Literature DB >> 12412325

Can robots make good models of biological behaviour?

B Webb1.   

Abstract

UNLABELLED: How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models--the representation of a hypothesis about a target system--are distinguished from several other relationships also termed "modelling" in discussions of scientific explanation. Seven dimensions on which simulation models can differ are defined and distinctions between them discussed: 1. RELEVANCE: whether the model tests and generates hypotheses applicable to biology. 2. Level: the elemental units of the model in the hierarchy from atoms to societies. 3. Generality: the range of biological systems the model can represent. 4. Abstraction: the complexity, relative to the target, or amount of detail included in the model. 5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour. 6. Performance match: to what extent the model behaviour matches the target behaviour. 7. Medium: the physical basis by which the model is implemented. No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments.

Mesh:

Year:  2001        PMID: 12412325     DOI: 10.1017/s0140525x01000127

Source DB:  PubMed          Journal:  Behav Brain Sci        ISSN: 0140-525X            Impact factor:   12.579


  29 in total

Review 1.  Biomimetic vibrissal sensing for robots.

Authors:  Martin J Pearson; Ben Mitchinson; J Charles Sullivan; Anthony G Pipe; Tony J Prescott
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2011-11-12       Impact factor: 6.237

Review 2.  Invertebrate central pattern generator circuits.

Authors:  Allen I Selverston
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2010-08-12       Impact factor: 6.237

3.  Simulation and robotics studies of salamander locomotion: applying neurobiological principles to the control of locomotion in robots.

Authors:  Auke Jan Ijspeert; Alessandro Crespi; Jean-Marie Cabelguen
Journal:  Neuroinformatics       Date:  2005

4.  A model of antennal wall-following and escape in the cockroach.

Authors:  T P Chapman; B Webb
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2006-06-08       Impact factor: 1.836

5.  Methods for improving simulations of biological systems: systemic computation and fractal proteins.

Authors:  Peter J Bentley
Journal:  J R Soc Interface       Date:  2009-03-04       Impact factor: 4.118

Review 6.  Biological and artificial cognition: what can we learn about mechanisms by modelling physical cognition problems using artificial intelligence planning techniques?

Authors:  Jackie Chappell; Nick Hawes
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2012-10-05       Impact factor: 6.237

Review 7.  Principles of goal-directed spatial robot navigation in biomimetic models.

Authors:  Michael Milford; Ruth Schulz
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2014-11-05       Impact factor: 6.237

Review 8.  Adaptation of sensor morphology: an integrative view of perception from biologically inspired robotics perspective.

Authors:  Fumiya Iida; Surya G Nurzaman
Journal:  Interface Focus       Date:  2016-08-06       Impact factor: 3.906

9.  On Strong Anticipation.

Authors:  N Stepp; M T Turvey
Journal:  Cogn Syst Res       Date:  2010-06-01       Impact factor: 3.523

Review 10.  Integrative Neuroscience of Paramecium, a "Swimming Neuron".

Authors:  Romain Brette
Journal:  eNeuro       Date:  2021-06-07
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