A Simple Aplysia-Like Spiking Neural Network to Generate Adaptive Behavior in Autonomous Robots

@article{Alnajjar2008ASA,
  title={A Simple Aplysia-Like Spiking Neural Network to Generate Adaptive Behavior in Autonomous Robots},
  author={Fady S. Alnajjar and Kazuyuki Murase},
  journal={Adaptive Behavior},
  year={2008},
  volume={16},
  pages={306 - 324}
}
In this article, we describe an adaptive controller for an autonomous mobile robot with a simple structure. [] Key Method The synaptic weights were modified dependent on the firing rates of the presynaptic modulation signal and that of the hidden-layer neuron by STDP. In experiments using a real robot, which uses a similar simple SNN controller, the robot adapted quickly to the given environment in a single trial by organizing the weights, acquired navigation and obstacle-avoidance behavior. In addition, it…

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References

SHOWING 1-10 OF 37 REFERENCES
Self-organization of Spiking Neural Network that Generates Autonomous Behavior in a Real Mobile Robot
TLDR
A self-organization algorithm based on a use-dependent synaptic potentiation and depotentiation at synapses of input layer to hidden layer and of hidden layer to output layer was formulated and implemented into the robot and incrementally organized the network.
Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots
TLDR
It is shown that evolution finds relatively quickly functional spiking controllers capable of navigating in irregularly textured environments without hitting obstacles using a very simple genetic encoding and fitness function.
Unsupervised learning and self-organization in networks of spiking neurons
TLDR
This chapter investigates possible mechanisms of unsupervised learning and self-organization in networks of spiking neurons, and describes a biologically plausible algorithm for these networks to find clusters in a high dimensional input space or a subspace of it.
Networks of Spiking Neurons: The Third Generation of Neural Network Models
  • W. Maass
  • Computer Science
    Electron. Colloquium Comput. Complex.
  • 1996
Evolving spike-timing-dependent plasticity for single-trial learning in robots.
  • E. D. Di Paolo
  • Biology
    Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
  • 2003
TLDR
Experimental single-trial learning in an evolved robot model of synaptic spike-timing-dependent plasticity shows that plasticity is used to sustain the neural activity corresponding both to the normal phototaxis condition and to the learned behaviour.
Networks of spiking neurons: the third generation of neural network models
  • W. Maas
  • Biology, Computer Science
  • 1997
Genetic algorithm for a fuzzy spiking neural network of a mobile robot
  • N. Kubota, H. Sasaki
  • Computer Science
    2005 International Symposium on Computational Intelligence in Robotics and Automation
  • 2005
TLDR
This paper applies a steady-state genetic algorithm for acquiring the suitable network structure through the interaction with the environment and shows the robot can update the network structure and learn the weights of FSNN according to the spatio-temporal context of the facing environment.
An Evolutionary Ecological Approach to the Study of Learning Behavior Using a Robot-Based Model
TLDR
This article explores the applicability of integrated neural networks with fixed connection weights and simple 'leaky-integrator' neurons as controllers for autonomous learning robots and shows that such a control system is capable of integrating reactive and learned behaviour without explicitly needing hand-designed modules, dedicated to a particular behavior, or an externally introduced reinforcement signal.
Functional significance of long-term potentiation for sequence learning and prediction.
TLDR
It is found that, due to a temporal asymmetry in the induction of NMDA-mediated LTP, firing patterns in a neuronal array that initially represent the current value of a sensory input will, after training, provide an experienced-based prediction of that input instead.
From Wheels to Wings with Evolutionary Spiking Circuits
TLDR
The motivation and methodology used to reach the EPFL indoor flying project goal are described as well as the results of a number of preliminary experiments on vision-based wheeled and flying robots.
...
...