Modulation of function and gated learning in a network memory.

By L. F. Abbott. 1990

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, methodology): “to emphasize the role of m, a modulatory signal used as a multiplicative factor that can enhance or reduce plasticity (Abbott, 1990).”
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation (2012, background): “Thus, while the dynamics of modulated Hebbian plasticity and modulated spike-timedependent plasticity (STDP) have been extensively investigated (Abbott, 1990; Montague et al., 1996; Florian, 2007; Porr and Wörgötter, 2007; Frémaux et al., 2010; Pfeiffer et al., 2010), the novelty of this work is the”
Computational Models of Neuromodulation (1998, background): “In a model of associative learning, Abbott (1990) proposes that neuro-modulation may serve as a mechanism for initiating and terminating learning.”
Evolutionary and Computational Advantages of Neuromodulated Plasticity (2008, background): “So far, this has been the most popular interpretation of neuromodulated plasticity (Abbott, 1990; Reynolds and Wickens, 2002; Porr and Wörgötter, 2007a).”
Solving the Distal Reward Problem with Rare Correlations (2013, background): “These findings provided inspiration for neural models that use neuromodulation to gate neural plasticity such that high modulation reinforces actions, while low modulation extinguishes actions (Abbott, 1990; Montague, Dayan, Person, & Sejnowski, 1995; Fellous & Linster, 1998; Porr & Wörgötter, 2007;”