Full Papers

8. Brain-Like Learning Directly from Dynamic Cluttered Natural Video

by Yuekai Wang, Xiaofeng Wu (Fudan University),

Juyang Weng (Fudan University & Michigan State University)

Abstract: It is mysterious how the brain of a baby figures out which part of a cluttered scene to attend to in the dynamic world. On one hand, the various backgrounds, where object may appear at different locations, make it difficult to find the object of interest. On the other hand, with the numbers of locations, types and variations in each type (e.g., rotation) increasing, conventional model-based search schemes start to break down. It is also unclear how a baby acquires concepts, such as locations and types. Inspired by brain anatomy, the work here is a computational synthesis from rich neurophysiological and behavioral data. Our hypothesis is that motor signals pay a critical role for the neurons in the brain to select the motorcorrelated pattern on the retina to respond. This work introduces a new biologically inspired mechanism – synapse maintenance in tight integration with Hebbian mechanisms to realize object detection and recognition from cluttered natural video while the motor manipulates (or correlate with) object of interest. Synapse maintenance is meant to automatically decide which synapse should be active during the firing of the post-synaptic neuron. With the synapse maintenance, each neuron automatically wires itself with the other parts of the brain-like network even when a dynamic object of interest, specified by the supervised motor, takes up only a small part of the retina in the presence of complex dynamic backgrounds.

Pages: 51-58

Full Text: PDF