Add to ORKGWe propose a neural network model inspired from a simulated cortex model. Also, a new paradigm for pat-tern recognition by oscillatory neural networks is pre-sented. The relaxation time of the oscillatory networks is used as a criterion for novelty detection. We compare the proposed Neural Network with Hopeld and back-propagation networks for a noisy digit recognition task. It is shown that the proposed network is more robust. This work could be a possible bridge between nonlinear dynamical systems and cognitive processes.
Novelty detection is a machine learning technique which identifies new or unknown information in dat...
Novelty detection is a very useful tool to differentiate between known {normal} and unknown (novelty...
Based on the behavior of living beings, which react mostly to external stimuli, we introduce a neura...
W e propose a neural network model inspired from a simulated cortex model. Also, a new paradigm for ...
We propose a spiking neural network model that is inspired from an oversimplified general structure ...
We propose a spiking neural network model that is inspired from an oversimplified general structure ...
Complex forms of pattern recognition is more widely used these days. Complex recognition problems ar...
Much evidence indicates that recognition memory involves two separable processes, recollection and f...
Synchronization of neural activity in the gamma band is assumed to play a significant role not only ...
Most contemporary neural network models deal with essentially static, perceptual problems of classif...
We present a single-layer recurrent neural network that im-plements novelty detection for spatiotemp...
This report discusses novelty detection in time series. Particularly novelty detection with clusteri...
Novelty detection is a fundamental biological problem that organisms must solve to determine whether...
In complex natural environments, sensory systems are constantly exposed to a large stream of inputs....
Neuroimaging research provides converging evidence in support of functional networks active under re...
Novelty detection is a machine learning technique which identifies new or unknown information in dat...
Novelty detection is a very useful tool to differentiate between known {normal} and unknown (novelty...
Based on the behavior of living beings, which react mostly to external stimuli, we introduce a neura...
W e propose a neural network model inspired from a simulated cortex model. Also, a new paradigm for ...
We propose a spiking neural network model that is inspired from an oversimplified general structure ...
We propose a spiking neural network model that is inspired from an oversimplified general structure ...
Complex forms of pattern recognition is more widely used these days. Complex recognition problems ar...
Much evidence indicates that recognition memory involves two separable processes, recollection and f...
Synchronization of neural activity in the gamma band is assumed to play a significant role not only ...
Most contemporary neural network models deal with essentially static, perceptual problems of classif...
We present a single-layer recurrent neural network that im-plements novelty detection for spatiotemp...
This report discusses novelty detection in time series. Particularly novelty detection with clusteri...
Novelty detection is a fundamental biological problem that organisms must solve to determine whether...
In complex natural environments, sensory systems are constantly exposed to a large stream of inputs....
Neuroimaging research provides converging evidence in support of functional networks active under re...
Novelty detection is a machine learning technique which identifies new or unknown information in dat...
Novelty detection is a very useful tool to differentiate between known {normal} and unknown (novelty...
Based on the behavior of living beings, which react mostly to external stimuli, we introduce a neura...