On the Computational Power of Spiking Neural P Systems with Self-Organization

Abstract

Neural-like computing models are versatile computing mechanisms in the field of artificial intelligence. Spiking neural P systems (SN P systems for short) are one of the recently developed spiking neural network models inspired by the way neurons communicate. The communications among neurons are essentially achieved by spikes, i. e. short electrical pulses. In terms of motivation, SN P systems fall into the third generation of neural network models. In this study, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced, and the computational power of the system is investigated and evaluated. It is proved that SN P systems with self-organization are capable of computing and accept the family of sets of Turing computable natural numbers. Moreover, with 87 neurons the system can compute any Turing computable recursive function, thus achieves Turing universality. These results demonstrate promising initiatives to solve an open problem arisen by Gh Păun.

In the central nervous system, there are abundant amount of computational intelligence precipitated throughout millions of years of evolution. The computational intelligence has provided plenty of inspirations to construct powerful computing models and algorithms¹,²,³. Neural-like computing models are a class of powerful models inspired by the way how neurons communicate. The communication among neurons is essentially achieved by spikes, i.e. short electrical pulses. The biological phenomenon has been intensively investigated in the field of neural computation⁴. Using different mathematic approaches to describe neural spiking behaviours, various neural-like computing models have been proposed, such as artificial neural networks⁵ and spiking neural networks⁶. In the field of membrane computing, a kind of distributed and parallel neural-like computation model, named spiking neural P systems (SN P systems), were proposed in 2006⁷. SN P systems are widely considered as a promising variant of the third generation of neural network models⁸.

Generally, an SN P system can be represented by a directed graph, where neurons are placed in nodes and the synapses are denoted using arcs. Every neuron can contain a number of spikes and a set of firing (or spiking) rules. Following the firing rules, a neuron can send information encoded in spikes to other neurons. Input neurons read spikes from the environment, and output neurons emit spikes into the environment. The computation result can be embodied in various ways. One of the common approaches is the time elapsed between the first two consecutive spikes sent into the environment⁹,¹⁰ and the total number of spikes emitted into the environment¹¹,¹²,¹³.

For the past decade, there have been quite a few research efforts put forward to SN P systems. Notably, SN P systems can generate and accept the sets of Turing computable natural numbers¹⁴, generate the recursively enumerable languages¹⁵,¹⁶ and compute the sets of Turing computable functions¹⁷. Inspired by different biological phenomena and mathematical motivations, lots of variants of SN P systems have been proposed, such as SN P systems with anti-spikes¹⁸,¹⁹, SN P systems with weight²⁰, SN P systems with astrocyte²¹, homogenous SN P systems²²,²³, SN P systems with threshold²⁴, fuzzy SN P systems²⁵,²⁶, sequential SN P systems²⁷, SN P systems with rules on synapses²⁸, SN P systems with structural plasticity²⁹. For applications, SN P systems are used to design logic gates, logic circuites³⁰ and operating systems³¹, perform basic arithmetic operations³²,³³, solve combinatorial optimization problems³⁴, diagnose fault of electric power systems³⁵.

SN P systems are known as a class of neural-like computing models under the framework of membrane computing³⁶. Spiking neural network (shortly named SNN) is a well known candidate of siking neural network models³⁷, which incorporates the concept of time into their operating model, besides neuronal and synaptic state in general artificial neural networks, The neuron in SNN cannot fire at each propagation cycle, but only when a membrane potential reaches a specific value. When a neuron fires, it generates a signal which travels to other neurons which, in turn, increase or decrease their potentials in accordance with this signal. In SN P systems, spiking rules, denoted by formal production in grammar theory of formal languages, is used to describe the neuron's spiking behaviour, which determine the conditions of triggering spiking, the number of spikes consumed, and the number of spikes emitting to the neighboring neurons. The spikes from different neurons can be accumulated in the target neuron for further spiking. In terms of motivation of models, SN P systems also fall into the spiking neural network models, i.e., the third generation of neural network models.

Since SN P systems have more fundamental data structure (spike trains, i.e., binary strings), it performs well in achieving significant computation power with using a small number of units (neurons). It was proved by Gh Păun that 49 neurons are sufficient for SN P systems to achieve Turing universality. But, for conventional artificial neural networks, it was shown that 886 sigmoid function based processors are needed to achieve Turing universality³⁸.

In the nervous system, synaptic plasticity forms the cell assemblies with the self-organization of neurons, which induces ordered or even synchronized neural dynamics replicating basic processes of long-term memory³⁹,⁴⁰. The self-organizing principle in the developing nervous system and its importance for preserving and continuing neural system development provide us insights on how neural-like networks might be reorganized and configured in response to environment changes. Enlightened by the biological fact, self-organizing artificial neural networks with unsupervised and supervised learning have been proposed and gain their popularity for visualisation and classification⁴¹,⁴². It is still an open problem as formulated by Gh Păun in ref. 43, to construct SN P systems with self-organization and to use the system to perform possible computer vision and pattern recognition tasks.

Results

In this research, a novel variant of SN P systems, namely SN P systems with self-organization, is proposed and developed. The system initially has no synapse, but the synapses can be dynamically formed during the computation, which exhibits the self-organization behaviour. In the system, creation and deletion rules are used to create and delete synapses. The applications of synapse creation and deletion rules are controlled by the states of the involved neurons, i.e., the number of spikes contained in the neurons. The computational power of the system is investigated as well. As a result, it demonstrates that SN P systems with self-organization can compute and accept any set of Turing computable natural numbers. Moreover, with 87 neurons, the system can compute any Turing computable recursive function, ergo achieves Turing universality.

