is found to be the clustering coefficient and not the small world
effect, or the existence of hubs in a network. These results are in contrast to
previous results which use phase oscillator models such as the Kuramoto model.
Furthermore, we show that the patterns of synchronization changes when one goes
to the gamma band. We attribute such a change to the change in the refractory
period of Izhikevich neurons which changes significantly with frequency.Comment: 7 figures, 1 tabl
Fast global oscillations in networks of integrate-and-fire neurons with low firing rates
We study analytically the dynamics of a network of sparsely connected
inhibitory integrate-and-fire neurons in a regime where individual neurons emit
spikes irregularly and at a low rate. In the limit when the number of neurons N
tends to infinity,the network exhibits a sharp transition between a stationary
and an oscillatory global activity regime where neurons are weakly
synchronized. The activity becomes oscillatory when the inhibitory feedback is
strong enough. The period of the global oscillation is found to be mainly
controlled by synaptic times, but depends also on the characteristics of the
external input. In large but finite networks, the analysis shows that global
oscillations of finite coherence time generically exist both above and below
the critical inhibition threshold. Their characteristics are determined as
functions of systems parameters, in these two different regimes. The results
are found to be in good agreement with numerical simulations.Comment: 45 pages, 11 figures, to be published in Neural Computatio
Chimeras in Leaky Integrate-and-Fire Neural Networks: Effects of Reflecting Connectivities
The effects of nonlocal and reflecting connectivity are investigated in
coupled Leaky Integrate-and-Fire (LIF) elements, which assimilate the exchange
of electrical signals between neurons. Earlier investigations have demonstrated
that non-local and hierarchical network connectivity often induces complex
synchronization patterns and chimera states in systems of coupled oscillators.
In the LIF system we show that if the elements are non-locally linked with
positive diffusive coupling in a ring architecture the system splits into a
number of alternating domains. Half of these domains contain elements, whose
potential stays near the threshold, while they are interrupted by active
domains, where the elements perform regular LIF oscillations. The active
domains move around the ring with constant velocity, depending on the system
parameters. The idea of introducing reflecting non-local coupling in LIF
networks originates from signal exchange between neurons residing in the two
hemispheres in the brain. We show evidence that this connectivity induces novel
complex spatial and temporal structures: for relatively extensive ranges of
parameter values the system splits in two coexisting domains, one domain where
all elements stay near-threshold and one where incoherent states develop with
multileveled mean phase velocity distribution.Comment: 12 pages, 12 figure
The effect of gap junctional coupling on the spatiotemporal patterns of Ca2+ signals and the harmonization of Ca2+-related cellular responses
The calcium ion (Ca²⁺), a universal signaling molecule, is widely recognized to play a fundamental role in the regulation of various biological processes. Agonist–evoked Ca²⁺ signals often manifest as rhythmic changes in the cytosolic free Ca²⁺ concentration (ccyt) called Ca²⁺ oscillations. Stimuli intensity was found to be proportional to the oscillation frequency and the evoked down-steam cellular response. Stochastic receptor expression in individual cells in a cell population inevitably leads to individually different oscillation frequencies and individually different Ca²⁺-related cellular responses. However, in many organs, the neighboring cells have to overcome their individually different sensitivity and produce a synchronized response. Gap junctions are integral membrane structures that enable the direct cytoplasmic exchange of Ca²⁺ ions and InsP₃ molecules between neighboring cells. By simulations, we were able to demonstrate how the strength of intercellular gap junctional coupling in relation to stimulus intensity can modify the spatiotemporal patterns of Ca²⁺ signals and harmonize the Ca²⁺-related cellular responses via synchronization of oscillation frequency. We demonstrate that the most sensitive cells are the wave initiator cells and that a highly sensitive region plays an important role in the determination of the Ca²⁺ phase wave direction. This sensitive region will then also progressively determine the global behavior of the entire system
Locking of correlated neural activity to ongoing oscillations
Population-wide oscillations are ubiquitously observed in mesoscopic signals
of cortical activity. In these network states a global oscillatory cycle
modulates the propensity of neurons to fire. Synchronous activation of neurons
has been hypothesized to be a separate channel of signal processing information
in the brain. A salient question is therefore if and how oscillations interact
with spike synchrony and in how far these channels can be considered separate.
Experiments indeed showed that correlated spiking co-modulates with the static
firing rate and is also tightly locked to the phase of beta-oscillations. While
the dependence of correlations on the mean rate is well understood in
feed-forward networks, it remains unclear why and by which mechanisms
correlations tightly lock to an oscillatory cycle. We here demonstrate that
such correlated activation of pairs of neurons is qualitatively explained by
periodically-driven random networks. We identify the mechanisms by which
covariances depend on a driving periodic stimulus. Mean-field theory combined
with linear response theory yields closed-form expressions for the
cyclostationary mean activities and pairwise zero-time-lag covariances of
binary recurrent random networks. Two distinct mechanisms cause time-dependent
covariances: the modulation of the susceptibility of single neurons (via the
external input and network feedback) and the time-varying variances of single
unit activities. For some parameters, the effectively inhibitory recurrent
feedback leads to resonant covariances even if mean activities show
non-resonant behavior. Our analytical results open the question of
time-modulated synchronous activity to a quantitative analysis.Comment: 57 pages, 12 figures, published versio
- …