Donald O. Hebb

Donald O. Hebb (1904–1985) was a Canadian psychologist whose theories linked learning to changes in neural connections and helped establish modern neuroscience and cognitive psychology.

Connect

Synaptic plasticity: The ability of connections between neurons to change in strength or effectiveness. Hebb proposed lasting changes in synaptic effectiveness as a basis for learning.
Hebbian theory: The theory that coordinated activity among neurons can strengthen their functional connections. This is the central learning principle associated with Hebb’s work.
The Organization of Behavior: Donald O. Hebb’s 1949 book proposing neural assemblies and activity-based accounts of learning and thought. This book presented the framework that made Hebb’s name enduring in psychology and neuroscience.
Cognitive neuroscience: The study of how brain systems support mental processes such as perception, memory, and language. Hebb helped connect psychological functions with organized patterns of neural activity.
Anti-Hebbian learning: A learning rule in which correlated neural activity weakens rather than strengthens a connection. It reverses the direction of the strengthening rule commonly associated with Hebb.
Neuron: An excitable cell that receives, processes, and transmits signals in the nervous system. Hebb described learning through the coordinated activity of interconnected neurons.
Hebbian learning: A learning rule in which connections strengthen when their input and output neurons are active together. It formalizes Hebb’s proposal that correlated neural activity changes connection strength.
Karl Lashley: An American psychologist who investigated how brain lesions affect learning and memory. Hebb worked with Lashley and developed theories addressing problems raised by his brain research.
Cognitive psychology: The scientific study of mental processes, including attention, memory, language, and reasoning. His theories gave cognitive processes a proposed neural organization.
Oja's rule: A normalized Hebbian learning rule that can extract principal components from input data. It modifies Hebbian strengthening to prevent unbounded growth of connection weights.