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

First Steps Towards a CPU Made of Spiking Neural P Systems

We consider spiking neural P systems as devices which can be used to perform some basic arithmetic operations, namely addition, subtraction, comparison and multiplica- tion by a fixed factor. The input to these systems are natural numbers expressed in binary form, encoded as appropriate sequences of spikes. A single system accepts as inputs num- bers of any size. The present work may be considered as a first step towards the design of a CPU based on the working of spiking neural P systems

PBIL for Optimizing Hyperparameters of Convolutional Neural Networks and STL Decomposition

The optimization of hyperparameters in Deep Neural Net-works is a critical task for the final performance, but it involves a high amount of subjective decisions based on previous researchers’ expertise. This paper presents the implementation of Population-based Incremen-tal Learning for the automatic optimization of hyperparameters in Deep Learning architectures. Namely, the proposed architecture is a combina-tion of preprocessing the time series input with Seasonal Decomposition of Time Series by Loess, a classical method for decomposing time series, and forecasting with Convolutional Neural Networks. In the past, this combination has produced promising results, but penalized by an incre-mental number of parameters. The proposed architecture is applied to the prediction of the 222Rn level at the Canfranc Underground Labora-tory (Spain). By predicting the lowlevel periods of 222Rn, the potential contamination during the maintenance operations in the experiments hosted in the laboratory could be minimized. In this paper, it is shown that Population-based Incremental Learning can be used for the choice of optimized hyperparameters in Deep Learning architectures with a reasonable computational cost.Ministerio de Economía y Competitividad MDM- 2015-050

Solving the 3-COL Problem by Using Tissue P Systems without Environment and Proteins on Cells

The 3-COL problem consists on deciding if the regions of a map can be coloured with only three colors bearing in mind that two adjacent regions must be coloured with di erent colors. It is a NP problem and it has been previously used in complexity studies in membrane computing to check the ability of a model for solving problems of such complexity class. Recently, tissue P systems with proteins on cells have been presented and its ability to solve NP-problems has been proved, but it remained as an open question to know if such model was still able to solve such problems if the environment was removed. In this paper we provide an a rmative answer to this question by showing a uniform family of tissue P systems without environment and with proteins on cells which solves the 3-COL problem in linear time

Explainability in Simplicial Map Neural Networks

Simplicial map neural networks (SMNNs) are topology-based neural networks with interesting properties such as universal approximation capability and robustness to adversarial examples under appropriate conditions. However, SMNNs present some bottlenecks for their possible application in high dimensions. First, no SMNN training process has been defined so far. Second, SMNNs require the construction of a convex polytope surrounding the input dataset. In this paper, we propose a SMNN training procedure based on a support subset of the given dataset and a method based on projection to a hypersphere as a replacement for the convex polytope construction. In addition, the explainability capacity of SMNNs is also introduced for the first time in this paper

Towards a Programming Language in Cellular Computing

AbstractSeveral solutions to hard numerical problems using P systems have been presented recently, and strong similarities in their designs have been noticed. In this paper we present a new solution, to the Partition problem, via a family of deterministic P systems with active membranes using 2-division. Then, we intend to show that the idea of a cellular programming language is possible (at least for some relevant family of NP-complete problems), indicating some “subroutines” that can be used in a variety of situations and therefore could be useful for designing solutions for new problems in the future

First Steps Towards a CPU Made of Spiking Neural P Systems

We consider spiking neural P systems as devices which can be used to perform some basic arithmetic operations, namely addition, subtraction, comparison and multiplica- tion by a fixed factor. The input to these systems are natural numbers expressed in binary form, encoded as appropriate sequences of spikes. A single system accepts as inputs num- bers of any size. The present work may be considered as a first step towards the design of a CPU based on the working of spiking neural P systems

Searching Partially Bounded Regions with P Systems

The problem of automatically marking the interior and exterior regions of a simple curve in a digital image becomes a hard task due to the noise and the intrinsic difficulties of the media where the image is taken. In this paper, we propose a definition of the interior of a partially bounded region and present a bio-inspired algorithm for finding it in the framework of Membrane Computing.Ministerio de Economía y Competitividad TIN2012-3743