Homogeneous spiking neural P systems working in sequential mode induced by maximum spike number

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes, where neurons work in parallel in the sense that each neuron that can fire should fire at each computation step, and neurons can be different in the sense that they can have different sets of spiking rules. In this work, we consider SN P systems with the restrictions: (1) all neurons are homogeneous in the sense that each neuron has the same set of rules; (2) at each step the neuron with the maximum number of spikes among the neurons that are active (can spike) will fire. These restrictions correspond to the fact that the system consists of only one kind of neurons and a global view of the whole network makes the system sequential. The computation power of homogeneous SN P systems working in the sequential mode induced by the maximum spike number is investigated. Specifically, it is proved that such systems are universal as both generating and accepting devices.     

Spiking neural P systems: An improved normal form

Spiking neural P systems (in short, SN P systems) are computing devices based on the way the neurons communicate through electrical impulses (spikes). These systems involve various ingredients; among them, we mention forgetting rules and the delay in firing rules. However, it is known that the universality can be obtained without using these two features. In this paper we improve this result in two respects: (i) each neuron contains at most two rules (which is optimal for systems used in the generative mode), and (ii) the rules in the neurons using two rules have the same regular expression which controls their firing. This result answers a problem left open in the literature, and, in this context, an incompleteness in some previous proofs related to the elimination of forgetting rules is removed. Moreover, this result shows a somewhat surprising uniformity of the neurons in the SN P systems able to simulate Turing machines, which is both of a theoretical interest and it seems to correspond to a biological reality. When a bound is imposed on the number of spikes present in a neuron at any step of a computation (such SN P systems are called finite), two surprising results are obtained. First, a characterization of finite sets of numbers is obtained in the generative case (this contrasts the case of other classes of SN P systems, where characterizations of semilinear sets of numbers are obtained for finite SN P systems). Second, the accepting case is strictly more powerful than the generative one: all finite sets and also certain arithmetical progressions can be accepted. A precise characterization of the power of accepting finite SN P systems without forgetting rules and delay remains to be found.     

On string languages generated by sequential spiking neural P systems based on the number of spikes

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In this work, we consider SN P systems with the following restriction: at each step the active neuron with the maximum (or minimum) number of spikes among the neurons that can spike will fire [if there is a tie for the maximum (or minimum) number of spikes stored in the active neurons, only one of the neurons containing the maximum (or minimum) is chosen non-deterministically]. We investigate the computational power of such sequential SN P systems that are used as language generators. We prove that recursively enumerable languages can be characterized as projections of inverse-morphic images of languages generated by such sequential SN P systems. The relationships of the languages generated by these sequential SN P systems with finite and regular languages are also investigated.     

脉冲神经膜系统在穷举使用规则下产生的二进制字符串语言

脉冲神经膜系统是基于大脑中神经元之间通过突触相瓦协作、处理脉冲的生物现象提出的一种新的模型,文中在穷举使用规则的情况下考虑将脉冲神经膜系统作为串语言产生器:当输出神经元发送出一个或多个神经脉冲时,用数字1表示,否则用数字0表示,当计算停止时,把产生的二进制串定义为系统的计算结果.在文中,作者让明了在穷举使用规则的情况下,具有一个神经元的脉冲神经膜系统可以刻画二进制有限语言,并且证明了在不限制神经元个数的情况下,该系统可以刻画递归可枚举语言.     

On spiking neural P systems

This work deals with several aspects concerning the formal verification of SN P systems and the computing power of some variants. A methodology based on the information given by the transition diagram associated with an SN P system is presented. The analysis of the diagram cycles codifies invariants formulae which enable us to establish the soundness and completeness of the system with respect to the problem it tries to resolve. We also study the universality of asynchronous and sequential SN P systems and the capability these models have to generate certain classes of languages. Further, by making a slight modification to the standard SN P systems, we introduce a new variant of SN P systems with a special I/O mode, called SN P modules, and study their computing power. It is demonstrated that, as string language acceptors and transducers, SN P modules can simulate several types of computing devices such as finite automata, a-finite transducers, and systolic trellis automata.     

