GenNet: A Platform for Hybrid Network Experiments

We describe General Network (GenNet), a software plugin for the real time experimental interface (RTXI) dynamic clamp system that allows for straightforward and flexible implementation of hybrid network experiments. This extension to RTXI allows for hybrid networks that contain an arbitrary number of simulated and real neurons, significantly improving upon previous solutions that were limited, particularly by the number of cells supported. The benefits of this system include the ability to rapidly and easily set up and perform scalable experiments with hybrid networks and the ability to scan through ranges of parameters. We present instructions for installing, running and using GenNet for hybrid network experiments and provide several example uses of the system

Robustness and fault tolerance make brains harder to study

Brains increase the survival value of organisms by being robust and fault tolerant. That is, brain circuits continue to operate as the organism needs, even when the circuit properties are significantly perturbed. Kispersky and colleagues, in a recent paper in Neural Systems & Circuits, have found that Granger Causality analysis, an important method used to infer circuit connections from the behavior of neurons within the circuit, is defeated by the mechanisms that give rise to this robustness and fault tolerance

The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference

Background: Wiener-Granger causality (“G-causality”) is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences. Originally developed in the context of econometric theory, it has since achieved broad application in the neurosciences and beyond. Prediction in the G-causality formalism is based on VAR (Vector AutoRegressive) modelling. New Method: The MVGC Matlab c Toolbox approach to G-causal inference is based on multiple equivalent representations of a VAR model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral density of the underlying process. It features a variety of algorithms for moving between these representations, enabling selection of the most suitable algorithms with regard to computational efficiency and numerical accuracy. Results: In this paper we explain the theoretical basis, computational strategy and application to empirical G-causal inference of the MVGC Toolbox. We also show via numerical simulations the advantages of our Toolbox over previous methods in terms of computational accuracy and statistical inference. Comparison with Existing Method(s): The standard method of computing G-causality involves estimation of parameters for both a full and a nested (reduced) VAR model. The MVGC approach, by contrast, avoids explicit estimation of the reduced model, thus eliminating a source of estimation error and improving statistical power, and in addition facilitates fast and accurate estimation of the computationally awkward case of conditional G-causality in the frequency domain. Conclusions: The MVGC Toolbox implements a flexible, powerful and efficient approach to G-causal inference. Keywords: Granger causality, vector autoregressive modelling, time series analysi

The Mechanism of Abrupt Transition between Theta and Hyper-Excitable Spiking Activity in Medial Entorhinal Cortex Layer II Stellate Cells

Recent studies have shown that stellate cells (SCs) of the medial entorhinal cortex become hyper-excitable in animal models of temporal lobe epilepsy. These studies have also demonstrated the existence of recurrent connections among SCs, reduced levels of recurrent inhibition in epileptic networks as compared to control ones, and comparable levels of recurrent excitation among SCs in both network types. In this work, we investigate the biophysical and dynamic mechanism of generation of the fast time scale corresponding to hyper-excitable firing and the transition between theta and fast firing frequency activity in SCs. We show that recurrently connected minimal networks of SCs exhibit abrupt, threshold-like transition between theta and hyper-excitable firing frequencies as the result of small changes in the maximal synaptic (AMPAergic) conductance. The threshold required for this transition is modulated by synaptic inhibition. Similar abrupt transition between firing frequency regimes can be observed in single, self-coupled SCs, which represent a network of recurrently coupled neurons synchronized in phase, but not in synaptically isolated SCs as the result of changes in the levels of the tonic drive. Using dynamical systems tools (phase-space analysis), we explain the dynamic mechanism underlying the genesis of the fast time scale and the abrupt transition between firing frequency regimes, their dependence on the intrinsic SC's currents and synaptic excitation. This abrupt transition is mechanistically different from others observed in similar networks with different cell types. Most notably, there is no bistability involved. ‘In vitro’ experiments using single SCs self-coupled with dynamic clamp show the abrupt transition between firing frequency regimes, and demonstrate that our theoretical predictions are not an artifact of the model. In addition, these experiments show that high-frequency firing is burst-like with a duration modulated by an M-current

