Computational Models of Neuromodulation

By Jean-Marc Fellous, C. Linster. 1998

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Neuromodulated plasticity (Fellous and Linster, 1998) is often used to implement reward-learning in neural networks (reviewed later in Section III-F).”
Evolutionary and Computational Advantages of Neuromodulated Plasticity (2008, methodology): “For a detailed description of the categories mentioned above refer to (Fellous and Linster, 1998).”
Evolving neuromodulatory topologies for reinforcement learning-like problems (2007, methodology): “Following, an overview of the algorithm is given.”
Neuromodulation and plasticity in an autonomous robot (2002, background): “During reward conditioning, the model learns to generate tonic and phasic signals that represent predictions and prediction errors, including precisely timed negative signals if expected rewards are omitted or delayed.”
Better Living Through Chemistry: Evolving GasNets for Robot Control (1998, background): “Of course there are a number of explicit computationalmodelling projects in which the mechanisms and functional role of various kinds of modulation are studied, based on speci c biological data (Fellous and Linster, 1998).”
Evolution of Reinforcement Learning in Uncertain Environments: A Simple Explanation for Complex Foraging Behaviors (2002, background): “To avoid the possible confusion of terms, we make a distinction between the notions of heterosynaptic learning (Dittman & Regehr, 1997; Schacher, Wu & Sun, 1997; Vogt & Nicoll, 1999) and neuromodulation of plasticity (Bailey, Giustetto, Huang, Hawkins, & Kandel, 2000; Fellous & Linster, 1998).”
Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios (2008, background): “Short term memory by means of neuromodulation was investigated in (Ziemke and Thieme, 2002) where a robot navigated in a T-maze and remembered turning directions according to visual clues in the maze.”
Robot coverage control by evolved neuromodulation (2013, background): “An extensive review of computational models of neuromodulation can be found in [15], and some recent models are reviewed in [23].”
Neuroevolution: from architectures to learning (2008, background): “The existing evidence points to the combined action of evolved value systems [58] and neuromodulatory effects [2, 18].”
Neural Plasticity and Minimal Topologies for Reward-Based Learning (2008, background): “(neuromodulation) [4] to reduce the complexity of models and find the minimal required mechanisms.”
Modeling functions of striatal dopamine modulation in learning and planning. (2001, methodology): “Effects of neuromodulators on neuronal membrane properties have been simulated with various modeling techniques (reviewed in Fellous and Linster, 1998).”
Evolved Sensitive Periods in Learning (2013, background): “Fellous and Linster (1998) present a review of work on these kinds of networks, revealing that modulatory signals have been used to affect network function in diverse ways, most of them biologically plausible , or at least biologically inspired.”
Actor-critic models of the basal ganglia: new anatomical and computational perspectives (2002, background): “…plasticity and neuromodulatory gating of synaptic plasticity have been demonstrated in neural tissues (Bailey et al., 2000; Dittman & Regehr, 1997; Fellous & Linster, 1998; Schacher et al., 1997; Vogt & Nicoll, 1999), and have been recognized to increase the computational complexity of synaptic…”