Before stating the results in mathematical forms, some notations should be introduced. N_mSPSO_all(cre_h, del_g, rule_r) (resp. N_mSPSO_acc(cre_h′, del_g′, rule_r′)) denotes the family of sets of numbers computed (resp. accepted) by SN P systems with self-organization of degree m, where h (resp. h′) indicates the maximal number of synapses that can be created using a synapse creation rule, g (resp. g′) is the maximal number of synapses that can be deleted by using a synapse deletion rule, r (resp. r′) is the maximal number of rules in each neuron, and the subscript all indicates the computation result is encoded by the number of spikes emitted into the environment (resp. the subscript acc indicates the system works in the accepting mode). If the parameters are not bounded, i.e., there is no limit imposed on them, then they are replaced with *. NRE denotes the family of Turing computable sets of numbers⁴⁴.

The main results of this work can be mathematically depicted by the following theorems.

Theorem 1. N_*SPSO_all(cre_*, del_*, rule₅) = NRE.

Theorem 2. N_*SPSO_acc(cre_*, del_*, rule₅) = NRE.

Theorem 3. There is a universal SN P system with self-organization having 87 neurons for computing functions.

These results show that SN P systems with self-organization are powerful computing models, i.e., they are capable of doing what Turing machine can do. Also, they provide potential and theoretical feasibility of using SN P systems to solve real-life problems, such as pattern recognition and classification.

In SN P system with self-organization, it has no initially designed synapses. The synapses can be created or deleted according to the information contained in involved neurons during the computation. In previous work, it was found that the information diversing ability of synapses had some programable feature for SN P systems, but the computation power of SN P systems without initial synapses is an open problem. Although this is not the first time the feature of creating or deleting synapses investigated in SN P systems, see e.g. SN P systems with structural plasticity, it is quite the first attempt to construct SN P systems has no initial synapses.

Methods

In this section, it starts by the mathematical definition of SN P system with self-organization, and then the computation power of SN P systems with self-organization is investigated as number generator, acceptor and function computing devices. It is proved in constructive ways that SN P systems with self-organization can compute and accept the family of sets of Turing computable natural numbers. With 87 neurons, such system can compute any Turing computable recursive function.

Spiking Neural P Systems with Self-Organization

Before introducing the definition of SN P system with self-organization, some prerequisites of basic concepts of formal language theory⁴⁵ are recalled.

For an alphabet V, V* denotes the set of all finite strings of symbols from V, the empty string is denoted by λ, and the set of all nonempty strings over V is denoted by V⁺. When V = {a} is a singleton, then we write simply a* and a⁺ instead of {a}*, {a}⁺. A regular expression over an alphabet V is defined as follows: (1) λ and each a ∈ V is a regular expression; (2) if E₁ and E₂ are regular expressions over V, then (E₁)(E₂), (E₁) ∪ (E₂), and (E₁)⁺ are regular expressions over V; (3) nothing else is a regular expression over V.

For each regular expression E, a language L(E) is associated, defined in the following way: (1) L(λ) = {λ} and L(a) = {a}, for all a ∈ V, (2) L((E₁)∪(E₂)) = L(E₁) ∪ L(E₂), L((E₁)(E₂)) = L(E₁)L(E₂) and L((E₁)⁺) = (L(E₁))⁺ for all regular expressions E₁, E₂ over V. Unnecessary parentheses can be omitted when writing a regular expression, and (E)⁺ ∪ {λ} can also be written as E*. By NRE we denote the family of Turing computable sets of numbers. (NRE is the family of length sets of recursively enumerable languages–those recognized by Turing machines).

An SN P system with self-organization of degree m ≥ 1 is a construct of the form

where

• O = {a} is a singleton, where a is called the spike;

• σ₁, σ₂, …, σ_m are neurons of the form σ_i = (n_i, R_i) with 1 ≤ i ≤ m, where

– is the initial number of spikes contained in neuron σ_i;

– R_i is a finite set of rules in neuron σ_i of the following three forms:

1. spiking rule: E/a^c → a^p; d, where E is a regular expression over O, d ≥ 0 and c ≥ p ≥ 0;

2. synapse creation rule: E′/a^c′ → +(a^p′, cre(i)), where E′ is a regular expression over O, cre(i) ⊆ {σ₁, σ₂, …, σ_m}/{σ_i} and c′ ≥ p′ > 1;

3. synapse deletion rule: E″/a^c″ → −(λ, del(i)), where E″ is a regular expression over O, del(i) ⊆ {σ₁, σ₂, …, σ_m}/{σ_i} and c″ ≥ 1;

• is the initial set of synapses, which means no synapse is initially set; at any moment t, the set of synapses is denoted by syn_t ⊆ {1, 2, …, m} × {1, 2, …, m}.

• in, out ∈ {1, 2, …, m} indicates the input and output neuron, respectively.

A spiking rule of the form E/a^c → a^p; d is applied as follows. If neuron σ_i contains k spikes, and a^k ∈ L(E), k ≥ c, then rule E/a^c → a^p; d ∈ R_i can be applied. It means that c spikes are consumed and removed from neuron σ_i, i.e., k − c spikes are remained, while the neuron emits p spikes to its neighboring neurons after d steps. (It is a common practice in membrane computing to have a global clock defined. The clock is used to mark the time of the whole system and ensure the system synchronization.) If d = 0, then the p spikes are emitted out immediately, if d = 1, then the p spikes are emitted in the next step, etc. If the rule is used in step t and d ≥ 1, then in steps t, t + 1, ..., t + d − 1 the neuron is closed (this corresponds to the refractory period from neurobiology), so that it cannot receive new spikes (if a neuron tries to send spikes to a neuron in close status, then these particular spikes will be lost). In the step t + d, the neuron fires and regains open status, so it can receive spikes (which can be used starting with the step t + d + 1, when the neuron can again apply rules). It is possible that p is associated with value 0. In this case, neuron σ_i consumes c spikes without emitting any spike. Spiking rule with p = 0 is also called forgetting rule, by which a pre-defined number of spikes can be removed out of the neuron. If E = a^c, then the rule can be written in the simplified form a^c → a^p; d, and if d = 0, then the rule can be simply written as E/a^c → a^p.

