cc 16(9): e3

Research Article

Designing Behaviour in Bio-inspired Robots Using Associative Topologies of Spiking-Neural-Networks

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  • @ARTICLE{10.4108/eai.3-12-2015.2262580,
        author={Cristian Jimenez-Romero and David Sousa-Rodrigues and Jeffrey Johnson},
        title={Designing Behaviour in Bio-inspired Robots Using Associative Topologies of Spiking-Neural-Networks},
        journal={EAI Endorsed Transactions on Collaborative Computing},
        volume={2},
        number={9},
        publisher={ACM},
        journal_a={CC},
        year={2016},
        month={5},
        keywords={spiking neurons, spike timing dependent plasticity, associative learning, robotics, agents simulation, articial life},
        doi={10.4108/eai.3-12-2015.2262580}
    }
    
  • Cristian Jimenez-Romero
    David Sousa-Rodrigues
    Jeffrey Johnson
    Year: 2016
    Designing Behaviour in Bio-inspired Robots Using Associative Topologies of Spiking-Neural-Networks
    CC
    EAI
    DOI: 10.4108/eai.3-12-2015.2262580
Cristian Jimenez-Romero1,*, David Sousa-Rodrigues1, Jeffrey Johnson1
  • 1: The Open University UK
*Contact email: cristian.jimenez-romero@open.ac.uk

Abstract

This study explores the design and control of the behaviour of agents and robots using simple circuits of spiking neurons and Spike Timing Dependent Plasticity (STDP) as a mechanism of associative and unsupervised learning. Based on a "reward and punishment" classical conditioning, it is demonstrated that these robots learnt to identify and avoid obstacles as well as to identify and look for rewarding stimuli. Using the simulation and programming environment NetLogo, a software engine for the Integrate and Fire model was developed, which allowed us to monitor in discrete time steps the dynamics of each single neuron, synapse and spike in the proposed neural networks. These spiking neural networks (SNN) served as simple brains for the experimental robots. The Lego Mindstorms robot kit was used for the embodiment of the simulated agents. In this paper the topological building blocks are presented as well as the neural parameters required to reproduce the experiments. This paper summarizes the resulting behaviour as well as the observed dynamics of the neural circuits. The Internet-link to the NetLogo code is included in the annex.