- Tytuł:
- Synchrony state generation : an approach using stochastic synapses
- Autorzy:
-
El-Laithy, K.
Bogdan, M. - Powiązania:
- https://bibliotekanauki.pl/articles/91844.pdf
- Data publikacji:
- 2011
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
temporal synchrony
artificial neural network
stochastic synapses
synchrony state generation
Hebbian-based learning - Opis:
- In this study, the generation of temporal synchrony within an artificial neural network is examined considering a stochastic synaptic model. A network is introduced and driven by Poisson distributed trains of spikes along with white-Gaussian noise that is added to the internal synaptic activity representing the background activity (neuronal noise). A Hebbian-based learning rule for the update of synaptic parameters is introduced. Only arbitrarily selected synapses are allowed to learn, i.e. update parameter values. Results show that a network using such a framework is able to achieve different states of synchrony via learning. Thus, the plausibility of using stochastic-based models in modeling the neural process is supported. It is also consistent with arguments claiming that synchrony is a part of the memory-recall process and copes with the accepted framework in biological neural systems.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 1; 17-25
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł