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

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Improvement of Copper Metal Leaching in Sulfuric Acid Solution by Simultaneous Use of Oxygen and Cupric Ions

A new concept for copper (Cu) metal leaching by the simultaneous use of cupric ions (Cu2+) and oxygen (O-2) was proposed to improve Cu metal leaching in sulfuric acid. According to this concept, Cu(2+)oxidizes Cu metal into cuprous ion (Cu+), and O(2)oxidizes Cu(+)into Cu2+. The improvement in Cu leaching efficiency from Cu metal was investigated experimentally in the sulfuric acid solution using Cu(2+)and O(2)simultaneously. Furthermore, the result was compared with that for the sulfuric acid solution containing neither Cu(2+)nor O(2)and with the sulfuric acid solution without Cu(2+)and O-2. When both Cu(2+)and O(2)were used in the leaching solution, the leaching rate of Cu from Cu metal powder was higher than at other leaching conditions, and the leaching efficiency of Cu increased to more than 99.9% in 1 mol/L sulfuric acid solution at 400 rpm and 50 degrees C with <= 75 mu m Cu metal powder, 1% pulp density, 10,000 mg/L initial Cu(2+)concentration, and 100 cc/min O(2)introduction. These results indicated that the leaching of Cu from Cu metal could be accelerated by adding Cu(2+)and O(2)in the sulfuric acid solution

Enhanced Cementation of Co2+ and Ni2+ from Sulfate and Chloride Solutions Using Aluminum as an Electron Donor and Conductive Particles as an Electron Pathway

Cobalt and nickel have become important strategic resources because they are widely used for renewable energy technologies and rechargeable battery production. Cementation, an electrochemical deposition of noble metal ions using a less noble metal as an electron donor, is an important option to recover Co and Ni from dilute aqueous solutions of these metal ions. In this study, cementation experiments for recovering Co2+ and Ni2+ from sulfate and chloride solutions (pH = 4) were conducted at 298 K using Al powder as electron donor, and the effects of additives such as activated carbon (AC), TiO2, and SiO2 powders on the cementation efficiency were investigated. Without additives, cementation efficiencies of Co2+ and Ni2+ were almost zero in both sulfate and chloride solutions, mainly because of the presence of an aluminum oxide layer (Al2O3) on an Al surface, which inhibits electron transfer from Al to the metal ions. Addition of nonconductor (SiO2) did not affect the cementation efficiencies of Co2+ and Ni2+ using Al as electron donor, while addition of (semi)conductors such as AC or TiO2 enhanced the cementation efficiencies significantly. The results of surface analysis (Auger electron spectroscopy) for the cementation products when using TiO2/Al mixture showed that Co and Ni were deposited on TiO2 particles attached on the Al surface. This result suggests that conductors such as TiO2 act as an electron pathway from Al to Co2+ and Ni2+, even when an Al oxide layer covered on an Al surface

Visual Domain Adaptation by Consensus-Based Transfer to Intermediate Domain

We describe an unsupervised domain adaptation framework for images by a transform to an abstract intermediate domain and ensemble classifiers seeking a consensus. The intermediate domain can be thought as a latent domain where both the source and target domains can be transferred easily. The proposed framework aligns both domains to the intermediate domain, which greatly improves the adaptation performance when the source and target domains are notably dissimilar. In addition, we propose an ensemble model trained by confusing multiple classifiers and letting them make a consensus alternately to enhance the adaptation performance for ambiguous samples. To estimate the hidden intermediate domain and the unknown labels of the target domain simultaneously, we develop a training algorithm using a double-structured architecture. We validate the proposed framework in hard adaptation scenarios with real-world datasets from simple synthetic domains to complex real-world domains. The proposed algorithm outperforms the previous state-of-the-art algorithms on various environments

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clustering method based on path similarities of XML data

High Reynolds Number Airfoil: From Wall-Resolved to Wall-Modeled LES

peer reviewedWall-Modeled Large-Eddy Simulation (WMLES) alleviates the near-wall grid requirement by employing a wall-model to reconstruct the wall shear-stress. In this way, WMLES simultaneously reduces the computational cost associated with Wall-Resolved LES (WRLES) and opens the door towards higher Reynolds numbers (Piomelli, Wall-modeled large-eddy simulations: present status and prospects. Springer, Netherlands, 2010, [1], Larsson et al, Mech Eng Rev, 3, 2016, [2]