Wednesday, June 6, 2007

Temporal Activity Patterns in Thermosensory Neurons of Freely Moving Caenorhabditis elegans Encode Spatial Thermal Gradients

Damon A. Clark, Christopher V. Gabel, Harrison Gabel, and Aravinthan D. T. Samuel
The Journal of Neuroscience, June 6, 2007, 27(23):6083-6090;

Our understanding of the operation of neurons and neuronal circuits has come primarily from probing their activity in dissected, anesthetized, or restrained animals. However, the behaviorally relevant operation of neurons and neuronal circuits occurs within intact animals as they freely perform behavioral tasks. The small size and transparency of the nematode Caenorhabditis elegans make it an ideal system for noninvasive, optical measurements of neuronal activity. Here, we use a high signal-to-noise version of cameleon, a fluorescent calcium-binding protein, to quantify the activity of the AFD thermosensory neuron of individual worms freely navigating spatial thermal gradients. We find that AFD activity is directly coupled to the worm's exploratory movements in spatial thermal gradients. We show that the worm is able, in principle, to evaluate and guide its own thermotactic behaviors with respect to ambient spatial thermal gradients by monitoring the activity of this single thermosensory neuron.

Enhanced Category Tuning Revealed by Intracranial Electroencephalograms in High-Order Human Visual Areas

Eran Privman, Yuval Nir, Uri Kramer, Svetlana Kipervasser, Fani Andelman, Miri Y. Neufeld, Roy Mukamel, Yehezkel Yeshurun, Itzhak Fried, and Rafael Malach
The Journal of Neuroscience, June 6, 2007, 27(23):6234-6242;

The functional organization of human sensory cortex was studied by comparing intracranial EEG (iEEG) recordings of local field potentials in neurosurgical patients with functional magnetic resonance imaging (fMRI) obtained in healthy subjects. Using naturalistic movie stimuli, we found a tight correlation between these two measures throughout the human sensory cortex. Importantly, the correlation between the iEEG and fMRI signals was site-specific, exhibiting neuroanatomically specific coupling. In several cortical sites the iEEG activity was confined strictly to one object category. This site selectivity was not limited to faces but included other object categories such as houses and tools. The selectivity of the iEEG signals to images of different object categories was remarkably higher when compared with the selectivity of the corresponding fMRI signals. A plausible interpretation of the fMRI and iEEG results concerns cortical organization in which object categories are organized in a mosaic of narrowly tuned object-selective clusters.

Cortical Connections of Area V4 in the Macaque

Ungerleider LG, Galkin TW, Desimone R, Gattass R.
Cereb Cortex. 2007 Jun 4

To determine the locus, full extent, and topographic organization of cortical connections of area V4 (visual area 4), we injected anterograde and retrograde tracers under electrophysiological guidance into 21 sites in 9 macaques. Injection sites included representations ranging from central to far peripheral eccentricities in the upper and lower fields. Our results indicated that all parts of V4 are connected with occipital areas V2 (visual area 2), V3 (visual area 3), and V3A (visual complex V3, part A), superior temporal areas V4t (V4 transition zone), MT (medial temporal area), and FST (fundus of the superior temporal sulcus [STS] area), inferior temporal areas TEO (cytoarchitectonic area TEO in posterior inferior temporal cortex) and TE (cytoarchitectonic area TE in anterior temporal cortex), and the frontal eye field (FEF). By contrast, mainly peripheral field representations of V4 are connected with occipitoparietal areas DP (dorsal prelunate area), VIP (ventral intraparietal area), LIP (lateral intraparietal area), PIP (posterior intraparietal area), parieto-occipital area, and MST (medial STS area), and parahippocampal area TF (cytoarchitectonic area TF on the parahippocampal gyrus). Based on the distribution of labeled cells and terminals, projections from V4 to V2 and V3 are feedback, those to V3A, V4t, MT, DP, VIP, PIP, and FEF are the intermediate type, and those to FST, MST, LIP, TEO, TE, and TF are feedforward. Peripheral field projections from V4 to parietal areas could provide a direct route for rapid activation of circuits serving spatial vision and spatial attention. By contrast, the predominance of central field projections from V4 to inferior temporal areas is consistent with the need for detailed form analysis for object vision.

