Citation, DOI & article data
Hebbian learning describes a type of activity-dependent modification of the strength of synaptic transmission at pre-existing synapses which plays a central role in the capacity of the brain to convert transient experiences into memory. According to Hebb et al 1, two cells or systems of cells that are repeatedly active simultaneously will eventually become associated so that the activity in one facilitates activity in the other. Hebb's theory is often summarized as "Cells that fire together wire together." 2 While this type of learning was first hypothesized in terms of mammalian brains, it has been used in the field of artificial intelligence (AI).
Hebbian learning is the subject of research in terms of AI applied to medical imaging in various areas, and has been implemented in diffusion tensor imaging analysis and fiber tractography. The diffusion anisotropy in biological tissues is not clear and Hebbian learning can be used to identify intersecting fiber tracts 3.
- 1. Morris R. D.O. Hebb: The Organization of Behavior, Wiley: New York; 1949. Brain Res Bull. 1999;50(5-6):437. doi:10.1016/s0361-9230(99)00182-3
- 3. Dilek Goksel Duru (2010). A Hebbian Learning Approach for Diffusion Tensor Analysis and Tractography, New Advances in Machine Learning, Yagang Zhang (Ed.), ISBN: 978-953-307-034-6, InTech, Available from: http://www.intechopen.com/books/new-advances-in-machine-learning/a-hebbian-learning-approach-fordiffusion-tensor-analysis-and-tractography.
- 2. Löwel S, Singer W. Selection of intrinsic horizontal connections in the visual cortex by correlated neuronal activity. Science. 1992 Jan 10;255(5041):209-12. doi: 10.1126/science.1372754. PMID: 1372754.