The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World

By J. Krichmar. 2008

138 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Molecular Biology

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, methodology): “, modulating activations rather than plasticity (Krichmar, 2008).”
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation (2012, background): “A low modulatory value, or tonic value, implies small changes and is useful in conditions of low relevance for learning, as opposed to phasic values (Aston-Jones and Cohen, 2005; Krichmar, 2008).”
Robot Cognitive Control with a Neurophysiologically Inspired Reinforcement Learning Model (2011, methodology): “A possible improvement of our model would be to replace the algorithmic implementation of the softmax function in our LPFC module by a modulation of extrinsic and inhibitory synaptic weights between competing neurons based on the level of noradrenergic innervation, as proposed by (Krichmar, 2008).”
Robot coverage control by evolved neuromodulation (2013, background): “The idea of relating neuromodulatory substances to properties and learning parameters has been explored [14], [22], but to the best of our knowledge has not previously been evolved.”
as a Robot Controller Neuromodulation as a Robot Controller A Brain-Inspired Strategy for Controlling Autonomous Robots (2009, background): “3) The effect of each of these neuromodulatory systems on downstream neuronal targets is similar, i.e., they cause target neural networks to sharpen, resulting in a winner-take-all response [1], [3], [4] Authorized licensed use limited to: Univ of Calif Irvine.”
Rare Neural Correlations Implement Robotic Conditioning with Delayed Rewards and Disturbances (2013, background): “The implementation of such functions is investigated in a number of computational models (Fellous and Linster, 1998; Doya, 1999, 2002) and neural robotic controllers (Krichmar, 2008; Cox and Krichmar, 2009), in particular with focus on the role of neuromodulation in attention (Avery et al.”