Estimation of acoustic communication channel capacity of an ocean waveguide disturbed by surface waves

2011 ◽  
Vol 130 (4) ◽  
pp. 2346-2346
Author(s):  
Thomas J. Hayward
Keyword(s):  
Surface Waves ◽  
Channel Capacity ◽  
Acoustic Communication ◽  
Communication Channel

scholarly journals Simulation of an Underwater Acoustic Communication Channel Characterized by Wind-Generated Surface Waves and Bubbles

2013 ◽  
Vol 38 (4) ◽  
pp. 642-654 ◽  
Author(s):  
Henry S. Dol ◽  
Mathieu E. G. D. Colin ◽  
Michael A. Ainslie ◽  
Paul A. van Walree ◽  
Jeroen Janmaat
Keyword(s):  
Surface Waves ◽  
Acoustic Communication ◽  
Communication Channel ◽  
Underwater Acoustic Communication ◽  
Underwater Acoustic

Capacity of a Single Spiking Neuron Channel

2009 ◽  
Vol 21 (6) ◽  
pp. 1714-1748 ◽  
Author(s):  
Shiro Ikeda ◽  
Jonathan H. Manton
Keyword(s):  
Information Processing ◽  
Channel Capacity ◽  
Information Transfer ◽  
Communication Channel ◽  
Discrete Distribution ◽  
Random Variable ◽  
Temporal Coding ◽  
Reasonable Assumption ◽  
Rate Coding ◽  
The Brain

Information transfer through a single neuron is a fundamental component of information processing in the brain, and computing the information channel capacity is important to understand this information processing. The problem is difficult since the capacity depends on coding, characteristics of the communication channel, and optimization over input distributions, among other issues. In this letter, we consider two models. The temporal coding model of a neuron as a communication channel assumes the output is τ where τ is a gamma-distributed random variable corresponding to the interspike interval, that is, the time it takes for the neuron to fire once. The rate coding model is similar; the output is the actual rate of firing over a fixed period of time. Theoretical studies prove that the distribution of inputs, which achieves channel capacity, is a discrete distribution with finite mass points for temporal and rate coding under a reasonable assumption. This allows us to compute numerically the capacity of a neuron. Numerical results are in a plausible range based on biological evidence to date.

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