Stimulus-dependent maximum entropy models of neural population codes

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. To be able to infer a model for this distribution from large-scale neural recordings, we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. The model is able to capture the single-cell response properties as well as the correlations in neural spiking due to shared stimulus and due to effective neuron-to-neuron connections. Here we show that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. As a result, the SDME model gives a more accurate account of single cell responses and in particular outperforms uncoupled models in reproducing the distributions of codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like surprise and information transmission in a neural population.Comment: 11 pages, 7 figure

Implementing Telemedicine in Medical Emergency Response: Concept of Operation for a Regional Telemedicine Hub

A regional telemedicine hub, providing linkage of a telemedicine command center with an extended network of clinical experts in the setting of a natural or intentional disaster, may facilitate future disaster response and improve patient outcomes. However, the health benefits derived from the use of telemedicine in disaster response have not been quantitatively analyzed. In this paper, we present a general model of the application of telemedicine to disaster response and evaluate a concept of operations for a regional telemedicine hub, which would create distributed surge capacity using regional telemedicine networks connecting available healthcare and telemedicine infrastructures to external expertise. Specifically, we investigate (1) the scope of potential use of telemedicine in disaster response; (2) the operational characteristics of a regional telemedicine hub using a new discrete-event simulation model of an earthquake scenario; and (3) the benefit that the affected population may gain from a coordinated regional telemedicine network

Multisensory Perceptual Learning of Temporal Order: Audiovisual Learning Transfers to Vision but Not Audition

Background: An outstanding question in sensory neuroscience is whether the perceived timing of events is mediated by a central supra-modal timing mechanism, or multiple modality-specific systems. We use a perceptual learning paradigm to address this question. Methodology/Principal Findings: Three groups were trained daily for 10 sessions on an auditory, a visual or a combined audiovisual temporal order judgment (TOJ). Groups were pre-tested on a range TOJ tasks within and between their group modality prior to learning so that transfer of any learning from the trained task could be measured by post-testing other tasks. Robust TOJ learning (reduced temporal order discrimination thresholds) occurred for all groups, although auditory learning (dichotic 500/2000 Hz tones) was slightly weaker than visual learning (lateralised grating patches). Crossmodal TOJs also displayed robust learning. Post-testing revealed that improvements in temporal resolution acquired during visual learning transferred within modality to other retinotopic locations and orientations, but not to auditory or crossmodal tasks. Auditory learning did not transfer to visual or crossmodal tasks, and neither did it transfer within audition to another frequency pair. In an interesting asymmetry, crossmodal learning transferred to all visual tasks but not to auditory tasks. Finally, in all conditions, learning to make TOJs for stimulus onsets did not transfer at all to discriminating temporal offsets. These data present a complex picture of timing processes

Metabolic Engineering of Cofactor F420 Production in Mycobacterium smegmatis

Cofactor F420 is a unique electron carrier in a number of microorganisms including Archaea and Mycobacteria. It has been shown that F420 has a direct and important role in archaeal energy metabolism whereas the role of F420 in mycobacterial metabolism has only begun to be uncovered in the last few years. It has been suggested that cofactor F420 has a role in the pathogenesis of M. tuberculosis, the causative agent of tuberculosis. In the absence of a commercial source for F420, M. smegmatis has previously been used to provide this cofactor for studies of the F420-dependent proteins from mycobacterial species. Three proteins have been shown to be involved in the F420 biosynthesis in Mycobacteria and three other proteins have been demonstrated to be involved in F420 metabolism. Here we report the over-expression of all of these proteins in M. smegmatis and testing of their importance for F420 production. The results indicate that co–expression of the F420 biosynthetic proteins can give rise to a much higher F420 production level. This was achieved by designing and preparing a new T7 promoter–based co-expression shuttle vector. A combination of co–expression of the F420 biosynthetic proteins and fine-tuning of the culture media has enabled us to achieve F420 production levels of up to 10 times higher compared with the wild type M. smegmatis strain. The high levels of the F420 produced in this study provide a suitable source of this cofactor for studies of F420-dependent proteins from other microorganisms and for possible biotechnological applications

Proteomic Analysis of Neisseria gonorrhoeae Biofilms Shows Shift to Anaerobic Respiration and Changes in Nutrient Transport and Outermembrane Proteins

Neisseria gonorrhoeae, the causative agent of gonorrhea, can form biofilms in vitro and in vivo. In biofilms, the organism is more resistant to antibiotic treatment and can serve as a reservoir for chronic infection. We have used stable isotope labeling by amino acids in cell culture (SILAC) to compare protein expression in biofilm and planktonic organisms. Two parallel populations of N. gonorrhoeae strain 1291, which is an arginine auxotroph, were grown for 48 h in continuous-flow chambers over glass, one supplemented with 13C6-arginine for planktonic organisms and the other with unlabeled arginine for biofilm growth. The biofilm and planktonic cells were harvested and lysed separately, and fractionated into three sequential protein extracts. Corresponding heavy (H) planktonic and light (L) biofilm protein extracts were mixed and separated by 1D SDS-PAGE gels, and samples were extensively analyzed by liquid chromatography-mass spectrometry. Overall, 757 proteins were identified, and 152 unique proteins met a 1.5-fold cutoff threshold for differential expression with p-values <0.05. Comparing biofilm to planktonic organisms, this set included 73 upregulated and 54 downregulated proteins. Nearly a third of the upregulated proteins were involved in energy metabolism, with cell envelope proteins making up the next largest group. Of the downregulated proteins, the largest groups were involved in protein synthesis and energy metabolism. These proteomics results were compared with our previously reported results from transcriptional profiling of gonococcal biofilms using microarrays. Nitrite reductase and cytochrome c peroxidase, key enzymes required for anaerobic growth, were detected as highly upregulated in both the proteomic and transcriptomic datasets. These and other protein expression changes observed in the present study were consistent with a shift to anaerobic respiration in gonococcal biofilms, although changes in membrane proteins not explicitly related to this shift may have other functions

Refinement and Pattern Formation in Neural Circuits by the Interaction of Traveling Waves with Spike-Timing Dependent Plasticity

Traveling waves in the developing brain are a prominent source of highly correlated spiking activity that may instruct the refinement of neural circuits. A candidate mechanism for mediating such refinement is spike-timing dependent plasticity (STDP), which translates correlated activity patterns into changes in synaptic strength. To assess the potential of these phenomena to build useful structure in developing neural circuits, we examined the interaction of wave activity with STDP rules in simple, biologically plausible models of spiking neurons. We derive an expression for the synaptic strength dynamics showing that, by mapping the time dependence of STDP into spatial interactions, traveling waves can build periodic synaptic connectivity patterns into feedforward circuits with a broad class of experimentally observed STDP rules. The spatial scale of the connectivity patterns increases with wave speed and STDP time constants. We verify these results with simulations and demonstrate their robustness to likely sources of noise. We show how this pattern formation ability, which is analogous to solutions of reaction-diffusion systems that have been widely applied to biological pattern formation, can be harnessed to instruct the refinement of postsynaptic receptive fields. Our results hold for rich, complex wave patterns in two dimensions and over several orders of magnitude in wave speeds and STDP time constants, and they provide predictions that can be tested under existing experimental paradigms. Our model generalizes across brain areas and STDP rules, allowing broad application to the ubiquitous occurrence of traveling waves and to wave-like activity patterns induced by moving stimuli

What lies between market and hierarchy? Insights from internalization theory and global value chain theory

In this paper, we suggest that internalization theory might be extended by incorporating complementary insights from GVC theory. More specifically, we argue that internalization theory can explain why lead firms might wish to externalize selected activities, but that it is largely silent on the mechanisms by which those lead firms might exercise control over the resultant externalized relationships with their GVC partners. We advance an explanation linking the choice of control mechanism to two factors: power asymmetries between the lead firms and their GVC partners, and the degree of codifiability of the information to be exchanged in the relationship