Neuronal coding
Dotaz
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[1st ed.] 63 s. ; 26 cm
- MeSH
- kybernetika MeSH
- modely neurologické MeSH
- neurom fyziologie MeSH
- neuronové sítě MeSH
- Publikační typ
- abstrakty MeSH
- kongresy MeSH
- Konspekt
- Knihovnictví. Informatika
- NLK Obory
- knihovnictví, informační věda a muzeologie
- neurovědy
[1st ed.] nestr. ; 30 cm
- MeSH
- neuronové sítě MeSH
- Publikační typ
- kongresy MeSH
- Konspekt
- Patologie. Klinická medicína
- NLK Obory
- 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.
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.
