[1st ed.] 63 s. ; 26 cm
- MeSH
- Cybernetics MeSH
- Models, Neurological MeSH
- Neuroma physiology MeSH
- Neural Networks, Computer MeSH
- Publication type
- Abstracts MeSH
- Congress MeSH
- Conspectus
- Knihovnictví. Informatika
- NML Fields
- knihovnictví, informační věda a muzeologie
- neurovědy
[1st ed.] nestr. ; 30 cm
- MeSH
- Neural Networks, Computer MeSH
- Publication type
- Congress MeSH
- Conspectus
- Patologie. Klinická medicína
- NML Fields
- neurovědy
Fast information transfer in neuronal systems rests on series of action potentials, the spike trains, conducted along axons. Methods that compare spike trains are crucial for characterizing different neuronal coding schemes. In this paper we review recent results on the notion of spiking randomness, and discuss its properties with respect to the rate and temporal coding schemes. This method is compared with other widely used characteristics of spiking activity, namely the variability of interspike intervals, and it is shown that randomness and variability provide two distinct views. We demonstrate that estimation of spiking randomness from simulated and experimental data is capable of capturing characteristics that would otherwise be difficult to obtain with conventional methods.
It is automatically assumed that the accuracy with which a stimulus can be decoded is entirely determined by the properties of the neuronal system. We challenge this perspective by showing that the identification of pure tone intensities in an auditory nerve fiber depends on both the stochastic response model and the arbitrarily chosen stimulus units. We expose an apparently paradoxical situation in which it is impossible to decide whether loud or quiet tones are encoded more precisely. Our conclusion reaches beyond the topic of auditory neuroscience, however, as we show that the choice of stimulus scale is an integral part of the neural coding problem and not just a matter of convenience.
- MeSH
- Acoustic Stimulation methods MeSH
- Algorithms * MeSH
- Humans MeSH
- Models, Neurological * MeSH
- Nerve Fibers physiology MeSH
- Neural Conduction physiology MeSH
- Cochlear Nerve physiology MeSH
- Computer Simulation utilization MeSH
- Stochastic Processes MeSH
- Loudness Perception physiology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
We define an optimal signal in parametric neuronal models on the basis of interspike interval data and rate coding schema. Under the classical approach the optimal signal is located where the frequency transfer function is steepest. Its position coincides with the inflection point of this curve. This concept is extended here by using Fisher information which is the inverse asymptotic variance of the best estimator and its dependence on the parameter value indicates accuracy of estimation. We compare the signal producing maximal Fisher information with the inflection point of the sigmoidal frequency transfer function.
