381 research outputs found
Spiking Neural P Systems with Anti-Spikes
Besides usual spikes employed in spiking neural P systems, we consider
"anti-spikes", which participate in spiking and forgetting rules, but also annihilate spikes
when meeting in the same neuron. This simple extension of spiking neural P systems
is shown to considerably simplify the universality proofs in this area: all rules become
of the form bc ! b0 or bc ! ¸, where b; b0 are spikes or anti-spikes. Therefore, the
regular expressions which control the spiking are the simplest possible, identifying only
a singleton. A possible variation is not to produce anti-spikes in neurons, but to consider
some "inhibitory synapses", which transform the spikes which pass along them into anti-
spikes. Also in this case, universality is rather easy to obtain, with rules of the above
simple forms.Junta de Andalucía P08 – TIC 0420
Spiking Neural P Systems with Addition/Subtraction Computing on Synapses
Spiking neural P systems (SN P systems, for short) are a class of distributed
and parallel computing models inspired from biological spiking neurons. In this paper,
we introduce a variant called SN P systems with addition/subtraction computing on
synapses (CSSN P systems). CSSN P systems are inspired and motivated by the shunting
inhibition of biological synapses, while incorporating ideas from dynamic graphs and
networks. We consider addition and subtraction operations on synapses, and prove that
CSSN P systems are computationally universal as number generators, under a normal
form (i.e. a simplifying set of restrictions)
Simulating FRSN P Systems with Real Numbers in P-Lingua on sequential and CUDA platforms
Fuzzy Reasoning Spiking Neural P systems (FRSN P systems,
for short) is a variant of Spiking Neural P systems incorporating
fuzzy logic elements that make it suitable to model fuzzy diagnosis knowledge
and reasoning required for fault diagnosis applications. In this sense,
several FRSN P system variants have been proposed, dealing with real
numbers, trapezoidal numbers, weights, etc. The model incorporating
real numbers was the first introduced [13], presenting promising applications
in the field of fault diagnosis of electrical systems. For this variant,
a matrix-based algorithm was provided which, when executed on parallel
computing platforms, fully exploits the model maximally parallel
capacities. In this paper we introduce a P-Lingua framework extension
to parse and simulate FRSN P systems with real numbers. Two simulators,
implementing a variant of the original matrix-based simulation
algorithm, are provided: a sequential one (written in Java), intended to
run on traditional CPUs, and a parallel one, intended to run on CUDAenabled
devices.Ministerio de Economía y Competitividad TIN2012-3743
Noise-induced synchronization and anti-resonance in excitable systems; Implications for information processing in Parkinson's Disease and Deep Brain Stimulation
We study the statistical physics of a surprising phenomenon arising in large
networks of excitable elements in response to noise: while at low noise,
solutions remain in the vicinity of the resting state and large-noise solutions
show asynchronous activity, the network displays orderly, perfectly
synchronized periodic responses at intermediate level of noise. We show that
this phenomenon is fundamentally stochastic and collective in nature. Indeed,
for noise and coupling within specific ranges, an asymmetry in the transition
rates between a resting and an excited regime progressively builds up, leading
to an increase in the fraction of excited neurons eventually triggering a chain
reaction associated with a macroscopic synchronized excursion and a collective
return to rest where this process starts afresh, thus yielding the observed
periodic synchronized oscillations. We further uncover a novel anti-resonance
phenomenon: noise-induced synchronized oscillations disappear when the system
is driven by periodic stimulation with frequency within a specific range. In
that anti-resonance regime, the system is optimal for measures of information
capacity. This observation provides a new hypothesis accounting for the
efficiency of Deep Brain Stimulation therapies in Parkinson's disease, a
neurodegenerative disease characterized by an increased synchronization of
brain motor circuits. We further discuss the universality of these phenomena in
the class of stochastic networks of excitable elements with confining coupling,
and illustrate this universality by analyzing various classical models of
neuronal networks. Altogether, these results uncover some universal mechanisms
supporting a regularizing impact of noise in excitable systems, reveal a novel
anti-resonance phenomenon in these systems, and propose a new hypothesis for