Synapse creation and deletion rules are used to create and delete synapses during the computation. Synapse creation rule E′/a^c′ → +(a^p′, cre(i)) is applied as follows. If neuron σ_i has k′ spikes such that a^k′ ∈ L(E′), k′ ≥ c′, then the synapse creation rule is applied with consuming c′ spikes, creating synapses to connect neuron σ_i to each neuron in cre(i) and emitting p′ spikes to each neuron in cre(i). If neuron σ_i has k″ spikes such that a^k″ ∈ L(E″) and k″ ≥ c″, then synapse deletion rule E″/a^c″ → −(λ, del(i)) is applied, removing c″ spikes from neuron σ_i and deleting all the synapses connecting neuron σ_i to the neurons from del(i). With the synapse creation and deletion rules, E′ and E″ are regular expressions over O = {a}, which regulate the application of synapse creation and deletion rules. This means that synapse creation and deletion rules can be used if and only if the neuron contain some particular numbers of spikes, i.e., the neuron is in some specific states. With the applications of synapse creation and deletion rules the system can dynamically rebuild its topological structure during the computation, which is herein defined as self-organization.

One neuron is specified as the input neuron, through which the system can read spikes from the environment. The output neuron has a synapse creation rule of the form E′/a^c′ → +(a^p′, {0}), where the environment is labelled by 0. By using the rule, the output neuron creates a synapse pointing to the environment, and then it can emit spikes into the environment along the created synapse.

For each time step, as long as there is one available rule in R_i, neuron σ_i must apply the rule. It is possible that there are more than one rule that can be used in a neuron at some moment, since spiking rules, synapse creation rules and synapse deletion rules may be associated with regular languages (according to their regular expressions). In this case, the neuron will non-deterministically uses one of the enabled rules. The system works sequentially in each neuron (at most one rule from each R_i can be used), and if parallelism is designed for the system, all the neurons at the same system level have at least one enabled rule activated.

The configuration of the system at certain moment is defined by three major factors which are the number of spikes contained in each neuron, the number of steps to wait until it becomes open and the current set of synapses. With the notion, the initial configuration of the system is 〈n₁/0, n₂/0, …, n_m/0, . Using the spiking, forgetting, synapse creation and deletion rules as described above, we can define transitions among configurations. Any sequence of transitions starting from the initial configuration is called a computation. A computation halts, also called successful, if it reaches a configuration where no rule can be applied in any neuron in the system. For each successful computation of the system, a computation result is generated, which is total the number of spikes sent to the environment by the output neuron.

System Π generates a number n as follows. The computation of the system starts from the initial configuration and finally halts, emitting totally n spikes to the environment. The set of all numbers computed in this way by Π is denoted by N_all(Π) (the subscript all indicates that the computation result is the total number of spikes emitted into the environment by the system). System Π can also work in the accepting mode. A number n is read through input neurons from the environment in form of spike train 10ⁿ⁻¹1, which will be stored in a specified neuron σ₁ in the form of f(n) spikes. If the computation eventually halts, then number n is said to be accepted by Π. The set of numbers accepted by Π is denoted by N_acc(Π).

It is denoted by N_mSPSO_all(cre_h, del_g, rule_r) (resp. N_mSPSO_acc(cre_h′, del_g′, rule_r′)) the family of sets of numbers computed (resp. accepted) by SN P systems with self-organization of degree m, where h (resp. h′) indicates the maximal number of synapses that can be created with using a synapse creation rule, g (resp. g′) is the maximal number of synapses that can be deleted with using a synapse deletion rule, r (resp. r′) is the maximal number of rules in each neuron, and the subscript all indicates the computation result is encoded by the number of spikes emitted into the environment (resp. the subscript acc indicates the system works in a accepting mode). If the parameters are not bounded, i.e., there is no limit imposed on them, then they are replaced with *.

In order to compute a function f : N^k → N by SN P systems with self-organization, k natural numbers n₁, n₂, …, n_k are introduced in the system by reading from the environment a spike train (which is a binary sequence) . The input neuron has a synapse pointing from the environment, by which the spikes can enter it. The input neuron reads a spike in each step corresponding to a digit 1 from the string z; otherwise, no spike is received. Note that exactly k + 1 spikes are introduced into the system through the input neuron, i.e., after the last spike, it is assumed that no further spike is coming to the input neuron. The output neuron has a synapse pointing to the environment from it, by which the spikes can be emitted to the environment. The result of the computation is the total number of spikes emitted into the environment by the output neuron, hence producing r spikes with r = f(n₁, n₂, …, n_k).

SN P systems with self-organization can be represented graphically, which is easier to understand than that in a symbolic way. A rounded rectangle with the initial number of spikes and rules is used to represent a neuron and a directed edge connecting two neurons represents a synapse.

In the following proofs, the notion of register machine is used. A register machine is a construct M = (m, H, l₀, l_h, I), where m is the number of registers, H is the set of instruction labels, l₀ is the start label, l_h is the halt label (assigned to instruction HALT), and I is the set of instructions; each label from H labels only one instruction from I, thus precisele following forms:

• l_i: (ADD(r), l_j, l_k) (add 1 to register r and then go to one of the instructions with labels l_j, l_k),

• l_i: (SUB(r), l_j, l_k) (if register r is non-zero, then subtract 1 from it, and go to the instruction with label l_j; otherwise, go to the instruction with label l_k),

• l_h: HALT (the halt instruction).