Hierarchies based on the number of cooperating systems of three-dimensional finite automata

The question of whether processing three-dimensional digital patterns is much more difficult than two-dimensional ones is of great interest from both theoretical and practical standpoints. Recently, owing to advances in many application areas, such as computer vision, robotics, and so forth, it has become increasingly apparent that the study of three-dimensional pattern processing is of crucial importance. Thus, the study of three-dimensional automata as a computational model of three-dimensional pattern processing has become meaningful. This article introduces a cooperating system of three-dimensional finite automata as one model of three-dimensional automata. A cooperating system of three-dimensional finite automata consists of a finite number of three-dimensional finite automata and a three-dimensional input tape where these finite automata work independently (in parallel). Those finite automata whose input heads scan the same cell of the input tape can communicate with each other, i.e., every finite automaton is allowed to know the internal states of other finite automata on the cell it is scanning at the moment. In this article, we continue the study of cooperating systems of three-dimensional finite automata, and mainly investigate hierarchies based on the number of their cooperating systems.     

Asynchronous spiking neural P systems

We consider here spiking neural P systems with a non-synchronized (i.e., asynchronous) use of rules: in any step, a neuron can apply or not apply its rules which are enabled by the number of spikes it contains (further spikes can come, thus changing the rules enabled in the next step). Because the time between two firings of the output neuron is now irrelevant, the result of a computation is the number of spikes sent out by the system, not the distance between certain spikes leaving the system. The additional non-determinism introduced in the functioning of the system by the non-synchronization is proved not to decrease the computing power in the case of using extended rules (several spikes can be produced by a rule). That is, we obtain again the equivalence with Turing machines (interpreted as generators of sets of (vectors of) numbers). However, this problem remains open for the case of standard spiking neural P systems, whose rules can only produce one spike. On the other hand we prove that asynchronous systems, with extended rules, and where each neuron is either bounded or unbounded, are not computationally complete.For these systems, the configuration reachability, membership (in terms of generated vectors), emptiness, infiniteness, and disjointness problems are shown to be decidable. However, containment and equivalence are undecidable.     

Complexity of control on finite automata

We consider control questions for finite automata viewed as input/output systems. In particular, we find estimates of the minimal number of states of an automaton able to control a given automaton. We prove that, on average, feedback closed-loop control automata do not have fewer states than open-loop control automata when the control objective is to steer the controlled automaton to a target state. We compare our approach to other ways of formalizing of formalizing analogous control objectives.     

Input-trees of finite automata and application to cryptanalysis

In this paper,Weights of output set and of input set for finite automata are discussed.For a weakly invertible finite automaton,we prove that for states with minimal output weight,the distruibution of input sets is uniform.Then for a kind of compound finite automata,we give weights of output set and of input set explicitly,and a characterization of their input-trees.For finite automaton public key cryptosystems,of which automata in public keys belong to such a kind of compound finite automata,we evaluate search amounts of exhaust search algorithms in average case and in worse case for both encryption and signature,and success ful probabilities of stochastic search algorithms for both encryption and signature.In addition,a result on mutual invertibility of inite automata is also given.     

Cooperating systems of four-dimensional finite automata

M. Blum and C. Hewitt first proposed two-dimensional automata as a computational model of two-dimensional pattern processing in 1967, and investigated their pattern recognition abilities. Since then, many researchers in this field have investigated many properties of automata on a two- or three-dimensional tape. However, the question of whether processing four-dimensional digital patterns is much more difficult than processing two- or three-dimensional ones is of great interest from both theoretical and practical standpoints. Thus, the study of four-dimensional automata as a computational model of four-dimensional pattern processing has been meaningful. This article introduces a cooperating system of four-dimensional finite automata as one model of four-dimensional automata. A cooperating system of four-dimensional finite automata consists of a finite number of four-dimensional finite automata and a four-dimensional input tape, where these finite automata work independently (in parallel). The finite automata whose input heads scan the same cell of the input tape can communicate with each other, i.e., every finite automaton is allowed to know the internal states of other finite automata on the cell it is scanning at the moment. In this article we mainly investigate the accepting powers of a cooperating system of seven-way four-dimensional finite automata. The seven-way four-dimensional finite automaton is a four-dimensional finite automaton whose input head can move east, west, south, north, up, down, or in the future, but not in the past, on a four-dimensional input tape.     

