Instability of Attractors in Auto-associative Networks with Bio-inspired Fast Synaptic Noise
نویسندگان
چکیده
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 observed in neurobiological systems. This results in a nonequilibrium condition 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.
منابع مشابه
Competition Between Synaptic Depression and Facilitation in Attractor Neural Networks
We study the effect of competition between short-term synaptic depression and facilitation on the dynamic properties of attractor neural networks, using Monte Carlo simulation and a mean-field analysis. Depending on the balance of depression, facilitation, and the underlying noise, the network displays different behaviors, including associative memory and switching of activity between different...
متن کاملA Self-Reconstructing Algorithm for Single and Multiple-Sensor Fault Isolation Based on Auto-Associative Neural Networks
Recently different approaches have been developed in the field of sensor fault diagnostics based on Auto-Associative Neural Network (AANN). In this paper we present a novel algorithm called Self reconstructing Auto-Associative Neural Network (S-AANN) which is able to detect and isolate single faulty sensor via reconstruction. We have also extended the algorithm to be applicable in multiple faul...
متن کاملMolecular Associative Memory with Spatial Auto-logistic Model for Pattern Recall
We propose a molecular associative memory model, by combining auto-logistic specifications which capture statistical dependencies within the local neighborhood systems of the exposed knowledge, with the bio-inspired DNA-based molecular operations which store and evolve the memory. Our model, characterized by only the local dependencies of the spatial binary data, allows to capture only a fewer ...
متن کاملComputing with dynamic attractors in neural networks.
In this paper we report on some new architectures for neural computation, motivated in part by biological considerations. One of our goals is to demonstrate that it is just as easy for a neural net to compute with arbitrary attractors--oscillatory or chaotic--as with the more usual asymptotically stable fixed points. The advantages (if any) of such architectures are currently being investigated...
متن کاملConversion of Temporal Correlations Between Stimuli to Spatial Correlations Between Attractors
It is shown that a simple modification of synaptic structures (of the Hop field type) constructed to produce auto-associative attractors, produces neural networks whose attractors are correlated with several (learned) patterns used in the construction of the matrix. The modification stores in the matrix a fixed sequence ofuncorrelated pattems. The network then has correlated attractors, provok...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005