Network Plasticity as Bayesian Inference

General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference. But a model for that has been missing. We propose that inherently stochastic features of synaptic plasticity and spine motility enable cortical networks of neurons to carry out probabilistic inference by sampling from a posterior distribution of network configurations. This model provides a viable alternative to existing models that propose convergence of parameters to maximum likelihood values. It explains how priors on weight distributions and connection probabilities can be merged optimally with learned experience, how cortical networks can generalize learned information so well to novel experiences, and how they can compensate continuously for unforeseen disturbances of the network. The resulting new theory of network plasticity explains from a functional perspective a number of experimental data on stochastic aspects of synaptic plasticity that previously appeared to be quite puzzling.Comment: 33 pages, 5 figures, the supplement is available on the author's web page http://www.igi.tugraz.at/kappe

Multi-spectrum retrieval of Venus IR surface emissivity maps from VIRTIS/VEX nightside measurements at Themis Regio

Surface emissivity maps in the infrared can contribute to explore Venus’ geology. Nightside radiance spectra at Themis Regio acquired by the IR mapping channel of the Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS-M-IR) aboard Venus EXpress (VEX) are used to derive emissivity data from the three accessible spectral surface windows at 1.02, 1.10, and 1.18 μm. The measured spectra are simulated by applying a full radiative transfer model. Neglecting geologic activity, a multi-spectrum retrieval algorithm is utilized to determine the emissivity maps of the surface target as parameter vectors that are common to many spectrally resolved images that cover this target. Absolute emissivity values are difficult to obtain due to strong interferences from other parameters. The true emissivity mean of the target cannot be retrieved, nor can the emissivity mean of a retrieved map be strictly preset. The retrieved map can exhibit trends with latitude and topography that are probably artificial. Once the trends have been removed in a post-processing step, it can be observed that the magnitude of the resulting spatial emissivity fluctuations around their mean value increases with increasing mean value. A linear transformation is applied that converts the de-trended map to exhibit a defined emissivity mean value called reference emissivity, here 0.5, yielding the ‘renormalized emissivity map’ with accordingly transformed fluctuations. It is verified that renormalized emissivity maps are largely independent of the emissivity mean before renormalization, of modifications to interfering atmospheric, surface, and instrumental parameters, and of selected details of the retrieval pipeline and data calibration and preprocessing. Extremely large emissivity retrieval errors due to imperfect or unconsidered forward model parameters are effectively avoided. If the absolute emissivity at a given bin of the target were known, the absolute emissivity map of the entire target could be computed according to the mentioned transformation, assuming absent true trends with latitude and topography. Until then, the renormalized emissivities are interpreted as spatial variations relative to the reference emissivity. They represent an important step toward the retrieval of absolute emissivities. Renormalized emissivity maps of Themis Regio at the three surface windows are determined from 64 measurement repetitions. Retrieval errors are estimated by a statistical evaluation of maps derived from various disjoint selections of spectra and using different assumptions on the interfering parameters. Double standard deviation errors for the three surface windows amount to 3%, 8%, and 4%, respectively, allowing geologic interpretation. A comparison to results from an earlier error analysis based on synthetic spectra shows that unconsidered time variations of interfering atmospheric parameters are a major error source. Spatial variations of the 1.02 μm surface emissivity of 20% that correspond to the difference between unweathered granitic and basaltic rocks would be easily detectable, but such variations are ruled out for the studied target area. Emissivity anomalies of up to 8% are detected at both 1.02 and 1.18 μm. At present sensitivity, no anomalies are identified at 1.10 μm, but anomalies exceeding the determined error level can be excluded. With single standard deviation significance, all three maps show interesting spatial emissivity variations

Error analysis for retrieval of Venus' IR surface emissivity from VIRTIS/VEX measurements

