Abstract Spiking neural P systems (SN P systems) are a mathematical model of neural networks, abstracting the way biological neurons communicate with spikes, developed within the framework of the membrane computing theory. Recently, driven by the boom of learning neural models, SN P systems have become a rapidly emerging research front. Consequently, many different variants of the learning models of SN P system prevail among the new research results. Although large proprietary deep learning models are still based on the continuous neural network paradigm, spiking neurons are attractive because of their low-energy demands. The purpose of this paper is to provide an up-to-date overview of learning paradigms and techniques for SN P systems. After a brief introduction of the structure and function of SN P systems, we summarise recent approaches to learning and adaptation in SN P systems, including Hebbian learning, Widrow-Hoff algorithm, fuzzy approaches, nonlinear SN P systems, gated and long short-term memory inspired SN P systems, convolutional SN P systems, and more.

Abstract In this paper, we study reversibility in sP colonies and in reaction systems. sP colony is a bio-inspired computational model formed from an environment and a finite set of agents. The current state of the environment is represented by a finite set of objects and the current state of the agent is given by a finite multiset of objects. By execution of a program from a set of programs associated with the agent, the agent can change the objects in its own state and possibly in the environment, too. Reaction systems are a bio-inspired computational model where reactants are transformed into products only if some inhibitors are not present. We define sP colonies without input influence and prove that to any reversible sP colony of such type an inverse sP colony can be constructed that performs inverse computation. In the second part of the paper, we show that the concept of a reversible reaction system and the notion of an inverse reaction system can be defined in a similar way, and partially reversible reaction systems can simulate reversible logic gates and reversible Turing machines.

In this paper we introduce and study P colonies where the environment is given as a string. These constructs, called automaton-like P colonies or APCol systems, behave like automata: during functioning, the agents change their own states and process