Preliminary to a Neural Network Model of Sonar-Based Target Discrimination in the Echolocating Bat

Abstract

It's known from previous research (Griffin, 1958) that echolocating bats such as the big brown bat Eptesicus fuscus, can discriminate between edible and inedible airborne targets using information carried in the echo returns. The goal of this project is to build a neural network model which can perform a rudimentary discrimination task using the same sonar targets. The model is to serve as a preliminary test of the network's ability to discriminate, categorize, and generalize from a limited database. We plan to use this model in a later study comparing the performance of our network with the behavioral data collected by Griffin, trying to duplicate as closely as possible the parameters of the task originally presented to bats.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1988
Accession Number
ADA205681

Entities

People

  • Cameron Brennan
  • Lynda Chen

Organizations

  • Brown University

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Applied Psychology
  • Biological Sciences
  • Biosonar
  • Classification
  • Computers
  • Databases
  • Ear
  • Identification
  • Information Science
  • Military Research
  • Neural Networks
  • Psychology
  • Recognition
  • Signal Processing
  • Target Discrimination
  • Three Dimensional
  • Universities

Fields of Study

  • Computer science

Readers

  • Acoustical Oceanography.
  • Aerospace Engineering
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks