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The paper presents a hierarchical spike timing neural network model developed in NEST simulator aimed to reproduce human decision making in simplified simulated visual navigation tasks. It includes multiple layers starting from retina photoreceptors and retinal ganglion cells (RGC) via thalamic relay including lateral geniculate nucleus (LGN), thalamic reticular nucleus (TRN), and interneurons (IN) mediating connections to the higher brain areas—visual cortex (V1), middle temporal (MT), and medial superior temporal (MTS) areas, involved in dorsal pathway processing of spatial and dynamic visual information. The last layer—lateral intraparietal cortex (LIP)—is responsible for decision making and organization of the subsequent motor response (saccade generation). We simulated two possible decision options having LIP layer with two sub-regions with mutual inhibitory connections whose increased firing rate corresponds to the perceptual decision about motor response—left or right saccade. Each stage of the model was tested by appropriately chosen stimuli corresponding to its selectivity to specific stimulus characteristics (orientation for V1, direction for MT, and expansion/contraction movement templates for MST, respectively). The overall model performance was tested with stimuli simulating optic flow patterns of forward self-motion on a linear trajectory to the left or to the right from straight ahead with a gaze in the direction of heading.

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Abstract-This paper presents the results of a comparative analysis between different approaches to experimental data storage and processing. Several studies related to the problem and some methods for solving it have been discussed. Different types of databases, ways of using them and the areas of their application are analyzed. For the purposes of the study, a relational database for storing and analyzing specific data from behavioral experiments was designed. The methodology and conditions for conducting the experiments are described. Three different indicators are analyzed, respectively: memory required to store the data, time to load the data from an external file into computer memory and iteration time across all records through one cycle. The obtained results show that for storing a large number of records (in the order of tens of millions rows) either dynamic arrays (stored on external media in binary file format), or an approach based on a local or remote database management system can be used. Regarding the data loading time, the fastest approach was the one that uses dynamic arrays. It outperforms significantly the approaches based on a local or remote database. The obtained results show that the dynamic arrays and the local data sets approaches iterated much faster across all data records than the remote database approach.

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The mill fans (MF) are centrifugal fans of the simplest type with flat radial blades adapted for simultaneous operation both like fans and also like mills. The key variable that could be used for diagnostic purposes is vibration amplitude of MF corpse. However its mode values include a great deal of randomness. Therefore the application of deterministic dependencies with correcting coefficients is non-effective for MF predictive modeling. Standard statistical and probabilistic (Bayesian) approaches are also inapplicable to estimate MF vibration state due to non-stationarity, non-ergodicity and the significant noise level of the monitored vibrations. Adequate for the case methods of computational intelligence (fuzzy logic, neural networks and more general AI techniques -the precedents' method or machine learning (ML)) must be used. The present paper describes promising initial results on applying the Case Based Reasoning (CBR) approach for intelligent diagnostic of the mill fan working capacity using its vibration state.