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)

Diagnosis of Brain Diseases via Multi-Scale Time-Series Model

The functional magnetic resonance imaging (fMRI) data and brain network analysis have been widely applied to automated diagnosis of neural diseases or brain diseases. The fMRI time series data not only contains specific numerical information, but also involves rich dynamic temporal information, those previous graph theory approaches focus on local topology structure and lose contextual information and global fluctuation information. Here, we propose a novel multi-scale functional connectivity for identifying the brain disease via fMRI data. We calculate the discrete probability distribution of co-activity between different brain regions with various intervals. Also, we consider nonsynchronous information under different time dimensions, for analyzing the contextual information in the fMRI data. Therefore, our proposed method can be applied to more disease diagnosis and other fMRI data, particularly automated diagnosis of neural diseases or brain diseases. Finally, we adopt Support Vector Machine (SVM) on our proposed time-series features, which can be applied to do the brain disease classification and even deal with all time-series data. Experimental results verify the effectiveness of our proposed method compared with other outstanding approaches on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and Major Depressive Disorder (MDD) dataset. Therefore, we provide an efficient system via a novel perspective to study brain networks

Asynchronous Spiking Neural P Systems with Anti-Spikes

Spiking neural P systems with anti-spikes (ASN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes and inhibitory spikes. ASN P systems working in the synchronous manner with standard spiking rules have been proved to be Turing completeness, do what Turing machine can do. In this work, we consider the computing power of ASN P systems working in the asynchronous manner with standard rules. As expected, the non-synchronization will decrease the computability of the systems. Specifically, asynchronous ASN P systems with standard rules can only characterize the semilinear sets of natural numbers. But, by using weighted synapses, asynchronous ASN P systems can achieve the equivalence with Turing machine again. It implies that weighted synapses has some "programming capacity" in the sense of achieving computing power. The obtained results have a nice interpretation: the loss in power entailed by removing the synchronization from ASN P systems can be compensated by using weighted synapses among connected neurons. ? 2014 Springer Science+Business Media New York