Towards an understanding of neuroscience for science educators

Advances in neuroscience have brought new insights to the development of cognitive functions. These data are of considerable interest to educators concerned with how students learn. This review documents some of the recent findings in neuroscience, which is richer in describing cognitive functions than affective aspects of learning. A brief overview is presented here of the techniques used to generate data from imaging and how these findings have the possibility to inform educators. There are implications for considering the impact of neuroscience at all levels of education – from the classroom teacher and practitioner to policy. This relatively new cross-disciplinary area of research implies a need for educators and scientists to engage with each other. What questions are emerging through such dialogues between educators and scientists are likely to shed light on, for example, reward, motivation, working memory, learning difficulties, bilingualism and child development. The sciences of learning are entering a new paradigm

Intra-axonal protein synthesis in development and beyond

Author ManuscriptProteins can be locally produced in the periphery of a cell, allowing a rapid and spatially precise response to the changes in its environment. This process is especially relevant in highly polarized and morphologically complex cells such as neurons. The study of local translation in axons has evolved from being primarily focused on developing axons, to the notion that also mature axons can produce proteins. Axonal translation has been implied in several physiological and pathological conditions, and in all cases it shares common molecular actors and pathways as well as regulatory mechanisms. Here, we review the main findings in these fields, and attempt to highlight shared principles.Portuguese Foundation for Science and Technology (FCT) in the context of the FCT funded University of Minho MD/PhD Program (SFRH/BD/52322/2013). U.H. was supported by grants from the National Institutes of Health (R01MH096702), the BrightFocus Foundation, and the Irma T. Hirschl Trus

Artificial Life and Natural Intelligence

Abstract. This paper reviews the neuroscience literature to sculpt a view of intelligence from the artificial life (ALife) perspective. Three key themes are used to motivate a journey down the low road to cognition. First, the origins of brain structures and dynamics exhibit considerable emergence at phylogenic, epigenetic, and ontogenetic levels. Second, AL-ife complexity measures have interesting parallels in theoretical neuro-science. Finally, the cerebral internalization of sensory stimuli and motor control explain, respectively, a) semantics in terms of differential com-plexity, and b) how neural evolution has overcome the limitations of simple emergence.

Unsupervised Learning of Relations

Abstract. Learning processes allow the central nervous system to learn relationships between stimuli. Even stimuli from different modalities can easily be associated, and these associations can include the learning of mappings between observable parameters of the stimuli. The data struc-tures and processing methods of the brain, however, remain very poorly understood. We investigate the ability of simple, biologically plausible processing mechanisms to learn such relationships when the data is rep-resented using population codes, a coding scheme that has been found in a variety of cortical areas. We require that the relationships are learned not just from the point of view of an omniscient observer, but rather the network itself must be able to make effective use of the learned re-lationship, within the population code representations. Using a form of Hebbian learning, local winner-take-all, and homeostatic activity regula-tion away from the periphery, we obtain a learning framework which is able to learn relationships from examples and then use the learned rela-tionships for a variety of routine nervous system tasks such as inference, de-noising, cue-integration, and decision making.

Price Discrimination in the Context of Vertical Differentiation: An Application to Canadian Wheat Exports

The ability of the Canadian Wheat Board (CWB) to price discriminate in wheat exports is examined. The conceptual model shows that the CWB's ability to exploit cost differences in pricing depends on the extent of differentiation between Canadian and U.S. wheat. This model is implemented using monthly confidential price data for exports to four markets from 1982 to 1994. The empirical results support the conclusions that (1) the CWB has market power emerging from product differentiation, (2) the CWB price discriminates across export markets, and (3) Alchian—Allen effects are important in pricing in markets valuing quality such as Japan and the United Kingdom. Copyright 2005, Oxford University Press.