منابع مشابه
Measurement and removal of echo integration noise
Pushing scientific echo sounders to the limit involves the consideration of ‘‘noise’’, which is inherently frequency dependent and which also depends on bottom depth. Here, noise is quantified by measurement with a standard echo sounder, the SIMRAD EK500, at 18, 38, 120 and 200 kHz. The use of empirical relationships of noise as a function of range to reduce echo integration is described in gen...
متن کاملVariability in noise-driven integrator neurons.
Neural variability in the presence of noise has been studied mainly in resonator neurons, such as Hodgkin-Huxley or FitzHugh-Nagumo models. Here we investigate this variability for integrator neurons, whose excitability is due to a saddle-node bifurcation of the rest state instead of a Hopf bifurcation. Using simple theoretical expressions for the interspike times distributions, we obtain coeff...
متن کاملNatural Dolphin Echo Recognition Using an Integrator Gateway Network
We have been studying the performance of a bottlenosed dolphin on a delayed matching-to-sample task to gain insight into the processes and mechanisms that the animal uses during echolocation. The dolphin recognizes targets by emitting natural sonar signals and listening to the echoes that return. This paper describes a novel neural network architecture, called an integrator gateway network, tha...
متن کاملAllocation of echo integrator output to small larval insect
In acoustic sampling for fish, thresholding is normally applied to eliminate the unwanted contribution of noise to the integrator output. Since thresholding discriminates against small targets. this technique cannot be used for the quantitative study of these small targets in the presence of larger ones, When the integrator output (area backscattering coefficient, .I'd) due to one size class of...
متن کاملState Noise Effects on the Stochastic Gradient Descent Optimization Method for Echo State Networks with Leaky Integrator Neurons
Executive Summary Echo state networks (ESNs) are a novel approach to modeling the nonlinear dynamical systems that abound in the sciences and engineering. They employ artificial recurrent neural networks in a way that has been independently proposed as a learning mechanism in biological brains and lead to a fast and simple algorithm for supervised training. ESNs are controlled by several global...
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ژورنال
عنوان ژورنال: Fisheries science
سال: 1995
ISSN: 0919-9268
DOI: 10.2331/fishsci.61.637