Venus' surface emissivity data in the infrared can serve to explore the planet's geology. The only global data with high spectral, spatial, and temporal resolution and coverage at present is supplied by nightside emission measurements acquired by the Visible and InfraRed Thermal Imaging Spectrometer VIRTIS-M-IR (1.0-5.1 μm) aboard ESA's Venus Express. A radiative transfer simulation and a retrieval algorithm can be used to determine surface emissivity in the nightside spectral transparency windows located at 1.02, 1.10, and 1.18 μm. To obtain satisfactory fits to measured spectra, the retrieval pipeline also determines auxiliary parameters describing cloud properties from a certain spectral range. But spectral information content is limited, and emissivity is difficult to retrieve due to strong interferences from other parameters. Based on a selection of representative synthetic VIRTIS-M-IR spectra in the range 1.0-2.3 μm, this paper investigates emissivity retrieval errors that can be caused by interferences of atmospheric and surface parameters, by measurement noise, and by a priori data, and which retrieval pipeline leads to minimal errors. Retrieval of emissivity from a single spectrum is shown to fail due to extremely large errors, although the fits to the reference spectra are very good. Neglecting geologic activity, it is suggested to apply a multi-spectrum retrieval technique to retrieve emissivity relative to an initial value as a parameter that is common to several measured spectra that cover the same surface bin. Retrieved emissivity maps of targets with limited extension (a few thousand km) are then additively renormalized to remove spatially large scale deviations from the true emissivity map that are due to spatially slowly varying interfering parameters. Corresponding multi-spectrum retrieval errors are estimated by a statistical scaling of the single-spectrum retrieval errors and are listed for 25 measurement repetitions. For the best of the studied retrieval pipelines, temporally varying interfering atmospheric parameters (cloud parameters, minor gas abundances) contribute errors in the order of 3%-10% of the true emissivity, depending on the surface window, the reference spectrum, and assuming statistical independence of the parameters. Temporally constant interfering parameters that spatially vary on a scale of 100 km (surface elevation, interfering emissivities) add 9%-16%. Measurement noise with a standard deviation of 10e-4 W/(m2 sr μm) leads to additional 1%-4%. Reasonable modifications of a priori mean values have negligible impacts. Retrieved maps are most reliable at 1.02 μm. There is an overall tendency for better results for cases with small cloud opacity, high surface elevation, high emissivity, and small observation angle, but this depends on the emissivity window, retrieval pipeline, and measurement repetition number. Calibration, preprocessing, and simulation errors can lead to additional errors. Based on the presented results, a subsequent paper will discuss emissivity data retrieval for a selected surface target

Lower atmosphere minor gas abundances as retrieved from Venus Express VIRTIS-M-IR data at 2.3 µm

Minor gas abundances in the lower atmosphere of Venus׳ southern hemisphere are investigated using spectroscopic nightside measurements recorded by the Visible and InfraRed Thermal Imaging Spectrometer aboard ESA’s Venus Express mission in the moderate spectral resolution infrared mapping channel (VIRTIS-M-IR, 1–5 µm, FWHM=17 nm). The entire usable data archive is utilized including only radiation spectra sampled at long detector exposure times (≥3.3 s) during eight Venus solar days between April 2006 and October 2008. Combined radiative transfer and retrieval techniques are applied for a simultaneous determination of total cloud opacity and H2O, CO, and OCS abundances from the 2.3 µm atmospheric transparency window that sounds the altitude range between about 30 and 45 km. A wavelength-dependent CO2 opacity correction is considered. Zonal averages of CO abundances at 35 km increase by about 35% from (22.9±0.8) ppmv at equatorial latitudes to (31.0±2.1) ppmv at 65 °S and then decrease to (29.4±2.4) ppmv at 80 °S The±figures refer to the statistical variability of retrieved abundances. In accordance with earlier results, the observed latitudinal variation of tropospheric CO is consistent with a Hadley cell-like circulation. Dawn side CO abundances at high latitudes are slightly smaller than dusk side values by about 7%. The latitudinal distribution of OCS at 35 km is anticorrelated with that of CO, ranging from about (1.15±0.2) ppmv at 65 °S to (1.60±0.2) ppmv at low latitudes (poleward decrease of 28%). Zonal averages of H2O abundances near 35 km slightly decrease toward the South Pole by about 10%, and the hemispheric average is (32.0±1.3) ppmv. A significant local time dependence of OCS and H2O is not observed. Detailed analyses of individual spectrum retrieval errors for different atmospheric models reveal that CO abundance results are reliable (error 4–7%), while H2O and OCS results have lower confidence (errors 30–47% and 41–86%, respectively). SO2 abundances cannot reliably be retrieved from VIRTIS-M-IR spectra

Surface emissivity retrieval from VIRTIS/VEX data in the Quetzalpetlatl quadrangle on Venus based on the new MSR multi-spectrum retrieval technique

Surface emissivity is difficult and error-prone to retrieve from VIRTIS measurements of Venus’ nightside. A detailed radiative transfer forward model simulation is used to generate synthetic spectra for given atmospheric and surface parameters. The new MSR multi-spectrum retrieval technique is applied to retrieve atmospheric and surface parameters that allow the synthetic spectra to fit the measurements. The incorporation of expected spatial-temporal correlations between parameters describing a selection of contiguous measurements leads to much more reliable parameters, as does the retrieval of surface emissivity of a surface bin as a parameter that is common to measurements that repeatedly cover that bin, thereby neglecting geologic activity. The method is applied to Quetzalpetlatl quadrangle including the Lada Terra rise and the Quetzalpetlatl corona. This area combines corona-dominated rises, rifted volcanic rises, and large coronae structures. Retrieved emissivity at 1.02 μm is related to regional geologic units