- Tytuł:
- Kernel analysis for estimating the connectivity of a network with event sequences
- Autorzy:
-
Tezuka, T.
Claramunt, C. - Tematy:
-
connectivity estimation
neural network
kernel methods
spike train - Pokaż więcej
- Data publikacji:
- 2017
- Powiązania:
- https://bibliotekanauki.pl/articles/91880.pdf Link otwiera się w nowym oknie
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 1; 17-31
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Opis:
- Estimating the connectivity of a network from events observed at each node has many applications. One prominent example is found in neuroscience, where spike trains (sequences of action potentials) are observed at each neuron, but the way in which these neurons are connected is unknown. This paper introduces a novel method for estimating connections between nodes using a similarity measure between sequences of event times. Specifically, a normalized positive definite kernel defined on spike trains was used. The proposed method was evaluated using synthetic and real data, by comparing with methods using transfer entropy and the Victor-Purpura distance. Synthetic data was generated using CERM (Coupled Escape-Rate Model), a model that generates various spike trains. Real data recorded from the visual cortex of an anaesthetized cat was analyzed as well. The results showed that the proposed method provides an effective way of estimating the connectivity of a network when the time sequences of events are the only available information.
- Dostawca treści:
- Biblioteka Nauki
Artykuł