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This paper proposes a neural network structure for spatio-temporal learning and recognition inspired by the long-term memory (LTM) model of the human cortex. Our structure is able to process real-valued and multidimensional sequences. This capability is attained by addressing three critical problems in sequential learning, namely the error tolerance, the(More)
In this paper, we present a novel biologically-inspired spatio-temporal sequence learning architecture of visual place cells to leverage autonomous navigation. The construction of the place cells originates from the well-known architecture of Hubel and Wiesel to develop simple to complex features in ventral stream of the human brain. To characterize the(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: • We propose a novel biologically inspired neural network architecture for the problem(More)
In this work, we propose a connectionist memory structure for spatio-temporal sequence learning and recognition inspired by the Long-Term Memory structure of human cortex. Besides symbolic data, our framework is able to continuously process real-valued multi-dimensional data stream. This capability is made possible by addressing three critical problems in(More)
This paper proposes an unsupervised salient object detection system that able to extract potential exogenous regions of interests which may be used in robotic navigation system. Biologically inspired, this approach has novel implications for robotic vision that can reduce the complexity in global image processing by focusing only on representative basins of(More)
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