Pseudo-random Number Generation Using Binary Recurrent Neural Networks
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Authors
Hughes, James M.
Issue Date
2007-04-26
Type
Thesis
Language
en_US
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Abstract
Pseudo-random number generators are deterministic functions that map, in most
cases, a state x to a new state x˙ using some update function in order to generate
pseudo-random data. Use of these numbers is an integral part of computer science,
stochastic physical & statistical simulation, and cryptography. Because of the deterministic
nature of these functions, it is impossible to speak of the resultant numbers
as truly random. Therefore, the primary goal of pseudo-random number generation
is to create values that are statistically identical to truly random numbers.
To this end, a few specific characteristics are desirable. First, there should be
a way to extract some value from the function (such as a single bit) at discrete
intervals that cannot be guessed with probability greater than 50% if only f, the
update function, is known. Additionally, functions with long limit cycles and whose
cycle lengths grow exponentially in the size of one or more system variables are among
those well-suited for random number generation. Finally, the values generated should
be independently and identically distributed over the given output interval.
Many different, well-established methods exist for generating pseudo-random
numbers. Each of these methods has advantages and disadvantages related to its efficiency
and effectiveness. In this project, we present the results of a unique pseudorandom
number generator created using binary recurrent neural networks trained
with two types of neuronal plasticity, anti-spike-timing dependent plasticity (anti-
STDP) and intrinsic plasticity (IP). We subject our results to industry-standard
random number generator test suites, in addition to performing empirical analysis
on the dynamics of our simulated networks. We show that the interaction of these
types of plasticity creates network dynamics well-suited for pseudo-random number
generation.
Materials made available to the public 3/20/2014 with consent of the author.
Materials made available to the public 3/20/2014 with consent of the author.
Description
39 p.
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U.S. copyright laws protect this material. Commercial use or distribution of this material is not permitted without prior written consent of the author.