Format

Send to

Choose Destination
Curr Opin Neurobiol. 2016 Apr;37:99-105. doi: 10.1016/j.conb.2016.01.014. Epub 2016 Feb 11.

Toward the neural implementation of structure learning.

Author information

1
Janelia Research Campus, 19700 Helix Dr., Ashburn, VA 20147, USA. Electronic address: gowan@janelia.hhmi.org.
2
Department of Brain and Cognitive Sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
3
Department of Psychology and Center for Brain Science, Harvard University, Northwest Building, 52 Oxford Street, Cambridge, MA 02138, USA.

Abstract

Despite significant advances in neuroscience, the neural bases of intelligence remain poorly understood. Arguably the most elusive aspect of intelligence is the ability to make robust inferences that go far beyond one's experience. Animals categorize objects, learn to vocalize and may even estimate causal relationships - all in the face of data that is often ambiguous and sparse. Such inductive leaps are thought to result from the brain's ability to infer latent structure that governs the environment. However, we know little about the neural computations that underlie this ability. Recent advances in developing computational frameworks that can support efficient structure learning and inductive inference may provide insight into the underlying component processes and help pave the path for uncovering their neural implementation.

PMID:
26874471
DOI:
10.1016/j.conb.2016.01.014
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center