ABSTRACT Vesicular neurotransmitter transporters package transmitter into the lumen of synaptic vesicles for quantal release. However, the number of transporters that localize to each vesicle is not known. In this issue of Neuron, a study by Daniels et al. using the Drosophila neuromuscular junction and mutations of the vesicular glutamate transporter suggests that one transporter may suffice to fill each vesicle.
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ABSTRACT: Recordings of brain electrophysiological activity provide the most direct reflect of neural function. Information contained in these signals varies as a function of the spatial scale at which recordings are done: from single cell recording to large scale macroscopic fields, e.g., scalp EEG. Microscopic and macroscopic measurements and models in Neuroscience are often in conflict. Solving this conflict might require the developments of a sort of bio-statistical physics, a framework for relating the microscopic properties of individual cells to the macroscopic or bulk properties of neural circuits. Such a framework can only emerge in Neuroscience from the systematic analysis and modeling of the diverse recording scales from simultaneous measurements. In this article we briefly review the different measurement scales and models in modern neuroscience to try to identify the sources of conflict that might ultimately help to create a unified theory of brain electromagnetic fields. We argue that seen the different recording scales, from the single cell to the large scale fields measured by the scalp electroencephalogram, as derived from a unique physical magnitude--the electric potential that is measured in all cases--might help to conciliate microscopic and macroscopic models of neural function as well as the animal and human neuroscience literature.Medical & Biological Engineering 05/2011; 49(5):511-20. DOI:10.1007/s11517-011-0769-4 · 1.50 Impact Factor
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ABSTRACT: Neuroimaging studies typically identify neural activity correlated with the predictions of highly parameterized models, like the many reward prediction error (RPE) models used to study reinforcement learning. Identified brain areas might encode RPEs or, alternatively, only have activity correlated with RPE model predictions. Here, we use an alternate axiomatic approach rooted in economic theory to formally test the entire class of RPE models on neural data. We show that measurements of human neural activity from the striatum, medial prefrontal cortex, amygdala, and posterior cingulate cortex satisfy necessary and sufficient conditions for the entire class of RPE models. However, activity measured from the anterior insula falsifies the axiomatic model, and therefore no RPE model can account for measured activity. Further analysis suggests the anterior insula might instead encode something related to the salience of an outcome. As cognitive neuroscience matures and models proliferate, formal approaches of this kind that assess entire model classes rather than specific model exemplars may take on increased significance.The Journal of Neuroscience : The Official Journal of the Society for Neuroscience 10/2010; 30(40):13525-36. DOI:10.1523/JNEUROSCI.1747-10.2010 · 6.75 Impact Factor