A fast filtering algorithm using the transmission mechanism of human auditory information and its application on quadruped robot speed tracking

This paper develops a fast filtering algorithm based on the transmission mechanism of human auditory information. By integrating the function of cochlea tuning and short-term synaptic plasticity, the derived details and the feasible parameter criterion under minimum error variance condition are discussed. For illustration, the developed fast filtering algorithm is applied to the speed tracking of quadruped robot walks. The comparison with the Kalman filtering method is given from two aspects, the tracking performance and the tracking speed. Both the two illustrate the high-efficiency of developed fast filtering algorithm.

[1]  Antanas Verikas,et al.  A multi-channel adaptive nonlinear filtering structure realizing some properties of the hearing system , 2005, Comput. Biol. Medicine.

[2]  K M MacLeod,et al.  A role for short-term synaptic facilitation and depression in the processing of intensity information in the auditory brain stem. , 2007, Journal of neurophysiology.

[3]  Brent Doiron,et al.  Short Term Synaptic Depression Imposes a Frequency Dependent Filter on Synaptic Information Transfer , 2012, PLoS Comput. Biol..

[4]  Heik Heinrich Hellmich,et al.  Synaptic plasticity in spiking neural networks (SP2INN): a system approach , 2003, IEEE Trans. Neural Networks.

[5]  Chi-Sang Poon,et al.  A kinetic model unifying presynaptic short-term facilitation and depression , 2009, Journal of Computational Neuroscience.

[6]  Bruce R. Blazar,et al.  Dendritic Discrimination of Temporal Input Sequences in Cortical Neurons , 2010 .

[7]  K. MacLeod.  Short-term synaptic plasticity and intensity coding , 2011, Hearing Research.

[8]  Anatol C. Kreitzer,et al.  Interplay between Facilitation, Depression, and Residual Calcium at Three Presynaptic Terminals , 2000, The Journal of Neuroscience.

[9]  E. Fortune,et al.  Short-Term Synaptic Plasticity Contributes to the Temporal Filtering of Electrosensory Information , 2000, The Journal of Neuroscience.

[10]  Yuriy Romanyshyn,et al.  Filtering signals in models of neurons and neural networks , 2011, Perspective Technologies and Methods in MEMS Design.

[11]  S. David,et al.  Does attention play a role in dynamic receptive field adaptation to changing acoustic salience in A1? , 2007, Hearing Research.

[12]  E. Fortune,et al.  Short-term synaptic plasticity as a temporal filter , 2001, Trends in Neurosciences.

[13]  J. Schnupp.  Auditory Filters, Features, and Redundant Representations , 2006, Neuron.

[14]  Frank C. Hoppensteadt,et al.  Bursts as a unit of neural information: selective communication via resonance , 2003, Trends in Neurosciences.