3,411 research outputs found
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
The Utility of Phase Models in Studying Neural Synchronization
Synchronized neural spiking is associated with many cognitive functions and
thus, merits study for its own sake. The analysis of neural synchronization
naturally leads to the study of repetitive spiking and consequently to the
analysis of coupled neural oscillators. Coupled oscillator theory thus informs
the synchronization of spiking neuronal networks. A crucial aspect of coupled
oscillator theory is the phase response curve (PRC), which describes the impact
of a perturbation to the phase of an oscillator. In neural terms, the
perturbation represents an incoming synaptic potential which may either advance
or retard the timing of the next spike. The phase response curves and the form
of coupling between reciprocally coupled oscillators defines the phase
interaction function, which in turn predicts the synchronization outcome
(in-phase versus anti-phase) and the rate of convergence. We review the two
classes of PRC and demonstrate the utility of the phase model in predicting
synchronization in reciprocally coupled neural models. In addition, we compare
the rate of convergence for all combinations of reciprocally coupled Class I
and Class II oscillators. These findings predict the general synchronization
outcomes of broad classes of neurons under both inhibitory and excitatory
reciprocal coupling.Comment: 18 pages, 5 figure
Topological exploration of artificial neuronal network dynamics
One of the paramount challenges in neuroscience is to understand the dynamics
of individual neurons and how they give rise to network dynamics when
interconnected. Historically, researchers have resorted to graph theory,
statistics, and statistical mechanics to describe the spatiotemporal structure
of such network dynamics. Our novel approach employs tools from algebraic
topology to characterize the global properties of network structure and
dynamics.
We propose a method based on persistent homology to automatically classify
network dynamics using topological features of spaces built from various
spike-train distances. We investigate the efficacy of our method by simulating
activity in three small artificial neural networks with different sets of
parameters, giving rise to dynamics that can be classified into four regimes.
We then compute three measures of spike train similarity and use persistent
homology to extract topological features that are fundamentally different from
those used in traditional methods. Our results show that a machine learning
classifier trained on these features can accurately predict the regime of the
network it was trained on and also generalize to other networks that were not
presented during training. Moreover, we demonstrate that using features
extracted from multiple spike-train distances systematically improves the
performance of our method
Models wagging the dog: are circuits constructed with disparate parameters?
In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken
Dimensions of Neural-symbolic Integration - A Structured Survey
Research on integrated neural-symbolic systems has made significant progress
in the recent past. In particular the understanding of ways to deal with
symbolic knowledge within connectionist systems (also called artificial neural
networks) has reached a critical mass which enables the community to strive for
applicable implementations and use cases. Recent work has covered a great
variety of logics used in artificial intelligence and provides a multitude of
techniques for dealing with them within the context of artificial neural
networks. We present a comprehensive survey of the field of neural-symbolic
integration, including a new classification of system according to their
architectures and abilities.Comment: 28 page
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