5,243 research outputs found
Arithmetic Operations with Spiking Neural P Systems with Rules and Weights on Synapses
The application of spiking neural P systems with rules and weights on synapses to arithmetic operations is discussed in this paper. We design specific spiking neural P systems with rules and weights on synapses for successfully performing addition, multiplication and the greatest common divisor. This is the first attempt to discuss the application of the new variant of spiking neural P systems, spiking neural P systems with rules and weights on synapses, and especially the use of spiking neural P systems to perform the greatest common divisor. Comparing with the results reported in the literature, smaller number of neurons are required to fulfill the arithmetic operations
Spiking Neural P Systems with Communication on Request
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Spiking Neural P Systems are Neural System models characterised by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural P systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these P systems, a speci ed number of spikes are consumed and a speci ed number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron.
In the present work, a novel communication strategy among neurons of Spiking Neural P Systems is proposed. In the resulting models, called Spiking Neural P Systems with Communication on Request, the spikes are requested from neighbouring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural P systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron).
The Spiking Neural P Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems
Spiking Neural P systems with weights
A variant of spiking neural P systems with positive or negative weights on synapses is introduced, where the rules of a neuron fire when the potential of that neuron equals a given value. The involved values—weights, firing thresholds, potential consumed by each rule—can be real (computable) numbers, rational numbers, integers, and natural numbers. The power of the obtained systems is investigated. For instance, it is proved that integers (very restricted: 1, −1 for weights, 1 and 2 for firing thresholds, and as parameters in the rules) suffice for computing all Turing computable sets of numbers in both the generative and the accepting modes. When only natural numbers are used, a characterization of the family of semilinear sets of numbers is obtained. It is shown that spiking neural P systems with weights can efficiently solve computationally hard problems in a nondeterministic way. Some open problems and suggestions for further research are formulated.Ministerio de Ciencia e Innovación TIN2009–13192Junta 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)
Spiking Neural P Systems with Structural Plasticity: Attacking the Subset Sum Problem
Spiking neural P systems with structural plasticity (in short,
SNPSP systems) are models of computations inspired by the function and
structure of biological neurons. In SNPSP systems, neurons can create
or delete synapses using plasticity rules. We report two families of solutions:
a non-uniform and a uniform one, to the NP-complete problem
Subset Sum using SNPSP systems. Instead of the usual rule-level nondeterminism
(choosing which rule to apply) we use synapse-level nondeterminism
(choosing which synapses to create or delete). The nondeterminism
due to plasticity rules have the following improvements from a
previous solution: in our non-uniform solution, plasticity rules allowed
for a normal form to be used (i.e. without forgetting rules or rules with
delays, system is simple, only synapse-level nondeterminism); in our uniform
solution the number of neurons and the computation steps are
reduced.Ministerio de Economía y Competitividad TIN2012-3743
Logic Negation with Spiking Neural P Systems
Nowadays, the success of neural networks as reasoning systems is doubtless.
Nonetheless, one of the drawbacks of such reasoning systems is that they work
as black-boxes and the acquired knowledge is not human readable. In this paper,
we present a new step in order to close the gap between connectionist and logic
based reasoning systems. We show that two of the most used inference rules for
obtaining negative information in rule based reasoning systems, the so-called
Closed World Assumption and Negation as Finite Failure can be characterized by
means of spiking neural P systems, a formal model of the third generation of
neural networks born in the framework of membrane computing.Comment: 25 pages, 1 figur
A CMOS Spiking Neuron for Brain-Inspired Neural Networks with Resistive Synapses and In-Situ Learning
Nanoscale resistive memories are expected to fuel dense integration of
electronic synapses for large-scale neuromorphic system. To realize such a
brain-inspired computing chip, a compact CMOS spiking neuron that performs
in-situ learning and computing while driving a large number of resistive
synapses is desired. This work presents a novel leaky integrate-and-fire neuron
design which implements the dual-mode operation of current integration and
synaptic drive, with a single opamp and enables in-situ learning with crossbar
resistive synapses. The proposed design was implemented in a 0.18 m CMOS
technology. Measurements show neuron's ability to drive a thousand resistive
synapses, and demonstrate an in-situ associative learning. The neuron circuit
occupies a small area of 0.01 mm and has an energy-efficiency of 9.3
pJspikesynapse
Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective
On metrics of density and power efficiency, neuromorphic technologies have
the potential to surpass mainstream computing technologies in tasks where
real-time functionality, adaptability, and autonomy are essential. While
algorithmic advances in neuromorphic computing are proceeding successfully, the
potential of memristors to improve neuromorphic computing have not yet born
fruit, primarily because they are often used as a drop-in replacement to
conventional memory. However, interdisciplinary approaches anchored in machine
learning theory suggest that multifactor plasticity rules matching neural and
synaptic dynamics to the device capabilities can take better advantage of
memristor dynamics and its stochasticity. Furthermore, such plasticity rules
generally show much higher performance than that of classical Spike Time
Dependent Plasticity (STDP) rules. This chapter reviews the recent development
in learning with spiking neural network models and their possible
implementation with memristor-based hardware
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