A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit <a rel="external noopener" href="http://cns.bu.edu/~steve/LevVerGroJOCN10TR.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<i title="Springer Nature">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2awqvcobm5bw3bostcqrykczhe" style="color: black;">Journal of Computational Neuroscience</a>
How spiking neurons cooperate to control behavioral processes is a fundamental problem in computational neuroscience. Such cooperative dynamics are required during visual perception when spatially distributed image fragments are grouped into emergent boundary contours. Perceptual grouping is a challenge for spiking cells because its properties of collinear facilitation and analog sensitivity occur in response to binary spikes with irregular timing across many interacting cells. Some models have<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10827-009-0211-1">doi:10.1007/s10827-009-0211-1</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/20111896">pmid:20111896</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lfxr54ucn5eofokrdaflwbcqei">fatcat:lfxr54ucn5eofokrdaflwbcqei</a> </span>
more »... demonstrated spiking dynamics in recurrent laminar neocortical circuits, but not how perceptual grouping occurs. Other models have analyzed the fast speed of certain percepts in terms of a single feedforward sweep of activity, but cannot explain other percepts, such as illusory contours, wherein perceptual ambiguity can take hundreds of milliseconds to resolve by integrating multiple spikes over time. The current model reconciles fast feedforward with slower feedback processing, and binary spikes with analog network-level properties, in a laminar cortical network of spiking cells whose emergent properties quantitatively simulate parametric data from neurophysiological experiments, including the formation of illusory contours; the structure of non-classical visual receptive fields; and self-synchronizing gamma oscillations. These laminar dynamics shed new light on how the brain resolves local informational ambiguities through the use of properly designed nonlinear feedback spiking networks which run as fast as they can, given the amount of uncertainty in the data that they process.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160316111653/http://cns.bu.edu/~steve/LevVerGroJOCN10TR.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a3/40/a34036734035d74be6e967080b832f6b2cb924a7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10827-009-0211-1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>