A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2006; you can also visit <a rel="external noopener" href="http://hal.inria.fr/docs/00/03/05/26/PDF/BerryTemamLNCS2005.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<i title="Springer Berlin Heidelberg">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
Carbon nanotubes are often seen as the only alternative technology to silicon transistors. While they are the most likely short-term alternative, other longer-term alternatives should be studied as well, even if their properties are less familiar to chip designers. While contemplating biological neurons as an alternative component may seem preposterous at first sight, significant recent progress in CMOS-neuron interface suggests this direction may not be unrealistic; moreover, biological<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11494669_38">doi:10.1007/11494669_38</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/x6gqlpcxufcelgtstvsv3l5sfy">fatcat:x6gqlpcxufcelgtstvsv3l5sfy</a> </span>
more »... are known to self-assemble into very large networks capable of complex information processing tasks, something that has yet to be achieved with other emerging technologies. The first step to designing computing systems on top of biological neurons is to build an abstract model of selfassembled biological neural networks, much like computer architects manipulate abstract models of transistors and circuits. In this article, we propose a first model of the structure of biological neural networks. We provide empirical evidence that this model matches the biological neural networks found in living organisms, and exhibits the smallworld graph structure properties commonly found in many large and self-organized systems, including biological neural networks. More importantly, we extract the simple local rules and characteristics governing the growth of such networks, enabling the development of potentially large but realistic biological neural networks, as would be needed for complex information processing/computing tasks. Based on this model, future work will be targeted to understanding the evolution and learning properties of such networks, and how they can be used to build computing systems.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20061127131506/http://hal.inria.fr/docs/00/03/05/26/PDF/BerryTemamLNCS2005.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/fb/a3/fba3fa18b5d8483553d6e6853a27a797fb7f235e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11494669_38"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>