As number generator

A register machine M computes a number n as follows. It starts by using initial instruction l₀ with all registers storing number 0. When it reaches halt instruction l_h, the number stored in register 1 is called the number generated or computed by register machine M. The set of numbers generated or computed by register machine M is denoted by N(M). It is known that register machines compute all sets of numbers which are Turing computable, hence they characterize NRE, i.e., N(M) = NRE, where NRE is the family of Turing computable sets of numbers⁴⁴.

Without loss of generality, it can be assumed that in the halting configuration, all registers different from the first one are empty, and that the first register is never decremented during the computation (i.e., its content is only added to). When the power of two number generating devices D₁ and D₂ are compared, number zero is ignored; that is, N(D₁) = N(D₂) if and only if N(D₁) − {0} = N(D₂) − {0} (this corresponds to the usual practice of ignoring the empty string in language and automata theory).

Theorem 4. N_*SPSO_all(cre_*, del_*, rule₅) = NRE.

Proof

It only has to prove NRE⊆N_*SPSO_all (cre_*, del_*, rule₅), since the converse inclusion is straightforward from the Turing-Church thesis (or it can be proved by the similar technical details in Section 8.1 in ref. 46, but is cumbersome). To achieve this, we use the characterization of NRE by means of register machines in the generative mode. Let us consider a register machine M = (m, H, l₀, l_h, I) defined above. It is assumed that register 1 of M is the output register, which is never decremented during the computation. For each register r of M, let s_r be the number of instructions of the form l_i: (SUB(r), l_j, l_k), i.e., the number of SUB instructions acting on register r. If there is no such SUB instruction, then s_r = 0, which is the case for the first register r = 1. In what follows, a specific SN P system with self-organization Π is constructed to simulate register machine M.

System Π consists of three modules–ADD, SUB and FIN modules. The ADD and SUB modules are used to simulate the operations of ADD and SUB instructions of M; and the FIN module is used to output a computation result.

In general, with any register r of M, a neuron σ_r in system Π is associated; the number stored in register r is encoded by the number of spikes in neuron σ_r. Specifically, if register r stores number n ≥ 0, then there are 5n spikes in neuron σ_r. For each label l_i of an instruction in M, a neuron is associated. During the simulation, when neuron receives 6 spikes, it becomes active and starts to simulate instruction l_i: (OP(r), l_j, l_k) of M: the process starts with neuron activated, operates on the number of spikes in neuron σ_r as requested by OP, then sends 6 spikes into neuron or , which becomes active in this way. Since there is no initial synapse in system Π, some synapses are created to pass spikes to target neurons with synapse creation rules, after that the created synapses will be deleted when simulation completes by synapse deletion rules. When neuron (associated with the halting instruction l_h of M) is activated, a computation in M is completely simulated by system Π.

The following describes the works of ADD, SUB, and FIN modules of the SN P systems with self-organization.

Module ADD (shown in Fig. 1): Simulating the ADD instruction l_i: (ADD(r), l_j, l_k).

Figure 1. Module ADD simulating the ADD instruction.

Initially, there is no synapse in system Π, and all the neurons have no spike with exception that neuron has 6 spikes. This means system Π starts by simulating initial instruction l₀. Let us assume that at step t, an instruction l_i: (ADD(r), l_j, l_k) has to be simulated, with 6 spikes present in neuron (like in the initial configuration) and no spike in any other neurons, except in those neurons associated with registers.

At step t, neuron has 6 spikes, and synapse creation rule is applied in , it generates three synapses connecting neuron to neurons , and σ_r. Meanwhile, it consumes 5 spikes (one spike remaining) and sends 5 spikes to each of neurons , and σ_r. The number of spikes in neuron σ_r is increased by 5, which simulates adding 1 to register r of M. At step t + 1, neuron deletes the three synapses created at step t by using rule . At the same moment, neuron uses synapse creation rule a⁵/a⁴ → + (a³, {l_j, l_k}), and creates two synapses to neurons and , as well as sends 3 spikes to each of the two neurons. At step t + 2, neuron deletes the two synapses by using synapse deletion rule a → − (λ, {l_j, l_k}). In neuron , there are 5 spikes at step t + 1 such that both of synapse creation rules a⁵/a⁴ → + (a³, {l_j}) and a⁵/a⁴ → + (a³, {l_k}) are enabled, but only one of them is non-deterministically used.

– If rule a⁵/a⁴ → + (a³, {l_j}) is chosen to use, neuron creates a synapse and sends 3 spikes to neuron . In this case, neuron accumulates 6 spikes, which means system Π starts to simulate instruction l_j of M. One step later, with one spike inside neuron uses rule a → − (λ, {l_j, l_k}) to delete the synapse to neuron , and neuron removes the 3 spikes (from neuron ) by the forgetting rule a³ → λ.

– If rule a⁵/a⁴ → + (a³, {l_k}) is selected to apply, neuron creates a synapse and sends 3 spikes to neuron . Neuron accumulates 6 spikes, which indicates system Π goes to simulate instruction l_k of M. One step later, neuron removes the 3 spikes by using forgetting rule a³ → λ, and the synapse from neuron to is deleted by using rule a → − (λ, {l_j, l_k}) in neuron .

