Operant conditioning in skinnerbots
Adaptive Behavior - Special issue on environment structure and behavior
Computational models of neuromodulation
Neural Computation
Understanding intelligence
Autonomous Robots
TD Models of reward predictive responses in dopamine neurons
Neural Networks - Computational models of neuromodulation
Temporal Difference Model Reproduces Anticipatory Neural Activity
Neural Computation
Timed delivery of reward signals in an autonomous robot
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Actor-Critic Models of Reinforcement Learning in the Basal Ganglia: From Natural to Artificial Rats
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Journal of Cognitive Neuroscience
Assessing Machine Volition: An Ordinal Scale for Rating Artificial and Natural Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Hierarchical Co-evolution of Cooperating Agents Acting in the Brain-Arena
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Design Principles and Constraints Underlying the Construction of Brain-Based Devices
Neural Information Processing
The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Robotics and Autonomous Systems
Anubis: Artificial neuromodulation using a bayesian inference system
Neural Computation
Neuronal assembly dynamics in supervised and unsupervised learning scenarios
Neural Computation
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In this paper we implement a computational model of a neuromodulatory system in an autonomous robot. The output of the neuromodulatory system acts as a value signal, modulating widely distributed synaptic changes. The model is based on anatomical and physiological properties of midbrain diffuse ascending systems, in particular parts of the dopamine and noradrenaline systems. 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. We test the robot's learning and behavior in different environmental contexts and observe changes in the development of the neuromodulatory system that depend upon environmental factors. Simulation of a computational model incorporating both reward-related and aversive stimuli leads to the emergence of conditioned reward and aversive behaviors. These studies represent a step towards investigating computational aspects of neuromodulatory systems in autonomous robots.