Structural complexity 1
Introduction to algorithms
Pulsed neural networks
P systems with active membranes: attacking NP-complete problems
Journal of Automata, Languages and Combinatorics
Introduction to Circuit Complexity: A Uniform Approach
Introduction to Circuit Complexity: A Uniform Approach
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Membrane Computing: An Introduction
Membrane Computing: An Introduction
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Theoretical Computer Science - Natural computing
A New Class of Symbolic Abstract Neural Nets: Tissue P Systems
COCOON '02 Proceedings of the 8th Annual International Conference on Computing and Combinatorics
Solving NP-Complete Problems Using P Systems with Active Membranes
UMC '00 Proceedings of the Second International Conference on Unconventional Models of Computation
Theoretical Computer Science
Computing with Membranes
A fast P system for finding a balanced 2-partition
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Normal forms for spiking neural P systems
Theoretical Computer Science
Solving numerical NP-complete problems with spiking neural P systems
WMC'07 Proceedings of the 8th international conference on Membrane computing
Computing with spiking neural p systems: traces and small universal systems
DNA'06 Proceedings of the 12th international conference on DNA Computing
Fundamenta Informaticae
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Recently the possibility of using spiking neural P systems for solving computationally hard problems has been considered. Such solutions assume that some (possibly exponentially large) pre-computed resources are given in advance, provided that their structure is "regular" and they do not contain neither "hidden information" that simplify the solution of specific instances, nor an encoding of all possible solutions (that is, an exponential amount of information that allows to cheat while solving the instances of the problem). In this paper we continue this research line, and we investigate the possibility of solving numerical NP-complete problems such as SUBSET SUM. In particular, we first propose a semi-uniform family of spiking neural P systems in which every system solves a specific instance of SUBSET SUM. Then, we exploit a technique used to calculate ITERATED ADDITION with Boolean circuits to obtain a uniform family of spiking neural P systems in which every system is able to solve any instance of SUBSET SUM of a fixed size. All the systems here considered are deterministic, and their size generally grows exponentially with respect to the instance size.