Therefore, from firing neuron , system Π adds 5 spikes to neuron σ_r and non-deterministically activates one of the neurons and , which correctly simulates the ADD instruction l_i: (ADD(r), l_j, l_k). When the simulation of ADD instruction is completed, the ADD module returns to its initial topological structure, i.e., there is no synapse in the module. The dynamic transformation of topological structure and the numbers of spikes in neurons of ADD module during the ADD instruction simulation with neuron or finally activated is shown in Figs 2 and 3. In the figures, the spiking rules are omitted for clear illustration, neurons are represented by circles with the number of spikes and directed edges is used to represent the synapses.

Figure 2. The dynamic transformation of topological structure and the numbers of spikes in neurons of ADD module during the ADD instruction simulation with neuron finally activated.

Figure 3. The dynamic transformation of topological structure and the numbers of spikes in neurons of ADD module during the ADD instruction simulation with neuron finally activated.

Module SUB (shown in Fig. 4): Simulating the SUB instruction l_i: (SUB(r), l_j, l_k).

Figure 4. Module SUB simulating the SUB instruction.

Given starting time stamp t, system Π simulates a SUB instruction l_i: (SUB(r), l_j, l_k). Let s_r be the number of SUB instructions acting on register r and the set of labels of instructions acting on register r be . Obviously, it holds .

At step t, neuron has 6 spikes, and becomes active by using synapse creation rule , creating synapses and sending 4 spikes to each of neurons , and σ_r. With 4 spikes inside, neurons and keep inactive at step t + 1 because no rule can be used. In neuron σ_r, it has the following two cases.

– If neuron σ_r has 5n (n > 0) spikes (corresponding to the fact that the number stored in register r is n, and n > 0), then by receiving 4 spikes from neuron , it accumulates 5n + 4 spikes and becomes active by using rule at step t + 1. It creates a synapse to each of neurons and with and sending 6 spikes to the neurons. By consuming 8 spikes, the number of spikes in neuron σ_r becomes 5n + 4 − 8 = 5(n − 1) + 1 (n ≥ 0) such that rule is enabled and applied at step t + 2. With application of the rule, neuron σ_r removes the synapses from neuron σ_r to neurons and , . Meanwhile, neurons and with s ≠ i remove the 6 spikes by using forgetting rule a⁶ → λ, and neuron removes the 10 spikes by forgetting rule a¹⁰ → λ. Neuron accumulates 10 spikes (4 spikes from neuron and 6 spikes from neuron σ_r), and rule a¹⁰/a⁹ → + (a⁶, {l_j}) is applied at step t + 2, creating a synapse and sending 6 spikes to neuron . In this case, neuron receives 6 spikes, which means system Π starts to simulate instruction l_j of M. One step later, the synapse from neuron to neuron is deleted by using synapse deletion rule a → − (λ, {l_j}).

– If neuron σ_r has no spike (corresponding to the fact that the number stored in register r is 0), then after receiving 4 spikes from neuron , it has 4 spikes and rule is used, creating a synapse to each of neurons and with and sending 3 spikes to the neurons. Neuron σ_r remains one spike, and synapse deletion rule is applied at step t + 2, removing the synapses from neuron σ_r to neurons and , . At the same moment, neurons and with s ≠ i remove the 3 spikes by using forgetting rule a³ → λ, and neuron removes 7 spikes using spiking rule a⁷ → λ. Having 7 spikes, Neuron becomes active by using rule a⁷/a⁶ → + (a⁶, {l_k}) at step t + 2, creating a synapse to neuron and sending 6 spikes to neuron . In this case, neuron receives 6 spikes, which means system Π starts to simulate instruction l_k of M. At step t + 3, neuron uses rule a → − (λ, {l_k}) to remove the synapse to neuron .

The simulation of SUB instruction performs correctly: System Π starts from having 6 spikes and becoming active, and ends in neuron receiving 6 spikes (if the number stored in register r is great than 0 and decreased by one), or in neuron receiving 6 spikes (if the number stored in register r is 0).

When the simulation of SUB instruction is completed, the SUB module returns to its initial topological structure, i.e., there is no synapse in the module. The dynamic transformation of topological structure and the numbers of spikes in involved neurons in the SUB instruction simulation with neuron (resp. neuron ) finally activated is shown in Fig. 5 (resp. Fig. 6).

Figure 5. The dynamic transformation of topological structure and the numbers of spikes in involved neurons in the SUB instruction simulation with neuron finally activated.

Figure 6. The dynamic transformation of topological structure and the numbers of spikes in involved neurons in the SUB instruction simulation with neuron finally activated.

Module FIN (shown in Fig. 7) – outputting the result of computation.

Figure 7. Outputting the computation result.

Assume that at step t the computation in M halts, i.e., the halting instruction is reached. In this case, neuron in Π receives 6 spikes. At that moment, neuron σ₁ contains 5n spikes, for the number n ≥ 1 stored in register 1 of M. With 6 spikes inside, neuron becomes active by using rule a⁶/a⁵ → +(a², {1}), creating a synapse to neuron σ₁ and sending 2 spikes to neuron σ₁. Neuron ends with one spike, and rule a → −(λ, {1}) is used, removing the synapse to neuron σ₁ one step later.

After neuron σ₁ receives the 2 spikes from neuron , the number of spikes in neuron σ₁ becomes 5n + 2 and rule a²(a⁵)⁺/a → +(λ, {0}) is enabled and applied at step t + 2. By using the rule, neuron σ₁ consumes one spike and creates a synapse to the environment. Neuron σ₁ contains 5n + 1 spikes such that spiking rule a(a⁵)⁺/a⁵ → a is used, consuming 5 spikes and emitting one spike to the environment at step t + 3. Note that the number of spikes in neuron σ₁ becomes 5(n − 1) + 1. So, if the number of spikes in neuron σ₁ is not one, then neuron σ₁ will fire again in the next step sending one spike into the environment. In this way, neuron σ₁ can fire for n times, i.e., until the number of spikes in neuron σ₁ reaches one. For each time when neuron σ₁ fires, it sends one spike into the environment. So, in total, neuron σ₁ sends n spikes into the environment, which is exactly the number stored in register 1 of M at the moment when the computation of M halts. When neuron σ₁ has one spike, rule a → −(λ, {0}) is used to remove the synapse from neuron σ₁ to the environment, and system Π eventually halts.

