NeuroNexus (Ann Arbor, Michigan, USA) 16-channel shank arrays were coupled with optical ferrules to record and stimulate simultaneously in the hippocampus. A single-shank H-style array was used, with 16 177 μm2 contacts spaced 100 μm apart along a 5 mm shaft. This length was sufficient to record simultaneously from PARP Inhibitor in clinical trials the CA1 and CA3 layers. The shaft was connected

to an Omnetics connector via a 21 mm flexible ribbon cable. Ground and reference wires were again separated from the contact sites and routed through stainless steel wires. NeuroNexus “activated” the electrode contacts via iridium oxide – a process that reduced impedance and they suggested would reduce optical stimulation artifacts (personal communication). Both the NeuroNexus and TDT arrays made use of a magnet-based coupling technique to the 16-channel 100 gain tethered recording headstage (Triangle Biosystems, Durham, NC, USA) to reduce movement artifacts (Figure ​Figure1J1J, red dots), a technique we have described previously (Rolston et al., 2009c, 2010b). Once the magnet was attached

with superglue, the NeuroNexus array could be situated onto our custom-designed and 3D-printed implantation holder3 (Figures 1H,J). This enabled the array shank and contacts to be positioned in parallel to the optical fiber (Figure ​Figure1J1J), and cemented in place with quick-drying super glue (Figure ​Figure1K1K). The fiber and shank thus were stereotactically inserted together, maintaining a fixed distance from each other throughout the experiment. The implantation device consists of a single post compatible with a Kopf Universal Holder (David Kopf Instruments, Tujunga, CA, USA) with

a single-prong plug that enabled easy swapping and customization depending on the implant configuration (Figure ​Figure1H1H). This allowed us to use the device to implant an optical ferrule in isolation – as in the MS – or in conjunction with a NeuroNexus array (Figure ​Figure1J1J) – as in the dorsal hippocampus. EXPERIMENTAL METHODS SURGERIES Two month old adult male Sprague–Dawley rats (250–300 g) were purchased from Charles River Laboratories (Wilmington, Cilengitide MA, USA). All animals were maintained within a 12/12 light/dark cycle vivarium with unlimited access to food and water. This work was conducted in accordance with Emory University’s Institute for Animal Care and Use Committee. Each subject underwent two surgical procedures. The first survival surgery introduced the optogenetic viral vector to the stimulation target – either the MS or the dorsal hippocampus. For medial septal stimulation, rats were anesthetized with 1.5–4% inhaled isoflurane, and a craniectomy was made 0.40 mm anterior and 2.00 mm lateral to bregma on the right side of the skull. A pulled-glass pipette attached to a stereotactically mounted injector (Nanoject; Drummond Scientific Co., Broomall, PA, USA) was used to inject 1.

The memory model infers the recognition from these small portions of the entire memory. When input data are observed, edges based on the data are extracted with regard to the

edge configuration of the model. In the activation step, the extracted edge, Ei, and previously encoded edge, Em, are compared. To check the correspondence between two edges, the inclusion relation is MDV3100 Androgen Receptor inhibitor applied for a comparison measure. As a condition of the activation between two edges, at least one value should be matched, and no mismatched values should exist. The activation function can be represented as follows: δ(Ei,Em)={1,if  (#  of  matched  value>Nm, #  of  mismatched  value=0)0,otherwise. (3) If one value of an edge is missing because of different edge lengths, we do not count this case as a mismatched value. Figure 4 shows the success and failure of activation. Figure 4 Conditions of edge activation: (a) successful and (b) failed activations. The top and bottom rows are for the input edges and encoded memory edges, respectively. Arrows with a cross indicate mismatches of the edges between the input and memory. The secondary step of the recognition mechanism is judgment. To judge the familiarity,

an activation-based memory mechanism is involved. The model investigates whether the activated edges construct a fully connected links. After the edges are selected in the memory, the connected links are activated consecutively. If two adjacent edges are activated simultaneously by the input, the connected link is finally assigned as an activated link. If edges are activated and connected with each other in every

dimension of the network, the input data are judged as old (see (4)): ∏iδ(Ei,Em)=1. (4) If the activated edges are fully connected in the memory network, it means that the combination of edges was previously encoded. The reason for this is that all of the encoded instances make a closed link set in the network model. Figure 5 shows ring-type and line-type networks that have been judged as old or new. As shown in Figure 5, different edges are activated simultaneously. The number of closed loops changes according to the input data and network connectivity. However, the number of loops does not indicate certainty of the recognition judgment. The criterion is whether a fully connected link exists or not. Figure 5 Network diagrams built using activated edges and links. Top graphs ((a)–(d)) represent ring-type networks and bottom graphs ((e)–(h)) represent Carfilzomib line-type networks. Among the graphs, (a), (b), (e), and (f) contain a fully connected links, … 3.2.2. Performance Measure As a performance measure, we use a confusion matrix. In a hypernetwork, an old input is always judged as old if we assume that there is no removal of edges or links in the memory. This means that a false negative does not occur. Likewise, results judged as new are constantly made from new inputs. Our concern is false-positive cases, where a new input is judged as old.

