Detail Neuronal Plasticity and Temporal Adaptivity: GasNet Robot Control Networks Bibliografi Author: Smith, Tom ; Husbands, Phil (Co-Author); O'Shea, Michael (Co-Author); Philippides, Andy (Co-Author) Topik: evolutionary robotics; artificial neural networks; neuromodulation; GasNets; neuronal plasticity Bahasa: (EN )     Penerbit: SAGE Publications     Tempat Terbit: London    Tahun Terbit: 2002     Jenis: Article - untuk jurnal ilmiah Fulltext: 161AB1034.pdf (1.49MB; 2 download) Abstract Designing controllers for autonomous robots is not an exact science, and there are few guiding principles on what properties of control systems are useful for what kinds of task. In this article we analyze the functional operation of robot controllers developed using evolutionary computation methods, to elucidate the strengths and weaknesses of the underlying control system class. By comparing and contrasting robot controllers based on two different classes of artificial neural network, the GasNet and NoGas networks, we show that the increased evolvability of the GasNet class on a visual shape discrimination task is due to the temporally adaptive nature of the GasNet, where neuronal plasticity mediated through the concentration of virtual neuromodulatory ?gases? occurs over a wide range of time courses. We argue that the availability of mechanisms operating over a wide range of potential time courses is a crucial property for controllers used to generate adaptive behavior over time, and that the design process should easily be able to adapt those time courses to the natural time scales in the environment. Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Lihat Sejarah Pengadaan  Konversi Metadata   Kembali

Copyright © 2006, 2007 Unika Atma Jaya, all rights reserved