the efficiency of high-frequency stimulation in Parkinson's disease
Dynamic threshold neural P systems
Pulse coupled neural networks (PCNN, for short) are models abstracting the synchronization behavior
observed experimentally for the cortical neurons in the visual cortex of a cat’s brain, and the intersecting
cortical model is a simplified version of the PCNN model. Membrane computing (MC) is a kind computation
paradigm abstracted from the structure and functioning of biological cells that provide models
working in cell-like mode, neural-like mode and tissue-like mode. Inspired from intersecting cortical
model, this paper proposes a new kind of neural-like P systems, called dynamic threshold neural P systems
(for short, DTNP systems). DTNP systems can be represented as a directed graph, where nodes are dynamic
threshold neurons while arcs denote synaptic connections of these neurons. DTNP systems provide a
kind of parallel computing models, they have two data units (feeding input unit and dynamic threshold
unit) and the neuron firing mechanism is implemented by using a dynamic threshold mechanism. The
Turing universality of DTNP systems as number accepting/generating devices is established. In addition,
an universal DTNP system having 109 neurons for computing functions is constructed.National Natural Science Foundation of China No 61472328Research Fund of Sichuan Science and Technology Project No. 2018JY0083Chunhui Project Foundation of the Education Department of China No. Z2016143Chunhui Project Foundation of the Education Department of China No. Z2016148Research Foundation of the Education Department of Sichuan province No. 17TD003
Asynchronous Spiking Neural P Systems with Multiple Channels and Symbols
Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computation systems, inspired from the way that the neurons process and communicate information by means of spikes. A new variant of SNP systems, which works in asynchronous mode, asynchronous spiking neural P systems with multiple channels and symbols (ASNP-MCS systems, in short), is investigated in this paper. There are two interesting features in ASNP-MCS systems: multiple channels and multiple symbols. That is, every neuron has more than one synaptic channels to connect its subsequent neurons, and every neuron can deal with more than one type of spikes. The variant works in asynchronous mode: in every step, each neuron can be free to fire or not when its rules can be applied. The computational completeness of ASNP-MCS systems is investigated. It is proved that ASNP-MCS systems as number generating and accepting devices are Turing universal. Moreover, we obtain a small universal function computing device that is an ASNP-MCS system with 67 neurons. Specially, a new idea that can solve ``block'' problems is proposed in INPUT modules
Nonlinear Hebbian learning as a unifying principle in receptive field formation
The development of sensory receptive fields has been modeled in the past by a
variety of models including normative models such as sparse coding or
independent component analysis and bottom-up models such as spike-timing
dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic
plasticity. Here we show that the above variety of approaches can all be
unified into a single common principle, namely Nonlinear Hebbian Learning. When
Nonlinear Hebbian Learning is applied to natural images, receptive field shapes
were strongly constrained by the input statistics and preprocessing, but
exhibited only modest variation across different choices of nonlinearities in
neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse
network activity are necessary for the development of localized receptive
fields. The analysis of alternative sensory modalities such as auditory models
or V2 development lead to the same conclusions. In all examples, receptive
fields can be predicted a priori by reformulating an abstract model as
nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural
statistics can account for many aspects of receptive field formation across
models and sensory modalities
P Systems with Anti-Matter
After a short introduction to the area of membrane computing (a branch
of natural computing), we introduce the concept of anti-matter in membrane computing.
First we consider spiking neural P systems with anti-spikes, and then we show the
power of anti-matter in cell-like P systems. As expected, the use of anti-matter objects
and especially of matter/anti-matter annihilation rules, turns out to be rather powerful:
computational completeness of P systems with anti-matter is obtained immediately, even
without using catalysts. Finally, some open problems are formulated, too
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