Biological smart sensing strategies in weakly electric fish

Biological sensory systems continuously monitor and analyze changes in real-world environments that are relevant to an animal`s specific behavioral needs and goals. Understanding the sensory mechanisms and information processing principles that biological systems utilize for efficient sensory data acquisition may provide useful guidance for the design of smart-sensing systems in engineering applications. Weakly electric fish, which use self-generated electrical energy to actively sense their environment, provide an excellent model system for studying biological principles of sensory data acquisition. The electrosensory system enables these fish to hunt and navigate at night without the use of visual cues. To achieve reliable, real-time task performance, the electrosensory system implements a number of smart sensing strategies, including efficient stimulus encoding, multi-scale virtual sensor arrays, task-dependent filtering and online subtraction of sensory expectation.

[1]  L. Maler,et al.  Plastic and Nonplastic Pyramidal Cells Perform Unique Roles in a Network Capable of Adaptive Redundancy Reduction , 2004, Neuron.

[2]  Eric I. Knudsen,et al.  Spatial aspects of the electric fields generated by weakly electric fish , 1975, Journal of comparative physiology.

[3]  Mark E. Nelson,et al.  Target Detection, Image Analysis, and Modeling , 2005 .

[4]  Bruce A. Carlson,et al.  From stimulus estimation to combination sensitivity: encoding and processing of amplitude and timing information in parallel, convergent sensory pathways , 2008, Journal of Computational Neuroscience.

[5]  L. Maler,et al.  Neural architecture of the electrosensory lateral line lobe: adaptations for coincidence detection, a sensory searchlight and frequency-dependent adaptive filtering , 1999, The Journal of experimental biology.

[6]  Leonard Maler,et al.  Receptive field organization across multiple electrosensory maps. I. Columnar organization and estimation of receptive field size , 2009, The Journal of comparative neurology.

[7]  M. E. Nelson,et al.  Characterization and modeling of P-type electrosensory afferent responses to amplitude modulations in a wave-type electric fish , 1997, Journal of Comparative Physiology A.

[8]  M. A. MacIver,et al.  Prey capture in the weakly electric fish Apteronotus albifrons: sensory acquisition strategies and electrosensory consequences. , 1999, The Journal of experimental biology.

[9]  Walter Heiligenberg,et al.  Principles of Electrolocation and Jamming Avoidance in Electric Fish , 1977, Studies of Brain Function.

[10]  Sailes K. Sengijpta.  Fundamentals of Statistical Signal Processing: Estimation Theory , 1995 .

[11]  M. Nelson,et al.  Logarithmic time course of sensory adaptation in electrosensory afferent nerve fibers in a weakly electric fish. , 1996, Journal of neurophysiology.

[12]  Mark E. Nelson,et al.  Modeling signal and background components of electrosensory scenes , 2005, Journal of Comparative Physiology A.

[13]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[14]  Simon Haykin,et al.  Adaptive Filter Theory 4th Edition , 2002 .

[15]  M. A. MacIver,et al.  Sensory acquisition in active sensing systems , 2006, Journal of Comparative Physiology A.

[16]  Maurice J Chacron,et al.  Temporal processing across multiple topographic maps in the electrosensory system. , 2008, Journal of neurophysiology.

[17]  R Ratnam,et al.  Nonrenewal Statistics of Electrosensory Afferent Spike Trains: Implications for the Detection of Weak Sensory Signals , 2000, The Journal of Neuroscience.

[18]  J. Bastian,et al.  Pyramidal-cell plasticity in weakly electric fish: a mechanism for attenuating responses to reafferent electrosensory inputs , 2004, Journal of Comparative Physiology A.

[19]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[20]  Mark E. Nelson,et al.  A Simple Model of Long-Term Spike Train Regularization , 2002, Neural Computation.

[21]  Curtis C Bell,et al.  Memory-based expectations in electrosensory systems , 2001, Current Opinion in Neurobiology.

[22]  Bruno A Olshausen,et al.  Sparse coding of sensory inputs , 2004, Current Opinion in Neurobiology.

[23]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[24]  Brent Doiron,et al.  Non-classical receptive field mediates switch in a sensory neuron's frequency tuning , 2003, Nature.

[25]  Carl D. Hopkins,et al.  Stimulus filtering and electroreception: Tuberous electroreceptors in three species of Gymnotoid fish , 2004, Journal of comparative physiology.

[26]  C. Carr,et al.  Peripheral organization and central projections of the electrosensory nerves in gymnotiform fish , 1982, The Journal of comparative neurology.

[27]  C. Koch,et al.  From stimulus encoding to feature extraction in weakly electric fish , 1996, Nature.

[28]  C A Shumway,et al.  Multiple electrosensory maps in the medulla of weakly electric gymnotiform fish. I. Physiological differences , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[29]  C A Shumway,et al.  Multiple electrosensory maps in the medulla of weakly electric gymnotiform fish. II. Anatomical differences , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.