Multi-walled carbon nanotubes (MWCNT) and carbon nanofibers (CNF) were created using chemical vapor deposition at growth temperatures between 500 and 750 C, which have increasing crystallinity with increasing growth temperature. We used Raman spectroscopy to analyze the samples. The intensity ratios compared to the G-band, and full-width at half-maximum, of all observable Raman bands in both the first and second-order region were investigated. Good match was observed for the defect related bands of the MWCNT samples and data found in the literature. Several second-order bands display a strong dependency to growth temperature. Similar growth temperature (and thus defect) dependencies were found between several first and second-order bands, which might aid in determining the physical causes of these bands. CNF show much weaker Raman features due to their low crystallinity, making them more difficult to analyse. The results of this work are used to give recommendations on how to investigate MWCNT and CNF crystallinity using Raman spectroscopy. Finally, we demonstrate that Raman spectroscopy can be used to distinguish between the MWCNT root and tip growth mechanism.
Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature.
Theories of episodic memory have proposed that individual memory traces are linked together by a representation of context that drifts slowly over time. Recent data challenge the notion that contextual drift is always slow and passive. In particular, changes in one's external environment or internal model induce discontinuities in memory that are reflected in sudden changes in neural activity, suggesting that context can shift abruptly. Furthermore, context change effects are sensitive to top-down goals, suggesting that contextual drift may be an active process. These findings call for revising models of the role of context in memory, in order to account for abrupt contextual shifts and the controllable nature of context change.
Little is known about the relationship between attention and learning during decision making. Using eye tracking and multivariate pattern analysis of fMRI data, we measured participants' dimensional attention as they performed a trial-and-error learning task in which only one of three stimulus dimensions was relevant for reward at any given time. Analysis of participants' choices revealed that attention biased both value computation during choice and value update during learning. Value signals in the ventromedial prefrontal cortex and prediction errors in the striatum were similarly biased by attention. In turn, participants' focus of attention was dynamically modulated by ongoing learning. Attentional switches across dimensions correlated with activity in a frontoparietal attention network, which showed enhanced connectivity with the ventromedial prefrontal cortex between switches. Our results suggest a bidirectional interaction between attention and learning: attention constrains learning to relevant dimensions of the environment, while we learn what to attend to via trial and error.
Eating is a learned process. Our desires for specific foods arise through experience. Both electrical stimulation and optogenetic studies have shown that increased activity in the lateral hypothalamus (LH) promotes feeding. Current dogma is that these effects reflect a role for LH neurons in the control of the core motivation to feed, and their activity comes under control of forebrain regions to elicit learned food-motivated behaviors. However, these effects could also reflect the storage of associative information about the cues leading to food in LH itself. Here, we present data from several studies that are consistent with a role for LH in learning. In the first experiment, we use a novel GAD-Cre rat to show that optogenetic inhibition of LH \(\gamma\)-aminobutyric acid (GABA) neurons restricted to cue presentation disrupts the rats' ability to learn that a cue predicts food without affecting subsequent food consumption. In the second experiment, we show that this manipulation also disrupts the ability of a cue to promote food seeking after learning. Finally, we show that inhibition of the terminals of the LH GABA neurons in ventral-tegmental area (VTA) facilitates learning about reward-paired cues. These results suggest that the LH GABA neurons are critical for storing and later disseminating information about reward-predictive cues.
Most of life is extinct, so incorporating some fossil evidence into analyses of macroevolution is typically seen as necessary to understand the diversification of life and patterns of morphological evolution. Here we test the effects of inclusion of fossils in a study of the body size evolution of afrotherian mammals, a clade that includes the elephants, sea cows and elephant shrews. We find that the inclusion of fossil tips has little impact on analyses of body mass evolution; from a small ancestral size (approx. 100 g), there is a shift in rate and an increase in mass leading to the larger-bodied Paenungulata and Tubulidentata, regardless of whether fossils are included or excluded from analyses. For Afrotheria, the inclusion of fossils and morphological character data affect phylogenetic topology, but these differences have little impact upon patterns of body mass evolution and these body mass evolutionary patterns are consistent with the fossil record. The largest differences between our analyses result from the evolutionary model, not the addition of fossils. For some clades, extant-only analyses may be reliable to reconstruct body mass evolution, but the addition of fossils and careful model selection is likely to increase confidence and accuracy of reconstructed macroevolutionary patterns.
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