Design of logic gates using spiking neural P systems with homogeneous neurons and astrocytes-like control

Spiking neural P systems with request rules

Spiking neural P systems (SN P systems) are bio-inspired neural-like computing models, which are obtained by abstracting the way of biological neurons’ spiking and communication by means of spikes in central nervous systems. SN P systems performed well in describing and modeling behaviors that occur simultaneously, yet weak at modeling complex systems with the limits of using a single spike. In this paper, drawing on the idea from colored petri nets, SN P systems with colored spikes are proposed, where a finite set of colors is introduced to mark the spikes such that each spike is associated with a unique color. The updated spiking rule is applied by consuming and emitting a number of colored spikes (with the same or different colors). The computation power of the systems is investigated. Specifically, it is shown that SN P systems with colored spikes having three neurons are sufficient to compute Turing computable sets of numbers, and such system having two neurons is able to compute the family of recursive functions. These results improved the corresponding ones on the number of neurons needed to construct universal SN P systems recently appeared in [Neurocomputing, 2016, 193(12): 193–200]. To our best knowledge, this is the smallest number of neurons used to construct Turing universal SN P systems as number generator and function computing device by far.

Spiking Neural P Systems With Colored Spikes

Spiking neural P systems (namely SN P systems, for short) are bio-inspired neural-like computing models under the framework of membrane computing, which are also known as a new candidate of the third generation of neural networks. In this work, a parallel image skeletonizing method is proposed with SN P systems with weights. Specifically, an SN P system with weighs is constructed to achieve the Zhang–Suen image skeletonizing algorithm. Instead of serial calculation like Zhang–Suen image skeletonizing algorithm, the proposed method can parallel process a certain number of pixels of an image by spiking multiple neurons simultaneously at any computation step. Demonstrating via the experimental results, our method shows higher efficiency in data-reduction and simpler skeletons with less noise spurs than the method developed in Diazpernil (Neurocomputing 115:81–91, 2013) in skeletonizing images like hand-written words.

A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights

Spiking neural P systems (SN P systems) are a class of parallel and distributed spiking neural network models, which are inspired from the way biological neurons spiking and communicating by means of spikes. White hole rules, abstracted from the biological observation of neural information rejection, were recently introduced into SN P systems, by which a neuron consumes its complete contents when it fires. In this work, SN P systems with white hole neurons are proposed, in which each neuron has only white hole rules. The computational power of general and bounded SN P systems with white hole neurons are obtained. Specifically, it is achieved in a constructive way that i) general SN P systems (having both bounded and unbounded) white hole neurons are Turing universal as number generators; ii) bounded SN P systems with white hole neurons can only characterize semi-linear sets of numbers. These results show that “information storage capacity” of certain key neurons provides some “programming capacity” useful for SN P systems achieving a desired computation power.

Spiking Neural P Systems With White Hole Neurons

An Spiking Neural P system with anti-spikes uses two types of objects called spikes and anti-spikes which can encode binary digits in a natural way. The step when system emits a spike or an anti-spike is associated with symbol 1 and 0, respectively. Here we consider these computing devices as language generators. They allow non-determinism between the rules ac → a and ac → ā, c ϵ ℕ, thus help to generate languages which cannot be generated using simple SN P systems.

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On string languages generated by spiking neural p systems with anti-spikes

Spiking neural P systems with anti-spikes (for short, SN PA systems) can encode the binary digits in a natural way using two types of objects called anti-spikes and spikes. In this paper, we use SN PA systems to perform the arithmetic operation like 2's complement, addition and subtraction of binary numbers. They are also used to simulate NAND and NOR gates.

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Computability of spiking neural p systems with anti-spikes

https://gdeepak.com/pubs/Modelling%20and%20analysis%20of%20spiking%20neural%20P%20systems%20with%20anti-spikesusing%20Pnet%20lab.pdf

Modelling and analysis of spiking neural P systems with anti-spikes using Pnet lab

This paper shows that deterministic P-timed Petri nets with inhibitory and test arcs can simulate an SN P system. A method is proposed to translate an SN P system into Petri net model and is illustrated with an example.

/pdf/modeling-spiking-neural-p-systems-using-timed-petri-nets-3751vh8ikl.pdf

Modeling spiking neural P systems using timed Petri nets

The concept of cooperation and distribution as known from grammar systems is introduced to spiking neural P systems (in short, SN P systems) in which each neuron has a finite number of sets (called components) of rules. During computations, at each step only one of the components can be active for the whole system and one of the enabled rules from this active component of each neuron fires. The switching between the components occurs under different cooperation strategies. This paper considers the terminating mode, in which the switching occurs when no rule is enabled in the active component of any neuron in the system. By introducing this new mechanism, the computational power of asynchronous and sequential SN P systems with standard rules is investigated. The results are that asynchronous standard SN P systems with two components and strongly sequential unbounded SN P systems with two components are Turing complete.

Venkata Padmavati Metta

Papers

On string languages generated by spiking neural p systems with anti-spikes

Computability of spiking neural p systems with anti-spikes

Modelling and analysis of spiking neural P systems with anti-spikes using Pnet lab

Modeling spiking neural P systems using timed Petri nets

Spiking Neural P Systems with Cooperating Rules