Studies of mind and brain : neural principles of learning, perception, development, cognition, and motor control

By Stephen Grossberg. 1982

Subfields: Neuroscience, Cognitive Science & Psychology

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “…advances in neuroscience suggest that plasticity in the brain is far more responsible for the manifestation of behaviors and skills than previously thought (LeDoux, 2003; Doidge, 2007; Grossberg, 2012), meaning that behavior is less determined by genetics and more by stimuli-driven plasticity.”
Repeated Augmented Rehearsal: A Simple but Strong Baseline for Online Continual Learning (2022, background): “…to learn new tasks, quantiﬁed by 1 T =1 a i,i , and the ability to avoid forgetting past tasks, quantiﬁed by T − 1 T B T , which draws a connection to the more general problem of the stability-plasticity trade-off in neural networks and continual learning [Grossberg, 2012, Delange et al., 2021].”
Continual learning: A comparative study on how to defy forgetting in classification tasks (2019, background): “Catastrophic interference is a direct result of a more general problem in neural networks, the so-called “stability-plasticity” dilemma [5].”
Continual Learning in Neural Networks (2019, background): “Catastrophic interference is a direct result of a more general problem in neural network, the so-called “stability–plasticity” dilemma [51].”
A Continual Learning Survey: Defying Forgetting in Classification Tasks (2019, background): “This is a direct result of a more general problem in neural networks, namely the stability-plasticity dilemma [20], with plasticity referring to the ability of integrating new knowledge, and stability retaining previous knowledge while encoding it.”
Nonlinear neural networks: Principles, mechanisms, and architectures (1988, methodology): “The additive model has continued to be a cornerstone of neural network research to the present day; see, for example, Amari and Arbib (1982) and Grossberg (1982).”
Class-Incremental Learning: A Survey (2023, background): “Since the data stream comes continually and requires training a lifelong time, incremental learning is also known as ‘ continual learning ’ [6] or ‘ lifelong learning ’ [9].”
Psychological Review (2003, methodology): “The theory is best viewed as an application of a more general theory of how cognitive and emotional processes interact (Grossberg, 1980, 1982a, 1984b, 1987).”
Competitive Learning: From Interactive Activation to Adaptive Resonance (1987, background): “One physiological design for F2 processing which has these properties is a gated dipole field (Grossberg, 1982a, 1987a).”
Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks (2021, background): “This phenomenon is known as the stability-plasticity dilemma [9, 10].”
DyGRAIN: An Incremental Learning Framework for Dynamic Graphs (2022, methodology): “The conventional incremental learning methods re-lieve catastrophic forgetting by adjusting stability-plasticity dilemma [ Grossberg, 2012 ] through replay instances or consolidating parameter space.”
ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network (1991, methodology): “This ART coding strategy has been shown to play a useful role in searching for appropriate recognition codes in response to predictive feedback (Grossberg, 1982b, 1984).”
3-D vision and figure-ground separation by visual cortex (2010, background): “Such an attentional focus can support new learning as it gives rise to a conscious perceptual experience (Grossberg, 1980, 1982a) .”