The firing irregularity as the firing characteristic orthogonal to the firing rate
نویسندگان
چکیده
منابع مشابه
The firing rate of neurons in the nucleus cuneiformis in response to formalin in male rat
Introduction: Although formalin-induced activity in primary afferent fibers and spinal dorsal horn is well described, the midbrain neural basis underlying each phase of behavior in formalin test has not been clarified. The present study was designed to investigate the nucleus cuneiformis (CnF) neuronal responses during two phases after subcutaneous injection of formalin into the hind paw...
متن کاملEstimating the Firing Rate
Neuronal activity is measured by the number of stereotyped action potentials, called spikes, elicited in response to a stimulus or the behavioral conditions of an animal. Any nonparametric method for grasping the time-varying rate of spike firing contains a single parameter that controls the jaggedness of the estimated rate, such as the binsize of the time histogram or the bandwidth of the kern...
متن کاملDoes High Firing Irregularity Enhance Learning?
In this note, we demonstrate that the high firing irregularity produced by the leaky integrate-and-fire neuron with the partial somatic reset mechanism, which has been shown to be the most likely candidate to reflect the mechanism used in the brain for reproducing the highly irregular cortical neuron firing at high rates (Bugmann, Christodoulou, & Taylor, 1997; Christodoulou & Bugmann, 2001), e...
متن کاملEstimating Instantaneous Irregularity of Neuronal Firing
Cortical neurons in vivo had been regarded as Poisson spike generators that convey no information other than the rate of random firing. Recently, using a metric for analyzing local variation of interspike intervals, researchers have found that individual neurons express specific patterns in generating spikes, which may symbolically be termed regular, random, or bursty, rather invariantly in tim...
متن کاملStochastic firing rate models
We review a recent approach to the mean-field limits in neural networks that takes into account the stochastic nature of input current and the uncertainty in synaptic coupling. This approach was proved to be a rigorous limit of the network equations in a general setting, and we express here the results in a more customary and simpler framework. We propose a heuristic argument to derive these eq...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Neuroscience
سال: 2010
ISSN: 1662-453X
DOI: 10.3389/conf.fnins.2010.03.00103