PMID: 17548798
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Tuesday, June 5, 2007

Probabilistic reasoning by neurons

Yang T, Shadlen MN.
Nature. 2007 Jun 3;

Our brains allow us to reason about alternatives and to make choices that are likely to pay off. Often there is no one correct answer, but instead one that is favoured simply because it is more likely to lead to reward. A variety of probabilistic classification tasks probe the covert strategies that humans use to decide among alternatives based on evidence that bears only probabilistically on outcome. Here we show that rhesus monkeys can also achieve such reasoning. We have trained two monkeys to choose between a pair of coloured targets after viewing four shapes, shown sequentially, that governed the probability that one of the targets would furnish reward. Monkeys learned to combine probabilistic information from the shape combinations. Moreover, neurons in the parietal cortex reveal the addition and subtraction of probabilistic quantities that underlie decision-making on this task.

PMID: 17546027


Victim of the Brain

1988 docudrama about "the ideas of Douglas Hofstadter". It was created by Dutch director Piet Hoenderdos. Features interviews with Doug ... all » Hofstadter and Dan Dennett. Dennett also stars as himself.

Original acquired from the Center for Research in Concepts and Cognition at Indiana University. Uploaded with permission from Douglas Hofstadter.

A Multi-Feature Part-Based Object Detection System

Google Tech Talks May 25, 2007 - Speaker: Bernd Heisele, Honda

I will start with an overview on object recognition systems which use local features and ... all » analyze their strengths and weaknesses. I will then present a general purpose part-based object detection system which we evaluated on a benchmark pedestrian detection data set . In a first step, the system computes feature maps from the training images. It then randomly extracts a large number of rectangular parts from the feature maps and clusters the parts based on their feature similarity and their x-y-location in the feature maps. The cluster centers build an initial set of part templates from which the system selects a subset using the gentle-boost algorithm. The localization of the parts during classification is performed by normalized cross-correlation of the part templates with feature maps. Three different types of feature maps were used in our experiments: Original gray value images, the magnitudes of the gradient, and Gabor filtered images. In experiments on a benchmark pedestrian detection database, we investigate how the number of the components, the feature type and the training data affects the detection performance. The system is compared to state-of-the-art pedestrian detectors.

A Labeled-Line Code for Small and Large Numerosities in the Monkey Prefrontal Cortex

Andreas Nieder and Katharina Merten
The Journal of Neuroscience, May 30, 2007, 27(22):5986-5993

How single neurons represent information about the magnitude of a stimulus remains controversial. Neurons encoding purely sensory magnitude typically show monotonic response functions ("summation coding"), and summation units are usually implemented in models of numerosity representation. In contrast, cells representing numerical quantity exhibit nonmonotonic tuning functions that peak at their preferred numerosity ("labeled-line code"), but the restricted range of tested quantities in these studies did not permit a definite answer. Here, we analyzed both behavioral and neuronal representations of a broad range of numerosities from 1 to 30 in the prefrontal cortex of monkeys. Numerosity-selective neurons showed a clear and behaviorally relevant labeled-line code for all numerosities. Moreover, both the behavioral and neuronal tuning functions obeyed the Weber–Fechner Law and were best represented on a nonlinearly compressed scale. Our single-cell study is in good agreement with functional imaging data reporting peaked tuning functions in humans, demonstrating neuronal precursors for human number competence in a nonhuman primate. Our findings also emphasize that the manner in which neurons encode and maintain magnitude information may depend on the precise task at hand as well as the type of magnitude to represent and memorize.


Sunday, June 3, 2007

Interactions between higher and lower visual areas improve shape selectivity of higher level neurons-Explaining crowding phenomena

Jehee JF, Roelfsema PR, Deco G, Murre JM, Lamme VA
Brain Res. 2007 Apr 12;

Recent theories of visual perception propose that feedforward cortical processing enables rapid and automatic object categorizations, yet incorporates a limited amount of detail. Subsequent feedback processing highlights high-resolution representations in early visual areas and provides spatial detail. To verify this hypothesis, we separate the contributions of feedforward and feedback signals to the selectivity of cortical neurons in a neural network simulation that is modeled after the hierarchical feedforward-feedback organization of cortical areas. We find that in such a network the responses of high-level neurons can initially distinguish between low-resolution aspects of objects but are 'blind' to differences in detail. After several feedback-feedforward cycles of processing, however, they can also distinguish between objects that differ in detail. Moreover, we find that our model captures recent paradoxical results of crowding phenomena, showing that spatial detail that is lost in visual crowding is nevertheless able to evoke specific adaptation effects. Our results thus provide an existence proof of the feasibility of novel theoretical models and provide a mechanism to explain various psychophysical and physiological results.

PMID: 17540349