Synchrony measures for biological neural networks
β Scribed by Paul F. Pinsky; John Rinzel
- Publisher
- Springer-Verlag
- Year
- 1995
- Tongue
- English
- Weight
- 908 KB
- Volume
- 73
- Category
- Article
- ISSN
- 0340-1200
No coin nor oath required. For personal study only.
β¦ Synopsis
Synchronous firing of a population of neurons has been observed in many experimental preparations; in addition, various mathematical neural network models have been shown, analytically or numerically, to contain stable synchronous solutions. In order to assess the level of synchrony of a particular network over some time interval, quantitative measures of synchrony are needed. We develop here various synchrony measures which utilize only the spike times of the neurons; these measures are applicable in both experimental situations and in computer models. Using a mathematical model of the CA3 region of the hippocampus, we evaluate these synchrony measures and compare them with pictorial representations of network activity. We illustrate how synchrony is lost and synchrony measures change as heterogeneity amongst cells increases. Theoretical expected values of the synchrony measures for different categories of network solutions are derived and compared with results of simulations.
π SIMILAR VOLUMES
Synchrony is surprisingly complex even in the simplest cases. One strategy for simplifying complex phenomena is to define a dimensionless measurement model with the aim of (1) finding order, (2) comparing complex phenomena, and (3) making decisions about statistical significance. However, a model is
A previously described neural-network model (Desmond 1991; Desmond and Moore 1988; Moore et al. 1989) predicts that both CS-onset-evoked and CS-offset-evoked stimulus trace processes acquire associative strength during classical conditioning, and that CR waveforms can be altered by manipulating the
To replace the traditional weighted average method, Choquet integrals or Sugeno integrals with respect to fuzzy measures are used to obtain a synthetic evaluation of a given object (or its quality, function, etc. respectively) with multi-attribute. Generally, it is not easy to determine fuzzy measur
Mammalian macular endorgans are linear bioaccelerometers located in the vestibular membranous labyrinth of the inner ear. In this paper, the organization of the endorgan is interpreted on physical and engineering principles. This is a necessary prerequisite to mathematical and symbolic modeling of i