137 research outputs found
Phase locking in networks of synaptically coupled McKean relaxation oscillators
We use geometric dynamical systems methods to derive phase equations for networks
of weakly connected McKean relaxation oscillators. We derive an explicit
formula for the connection function when the oscillators are coupled with chemical
synapses modeled as the convolution of some input spike train with an appropriate
synaptic kernel. The theory allows the systematic investigation of the way in
which a slow recovery variable can interact with synaptic time scales to produce
phase-locked solutions in networks of pulse coupled neural relaxation oscillators.
The theory is exact in the singular limit that the fast and slow time scales of the
neural oscillator become effectively independent. By focusing on a pair of mutually
coupled McKean oscillators with alpha function synaptic kernels, we clarify the role
that fast and slow synapses of excitatory and inhibitory type can play in producing
stable phase-locked rhythms. In particular we show that for fast excitatory synapses
there is coexistence of a stable synchronous, a stable anti-synchronous, and one stable
asynchronous solution. For slower synapses the anti-synchronous solution can
lose stability, whilst for even slower synapses it can regain stability. The case of
inhibitory synapses is similar up to a reversal of the stability of solution branches.
Using a return-map analysis the case of strong pulsatile coupling is also considered.
In this case it is shown that the synchronous solution can co-exist with a continuum
of asynchronous states
A continuum of weakly coupled oscillatory McKean neurons
The McKean model of a neuron possesses a one dimensional fast voltage-like variable and a slow
recovery variable. A recent geometric analysis of the singularly perturbed system has allowed an
explicit construction of its phase response curve [S Coombes 2001 Phase-locking in networks of
synaptically coupled McKean relaxation oscillators, Physica D, Vol 160, 173-188]. Here we use
tools from coupled oscillator theory to study weakly coupled networks of McKean neurons. Using
numerical techniques we show that the McKean system has traveling wave phase-locked solutions
consistent with that of a network of more biophysically detailed Hodgkin-Huxley neurons
シナプス ケツゴウ ニューロン モデル ノ ブンキ カイセキ
We investigate bifurcations of periodic solutions in model equations of neurons coupled through the characteristics of synaptic transmissions with a time delay. The model can be considered as a dynamical system whose solution includes jumps depending on a condition related to the behavior of the trajectory. Although the solution is discontinuous, we can define the Poincare map as a synthesis of successive submaps, and give its derivatives for obtaining periodic points and their bifurcations.Using our proposed method, we clarify mechanisms of bifurcations among synchronized oscillations with phase-locking patterns by analyzing periodic solutions observed in a model of coupled Hodgkin-Huxley equations. Moreover we illustrate a mechanism of the generation of chaotic itinerancy or the phenomenon of chaotic transitions among several quasi-stable states, which corresponds to associative dynamics or memory searching process in real neurons, by the analysis of four-coupled Bonhoffer-van der Pol equations
Criteria for robustness of heteroclinic cycles in neural microcircuits.
Copyright © 2011 Ashwin et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.We introduce a test for robustness of heteroclinic cycles that appear in neural microcircuits modeled as coupled dynamical cells. Robust heteroclinic cycles (RHCs) can appear as robust attractors in Lotka-Volterra-type winnerless competition (WLC) models as well as in more general coupled and/or symmetric systems. It has been previously suggested that RHCs may be relevant to a range of neural activities, from encoding and binding to spatio-temporal sequence generation.The robustness or otherwise of such cycles depends both on the coupling structure and the internal structure of the neurons. We verify that robust heteroclinic cycles can appear in systems of three identical cells, but only if we require perturbations to preserve some invariant subspaces for the individual cells. On the other hand, heteroclinic attractors can appear robustly in systems of four or more identical cells for some symmetric coupling patterns, without restriction on the internal dynamics of the cells
Modeling rhythmic patterns in the hippocampus
We investigate different dynamical regimes of neuronal network in the CA3
area of the hippocampus. The proposed neuronal circuit includes two fast- and
two slowly-spiking cells which are interconnected by means of dynamical
synapses. On the individual level, each neuron is modeled by FitzHugh-Nagumo
equations. Three basic rhythmic patterns are observed: gamma-rhythm in which
the fast neurons are uniformly spiking, theta-rhythm in which the individual
spikes are separated by quiet epochs, and theta/gamma rhythm with repeated
patches of spikes. We analyze the influence of asymmetry of synaptic strengths
on the synchronization in the network and demonstrate that strong asymmetry
reduces the variety of available dynamical states. The model network exhibits
multistability; this results in occurrence of hysteresis in dependence on the
conductances of individual connections. We show that switching between
different rhythmic patterns in the network depends on the degree of
synchronization between the slow cells.Comment: 10 pages, 9 figure
Mode locking in a spatially extended neuron model: active soma and compartmental tree
Understanding the mode-locked response of excitable systems to periodic forcing has important applications in neuroscience. For example it is known that spatially extended place cells in the hippocampus are driven by the theta rhythm to generate a code conveying information about spatial location. Thus it is important to explore the role of neuronal dendrites in generating the response to periodic current injection. In this paper we pursue this using a compartmental model, with linear dynamics for each compartment, coupled to an active soma model that generates action potentials. By working with the piece-wise linear McKean model for the soma we show how the response of the whole neuron model (soma and dendrites) can be written in closed form. We exploit this to construct a stroboscopic map describing the response of the spatially extended model to periodic forcing. A linear stability analysis of this map, together with a careful treatment of the non-differentiability of the soma model, allows us to construct the Arnol'd tongue structure for 1:q states (one action potential for q cycles of forcing). Importantly we show how the presence of quasi-active membrane in the dendrites can influence the shape of tongues. Direct numerical simulations confirm our theory and further indicate that resonant dendritic membrane can enlarge the windows in parameter space for chaotic behavior. These simulations also show that the spatially extended neuron model responds differently to global as opposed to point forcing. In the former case spatio-temporal patterns of activity within an Arnol'd tongue are standing waves, whilst in the latter they are traveling waves
Macroscopic equations governing noisy spiking neuronal populations
At functional scales, cortical behavior results from the complex interplay of
a large number of excitable cells operating in noisy environments. Such systems
resist to mathematical analysis, and computational neurosciences have largely
relied on heuristic partial (and partially justified) macroscopic models, which
successfully reproduced a number of relevant phenomena. The relationship
between these macroscopic models and the spiking noisy dynamics of the
underlying cells has since then been a great endeavor. Based on recent
mean-field reductions for such spiking neurons, we present here {a principled
reduction of large biologically plausible neuronal networks to firing-rate
models, providing a rigorous} relationship between the macroscopic activity of
populations of spiking neurons and popular macroscopic models, under a few
assumptions (mainly linearity of the synapses). {The reduced model we derive
consists of simple, low-dimensional ordinary differential equations with}
parameters and {nonlinearities derived from} the underlying properties of the
cells, and in particular the noise level. {These simple reduced models are
shown to reproduce accurately the dynamics of large networks in numerical
simulations}. Appropriate parameters and functions are made available {online}
for different models of neurons: McKean, Fitzhugh-Nagumo and Hodgkin-Huxley
models
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