Hebbian synapses: biophysical mechanisms and algorithms.

By T. H. Brown, E. W. Kairiss, C. L. Keenan. 1990

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Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Those advances in the 1970s and 1980s lead to the mathematical formulation of synaptic learning dynamics (Lisman, 1989; Brown et al., 1990), e.g. the BCM rule, inspired by the cat’s visual cortex (Bienenstock et al., 1982), and the Oja’s rule (Oja, 1982).”
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation (2012, methodology): “The Hebbian paradigm (Hebb, 1949; Marr, 1969; Stent, 1973; Brown et al., 1990; Bi and Poo, 2001; Gerstner and Kistler, 2002a; Cooper, 2005), which states that neurons that fire together, wire together, is a ubiquitous paradigm in neuroscience that has been substantially validated through neural reco”
A computational theory of hippocampal function, and empirical tests of the theory. (2006, background): “…can then be transformed into an even sparser firing activity in CA3 by a threshold effect.5 Third, nonassociative plasticity of mossy fibres (see Brown et al., 1989, 1990) might have a useful effect in enhancing the signal-to-noise ratio, in that a consistently firing mossy fibre would produce…”
Computational Models of Neuromodulation (1998, background): “Learning is achieved according to a synaptic modiﬁcation rule (or equation) that relates synaptic strength and presynaptic and postsynaptic activities (Brown, Kairiss, & Keenan, 1990; Hasselmo, 1995; Zador, Koch, & Brown, 1990).”
The mechanisms for pattern completion and pattern separation in the hippocampus (2013, background): “We (Rolls and Kesner, 2006) have hypothesized that nonassociative plasticity of mossy fibres (see Brown et al., 1989, 1990) might have a useful effect in enhancing the signal-tonoise ratio, in that a consistently firing mossy fiber would produce non-linearly amplified currents in the postsynaptic…”
Hippocampal mediation of stimulus representation: A computational theory (1993, methodology): “…than the correlational or Hebbian learning rules which have been used to describe synaptic plasticity (e.g., Stanton and Sejnowski, 1989; Brown et al., 1990), the Rescorla-Wagner/LMS rule can be built up from simple circuits using known synaptic modification rules (Donegan et al., 1989;…”
A Simple Hebbian/Anti-Hebbian Network Learns the Sparse, Independent Components of Natural Images (2006, background): “The occurrence of Hebbian-like mechanisms leading to long-term changes (long-term potentiation, LTP) in the brains of a range of animals is fairly well substantiated (see Brown, Kairiss, & Keenan, 1990, for a review).”