Conductance Ratios and Cellular Identity

Recent experimental evidence suggests that coordinated expression of ion channels plays a role in constraining neuronal electrical activity. In particular, each neuronal cell type of the crustacean stomatogastric ganglion exhibits a unique set of positive linear correlations between ionic membrane conductances. These data suggest a causal relationship between expressed conductance correlations and features of cellular identity, namely electrical activity type. To test this idea, we used an existing database of conductance-based model neurons. We partitioned this database based on various measures of intrinsic activity, to approximate distinctions between biological cell types. We then tested individual conductance pairs for linear dependence to identify correlations. Contrary to experimental evidence, in which all conductance correlations are positive, 32% of correlations seen in this database were negative relationships. In addition, 80% of correlations seen here involved at least one calcium conductance, which have been difficult to measure experimentally. Similar to experimental results, each activity type investigated had a unique combination of correlated conductances. Finally, we found that populations of models that conform to a specific conductance correlation have a higher likelihood of exhibiting a particular feature of electrical activity. We conclude that regulating conductance ratios can support proper electrical activity of a wide range of cell types, particularly when the identity of the cell is well-defined by one or two features of its activity. Furthermore, we predict that previously unseen negative correlations and correlations involving calcium conductances are biologically plausible

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Robust transmission of rate coding in the inhibitory Purkinje cell to cerebellar nuclei pathway in awake mice.

Neural coding through inhibitory projection pathways remains poorly understood. We analyze the transmission properties of the Purkinje cell (PC) to cerebellar nucleus (CN) pathway in a modeling study using a data set recorded in awake mice containing respiratory rate modulation. We find that inhibitory transmission from tonically active PCs can transmit a behavioral rate code with high fidelity. We parameterized the required population code in PC activity and determined that 20% of PC inputs to a full compartmental CN neuron model need to be rate-comodulated for transmission of a rate code. Rate covariance in PC inputs also accounts for the high coefficient of variation in CN spike trains, while the balance between excitation and inhibition determines spike rate and local spike train variability. Overall, our modeling study can fully account for observed spike train properties of cerebellar output in awake mice, and strongly supports rate coding in the cerebellum

Comparison of CN simulations with 500 vs. 50 PC AST inputs.

<p><b>A.</b> The unitary conductance was divided by 10 for 500 inputs to result in a matching mean inhibitory input conductance. <b>B,D,E.</b> Conventions as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005578#pcbi.1005578.g006" target="_blank">Fig 6</a>. <b>C.</b> The 500 PC inputs lead to a much reduced spike rate. <b>F.</b> Difference between (E) and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005578#pcbi.1005578.g006" target="_blank">Fig 6B</a>. PSTH mean peak frequency changes are generally similar, but higher by up to 14 Hz for G_ex: L with 50 PC inputs.</p

Recorded and simulated peri-stimulus time histograms (PSTH) for respiration.

<p>The median respiratory interval in our data set was 242 ms (respiratory frequency of 3.9 Hz), therefore approximately one full respiratory cycle is shown to each side of the event trigger. <b>A,B.</b> Spike raster plot for sample PC recording (top) and average PSTH (bottom). <b>C,D.</b> Spike raster plot for PC AST made from the shown sample average PSTH (A) convolved into the rate template from a different recorded PC without respiratory modulation at the time of each respiratory event. <b>E,F.</b> Raster plot (top) and average PSTH (bottom) of a sample CN neuron aligned to respiration. Note that this CN neuron is not recorded at the same time and its phase of modulation is not driven by the PC neuron shown in panel A,B. <b>G,H.</b> Simulated CN neuron respiratory PSTH resulting from simulation with 50% of PC inputs incorporating respiratory modulation as depicted in C,D). The dot sizes in the raster plots were adapted to the mean rate of each spike train to best depict modulation. Note that the phase of the modulation in the CN simulation is not targeted to match the phase of the CN recording, but is the inverse of the phase of respiratory modulation in the PC ASTs (Fig 5D) due to the inhibitory nature of PC inputs onto CN neurons.</p