Notes on spiking neural P systems and finite automata

Abstract

Summary. Spiking neural P systems (in short, SNP systems) are membrane computing models inspired by the pulse coding of information in biological neurons. SNP systems with standard rules have neurons that emit at most one spike (the pulse) each step, and have either an input or output neuron connected to the environment. SNP transducers were introduced, where both input and output neurons were used. More recently, SNP modules were introduced which generalize SNP transducers: extended rules are used (more than one spike can be emitted each step) and a set of input and output neurons can be used. In this work we continue relating SNP modules and finite automata: (i) we amend previous constructions for DFA and DFST simulations, (ii) improve the construction from three neurons down to one neuron, (iii) DFA with output are simulated, and (iv) we generate automatic sequences using results from (iii).

Snapse: A Visual Tool for Spiking Neural P Systems

“…In particular, allowing users to use bitstrings as inputs in place of spike trains, and to use regular expressions that use variables such as that used in the Encoder in [51] are helpful. We aim to support the use of input neurons, in order to simulate SN P systems as transducers as in [22,23] and more recently in [25]. Snapse can also be extended to allow support for other variants of SN P systems like SN P systems with rules on synapses [52], with anti-spikes [53] and dynamic variants such as [54][55][56].…”

“…There are also works that focus on simulating SN P systems, as they are parallel in nature, in GPUs such as [9,10] and more recently in [11][12][13]. Much theoretical work has been done on SN P systems, e.g., their normal forms [14][15][16], formal representations [17][18][19], and their relations to classical models of computation [20][21][22][23][24][25] with a short and recent survey in [26]. After much theoretical work, more recently the work to apply SN P systems to real-world problems becomes even more active, with some early works on image processing e.g., [27] and more recently in [28], use for cryptography [29][30][31], use of evolutionary algorithms to design SN P systems [32][33][34], in pattern recognition [35,36], computational biology [37], with a recent survey in [38].…”