Elucidation on the Effect of Operating Temperature to the Transport Properties of Polymeric Membrane Using Molecular Simulation Tool

Existing reports of gas transport properties within polymeric membrane as a direct consequence of operating temperature are in a small number and have arrived in diverging conclusion. The scarcity has been associated to challenges in fabricating defect free membranes and empirical investigations of gas permeation performance at the laboratory scale that are often time consuming and costly. Molecular simulation has been proposed as a feasible alternative of experimentally studied materials to provide insights into gas transport characteristic. Hence, a sequence of molecular modelling procedures has been proposed to simulate polymeric membranes at varying operating temperatures in order to elucidate its effect to gas transport behaviour. The simulation model has been validated with experimental data through satisfactory agreement. Solubility has shown a decrement in value when increased in temperature (an average factor of 1.78), while the opposite has been observed for gas diffusivity (an average factor of 1.32) when the temperature is increased from 298.15Â K to 323.15Â K. In addition, it is found that permeability decreases by 1.36 times as the temperature is increased

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison

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)

Prediction-error neurons in circuits with multiple neuron types: formation, refinement, and functional implications.

SignificanceAn influential idea in neuroscience is that neural circuits do not only passively process sensory information but rather actively compare them with predictions thereof. A core element of this comparison is prediction-error neurons, the activity of which only changes upon mismatches between actual and predicted sensory stimuli. While it has been shown that these prediction-error neurons come in different variants, it is largely unresolved how they are simultaneously formed and shaped by highly interconnected neural networks. By using a computational model, we study the circuit-level mechanisms that give rise to different variants of prediction-error neurons. Our results shed light on the formation, refinement, and robustness of prediction-error circuits, an important step toward a better understanding of predictive processing

Data from: Defensive symbionts mediate species coexistence in phytophagous insects

1. Competition of two species for the same resource is expected to result in competitive exclusion of the inferior competitor. In natural communities, however, other antagonists and symbionts moderate competition. Thus we have to go beyond studying pairwise interactions. 2. Natural enemies may facilitate coexistence if they affect the superior competitor more strongly, or they can hinder coexistence via apparent competition. Less well studied is the role of symbionts, which may influence species coexistence in conjunction with enemies. 3. Eukaryotes commonly harbor microbial endosymbionts that provide protection against natural enemies, but are costly in their absence. Such defensive symbionts could thus mediate coexistence of species competing for the same resource, both in the presence and in the absence of enemies, but as yet there is little evidence for this claim. 4. We addressed this proposed role of defensive symbionts in replicated simple communities consisting of two aphid species sharing the same host plant and the same natural enemy, a parasitoid wasp. Both, one, or neither species were infected with a resistance-conferring symbiont, and they competed in the absence as well as the presence of parasitoids. 5. The symbiont had significant effects in the absence of parasitoids by lowering competitive ability especially in one species, but the effects were more dramatic in the presence of parasitoids. With both species protected by the symbiont, parasitoid densities remained low and both aphid species persisted. When neither species was protected, parasitoids drove both species to extinction. Surprisingly, the same outcome was observed when only one species was protected. The susceptible species supported high densities of parasitoids that also killed the resistant aphids via mechanisms other than parasitism, presumably by disturbing them to the point of starvation. This is an intriguing form of apparent competition. 6. Our results demonstrate an important role of defensive symbionts in insect communities through modifying species interactions. This highlights the need for experimental data when studying species coexistence in competitive networks. Furthermore, the observation that a susceptible host can negatively affect a resistant host via a shared parasitoid is an instructive insight for biological control