Membrane Properties and the Balance between Excitation and Inhibition Control Gamma-Frequency Oscillations Arising from Feedback Inhibition

Computational studies as well as in vivo and in vitro results have shown that many cortical neurons fire in a highly irregular manner and at low average firing rates. These patterns seem to persist even when highly rhythmic signals are recorded by local field potential electrodes or other methods that quantify the summed behavior of a local population. Models of the 30–80 Hz gamma rhythm in which network oscillations arise through ‘stochastic synchrony’ capture the variability observed in the spike output of single cells while preserving network-level organization. We extend upon these results by constructing model networks constrained by experimental measurements and using them to probe the effect of biophysical parameters on network-level activity. We find in simulations that gamma-frequency oscillations are enabled by a high level of incoherent synaptic conductance input, similar to the barrage of noisy synaptic input that cortical neurons have been shown to receive in vivo. This incoherent synaptic input increases the emergent network frequency by shortening the time scale of the membrane in excitatory neurons and by reducing the temporal separation between excitation and inhibition due to decreased spike latency in inhibitory neurons. These mechanisms are demonstrated in simulations and in vitro current-clamp and dynamic-clamp experiments. Simulation results further indicate that the membrane potential noise amplitude has a large impact on network frequency and that the balance between excitatory and inhibitory currents controls network stability and sensitivity to external inputs

Dynamic Effective Connectivity of Inter-Areal Brain Circuits

Anatomic connections between brain areas affect information flow between neuronal circuits and the synchronization of neuronal activity. However, such structural connectivity does not coincide with effective connectivity (or, more precisely, causal connectivity), related to the elusive question “Which areas cause the present activity of which others?”. Effective connectivity is directed and depends flexibly on contexts and tasks. Here we show that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity. Integrating simulation and semi-analytic approaches, we study mesoscale network motifs of interacting cortical areas, modeled as large random networks of spiking neurons or as simple rate units. Through a causal analysis of time-series of model neural activity, we show that different dynamical states generated by a same structural connectivity motif correspond to distinct effective connectivity motifs. Such effective motifs can display a dominant directionality, due to spontaneous symmetry breaking and effective entrainment between local brain rhythms, although all connections in the considered structural motifs are reciprocal. We show then that transitions between effective connectivity configurations (like, for instance, reversal in the direction of inter-areal interactions) can be triggered reliably by brief perturbation inputs, properly timed with respect to an ongoing local oscillation, without the need for plastic synaptic changes. Finally, we analyze how the information encoded in spiking patterns of a local neuronal population is propagated across a fixed structural connectivity motif, demonstrating that changes in the active effective connectivity regulate both the efficiency and the directionality of information transfer. Previous studies stressed the role played by coherent oscillations in establishing efficient communication between distant areas. Going beyond these early proposals, we advance here that dynamic interactions between brain rhythms provide as well the basis for the self-organized control of this “communication-through-coherence”, making thus possible a fast “on-demand” reconfiguration of global information routing modalities

Kinetic and spectroscopic study of catalysts for water-gas shift and nitrogen oxide removal