The dynamic transformation of topological structure of the FIN module and the numbers of spikes in the neurons of FIN module and in the environment are shown in Fig. 8.

Figure 8. The dynamic transformation of topological structure of the FIN module and the numbers of spikes in the neurons of FIN module and the environment.

Based on the description of the work of system Π above, the register machine M is correctly simulated by system Π, i.e., N(M) = N_all(Π). We can check that each neuron in system Π has at most three rules, and no limit is imposed on the numbers of neurons and the synapses that can be created (or deleted) by using one synapse creation (or deletion) rule. Therefore, it concludes N_*SPSO_all(cre_*, del_*, rule₅) = NRE.

This concludes the proof.

As number acceptor

Register machine can work in the accepting mode. Number n is accepted by register machine M′ as follows. Initially, number n is stored in the first register of M′ and all the other registers are empty. If the computation starting in this configuration eventually halts, then the number n is said to be accepted by register machine M′. The set of numbers accepted by register machine M′ is denoted by N_acc(M′). It is known that all the sets of numbers in NRE can be accepted by register machine M′, even using the deterministic register machine; i.e. the machine with the ADD instructions of the form l_i: (ADD(r), l_j, l_k) where l_j = l_k (in this case, the instruction is written in the form l_i: (ADD(r), l_j))⁴⁴.

Theorem 5. N_*SPSO_acc(cre_*, del_*, rule₅) = NRE.

Proof

It only has to prove NRE ⊆ N_*SPSO_acc (cre_*, del_*, rule₅), since the converse inclusion is straightforward from the Turing-Church thesis. In what follows, an SN P system Π′ with self-organization working in accepting mode is constructed to simulate a deterministic register machine M′ = (m, H, l₀, l_h, I) working in the acceptive mode. Actually, the proof is given by modifying the proof of Theorem 4.

Each register r of M′ is associated with a neuron σ_r in system Π′, and for each instruction l_i of M′ a neuron is associated. A number n stored in register r is represented by 5n spikes in neuron σ_r.

The system Π′ consists of an INPUT module, deterministic ADD and SUB modules. The INPUT module is shown in Fig. 9, where all neurons are initially empty with the exception that input neuron σ_in has 8 spikes. Spike train 10ⁿ⁻¹1 is introduced into the system through input neuron σ_in, where the internal between the two spikes in the spike train is (n + 1) − 1 = n, which indicates that number n is going to be accepted by system Π′.

Figure 9. The INPUT module of system Π′.

Assuming at step t neuron σ_in receives the first spike. At step t + 1, neuron σ_in contains 9 spikes, and rule a⁹/a⁶ → +(a⁶, {I₁, I₂}) is used, creating a synapse from neuron σ_in to neurons and . Meanwhile, neuron σ_in sends 6 spikes to the two neurons. In neuron σ_in, 6 spikes are consumed and 3 spikes remain. With 6 spikes inside, neurons and become active at step t + 2. Neuron uses rule a⁶/a → +(λ, {I₂}) to create a synapse to neuron ; and neuron uses rule a⁶/a → +(λ, {I₁, 1}) to create a synapse to each of neurons and σ₁. Each of neurons and has 5 spikes left. From step t + 3 on, neurons and fire and begin to exchange 5 spikes between them. In this way, neuron σ₁ receives 5 spikes from neurons at each step.

At step t + n, neuron σ_in receives the second spike from the environment, accumulating 4 spikes inside. At step t + n + 1, neuron σ_in fires for the second time by using spiking rule a⁴/a³ → a³, sending 3 spikes to neurons and . Each of neurons and accumulates 8 spikes. At step t + n + 2, neuron uses synapse creation rule a⁸/a⁶ → +(a⁶, {l₀}), creating a synapse to neuron and sending 6 spikes to neuron . This means that system Π′ starts to simulate the initial instruction l₀ of register machine M′. Meanwhile, neuron uses synapse deletion rule a⁸/a → −(λ, {I₁}), removing the synapse from neuron .. to neuron . In the next step, neuron creates a synapse to neuron σ₁ and sends 5 spikes to neuron σ₁ by using rule a⁷ → +(a⁵, {1}).

From step t + 3 to t + n + 1, neuron σ₁ receives 5 spikes in each step from neuron , thus in total accumulating 5(n − 1) spikes. Neuron σ₁ receives no spike at step t + n + 2, and gets 5 spikes from neuron at step t + n + 3. After that, no more spikes are sent to neuron . Neuron σ₁ contains 5n spikes, which indicates the number to be accepted by register machine M′ is n. At step t + n + 4, neuron uses rule a² → −(λ, {1}), deleting the synapse to neuron σ₁.

The dynamic transformation of topological structure of INPUT module and the numbers of spikes in the neurons of INPUT module are shown in Fig. 10.

Figure 10. The dynamic transformation of topological structure of INPUT module and the numbers of spikes in the neurons of INPUT module.

