Learning Multi-Sensory integration with Self-Organization and Statistics

Recently, we presented a self-organized artificial neural network algorithm capable of learning a latent variable model of its high-dimensional input and to optimally integrate that input to compute and population-code a probability density function over the values of the latent variables of that model. We did take our motivation from natural neural networks and reported on a simple experiment with simulated multi-sensory data. However, we focused on presenting the algorithm and evaluating its performance, leaving a comparison with natural cognition for future work. In this paper, we show that our algorithm behaves similar, in important behavioral and neural aspects, to a prime example of natural multi-sensory integration: audio-visual object localization.

@InProceedings{BW13a, 
 	 author =  {Bauer, Johannes and Wermter, Stefan},  
 	 title = {Learning Multi-Sensory integration with Self-Organization and Statistics}, 
 	 booktitle = {Ninth International Workshop on Neural-Symbolic Learning and Reasoning (NeSy’13)},
 	 editors = {Garcez, A.; Lamb, L.; Hitzler, P.},
 	 number = {},
 	 volume = {},
 	 pages = {7--12},
 	 year = {2013},
 	 month = {Aug},
 	 publisher = {},
 	 doi = {}, 
 }