Instability of attractors in auto–associative networks with bio–inspired fast synaptic noise Torres Agudo, Joaquín Cortes, J. M. Marro Borau, Joaquín Inteligencia artificial Artificial intelligence We studied auto–associative networks in which synapses are noisy on a time scale much shorter that the one for the neuron dynamics. In our model a presynaptic noise causes postsynaptic depression as recently ob- served in neurobiological systems. This results in a nonequilibrium condi- tion in which the network sensitivity to an external stimulus is enhanced. In particular, the fixed points are qualitatively modified, and the system may easily scape from the attractors. As a result, in addition to pattern recognition, the model is useful for class identification and categorization. 2022-11-10T11:53:11Z 2022-11-10T11:53:11Z 2006-04-16 conference output Published version: Torres, J.J., Cortés, J.M., Marro, J. (2005). Instability of Attractors in Auto-associative Networks with Bio-inspired Fast Synaptic Noise. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. [https://doi.org/10.1007/11494669_21] https://hdl.handle.net/10481/77887 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Springer