Notch signalling is critical for the development of the nervous system. In the zebrafish mind-bomb mutants, disruption of E3 ubiquitin ligase activity inhibits Notch signalling. In these utant embryos, precocious development of primary neurons leading to depletion of neural progenitor cells results in a neurogenic phenotype characterized by defects in neural patterning and brain development. Cyclin-dependent kinase 5 (Cdk5), a predominant neuronal kinase, is involved in a variety of essential functions of the nervous system. Most recently, mammalian studies on Notch and Cdk5 regulating each other’s function have been emerging. The status of Cdk5 in the mindbomb mutant embryos with excessive primary neurons is not known. In situ hybridization of the zebrafish mindbomb mutant embryos uncovered a robust upregulation in Cdk5 expression but with a reduced Cdk5 activity. The implications of these findings in both the mammalian system and zebrafish are discussed in this mini-review to provide a glimpse into the relationship between Notch and Cdk5 that may explain certain neurodevelopmental defects associated with either mutations in ubiquitin ligase or altered expression of Cdk5. and Corresponding author: Jyotshna Kanungo
The analysis of information coding in neurons requires methods that measure different properties of neuronal signals. In this paper we review the recently proposed measure of randomness and compare it to the coefficient of variation, which is the frequently employed measure of variability of spiking neuronal activity. We focus on the problem of the spontaneous activity of neurons, and we hypothetize that under defined conditions, spontaneous activity is more random than evoked activity. This hypothesis is supported by contrasting variability and randomness obtained from experimental recordings of olfactory receptor neurons in rats., L. Košťál, P. Lánský., and Obsahuje biblografii a biblografické odkazy
Generalization phenornena which také plače in two different assembly neural networks are considered in the paper. Either of these two assembly networks is artificially partitioned into several subiietworks according to the number of classes that the network has to recognize. Hebb’s cissernblies are formed in the networks. One of the assembly networks is with binary connections, the other is with analog ones. Recognition abilities of the networks are compared on the task of handwritten character recognition. The third neural network of a perceptron type is considered in the paper for comparison with the previous ones. This latter network works according to the nearest-neighbor method. Computer simulation of all three neural networks was performed. Experirnents showed that the assembly network with binary connections has approximately the same recognition accuracy as the network realizing the nearest-neighbor technique.