1,952 research outputs found
Emergence of Synchronous Oscillations in Neural Networks Excited by Noise
The presence of noise in non linear dynamical systems can play a constructive
role, increasing the degree of order and coherence or evoking improvements in
the performance of the system. An example of this positive influence in a
biological system is the impulse transmission in neurons and the
synchronization of a neural network. Integrating numerically the Fokker-Planck
equation we show a self-induced synchronized oscillation. Such an oscillatory
state appears in a neural network coupled with a feedback term, when this
system is excited by noise and the noise strength is within a certain range.Comment: 12 pages, 18 figure
Potential mechanisms for imperfect synchronization in parkinsonian basal ganglia
Neural activity in the brain of parkinsonian patients is characterized by the
intermittently synchronized oscillatory dynamics. This imperfect
synchronization, observed in the beta frequency band, is believed to be related
to the hypokinetic motor symptoms of the disorder. Our study explores potential
mechanisms behind this intermittent synchrony. We study the response of a
bursting pallidal neuron to different patterns of synaptic input from
subthalamic nucleus (STN) neuron. We show how external globus pallidus (GPe)
neuron is sensitive to the phase of the input from the STN cell and can exhibit
intermittent phase-locking with the input in the beta band. The temporal
properties of this intermittent phase-locking show similarities to the
intermittent synchronization observed in experiments. We also study the
synchronization of GPe cells to synaptic input from the STN cell with
dependence on the dopamine-modulated parameters. Dopamine also affects the
cellular properties of neurons. We show how the changes in firing patterns of
STN neuron due to the lack of dopamine may lead to transition from a lower to a
higher coherent state, roughly matching the synchrony levels observed in basal
ganglia in normal and parkinsonian states. The intermittent nature of the
neural beta band synchrony in Parkinson's disease is achieved in the model due
to the interplay of the timing of STN input to pallidum and pallidal neuronal
dynamics, resulting in sensitivity of pallidal output to the phase of the
arriving STN input. Thus the mechanism considered here (the change in firing
pattern of subthalamic neurons through the dopamine-induced change of membrane
properties) may be one of the potential mechanisms responsible for the
generation of the intermittent synchronization observed in Parkinson's disease.Comment: 27 pages, 9 figure
Phase locking below rate threshold in noisy model neurons
The property of a neuron to phase-lock to an oscillatory stimulus before adapting its spike rate to the stimulus frequency plays an important role for the auditory system. We investigate under which conditions neurons exhibit this phase locking below rate threshold. To this end, we simulate neurons employing the widely used leaky integrate-and-fire (LIF) model. Tuning parameters, we can arrange either an irregular spontaneous or a tonic spiking mode. When the neuron is stimulated in both modes, a significant rise of vector strength prior to a noticeable change of the spike rate can be observed. Combining analytic reasoning with numerical simulations, we trace this observation back to a modulation of interspike intervals, which itself requires spikes to be only loosely coupled. We test the limits of this conception by simulating an LIF model with threshold fatigue, which generates pronounced anticorrelations between subsequent interspike intervals. In addition we evaluate the LIF response for harmonic stimuli of various frequencies and discuss the extension to more complex stimuli. It seems that phase locking below rate threshold occurs generically for all zero mean stimuli. Finally, we discuss our findings in the context of stimulus detection
The type II phase resetting curve is optimal for stochastic synchrony
The phase-resetting curve (PRC) describes the response of a neural oscillator
to small perturbations in membrane potential. Its usefulness for predicting the
dynamics of weakly coupled deterministic networks has been well characterized.
However, the inputs to real neurons may often be more accurately described as
barrages of synaptic noise. Effective connectivity between cells may thus arise
in the form of correlations between the noisy input streams. We use constrained
optimization and perturbation methods to prove that PRC shape determines
susceptibility to synchrony among otherwise uncoupled noise-driven neural
oscillators. PRCs can be placed into two general categories: Type I PRCs are
non-negative while Type II PRCs have a large negative region. Here we show that
oscillators with Type II PRCs receiving common noisy input sychronize more
readily than those with Type I PRCs.Comment: 10 pages, 4 figures, submitted to Physical Review
Instability of synchronized motion in nonlocally coupled neural oscillators
We study nonlocally coupled Hodgkin-Huxley equations with excitatory and
inhibitory synaptic coupling. We investigate the linear stability of the
synchronized solution, and find numerically various nonuniform oscillatory
states such as chimera states, wavy states, clustering states, and
spatiotemporal chaos as a result of the instability.Comment: 8 pages, 9 figure
- …