A bio-inspired multisensory stochastic integration algorithm

作者：Porras Alex^*; Llinas Rodolfo R

来源：Neurocomputing, 2015, 151: 11-33.

DOI：10.1016/j.neucom.2014.06.080

• Bioinspired
• Multisensory
• Stochastic
• SPGD
• Intensionality vector
• Motor output

The present paper describes a new stochastic multisensory integration system capable of combining a number of co-registered inputs, integrating different aspects of the external world, into a common premotor coordinate metric.
In the present solution, the model uses a Stochastic Gradient Descent (SGD) algorithm to blend different sensory inputs into a single premotor intensionality vector. This is done isochronally, as the convergence time is independent of the number and type of parallel sensory inputs. This intensionality vector, generated based on "the sum over histories" [1], makes this implementation ideal to govern noncontinuous control systems. The rapid convergence of the SGD [2-7] is also used to compare with its biological equivalent in vertebrates -the superior tectum- to evaluate limits of convergence, precision and variability. The overall findings indicate that mathematical modeling is effective in addressing multisensory transformations resembling biological systems.

• 出版日期2015-3-3