120 research outputs found
Semantics of deductive databases with spiking neural P systems
The integration of symbolic reasoning systems based on logic and connectionist systems based on thefunctioning of living neurons is a vivid research area in computer science. In the literature, one can findmany efforts where different reasoning systems based on different logics are linked to classic artificialneural networks. In this paper, we study the relation between the semantics of reasoning systems basedon propositional logic and the connectionist model in the framework of membrane computing, namely,spiking neural P systems. We prove that the fixed point semantics of deductive databases without nega- tion can be implemented in the spiking neural P systems model and such a model can also deal withnegation if it is endowed with anti-spikes and annihilation rules
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
Semantics of Deductive Databases in a Membrane Computing Connectionist Model
The integration of symbolic reasoning systems based on logic and connectionist
systems based on the functioning of living neurons is a vivid research area in
computer science. In the literature, one can found many e orts where di erent reasoning
systems based on di erent logics are linked to classic arti cial neural networks. In this
paper, we study the relation between the semantics of reasoning systems based on propositional
logic and the connectionist model in the framework of membrane computing,
namely, spiking neural P systems. We prove that the xed point semantics of deductive
databases and the immediate consequence operator can be implemented in the spiking
neural P systems model
Semantics of Deductive Databases in a Membrane Computing Connectionist Model
The integration of symbolic reasoning systems based on logic and connectionist
systems based on the functioning of living neurons is a vivid research area in
computer science. In the literature, one can found many e orts where di erent reasoning
systems based on di erent logics are linked to classic arti cial neural networks. In this
paper, we study the relation between the semantics of reasoning systems based on propositional
logic and the connectionist model in the framework of membrane computing,
namely, spiking neural P systems. We prove that the xed point semantics of deductive
databases and the immediate consequence operator can be implemented in the spiking
neural P systems model
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)
Exploring the landscapes of "computing": digital, neuromorphic, unconventional -- and beyond
The acceleration race of digital computing technologies seems to be steering
toward impasses -- technological, economical and environmental -- a condition
that has spurred research efforts in alternative, "neuromorphic" (brain-like)
computing technologies. Furthermore, since decades the idea of exploiting
nonlinear physical phenomena "directly" for non-digital computing has been
explored under names like "unconventional computing", "natural computing",
"physical computing", or "in-materio computing". This has been taking place in
niches which are small compared to other sectors of computer science. In this
paper I stake out the grounds of how a general concept of "computing" can be
developed which comprises digital, neuromorphic, unconventional and possible
future "computing" paradigms. The main contribution of this paper is a
wide-scope survey of existing formal conceptualizations of "computing". The
survey inspects approaches rooted in three different kinds of background
mathematics: discrete-symbolic formalisms, probabilistic modeling, and
dynamical-systems oriented views. It turns out that different choices of
background mathematics lead to decisively different understandings of what
"computing" is. Across all of this diversity, a unifying coordinate system for
theorizing about "computing" can be distilled. Within these coordinates I
locate anchor points for a foundational formal theory of a future
computing-engineering discipline that includes, but will reach beyond, digital
and neuromorphic computing.Comment: An extended and carefully revised version of this manuscript has now
(March 2021) been published as "Toward a generalized theory comprising
digital, neuromorphic, and unconventional computing" in the new open-access
journal Neuromorphic Computing and Engineerin
Explaining Cognitive Computing Through the Information Systems Lens
Cognitive computing (COC) aims to embed human cognition into computerized models. However, there is no scientific classification that delineates the nature of Cognitive Computing. Unlike the medical and computer science fields, Information Systems (IS) has conducted very little research on COC. Although the potential to make important research contributions in this area is great, we argue that the lack of a cohesive interpretation of what constitutes COC has led to inferior COC research in IS. Therefore, we need first to clearly identify COC as a phenomenon to be able to identify and guide prospective research areas in IS. In this research, a phenomenological approach is adopted using thematic analysis to the published literature in COC research. Then, we discuss how IS may contribute to the development of design science artifacts under the COC umbrella. In addition, the paper raises important questions for future research by highlighting how IS researchers could make meaningful contributions to this emerging topic
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