Prithwineel Paul

In this paper, we introduce a novel method for the classification of English letters using a variant of spiking neural membrane system, i.e., spiking neural P systems with structural plasticity (SNPSP) and spike time dependent plasticity rules. In this model, the creation and deletion of the synaptic connections between any two neurons are simulated by plasticity rules. Furthermore, in the learning model proposed in this paper, the weights associated with the synapses of SNPSP models are updated using Gradient descent via backpropagation-through-time and Hebbian plasticity rules. In the test case, without the noise the SNPSP models achieve average accuracy of 99.79%. Furthermore, SNPSP model outperforms the existing SNP based models for test cases with middle and higher noises. The dataset analyzed in this study was synthesized by modifying 7×5-pixel encodings of English letters with random bit flips for the purpose of demonstrating a concept; however, it is not representative of general image-classification tasks.

In this paper, we use generalized spiking neural P systems model with structural plasticity (SNPSP) for solving the SAT problem. It is an interesting variant of the spiking neural P systems (SNPS) model. In generalized SNPS with structural plasticity models, more than one synaptic connection can be established between two neurons using plasticity rules. The system proposed in the paper solves SAT problem in a deterministic and uniform manner. More specifically, the solution belongs to the category of EXP-uniform, i.e., the initial configuration of the system, a deterministic Turing machine cannot use less than an exponential number of computational steps. The time required to solve SAT problem is linear in n and m where n and m are the numbers of variables and clauses respectively. Our work is the first solution to use pre-computed resources with structural dynamism, that is plasticity rules in this case, to solve the SAT problem. Also, no delay is associated with the rules of the SNPS system proposed in this paper.

Quantum cryptography uses the properties of quantum mechanics to establish secure communication. In this paper, we at first summarize the concepts of qubits, superposition, entanglement, and the No-Cloning theorem and then we summarize quantum key distribution protocols based on these concepts. In particular, we illustrate BB84 and E91 protocols. These protocols explain the use of the properties of the quantum computing which protect key exchanges against eavesdropping. Quantum digital signatures and quantum secret sharing have shown that quantum cryptography is extremely flexible in ensuring data integrity and confidentiality. However, its potential also gives rise to problems such as channel loss in a quantum medium and error correction. This paper highlights the main aspects of quantum cryptography which works as a solution to the vulnerabilities inherent in classical cryptographic methods, particularly in light of threats from quantum computing. Furthermore, we discuss different quantum cryptographic protocols and their challenges, limitations, and applications. Also, we discuss different postquantum cryptographic (PQC) algorithms along with their comparative analysis and challenges in the implementation of these algorithms. Next, we explain the comparisons of different types of cryptography, i.e., classical, quantum and post-quantum cryptography which is followed by a discussion on the effect of artificial intelligence in quantum and postquantum cryptography. Finally, we end the paper with a discussion on applications of quantum cryptography and future directions of research in quantum and post-quantum cryptography.

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.

Spiking neural P systems (SN P systems) are computing models based on the third generation of neuron models known as spiking neurons. Recent results in neuroscience highlight the importance of extrasynaptic activities of neurons, that is, features and functioning of neurons outside their synapses. Previously it was thought that signals such as neuropeptides only assist neurons, but recently such signals have been given additional importance. Inspired by recent results, we define wireless SN P systems (WSN P systems). In WSN P systems, no synapses exist: regular expressions associated with each neuron are used to decide which spikes it receives. We provide two semantics of how to “interpret” the spikes released by neurons. A specific register machine is simulated to show the different style of programming WSN P systems compared to programming standard SN P systems and other variants. This style emphasizes a trade-off: WSN P systems can be more “flexible” since they are not limited by their synapses for sending spikes; however, losing the useful directed graph structure requires careful design of rules and expressions associated with each neuron. We use linear prime number encodings in constructing the expressions and rules of the neurons to prove that WSN P systems are Turing-complete in both spike semantics.