1,212 research outputs found
Computing with Spiking Neural P Systems: Traces and Small Universal Systems
Recently, the idea of spiking neurons and thus of computing
by spiking was incorporated into membrane computing, and so-called
spiking neural P systems (abbreviated SN P systems) were introduced.
Very shortly, in these systems neurons linked by synapses communicate
by exchanging identical signals (spikes), with the information encoded
in the distance between consecutive spikes. Several ways of using such
devices for computing were considered in a series of papers, with universality
results obtained in the case of computing numbers, both in the
generating and the accepting mode; generating, accepting, or processing
strings or infinite sequences was also proved to be of interest.
In the present paper, after a short survey of central notions and results
related to spiking neural P systems (including the case when SN P
systems are used as string generators), we contribute to this area with
two (types of) results: (i) we produce small universal spiking neural P
systems (84 neurons are sufficient in the basic definition, but this number
is decreased to 49 neurons if a slight generalization of spiking rules
is adopted), and (ii) we investigate the possibility of generating a language
by following the trace of a designated spike in its way through the
neurons.Ministerio de Educación y Ciencia TIN2005-09345-C03-0
Membrane computing: traces, neural inspired models, controls
Membrane Computing:Traces, Neural Inspired Models, ControlsAutor: Armand-Mihai IonescuDirectores: Dr. Victor Mitrana (URV)Dr. Takashi Yokomori (Universidad Waseda, Japón)Resumen Castellano:El presente trabajo está dedicado a una área muy activa del cálculo natural (que intenta descubrir la odalidad en la cual la naturaleza calcula, especialmente al nivel biológico), es decir el cálculo con membranas, y más preciso, a los modelos de membranas inspirados de la funcionalidad biológica de la neurona.La disertación contribuye al área de cálculo con membranas en tres direcciones principales. Primero, introducimos una nueva manera de definir el resultado de una computación siguiendo los rastros de un objeto especificado dentro de una estructura celular o de una estructura neuronal. A continuación, nos acercamos al ámbito de la biología del cerebro, con el objetivo de obtener varias maneras de controlar la computación por medio de procesos que inhiben/de-inhiben. Tercero, introducimos e investigamos en detallo - aunque en una fase preliminar porque muchos aspectos tienen que ser clarificados - una clase de sistemas inspirados de la manera en la cual las neuronas cooperan por medio de spikes, pulsos eléctricos de formas idénticas.English summary:The present work is dedicated to a very active branch of natural computing (which tries to discover the way nature computes, especially at a biological level), namely membrane computing, more precisely, to those models of membrane systems mainly inspired from the functioning of the neural cell.The present dissertation contributes to membrane computing in three main directions. First, we introduce a new way of defining the result of a computation by means of following the traces of a specified object within a cell structure or a neural structure. Then, we get closer to the biology of the brain, considering various ways to control the computation by means of inhibiting/de-inhibiting processes. Third, we introduce and investigate in a great - though preliminary, as many issues remain to be clarified - detail a class of P systems inspired from the way neurons cooperate by means of spikes, electrical pulses of identical shapes
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
We present PyCARL, a PyNN-based common Python programming interface for
hardware-software co-simulation of spiking neural network (SNN). Through
PyCARL, we make the following two key contributions. First, we provide an
interface of PyNN to CARLsim, a computationally-efficient, GPU-accelerated and
biophysically-detailed SNN simulator. PyCARL facilitates joint development of
machine learning models and code sharing between CARLsim and PyNN users,
promoting an integrated and larger neuromorphic community. Second, we integrate
cycle-accurate models of state-of-the-art neuromorphic hardware such as
TrueNorth, Loihi, and DynapSE in PyCARL, to accurately model hardware latencies
that delay spikes between communicating neurons and degrade performance. PyCARL
allows users to analyze and optimize the performance difference between
software-only simulation and hardware-software co-simulation of their machine
learning models. We show that system designers can also use PyCARL to perform
design-space exploration early in the product development stage, facilitating
faster time-to-deployment of neuromorphic products. We evaluate the memory
usage and simulation time of PyCARL using functionality tests, synthetic SNNs,
and realistic applications. Our results demonstrate that for large SNNs, PyCARL
does not lead to any significant overhead compared to CARLsim. We also use
PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and
demonstrate a significant performance deviation from software-only simulations.
PyCARL allows to evaluate and minimize such differences early during model
development.Comment: 10 pages, 25 figures. Accepted for publication at International Joint
Conference on Neural Networks (IJCNN) 202
Extended SNP Systems with States
We consider (extended) spiking neural P systems with states, where the
applicability of rules in a neuron not only depends on the presence of su ciently many
spikes (yet in contrast to the standard de nition, no regular checking sets are used), but
also on the current state of the neuron. Moreover, a spiking rule not only sends spikes,
but also state information to the connected neurons. We prove that this variant of the
original model of extended spiking neural P systems can simulate register machines with
only two states, even in the basic non-extended variant
Spiking Neural P Systems: A Short Introduction and New Normal Forms
Spiking neural P systems are a class of P systems inspired from the way
the neurons communicate with each other by means of electrical impulses (called
\spikes"). In the few years since this model was introduced, many results related
to the computing power and e ciency of these computing devices were reported.
The present paper quickly surveys the basic ideas of this research area and the basic
results, then, as typical proofs about the universality of spiking neural P systems,
we present some new normal forms for them. Speci cally, we consider a natural
restriction in the architecture of a spiking neural P system, to have neurons of a
small number of types (i.e., using a small number of sets of rules). We prove that
three types of neurons are su cient in order to generate each recursively enumerable
set of numbers as the distance between the rst two spikes emitted by the system;
the problem remains open for accepting SN P systems. The paper ends with the
complete bibliography of this domain, at the level of April 2009.Ministerio de Educación y Ciencia TIN2006-13452Junta de Andalucía P08-TIC-0420
Spiking Neural P Systems. Recent Results, Research Topics
After a quick introduction of spiking neural P systems (a class of P systems
inspired from the way neurons communicate by means of spikes, electrical impulses
of identical shape), and presentation of typical results (in general equivalence
with Turing machines as number computing devices, but also other issues, such as
the possibility of handling strings or infinite sequences), we present a long list of
open problems and research topics in this area, also mentioning recent attempts to
address some of them. The bibliography completes the information offered to the
reader interested in this research area.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía TIC-58
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.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía TIC-58
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