A Top-Down Approach for a Synthetic Autobiographical Memory System

Autobiographical memory AM refers to the organisation of one's experience into a coherent narrative. The exact neural mechanisms responsible for the manifestation of AM in humans are unknown. On the other hand, the field of psychology has provided us with useful understanding about the functionality of a bio-inspired synthetic AM SAM system, in a higher level of description. This paper is concerned with a top-down approach to SAM, where known components and organisation guide the architecture but the unknown details of each module are abstracted. By using Bayesian latent variable models we obtain a transparent SAM system with which we can interact in a structured way. This allows us to reveal the properties of specific sub-modules and map them to functionality observed in biological systems. The top-down approach can cope well with the high performance requirements of a bio-inspired cognitive system. This is demonstrated in experiments using faces data.

[1]  Mathew H. Evans,et al.  Machines Learning - Towards a New Synthetic Autobiographical Memory , 2014, Living Machines.

[2]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Peter Ford Dominey,et al.  Successive Developmental Levels of Autobiographical Memory for Learning Through Social Interaction , 2014, IEEE Transactions on Autonomous Mental Development.

[4]  Peter Ford Dominey,et al.  Embodied Simulation Based on Autobiographical Memory , 2013, Living Machines.

[5]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[6]  Neil D. Lawrence,et al.  Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..

[7]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[8]  Neil D. Lawrence,et al.  Manifold Relevance Determination , 2012, ICML.

[9]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[10]  Neil D. Lawrence,et al.  Deep Gaussian Processes , 2012, AISTATS.

[11]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[12]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[13]  Neil D. Lawrence,et al.  Variational Inference for Uncertainty on the Inputs of Gaussian Process Models , 2014, ArXiv.

[14]  A. Pouget,et al.  Probabilistic brains: knowns and unknowns , 2013, Nature Neuroscience.

[15]  Paul F. M. J. Verschure,et al.  A Framework for Mobile Robot Navigation Using a Temporal Population Code , 2012, Living Machines.

[16]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[17]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[18]  Технология.  Springer Science+Business Media , 2013 .