University of Stirling

About: University of Stirling is a education organization based out in Stirling, Stirling, United Kingdom. It is known for research contribution in the topics: Population & Polyunsaturated fatty acid. The organization has 7722 authors who have published 20549 publications receiving 732940 citations. The organization is also known as: Stirling University.

Optic-Flow and Cognitive Factors in Time-to-Collision Estimates

Abstract: Time-to-collision (Tc) estimates were obtained from twenty-four subjects who viewed film clips for varying lengths of time. The film clips showed the view from a moving car travelling towards a stationary target car, but ended 100 m before reaching the target. Viewing time varied from 2 to 6 s, approach velocity from 40 to 100 km h-1, and Tc from 3.6 to 9.0s. It was hypothesised that, if time were needed to calculate Tc, the accuracy of Tc estimates would increase with viewing time up to some maximum. However, the results showed no effect of viewing time, and this was taken to indicate that estimates were based upon information directly available from the changing optic array at the eye of the observer. A significant velocity effect was found, accuracy increasing with velocity. Since velocity was inversely correlated with Tc, this probably implies that accuracy decreases with increasing Tc. Sex differences were found, with males giving higher and more accurate estimates than females. The relevance of these findings to the nature of Tc information is discussed.

Functional genomics reveals increases in cholesterol biosynthetic genes and highly unsaturated fatty acid biosynthesis after dietary substitution of fish oil with vegetable oils in Atlantic salmon (Salmo salar)

Abstract: There is an increasing drive to replace fish oil (FO) in finfish aquaculture diets with vegetable oils (VO), driven by the short supply of FO derived from wild fish stocks However, little is known of the consequences for fish health after such substitution The effect of dietary VO on hepatic gene expression, lipid composition and growth was determined in Atlantic salmon (Salmo salar), using a combination of cDNA microarray, lipid, and biochemical analysis FO was replaced with VO, added to diets as rapeseed (RO), soybean (SO) or linseed (LO) oils Dietary VO had no major effect on growth of the fish, but increased the whole fish protein contents and tended to decrease whole fish lipid content, thus increasing the protein:lipid ratio Expression levels of genes of the highly unsaturated fatty acid (HUFA) and cholesterol biosynthetic pathways were increased in all vegetable oil diets as was SREBP2, a master transcriptional regulator of these pathways Other genes whose expression was increased by feeding VO included those of NADPH generation, lipid transport, peroxisomal fatty acid oxidation, a marker of intracellular lipid accumulation, and protein and RNA processing Consistent with these results, HUFA biosynthesis, hepatic β-oxidation activity and enzymic NADPH production were changed by VO, and there was a trend for increased hepatic lipid in LO and SO diets Tissue cholesterol levels in VO fed fish were the same as animals fed FO, whereas fatty acid composition of the tissues largely reflected those of the diets and was marked by enrichment of 18 carbon fatty acids and reductions in 20 and 22 carbon HUFA This combined gene expression, compositional and metabolic study demonstrates that major lipid metabolic effects occur after replacing FO with VO in salmon diets These effects are most likely mediated by SREBP2, which responds to reductions in dietary cholesterol These changes are sufficient to maintain whole body cholesterol levels but not HUFA levels

Temporal Sequence Learning, Prediction, and Control: A Review of Different Models and Their Relation to Biological Mechanisms

Abstract: In this review, we compare methods for temporal sequence learning (TSL) across the disciplines machine-control, classical conditioning, neuronal models for TSL as well as spike-timing-dependent plasticity (STDP). This review introduces the most influential models and focuses on two questions: To what degree are reward-based (e.g., TD learning) and correlationbased (Hebbian) learning related? and How do the different models correspond to possibly underlying biological mechanisms of synaptic plasticity? We first compare the different models in an open-loop condition, where behavioral feedback does not alter the learning. Here we observe that reward-based and correlation-based learning are indeed very similar. Machine control is then used to introduce the problem of closed-loop control (e.g., actor-critic architectures). Here the problem of evaluative (rewards) versus nonevaluative (correlations) feedback from the environment will be discussed, showing that both learning approaches are fundamentally different in the closed-loop condition. In trying to answer the second question, we compare neuronal versions of the different learning architectures to the anatomy of the involved brain structures (basal-ganglia, thalamus, and cortex) and the molecular biophysics of glutamatergic and dopaminergic synapses. Finally, we discuss the different algorithms used to model STDP and compare them to reward-based learning rules. Certain similarities are found in spite of the strongly different timescales. Here we focus on the biophysics of the different calciumrelease mechanisms known to be involved in STDP.