On the power of elementary features in spiking neural P systems

Since their first publication in 2006, spiking neural (SN) P systems have already attracted the attention of a lot of researchers. This might be owing to the fact that this abstract computing device follows basic principles known from spiking neural nets, but its implementation is discrete, using membrane computing background. Among the elementary properties which confer SN P systems their computational power one can count the unbounded fan-in (indegree) and fan-out (outdegree) of each “neuron”, synchronicity of the whole system, the possibility of delaying and/or removing spikes in neurons, the capability of evaluating arbitrary regular expressions in neurons in constant time and some others. In this paper we focus on the power of these elementary features. Particularly, we study the power of the model when some of these features are disabled. Rather surprisingly, even very restricted SN P systems keep their universal computational power. Certain important questions regarding this topic still remain open.     

A formal design of an Arabic Text Formatter for microcomputers

This work presents the formal design of an Arabic Text Formatter (ATF), that may help many users in the Arab world. The design procedure is based on an automata approach to describe and recognize the Arabic characters. Thus a scanner is constructed in order to deal with the input stream and to recognize the words being written as well as to produce output tokens.Actually the form of an Arabic letter depends on its position in word. Therefore, the character generator should produce the correct shape to match the rules of writing the Arabic words. Here, we employ a keyboard that has a unique form for each character, however, the justification and the proper choice of a symbol format is carried out by the designed scanner.For the scanner, the regular expressions, the nondeterministic finite automata and the deterministic finite automata are given. The system commands and their corresponding actions are also pointed out.     

What can a neuron learn with spike-timing-dependent plasticity?

Spiking neurons are very flexible computational modules, which can implement with different values of their adjustable synaptic parameters an enormous variety of different transformations F from input spike trains to output spike trains. We examine in this letter the question to what extent a spiking neuron with biologically realistic models for dynamic synapses can be taught via spike-timing-dependent plasticity (STDP) to implement a given transformation F. We consider a supervised learning paradigm where during training, the output of the neuron is clamped to the target signal (teacher forcing). The well-known perceptron convergence theorem asserts the convergence of a simple supervised learning algorithm for drastically simplified neuron models (McCulloch-Pitts neurons). We show that in contrast to the perceptron convergence theorem, no theoretical guarantee can be given for the convergence of STDP with teacher forcing that holds for arbitrary input spike patterns. On the other hand, we prove that average case versions of the perceptron convergence theorem hold for STDP in the case of uncorrelated and correlated Poisson input spike trains and simple models for spiking neurons. For a wide class of cross-correlation functions of the input spike trains, the resulting necessary and sufficient condition can be formulated in terms of linear separability, analogously as the well-known condition of learnability by perceptrons. However, the linear separability criterion has to be applied here to the columns of the correlation matrix of the Poisson input. We demonstrate through extensive computer simulations that the theoretically predicted convergence of STDP with teacher forcing also holds for more realistic models for neurons, dynamic synapses, and more general input distributions. In addition, we show through computer simulations that these positive learning results hold not only for the common interpretation of STDP, where STDP changes the weights of synapses, but also for a more realistic interpretation suggested by experimental data where STDP modulates the initial release probability of dynamic synapses.     

On the Computational Complexity of P Automata

We characterize the classes of languages over finite alphabets which may be described by P automata, i.e., accepting P systems with communication rules only. Motivated by properties of natural computing systems, and the actual behavior of P automata, we study computational complexity classes with a certain restriction on the use of the available workspace in the course of computations and relate these to the language classes described by P automata. We prove that if the rules of the P system are applied sequentially, then the accepted language class is strictly included in the class of languages accepted by one-way Turing machines with a logarithmically bounded workspace, and if the rules are applied in the maximally parallel manner, then the class of context-sensitive languages is obtained.     

Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity

The persistent modification of synaptic efficacy as a function of the relative timing of pre- and postsynaptic spikes is a phenomenon known as spike-timing-dependent plasticity (STDP). Here we show that the modulation of STDP by a global reward signal leads to reinforcement learning. We first derive analytically learning rules involving reward-modulated spike-timing-dependent synaptic and intrinsic plasticity, by applying a reinforcement learning algorithm to the stochastic spike response model of spiking neurons. These rules have several features common to plasticity mechanisms experimentally found in the brain. We then demonstrate in simulations of networks of integrate-and-fire neurons the efficacy of two simple learning rules involving modulated STDP. One rule is a direct extension of the standard STDP model (modulated STDP), and the other one involves an eligibility trace stored at each synapse that keeps a decaying memory of the relationships between the recent pairs of pre- and postsynaptic spike pairs (modulated STDP with eligibility trace). This latter rule permits learning even if the reward signal is delayed. The proposed rules are able to solve the XOR problem with both rate coded and temporally coded input and to learn a target output firing-rate pattern. These learning rules are biologically plausible, may be used for training generic artificial spiking neural networks, regardless of the neural model used, and suggest the experimental investigation in animals of the existence of reward-modulated STDP.     

An invariance principle for maintaining the operating point of a neuron

Sensory neurons adapt to changes in the natural statistics of their environments through processes such as gain control and firing threshold adjustment. It has been argued that neurons early in sensory pathways adapt according to information-theoretic criteria, perhaps maximising their coding efficiency or information rate. Here, we draw a distinction between how a neuron's preferred operating point is determined and how its preferred operating point is maintained through adaptation. We propose that a neuron's preferred operating point can be characterised by the probability density function (PDF) of its output spike rate, and that adaptation maintains an invariant output PDF, regardless of how this output PDF is initially set. Considering a sigmoidal transfer function for simplicity, we derive simple adaptation rules for a neuron with one sensory input that permit adaptation to the lower-order statistics of the input, independent of how the preferred operating point of the neuron is set. Thus, if the preferred operating point is, in fact, set according to information-theoretic criteria, then these rules nonetheless maintain a neuron at that point. Our approach generalises from the unimodal case to the multimodal case, for a neuron with inputs from distinct sensory channels, and we briefly consider this case too.     

Reversible spiking neural P systems

Spiking neural (SN) P systems are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes. In this work, we investigate reversibility in SN P systems, as well as the computing power of reversible SN P systems. Reversible SN P systems are proved to have Turing creativity, that is, they can compute any recursively enumerable set of non-negative integers by simulating universal reversible register machine.     

赋权型自动机的不同模型研究

自动机理论是理论计算机科学的基础理论之一,在很多领域自动机有着广泛的应用,有穷状态自动机是正则语言的识别机器,通常分为确定型与非确定型两种模型,其识别语言的能力是等价的。赋权自动机是另一类重要的自动机模型,自动机的每条转移规则和状态可以赋以某一代数结构上的某一数值,从而可以计算输入字符串的权值。任何有穷状态自动机都可以视为一特殊赋权自动机,因此赋权自动机功能更强大,应用更为广泛。     

线性有限自动机输入输出集的性质

证明了两个线性有限自动机化合而得到的自动机具有输入输出均匀的性质,建立了由两个延迟1步弱可逆线性有限自动化合后得到的自动机的输入集个数与化合前自动机输入集个数的等式关系。     

On synchronized evolution of the network of automata

One of the tasks in machine learning is to build a device that predicts each next input symbol of a sequence as it takes one input symbol from the sequence. We studied new approaches to this task. We suggest that deterministic finite automata (DFA) are good building blocks for this device, together with genetic algorithms (GAs), which let these automata "evolve" to predict each next input symbol of the sequence. Moreover, we study how to combine these highly fit automata so that a network of them would compensate for each others' weaknesses and predict better than any single automaton. We studied the simplest approaches to combine automata: building trees of automata with special-purpose automata, which may be called switchboards. These switchboard automata are located on the internal nodes of the tree, take an input symbol from the input sequence just as other automata do, and predict which subtree will make a correct prediction on each next input symbol. GAs again play a crucial role in searching for switchboard automata. We studied various ways of growing trees of automata and tested them on sample input sequences, mainly note pitches, note durations and up/down notes of Bach's Fugue IX. The test results show that DFAs together with GAs seem to be very effective for this type of pattern learning task