[15]. They found external situational impact factors and verified the rationality. In recent years, a comprehensive analysis INK 128 solubility named the Scheme for the Comparative Analysis of Public Environmental Decision-Making (SCAPE) has been developed [16]. The basic idea is that since we cannot understand

every person exactly, why not take individuals as a black box with a system opinion and build a model based on input and output to identify the relationship between behavior and motivations? 2.2. Influencing Factors of Travel Mode Choice Understanding the influencing factors of proenvironmental travel behavior and their individual influencing path is the prerequisite for the scientific development of transportation decision-making and intervention strategies. The factors that influence proenvironmental travel can be divided into individual characteristics, social characteristics, and situational variables. In practice, behavioral change theory prevails to promote the voluntary reduction of car use and a shift to proenvironmental travel, of which some classic economic indicators (such as prices and taxes) and land use, transportation network, and behavior-oriented traffic systems have always been welcomed

[17, 18]. Understanding individual travel decision making is considered the key to promoting large-scale change in proenvironmental travel by public policy and education. The quality of the public transport service, travel characteristics, and personal characteristics are confirmed to exert a significant impact on travel choices [19]. Researchers usually choose factors (variables) according to the purpose of specific research topic and considering the difficulties in data collecting. In this paper, we mainly considered the following aspects when selecting variables as done in similar research: (1) the variables group should effectively express the main relationship between travel decision and factors; (2) when satisfying the first consideration,

a feasible and economical investigation should be taken into our consideration. 3. Model Traditionally, aggregate models were always used to study the influence of psychological variables, but when attempting to investigate the influence of some situational factors, a disaggregate model will be more appropriate. The main reason is that the situational factors can be measured although they Carfilzomib are varied, while psychological variables cannot be measured directly, although they are relatively stable [20]. The basic assumption of the disaggregate model is that when travelers are faced with travel mode choices, the “utility value” of choices can be used to describe travelers’ preference for each travel mode. Utility is the function of the selected object’s properties and the decision makers’ characteristics. The disaggregate model is based on utility maximization theory and random utility theory.

For finding more appropriate cluster centers, a generalized FCM optimized by PSO algorithm [17] was proposed. Shadowed sets are considered as a conceptual and algorithmic bridge between rough sets and fuzzy sets, thereby incorporate the generic merits, and have been successfully used for unsupervised learning. order TBC-11251 Shadowed sets introduce (0,1) interval to denote the belongingness of those clustering points, and the uncertainty among patterns lying in the shadowed

region is efficiently handled in terms of membership. Thus, in order to disambiguate and capture the essence of a distribution, recently the concept of shadowed sets has been introduced [18], which can also raise the efficiency in the iteration process of the new prototypes by eliminating some “bad points” that have bad influence

on cluster structure [19, 20]. Compared with FCM, the capability of shadowed c-means is enhanced when dealing with outlier [21]. Although lots of clustering algorithms based on FCM, PSO, or shadowed sets were proposed, most of them need to input the preestimated cluster number C. To obtain the desirable cluster partitions in a given data, commonly C is set manually, and this is a very subjective and somewhat arbitrary process. A number of approaches have been proposed to select the appropriate C. Bezdek et al. [22] suggested the rule of thumb C ≤ N1/2 where the upper bound must be determined based on knowledge or applications about the data. Another approach is to use a cluster validity index as a measure criterion about the data partition, such as Davies-Bouldin (DB) [23], Xie-Beni (XB) [24], and Dunn

[25] indices. These indices often follow the principle that the distance between objects in the same cluster should be as small as possible and the distance between objects in different clusters should be as large as possible. They have also been used to acquire the optimal number of clusters C according to their maximum or minimum value. Therefore, we wish to find the best C in some range, obtain cluster partitions by considering compactness and intercluster separation, and reduce the sensitivity to initial values. Here, we propose a modified algorithm named as SP-FCM which Entinostat integrates the merits of PSO and interleaves shadowed sets between stabilization iterations. And it can automatically estimate the optimal cluster number with a faster initialization than our previous approach. The structure of the paper is as follows. Section 2 outlines all necessary prerequisites. In Section 3, a new clustering approach called SP-FCM is presented for automatically finding the optimal cluster number. Section 4 includes the results of experiments involving UCI data sets, yeast gene expression data sets, and real data set. In Section 5, main conclusions are covered. 2.