Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networks.

Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Seminal theoretical results on dynamics of such networks are based on the assumption that synaptic strengths depend on the type of neurons they connect, but are otherwise statistically independent. Recent synaptic physiology datasets however highlight the prominence of specific connectivity patterns that go well beyond what is expected from independent connections.

Relating local connectivity and global dynamics in recurrent excitatory-inhibitory networks.

How the connectivity of cortical networks determines the neural dynamics and the resulting computations is one of the key questions in neuroscience. Previous works have pursued two complementary approaches to quantify the structure in connectivity. One approach starts from the perspective of biological experiments where only the local statistics of connectivity motifs between small groups of neurons are accessible.

Recurrent networks endowed with structural priors explain suboptimal animal behavior.

The strategies found by animals facing a new task are determined both by individual experience and by structural priors evolved to leverage the statistics of natural environments. Rats quickly learn to capitalize on the trial sequence correlations of two-alternative forced choice (2AFC) tasks after correct trials but consistently deviate from optimal behavior after error trials. To understand this outcome-dependent gating, we first show that recurrent neural networks (RNNs) trained in the same 2AFC task outperform rats as they can readily learn to use across-trial information both after correct and error trials.

Dimensional reduction of emergent spatiotemporal cortical dynamics via a maximum entropy moment closure.

Modern electrophysiological recordings and optical imaging techniques have revealed a diverse spectrum of spatiotemporal neural activities underlying fundamental cognitive processing. Oscillations, traveling waves and other complex population dynamical patterns are often concomitant with sensory processing, information transfer, decision making and memory consolidation. While neural population models such as neural mass, population density and kinetic theoretical models have been used to capture a wide range of the experimentally observed dynamics, a full account of how the multi-scale dynamics emerges from the detailed biophysical properties of individual neurons and the network architecture remains elusive.

A Mechanism for Synaptic Copy Between Neural Circuits.

Cortical oscillations are central to information transfer in neural systems. Significant evidence supports the idea that coincident spike input can allow the neural threshold to be overcome and spikes to be propagated downstream in a circuit. Thus, an observation of oscillations in neural circuits would be an indication that repeated synchronous spiking may be enabling information transfer.

A coarse-graining framework for spiking neuronal networks: from strongly-coupled conductance-based integrate-and-fire neurons to augmented systems of ODEs.

Homogeneously structured, fluctuation-driven networks of spiking neurons can exhibit a wide variety of dynamical behaviors, ranging from homogeneity to synchrony. We extend our partitioned-ensemble average (PEA) formalism proposed in Zhang et al. (Journal of Computational Neuroscience, 37(1), 81-104, 2014a) to systematically coarse grain the heterogeneous dynamics of strongly coupled, conductance-based integrate-and-fire neuronal networks.

Analysis of Six β-Lactam Residues in Milk and Egg by Micellar Electrokinetic Chromatography with Large-Volume Sample Stacking and Polarity Switching.

A new micellar electrokinetic chromatography method with large-volume sample stacking and polarity switching was developed to analyze amoxicllin, cephalexin, oxacillin, penicillin G, cefazolin, and cefoperazone in milk and egg. The important parameters influencing separation and enrichment factors were optimized. The optimized running buffer consisted of 10 mM phosphate and 22 mM SDS at pH 6.

Determination of eight triazine herbicide residues in cereal and vegetable by micellar electrokinetic capillary chromatography with on-line sweeping.

A new method was developed for the determination of eight triazine herbicide residues in cereal and vegetable samples by on-line sweeping technique in micellar electrokinetic capillary chromatography (MEKC). Some factors affecting analyte enrichment and separation efficiency were examined. The optimum buffer was composed of 25 mM borate, 15 mM phosphate, 40 mM sodium dodecylsulfate (SDS) and 3% (v/v) of 1-propanol at pH 6.

On-line preconcentration and determination of six sulfonylurea herbicides in cereals by MEKC with large-volume sample stacking and polarity switching.

A new MEKC method with large-volume sample stacking and polarity switching was developed for on-line preconcentration and detection of sulfonylurea herbicide (SUH) residues in cereals, including nicosulfuron (NS), thifensulfuon (methyl) (TFM), tribenuron-methly (TBM), sulfometuron-methyl (SMM), pyrazosulfuron-ethyl (PSE), and chlorimuron-ethyl (CME). In order to achieve a high resolution and enrichment factor, several parameters were optimized, such as the pH of the running buffer, the concentration of the BGE and the SDS, the separate voltage, the sample size, the pH, and the electrolyte concentration of the sample. The optimal running buffer was composed of 30 mM borate and 80 mM SDS at pH 7.