Neuromodulation and plasticity in an autonomous robot

By Olaf Sporns, William H. Alexander. 2002

105 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Short-term plasticity as cause–effect hypothesis testing in distal reward learning (2014, background): “The utility of modulatory dynamics in models of reward learning and behavior is also validated by closed-loop robotic neural controllers (Ziemke and Thieme, 2002; Sporns and Alexander, 2002; Alexander and Sporns, 2002; Sporns and Alexander, 2003; Soltoggio et al, 2008; Cox and Krichmar, 2009).”
Mapping Information Flow in Sensorimotor Networks (2006, background): “Plastic changes were under the control of a value system [55,56] capable of influencing the coupling between ColRGBY and Sal in the presence of changes in innately salient sensory stimulation, analogous to biological neuromodulatory systems (e.”
Neuromodulation in a learning robot: interactions between neural plasticity and behavior (2003, unknown): “Our model was tested in computer simulation [3] and in an autonomous robot [2,3].”
An Embodied Model of Learning, Plasticity, and Reward (2002, methodology): “The experimental methods were similar to those used in an earlier version of the model (Sporns and Alexander, 2002).”
The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World (2008, background): “Some of these reinforcement learning robot controllers are neurally inspired by reward expectation signals found in the dopaminergic system of the brain (Alexander & Sporns, 2002; Arleo, Smeraldi, & Gerstner, 2004; Krichmar & Edelman, 2002; Sporns & Alexander, 2002).”
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation (2012, background): “, 2010), making neuromodulation particularly suitable for modeling and implementing learning processes (Sporns and Alexander, 2002; Doya, 2002; Doya and Uchibe, 2005; Soula et al., 2005; Farries and Fairhall, 2007; Krichmar, 2008; Cox and Krichmar, 2009).”
Evolving neuromodulatory topologies for reinforcement learning-like problems (2007, methodology): “Following, an overview of the algorithm is given.”
Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios (2008, background): “Learning and adaptivity were shown in navigation tasks in (Sporns and Alexander, 2002) where a neural architecture was manually designed to update weights according to reinforcement signals.”
Please Scroll down for Article Connection Science Developmental Robotics: a Survey Developmental Robotics: a Survey (2003, unknown): “…i.e. they inﬂuence big populations of neurons; (b) their activation is temporally speciﬁc, that is, their effects are transient and short-lasting; and (c) spatially uniform, i.e. they affect widespread regions of the brain, while acting as a single global signal (Sporns and Alexander 2002).”
Copying and Evolution of Neuronal Topology (2008, background): “standard assumption in many models that neuromodulation can alter learning rates [66,67].”
Neuromodulation Improves the Evolution of Forward Models (2016, background): “Inspired by this natural phenomenon, researchers have added an abstraction of neuromodulation to NNs to improve short-term memory [34], evolvability [23], learning and adaptability [26], and finegrained plasticity control [24].”
Rare Neural Correlations Implement Robotic Conditioning with Delayed Rewards and Disturbances (2013, background): “The increasing evidence of the important role of neuromodulation in reward-driven learning led to the formulation of models of modulated plasticity with rate-based neurons (e.g., Montague et al., 1996; Alexander and Sporns, 2002; Sporns and Alexander, 2002; Ziemke and Thieme, 2002; Soltoggio et al.,”
Evolutionary and Computational Advantages of Neuromodulated Plasticity (2008, background): “Learning and adaptivity were shown in navigation tasks in (Sporns and Alexander, 2002).”
Robot coverage control by evolved neuromodulation (2013, background): “A number of these studies focus on neuromodulatory subsystems with projections that have diffuse action on the synapses of more classical neurons [36], [31], [34], [29].”