Neural changes when actions change: Adaptation of strong and weak expectations

Repeated experiences with an event create the expectation that subsequent events will expose an analog structure. These spontaneous expectations rely on an internal model of the event that results from learning. But what happens when events change? Do experience-based internal models get adapted instantaneously, or is model adaptation a function of the solidity of, i.e., familiarity with, the corresponding internal model? The present fMRI study investigated the effects of model solidity on model adaptation in an action observation paradigm. Subjects were made acquainted with a set of action movies that displayed an altered script when encountered again in the scanning session. We found model adaptation to result in an attenuation of the premotor-parietal network for action observation. Model solidity was found to modulate activation in the parahippocampal gyrus and the anterior cerebellar lobules, where increased solidity correlated with activity increase. Finally, the comparison between early and late stages of learning indicated an effect of model solidity on adaptation rate. This contrast revealed the involvement of a fronto-mesial network of Brodmann area 10 and the ACC in those states of learning that were signified by high model solidity, no matter if the memorized original or the altered action model was the more solid component. Findings suggest that the revision of an internal model is dependent on its familiarity. Unwarranted adaptations, but also perseverations may thus be prevented

Nonlinear optical properties of push–pull polyenes for electro-optics

Improved nonlinear organic chromophores of varying conjugation length with either thiobarbituric acid or 3-dicyanomethylene-2,3-dihydrobenzothiophene-1,1-dioxide (FORON® Blue) acceptors have been synthesized and investigated for their nonlinear optical properties. Very large quadratic hyperpolarizabilities β(−2ω; ω, ω) have been found, up to 25,700×10^(−48) esu at λ=1.91 μm. In a guest–host polymer very high electro-optic (EO) coefficients, of up to 55 pm/V, have been determined at λ=1.31 μm with 20-wt % chromophore loading. We find good agreement between molecular parameters evaluated by electric-field-induced second-harmonic generation (EFISH) and the measurements of guest–host solid–solid solutions. The latter method is well suited to the determination of the product of dipole moment μ and hyperpolarizability β quickly and reliably at the wavelength of interest for EO applications without the complications associated with EFISH measurements

Estimating the functional dimensionality of neural representations.

Recent advances in multivariate fMRI analysis stress the importance of information inherent to voxel patterns. Key to interpreting these patterns is estimating the underlying dimensionality of neural representations. Dimensions may correspond to psychological dimensions, such as length and orientation, or involve other coding schemes. Unfortunately, the noise structure of fMRI data inflates dimensionality estimates and thus makes it difficult to assess the true underlying dimensionality of a pattern. To address this challenge, we developed a novel approach to identify brain regions that carry reliable task-modulated signal and to derive an estimate of the signal's functional dimensionality. We combined singular value decomposition with cross-validation to find the best low-dimensional projection of a pattern of voxel-responses at a single-subject level. Goodness of the low-dimensional reconstruction is measured as Pearson correlation with a test set, which allows to test for significance of the low-dimensional reconstruction across participants. Using hierarchical Bayesian modeling, we derive the best estimate and associated uncertainty of underlying dimensionality across participants. We validated our method on simulated data of varying underlying dimensionality, showing that recovered dimensionalities match closely true dimensionalities. We then applied our method to three published fMRI data sets all involving processing of visual stimuli. The results highlight three possible applications of estimating the functional dimensionality of neural data. Firstly, it can aid evaluation of model-based analyses by revealing which areas express reliable, task-modulated signal that could be missed by specific models. Secondly, it can reveal functional differences across brain regions. Thirdly, knowing the functional dimensionality allows assessing task-related differences in the complexity of neural patterns

Measures of Neural Similarity

One fundamental question is what makes two brain states similar. For example, what makes the activity in visual cortex elicited from viewing a robin similar to a sparrow? One common assumption in fMRI analysis is that neural similarity is described by Pearson correlation. However, there are a host of other possibilities, including Minkowski and Mahalanobis measures, with each differing in its mathematical, theoretical, and neural computational assumptions. Moreover, the operable measures may vary across brain regions and tasks. Here, we evaluated which of several competing similarity measures best captured neural similarity. Our technique uses a decoding approach to assess the information present in a brain region, and the similarity measures that best correspond to the classifier’s confusion matrix are preferred. Across two published fMRI datasets, we found the preferred neural similarity measures were common across brain regions but differed across tasks. Moreover, Pearson correlation was consistently surpassed by alternatives