The deterministic ADD module is shown in Fig. 11, whose function is rather clear. By receiving 6 spikes, neuron becomes active, creating a synapse and sending 5 spikes to each of neurons σ_r, and . The number of spikes in neuron σ_r is increased by 5, which simulates the number stored in register 1 is increased by one. In the next step, neuron uses rule , removing the synapses from neuron to neurons σ_r, and . In neurons and , there are 5 spikes. The two neurons become active by using rule a⁵/a⁴ → +(a³, {l_j}). Each of them creates a synapse to neuron and emits 3 spikes to neuron . In this way, neuron accumulates 6 spikes inside, which means the system Π′ goes to simulate instruction l_j of M′. The synapses from neuron and to neuron will be removed by using synapse deletion rule a → −(λ, {l_j}) in neurons and .

Figure 11. The deterministic ADD module of system Π′.

Module SUB remains unchanged, as shown in Fig. 4. Module FIN is removed, with neuron remaining in the system, but having no rule inside. When neuron receives 6 spikes, it means that the computation of register machine M′ reaches instruction l_h and stops. Having 6 spikes inside, neuron cannot become active for no rule can be used. In this way, the work of system Π′ halts.

Based on the description of the implementation of system Π′ above, it is clear that the register machine M′ in acceptive mode is correctly simulated by the system Π′ working in acceptive mode, i.e., N_acc(M′) = N_acc(Π′).

We can check that each neuron in system Π′ has at most five rules, and no limit is imposed on the numbers of neurons and the synapses that can be created (or deleted) with using one synapse creation (or deletion) rule. Therefore, it concludes N_*SPSO_acc(cre_*, del_*, rule₅) = NRE.

As function computing device

A register machine M can compute a function f : N^k → N as follows: the arguments are introduced in special registers r₁, r₂, …, r_k (without loss of the generality, it is assumed that the first k registers are used). The computation starts with the initial instruction l₀. if the register machine halts, i.e., reaches HALT instruction l_h, the value of the function is placed in another specified register, labelled by r_t, with all registers different from r_t storing number 0. The partial function computed by a register machine M in this way is denoted by M(n₁, n₂, …, n_k). All Turing computable functions can be computed by register machine in this way.

Several universal register machines for computing functions were defined. Let (φ₀, φ₁,…) be a fixed admissible enumeration of the unary partial recursive functions. A register machine M_u is said to be universal if there is a recursive function g such that for all natural numbers x, y we have φ_x(y) = M_u(g_(x), y). As addressed by Minsky, universal register machine can compute any φ_x(y) by inputting a couple of numbers g(x) and y in registers 1 and 2, and the result can be obtained in register 0⁴⁷.

In the following proof of universality, a specific universal register machine M_u from⁴⁷ is used, the machine M_u = (8, H, l₀, l_h, I) presented in Fig. 12. In this universal register machine M_u, there are 8 registers (numbered from 0 to 7) and 23 instructions, and the last instruction is the halting one. As described above, the input numbers (the “code” of the partial recursive function to compute and the argument for this function) are introduced in registers 1 and 2, and the result is outputted in register 0 when the machine M_u halts.

Figure 12. The universal register machine M_u from⁴⁷.

A modification is necessary to be made in M_u, because the subtraction operation in the register where the result is placed is not allowed in the construction of the previous Theorems, but register 0 of M_u is subject of such operations. That is why an extra register is needed - labeled with 8 - and the halt instruction l_h of M_u should be replaced by the following instructions:

Therefore, the modified universal register machine has 9 registers, 24 ADD and SUB instructions, and 25 labels. The result of a computation of is stored in register 8

Theorem 6 There is a universal SN P system with self-organization having 87 neurons for computing functions.

Proof

An SN P system with self-organization Π″ is constructed to simulate the computation of the universal register machine . Specifically, the system Π″ consists of deterministic ADD modules, SUB modules, as well as an INPUT module and an OUTPUT module. The deterministic ADD module shown in Fig. 11 and SUB module shown in Fig. 4 can be used here to simulate the deterministic ADD instruction and SUB instruction of . The INPUT module introduces the necessary spikes into the system by reading a spike train from the environment, and the OUTPUT module outputs the computation result.

With each register r of , a neuron σ_r in system Π″ is associated; the number stored in register r is encoded by the number of spikes in neuron σ_r. If register r holds the number n ≥ 0, then neuron σ_r contains 5n spikes. With each instruction l_i in .., a neuron in system Π″ is associated. If neuron has 6 spikes inside, it becomes active and starts to simulate the instruction l_i. When neuron (associated with the label l′_h of the halting instruction of ) receives 6 spikes, the computation in is completely simulated by the system Π″; the number of spikes emitted into the environment from the output neuron, i.e., neuron σ₈, corresponds to the result computed by (stored in register 8).

The tasks of loading 5g(x) spikes in neuron σ₁ and 5y spikes in neuron σ₂ by reading the spike train 10^g(x)−110^y−11 through input neuron σ_in can be carried out by the INPUT module shown in Fig. 13.

Figure 13. The INPUT module of system Π″.

Initially, all the neurons contain no spike inside, with the exception that neuron σ_in has 15 spikes. It is assumed at step t neuron σ_in reads the first spike from the environment. With 16 spikes inside, neuron σ_in becomes active by using rule a¹⁶/a⁶ → +(a⁶, {I₁, I₂}) at step t + 1. It creates a synapse and sends 6 spikes to each of neurons and . Subsequently, neuron σ_in keeps inactive (for no rule can be used) until the second spike arrives at step t + g(x). Neuron has 6 spikes and uses rule a⁶/a → +(λ, {I₂, 1}) at step t + 2 it creates a synapse to neurons and σ₁ and sends 5 spikes to each of the two neurons. Meanwhile, neuron creates a synapse to neuron and sends 5 spikes to it. From step t + 3 on, neuron sends 5 spikes to neuron and exchanges 5 spikes with neuron in each step.