Nitrogen oxides (NOx) are formed in high temperature combustion processes such as in power generation and motor vehicles. Increasingly stringent regulation of these harmful emissions continues to drive interest in developing, understanding and studying new catalytic formulations for exhaust aftertreatment. For mobile sources, predominantly heavy duty diesel engines, selective catalytic reduction (SCR) with NH3 has become the principal means of NO x abatement. An alternative technology developed, but now surpassed by SCR, is NOx Storage Reduction (NSR) catalysis. Both technologies have been studied in our laboratory and are the basis for this dissertation. We studied seven different lean NOx trap (LNT) monolith formulations for NSR ranging from 0.6 to 6.2 wt.% Pt and 4 to 20 wt.% Ba loadings on γ-Al 2O3. The noble metal component of a LNT oxidizes NO to NO 2 aiding in the storage of NO2 on the alkaline earth component. Before the storage component saturates, a reductant such as H2 is introduced into the vehicular exhaust and the stored NOx is released and reduced to N2. Once the storage component is free of NOx, reductant flow is ceased and storage is begun anew. Our research focused on understanding the effects that CO2 and H2O have on the storage capacity of the LNT over short as well as extended periods of time. We found that for high Ba loadings, CO 2 had a consistently detrimental effect on the fast NOx storage capacity (NSC), defined as the amount of NOx the catalyst can store before 1% of the inlet NOx is measured in the reactor outlet. Over long NOx storage periods, CO2 continued to inhibit storage compared to the same catalyst in CO2 free conditions. On low loadings of Ba, however, the inhibition of CO2 was significantly reduced. We found that the loading dependent characteristics of the Ba phase affected the way in which CO2 adsorbed on the storage component, which greatly affected the stability of the species on lower Ba loadings. The less stable adsorbed CO2 proved less competitive for NOx sorption sites, explaining the weak reduction of NSC by CO2 on low Ba loadings. Contrary to CO2, H2O exhibited both beneficial and inhibitory effects on the NSC. Over long periods of time, and at high Ba loadings, the addition of H2O in the feed increased the NSC, attributed to enhanced O2 spillover on the hydroxylated Ba surface allowing greater access to available NOx storage sites. When the Ba loading was reduced, the interaction sphere of Pt particles with the Ba storage component required for O2 to spillover to assist in NOx storage was reduced. Thus, despite the enhanced spillover capacity of oxygen on the hydroxylated storage component other NSC decreasing effects of H2O addition, such as Ba agglomeration, became more dominant and reduced the NSC. Recent developments in selective catalytic reduction have shown Cu and Fe/chabazite (CHA) based zeolites to be particularly well suited to sustaining high catalytic rates without degradation in the harsh environment of diesel engine exhaust. Little has been published about these catalysts as the academic community has just recently learned about the materials and their commercial implementation. Using operando X-ray absorption spectroscopy, combined with first-principles thermodynamics simulations and kinetic analysis, we have studied the nature of the Cu active site on Cu/SSZ-13, Cu/SAPO-34 and Cu/ZSM-5. Examining the catalysts under operando standard SCR conditions (300 ppm NO, 300 ppm NH3, 5% O2, 5% H2O and 5% CO2) showed the catalyst to be in a mixed Cu(I)-Cu(II) oxidation state. Neither the amount of Cu(I) nor Cu(II) individually correlated with the different rates measured on the various zeolite catalysts, and so we proposed that the SCR reaction progresses via a redox mechanism requiring both Cu(I) and Cu(II). First principles thermodynamic calculations found that the redox couple of Cu(I)H2O and Cu(II)(OH)2 were the most thermodynamically stable species of any of the OxHy variants modeled on Cu. The redox nature of the Cu active site was further investigated in a follow up study isolating the reducing portion of the SCR by removing O 2 from the reaction feed. Cutting off O2 drove the catalyst into a highly reduced state dominated by Cu(I) while removing a reductant drove the Cu into the fully oxidized state. Our research shows that not only is redox a vital part of the SCR reaction on Cu/zeolites, but that the oxidation state of the active site is highly sensitive to the gas environment. The water-gas shift (WGS) reaction is an industrially important step in H2 generation from steam reforming. I have had the opportunity to contribute to a number of studies in WGS by studying the catalysts in FTIR. We studied numerous catalytic formulations including Fe promoted Pd/Al 2O3 and Au/TiO2. We found that the Fe promoted the WGS rate of the catalyst by a factor of 160 compared to the Fe free Pd/Al 2O3. The reduced Fe promoter efficiently split H2O, typically the role performed by reducible supports, and the nearby noble metal particles provided spillover H2 to maintain the reduced Fe phase necessary to split H2O. Our study of Au/TiO2 involved the development of a modified operando transmission IR cell with ultra-low dead volume allowing for fast switching isotope experiments over the catalyst. The isotope switching experiments showed that only CO adsorbed on Au0 sites was an active surface intermediate at 120°C. Counting the amount of active surface Au atoms for the reaction ruled out the Au particle surface and perimeter atoms as the dominant active sites and confirmed our previous finding that the active site was composed mostly of low coordinated corner Au atoms

Synthesis of Nitro Plastics

Abstract not availabl