Tactile Discrimination Using Template Classifiers: Towards a Model of Feature Extraction in Mammalian Vibrissal Systems
Rats and other whiskered mammals are capable of making sophisticated sensory discriminations using tactile signals from their facial whiskers (vibrissae). As part of a programme of work to develop biomimetic technologies for vibrissal sensing, including whiskered robots, we are devising algorithms for the fast extraction of object parameters from whisker deflection data. Previous work has demonstrated that radial distance to contact can be estimated from forces measured at the base of the whisker shaft. We show that in the case of a moving object contacting a whisker, the measured force can be ambiguous in distinguishing a nearby object moving slowly from a more distant object moving rapidly. This ambiguity can be resolved by simultaneously extracting object position and speed from the whisker deflection time series – that is by attending to the dynamics of the whisker’s interaction with the object. We compare a simple classifier with an adaptive EM (Expectation Maximisation) classifier. Both systems are effective at simultaneously extracting the two parameters, the EM-classifier showing similar performance to a handpicked template classifier. We propose that adaptive classification algorithms can provide insights into the types of computations performed in the rat vibrissal system when the animal is faced with a discrimination task.
KeywordsRadial Distance Tactile Stimulus Tactile Discrimination Tactile Signal Hall Effect Sensor
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- 4.Carvell, G.E., Simons, D.J.: Biometric analyses of vibrissal tactile discrimination in the rat. J. Neurosci. 10(8), 2638–2648 (1990)Google Scholar
- 5.Dehnhardt, G., Ducker, G.: Tactual discrimination of size and shape by a california sea lion (zalophus californianus). Animal Learning and Behavior 24(4), 366–374 (1996)Google Scholar
- 6.Evans, M., Fox, C.W., Pearson, M.J., Prescott, T.J.: Spectral template based classification of robotic whisker sensor signals in a floor texture discrimination task. In: Kyriacou, T., Nehmzow, U., Melhuish, C., Witkowski, M. (eds.) Proceedings of Towards Autonomous Robotic Systems (TAROS 2009), pp. 19–24 (2009)Google Scholar
- 8.Fox, C., Evans, M., Pearson, M., Prescott, T.J.: Towards temporal inference for shape recognition from whiskers. In: Ramamoorthy, S., Hayes, G.M. (eds.) Towards Autonomous Robotic Systems, pp. 226–233 (2008)Google Scholar
- 9.Fox, C.W., Mitchinson, B., Pearson, M.J., Pipe, A.G., Prescott, T.J.: Contact type dependency of texture classification in a whiskered mobile robot. Autonomous Robots (2009)Google Scholar
- 13.Lepora, N.F., Evans, M., Fox, C.W., Diamond, M.E., Gurney, K., Prescott, T.J.: Naive bayes texture classification applied to whisker data from a moving robot. In: World Congress on Computational Intelligence (submitted 2010)Google Scholar
- 15.Pearson, M.J., Mitchinson, B., Welsby, J., Pipe, T.G., Prescott, T.J.: Scratchbot: Active tactile sensing in a whiskered mobile robot. Submitted to SAB (2010)Google Scholar
- 17.Ullman, S., Vidal-Naquet, M., Sali, E.: Visual features of intermediate complexity and their use in classification. Nat. Neurosci. 5(7), 682–687 (2002)Google Scholar