At step t + g(x), neuron σ_in receives the second spike from the environment. By then it accumulates 11 spikes inside. At step t + g(x) + 1, neuron σ_in fires by using spiking rule a¹¹/a³ → a³, and sends 3 spikes to neurons and . Each of neurons and .. contains 8 spike, which will be remained in σ_in.

At step t + g(x) + 2, neuron applies synapse deletion rule a⁸/a → −(λ, {I₁}) and removes the synapse to neuron , meanwhile neuron removes the synapse to neuron . The two neurons stop to exchange spikes with each other. At step t + g(x) + 3, neuron has 7 spikes and fires by using spiking rule a⁷/a⁵ → a⁵, and sends 5 spikes to neuron σ₁. In the next step, neuron removes the synapse to neuron σ₁, and cannot send spikes to neuron σ₁.

In general, in each step from step t + 3 to t + g(x) +1, neuron σ₁ receives 5 spikes from neuron , in total receiving 5(g(x) − 1) spikes; at step t + g(x) + 2, no spike arriving in neuron σ₁, and at step t + g(x) + 3, 5 spikes reaching neuron σ₁. In this way, neuron σ₁ accumulates 5g(x) spikes, which simulates number g(x) is stored in register 1 of .

At step t + g(x) + 2, neuron σ_in contains 8 spikes such that rule a⁸/a⁶ → + (a⁶, {I₃, I₄}) is used, creating a synapse and sends 6 spikes to each of neurons and . At step t + g(x) + 3, neurons and create synapses to each other, meanwhile neuron creates a synapse to neuron σ₂. From step t + g(x) + 4 on, neuron begins to exchange 5 spikes with and send 5 spikes to neuron σ₂ in each step. At step t + g(x) + y, neuron σ_in receives the third spike from the environment, accumulating 3 spikes inside. One step later, it fires by using spiking rule a³/a² → a², sending 2 spikes to neurons , , and . With 2 spikes inside, neuron σ₁ removes its synapse to neuron σ₁ by rule a² → −(λ, {1}); while neuron forgets the two spikes by forgetting rule a² → λ. By receiving 2 spikes from neuron σ_in, neurons and contain 7 spikes. At step t + g(x) + y + 2, neuron fires by using rule a⁷/a³ → a³ and sends 3 spikes to neurons σ₂ and . The number of spikes in neuron σ₂ is 5(y − 1) + 3. Neuron consumes 6 spikes, creates a synapse and sends 6 spikes to neuron . This means system Π'' starts to simulate the initial instruction l₀ of .

At step t + g(x) + y + 3, neuron has 4 spikes, and it becomes active by rule a⁴/a → −(λ, {I₄}), and removes its synapse to neuron and ends with 3 spikes. In the next step, neuron fires by using spiking rule a³/a² → a², emitting 2 spikes to neuron σ₂. In this way, the number of spikes in neuron σ₂ becomes 5(y − 1) + 3 + 2 = 5y, which indicates number y is stored in register 2 of .

The deterministic ADD module shown in Fig. 11 and SUB module shown in Fig. 4 can be used to simulate ADD and SUB instructions of . The FIN module shown in Fig. 7 can be used to output the computation result with changing neuron σ₁ into σ₈.

Until now, we have used

• 9 neurons for 9 registers,

• 25 neurons for 25 labels,

• 20 neurons for 10 ADD instructions,

• 28 neurons for 14 SUB instruction,

• 5 additional neurons in the INPUT module,

which comes to a total of 87 neurons.

This concludes the proof.

Discussion and Future Works

In this work, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced. As results, it is proven that the systems are Turing universal, i.e., they can compute and accept the family of sets of Turing computable natural numbers. With 87 neurons, the system can compute any Turing computable recursive function, thus achieving Turing universality.

There has been a research focus on the construction of small universal SN P with less computing resource, i.e. less number of neuron in use¹⁷,⁴⁸,⁴⁹,⁵⁰,⁵¹,⁵². It is of interest that whether we can reduce the number of neurons in universal SN P systems with self-organization as function computing devices. A possible way is to construct ADD-ADD, ADD-SUB and SUB-ADD modules to perform particular consecutive ADD-ADD, ADD-SUB, and SUB-ADD instructions of .

SN P systems with learning function/capabiliy is a promising direction. Learning strategies and feedback mechanism have been intensively studied and investigated in conventional artificial neural networks. It is worthy to look into these techniques and transplant these ideas into SN P systems with self-organization.

In research of using artificial neural networks to recognize digital English letters, database MNIST (Mixed National Institute of Standards and Technology database) is widely used for training various letter recognition systems⁵³, and for training and testing in the field of machine learning⁵⁴. For further research, SN P systems with self-organization may be used to recognize handwritten digits letters and other possible pattern recognition problems. Since the data structure of SN P systems is binary sequences, an extra task of transmitting letters or pictures into binary sequences should be addressed. A possible way is transmitting digital numbers of pixels of pictures to binary form. Also, local binary pattern method, can be used to transmit pictures to binary forms.

Bioinformatics is s an interdisciplinary field that develops methods and software tools for understanding biological data⁵⁵. Artificial intelligence based methods and data mining strategy have been used in processing biological data, see e.g.56,57,58,59,60,61,62, it is worthy to processing biological data by SN P systems, such as DNA motif finding⁶³,⁶⁴, nuclear export signal identification⁶⁵,⁶⁶.

Additional Information

How to cite this article: Wang, X. et al. On the Computational Power of Spiking Neural P Systems with Self-Organization. Sci. Rep. 6, 27624; doi: 10.1038/srep27624 (2016).