Multi-level neural communication across spatial and temporal scales supports various functions in an animal brain, including sensing, movement, memory formation, and decision-making. In this primer, we provide examples of the vast structural and functional mechanisms that implement communication, with a focus on cellular, network and behavioral levels. We then discuss the growing neural data and computational models that can be used to study these mechanisms with a few hands-on model examples. To unify the presented topics, we suggest a computational model to test a recently proposed spatial computing hypothesis for item encoding and retrieval in the brain during a short-term memory task.

Shortened time to knowledge discovery and adapting prior domain knowledge is a challenge for computational and data-intensive communities such as e.g., bioinformatics and neuroscience. The challenge for a domain scientist lies in the actions to obtain guidance through query of massive information from diverse text corpus comprising of a wide-ranging set of topics when: investigating new methods, developing new tools, or integrating datasets. In this paper, we propose a novel "domain-specific topic model" (DSTM) to discover latent knowledge patterns about relationships among research topics, tools and datasets from exemplary scientific domains. Our DSTM is a generative model that extends the Latent Dirichlet Allocation (LDA) model and uses the Markov chain Monte Carlo (MCMC) algorithm to infer latent patterns within a specific domain in an unsupervised manner. We apply our DSTM to large collections of data from bioinformatics and neuroscience domains that include more than 25,000 of papers over the last ten years, featuring hundreds of tools and datasets that are commonly used in relevant studies. Evaluation experiments based on generalization and information retrieval metrics show that our model has better performance than the state-of-the-art baseline models for discovering highly-specific latent topics within a domain. Lastly, we demonstrate applications that benefit from our DSTM to discover intra-domain, cross-domain and trend knowledge patterns.

Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration of recommenders in science gateways in order to spur research productivity, we present a novel "OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons).

Neuro-scientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. Although science gateways have democratized relevant high performance/throughput resources, users require expert knowledge about programming and infrastructure configuration that is beyond the repertoire of most neuroscience programs. These factors become deterrents for the successful adoption and the ultimate diffusion (i.e., systemic spread) of science gateways in the neuroscience community. In this paper, we present a novel intuitionistic fuzzy logic based conversational recommender that can provide guidance to users when using science gateways for research and education workflows. The users interact with a context-aware chatbot that is embedded within custom web-portals to obtain simulation tools/resources to accomplish their goals. In order to ensure user goals are met, the chatbot profiles a user's cyberinfrastructure and neuroscience domain proficiency level using a `usability quadrant' approach. Simulation of user queries for an exemplary neuroscience use case demonstrates that our chatbot can provide step-by-step navigational support and generate distinct responses based on user proficiency.

Machine learning techniques underlying Big Data analytics have the potential to benefit data intensive communities in e.g., bioinformatics and neuroscience domain sciences. Today's innovative advances in these domain communities are increasingly built upon multi-disciplinary knowledge discovery and cross-domain collaborations. Consequently, shortened time to knowledge discovery is a challenge when investigating new methods, developing new tools, or integrating datasets. The challenge for a domain scientist particularly lies in the actions to obtain guidance through query of massive information from diverse text corpus comprising of a wide-ranging set of topics. In this paper, we propose a novel "domain-specific topic model" (DSTM) that can drive conversational agents for users to discover latent knowledge patterns about relationships among research topics, tools and datasets from exemplar scientific domains. The goal of DSTM is to perform data mining to obtain meaningful guidance via a chatbot for domain scientists to choose the relevant tools or datasets pertinent to solving a computational and data intensive research problem at hand. Our DSTM is a Bayesian hierarchical model that extends the Latent Dirichlet Allocation (LDA) model and uses a Markov chain Monte Carlo algorithm to infer latent patterns within a specific domain in an unsupervised manner. We apply our DSTM to large collections of data from bioinformatics and neuroscience domains that include hundreds of papers from reputed journal archives, hundreds of tools and datasets. Through evaluation experiments with a perplexity metric, we show that our model has better generalization performance within a domain for discovering highly specific latent topics.

Neurotransmitter homeostasis in and around a synapse involves complex random processes such as diffusion, molecular binding, and uptake by glial transporters. A three-dimensional stochastic diffusion model of a synapse was developed to provide molecular-level details of neurotransmitter homeostasis not predicted by alternative models based on continuum approaches. The development was illustrated through an example case cortico-accumbens synapse that successfully integrated neuroadaptations observed after chronic cocaine. By incorporating cystine-glutamate exchanger as a nonsynaptic release site for glutamate, the stochastic model was used to quantify the relative contributions of synaptic and nonsynaptic sources to extracellular concentration and to estimate molecular influx rates into the perisynapse. A perturbation analysis showed that among the parameters considered, variation in surface density of glial transporters had the largest effect on glutamate concentrations. The stochastic diffusion model of the example synapse was further generalized to characterize glial morphology by studying the role of diffusion path length in supporting neurotransmitter gradients and isolating the synapse. For the same set of parameters, diffusion path length was found to be proportional to the gradient supported.

This paper proposes a learning method for the compensation of uncertainties, for a class of nonlinear systems. A sliding model control strategy is used for the robust control design after a prior stable learning phase. Gaussian networks are used to identify the uncertainties during this learning phase. Learning and control bounds are guaranteed by properly constructing the training structure. The proposed technique has been validated using a hardware example case of an electromechanical system. Experiments have shown that the inclusion of the proposed learning technique in the robust control design results in improved system performance.

Identification and control designs are considered using neural networks for a class of nonlinear partially known dynamic systems. Real-time implementation of two designs, a neural identifier and a proposed neural controller, using an experimental system, comparisons with two other neural networks as well as conventional schemes, and an implementation architecture are reported. The proposed control design facilitates incorporation of available knowledge about the structure of the system. The study also illustrates the inherent capability of neural networks to handle nonlinearities and perform control effectively for a real world system, based on minimal system information.