Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes

Humans and other animals have been shown to perform near-optimally in multi-sensory integration tasks. Probabilistic population codes (PPCs) have been proposed as a mechanism by which optimal integration can be accomplished. Previous approaches have focussed on how neural networks might produce PPCs from sensory input or perform calculations using them, like combining multiple PPCs. Less attention has been given to the question of how the necessary organisation of neurons can arise and how the required knowledge about the input statistics can be learned. In this paper, we propose a model of learning multisensory integration based on an unsupervised learning algorithm in which an artificial neural network learns the noise characteristics of each of its sources of input. Our algorithm borrows from the selforganising map the ability to learn latent-variable models of the input and extends it to learning to produce a PPC approximating a probability density function over the latent variable behind its (noisy) input. The neurons in our network are only required to perform simple calculations and we make few assumptions about input noise properties and tuning functions. We report on a neurorobotic experiment in which we apply our algorithm to multi-sensory integration in a humanoid robot to demonstrate its effectiveness and compare it to human multi-sensory integration on the behavioural level. We also show in simulations that our algorithm performs near-optimally under certain plausible conditions, and that it reproduces important aspects of natural multi-sensory integration on the neural level.

@Article{BDW14, 
 	 author =  {Bauer, Johannes and Dávila-Chacón, Jorge and Wermter, Stefan},  
 	 title = {Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes}, 
 	 journal = {Connection Science},
 	 number = {4},
 	 volume = {27},
 	 pages = {1--19},
 	 year = {2014},
 	 month = {},
 	 publisher = {Taylor & Francis},
 	 doi = {10.1080/09540091.2014.971224}, 
 }