Prefrontal cortex activation reflects efficient exploitation of higher-order statistical structure

Since everyday actions are statistically structured, knowing which action a person has just completed allows predicting the most likely next action step. Taking even more than the preceding action into account improves this predictability, but also causes higher processing costs. Using fMRI, we investigated whether observers exploit 2nd-order statistical regularities preferentially if information on possible upcoming actions provided by 1st-order regularities is insufficient. We hypothesized that anterior prefrontal cortex balances whether or not 2nd order information should be exploited. Participants watched videos of actions that were structured by 1st- and 2nd-order conditional probabilities. Information provided by the 1st and by the 2nd order was manipulated independently. BOLD activity in the action observation network was more attenuated the more information on upcoming actions was provided by 1st- order structure, reflecting expectation suppression for more predictable actions. Activation in posterior parietal sites decreased further with 2nd-order information, but increased in temporal areas. As expected, 2nd-order information was integrated more when less 1st-order information was provided, and this interaction was mediated by anterior prefrontal cortex (BA 10). Observers spontaneously used both the present and the preceding action to predict the upcoming action, and integration of the preceding action was enhanced when the present action was uninformative.This research was supported by the University of Münster, Germany, and the Max-Planck-Society

Effects of La substitution on superconducting state of CeCoIn5

We report effects of La substitution on superconducting state of heavy fermion superconductor CeCoIn5, as seen in transport and magnetization measurements. As opposed to the case of conventional superconductors, pair breaking by nonmagnetic La results in depression of Tc and indicates strong gap anisotropy. Upper critical field Hc2 values decrease with increased La concentration, but the critical field anisotropy, gamma=Hc2(a)/Hc2(c), does not change in the Ce_{1-x}La_xCoIn5 (x=0-0.15). The electronic system is in the clean limit for all values of x.Comment: Submitted to Phys. Rev.

Competition between phonon superconductivity and Kondo screening in mixed valence and heavy fermion compounds

We consider competition of Kondo effect and s-wave superconductivity in heavy fermion and mixed valence superconductors, using the phenomenological approach for the periodic Anderson model. Similar to the well known results for single-impurity Kondo effect in superconductors, we have found principal possibility of a re-entrant regime of the superconducting transition temperature, $T_c$, in heavy fermion superconductors in a narrow range of model parameters and concentration of f-electrons. Suppression of $T_c$ in mixed valence superconductors is much weaker. Our theory has most validity in the low-temperature Fermi liquid regime, without re-entrant behavior of $T_c$. To check its applicability, we performed the fit for the $x$-dependence of $T_c$ in Ce$_{1-x}$La$_x$Ru$_3$Si$_2$ and obtained an excellent agreement with the experimental data, although no re-entrance was found in this case. Other experimental data are discussed in the light of our theoretical analysis. In particular, we compare temperatures of the superconducting transition for some known homologs, i.e., the analog periodic lattice compounds with and without f-elements. For a few pairs of homologs superconductivity exists only in the heavy fermion materials, thus confirming uniqueness of superconductivity mechanisms for the latter. We suggest that for some other compounds the value of $T_c$ may remain of the same order in the two homologs, if superconductivity originates mainly on some light Fermi surface, but induces sizable superconducting gap on another Fermi surface,for which hybridization or other heavy fermion effects are more significant.Comment: 11 pages, 4 figures, pd

Polymer waveguides with optimized overlap integral for modal dispersion phase-matching

Modal dispersion phase-matched second harmonic generation is demonstrated in new poled polymer waveguide geometries with a nonlinear optical core consisting of two side-chain polymers with different glass transition temperatures. After poling above and between the respective glass transitions, the sign of the nonlinear optical coefficient is reversed in the two polymers, thereby improving the overlap integral. Conversion efficiencies up to 7%/W cm(2) were achieved in the first experiments

Polymer waveguides with optimized overlap integral for modal dispersion phase-matching

Modal dispersion phase-matched second harmonic generation is demonstrated in new poled polymer waveguide geometries with a nonlinear optical core consisting of two side-chain polymers with different glass transition temperatures. After poling above and between the respective glass transitions, the sign of the nonlinear optical coefficient is reversed in the two polymers, thereby improving the overlap integral. Conversion efficiencies up to 7%/W cm(2) were achieved in the first experiments