1,153 research outputs found
Spontaneous spiking in an autaptic Hodgkin-Huxley set up
The effect of intrinsic channel noise is investigated for the dynamic
response of a neuronal cell with a delayed feedback loop. The loop is based on
the so-called autapse phenomenon in which dendrites establish not only
connections to neighboring cells but as well to its own axon. The biophysical
modeling is achieved in terms of a stochastic Hodgkin-Huxley model containing
such a built in delayed feedback. The fluctuations stem from intrinsic channel
noise, being caused by the stochastic nature of the gating dynamics of ion
channels. The influence of the delayed stimulus is systematically analyzed with
respect to the coupling parameter and the delay time in terms of the interspike
interval histograms and the average interspike interval. The delayed feedback
manifests itself in the occurrence of bursting and a rich multimodal interspike
interval distribution, exhibiting a delay-induced reduction of the spontaneous
spiking activity at characteristic frequencies. Moreover, a specific
frequency-locking mechanism is detected for the mean interspike interval.Comment: 8 pages, 10 figure
Travelling waves in a model of quasi-active dendrites with active spines
Dendrites, the major components of neurons, have many different types of branching structures and are involved in receiving and integrating thousands of synaptic inputs from other neurons. Dendritic spines with excitable channels can be present in large densities on the dendrites of many cells. The recently proposed Spike-Diffuse-Spike (SDS) model that is described by a system of point hot-spots (with an integrate-and-fire process) embedded throughout a passive tree has been shown to provide a reasonable caricature of a dendritic tree with supra-threshold dynamics. Interestingly, real dendrites equipped with voltage-gated ion channels can exhibit not only supra-threshold responses, but also sub-threshold dynamics. This sub-threshold resonant-like oscillatory behaviour has already been shown to be adequately described by a quasi-active membrane. In this paper we introduce a mathematical model of a branched dendritic tree based upon a generalisation of the SDS model where the active spines are assumed to be distributed along a quasi-active dendritic structure. We demonstrate how solitary and periodic travelling wave solutions can be constructed for both continuous and discrete spine distributions. In both cases the speed of such waves is calculated as a function of system parameters. We also illustrate that the model can be naturally generalised to an arbitrary branched dendritic geometry whilst remaining computationally simple. The spatio-temporal patterns of neuronal activity are shown to be significantly influenced by the properties of the quasi-active membrane. Active (sub- and supra-threshold) properties of dendrites are known to vary considerably among cell types and animal species, and this theoretical framework can be used in studying the combined role of complex dendritic morphologies and active conductances in rich neuronal dynamics
Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate model
Several firing patterns experimentally observed in neural populations have
been successfully correlated to animal behavior. Population bursting, hereby
regarded as a period of high firing rate followed by a period of quiescence, is
typically observed in groups of neurons during behavior. Biophysical
membrane-potential models of single cell bursting involve at least three
equations. Extending such models to study the collective behavior of neural
populations involves thousands of equations and can be very expensive
computationally. For this reason, low dimensional population models that
capture biophysical aspects of networks are needed.
\noindent The present paper uses a firing-rate model to study mechanisms that
trigger and stop transitions between tonic and phasic population firing. These
mechanisms are captured through a two-dimensional system, which can potentially
be extended to include interactions between different areas of the nervous
system with a small number of equations. The typical behavior of midbrain
dopaminergic neurons in the rodent is used as an example to illustrate and
interpret our results.
\noindent The model presented here can be used as a building block to study
interactions between networks of neurons. This theoretical approach may help
contextualize and understand the factors involved in regulating burst firing in
populations and how it may modulate distinct aspects of behavior.Comment: 25 pages (including references and appendices); 12 figures uploaded
as separate file
Noise-induced escape in an excitable system
We consider the stochastic dynamics of escape in an excitable system, the FitzHugh-Nagumo (FHN) neuronal model, for different classes of excitability. We discuss, first, the threshold structure of the FHN model as an example of a system without a saddle state. We then develop a nonlinear (nonlocal) stability approach based on the theory of large fluctuations, including a finite-noise correction, to describe noise-induced escape in the excitable regime. We show that the threshold structure is revealed via patterns of most probable (optimal) fluctuational paths. The approach allows us to estimate the escape rate and the exit location distribution. We compare the responses of a monostable resonator and monostable integrator to stochastic input signals and to a mixture of periodic and stochastic stimuli. Unlike the commonly used local analysis of the stable state, our nonlocal approach based on optimal paths yields results that are in good agreement with direct numerical simulations of the Langevin equation
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Synthesis of neuromorphic circuits with neuromodulatory properties
The field of neuromorphic engineering shows great promise in delivering novel devices inspired by biological principles that would undertake sensory and processing tasks with an unprecedented level of efficiency. In order to achieve that, engineers are required to understand and implement the many complex biological regulatory mechanisms that allow the nervous system to robustly operate and adapt over scales covering many orders of magnitude, while at the same time using unreliable and noisy components.
As a step towards that, this thesis aims at discussing and implementing the principles of neuromodulation in neuromorphic hardware, mechanisms which allow neurons to change and regulate their behaviour through the continuous control of their internal currents. We discuss how neural dynamics and its modulation can be broken down into four essential feedback loops, and we introduce a simplified model of the neural membrane respecting this fundamental structure. We present a novel methodology for controlling the neuron's behaviour through the shaping of its I-V curves in distinct timescales, thus characterising the behaviour of the neural circuit through its input-output properties. We show how modulation of the feedback loops affects the behaviour, and importantly, captures the transition between spiking and bursting oscillatory regimes, two major signalling modes of neurons. We then show how the architecture can be easily implemented using well-known neuromorphic building blocks based on subthreshold MOSFET circuits. Finally, we discuss how the excitability switch captured by the model can be exploited in simple network settings, thus opening up the possibility for future research into novel architectures where the control of cellular properties is utilised to shape the global behaviour of the network
Bistable dynamics underlying excitability of ion homeostasis in neuron models
When neurons fire action potentials, dissipation of free energy is usually
not directly considered, because the change in free energy is often negligible
compared to the immense reservoir stored in neural transmembrane ion gradients
and the long-term energy requirements are met through chemical energy, i.e.,
metabolism. However, these gradients can temporarily nearly vanish in
neurological diseases, such as migraine and stroke, and in traumatic brain
injury from concussions to severe injuries. We study biophysical neuron models
based on the Hodgkin-Huxley (HH) formalism extended to include time-dependent
ion concentrations inside and outside the cell and metabolic energy-driven
pumps. We reveal the basic mechanism of a state of free energy-starvation (FES)
with bifurcation analyses showing that ion dynamics is for a large range of
pump rates bistable without contact to an ion bath. This is interpreted as a
threshold reduction of a new fundamental mechanism of 'ionic excitability' that
causes a long-lasting but transient FES as observed in pathological states. We
can in particular conclude that a coupling of extracellular ion concentrations
to a large glial-vascular bath can take a role as an inhibitory mechanism
crucial in ion homeostasis, while the Na/K pumps alone are insufficient
to recover from FES. Our results provide the missing link between the HH
formalism and activator-inhibitor models that have been successfully used for
modeling migraine phenotypes, and therefore will allow us to validate the
hypothesis that migraine symptoms are explained by disturbed function in ion
channel subunits, Na/K pumps, and other proteins that regulate ion
homeostasis.Comment: 14 pages, 8 figures, 4 table
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