25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Dirichlet Process with Mixed Random Measures: A Nonparametric Topic Model for Labeled Data

We describe a nonparametric topic model for labeled data. The model uses a mixture of random measures (MRM) as a base distribution of the Dirichlet process (DP) of the HDP framework, so we call it the DPMRM. To model labeled data, we de ne a DP distributed random measure for each label, and the resulting model generates an unbounded number of topics for each label. We apply DP-MRM on single-labeled and multi-labeled corpora of documents and compare the performance on label prediction with MedLDA, LDA-SVM, and Labeled-LDA. We further enhance the model by incorporating ddCRP and modeling multi-labeled images for image segmentation and object labeling, comparing the performance with nCuts and rddCRP

Do You Feel What I Feel? Social Aspects of Emotions in Twitter Conversations

We present a computational framework for understanding the social aspects of emotions in Twitter conversations. Using unannotated data and semisupervised machine learning, we look at emotional transitions, emotional influences among the conversation partners, and patterns in the overall emotional exchanges. We find that conversational partners usually express the same emotion, which we name Emotion accommodation, but when they do not, one of the conversational partners tends to respond with a positive emotion. We also show that tweets containing sympathy, apology, and complaint are significant emotion influencers. We verify the emotion classification part of our framework by a human-annotated corpus

Direct Wiring of Eutectic Gallium-Indium to a Metal Electrode for Soft Sensor Systems

For wider applications of the liquid metal-based stretchable electronics, an electrical interface has remained as a crucial issue, due to its fragile electromechanical stability and complex fabrication steps. In this study, a direct writing-based technique is introduced to form the writing paths of conductive liquid metal (eutectic Gallium-Indium, eGaIn) and electrical connections to off-the-shelf metal electrodes in a single process. Specifically, by extending eGaIn wires written on a silicone substrate, the eGaIn wires were physically connected to the five different metal electrodes, of which stability as an electrical connection was investigated. Among the five different surface materials, the metal electrode finished by electroless nickel immersion gold (ENIG) had reproducible and low contact resistance without time-dependent variation. In our experiments, it was verified that the electrode part made by an ENIG-finished flexible flat cable (FFC) were mechanically (strain???100 %, pressure???600 kPa) and thermally (temperature???180 Celsius) durable. By modifying trajectories of eGaIn wires, soft sensor systems were fabricated and tested to measure finger joint angles and ground reaction forces, composed with 10 sensing units, respectively. The proposed method enables the eGaIn-based soft sensors or circuits to be connected to the typical electronic components through a FFC or weldable surfaces, using only off-the-shelf materials without additional mechanical or chemical treatments

Direct Writing-based Wiring of Liquid Metal to a Metal Electrode for Soft Sensor Systems

As demands for stretchable electronics have increased in the field of wearable devices, liquid metal, such as eutectic Gallium-Indium (eGaIn), has gained much attention due to its metallic conductivity with liquid reconfigurability. Although various applications have been suggested using eGaIn, an electrical connection has remained as a technical challenge. Wires have been directly inserted into the microfluidic channel filled with eGaIn, resulting in electromechanically unstable connection, bulky size, and time consuming fabrication steps for the electrode part. In this study, a novel solution for the electrode is proposed, connecting eGaIn wires directly to the metal electrode based on direct writing of eGaIn. The two electrode materials were considered as candidates, including electroless nickel immersion gold (ENIG) and immersion tin (Im-Sn) plated surfaces. Among them, only the ENIG-finished surface had stable electrical connection with eGaIn, allowing sufficiently low contact resistance. The suggested electrode part was mechanically durable under strain up to 100 %. As an application, a sensing skin embedding 10 sensing units was fabricated based on direct ink writing, using a flexible flat cable finished by ENIG plating

Modeling Topic Hierarchies with the Recursive Chinese Restaurant Process

Topic models such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet processes (HDP) are simple solutions to discover topics from a set of unannotated documents. While they are simple and popular, a major shortcoming of LDA and HDP is that they do not organize the topics into a hierarchical structure which is naturally found in many datasets. We introduce the recursive Chinese restaurant process (rCRP) and a nonparametric topic model with rCRP as a prior for discovering a hierarchical topic structure with unbounded depth and width. Unlike previous models for discovering topic hierarchies, rCRP allows the documents to be generated from a mixture over the entire set of topics in the hierarchy. We apply rCRP to a corpus of New York Times articles, a dataset of MovieLens ratings, and a set of Wikipedia articles and show the discovered topic hierarchies. We compare the predictive power of rCRP with LDA, HDP, and nested Chinese restaurant process (nCRP) using heldout likelihood to show that rCRP outperforms the others. We suggest two metrics that quantify the characteristics of a topic hierarchy to compare the discovered topic hierarchies of rCRP and nCRP. The results show that rCRP discovers a hierarchy in which the topics become more specialized toward the leaves, and topics in the immediate family exhibit more affinity than topics beyond the immediate family.This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0026507)

Modeling topic hierarchies with the recursive chinese restaurant process

Topic models such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet processes (HDP) are simple solutions to discover topics from a set of unannotated documents. While they are simple and popular, a major shortcoming of LDA and HDP is that they do not organize the top-ics into a hierarchical structure which is naturally found in many datasets. We introduce the recursive Chinese restau-rant process (rCRP) and a nonparametric topic model with rCRP as a prior for discovering a hierarchical topic structure with unbounded depth and width. Unlike previous models for discovering topic hierarchies, rCRP allows the documents to be generated from a mixture over the entire set of topics in the hierarchy. We apply rCRP to a corpus of New York Times articles, a dataset of MovieLens ratings, and a set of Wikipedia articles and show the discovered topic hierarchies. We compare the predictive power of rCRP with LDA, HDP, and nested Chinese restaurant process (nCRP) using held-out likelihood to show that rCRP outperforms the others. We suggest two metrics that quantify the characteristics of a topic hierarchy to compare the discovered topic hierarchies of rCRP and nCRP. The results show that rCRP discovers a hierarchy in which the topics become more specialized to-ward the leaves, and topics in the immediate family exhibit more affinity than topics beyond the immediate family